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Doing Research: A New Researcher’s Guide pp 1–15 Cite as

What Is Research, and Why Do People Do It?

  • James Hiebert 6 ,
  • Jinfa Cai 7 ,
  • Stephen Hwang 7 ,
  • Anne K Morris 6 &
  • Charles Hohensee 6  
  • Open Access
  • First Online: 03 December 2022

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Part of the book series: Research in Mathematics Education ((RME))

Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

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Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

An image of the book's description with the words like research, science, and inquiry and what the word research meant in the scientific world.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

An image represents the observation required in the scientific inquiry including planning and explaining.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

An image represents the data explanation as it is not limited and takes numerous non-quantitative forms including an interview, journal entries, etc.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

An image represents the scientific inquiry definition given by the editors of Britannica and also defines the hypothesis on the basis of the experiments.

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

An image represents the rationale and the prediction for the scientific inquiry and different types of information provided by the terms.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

An image represents the cycle of events that take place before making predictions, developing the rationale, and studying the prediction and rationale multiple times.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

Agnes, M., & Guralnik, D. B. (Eds.). (2008). Hypothesis. In Webster’s new world college dictionary (4th ed.). Wiley.

Google Scholar  

Britannica. (n.d.). Scientific method. In Encyclopaedia Britannica . Retrieved July 15, 2022 from https://www.britannica.com/science/scientific-method

Brownell, W. A., & Moser, H. E. (1949). Meaningful vs. mechanical learning: A study in grade III subtraction . Duke University Press..

Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V., Cirillo, M., Kramer, S. L., & Hiebert, J. (2019b). Posing significant research questions. Journal for Research in Mathematics Education, 50 (2), 114–120. https://doi.org/10.5951/jresematheduc.50.2.0114

Article   Google Scholar  

Cambridge University Press. (n.d.). Hypothesis. In Cambridge dictionary . Retrieved July 15, 2022 from https://dictionary.cambridge.org/us/dictionary/english/hypothesis

Cronbach, J. L. (1957). The two disciplines of scientific psychology. American Psychologist, 12 , 671–684.

Cronbach, L. J. (1975). Beyond the two disciplines of scientific psychology. American Psychologist, 30 , 116–127.

Cronbach, L. J. (1986). Social inquiry by and for earthlings. In D. W. Fiske & R. A. Shweder (Eds.), Metatheory in social science: Pluralisms and subjectivities (pp. 83–107). University of Chicago Press.

Hay, C. M. (Ed.). (2016). Methods that matter: Integrating mixed methods for more effective social science research . University of Chicago Press.

Merriam-Webster. (n.d.). Explain. In Merriam-Webster.com dictionary . Retrieved July 15, 2022, from https://www.merriam-webster.com/dictionary/explain

National Research Council. (2002). Scientific research in education . National Academy Press.

Weis, L., Eisenhart, M., Duncan, G. J., Albro, E., Bueschel, A. C., Cobb, P., Eccles, J., Mendenhall, R., Moss, P., Penuel, W., Ream, R. K., Rumbaut, R. G., Sloane, F., Weisner, T. S., & Wilson, J. (2019a). Mixed methods for studies that address broad and enduring issues in education research. Teachers College Record, 121 , 100307.

Weisner, T. S. (Ed.). (2005). Discovering successful pathways in children’s development: Mixed methods in the study of childhood and family life . University of Chicago Press.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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Frequently asked questions

What is a research project.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

Frequently asked questions: Writing a research paper

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

  • Researchability
  • Feasibility and specificity
  • Relevance and originality

Research questions anchor your whole project, so it’s important to spend some time refining them.

In general, they should be:

  • Focused and researchable
  • Answerable using credible sources
  • Complex and arguable
  • Feasible and specific
  • Relevant and original

All research questions should be:

  • Focused on a single problem or issue
  • Researchable using primary and/or secondary sources
  • Feasible to answer within the timeframe and practical constraints
  • Specific enough to answer thoroughly
  • Complex enough to develop the answer over the space of a paper or thesis
  • Relevant to your field of study and/or society more broadly

Writing Strong Research Questions

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

The main guidelines for formatting a paper in Chicago style are to:

  • Use a standard font like 12 pt Times New Roman
  • Use 1 inch margins or larger
  • Apply double line spacing
  • Indent every new paragraph ½ inch
  • Include a title page
  • Place page numbers in the top right or bottom center
  • Cite your sources with author-date citations or Chicago footnotes
  • Include a bibliography or reference list

To automatically generate accurate Chicago references, you can use Scribbr’s free Chicago reference generator .

The main guidelines for formatting a paper in MLA style are as follows:

  • Use an easily readable font like 12 pt Times New Roman
  • Set 1 inch page margins
  • Include a four-line MLA heading on the first page
  • Center the paper’s title
  • Use title case capitalization for headings
  • Cite your sources with MLA in-text citations
  • List all sources cited on a Works Cited page at the end

To format a paper in APA Style , follow these guidelines:

  • Use a standard font like 12 pt Times New Roman or 11 pt Arial
  • If submitting for publication, insert a running head on every page
  • Apply APA heading styles
  • Cite your sources with APA in-text citations
  • List all sources cited on a reference page at the end

No, it’s not appropriate to present new arguments or evidence in the conclusion . While you might be tempted to save a striking argument for last, research papers follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the results and discussion sections if you are following a scientific structure). The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

The conclusion of a research paper has several key elements you should make sure to include:

  • A restatement of the research problem
  • A summary of your key arguments and/or findings
  • A short discussion of the implications of your research

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

The introduction of a research paper includes several key elements:

  • A hook to catch the reader’s interest
  • Relevant background on the topic
  • Details of your research problem

and your problem statement

  • A thesis statement or research question
  • Sometimes an overview of the paper

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Building a program of research

Affiliation.

  • 1 School of Nursing, University of California, San Francisco, 2 Koret Way, San Francisco, CA 94143-0608, USA. [email protected]
  • PMID: 19566633
  • DOI: 10.1111/j.1742-7924.2009.00115.x

This article provides highlights of a talk titled, "Building a Program of Research," given at the Japan Academy of Nursing Science's 28th annual meeting, Fukuoka, Japan, on 13 December 2008. A program of research is defined as a coherent expression of a researcher's area of interest that has public health significance, builds from the published research literature in the field, has relevance for clinical nursing practice, and captures the passion and commitment of the researcher. The Outcomes Model for Health Care Research is proposed as a framework for how to develop and articulate a program of research. Eight steps are proposed to help a new researcher to think about how to build a program of research.

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  • Nursing Research / organization & administration*
  • Outcome and Process Assessment, Health Care / organization & administration*
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definition of a research program

Home Market Research

What is Research: Definition, Methods, Types & Examples

What is Research

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

  • Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

  • Identify potential and new customers
  • Understand existing customers
  • Set pragmatic goals
  • Develop productive market strategies
  • Address business challenges
  • Put together a business expansion plan
  • Identify new business opportunities
  • Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
  • The analysis is based on logical reasoning and involves both inductive and deductive methods.
  • Real-time data and knowledge is derived from actual observations in natural settings.
  • There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
  • It creates a path for generating new questions. Existing data helps create more research opportunities.
  • It is analytical and uses all the available data so that there is no ambiguity in inference.
  • Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

  • Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

  • Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

  • Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

what is research

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods.

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

  • One-to-one Interview
  • Focus Groups
  • Ethnographic studies
  • Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

  • Survey research
  • Descriptive research
  • Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

  • Valid – founded, logical, rigorous, and impartial.
  • Accurate – free of errors and including required details.
  • Reliable – other people who investigate in the same way can produce similar results.
  • Timely – current and collected within an appropriate time frame.
  • Complete – includes all the data you need to support your business decisions.

Gather insights

What is a research - tips

  • Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
  • Keep track of the frequency with which each of the main findings appears.
  • Make a list of your findings from the most common to the least common.
  • Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
  • Prepare conclusions and recommendations about your study.
  • Act on your strategies
  • Look for gaps in the information, and consider doing additional inquiry if necessary
  • Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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Department of Health & Human Services

Definitions

ORI  Introduction  to RCR: Chapter 3. The Protection of Human Subjects

Researchers are responsible for obtaining appropriate approval before conducting research involving human subjects. The need for approval rests on three seemingly obvious but not always easy-to-interpret considerations: 1) whether the work qualifies as research, 2) whether it involves human subjects, and 3) whether it is exempt. All three considerations are discussed in the Common Rule and guide decision making about the use of human subjects in research. The authority to make decisions about the need for approval rests with the Institutional Review Board (IRB, discussed below) or other appropriate institutional officials.

Research. The Common Rule defines research as “systematic investigation, including research development, testing and evaluation, designed to develop or contribute to generalizable knowledge” (§ 46.102(d), see box, next page, for full definition). This means that a project or study is research if it:

  • is conducted with the intention of drawing conclusions that have some general applicability and
  • uses a commonly accepted scientific method.

The random collection of information about individuals that has no general applicability is not research. Scientific investigation that leads to generalizable knowledge is.

Human subjects. Human subjects are “living individual(s) about whom an investigator conducting research obtains: (1) data through intervention or interaction with the individual; or (2) identifiable private information” (§ 46.102(f), see box, next page, for full definition). Humans are considered subjects and covered by Federal regulations if the researcher:

  • interacts or intervenes directly with them, or
  • collects identifiable private information.

If one of these two conditions applies and if the project or study qualifies as research, then institutional approval is needed before any work is undertaken.

Exempt research. Some studies that involve humans may be exempt from the requirements in the Federal regulations. Studies that fall into the following categories could qualify for exemptions, including:

  • research conducted in established or commonly accepted educational settings;
  • research involving the use of educational tests;
  • research involving the collection or study of existing data, documents, records, pathological specimens, or diagnostic specimens, if unidentifiable or publicly available;
  • research and demonstration projects which are conducted by or subject to the approval of department or agency heads; or
  • taste and food quality evaluation and consumer acceptance studies.

It is critically important to note, however, that decisions about whether studies are exempt from the requirements of the Common Rule must be made by an IRB or an appropriate institutional official and not by the investigator.

45 CFR 46.102

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Institutional Review Board

Health Sciences and Minimal Risk Research IRBs

Research vs. Quality Improvement and Program Evaluation

Determining whether a project constitutes human subjects research rather than quality improvement or program evaluation involves multiple factors. The federal definition of research is “a systematic investigation, including research development, testing and evaluation, designed to develop or contribute to generalizable knowledge. Activities which meet this definition constitute research for purposes of this policy, whether or not they are conducted or supported under a program which is considered research for other purposes.” This is an important distinction to make because it determines whether IRB review and oversight of a project is needed because IRB oversight is limited to human subjects research.

The IRBs Office is frequently asked to make a formal determination that a project falls outside of the federal definition of research. Often, IRB review of these projects isn’t required; however, formal IRB determinations that the projects do not require IRB oversight are requested in anticipation of such documentation being required for journals, conferences, funding sources and others.

The materials below are intended to assist study teams in determining whether a project requires submission to the IRB as a research project involving human subjects. If the project involves some characteristics of a research project, submission to the IRB for review is expected. To address the issue of documentation, the IRBs Office also has developed a tool that can provide self-certification that the project does not require IRB review and oversight.

IRB QI/Program Evaluation Self-Certification Tool

This tool allows study teams to make the decision about whether their project constitutes the definition of research under the Common Rule (45 CFR 46) independent of the IRB. The tool is designed to help determine whether the project constitutes research or whether it is quality improvement or program evaluation, such that IRB review isn’t required.

NOTE: This tool is not designed to determine all of the cases when a project falls outside of the IRB’s purview. This tool is only for determining if a project is QI/Program Evaluation, rather than research. The tool should not be used for public health surveillance projects, as these projects require consultation with the IRBs Office Director. The IRBs Office has additional resources that can help determine the need for IRB review based on inclusion of human subjects, as well as qualification for exemption.

For assistance answering the questions in the IRB QI\Program Evaluation Self-Certification Tool, please review the following resources:

  • QI\Program Evaluation Self-Certification Tool Guidance This guidance outlines how to complete the Self-Certification Tool with a breakdown of each question.
  • IRB QI\Program Evaluation Decision Tree This Decision tree provides an additional resource for assistance in determining whether a project constitutes human subjects research (and subsequently requires IRB review) or quality improvement\program evaluation. The decision tree forms the basis for the questions in the IRB QI\Program Evaluation Self-Certification Tool (see above), but it does not provide a certification upon completion.
  • Characteristics of Research, Quality Improvement and Program Evaluation Activities This table is intended to help in determining whether a project requires submission to the IRB as a research project involving human subjects. If the project involves some characteristics of a research project, submission to the IRB for review is expected. Please contact the IRBs Office with any questions or for assistance in making a determination.

Please note, HIPAA Privacy and Security Rule Regulations may still apply to your project even though IRB review isn’t required. If you have questions or concerns about IRB review requirements after reviewing the above materials, please contact the IRBs Office for additional assistance.

Defining Research with Human Subjects

A study is considered research with human subjects if it meets the definitions of both research AND human subjects, as defined in the federal regulations for protecting research subjects.

Research.  A systematic inquiry designed to answer a research question or contribute to a field of knowledge, including pilot studies and research development.

Human subject:  A living individual about whom an investigator (whether professional or student) conducting research:

  • Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or
  • Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.

The following sections will explain some of the words in the previous definitions.

The regulatory language:

A systematic inquiry designed to answer a research question or contribute to a field of knowledge, including pilot studies and research development.

The explanation:

Understanding what constitutes a systematic inquiry varies among disciplines and depends on the procedures and steps used to answer research questions and how the search for knowledge is organize and structured.

Pilot Studies and Research Development

Pilot studies are designed to conduct preliminary analyses before committing to a full-blown study or experiment.

Research development includes activities such as convening a focus group consisting of members of the proposed research population to help develop a culturally appropriate questionnaire.

Practical applications:

  • You are conducting a pilot study or other activities preliminary to research; or
  • You have designed a study to collect information or biospecimens in a systematic way to answer a research question; or
  • You intend to study, analyze, or otherwise use existing information or biospecimens to answer a research question.

Human Subjects

Human subjects are living individuals about whom researchers obtain information or biospecimens through interaction, intervention, or observation of private behavior, to also include the use, study, and analysis of said information or biospecimens.

Obtaining, using, analyzing, and generating identifiable private information or identifiable biospecimens that are provided to a researcher is also considered to be human subjects.

To meet the definition of human subjects, the data being collected or used are about people. Asking participants questions about their attitudes, opinions, preferences, behavior, experiences, background/history, and characteristics, or analyzing demographic, academic or medical records, are just some examples of human subjects data.

  • Interacting with people to gather data about them using methods such as interviews, focus groups, questionnaires, and participant observation; or
  • Conducting interventions with people such as experiments or manipulations of subjects or subjects' environments; or
  • Observing or recording behavior, whether in-person and captured in real time or in virtual spaces, like social media sites (e.g., Twitter) or online forums (e.g., Reddit); or
  • Obtaining existing information about individuals, such as students’ school records or patients’ health records, or data sets provided by another researcher or organization.

Interactions and Interventions

Interventions are manipulations of the subject or the subject's environment, for example is a behavioral change study using text messages about healthy foods.

Interactions include communication or interpersonal contact between investigator and participant.

A study may include both interventions and interactions.

Interactions and interventions do not require in-person contact, but may be conducted on-line.

Private Information

Private information  includes information or biospecimens: 1) about behavior that occurs in a context in which an individual can reasonably expect that no observation or recording is taking place; 2) that has been provided for specific purposes by an individual; and 3) that the individual can reasonably expect will not be made public (for example, a medical record).

Private information must be individually identifiable (i.e., the identity of the subject is or may readily be ascertained by the investigator or associated with the information) in order for the information to constitute research involving human subjects.

The regulations are clear that it is the subjects’ expectations that determine what behaviors, biospecimens, and identifiable information must be considered private. Subjects’ understanding of what privacy means are not universal, but are very specific and based on multiple interrelated factors, such as the research setting, cultural norms, the age of the subjects, and life experiences. For example, in the United States, health records are considered private and protected by law, but in some countries, health information is not considered private but are of communal concern. 

Identifiable Information

The identity of the subject is associated with the data gathered from the subject(s) existing data about the subjects. Even if the data (including biospecimens) do not include direct identifiers, such as names or email addresses, the data are considered identifiable if names of individuals can easily be deduced from the data.

If there are keys linking individuals to their data, the data are considered identifiable.

Levels of Review

Not all projects that meet the definition of research with human subjects need review by the actual committee. For example, projects that pose  negligible risk to participants may be reviewed and recommended for approval by IRB staff ; other projects may need to undergo review and approval by at least one member of the IRB committee or a quorum of the full board. Determination as to the need for review should always be made by the IRB staff.

Examples of Studies That MAY Meet the Definition of Research with Human Subjects

The following examples will likely require further consultation with an IRB staff member.

Analysis of existing information with no identifiers

If researchers have no interaction with human subjects, but will be conducting a secondary analysis of existing data without individual identifiers, the analysis of those data may not be research with human subjects. 

Expert consultation

Key words in the definition of a human subject are "a living individual about whom" a researcher obtains, uses, studies, analyzes, or generates information. People can provide you information that is not about them but is important for the research. For example, a researcher may contact non-governmental organizations to ask about sources of funding.

Program evaluations and quality improvement studies

Program evaluations are generally intended to query whether a particular program or curriculum meets its goals. They often involve pre- and post-surveys or evaluations.

Some program evaluations include a research component. If data are collected about the characteristics of the participants to analyze the relationship between demographic variable and success of the program, the study may become research with human subjects.  Research question:  Are there different learning outcomes associated with different levels of participant confidence?

Classroom research

Classes designed to teach research methods such as fieldwork, statistical analysis, or interview techniques, may assign students to conduct interviews, distribute questionnaires, or engage in participant observation. If the purpose of these activities is solely pedagogical and are not designed to contribute to a body of knowledge, the activities do not meet the definition of research with human subjects. 

Vignettes: Applying the Definitions

Art in Cambodia

An art history student wants to study art created by Cambodians in response to the massacres committed by the Khmer Rouge. The art she will study includes paintings, sculpture, video, and the performing arts.

Much of the research will be archival, using library and online resources. In addition, she will visit Cambodia. While there, she will speak with several museum curators for assistance locating and viewing art collections related to the massacres.

Is this research with human subjects?

No. Although the student will speak with curators, they are not the subjects of her research and she is not interested in learning anything about them. They will, in effect, serve as local guides.

What would make the study research with human subjects?

The student interviews people as they interact with art to understand the role of the arts in evoking and/or coming to terms with traumatic past events. She interviews people who view the art, such as visitors to museums, and discusses what the art means to them. She may collect information about their experiences during the genocide and compare those experiences with their reactions to the art. 

Bank-Supported Micro-Finance in Chile

A researcher is interested in the practice of microfinance in the Chilean Mapuche community. She meets with bankers and asks about the criteria for granting loans, the demographics of the people who receive loans, the types of businesses to which the bank prefers to grant loans, how many loans they give, the payback rates, and other data about the bank’s loan practices.

No. Although the researcher is interviewing bankers, the bankers are only providing information about their banking practices and are not providing any information about themselves. The questions are about “what” rather than “about whom.” The bankers are not human subjects. This type of interview is sometimes referred to as expert consultation.

The researcher explores the impact of small loans, both intended and unintended, on the recipients of the loans. The researcher interviews the recipients of the loans and gathers information from them about their lives before and after they received funding, how the loans affected their relationships with family members and other community members, the impact of the loans on their aspirations, and so on. He asks “about whom” questions designed to understand the impact of micro-loans.

Developing Teaching Materials

A researcher goes to a country in which the infrastructure has been severely damaged to help rebuild schools. The student interviews community members about what curricular materials they need, develops some materials, and teaches a math class.

No. Although interviews are conducted, the intent of interviewing is to assist in resource development rather than answer a research question designed to contribute to a field of knowledge.

If the researcher does pre- and post-testing to assess student learning in his class, is this research with human subjects?

No. The intent is to find out if the materials are effective. This is sometimes referred to as program assessment.

What would make this research with human subjects?

The researcher studies the impact of nutrition and personal variables on learning. He assesses the nutritional composition of the local diet, assesses students’ general health, and compares those data with test scores. He also measures motivation, family composition, and other characteristics of the students using written questionnaires.

Water Conservation

A researcher wants to find out if the campus water conservation program is effective. She will gather some information about water volume usage from the University engineering department. She will also survey residential students about their water usage habits over the last six months, their perceptions of the campus drought education program, and their reactions to the incentives offered by the program (water-saving competitions, free water-saving devices, etc.) She will report her findings to the program’s steering committee and administrators.

No. Although the researcher will systematically survey other students and will be collecting information about them, her intention is to assess the effectiveness of the conservation program.

The researcher designs an online survey to collect information that may help understand factors that influence the residential students’ responses to the conservation program. She asks questions about green attitudes and behaviors, positions on social and political issues, as well as motivation and narcissism.

Campus IRB Guides

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  • Policy & Compliance
  • Human Subjects

Definition of Human Subjects Research

  • Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or
  • Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens."

Decision Tool. Am I doing Human Subjects Research? Find out here.

Decision Tool: Am I Doing Human Subjects Research?  

The questionnaire is a tool to assist you with determining whether your project involves non-exempt human subjects research, meets the criteria for exempt human subjects research, or does not involve human subjects research. 

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Human Subjects Research Infographic

This resource summarizes the definition of human subjects research and provides examples of human subjects research projects. It also describes what you will need when you are preparing your NIH application and what is required if you are funded.

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Exempt Human Subjects Research Infographic

This resource is a guide to simplify the understanding of the exemptions from the federal regulations for the protection of human subjects research. It summarizes Exemptions 1, 2, 3, 4, 5, 6, 7 and 8, providing basic definitions, examples of studies that meet and do not meet the criteria of the exemption, and aspects one must consider when engaged in exempt or non-exempt human subjects research. 

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Research Involving Private Information or Biospecimens Flowchart

Studies involving the use of human specimens or data may or may not be considered to be research involving human subjects, depending on the details of the materials to be used. Use this flowchart to help determine if studies involving private information or biospecimens may meet the definition of human subjects research.

Public Health Surveillance Exclusions

Public Health Surveillance Exclusions

Learn about research activities which may qualify for a public health surveillance exclusion. Find useful information, key resources, and instructions for NIH applicants and offerors.

This page last updated on: January 13, 2020

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Special Research Program Guidelines

Definition and purpose.

Special Research Programs (SRPs) exist at UC Irvine to provide a structure for collaborative research activities that do not fit the definition and purpose of an Organized Research Unit (ORU), a Campus Center, or a School Center. An SRP may, for example, be formed in response to a special funding opportunity, as the campus branch of a University of California research program, in conjunction with a major Federal center grant, or as part of a federal or state governmental initiative. An SRP may also coordinate and administer unique research technologies or facilities used by researchers from multiple disciplines and schools. SRPs may be established for three or five years or, in special circumstances, an indefinite period.

The Office of the President's Administrative Policies and Procedures Concerning Organized Research Units specify that the term "Center" may be used for research units not formally constituted as ORUs. An SRP that is based exclusively at UC Irvine may, therefore, use the title “Center”. An SRP that is a branch of a University of California research program may use whatever title has been designated system-wide.

Establishment and Administrative Procedures

Designation as a Special Research Program is approved by the Vice Chancellor for Research, in consultation with the Academic Senate Council on Research, Computing, and Libraries (CORCL).

The research goals of the SRP should complement the academic goals of the University. Faculty loosely organized around a common set of research problems do not constitute an SRP.

An SRP may not be established if its objectives are essentially the same as those of an existing department or research unit.

Generally, the establishment or renewal date for SRPs will be January 1 or July 1.

Appointment of Director

The Director of an SRP must be a tenured faculty member and is generally appointed by the Vice Chancellor for Research.

The Director reports to the Vice Chancellor for Research and is responsible for the administrative functions of the SRP, as well as for guidance of the unit's activities in accordance with its established goals. Prior to and during their appointment, Directors are asked to disclose any potential conflicts of interest with their role as Director of the SRP.  The Director is reviewed at the same time as the SRP.

Administrative Operations

Faculty affiliated with SRPs are expected to follow all University of California policies and the policies set forth by their respective academic departments related to academic responsibilities, including teaching workload, faculty commitment of effort, faculty compensation, honoraria, travel, and sabbatical leave. The SRP reports to the Vice Chancellor for Research and must follow administrative review and approval processes set forth by the Vice Chancellor and/or campus policy. All employment opportunities in an SRP will be created and filled in accordance with University policies and procedures, and with full awareness of the normally finite life of an SRP.

An SRP does not have jurisdiction over courses or curricula and cannot offer formal courses for credit. It may perform other academic functions ordinarily carried out by departments of instruction and Organized Research Units; that is, organize research conferences and meetings, advise on graduate curricula, support graduate research, and manage training programs in conjunction with departments of instruction. It may employ graduate students and conduct other administrative activities in the pursuit of its goals.

In support of the University’s commitment to attaining excellence through diversity, every SRP is expected to maintain an environment that values differences and is free from discrimination and harassment.

Application and Review Procedures

An SRP may be proposed by any group of faculty members. They should first consider the appropriateness of the proposed SRP as a School Center, a Campus Center, or an Organized Research Unit and, if appropriate, apply under those mechanisms.

A written proposal for the SRP (typically no more than 20 pages) should be submitted and address the following points:

  • Scholarly justification. Describe the important research justifications for establishing the SRP.  Explain why the proposed SRP is not appropriate as an ORU or school/campus center.
  • Describe the investigators who will be involved in the SRP, how they will be involved, and the importance of the SRP to their research.
  • Describe how the SRP will be administered, and what resources will be available (space, administration, financial).  Describe the proposed Director and any other leadership.  Documents for commitments should be included as attachments.
  • If the proposed SRP is based on a funding initiative, provide information about the funding, including magnitude and duration, and copies of grant reviews.
  • Value added. Describe how establishment of the SRP will provide value added to the university, and what opportunities would be missed if it did not exist.

The SRP proposal will be reviewed within the Office of Research (OR).  The review will be carried by an ad hoc committee, with representation from the Academic Senate Council on Research, Computing, and Libraries (CORCL).  After the review, the Vice Chancellor for Research may establish the SRP and appoint the Director, typically for a period of 5 years.

Periodic reviews of SRPs (typically every 5 years) will be conducted by OR in consultation with CORCL. An ad-hoc review committee will be constituted by OR that includes a CORCL representative. The review will assess the SRP’s activities with regard to the stated purpose, present functioning, and future plans. Detailed instructions will be provided to the SRP in advance of the review. The SRP will be evaluated for its progress and scholarly and scientific contributions, similar to the review of an Organized Research Unit. CORCL will be provided with the review. The VCR may continue the SRP and reappoint the Director.

When a program review is concluded with the continuation of the SRP and/or Director for an additional term, any suggestions or comments that the Vice Chancellor feels may be helpful in planning for the next term will be communicated in writing to the SRP Director. The Director will be asked to respond to those comments in the next annual report.

In addition to the program review, each SRP may periodically undergo an administrative review. This review of the management and operations of the SRP may take place before a leadership transition, such as when a new Director is appointed, or in conjunction with a regularly scheduled SRP program review, or as needed.

Budget and Financial Considerations

Activities of SRPs may be funded by a combination of University budget allocations, extramural funds, service income or endowments. Funding allocations from the Office of Research, if any, will be based on the fiscal year, July 1st to June 30th.

Annual Reports

At the end of each academic year, SRPs shall submit to the Vice Chancellor for Research an annual report that includes:

  • A summary of activities carried out by the SRP during the past year.
  • Any significant changes during the past year.
  • Numbers of FTE of professional, technical, administrative and clerical personnel employed.
  • Sources and amounts (on an annual basis) of support funds, including income from the sale of publications and from other services.
  • Expenditures, distinguishing use of funds for administrative support, matching funds, direct research and other specific uses. A copy of the June 30th Final Ledger will satisfy this requirement.
  • Any other information relevant to a unit's effectiveness, including updated plans.

Inquiries and Questions

Requests for consideration as an SRP should be directed to:

Vice Chancellor for Research Office of Research [email protected]

Questions About Special Research Programs?

Hung Fan Associate Vice Chancellor for Strategic Initiatives [email protected]

Jill Yonago Kay Director, Research Policy (949) 824-1410 [email protected]

Human Research Protection Program Office of Research Regulatory Support

Defining research with human subjects.

HHS regulations for the protection of human subjects involved in research apply to projects determined to be research involving human subjects.

Revised Common Rule (2018 Requirements): Research and Human Subject Definitions

Research means a systematic investigation, including research development, testing, and evaluation, designed to develop or contribute to generalizable knowledge. Activities that meet this definition constitute research for purposes of this policy, whether or not they are conducted or supported under a program that is considered research for other purposes. For example, some demonstration and service programs may include research activities.

For purposes of the revised Common Rule regulations for the protection of human subjects, the following activities are deemed not to be research:

(1) Scholarly and journalistic activities (e.g., oral history, journalism, biography, literary criticism, legal research, and historical scholarship), including the collection and use of information, that focus directly on the specific individuals about whom the information is collected.

(2) Public health surveillance activities, including the collection and testing of information or biospecimens, conducted, supported, requested, ordered, required, or authorized by a public health authority. Such activities are limited to those necessary to allow a public health authority to identify, monitor, assess, or investigate potential public health signals, onsets of disease outbreaks, or conditions of public health importance (including trends, signals, risk factors, patterns in diseases, or increases in injuries from using consumer products). Such activities include those associated with providing timely situational awareness and priority setting during the course of an event or crisis that threatens public health (including natural or man-made disasters).

(3) Collection and analysis of information, biospecimens, or records by or for a criminal justice agency for activities authorized by law or court order solely for criminal justice or criminal investigative purposes.

(4) Authorized operational activities (as determined by each agency) in support of intelligence, homeland security, defense, or other national security missions.

MSU Requirements

If the activity meets this definition, even if the activity is conducted under a demonstration, service, or other program, the activity is considered research. Presently, MSU master's theses and Ph.D. dissertations are considered to be designed to develop or contribute to generalizable knowledge.

However, in some MSU courses, students collect data from humans by using professional research methods, even though the student's work is not expected to contribute to generalizable knowledge. For those student classroom activities that do not meet the federal definition of research because they are not designed to develop or contribute to generalizable knowledge, IRB review is not required. In these instances, the instructors are responsible for assuring that human subjects are protected. However, if such activities meet the definition of human subject research or a clinical investigation, the activity must be reviewed and approved by the IRB prior to initiation of the activity. Visit HRPP Manual Section 6-9-A, Special Considerations: Student Classroom Research , for more information.

Human subject means a living individual about whom an investigator (whether professional or student) conducting research:

Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or

Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.

Intervention includes both physical procedures by which information or biospecimens are gathered (e.g., venipuncture) and manipulations of the subject or the subject's environment that are performed for research purposes.

Interaction includes communication or interpersonal contact between investigator and subject.

Private information includes information about behavior that occurs in a context in which an individual can reasonably expect that no observation or recording is taking place, and information that has been provided for specific purposes by an individual and that the individual can reasonably expect will not be made public (e.g., a medical record).

Identifiable private information is private information for which the identity of the subject is or may readily be ascertained by the investigator or associated with the information.

An identifiable biospecimen is a biospecimen for which the identity of the subject is or may readily be ascertained by the investigator or associated with the biospecimen.

Pre-2018 Common Rule: Research and Human Subject Definitions

Research means a systematic investigation, including research development, testing and evaluation, designed to develop or contribute to generalizable knowledge. Activities which meet this definition constitute research for purposes of this policy, whether or not they are conducted or supported under a program which is considered research for other purposes. For example, some demonstration and service programs may include research activities.

Human subject means a living individual about whom an investigator (whether professional or student) conducting research obtains

Data through intervention or interaction with the individual, or

Identifiable private information.

Interventio n includes both physical procedures by which data are gathered (for example, venipuncture) and manipulations of the subject or the subject's environment that are performed for research purposes.

Private information includes information about behavior that occurs in a context in which an individual can reasonably expect that no observation or recording is taking place, and information which has been provided for specific purposes by an individual and which the individual can reasonably expect will not be made public (for example, a medical record). Private information must be individually identifiable (i.e., the identity of the subject is or may readily be ascertained by the investigator or associated with the information) in order for obtaining the information to constitute research involving human subjects.

Contact the MSU IRB with Any Questions

Application of the definitions for research involving human subjects to certain activities is not always straightforward. Contact the MSU IRB office with any questions.

Activities Requiring Review

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  • PMC9650106.1 ; 2022 Jan 28
  • ➤ PMC9650106.2; 2022 Nov 1

Research Software vs. Research Data I: Towards a Research Data definition in the Open Science context

Teresa gomez-diaz.

1 Laboraroire d'Informatique Gaspard-Monge, CNRS, Paris-Est, France

Tomas Recio

2 Universidad Antonio de Nebrija, Madrid, Spain

Associated Data

Underlying data.

Data underlying the arguments presented in this article can be found in the references, footnotes and Box 1 .

Version Changes

Revised. amendments from version 1.

This version considers the comments of the reviewers to better explain and illustrate some of the concepts presented in the article. In particular we have stressed the importance of the scientific production context for the RS and RD definitions. We have as well introduced new references related to the concepts of data and information, to further illustrate our view on the complexity of the data concept, and a new reference to complete the studied landscape for the proposed RD definition. As asked by the Referees, we have moved the translations of French and Spanish quotes to the main text. See our answers to the referee reports to complete the differences with the version 1 of this article.

Peer Review Summary

Background: Research Software is a concept that has been only recently clarified. In this paper we address the need for a similar enlightenment concerning the Research Data concept.

Methods: Our contribution begins by reviewing the Research Software definition, which includes the analysis of software as a legal concept, followed by the study of its production in the research environment and within the Open Science framework. Then we explore the challenges of a data definition and some of the Research Data definitions proposed in the literature.

Results: We propose a Research Data concept featuring three characteristics: the data should be produced (collected, processed, analyzed, shared & disseminated) to answer a scientific question, by a scientific team, and has yield a result published or disseminated in some article or scientific contribution of any kind.

Conclusions: The analysis of this definition and the context in which it is proposed provides some answers to the Borgman’s conundrum challenges, that is, which Research Data might be shared, by whom, with whom, under what conditions, why, and to what effects. They are completed with answers to the questions: how? and where?

1. Introduction

Each particle of the Universe, known or unknown by what is widely accepted as Science, is information. Different datasets can be associated to each particle to convey information, as, for example: where has this particle been discovered? By whom? At what time? Is this particle a constituent element of a rock, or a plant, or … ? Indeed, as living entities of the Earth planet, … we are all part of this Universe and every atom in our bodies came from a star that exploded … , therefore … we are all stardust … . 1

So long ago that we have never been able to give a precise date, information started to be fixed in cave paintings, figurines, and bone cravings, which have been found in caves like Altamira 2 or Lascaux 3 . That is, some human beings intentionally fixed information on a support. Much more recently, languages have been developed to deal with information, fixing and exchanging it in clay bricks, papyrus, monument walls, and paper books. Even more recently, information has been fixed in films, photographs, and has finally adopted digital formats.

Scientists study all kinds of subjects and objects: persons, animals, trees and plants and other living beings, philosophies and philosophers, artists and artworks, mathematical theories, music, languages, societies, cities, Earth and many other planets and exoplanets, clouds, weather and climate, stars and galaxies, as well as other animate or inanimate objects, molecules, particles, nanoparticles and viruses, nowadays including digital objects such as computer programs. Some of these items, like images, texts, and music etc. may have associated intellectual property rights; but others, like statistics or geographical data, may not. Yet, they may be affected by other legal contexts, such as, for example, the one given by the EU INSPIRE Directive 1 for spatial data, concerning any data with a direct or indirect reference to a specific location or geographical area.

Now, in our digital era, most of the above subjects under consideration are handled by humans using computers, through numerical data. Scientists present new theories and results built and produced with numerical simulations and through the analysis of numerical datasets. They are usually stored in databases, manipulated or produced in digital environments using existing software, either Free/Open Source Software (FLOSS) 4 or commercial, or by means of software developed by research teams to address specific problems 2 , 3 .

In this specific scientific context, the aims and developments of Open Science practices are particularly relevant. Indeed, as remarked by 4 : "We must all accept that science is data and that data are science … ". Therefore, in this article we take into consideration the following definition of Open Science, in which the open access to Research Data (RD) and to Research Software (RS) is part of the core pillars 5 :

Open Science is the political and legal framework where research outputs are shared and disseminated in order to be rendered visible, accessible and reusable.

A more transversal and global vision can be found in the UNESCO Recommendation on Open Science 5 , 6 . See also 7 for another relevant example of ongoing work on the Open Science concept. But in this paper, following the analysis and the conclusions of 5 , we focus here on this restricted framework as more suitable for our purposes.

Among the most important kinds of research outputs of any scientific work, we focus on the trio formed by articles , software and data. Actually, among all the possible duos, the couple RS and RD present more similarities, although a light list of differences between software and data have been mentioned in 8 and 9 . On the other hand, regarding other duos, we think that differences are much stronger. For instance, unlike the dissemination of published articles, usually at the hands of scientific editors, the dissemination of software and data that have been produced in the research process is mostly at the hands of their producers, the research team. The analogies between RS and RD have been already summarily highlighted in 10 , such as those concerning the release protocols of RD and RS, which raises the same questions, at the same time, in the production context. As a direct consequence, it seems suitable to propose a similar dissemination procedure for both kinds of research outputs 11 .

Indeed, let us remark that, as mentioned in 11 , 12 , both RS and RD dissemination might involve the use of licenses to set their sharing conditions, such a core issue. Information about RS licenses and licensing can be found at the Free Software Foundation (FSF) 6 , the Open Source Initiative (OSI) 7 , and the Software Package Data Exchange (SPDX) 8 . The SPDX licenses list also includes licenses that can be used for databases, like the Creative Commons licenses 9 or the Open Data Commons Licenses 10 , see for example 13 .

Other similarities regarding RS and RD are related to management plans: for example, Data Management Plans are nowadays required by research funders (see for example 14 , 15 ) and, in the same mood, Software Management Plans have been recently proposed, see 16 and the references therein.

Finally, concerning evaluation, as observed in 3 , similar evaluation protocols can be proposed for both RS and RD.

Leaving aside the common issues in RS and RD for licensing and management plans, that have been already studied in the above mentioned references, the RS and RD dissemination and evaluation analogies are more closely analyzed in the article 12 that follows the present work, including FAIR related issues 17 and 5Stars Open Data 11 . On the other hand, in the current article we focus on the conceptual analogies of RS and RD, and their consequences (see Section 5 ).

As we will argue in the next sections, a definition for RD can be proposed following the main features of the RS definition given in our recent work 3 , 18 . However, we consider that formulating such proposal still remains a challenging issue that we dare to address here. In fact, although one of the most widely accepted RD definitions is the one proposed by the OECD (2007) 19 , other works have shown the difficulties to fix such a definition 20 , 21 . Indeed, establishing this concept has important and not well settled consequences, for example, concerning the context of RD sharing, as highlighted by C. Borgman in 22 :

Data sharing is thus a conundrum. […] The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice.

It is the intention of our present work to bring some answers to these questions.

The plan of this article is as follows. The next section introduces the concept of RS after a summary presentation of the key points involved in the notion of software as a legal object. Section 3 is devoted to discuss the different issues involved in the challenge towards a precise definition of data (in the more comprehensive sense of this concept). Section 4 describes partially the landscape of existing work addressing the RD definition, enumerating, again, some difficulties to settle such a concept.

There we propose our RD definition, based in three characteristics: the data should be produced (collected, processed, analyzed, shared & disseminated) to answer a scientific question, by a scientific team, and has yield a result published or disseminated in some article or scientific contribution of any kind. Comparisons with other RD definitions are examined.

The last and final section concludes with the proposition of some specific answers to Borgman’s conundrum challenges 22 . Let us remark that these conundrum challenges involve as well RD dissemination issues that are studied in detail in the article that follows this work 12 , which also includes the analysis of RD evaluation and FAIR issues.

The reader of the current work should be aware that its authors are not legal experts. Thus, in order to address our goals in this article, we have analyzed (French, Spanish, European and USA) legal documents and articles written by law experts 1 , 13 , 20 , 21 , 23 – 34 , but from the scientist’s point of view. Yet, a deeper understanding of legal issues may require the intervention of legal specialists.

Following the standard scientific protocol, the authors of this work (mathematicians) have, first, detected a problem – the need to provide a more suitable RD definition. Then, they have observed the involved landscape and studied the related literature; have focused on and structured different components of the problem; finally, they have proposed what they believe could be a solution for the challenge under consideration. As in any other research work, we, authors of the present work, believe that our proposal should be examined by the scientific community in order to evaluate its correctness, and to help improving it, if needed, advancing towards a better solution.

2. Research Software

In this section we bring together some of the existing definitions of software as a legal object (see references below). We also recall our definition of RS coming from 3 , 18 .

2.1. Software is a legal object

In what follows we refer to the documents 26 – 29 dealing with a definition of software as a legal object. Note that the terms computer program , software , logiciel (in French), programa de ordenador (in Spanish) are synonyms in this work. The terms source code (or código fuente in Spanish), compiled code (or code compilé , código compilado ) correspond to subsets of a computer program.

The first definition that we would like to consider comes from the Directive 2009/24/EC of the European Parliament 26 , that states:

For the purpose of this Directive, the term “computer program” shall include programs in any form, including those which are incorporated into hardware. This term also includes preparatory design work leading to the development of a computer program provided that the nature of the preparatory work is such that a computer program can result from it at a later stage.

Moreover, in the Spanish Boletín Oficial del Estado n. 97 (1996) 27 we can find 12 :

A los efectos de la presente Ley se entenderá por programa de ordenador toda secuencia de instrucciones o indicaciones destinadas a ser utilizadas, directa o indirectamente, en un sistema informático para realizar una función o una tarea o para obtener un resultado determinado, cualquiera que fuere su forma de expresión y fijación. […] comprenderá también su documentación preparatoria.
[For the purpose of this Law, a computer program shall be understood as any sequence of instructions or indications intended to be used, directly or indirectly, in a computer system to perform a function or a task or to obtain a certain result, whatever expression and fixation form it can take. […] it can also include its preparatory documentation.]

Likewise, in the French Journal officiel de la République française (1982) 29 we can read:

Logiciel : Ensemble des programmes, procédés et règles, et éventuellement de la documentation, relatifs au fonctionnement d’un ensemble de traitement de données (en anglais : software). [ Software : All programs, procedures and rules, and possibly documentation, related to the performance of some data processing (in English: software).].

And in the French Code de la propriété intellectuelle (current regulation) 28 , Article L112-2, we can find:

Les logiciels, y compris le matériel de conception préparatoire, sont considérés notamment comme œuvres de l’esprit au sens du présent code. [Software, including the preparatory material, is considered as works protected by the present code.]

We observe that, in the above mentioned documents, the concept of software or computer program, logiciel or programa de ordenador refers to the set of instructions, of any kind, that are to be used in a computer system (including hardware). It is a work protected by the author rights. It can include the source code, the compiled code, and, eventually, the associated documentation and the preparatory material. It can be related to some data processing or to other tasks to be implemented in a computer system.

In order to complete this legal vision of the software concept we refer to item (11) of 26 :

For the avoidance of doubt, it has to be made clear that only the expression of a computer program is protected and that ideas and principles which underlie any element of a program, including those which underlie its interfaces, are not protected by copyright under this Directive. In accordance with this principle of copyright, to the extent that logic, algorithms and programming languages comprise ideas and principles, those ideas and principles are not protected under this Directive. In accordance with the legislation and case-law of the Member States and the international copyright conventions, the expression of those ideas and principles is to be protected by copyright.

Indeed, there is a difference between the concepts of algorithm and software from the legal point of view, as there is a difference between the mere idea for the plot of a novel and the final written work. Several persons could have the same idea for the plot, but its realization in a final document will deliver different novels by different writers, as the novel will reflect the personality of its author. Similarly, an algorithm remains on the side of ideas, and as such, it is not protected by copyright laws. On the other side, poetry, novels and software are protected under copyright laws. Moreover, a computer program can implement several algorithms, and the same algorithm can be implemented in several programs.

Finally, note the nature of software as a digital object underlying all the above considerations.

2.2. Software as a research output: definition of Research Software

Beyond the vision of software as a legal object, we bring here the concept of Research Software (RS) as a scientific production, as defined in 3 , 18 :

Research Software is a well identified set of code that has been written by a (again, well identified) research team. It is software that has been built and used to produce a result published or disseminated in some article or scientific contribution. Each research software encloses a set (of files) that contains the source code and the compiled code. It can also include other elements as the documentation, specifications, use cases, a test suite, examples of input data and corresponding output data, and even preparatory material.

Thus, Section 2.1 of 3 introduces several definitions regarding the notions of scientific and research software as found in the literature, as a way to support the above definition, while 18 provides complementary analysis on this concept. Note that this definition does not take into consideration if the RS status is “ongoing” or “finalized”, and does not regard if the RS has been disseminated, its quality or scope, its size, or if it is documented, maintained, used only by the development team for the production of an article, or it is currently used in several labs … 2 .

Different recent works on the RS concept can be found, for example, on 35 and the references therein, where the RDA FAIR for Research Software (FAIR4RS) working group 13 proposes a definition of RS full of subtleties and details, albeit, perhaps, of complex interpretation in practice.

We observe, following our proposed definition, that RS can be characterized through three main features:

  • • the goal of the RS development is to do research. As stated by D. Kelly: it is developed to answer a scientific question 36 ,
  • • it has been written by a research team,
  • • the RS is involved in the obtention of the results presented in scientific articles (as the most important means for scientific exchange are still articles published in scientific journals) or by any other kind of recognized scientific means.

Note that documentation, licenses, examples, data, tests, Software Management Plans and other related information and materials can also be part of the set of files that constitutes a specific RS. Remark that the data we refer to in this list will qualify as RD (as defined in Section 4 ) if they have been produced by a research team, that can be the same team that has produced the RS, but not necessarily (notice that the role of the research team involved in the development of a RS has been thoroughly studied in Section 2.2 of 3 ). Indeed, Section 2.1 above shows that the preparatory design work and documentation are part of the software, and these are documents that can be included in the released version of a RS, following the choice of the RS producer team. There can be other elements as for example tests, input and output files to illustrate how to use the RS, licenses, etc. To include these elements in the released RS correspond to best practices that facilitate RS reuse. In our view, the release of a RD (see Section 4 and 12 ) can follow similar practices, that is, to include a documentation, some use examples, a license, a data management plan … this is to be decided by the producer team.

The initial origin of this RS definition is to be found in 2 , that contains a detailed and complete study comparing articles and software produced in a typical (French) research lab. As remarked in received comments and Referee reports to this article, this RS definition (as well as the RD definition proposed in Section 4 ) is placed in what can be considered as a narrow context, emphasizing the role of the scientific production context. The relevance of such context is widely accepted by the scientific community in the case of articles: not every article published in a newspaper qualifies as a research article, that requires to be released in a scientific journal and subject to a referee procedure. Similarly, the importance of the production context has been already highlighted in the case of data, regarding those that qualify as cultural data 23 .

Besides, our definition does not include as RS neither commercial software nor existing Free/Open Source Software (FLOSS) or other software developed outside Academia, a restriction which does not exclude that RS (or research articles, data...) can be produced in other contexts like private laboratories, for example. Rather, this means that we are not considering here differences between private or public funding of research. As a matter of fact, a research team can use RS produced by other teams for their scientific work, as well as FLOSS or other software developed outside the scientific community, but the present work is centered in the making-of aspects which are pertinent for the proposed definition. Obviously, a RS that has been initially developed in a research lab can evolve to become commercial software or just evolve outside its initial academic context. The above definition concerns its early, academic life.

Moreover, a RS development team may not just use software produced by other teams, but also include external software as a component inside the ongoing computer program, a procedure that could be facilitated by the FLOSS licenses. We consider that this external component qualifies as RS if it complies with the three characteristics given in the above definition. Moreover, the producers of the final RS should clearly identify the included external components, and their licenses. They should also highlight the used or included RS components, by means of a correct citation form 3 , 8 , 11 , 37 – 39 .

Furthermore, a RS may involve other software components that can remain external , and that are not included in the RS development and release. It is then left to the users the task to recover and install them, and to assemble these external components in order to get a running environment. Another situation, as the one we have analyzed in 18 , deals with the RS developed within a given software environment which is not perhaps fully disseminated with the RS. For example, the GeoGebra code developed by T. Recio and collaborators 14 does not disseminate the whole GeoGebra software 15 , but only some parts that are relevant for their goals and that include their code.

See 2 , 3 , 18 for more discussions and references that have motivated the RS definition we have sketched in this section.

3. The challenges of a data definition

As stated in 40 :

“Data” is a difficult concept to define, as data may take many forms, both physical and digital.

For example, unlike software, data is, as a legal object, much more difficult to grasp. In fact, according to 33 , data is not a legal concept, as it does not fall into a specific legal regime. For example, data can be either mere information or une œuvre , a work with associated intellectual property, when it involves creative choices in its production that reflect the author’s personality 32 . The Knowledge Exchange report 21 provides guidelines that can be used to assess the legal status of research data, and mentions:

It is important to know the legal status of the data to be shared. […] not all data are protected by law, and not every use of protected research data requires the author’s consent. […] Whether data are in fact protected must be determined on a case-by-case basis.

In relation with this legal context of data sharing and reuse, a very complete framework is introduced in 23 :

Les problématiques liées à la réutilisation nécessitent une maîtrise parfaite du droit de la propriété intellectuelle, du droit à l’image, du droit des données personnelles, du respect à la vie privée et du secret de la statistique, du droit des affaires, du droit de la concurrence, du droit de la culture, du droit européen et des règles de l’économie publique. [The issues related to reuse require a perfect mastership of intellectual property rights, image rights, personal data rights, respect for private life and statistical confidentiality, business law, competition law, cultural law, European law and the rules of the public economy.]

Another list of legal issues related to data is provided by 33 , similar but not equal to the one in the previous quote. Yet, it is also necessary to consider other legal contexts concerning, for example, les données couvertes par le secret médical ou le secret industriel et commercial [Data covered by medical secret or by the industrial and commercial secret] 16 . Let us remark that the section Applicable Laws and Regulations of 15 provides a broad overview of regulatory aspects that need to be taken into consideration when developing disciplinary RD management protocols in the European context. But, as declared in the introduction, it is not our intention to go deeper into these legal aspects, that should be also regarded from the perspective of many different laws.

The underlying problem is that data can refer to many different subjects or objects. We need to simplify the context to help us setting a manageable concept of research data adapted to the scientific framework. For this purpose we present here two relevant data definitions found in the data scientific literature.

The OECD data definition in its Glossary of Statistical Terms 17 states that:

DATA Definition: Characteristics or information, usually numerical, that are collected through observation. Context: Data is the physical representation of information in a manner suitable for communication, interpretation, or processing by human beings or by automatic means (Economic Commission for Europe of the United Nations (UNECE)), “Terminology on Statistical Metadata”, Conference of European Statisticians Statistical Standards and Studies, No. 53, Geneva, 2000.

Also, as a relevant precedent, let us quote here the data definition of the Committee for a Study on Promoting Access to Scientific and Technical Data for the Public Interest , as mentioned in 41 :

A data set is a collection of related data and information – generally numeric, word oriented, sound, and/or image – organized to permit search and retrieval or processing and reorganizing. Many data sets are resources from which specific data points, facts, or textual information is extracted for use in building a derivative data set or data product. A derivative data set, also called a value-added or transformative data set, is built from one or more preexisting data set(s) and frequently includes extractions from multiple data sets as well as original data (Committee for a Study on Promoting Access to Scientific and Technical Data for the Public Interest, 1999, p. 15).

We can notice that both definitions combine the concepts of data and information, yielding, again, to a challenging situation. Thus, to better grasp the connection between both terms we have consulted several sources of different nature, see Box 1 . Note that we can find in Box 1 that information among the data synonyms in the Larousse dictionary, but data is not among the information synonyms. On the other hand, Wikipedia mentions that both terms can be used interchangeably, but that they have different meanings.

A promenade around the data and information concepts.

I.1 Diccionario de la lengua española of the Real Academia Española

  • • Definition of dato ( https://dle.rae.es/dato )
  • – Del latín datum ‘lo que se da’.
  • – 1. m. Información sobre algo concreto que permite su conocimiento exacto o sirve para deducir las consecuencias derivadas de un hecho. A este problema le faltan datos numéricos.
  • – 2. m. Documento, testimonio, fundamento.
  • – 3. m. Inform. Información dispuesta de manera adecuada para su tratamiento por una computadora.
  • • Definition of información ( https://dle.rae.es/informaci%C3%B3n )
  • – Del latín informatio, - o ¯ nis ‘concepto’, ‘explicación de una palabra’.
  • – 1. f. Acción y efecto de informar.
  • – 2. f. Oficina donde se informa sobre algo.
  • – 3. f. Averiguación jurídica y legal de un hecho o delito.
  • – 4. f. Pruebas que se hacen de la calidad y circunstancias necesarias en una persona para un empleo u honor. U. m. en pl.
  • – 5. f. Comunicación o adquisición de conocimientos que permiten ampliar o precisar los que se poseen sobre una materia determinada.
  • – 6. f. Conocimientos comunicados o adquiridos mediante una información.
  • – 7. f. Biol. Propiedad intrínseca de ciertos biopolímeros, como los ácidos nucleicos, originada por la secuencia de las unidades componentes.
  • – 8. f. desus. Educación, instrucción.

I.2 Diccionnaire Larousse de la langue française

  • • Definition of donnée ( https://www.larousse.fr/dictionnaires/francais/donn%c3%a9e/26436 )
  • – Ce qui est connu ou admis comme tel, sur lequel on peut fonder un raisonnement, qui sert de point de départ pour une recherche (ex. Les données actuelles de la biologie).
  • – Idée fondamentale qui sert de point de départ, élément essentiel sur lequel est construit un ouvrage (ex. Les données d’une comédie).
  • – Renseignement qui sert de point d’appui (ex. Manquer de données pour faire une analyse approfondie).
  • – Représentation conventionnelle d’une information en vue de son traitement informatique.
  • – Dans un problème de mathématiques, hypothèse figurant dans l’énoncé.
  • – Résultats d’observations ou d’expériences faites délibérément ou à l’occasion d’autres tâches et soumis aux méthodes statistiques.
  • • Definition of information ( https://www.larousse.fr/dictionnaires/francais/information/42993 )
  • – Action d’informer quelqu’un, un groupe, de le tenir au courant des événements : La presse est un moyen d’information.
  • – Indication, renseignement, précision que l’on donne ou que l’on obtient sur quelqu’un ou quelque chose: Manquer d’informations sur les causes d’un accident. (Abréviation familière : info.)
  • – Tout événement, tout fait, tout jugement porté à la connaissance d’un public plus ou moins large, sous forme d’images, de textes, de discours, de sons. (Abréviation familière : info.)
  • – Nouvelle communiquée par une agence de presse, un journal, la radio, la télévision. (Abréviation familière : info.)
  • – Cybernétique. Mesure de la diversité des choix dans un répertoire de messages possibles.
  • – Droit. Instruction préparatoire, diligentée par le juge d’instruction en vue de rechercher et de rassembler les preuves d’une infraction, de découvrir l’auteur, de constituer à charge et à décharge le dossier du procès pénal. (Elle est close par un non-lieu ou par un renvoi devant une juridiction répressive. En matière criminelle, l’instruction est à double degré [juge d’instruction, chambre d’accusation].)
  • – Informatique. Élément de connaissance susceptible d’être représenté à l’aide de conventions pour être conservé, traité ou communiqué.

I.3 Wikipedia

Extract from the Data page of Wikipedia ( https://en.wikipedia.org/wiki/Data ):

Data are characteristics or information, usually numeric, that are collected through observation. In a more technical sense, data are a set of values of qualitative or quantitative variables about one or more persons or objects, while a datum (singular of data) is a single value of a single variable. […] Although the terms “data” and “information” are often used interchangeably, these terms have distinct meanings. […] data are sometimes said to be transformed into information when they are viewed in context or in post-analysis. However, […] data are simply units of information.

Moreover, in 42 and in the web page of ISKO 18 , when discussing in detail the concept of data, an etymological and linguistic vision is also the starting point, and among other sources also, it mentions Wikipedia. The conclusion in 42 (section 2.5):

Therefore, our conclusion of this Section is that Kaase’s (2001, 3251) definition seems the most fruitful one suggested thus far: Data are information on properties of units of analysis.

See also 43 – 45 where ours readers can find further reflections on the concepts of data, information, knowledge, understanding, evidence and wisdom.

Such reflections bring to us an eclectic panorama on the ingredients that could form a data definition and their relation with the concept of information, attesting the involved difficulties in such goal.

Focusing in the scientific context, we can illustrate this complexity in full terms referring to the French Code de l’environnement 30 . In its Article L-124-2 19 we can appreciate the subtleties of the definition of environmental data in the following description:

Est considérée comme information relative à l’environnement au sens du présent chapitre toute information disponible, quel qu’en soit le support, qui a pour objet : 1. L’état des éléments de l’environnement, notamment l’air, l’atmosphère, l’eau, le sol, les terres, les paysages, les sites naturels, les zones côtières ou marines et la diversité biologique, ainsi que les interactions entre ces éléments ; 2. Les décisions, les activités et les facteurs, notamment les substances, l’énergie, le bruit, les rayonnements, les déchets, les émissions, les déversements et autres rejets, susceptibles d’avoir des incidences sur l’état des éléments visés au point 1 ; 3. L’état de la santé humaine, la sécurité et les conditions de vie des personnes, les constructions et le patrimoine culturel, dans la mesure où ils sont ou peuvent être altérés par des éléments de l’environnement, des décisions, des activités ou des facteurs mentionnés ci-dessus ; 4. Les analyses des coûts et avantages ainsi que les hypothèses économiques utilisées dans le cadre des décisions et activités visées au point 2 ; 5. Les rapports établis par les autorités publiques ou pour leur compte sur l’application des dispositions législatives et réglementaires relatives à l’environnement. [For the purposes of this chapter, information relating to the environment is considered to be any information available, whatever the medium, the purpose of which is: 1. The state of the elements of the environment, namely the air, atmosphere, water, soil, land, landscapes, natural sites, coastal or marine areas and biological diversity, as well as the interactions between these elements; 2. Decisions, activities and factors, namely substances, energy, noise, radiation, waste, emissions, spills and other discharges, likely to have an impact on the state of the elements concerned in point 1; 3. The state of human health, safety and living conditions of people, buildings and cultural heritage, insofar as they are or may be altered by elements of the environment, decisions, activities or the factors mentioned above; 4. The analyses of costs and advantages as well as the economic assumptions used in the context of the decisions and activities referred to in point 2; 5. Reports drawn up by public authorities or on their behalf on the application of legislative and regulatory provisions related to the environment. ] .

To be compared with the much more easier to understand concept of geographical data as introduced by the Article L127-1 20 of the same Code de l’environnement 30 :

Donnée géographique, toute donnée faisant directement ou indirectement référence à un lieu spécifique ou une zone géographique ; [Geographic data, any data that refers directly or indirectly to a specific place or geographic area;]

Another example to show the complexity of the representation and manipulation of data and information that we would like to mention here corresponds to the linguistic research work developed at the Laboratoire d’informatique Gaspard-Monge, where one of the authors of the present work resides, see for example the doctoral thesis 46 , 47 .

An additional factor that adds complexity to the concept of scientific data has to do with the potential use(s) and sharing of these data. As remarked by the OECD Glossary of Statistical Terms 21 :

The context provides detailed background information about the definition, its relevance, and in the case of data element definitions, the appropriate use(s) of the element described.

The importance of the context is also noted in 22 :

… research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context.

This opens the door to a series of complex issues. For example, to the need for complementary, technical information or documentation associated to a given dataset in order to facilitate its reuse. See 48 (p.16) (and also 40 ) that highlights the difficulties raised by the concept of temperature related data, as explained by a CENS biologist:

There are hundreds of ways to measure temperature. “The temperature is 98” is low-value compared to, “the temperature of the surface, measured by the infrared thermopile, model number XYZ, is 98.” That means it is measuring a proxy for a temperature, rather than being in contact with a probe, and it is measuring from a distance. The accuracy is plus or minus.05 of a degree. I [also] want to know that it was taken outside versus inside a controlled environment, how long it had been in place, and the last time it was calibrated, which might tell me whether it has drifted.

Another instance to further illustrate the complexity of technical information associated to a data set in the STRENDA Guidelines that have been developed to assist authors to provide data describing their investigations of enzyme activities. 22

Other examples from the collection of complex issues associated to data use(s) and sharing conditions are:

  • • 23 (p.11) The concept of right of access , involving the meaning of public information, requiring three characteristics: the existence of a document, of administrative nature, and in the possession of the Public Administration.
… l’utilisation d’une information publique par toute personne qui le souhaite à d’autres fins que celles de la mission de service public pour les besoins de laquelle les documents ont été élaborés ou détenus. [… the use of public information by anyone who wishes it for other purposes than those of the original needs for which the documents were prepared or held by the public service mission.].

finds a strong formulation for scientific data in 49 :

The value of data lies in their use. Full and open access to scientific data should be adopted as the international norm for the exchange of scientific data derived from publicly funded research. The public-good interests in the full and open access to and use of scientific data need to be balanced against legitimate concerns for the protection of national security, individual privacy, and intellectual property.

For more information on ‘re-use’ see, for example, 20 , 25 , 32 , 48 .

  • • 23 (p.10) The evolution from the right of access to documents from the Public Administration to the right of reuse of public information.
  • 1 public information derived from a document produced or hold by the Administration,
  • 2 there are no other intellectual property rights owners,
  • 3 data do not affect personal or private issues of people.
  • • 22 (p. 1060) The concept of data sharing in a scientific context:
For the purposes of this article, data sharing is the release of research data for use by others. Release may take many forms, from private exchange upon request to deposit in a public data collection. Posting datasets on a public website or providing them to a journal as supplementary materials also qualifies as sharing.
  • • The importance of licenses to set the sharing and re-use conditions as highlighted in 5 , 11 , 13 , 50 .
Open data are data in an open format that can be freely used, re-used and shared by anyone for any purpose.
  • • 53 also provides a classification of scientific data in four types: observational, experimental, computational and reference data sets.
  • • The FAIR guiding principles 17 are studied in the article that follows this work 12 .
  • • The recent and relevant introduction of the term Big Data 24 , that refers to the exploitation of larger amounts of data. They can appear in medical research, meteorology, genomics, astronomy, demographic studies … and in real life, as we live all in a digital world where we generate large amounts of data every day by the use of phones and computers to do work, travelling, e-mail, business, shopping etc. 42 . Big data is associated mainly to four “V” characteristics: Volume, Variety, Velocity, Veracity, and others can be found for example in the mentioned Wikipedia page and in the references mentioned there. See also 54 .

Closing the conceptual loop developed in this section, let us remark, again, that legal aspects arise quite naturally in the above list of items. Among others, some aspects are related to the fact that the datasets are usually organized in databases, where data is arranged in a systematic or methodical way and is individually accessible by electronic or other means 13 , 20 , 21 , 24 , 28 . The intellectual property rights can apply to the content of a database, the disposition of its elements and to the tools that make it working (for example software). The sui generis database rights primarily protects the producer of the database and may prohibit, for instance, the extraction and/or reuse of all or a substantial part of its content 24 .

Finally, let us quote here this paragraph from the OpenAIRE project report 20 (p.19) that highlights the difficulties to set a research data definition in the context of legal studies:

From a legal point of view, one of the very basic questions of this study is which kind of potentially protected data we are dealing with in the context of e-infrastructures for publications and research data such as OpenAIREplus. The term “research data” in this context does not seem to be very helpful, since there is no common definition of what research data basically is. It seems rather that every author or research study in this context uses its own definition of the term. Therefore, the term “research data” will not be strictly defined, but will include any kind of data produced in the course of scientific research, such as databases of raw data, tables, graphics, pictures or whatever else.

We can remark, that although the preceding quote does not provide a strict definition of research data, it highlights the relevance of the production context, as we have already mentioned in Section 2.2 .

4. Data as a research output: towards a definition for Research Data

In the previous section we have exemplified the complexity of the concept of data through different approaches. In this section we focus on the research data concept, proposing here a RD definition, directly derived from the RS definition presented in Section 2.2 . To this aim we start by gathering some previous definitions that are particularly relevant for our proposal.

The first one is the White House document 34 , and in particular the Intangible property section where we can find the following definition.

Research data is defined as the recorded factual material commonly accepted in the scientific community as necessary to validate research findings, but not any of the following: preliminary analyses, drafts of scientific papers, plans for future research, peer reviews, or communications with colleagues.

Let us remark that, according to 34 this definition explicitly excludes:

(A) Trade secrets, commercial information, materials necessary to be held confidential by a researcher until they are published, or similar information which is protected under law; and (B) Personnel and medical information and similar information the disclosure of which would constitute a clearly unwarranted invasion of personal privacy, such as information that could be used to identify a particular person in a research study.

The above RD definition has been extended in 55 , emphasizing, among other aspects, the scientific purpose of the recorded factual material and the link with the scientific community.

A second basic inspiration for our proposal is the Directive for Open Data 25 that states:

(Article 2 (27)) The volume of research data generated is growing exponentially and has potential for re-use beyond the scientific community. […] Research data includes statistics, results of experiments, measurements, observations resulting from fieldwork, survey results, interview recordings and images. It also includes meta-data, specifications and other digital objects. Research data is different from scientific articles reporting and commenting on findings resulting from their scientific research. […] (Article 2 (9)) ‘research data’ means documents in a digital form, other than scientific publications, which are collected or produced in the course of scientific research activities and are used as evidence in the research process, or are commonly accepted in the research community as necessary to validate research findings and results;

The third pillar that we consider essential to support our proposal is the OECD report 19 (p.13) where we can find one of the most largely accepted and adopted definitions of RD:

Research data are defined as factual records (numerical scores, textual records, images and sounds) used as primary sources for scientific research, and that are commonly accepted in the scientific community as necessary to validate research findings. A research data set constitutes a systematic, partial representation of the subject being investigated. This term does not cover the following: laboratory notebooks, pre-liminary analyses, and drafts of scientific papers, plans for future research, peer reviews, or personal communications with colleagues or physical objects (e.g. laboratory samples, strains of bacteria and test animals such as mice). Access to all of these products or outcomes of research is governed by different considerations than those dealt with here.

Finally, let us bring here the research data definition coming from the “Concordat on Open Research Data” 25 signed by the research councils of the UK Research and Innovation (UKRI) organisation 26 :

Research data are the evidence that underpins the answer to the research question, and can be used to validate findings regardless of its form (e.g. print, digital, or physical). These might be quantitative information or qualitative statements collected by researchers in the course of their work by experimentation, observation, modelling, interview or other methods, or information derived from existing evidence. Data may be raw or primary (e.g. direct from measurement or collection) or derived from primary data for subsequent analysis or interpretation (e.g. cleaned up or as an extract from a larger data set), or derived from existing sources here the rights may be held by others.

Let us observe that this last definition highlights the important role of data as a tool to find an answer to a scientific question, coinciding with the first characteristic of our RS definition, and also agreeing with 40 (p. 508): … data from scientific sensors are a means and not an end for their own research.

A remarkable “positive” aspect of these four definitions is that they separate the data from the subject under study, and establish what is, or is not, RD. This is relevant, as the legal context of the subjects under study sets up the legal (and ethical ) context of the RD.

We must say that we do not agree completely with all the terms in these definitions. For example, regarding the exclusion of the laboratory notebooks as RD elements, as we think they can be used to generate input data for other studies (how a laboratory works, which is the information that appears in some notebooks depending on the scientific matter). We think that these information and data can be of interest for other researchers.

Some other “negative” aspects: the role of the data producers does not appear in the above definitions, although it is more or less implicit when they refer to the connection with the scientific community. Indeed, their role is very important as observed in 48 (p.6):

Data creators usually have the most intimate knowledge about a given dataset, gained while designing, collecting, processing, analyzing and interpreting the data. Many individuals may participate in data creation, hence knowledge may be distributed among multiple parties over time.

Certainly, as for each research output, the producer team is the guarantor of the data quality, in particular to ensure that the data are not outdated, erroneous, falsified, irrelevant, and unusable. Note that this is particularly relevant in the case of RD, as a consequence of the lack of a widely accepted RD publication procedures, compared to the existing ones for articles in scientific journals, where the responsibility of the quality of the publication is somehow shared by the authors, the journal editors, and the reviewers. This is also confirmed by 56 (p. 73):

The concept of data quality is determined by multiple factors. The first is trust. This factor is complex in itself. […] Giarlo (2013) also mentions trust in first place, stating that it depends on subjective judgments on authenticity, acceptability or applicability of the data. Trust is also influenced by the given subject discipline, the reputation of those responsible for the creation of the data, and the biases of the persons who are evaluating the data.

Even more, note that, as remarked in 23 the quality of the producer legal entity defines the cultural quality of the data in legal terms, yielding in this way the qualification of cultural data.

On the other hand, in some of the above definitions, the RD scientific purpose is focused in its role to validate research findings , although RD can be reused for many other finalities in the scientific context as, for instance, to generate new knowledge, i.e. as primary sources for new scientific findings. Let us observe that these are two of the four rationales for data sharing examined in 22 .

Bearing all these arguments in mind, we propose the following RD definition.

Research data is a well identified set of data that has been produced (collected, processed, analyzed, shared & disseminated) by a (again, well identified) research team. The data has been collected, processed and analyzed to produce a result published or disseminated in some article or scientific contribution. Each research data encloses a set (of files) that contains the dataset maybe organized as a database, and it can also include other elements as the documentation, specifications, use cases, and any other useful material as provenance information, instrument information, etc. It can include the research software that has been developed to manipulate the dataset (from short scripts to research software of larger size) or give the references to the software that is necessary to manipulate the data (developed or not in an academic context).

We can summarize the above definition in the following three main characteristics:

  • • the goal of the collection and analysis is to do research, that is, to answer a scientific question (which includes the validation of research findings),
  • • it has been produced by a research team,
  • • the RD is involved in the obtention of the results presented in scientific articles (as the most important means for scientific exchange are still articles published in scientific journals) or by any other kind of recognized scientific means.

We provide here some further considerations concerning this proposal. First, it is clear that we have followed closely the RS definition in Section 2.2 , in order to formulate this RD counterpart, which involves the transaltion of some RS features of strict digital nature to RD. This does not mean that we do not consider non digital data as possible RD, but rather we assume that the information extracted from the physical samples has been already treated as digital information to be manipulated in a computer system, which simplifies the manipulation of physical data and its inclusion in the proposed RD definition.

Secondly, we emphasize that our RD definition also follows the consideration of a restricted research production context, as in the case of our RS definition. But this limited context to set the RD definition does not mean that e.g. public sector data can not be used in the research work. Rather it means that the external components that have not been directly collected/produced by the research team should be well identified, indicating their origin, where the data is available, which is the license that allows the reuse. It is also necessary to indicate if the data has been reused (processed) without modification, or if some adaptations have been necessary for the analysis. External data components can have any origin, not just public sector. As we have highlighted in Section 3 , the production context of the data may have a lot of importance, as data can be difficult to interpret once removed from their initial context 22 .

Third, note that, according to our definition, documentation, licenses, Data Management Plans and other documents can also be part of the set of files that constitutes the RD. Moreover, as explained in Section 2.2 , a RS can also include data in the list of included materials that could also be qualified as RD. There are here a broad spectrum of possibilities, according to the size, the importance given by the research team and the chosen strategy in the dissemination stage. If the RD is considered of little size and less importance than the RS, it can be just included and disseminated as part of the software, and also the other way around, when the RS is considered less important than the RD, as for example when the software development effort is much less important than the time and effort invested in the data collection and analysis. It can also happen that both outputs are considered as of equal value, and can be disseminated separately. In this case it is important that both outputs are linked in order to allow other researchers to find easily the other output.

In a similar manner as for RS, RD can include other data components, and some can also qualify as research data. The RD producer team should explain how these components have been selected, mixed and analyzed, and highlight the reuse of other RD components by means of a correct citation form, see for example, 38 , 41 , 57 .

Moreover, software and data can have several versions and releases, and they can be manipulated alike and with similar tools (forges, etc …) 37 , 58 , 59 . One of the differences that we have detected between RS and RD is that while some research teams can decide to give access to early stages of the software development, what we observe in the consulted work is that RD is expected in its final form, ready for reuse, as mentioned in 22 :

If the rewards of the data deluge are to be reaped, then researchers who produce those data must share them, and do so in such a way that the data are interpretable and reusable by others.

This difference is a consequence of the distinct nature of the building process of both objects. In the FLOSS community, we find the release early, release often principle associated to the development of the Linux kernel 60 and to Agile developments. 27 This principle may not have the same sense in the building of a dataset for which a research team collects, processes and analyzes data with a very particular research purpose, maybe difficult to share with a large or external community in the early stages of the RD production.

Yet, in this work, we do not address some production issues like best software development practices or data curation, as they are out of the scope of the present article, and could be the object of future work. It is not that we do not give enough appreciation to these important issues, as they are part of the 3rd step of the proposed CDUR evaluation protocol for RS and RD, see sections 2.3 and 3.3 of 12 . For us, the research team decides when the research outputs have reached the right status for its dissemination. Neither we do enter in the different roles (see 22 ) that may appear in the RD team, taking care of actions involving: collection, cleaning, selection, documentation, analysis, curation, preservation, maintenance, or the role of Data Officer proposed in 15 .

5. Conclusion

While some authors highlight differences between software and data 8 , 9 , the present article leans toward profiting from the similarities shared by RS and RD. For example, taking into consideration the difference between the definition of software and the definition of RS has driven us to the proposition of a RD definition that is independent from the definition of data. Likewise, along the above sections we have emphasized other characteristics of RD that are grounded in the RS features. As a side effect of this approach, the fact that we can easily adopt issues from the RS definition formulation to RD, confirms and validates our proposed RS definition.

In the introduction we have mentioned Borgman’s conundrum challenges related to RD 22 :

The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice.

In our experience, Borgman's conundrum challenges correspond to questions that appear regularly at different stages of the RD production. We think that to provide the vision developed in Section 4 could be of help to deal with these questions, as a first step to tackle some problems in a well determined situation. Moreover, the view proposed in Section 4 is extended and completed with the dissemination and evaluation protocols of 12 . Our experience of many years confirms the need of these protocols for RS, and we think that they will be appropriated, useful and relevant for RD as well.

As a test for the soundness of the proposed RD definition we have used the conundrum queries as a benchmark, checking if our definition allows us to provide answers to the different questions, as well as to two extra ones that we consider equally relevant, namely how and where to share RD:

Which data might be shared? Following the arguments supporting our RD definition, we think that it is a decision of the research team: similarly to the stage in which the team decides to present some research work in the form of a document for its dissemination as a preprint, or a journal article, a conference paper, a book … the team should decide which data might be shared, in which form and when (following maybe funder or institutional Open Science requirements).

By whom? The research team that has collected, processed, analyzed the RD, and decided to share and disseminate it. That is the RD producer team, as stated in the second characteristic of our RD definition. On the other hand, data ownership issues have been discussed for example in 20 , 21 , 32 , 61 – 63 .

How? As observed in the precedent sections, the How? should follow some kind of dissemination procedure like the one proposed in 11 , 12 in order to identify correctly the RD set of files, to set a title and the list of persons in the producer team (that can be completed with their different roles), to determine the important versions and associated dates, to give a documentation, to verify the legal 21 , 33 (and ethical ) context of the RD and give the license to settle the sharing conditions 13 , etc. which can include the publication of a data paper and decisions about in which form and when the RD should be disseminated, maybe following grant funders or institutional Open Science requirements). In order to increase the return on public investments in scientific research, RD dissemination could respect principles and follow guidelines as described in 17 , 19 . Further analysis on RD dissemination issues can be found in 12 .

Where? There are different places to disseminate a RD, including the web pages of the producer team, of the funded project, or in a existing data repository. Let us remark that the Registry of Research Data Repository 28 is a global registry of RD repositories that covers repositories from different academic disciplines. It is funded by the German Research Foundation (DFG) 29 and it can help to find the right repository. Note that the Science Europe report 64 provides criteria for the selection of trustworthy repositories to deposit RD.

With whom? Each act of scholar communication has its own target public, and initially, the RD dissemination strategy can target the same public as the one that could be interested in the corresponding research article. But it can happen that the RD is of interdisciplinary value, possibly wider than the initial discipline associated to the scientific publication, and to assess what is the public involved in this larger context can be difficult. Indeed, as observed by 22 :

An investigator may be part of multiple, overlapping communities of interest, each of which may have different notions of what are data and different data practices. The boundaries of communities of interest are neither clear nor stable.

So, it can be complex to determine the community of interest for a particular RD, but this also happens for articles, see for example the studies on HIV/AIDS 65 making reference to automatic reasoning in elementary geometry in studies in its reference number 12, and it seems to us that this has never been an obstacle for sharing a publication. Thus 22 :

… the intended users may vary from researchers within a narrow specialty to the general public.

Under what conditions? As described previously, and in parallel with the case of RS, the sharing conditions are to be found in the license that goes with the RD, such as a Creative Commons license 30 or other licenses to settle the attribution, re-use, mining … conditions 13 . For example, in France, the law of 2016 for a Digital Republic Act sets in a Décret the list of licenses that can be used for RS or RD release 31 , 32 .

Why and to what effects? There maybe different reasons to release some RD, from the contribution to build more solid, and easy to validate science, to just comply with the recommendations or requirements of the funder of a project, of the institutions supporting the research team, or those of a scientific journal, including Open Science issues 5 . The works 22 , 49 give a thorough analysis on this subject. As documented there and already mentioned in Section 3 :

“The value of data lies in their use. Full and open access to scientific data should be adopted as the international norm for the exchange of scientific data derived from publicly funded research.”

As remarked in 5 and in the work analyzed there, the evaluation step is an important enabler in order to improve the adoption of Open Science best practices and to increase RD sharing and open access. To disseminate high quality RD outputs is a task that requires time, work and hands willing to verify the quality of the data, to write the associated documentation, etc. Incentives are needed to motivate the teams to accomplish these tasks. RD dissemination also asks for the establishment of best citation practices and evolution in the protocols of research evaluation. In particular, following the parallelism present all along this work, the CDUR protocol 3 proposed for RS evaluation can be also proposed for RD as developed in the article that extends the present work 12 .

Data availability

Acknowledgments.

With many thanks to the Referees, to the Departamento de Matemáticas, Estadística y Computación de la Universidad de Cantabria (Spain) for hospitality, and to Prof. T. Margoni for useful comments and references.

[version 2; peer review: 3 approved]

Funding Statement

This work is partially funded by the CNRS-International Emerging Action (IEA) PREOSI (2021-22).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

1 “We Are Star Dust” - Symphony of Science, https://www.youtube.com/watch?v=8g4d-rnhuSg

2 Cave of Altamira and Paleolithic Cave Art of Northern Spain, https://whc.unesco.org/en/list/310/

3 Prehistoric Sites and Decorated Caves of the Vézère Valley, https://whc.unesco.org/en/list/85/

4 https://en.wikipedia.org/wiki/Free_and_open-source_software

5 https://en.unesco.org/science-sustainable-future/open-science/recommendation

6 https://www.fsf.org/licensing/

7 https://opensource.org/licenses

8 https://spdx.org/licenses/

9 https://creativecommons.org/licenses/?lang=en

10 https://opendatacommons.org/licenses/

11 https://5stardata.info/en/

12 Note that the authors of this article provide their own translations. Authors prefer to keep the original text for two reasons. First, because of the legal nature of the involved quotations. Second, for French or Spanish speaking readers to enjoy it, very much in line with the Helsinki Initiative on Multilingualism in Scholarly Communication (2019), see https://doi.org/10.6084/m9.figshare.7887059 . These translations have been helped by Google Translate, https://translate.google.com/ and Linguee, https://www.linguee.fr/ .

13 https://www.rd-alliance.org/groups/fair-research-software-fair4rs-wg

14 https://matek.hu/zoltan/issac-2021.php

15 https://swmath.org/software/4203

16 See, for example, https://www.senat.fr/dossier-legislatif/pjl16-504.html

17 https://stats.oecd.org/glossary/detail.asp?ID=532

18 https://www.isko.org/cyclo/data

19 https://www.legifrance.gouv.fr/codes/article_lc/LEGIARTI000006832922/

20 https://www.legifrance.gouv.fr/codes/section_lc/LEGITEXT000006074220/LEGISCTA000022936254/

21 The entries of the glossary https://stats.oecd.org/glossary/ have several parts including Definition and Context as shown in the Data definition included in Section 3 . This quotation appears when placing the pointer over the Context part of the Data entry.

22 https://www.beilstein-institut.de/en/projects/strenda/guidelines/

23 https://en.wikipedia.org/wiki/Open_data

24 https://en.wikipedia.org/wiki/Big_data

25 https://www.ukri.org/wp-content/uploads/2020/10/UKRI-020920-ConcordatonOpenResearchData.pdf

26 https://www.ukri.org/

27 https://en.wikipedia.org/wiki/Agile_software_development

28 https://www.re3data.org/

29 http://www.dfg.de/

30 https://creativecommons.org/

Reviewer response for version 2

Joachim schopfel.

1 GERiiCO Labor, University of Lille, Lille, France

The second version is fine with me. The authors replied to all comments; they fixed some issues, and they provided complementary arguments for other issues. I do not share all their viewpoints but that is science and not a problem. The paper is interesting and relevant.

Is the work clearly and accurately presented and does it cite the current literature?

If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable

Are all the source data underlying the results available to ensure full reproducibility?

No source data required

Is the study design appropriate and is the work technically sound?

Are the conclusions drawn adequately supported by the results?

Are sufficient details of methods and analysis provided to allow replication by others?

Reviewer Expertise:

Information science

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Remedios Melero

1 Instituto de Agroquímica y Tecnología de Alimentos, CSIC, Valencia, Spain

I do not have any further comments.

Open science, open research data, scholarly publications, open access policies

Tibor Koltay

1 Institute of Learning Technologies, Eszterházy Károly University, Eger, Hungary

I am satisfied with the author’s reply, and found the other two reviews’ comments intriguing and useful for the authors. I have no further comments.

Reviewer response for version 1

The research data management is a central dimension of the development of scientific research and related infrastructures. Also, any original attempt to define research data is welcome and helpful for the understanding of this field. This conceptual paper will be a valuable contribution to the discussion on the research but. Yet, it should be improved, and a couple of more or less minor issues should be fixed.

  • First, all cited text should be systematically translated into English.
  • The main concepts (such as data, information, knowledge...) should be defined from the beginning on and not only later (section 3). The definitions should not be based on Wikipedia, Larrousse etc but on academic works in the field of information science (eg ISKO). 
  • Open science is a fuzzy concept, an umbrella term or even a "boundary object" (as Samuel Moore put it). But it should be made clear that open science is more than "sharing and dissemination of research outputs" (as in the [5] citation). 
  • The former comment is important because the approach of the paper is in some kind limited or reduced to the aspect of "research output". Generally, in the research process, research software and research data are not only output but also tools (software) and input (data). This needs clarification.
  • In the same context, the paper cites Wikipedia with " We must all  accept that science is data and that data are science". I have two problems with this: nobody must accept anything in science, all is matter of discussion; and this sentence is either trivial or it makes no sense. My advice would be to avoid these kind of sentences.
  • Later on, the paper presents "analogies" between RS and RD. Analogy, even if it exists, does not mean "similarity", and I think that this comparison is somehow misleading because the underlying assumption is not entirely correct ("a definition for RD can be proposed following the main features of the RS definition"). Software and data are different objects, with different issues (IP protection, communities etc.); the analysis of RS may be helpful for a better understanding of RD but this does not mean that both are more or less similar or even "fungible".
  • In section 3, I would suggest that the paper tries to describe the relationship between RS and RD, perhaps with "use cases". 
  • I admit that the authors are not legal experts but section 3 should be more explicit (and perhaps shorter and more restrictive) about the different laws and legal frameworks. Are you speaking about French laws? Or about the EU regulation?
  • Another, related issue is the data typology. The paper is about research data but section 3 mentions (and apparently does not differentiate) environmental data, cultural data and public sector information. 
  • My suggestion would be to improve the structure of section 3 and to distinguish between concepts, typology, legal status and reuse/policy (subsections).
  • Section 4: I already mentioned it above - RD is not only output but also input, with different issues (third party rights etc). This requires clarification.
  • At the end of section 4, the paper states that "documentation, licenses, Data Management Plans and other documents can also be part of the set of files that constitutes the RD". The meaning of this statement requires attention, as well as its implications. Does this mean that "RDM and other documents" are data? Or that they may be part of a RD deposit? But again (see above, comment 5), a statement that "all is data" is not helpful; it may make sense as a political catchword but not in an academic paper.
  • Last comment: I like very much Borgman's assessment of RD and her "conundrum challenges" but I have a somewhat different understanding of the meaning of this - for me, these "challenges" are questions that require attention and evaluation in a given situation, not for all RD in a general way. For me, they provide a kind of "reading grid" to analyse a specific data community, or a specific instrument or infrastructure or workflow; but they don't require or demand a comprehensive response as such provided by the paper.
  • Anyway, the paper is an interesting contribution to the academic research on RD, and I am looking forward to read a revised and improved version. Thank you!

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

LIGM, Gustave Eiffel University & CNRS, France

Many thanks to you, Joachim Schopfel, for your interesting comments that give us the opportunity to improve this work. A new version is in preparation, but we provide here some answers to your comments.

1. [translations into English]

Translations are included as footnotes, they will be moved to the main text.

2. [information science (eg ISKO).]

Many thanks for this reference, we are looking into it.

3. [Open science is a fuzzy concept…]

As indicated in the introduction: A more transversal and global vision can be found in the ongoing work for the UNESCO Recommendation on Open Science [Reference 6]. See also [Reference 7] . We will explain better this point.

4. [the paper is in some kind limited or reduced to the aspect of "research output". Generally, in the research process, research software and research data are not only output but also tools (software) and input (data). This needs clarification.]

In our view, each "research output" is a potential input for new research work. For example a RS can be a tool to manipulate data or an input for a new RS, this can be in the form of a component, or in the form of a new version done by the initial research team or another one. A RD can be used by other teams (as a tool) to understand some problem, it can be modified to produce a new RD, or it can be included as part of a larger data set, that can be as well a new RD. To better understand the production context is not, in our view, a limitation. But you are right, this point needs clarification.

5. [cites Wikipedia with " We must all accept that science is data and that data are science ".]

Please note that this cited phrase comes from [Reference 4], and as indicated to Referee T. Koltay, we have chosen to do this reference in a slightly different manner as done in the Borgman’s work, where we have found it.

6.1. [similarity/analogy]

When consulting Cambridge English Learner’s Dictionary dictionary we find:

analogy: a comparison that shows how two things are similar

6.2. [Software and data are different objects, with different issues (IP protection, communities etc.); the analysis of RS may be helpful for a better understanding of RD but this does not mean that both are more or less similar or even "fungible".]

It is one of the intentions of the present work to show the differences between data and software form the legal point of view. While software finds a somehow clear and simple presentation (Section 2.1), data is much more difficult to grasp, as studied in Section 3. But this is not an obstacle to present an unified vision of RS and RD as research outputs, as we can see in the RS and RD proposed definitions. The fact that we can propose a similar formulation for both definitions allows us to propose similar dissemination and evaluation protocols as you can find in the article that follows this work [Reference 13]. The fact that we can deal with RS and RD in a similar way does not mean that they are similar.

7. [describe the relationship between RS and RD, perhaps with "use cases".]

It seems to us that it is quite usual for the targeted research audience to use and/or produce RS and/or RD as part of their everyday research practices, and that this point does not require further explanation. Examples can be found easily in the literature, as for example in the bibliography included at the end of this work.

8. [I admit that the authors are not legal experts but section 3 should be more explicit (and perhaps shorter and more restrictive) about the different laws and legal frameworks. Are you speaking about French laws? Or about the EU regulation?]

As indicated in the introduction, we have consulted legal texts and legal experts’ work in order to understand and explain the legal context in which we place this work. We have consulted French, European and USA texts, and selected the parts that we have used to document the article. We consider that our role is restricted to this intention, due to the lack or further expertise in legal matters, which does not hide the efforts we have put in to understand and to explain some legal issues. But we are unable to give more information on the regulations that can be taken into consideration, as this is the role of legal experts in the light of a well defined setting.

9. [Another, related issue is the data typology. The paper is about research data but section 3 mentions (and apparently does not differentiate) environmental data, cultural data and public sector information.]

The goal of Section 3 is to show the difficulties existing to set a data definition from the legal point of view, which is a very different context as the one existing for software, as shown in Section 2.1. The case of cultural data is very interesting, as legally speaking [Reference 19] the quality of the producer legal entity defines the cultural quality of the data . Then we can establish the parallel with the quality of research for some data set, as the consequence of the research quality of the producer team. Data typology could be the object of future work.

10. [My suggestion would be to improve the structure of section 3 and to distinguish between concepts, typology, legal status and reuse/policy (subsections).]

We will consider this suggestion

11. [Section 4: I already mentioned it above - RD is not only output but also input, with different issues (third party rights etc). This requires clarification.]

As already explained, we study in here the production aspects, and other aspects are presented in [Reference 13]. But you are right, this needs better explanation.

12. [At the end of section 4, the paper states that "documentation, licenses, Data Management Plans and other documents can also be part of the set of files that constitutes the RD". ]

Section 2.1 shows that the preparatory design work and documentation are part of the software, and these are documents that can be included in the released version of a RS, following the choice of the RS producer team. There can be other elements as for example tests, input and output files to illustrate how to use the RS, licenses, etc. To include these elements in the released RS correspond to best practices that facilitate RS reuse. In our view, to release a RD can follow similar practices, that is, to include a documentation, some use examples, a license, a data management plan…this is to be decided by the producer team.

13. [Last comment: I like very much Borgman's assessment of RD and her "conundrum challenges" but I have a somewhat different understanding of the meaning of this - for me, these "challenges" are questions that require attention and evaluation in a given situation, not for all RD in a general way. For me, they provide a kind of "reading grid" to analyse a specific data community, or a specific instrument or infrastructure or workflow; but they don't require or demand a comprehensive response as such provided by the paper.]

In our experience, Borgman's conundrum challenges correspond to questions that appear regularly at different stages of the RD production. We think that to provide such vision as the one exposed in Section 4 could be of help to deal with these questions, and, as you said, as a first step to tackle some problems in a well determined situation. Moreover, this view proposed in Section 4 is extended and completed with the dissemination and evaluation protocols proposed in [Reference 13]. Our experience of many years confirms the need of these protocols for RS, and we think that they will be appropriated, useful and relevant for RD as well.

Teresa Gomez-Diaz and Tomas Recio

The authors proposed a Research Data (RD) definition "based in three characteristics: the data should be produced (collected, processed, analyzed, shared & disseminated) to answer a scientific question, by a scientific team, and has yield a result published or disseminated in some article or scientific contribution of any kind." From my point of view this definition restricts RD to those that are published by a scientific team, however what about the citizen science, or data produced by non-scientist staff? What about any other data that do not deserve be published but help to further research?

Authors say: "the RS is involved in the obtention of the results presented in scientific articles" - This is not necessarily true. RS is not always involved in the obtention of results because it can be developed for any other purpose, again the authors make a very strict definition.

Authors say: "As a matter of fact, a research team can use RS produced by other teams for their scientific work, as well as FLOSS or other software developed outside the scientific community, but the present work is centered in the making-of aspects which are pertinent for the proposed definition." - This restricts the definition of Research Software (RS) a lot by excluding all FLOSS produced by non-academic members.

The authors have missed any mention to the  Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information, in which RD are defined and included as part of the public sector. In fact, the authors have cited it but they have not commented/mentioned the fact that RD has a wider meaning and that according to this Directive are considered public sector information, and they need not necessarily be published in a scientific journal but shared.

Definitions given by dictionaries are not particularly relevant to the scientific context/environment. I think this part should be omitted, it only adds some definitions in the authors' own languages.

"For example, to the need for complementary, technical information associated to a given dataset in order to facilitate its reuse." - This is part of the FAIR principles which are not mentioned/linked to this comment. Obviously, a dataset without any information about how data have been produced/obtained, etc. are not valuable.

Authors write: "In here, the research outputs have reach a status in which the research team is happy enough for its dissemination." - This seems a very naïve assertion. Because the authors "do not consider production issues like best software development practices or data curation", it seems they do not care about these important issues.

Conclusions again repeat the proposal of a RD definition. Concepts like linked data, FAR data, and open data have not been treated in the article. Their definition of RD is very strict and narrow, and they have not considered any semantic issues about data and the benefits and implications of being a 5star open data . Their definition is far from the 4th or 5th step of the stars.

In general, from my point of view, the article does not add any new ideas about RD definition and restricts it to data produced by scientific teams.

Many thanks to you, Remedios Melero, for these very interesting comments. We are preparing a new version of this article and we will include several of the proposed corrections. Meanwhile, we would like to provide in here some preliminary comments.

1. [this definition restricts RD to those that are published by a scientific team, however what about the citizen science, or data produced by non-scientist staff?]

[the article does not add any new ideas about RD definition and restricts it to data produced by scientific teams.]

It would be strange to consider any article published in a newspaper as a scientific publication.

On the other hand, scientists may read the newspapers and many other documents, including tweets, and may use these documents as input information for a research work. As already explained in our answer to Rob Hooft’s comment, yes, we have chosen a restricted definition for RD. It allows us to provide the answers to the Borgman’s conundrum challenges that are in the Conclusion section. As far as we know, we have not found in the consulted literature the proposition of such kind of answers in this complete view. Moreover, as the RD definition finds a similar formulation as the RS one, we can also translate RS dissemination and evaluation protocols to RD [Reference 13]. Once we understand well the restricted context, it can be studied its extension and then see which are the answers to Borgman’s conundrum challenges and the dissemination and evaluation protocols that can be proposed in the extended context.

The fact that we do not include e.g. public sector data as RD is different from the claim that these data cannot be used as input for a research work. As explained in section 3.2 of [Reference 13], these external data components should be correctly presented and referenced, and some can also fall in the category of RD.

2. [RS is not always involved in the obtention of results because it can be developed for any other purpose, again the authors make a very strict definition.]

[This restricts the definition of Research Software (RS) a lot by excluding all FLOSS produced by non-academic members.]

You are right, this point should be explained better. To obtain a research result may involve the use of software (FLOSS or not FLOSS), the development of software to support some work or service, and the development of RS by the research team as explained in [References 3, 14]. Note that RS can be also disseminated as FLOSS, which is the usual practice in the work of T. Recio and in the research lab of T. Gomez-Diaz. This is also similar for data and RD, that can be disseminated as open data, as well as for publications and research articles as seen in the previous point.

3. [Research data defined in the  Directive (EU) 2019/1024 ]

This definition was included in the preparation versions of the present article, and it will be included again in the new version in preparation, following your advice.

4. [Definitions given by dictionaries]

In the difficulties to explain easily the concepts of data and information we have ended in the consultation of several dictionaries, including some in English. Some of the found definitions, mainly in Spanish and French have attracted our attention and we have decided to included them in Box 1. This box can be easily skipped by readers not interested in these definitions.

We prefer to leave the reading of the content of this box at the choice of readers.

5. [FAIR and "For example, to the need for complementary, technical information associated to a given dataset in order to facilitate its reuse."]

Please note that FAIR principles appear in the [Reference 55] dated 2016, while [Reference 36] that we have chosen to illustrate the need for complementary, technical information is dated 2012. Moreover, this is also related to the importance of context, that is explained in the OECD Glossary of Statistical Terms, with PDF and WORD download versions dated 2007 [ https://stats.oecd.org/glossary/download.asp ]. On the other hand, FAIR principles are considered in the second part of this work [Reference 13], as they are related to dissemination issues. We will also mention them in the second version of this first part.

6. ["In here, the research outputs have reached a status in which the research team is happy enough for its dissemination."]

[authors "do not consider production issues like best software development practices or data curation", it seems they do not care about these important issues.]

You are right, this point should be better explained in the new version of the article. It is not that we do not care about these important issues, as they are part of the 3 rd step of the proposed CDUR evaluation protocol for RS and RD, see sections 2.3 and 3.3 of [Reference 13].

7. [Concepts like linked data, FAIR data, and open data have not been treated in the article. Their definition of RD is very strict and narrow, and they have not considered any semantic issues about data and the benefits and implications of being a 5star open data . Their definition is far from the 4th or 5th step of the stars.]

Please note that FAIR data and open data are treated in [Reference 13]. We will include in the second version the mention of the 5star open data, many thanks for this reference.

Teresa Gomez-Diaz, Tomas Recio

The content of the first two paragraphs of the paper (especially the first one) seems to be less appropriate, compared to the purpose of your paper. I would thus advise you to consider rewriting these paragraphs.

Your practice of providing the cited texts in the original language (French or Spanish) and providing the translations of these passages only in the footnotes is unusual and may be not appropriate for a readership that probably reads and writes only in English, or is not familiar with Spanish and/or French texts. As I see it, if you would want to make a favour to your readers, who prefer French or Spanish, the solution could be reverse this order, i.e. putting the original texts into the footnotes.

Other remarks

I think that it would be better if the following sentence would be changed as follows:

  • “Indeed, as remarked by Hanson et al ., we must all accept that science is data and that data are science… 4 ”

This regards not only the form of citing, but content, because this remark comes from Borgman’s Conundrum, cited in your paper a couple of times.

You describe three main characteristics of RS:

  • “the goal of the RS development is to do research. As stated by D. Kelly: it is developed to answer a scientific question 32 ,
  • it has been written by a research team,
  • the RS is involved in the obtention of the results presented in scientific articles (as the most important means for scientific exchange are still articles published in scientific journals).”

In general, these three claims are correct. However, the first one of them is a little awkward. I would thus change it to anything like “the goal of the RS development is to support research. As stated by Kelly, it is developed to answer a general, or a specific scientific question. Writing the software requires close involvement of someone with deep domain knowledge in the application area related to the question. 32 ”. Theses sentences however may prove redundant, because you provide a more complete definition:

  • “Research Software is a well identified set of code that has been written by a (again, well identified) research team…If take this, linger definition only, the expression “(again, well identified)” should be deleted.

You write that “Indeed, there is a difference between the concepts of algorithm and software from the legal point of view, as there is a difference between the mere idea for the plot of a novel and the final written work.” This is a brilliant idea, although I believe that it should not be restricted to the legal point of view.

In my view, it seems to be dangerous to write about copyright issues without being legal experts. Personally, I have only basic knowledge of copyright laws, so I cannot judge the correctness of all your argument. Fortunately, what you describe is also related to different issues.

I do not see any further problems. Therefore, I will not enumerate passages that are correct and rather straightforward. My suggestion is however, that you carefully review you text in order to reach clarity of your argument.

Many thanks to you, Tibor Koltay, for these very interesting comments. We are preparing a new version of this article and we will include several of the proposed corrections. Meanwhile, we would like to provide in here some preliminary comments.

1. [first two paragraphs]

We have chosen to start in a ''light'' manner an article that can ask for some effort to be understood, this is our author’s choice. It is the reader’s choice to skip these two first paragraphs or to enjoy them, as this does not have any consequence for the understanding of the content of the article.

2. [translations to English]

We agree with you, the translations to English in the footnotes may hinder the fluent reading of this work, we will modify the presentation.

3. [Hanson et al. Reference]

You are right, we have found this reference in Borgman’s work, but we have consulted the original article and we have chosen to do this reference in a slightly different manner.

4. [RS definition characteristics]

We will modify the phrase to include your proposition as follows: “the goal of the RS development is to do or to support research’’. Please note that the composition of a research team involved in the

development of a RS has been thoroughly studied in section 2.2 of [Reference 3]. We will include this reference to clarify this point as you ask. Please, also note that long developments may involve many different contributions from developers with different status. As copyright issues enter into play, it is important that the RS developers and contributors are correctly listed.

5. [Algorithms and software]

Comparisons between algorithms and software can be done in several contexts, for example in mathematics, or in computer science, among others. We have highlighted the legal aspects as we detect regularly the confusion between these two concepts, and the [Reference 22] providers a pretty clear explanation.

6. [Copyright issues]

Please note that one of the authors has study copyright issues in order to write [Reference 2], work that has been validated by several experts, including legal experts. On the other hand, we are regularly in contact and follow the work of legal experts, in such a manner as to provide us with the necessary confidence to deal with copyright issues in the way we propose in this article. The remark included at the end of the Introduction gives the necessary warning to our readers on this point.

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Innovation, science and economic development canada's 2024–2025 departmental plan, on this page, from the minister, companies, investment and growth.

  • Science, Technology, Research and Commercialization

People, Skills and Communities

Internal services, future-oriented condensed statement of operations, human resources, corporate information, supplementary information tables, federal tax expenditures, definitions.

It is our pleasure to present the 2024–25 Departmental Plan for Innovation, Science and Economic Development Canada (ISED), which lays out the key priorities the Department is working to advance for the benefit of all Canadians.

In 2024–25, ISED will continue working with the Innovation, Science and Economic Development Portfolio and other federal partners to bolster Canadian innovation by fostering competitive, sustainable and inclusive economic growth.

As Canada transitions to a net-zero economy, ISED has entered into special agreements with industry partners such as NextStar Energy, Volkswagen PowerCo, and Northvolt Batteries North America to increase Canada's production of lithium battery cells and electric vehicles (EVs). To help ensure Canada's economy remains a competitive destination for investment following the introduction of the Inflation Reduction Act in the United States, these unprecedented agreements will advance the country's position as an EV manufacturer. To complement these efforts and support Canada's Critical Minerals Strategy, the Strategic Innovation Fund (SIF) will also invest in projects that will prioritize the manufacturing, processing, and recycling of critical minerals.

Recognizing the growing importance of artificial intelligence (AI), ISED will seek to accelerate the adoption and commercialization of AI by investing in the Pan-Canadian Artificial Intelligence Strategy (PCAIS). In 2024-25, the PCAIS will provide funding of up to $20 million to institutions in across the country to advance AI innovations and research. Further advancing the PCAIS, ISED's Advanced Manufacturing Global Innovation Cluster, led by Next Generation Manufacturing Canada, will provide $19 million in funding for 12 AI projects in Canada's manufacturing sector. These projects are expected to enhance the competitiveness of Canadian manufacturing through the commercialization of Canadian AI innovations, while increasing manufacturing capacity in the country.

The new National Quantum Strategy (NQS), in turn, will continue to ensure Canada's leadership in quantum technology. Under the NQS, the Department, in collaboration with key partners including the regional development agencies, will continue to support the development of Canada's quantum research and the commercialization of quantum-ready technologies through the Quantum Industry Canada program.

The Department's Universal Broadband Fund (UBF) will continue to support the expansion of broadband, connecting underserved rural, remote, and Indigenous communities with access to high-speed Internet. The Government of Canada, in partnership with other orders of government and private sector stakeholders, has leveraged UBF to secure high-speed internet access for 93.5 percent of Canadian households and is on track to exceed its goal of connecting 98 percent of Canadian households by 2026 and 100 percent by 2030.

Supporting fundamental research continues to be a priority for ISED. The Department is working to promote a strong, vibrant science and research community across Canada, anchored by a diverse pool of world-class researchers. Through the Biomanufacturing and Life Sciences Strategy, ISED is looking to grow a strong, competitive domestic life sciences sector, with cutting-edge biomanufacturing capabilities, which will create good jobs for Canadians and ensure Canada is prepared for future pandemics and health emergencies. Specifically, the Department is investing $225 million in AbCellera—the Canadian biotech company that helped develop the first antibody therapy treatment for COVID-19—to fund research and clinical trials projects, and to build a new manufacturing plant that will produce anti-body therapies for clinical trials.

In addition, scientific excellence will be supported through the Strategic Science Fund. Contribution agreements are being finalized with a diverse portfolio of organizations selected for funding by an independent expert panel. The funding will support of the organizations in advancing program objectives to support research, talent, knowledge mobilization and science culture. To help post-secondary institutions maintain and enhance security posture and support them in identifying, assessing, and mitigating risks to research security, ISED will continue to fund the Cybersecurity Initiatives Program.

In 2024–25, ISED will continue to support small and medium-sized enterprises (SMEs) —the backbone of the Canadian economy. In addition to the Department's ongoing support for women-owned and Black-owned businesses—through their respective entrepreneurship strategies, ISED will continue to eliminate barriers to access for under-represented entrepreneurs by providing support through the 2SLGBTQI+ Entrepreneurship Program. The program will deliver critical business advice, create resources, provide mentorship, and will collect the data needed to better understand the needs of 2SLGBTQI+ entrepreneurs and the challenges they face. Additionally, the Canada Digital Adoption Program will continue to support SMEs to modernize their operations and adopt e-commerce technologies, bolstering their growth and competitiveness.

Finally, ISED will support tourism in Canada by delivering on the Federal Tourism Growth Strategy, which includes targeted actions to help the Tourism sector recover from the pandemic and grow the Indigenous tourism industry. Specifically, the Department will strengthen economic reconciliation through the $20 million Indigenous Tourism Fund, which will support the scale-up of signature Indigenous tourism experiences and help build the capacity of micro and/or small Indigenous tourism businesses.

Funding from Budget 2023 will support additional tourism programming including $50 million to Destination Canada to attract major international conventions, conferences, and events to Canada; and $108 million to the Regional Development Agencies to support communities, small businesses, and non-profit organizations in developing local projects and events. With the goal of positioning Canada as a premier outdoor, nature-based destination, ISED will also leverage the country's natural landscape and network of trails in developing a new Trails Tourism Strategy.

We invite you to read this report to learn more about how ISED, along with its portfolio partners, is working with Canadians of all backgrounds and in all regions—urban and rural—to position Canada as a leader in the global economy.

definition of a research program

The Honourable François-Philippe Champagne Minister of Innovation, Science and Industry

definition of a research program

The Honourable Mary Ng Minister of Export Promotion, International Trade and Economic Development

definition of a research program

The Honourable Soraya Martinez Ferrada Minister of Tourism and Minister responsible for the Economic Development Agency of Canada for the Regions of Quebec

definition of a research program

The Honourable Gudie Hutchings Minister of Rural Economic Development and Minister responsible for the Atlantic Canada Opportunities Agency

definition of a research program

The Honourable Rechie Valdez Minister of Small Business

Plans to deliver on core responsibilities and internal services

Core responsibilities and internal services, description, quality of life impacts, results and targets, plans to achieve results, snapshot of planned resources in 2024-25, related government priorities, program inventory.

Provide support to help grow small, medium and large Canadian businesses into globally competitive, high-impact firms; ensure a fair and competitive marketplace; promote the conditions that support competitive prices and product choices, including in the telecommunications sector; simplify government programming, promote efforts to reduce red tape for businesses, putting in place the right conditions for market-driven innovation and promoting growth and an economy that works for everyone; reduce barriers to the movement of goods, services, capital and labour; grow Canada's tourism sector.

The Companies, Investment and Growth core responsibility is most closely related to the "Prosperity" domain of Canada's Quality of Life Framework, but it also touches on the "Good Governance" and "Environment" domains.

The "firm growth", "gross-domestic product per capita", and "investment in in-house research and development" indicators under the "Prosperity" domain are directly related to this core responsibility, for which the key outcome is to provide support to small, medium and large Canadian businesses to innovate and grow. To assess this outcome, ISED tracks the revenue growth rate and value of business expenditures on research and development (BERD) of the firms it supports.

Additionally, the "Good Governance" domain measures Canadians' confidence in institutions. Several entities under ISED's Companies, Investment and Growth core responsibility, including Measurement Canada, the Office of the Superintendent of Bankruptcy, Corporations Canada, the Competition Bureau and the Canadian Intellectual Property Office, focus on fostering conditions for market-driven innovation and creating a fair and competitive marketplace for businesses, investors and consumers.

Under the "Environment" domain, the "greenhouse gas emissions" indicator relates to the following departmental results indicator: "annual incremental reductions in GHG emissions attributable to ISED-supported technologies".

The following tables show, for each departmental result related to Companies, Investment and Growth, the indicators, the results from the three most recently reported fiscal years, the targets and target dates approved for 2024–25.

Table 1: Indicators, results, and targets for departmental result: Canada has a clean and sustainable economy.

Table 2: indicators, results and targets for departmental result: canadian businesses and industries are innovative and growing., table 3: indicators, results and targets for departmental result: businesses, investors and consumers are confident in the canadian marketplace, including the digital economy..

The financial, human resources and performance information for ISED's program inventory is available on GC InfoBase Footnote i .

Departmental Result: Canadian businesses and industries are innovative and growing

Innovation and adaptation on the part of Canadian enterprises—from start-up to scale-up—are vital to success in job creation, economic productivity, and trade. To spur creativity in support of Canada's economic recovery, growth, and competitiveness, ISED will continue to work closely with entrepreneurs, businesses and industry sectors to build on areas of traditional Canadian advantage while also capitalizing on emerging opportunities. Innovation and adaptation on the part of Canadian enterprises—from start-up to scale-up—are vital to success in job creation, economic productivity, and trade. To spur creativity in support of Canada's economic recovery, growth, and competitiveness, ISED will continue to work closely with entrepreneurs, businesses and industry sectors to build on areas of traditional Canadian advantage while also capitalizing on emerging opportunities.

Growing Canada's innovation ecosystems

Innovation Canada Footnote ii offers programs and services to help Canadian businesses innovate and grow by accessing client-centered, simplified support to advance research and the development and commercialization of innovative technologies and products. Flagship programs like the Strategic Innovation Fund (SIF), the Global Innovation Clusters (GICs) and Innovative Solutions Canada are instrumental in fostering industrial innovation and growth while advancing other governmental priorities like reducing greenhouse gas emissions (GHG). With renewed funding of $750 million from Budget 2022, the five GIC's will continue their efforts to advance Canada's innovation ecosystems, promote investments in innovation and commercialization, expand their national and global presence, deepen their collaborations, and support the growth and scale-up of Canadian small and medium-sized enterprises (SMEs). In 2024–25, the GIC program will increase industry co-investments by raising the industry match ratio to $1.50 for every dollar committed by non-GIC partners by 2028. In addition, the program is expected to establish new collaborations involving funding partnerships for the Pan-Canadian Artificial Intelligence Strategy (PCAIS) and Canada's National Quantum Strategy. For instance, as part of the PCAIS, the Advanced Manufacturing Cluster Footnote iii launched the AI for Manufacturing funding challenge, calling on companies to submit project proposals focused on commercializing AI or machine learning solutions in manufacturing. As a result, NGen, the organization leading the Advanced Manufacturing Cluster, will provide $19 million in funding for 12 AI projects in Canada's manufacturing sector. These projects are expected to enhance the competitiveness of Canadian manufacturing through the commercialization of Canadian AI innovations while building manufacturing capacity in the country.

In 2024–25, SIF will continue to support industrial transformation and growth, building on its $17 billion in research and development (R&D) investments to ensure the long-term sustainability of Canadian businesses in key industrial sectors including automotive, critical minerals and batteries, life sciences, semiconductors, aerospace, natural resources, and agri-food.

Through the Net Zero Accelerator Initiative, SIF will support Canada's goal of becoming a leader in clean technologies and help advance Canada's commitment to achieving net-zero emissions by 2050. For instance, SIF will advance the development of the battery innovation ecosystem by providing funding for the construction of E-One Moli's new $1 billion battery manufacturing and R&D facility in British Columbia, to support the production of lithium-ion batteries for industrial and consumer applications. SIF will also invest in electric vehicle (EV) battery manufacturing projects such as those by Volkswagen PowerCo, NextStar Energy and Northvolt Batteries North America, which will help reduce GHG emissions, decarbonize the transportation sector and strengthen Canada's domestic manufacturing capacity. ISED's partnership with Northvolt, in particular, will support the construction of its new EV battery manufacturing facility in Quebec—a $7 billion dollar investment—that will produce some of the greenest batteries in the world given its low carbon footprint. These investments in clean technology solutions demonstrate ISED's ongoing work toward meeting Canada's ambitious emissions reductions targets and securing Canada's battery supply chain. Supported by these investments, Canada ranked first in Bloomberg NEF's 2023 Global Lithium-Ion Battery Supply Chain Ranking.

Under Canada's Critical Minerals Strategy, in 2024–25, SIF will use targeted funds to accelerate investments in critical mineral projects, specifically prioritizing advanced manufacturing, processing, and recycling applications. To meet the rising demand for critical minerals and related manufactured products, ISED's investments will increase the supply of responsibly sourced critical minerals, while promoting innovation and sustainable practices across domestic and global critical minerals value chains. Since Canada is a leading global producer of many minerals, including nickel, potash, aluminum and uranium, investments in critical minerals have the potential to spur economic growth and manufacturing capabilities both domestically and internationally. Through ISED's expected  $551.3 million contribution to Umicore, a circular materials technology company, the federal government, in partnership with the Government of Ontario, will support Umicore's decision to build a new manufacturing facility in Loyalist Township, Ontario, to produce essential EV battery components using critical minerals such as nickel, lithium, and cobalt. This facility, the first of its kind in North America, will strengthen Canada's domestic EV and battery supply chain.

Supporting the industrial transformation of key sectors

Since March 2020, more than $2.1 billion has been invested in rebuilding Canada's vaccines, therapeutics and biomanufacturing capacity. In alignment with the Biomanufacturing and Life Sciences Strategy, ISED will support a $61 million project by Edesa Biotech—a biopharmaceutical R&D company focused on developing and commercializing novel clinical-stage drugs for autoimmune and inflammatory diseases, such as acute respiratory distress syndrome (ARDS). ISED is also investing through SIF to support a $119.3 million project by Pharmascience Inc. to expand its facility in Candiac, Quebec, by 2,500 square metres, increasing its capacity to produce sterile injectables. Both projects will be instrumental in increasing innovation in the life sciences sector, contributing to Canada's talent pipeline by creating new jobs, and developing world-class expertise and infrastructure to build a competitive biomanufacturing and life sciences industry.

Budget 2022 announced $30 million over four years to expand the CAN Health Network across Canada. By connecting health organizations with Canadian companies from coast-to-coast-to-coast, the Government of Canada's investment in expanding the CAN Health Network will support innovation in the health technology sector, grow businesses, create good well-paying jobs and generate prosperity for Canadians.

SIF will continue to provide targeted support to the aerospace industry—one of the most innovative and export-driven sectors in Canada, contributing close to $27 billion and more than 210,000 jobs to the economy. The SIF investment of $350 million to support the new Initiative for Sustainable Aviation Technology— a pan-Canadian, industry-led aerospace network focused on funding collaborative R&D projects with companies of all sizes across the Canadian supply chain—will help accelerate the green industrial transformation of the aerospace industry, generate high-value jobs, strengthen the sector's supply chains, and position Canada as a global leader in sustainable aviation.

Through the National Shipbuilding Strategy Value Proposition (NSS VP), the department will help ensure the long-term sustainability of the Canadian marine industry by requiring that shipyards with large vessel contracts support investments in three key areas—human resources development, technology investment and industrial development—that are equal to 0.5% of the value of the contracts they receive. For 2024–25, Irving Shipbuilding Inc. and Seaspan's Vancouver Shipyards have forecasted more than $41 million in combined NSS VP investments in Canada's marine industry.

Helping businesses navigate government support

Connecting businesses with government programs and supports at the federal and the provincial, and territorial level to facilitate growth and innovation remains a key priority for ISED. In 2024–25, ISED will liaise with government partners to optimize service provided through the Business Benefits Finder, a platform that uses a client-centred approach to streamline the process of connecting businesses with the services and programs they need to bolster their performance. ISED's Accelerated Growth Service, which helps entrepreneurs and businesses innovate and scale up through its advisory and growth services, will complement the Business Benefits Finder by assisting existing, innovative businesses in accessing government programs and services.

Similarly, the Global Hypergrowth Project (GHP) will support businesses in scaling-up and growing by convening various federal, provincial, and broader ecosystem partners to identify programming gaps and opportunities for Canadian companies. Announced in July 2023, the GHP will help businesses develop into anchor firms, which are responsible for creating business clusters and incubating other businesses, by assisting them in navigating complex regulatory regimes, expanding to new markets, and acquiring the right talent. To achieve these goals, eight Canadian businesses have been selected to receive tailored support through the project, including Clarius Mobile Health Corp. With help from the GHP, Clarius will be able to propel its growth and make its innovative portable hand-held ultrasound imaging device available to more clinicians around the world.

In 2024–25, ISED's national BizPaL office will continue supporting cross-jurisdictional partnerships focused on helping Canadian businesses find and access business licences and reducing the burden of duplication for businesses to meet regulatory, permitting and licensing requirements. The program will help users navigate Canada's regulatory landscape via an interactive artificial intelligence interface, improving access to timely, complete information and analytics through an Open Service Platform and the Service for Regulators project.

Increasing access to capital for Canadian businesses

ISED, through the Canada Small Business Financing Program (CSBFP), will continue increasing the availability of financing for Canadian small businesses looking to start up, expand, modernize, and innovate. In 2024–25, the CSBFP will gather information to support and inform a statutory comprehensive review report assessing the extent to which the program has met its goals over the last five years, including examining the provisions and operations of the Canada Small Business Financing Act . The report will propose recommendations to ensure that the CSBFP continues to adapt to meet the current economic conditions and future needs of small businesses.

As announced in Budget 2021, ISED's Venture Capital Catalyst Initiative (VCCI), managed by the Business Development Bank of Canada, will support innovation and job creation in Canada by increasing the availability of private sector capital for Canadian entrepreneurs with high-growth potential, particularly for those in the life sciences sector and those belonging to under-represented groups. VCCI will accomplish this through three streams: $350 million for funds-of-funds, a $50 million for supporting VC investments in life science technologies, and a $50 million for an inclusive growth stream dedicated to increase access to VC programs for underrepresented groups. In 2024–25, funding recipients for the second intake of the inclusive growth stream and will invest up to $25 million in five Canadian VC funds through this stream. Through VCCI's inclusive growth stream, ISED aims to advance diversity, equity, and inclusion in the Canadian VC ecosystem by providing additional capital to invest in innovative under-represented entrepreneurs.

Supporting a modern telecommunications network

As the demand for spectrum and telecommunications services continues to grow, ISED will continue to advance Canada's position as a global centre for innovation and world-class wireless infrastructure. In 2024–25, the department will launch a new Non-Competitive Local Licensing framework, offering 80 MHz of mid-band spectrum, which will provide users— including wireless Internet service providers, vertical industries, and Indigenous communities—with localized access to shared 5G spectrum.

With a commitment to fostering greater universal connectivity, ISED will implement a new Access Licensing Framework in 2024–25 to facilitate greater access to unused licence spectrum in rural and remote areas, including supporting the expansion of broadband services. Under this framework, ISED will also consult on and implement an Indigenous Priority Window, which will allow Indigenous-led businesses and Indigenous service providers access to spectrum that strengthens their Internet connectivity, improves access to emergency response services, and establishes reliable cell service.

Reinvigorating tourism in Canada

Building on the progress made last year, ISED will continue to implement new strategies to advance the long-term growth of the tourism sector. In 2024–25, under the Federal Tourism Growth Strategy (FTGS), the department will coordinate and promote measures to support the growth of Canada's tourism industry, particularly positioning Indigenous communities as attractive tourist destinations, by continuing to implement the $108 million Tourism Growth Program, through the regional development agencies, to deliver key tourism projects.

As part of the FTGS, ISED will collaborate with its federal, provincial, and territorial partners, industry stakeholders and Indigenous people to ensure that the needs of tourism businesses are being served. The FTGS is based on five strategic priorities: investing in tourism assets, embracing recreation and the great outdoors, partnering to grow Indigenous tourism, attracting more international events, and improving coordination through a Ministerial Tourism Growth Council. ISED will also provide $50 million to Destination Canada, with the expectation of hosting more business events in Canada and invest in maintaining Canada's network of trails and outdoor spaces through the Trails Tourism Strategy.

Indigenous tourism is a key segment of the industry that differentiates Canada as a premier tourist destination. ISED's continued support of this segment through the $20 million Indigenous Tourism Fund (ITF), announced in Budget 2022, will help rejuvenate the Indigenous tourism industry and ensure long-term, sustainable growth for Indigenous businesses and communities. As a component of the ITF, the department is collaborating with the Indigenous Tourism Association of Canada to roll out the first $10 million of the Micro and Small Business Stream, which will provide financial assistance, specifically non-repayable contributions of up to $25,000, to support export-ready micro and small Indigenous tourism businesses.

Developing talent for the digital economy

As Canada's key industrial sectors continue to evolve and digitize, ISED remains committed to equipping youth, students, graduates, and mid-career workers with industry-relevant experience to enhance their professional experience and preparedness.

In 2024–25, ISED will continue to administer programs aimed at increasing digital skills and employment experience among Canadian youth, including the Digital Skills for Youth (DS4Y) program, the Computers for Schools Internship program (CFSI), and the Business + Higher Education Roundtable (BHER). Through DS4Y, which connects post-secondary graduates with internships in small businesses and not-for-profit organizations, nine not-for-profit organizations have been selected to provide 179 internships that will equip youth with the skills and experience needed to transition to career-oriented employment. In 2024–25, the CFSI will provide 148 internships to help young people develop digital skills through paid, on-the-job experience refurbishing digital devices, which will increase their employability and marketability to prospective employers. Likewise, the BHER will seek to help emerging talent develop skills that will prepare them for the labour market and support business innovation and growth. It will aim to create over 7,500 work integrated learning opportunities for post-secondary students in 2024–25.

Launched in 2022–23, the Upskilling for Industry Initiative (UII) has committed $125 million in 2024–25 to support employers and SMEs, in better identifying their skills needs and develop new upskilling programming to meet them. Through Palette Skills Inc., the selected lead delivery participant , Footnote iv UII will continue to support demand-driven short-cycle programs to meet the needs of employers in six high-growth sectors: digital technology, cybersecurity, agriculture technology, advanced manufacturing, clean technology and biomanufacturing. ISED expects to connect 15,000 Canadians, including those from under-represented groups, with new work opportunities by March 31, 2025, creating a responsive pipeline of upskilled workers for Canadian industry.

Departmental Result: Businesses, investors, and consumers are confident in the Canadian marketplace, including in the digital economy.

Creating equitable market conditions is critical to Canada's economic growth on the global stage. Through a focus on modernizing key regulatory frameworks and intellectual property products and services, ISED will continue its progress towards leveling the playing field for businesses, investors, and consumers, creating a robust and fair marketplace that balances economic growth with national security considerations.

Modernizing Canada's marketplace regulatory frameworks

In 2024–25, the Competition Bureau will support the government's efforts to modernize and update the Competition Act Footnote v to promote and strengthen competitive markets. The Affordable Housing and Groceries Act received Royal Assent on December 15, 2023. Among other measures to make life more affordable for Canadians, the Act empowers the Bureau to take action against collaborations that stifle competition and consumer choice, in particular situations where large grocers prevent smaller competitors from establishing operations nearby. It also removed the efficiencies defence, which allowed anti-competitive mergers to survive challenges if corporate efficiencies offset the harm to competition, even when Canadian consumers would pay higher prices and have fewer choices. With a modernized Competition Act, the Bureau will be better able to protect and promote competition in Canada leading to lower prices, while stimulating innovation and economic growth. The Bureau will also continue its work with regulators and policymakers to assess the impact of new or existing policies on competition, championing the essential role of competition in the economy. Through the Canadian Digital Regulators Forum, the Bureau will strengthen information sharing and collaboration on digital markets and artificial intelligence (AI) with the Office of the Privacy Commissioner of Canada and the Canadian Radio-television and Telecommunications Commission. To ensure that anti-competitive behaviour is detected and deterred, the Bureau will continue to implement proactive monitoring and enforcement measures in areas such as deceptive marketing practices, cartels, problematic mergers, and digital services.

To protect the integrity of the Canadian insolvency system, ISED's Office of the Superintendent of Bankruptcy (OSB) will continue to modernize its directives, regulations, and IT systems in 2024–25 to reduce unnecessary burden on those it regulates and support confidence in the Canadian marketplace. Through its newly launched Debtor Compliance Management System, the OSB will leverage AI capabilities to detect and address debtor non-compliance.

In 2024–25, Measurement Canada, which has the legislative mandate to approve and oversee all measuring devices used in Canadian financial transactions, will prioritize modernizing and renewing legislation governing trade measurement, especially the approval and inspection of electric vehicle charging devices.

Through legislative amendments to the Investment Canada Act , ISED will continue to strengthen Canada's reputation as the world's top destination to invest . The amendments propose new regulatory measures for foreign investments that aim to improve compliance and information sharing with international counterparts concerning foreign investment reviews and national security assessments. Additionally, the amendments will grant the Minister of Innovation, Science and Industry the authority to impose interim conditions during a national security review of investments and accept binding undertakings from investors to mitigate any national security risks. These measures will not only foster a stronger investment climate but also grant ISED greater authority in reviewing any national security threats from foreign investments. Through timely processing of foreign investment reviews under the Act, ISED will assess foreign investments in Canada for likely net economic benefits and potential national security injury as well.

Promoting compliance among federally incorporated businesses

In 2024–25, ISED will notify corporations of the new reporting requirements under the Canada Business Corporations Act (CBCA), whereby federally incorporated businesses must proactively submit information on their beneficial owners or individuals with significant control of their business. The publicly accessible beneficial ownership registry, governed under the CBCA, will provide information about the ownership and control of Canadian corporations governed under the CBCA to help reduce the misuse of these corporations and strengthen the detection of tax evasion and fraudulent activities through improved transparency of beneficial ownership.

Promoting and protecting consumer interests

As consumer spending patterns and trends change because of inflation and rising food prices, ISED will continue to ensure that the voice of the consumer is represented through the Contributions Program for Non-Profit Consumer and Voluntary Organizations. In 2024–25, the department will provide $1.7 million to consumer advocacy organizations in Canada to conduct research projects, addressing issues such as digital consumer protection, systemic barriers to vulnerable consumers, affordability, and sustainable consumption. The program will also receive a $3.3 million budget increase to strengthen support for consumer advocacy, with a particular focus on areas where consumers have expressed great concern, including retail practices and rising grocery prices.

Advancing inclusive economic growth through intellectual property

In 2024–25, ISED will work toward making Canada's intellectual property (IP) system more inclusive, with a particular focus on the intersection of IP and the protection of Indigenous knowledge and cultural expressions.

IP protections, services and resources remain widely underutilized by Indigenous businesses and entrepreneurs in Canada as they face barriers to accessing Canada's IP system. As a result, the Canadian marketplace has been flooded with fraudulent imported Indigenous arts and goods, posing a threat to the economic viability of Indigenous communities. To address these barriers and support the protection of Indigenous IP, ISED will provide $150,000 in grants to Indigenous organizations through Indigenous Intellectual Property Program (IIPP) grants. The program will fund both small scale Indigenous-led projects, up to $15,000, and larger, more complex projects up to $50,000. The IIPP grants will also fund representatives from Indigenous organizations, up to $5,000, for their participation in World Intellectual Property Organization events and negotiations related to intellectual property, Indigenous knowledge, and Indigenous cultural expressions policy.

Through the IP Clinics Program—a grant program designed to foster the development of future IP experts by increasing university students' exposure to IP issues—ISED will provide $400,000 to eligible projects with a commitment to improving the understanding of IP and increasing access to IP services, including for women and Indigenous-led businesses. In support of the Government of Canada's Intellectual Property Strategy, the program will continue to deliver low cost or free IP services and resources to promote inclusive access.

In an effort to provide quality, timely IP services and resources to innovators, as well as safeguard IP rights, the Canadian Intellectual Property Office (CIPO), an agency under ISED, will continue optimizing turnaround times and enhancing client services through new technologies. Specifically, CIPO will continue implementing its Trademark Recovery Plan to reduce application backlogs by increasing examination capacity to restore turnaround times to internationally comparable standards by 2026.

As part of the National IP Strategy, CIPO will continue to advance IP awareness among Canadian entrepreneurs and innovators through the IP Awareness and Education Program. In 2024–25, CIPO will offer a number of training opportunities and information resources on IP, developed in partnership with Indigenous organizations, government departments, businesses, academia, and regional offices. For instance, through the IP Village—a collaboration between Canada's leading IP organizations—ISED will deliver a range of targeted educational resources and tools to support SMEs and women, Indigenous and Black entrepreneurs in managing and levering IP assets as part of their business and growth strategies.

Departmental Result: Canada has a clean and sustainable economy.

As Canada transitions to a cleaner, more sustainable economy, ISED remains committed to supporting the development of a globally competitive clean technology sector that addresses environmental challenges and provides high quality jobs for Canadians. Through the provision of key supports to researchers and businesses to develop and adopt clean technologies and products, ISED will contribute to Canada's annual incremental reductions in GHG emissions and the number of clean technology projects underway, helping Canada progress towards its net-zero emissions goal by 2050.

Helping to grow the clean technology sector

In 2024–25, ISED will support project proponents that have the potential to transition the Canadian economy toward a net-zero future and seek to deploy clean technology in economic sectors and technology areas such as carbon capture and storage, agriculture, electricity, and clean fuels, including hydrogen. The department will continue offering services and resources to Canadian companies to facilitate the development and financing of transformative projects in Canada's industrial sectors, while helping Canada achieve its 2030 and 2050 emissions reduction targets and its transition toward a competitive, low-carbon economy. These activities include acting as a central point of contact in the government for project proponents; coordinating and identifying funding opportunities, in partnership with key federal departments and their funding programs; and working with companies to position high-potential project concepts for success. 

Leveraging improved data on clean technologies

As the federal focal point for clean technology, ISED's Clean Growth Hub—an inter-departmental initiative co-led by ISED and Natural Resources Canada and in partnership with 16 other departments and agencies—will continue to ensure that clean technology stakeholders are better equipped to make decisions related to clean technology innovation and deployment.

In 2024–25, the Hub will address emerging needs of clean technology stakeholders, including: leveraging government procurement to support clean technology development, facilitating stronger collaborations between clean technology innovators and adopters; strengthening awareness of unique regional clean technology needs, and leveraging new digital resources to improve client experience. It will also play an important role as a focal point in facilitating connections, coordinating leading clean tech companies across Canada, and providing assistance to new or existing clients, ranging from young clean tech innovators with high potential for disruption to large firms in high-emitting industries. Through the implementation of its strategy and action plan to advance reconciliation, equity, diversity and inclusion, which seek to better understand and address the needs of under-represented groups in the clean tech sector, the Hub will proactively leverage existing government efforts to increase inclusion and provide targeted support to these groups.  

In parallel, ISED, in collaboration with Statistics Canada and Natural Resources Canada, will publish macroeconomic, industry and administrative data related to Canada's cleantech sector to help support private and public sector decision making. The Clean Growth Hub, which is the only source of authoritative data on Canada's clean tech sector, will conduct an analysis of economic trends and policy impacts on the sector. The Hub will further publish data measuring the contribution of Canada's cleantech sector to the Canadian economy— including data on employment and disaggregated data on the basis of labour force characteristics— and measure various economic, social, environmental and governance indicators. With the aid of the administrative data pillar of the Clean Tech Data Strategy, the Hub will work with federal programs in improving the consistency of data collection on federal cleantech investments and will lead an annual data collection exercise on these investments, which will allow for deeper analysis and understanding of federal programs that support clean technology.

Canada, like many other countries, is dependent on global markets for its supply of critical minerals and lithium batteries. This reliance on external supply chains, combined with a lack of global market share in this industry, creates a risk that Canada will not be able to successfully transition to a low-carbon economy. Additionally, as the battery market is already very competitive, there is a risk that firms receiving funding will not be able to compete in the global market. To mitigate these risks, Canada will continue to strengthen its domestic supply chain through the Government of Canada's recently announced special agreements with industry partners—NextStar Energy, Volkswagen PowerCo, and Northvolt Batteries North America— to anchor the production of lithium batteries in Canada, helping to develop more resilient supply chains. These three major investments will help solidify Canada's position in the global supply chain and attract the business of automotive manufacturers and critical minerals suppliers, thereby creating a sustainable domestic ecosystem.

Additionally, in light of increasing interest rates and inflation combined with budgetary constraints , there is a risk that some programs, such as the Strategic Innovation Fund, the Global Innovation Clusters and the Accelerated Growth Service may be unable to deliver the planned level of activities and projects in 2024–25. In response, ISED has implemented several measures to minimize disruptions to funding and planned activities. For example, ISED is developing strategies to manage potential project cancellations or delays, including project pipelines that can be leveraged to reinvest this funding. The department is also conducting forecasting exercises with funding recipients to ensure that their spending is on track and that potential lapses are identified and addressed as early as possible. Furthermore, ISED will implement quarterly results monitoring and reporting, through committee reviews, and improve strategies for recipient selection to ensure that the expected results of projects are met.

Snapshot of planned resources in 2024–25

  • Planned spending: $4,279,156,052
  • Planned full-time resources: 4,352

In 2024–25, ISED will continue to ensure that its programs, policies, and initiatives are responsive, inclusive, and reflective of the diverse experiences and realities of Canadians in order to address inequities and barriers. For example, several Innovation Canada programs now require recipient organizations to develop inclusion, diversity, equity and accessibility strategies outlining how they will ensure fair and equitable access for applicants and highlighting targeted initiatives to address barriers and gaps for under-represented groups. In addition, the ElevateIP program will establish partnerships with organizations representing equity-deserving groups to develop more inclusive programming.

Following ISED's commitment to producing more inclusive outcomes for women and under-represented groups, gender and diversity plans are now mandated for all recipients of Strategic Innovation Fund support. The program requires recipient organizations to develop a comprehensive gender and diversity plan at the start of their projects in an effort to ensure that they are actively cultivating environments that are more equitable, inclusive and accessible. Similarly, under the Industrial and Technological Benefits Policy, firms bidding on applicable defence procurements are required to describe their approach to increasing gender equity and diversity in their corporate structure and broader supply chains in Canada. A key component of developing an effective GBA Plus framework is identifying how initiatives can be tailored to meet the diverse needs of the people most impacted. Programs such as the Global Innovation Clusters will be implementing a series of measures creating opportunities specifically for Indigenous peoples, for example—to provide meaningful work experience for people from equity-deserving groups. The clusters are also offering workshops and formal training in response to the industry's need for members to develop their talent, gain knowledge or learn new skills. Under each Cluster, the program also promotes the 50 – 30 Challenge that strives for gender parity (50% women and/or non-binary people) and significant representation (30%) of members of under-represented groups, on its board of directors.

ISED will continue applying a GBA Plus lens to its intellectual property programming as well. The IP Clinics and Patent Collective programs specifically support recipients from traditionally under-represented groups, such as women and Indigenous entrepreneurs, in need of network development and funding support for IP advice. CIPO, in general, will continue its collaboration with the international IP community to develop a stronger understanding of GBA Plus and address the existing gender gaps in the innovation ecosystem.

In 2024–25, the department will conduct various educational and awareness raising activities aimed at encouraging greater participation of equity deserving groups. For example, ExploreIP will be targeting underrepresented groups, including Indigenous, Black, women, 2SLGBTQI+, disabled and other diverse entrepreneurs, to increase program awareness and will highlight the importance of inclusion, diversity, equity, and accessibility among staff as they pertain to their duties. Corporations Canada at ISED will continue its broad and inclusive digital awareness campaign on how to effectively access information and resources, ensuring program requirements are accessible, reliable and not impeded by technological barriers.

ISED is committed to advancing Canada's efforts toward developing the United Nations 2030 Agenda for Sustainable Development and the UN's Sustainable Development Goals (SDGs). While ISED is a strong proponent for all 17 SDGs, its policies, programs, and initiatives mostly advance actions on SDG 9—Industry, Innovation and Infrastructure to "build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation." The department's key programs, such as the Strategic Innovation Fund, Canada's five Global Innovation Clusters and Innovative Solutions Canada will support work in: research and development, technology adoption, investments in science and research, efforts to attract anchor firms through foreign investment and measures to create innovative ecosystems. ISED will also contribute to this SDG through Canada's Intellectual Property Strategy, as Canadian innovators and businesses will continue to protect and leverage their IP. In terms of promoting green infrastructure as part of the SDG, SIF's Net-Zero Accelerator initiative, Sustainable Development Technology Canada (SDTC) and ISC will be supporting clean technology innovation and Canada's clean growth policies.

ISED is also a key contributor to the following SDGs: SDG 7—Affordable and Clean Energy, SDG 8 —Decent Work and Economic Growth, SDG 12—Responsible Consumption and Production; and SDG 13 —Climate Action. Since the department is a strong advocate for Canada's climate commitments, the SIF's NZA and SDTC provide targeted investments in projects advancing SDG 7 and SDG 13— such as ones focused on developing clean technologies, batteries, critical minerals and electric vehicles— and support for initiatives promoting globally competitive clean technology solutions that will help Canada achieve its net-zero target by 2050. In addition to these flagship ISED programs, the Business Benefits Finder, the Accelerated Growth Service, and the Upskilling for Industry Initiative will be advancing SDG 8 as well. These programs will encourage inclusive and sustainable economic growth by connecting Canadians with relevant government programs, and funding employer driven-approaches to redeploying workers in high-growth sectors.

More information on ISED's contributions to Canada's Federal Implementation Plan on the 2030 Agenda and the Federal Sustainable Development Strategy can be found in ISED's Departmental Sustainable Development Strategy . Footnote vi

Companies, Investment and Growth is supported by the following programs in the program inventory:

  • Business Innovation
  • Spectrum and Telecommunications
  • Digital Service
  • Economic Outcomes from Procurement
  • Support for Small Business
  • Talent Development
  • Intellectual Property
  • Competition Law Enforcement and Promotion
  • Marketplace Protection and Promotion
  • Clean Technology and Clean Growth

Supporting information on planned expenditures, human resources, and results related to ISED's program inventory is available on GC InfoBase . Footnote vii

Science, Research, Technology and Commercialization

Support and enable business-led investment and strategic collaborations for leading- edge technology development and commercialization; maintain and strengthen Canada's research excellence, including support for fundamental science, experimentation, and exploration to address global challenges.

The Science, Technology, Research and Commercialization core responsibility aligns with the "Prosperity" domain of the Quality of Life Framework. The program within this core responsibility focuses on maintaining and strengthening Canada's research excellence, including support for fundamental science, experimentation, and exploration to address global challenges.

Under the "Prosperity" domain, the "investment in in-house research and development" indicator aligns with ISED's departmental results indicator, "percentage of Canada's higher education research and development funded by business." Both indicators recognize the importance of investments in research and development to support innovation and the commercialization of new products, services, and technologies. ISED also tracks Canada's rank among OECD nations on the average relative citation (ARC) score of science and research publications. Key initiatives under this core responsibility focus on the transfer of knowledge within the Canadian research ecosystem.

The following tables show, for each departmental result related to Science, Technology, Research and Commercialization, the indicators, the results from the three most recently reported fiscal years, the targets and target dates approved for 2024–25.

Table 4: Indicators, results, and targets for departmental result: Canadian science, technology and innovation (ST&I) research contributes to knowledge transfer.

The financial, human resources and performance information for Innovation, Science and Economic Development Canada's program inventory is available on GC InfoBase . Footnote viii

Departmental Result: Canadian science, technology and innovation (ST&I) research contributes to knowledge transfer.

ISED aims to foster an innovative economy, improve the health and well-being of Canadians, and optimize federal investment in ST&I. To achieve these goals, in 2024–25 ISED will continue working with various third-party organizations (TPOs) to advance federal research priorities and fill ecosystem gaps—primarily through the newly established Strategic Science Fund (SSF). The Department will continue to promote collaboration between domestic and international research organizations, support the development of pan-Canadian science and research-related strategies, and provide the Prime Minister and Cabinet with advice related to key scientific issues through the Office of the Chief Science Advisor. ISED will also provide policy advice on cyber security and research security considerations for international research and development (R&D) investments in academia through the implementation of the Government of Canada's Digital Research Infrastructure Strategy.

Supporting coordinated federal investments

The government allocates funding to TPOs that play an important role in the Canadian science and research ecosystem by seizing unique opportunities, filling gaps in federal programming in areas of priority to the government, providing services nationally, or deriving advantages from ST&I activities being delivered at arms-length from the federal government. TPOs are independent, not-for-profit organizations that have highly diverse mandates and areas of expertise and cover a wide spectrum of ST&I activities.

In response to the need for greater transparency and accountability related to funding decisions, Budget 2019 established the SSF, administered jointly by ISED and Health Canada. This new competitive approach to investing in TPOs affirms the importance of a credible, principles-based merit-review process informed by the advice of independent external experts chosen based on high ethical standards, expertise, and diversity of perspectives. This approach was recommended by an independent advisory panel and external monitoring of this approach will ensure that it is fair and appropriately targeted.This marks a foundational change in how funding is provided to organizations in the science and research ecosystem. Through this coordinated approach, ISED and Health Canada are better equipped to make funding decisions and assess where there may be gaps in the ecosystem.

The SSF will act as a key funding vehicle for the science and research community and will ensure clear alignment with program objectives and expected outcomes. Activities funded under the SSF are expected to enhance internationally competitive, leading-edge research in areas critical to the health, and the economic and social well-being of Canadians; to develop, attract and retain world-class research and innovation talent in scientific areas that are aligned with Canada's priorities; to accelerate the exchange of research results and the translation of this knowledge into action in Canada and abroad; and to strengthen evidence-based decision making, innovation skills development and science culture.

Following a competitive process, 24 successful applicants have been selected Footnote ix to receive funding through the SSF in 2024-25, pending the finalization of contribution agreements. This first year of a five-year cycle of funding under the SSF will allow the government to provide direct financial support for organizations to advance both fundamental and applied research. ISED, in partnership with Health Canada, will work closely with SSF recipient organizations in 2024–25 to ensure their activities are aligned and positioned to meet expected program outcomes.

Accelerating the adoption and commercialization of artificial intelligence

In 2024-25, the Department will continue to support research and development in key emerging sectors such as artificial intelligence (AI) through the ongoing implementation of the Pan-Canadian Artificial Intelligence Strategy (PCAIS).

The PCAIS aims to drive the adoption of AI across Canada's economy and society. Through the three pillars—commercialization, standards, and talent and research, the PCAIS seeks to connect Canada's world-class talent and research capacity with federal programs that facilitate commercialization and technology adoption in order to ensure that Canadian ideas and knowledge are mobilized and commercialized domestically.

In support of the commercialization and talent and research pillars of the PCAIS, the $60 million fund for the national AI institutes —Amii in Edmonton, Mila in Montréal, and the Vector Institute in Toronto— aims to help translate research in AI into commercial applications and increase the capacity of Canadian businesses to adopt these new technologies. These three not-for-profit corporations are each receiving funding of up to $20 million over five years to support the advancement of AI research, training, and innovation. For example, Amii will advance leading-edge research in AI by funding academic research and encouraging industry leaders to invest in Alberta's world-leading talent and expertise. In 2024‒25, contributions to these institute will support capacity-building among business, health, and not-for-profit partners.

In 2024‒25, ISED will continue to monitor and support the ongoing implementation of the Digital Research Infrastructure (DRI) Strategy, to ensure Canadian researchers have the tools they need to conduct leading-edge research. Under the strategy, ISED is providing funding to the Digital Research Alliance of Canada (DRAC) for the planning, procurement, installation, operation and allocation of computing infrastructure to increase computing capacity for AI researchers. In 2024‒25, DRAC will continue to coordinate and deliver national services in advanced research computing, research data management and research software, while also promoting innovation and expanding the network of support and resources that are available to academic and research communities.

CANARIE, the Canadian Network for the Advancement of Research, Industry and Education (CANARIE) will advance the DRI Strategy by funding initiatives such as the expansion and maintenance of the National Research and Education Network (NREN)—which connects more than 750 Canadian universities, colleges, cégeps, research hospitals, government research labs, school boards, business incubators and accelerators. Specifically, through the Digital Accelerator for Innovation and Research (DAIR) program, CANARIE will continue to accelerate innovation and the commercialization of products and services by providing Canadian start-ups with free cloud resources and access to expertise in next generation technologies.

Enhancing Canada's research ecosystem and leading-edge technology development in quantum science

In 2024–25 the National Quantum Strategy (NQS) Secretariat will continue to oversee the coordination and integration of quantum R&D in Canada, guiding investments through the strategy's three pillars: quantum research, talent and commercialization. To do so, the secretariat will continue to work with key partners such as the Quantum Advisory Council, the Natural Sciences and Engineering Research Council of Canada, the National Research Council of Canada, Mitacs, Canada's Global Innovation Clusters, Innovative Solutions Canada (ISC), and ISED's Regional Development Agencies to look for solutions in several quantum technology areas such as computing, software, communications and sensors.

Additionally, under the NQS, ISED will provide another year of funding to Quantum Industry Canada, a consortium of Canadian quantum technology industries, which will increase internal capacity and improve the effectiveness of knowledge transfer between key groups to support the development, scaling and commercialization of innovations.

In 2024–25, work will continue developing of the Pan-Canadian Genomics Strategy in partnership with the NRC, taking into consideration the 'What We Heard' report published in the spring of 2022, which underscored Canada's potential in genomics with existing strengths in genomics research.

Canada has a small number of lab-to-market programs that aim to increase commercialization awareness and skills among early researchers and students. Budget 2022 provided funding for a national lab-to-market platform for students and researchers at post-secondary institutions to explore the commercial potential of their work, with the aim of filling gaps in funding to support post-secondary institutions in creating or expanding lab-to-market programming. In 2024-25, ISED will work with program delivery partners to launch the new national lab-to-market platform whereby successful post-secondary institution applicants will receive funding to develop shared training curriculum, leverage respective areas of sectoral or technology specialization, and engage in cross-promotion and networking as they build and expand their lab-to-market program offerings. A new annual survey, to be launched in fall 2024, will assess how knowledge created at Canada's universities, colleges and research institutes generates commercial outcomes, as well as broader social and economic benefits for Canadians.

Strengthening international scientific collaboration

In 2024–25, in collaboration with Global Affairs Canada, ISED will be joining Horizon Europe, the world's largest collaborative science, research and innovation program. As a result, Canadian researchers and innovators will be able to access a broader range of research opportunities in areas such as health; culture, creativity and inclusive society; civil security for society; digital, industry and space; climate, energy and mobility; and, food, bioeconomy, natural resources, agriculture & environment. The benefits of association to Horizon Europe include the ability to lead projects, receive direct funding and collaborate with European partners and other associated countries in various research and innovation areas.

Investing in Cybersecurity

In 2024–25, the Cyber Security Innovation Network (CSIN) program will seek to enhance R&D, increase commercialization, and further support the development of skilled cyber security talent across Canada. ISED's role is to implement, oversee and monitor the CSIN program, as well as provide funding for the selected successful projects. The CSIN program will help foster a strong national cyber security ecosystem and position Canada as a global leader in cyber security.

As the program's lead, the National Cybersecurity Consortium (NCC) launched an initial call for proposals in April 2023, which led to a partnership between telecommunications company Ericsson, and researchers from Concordia University, the University of Waterloo, and the University of Manitoba for a project to investigate the security of 5G networks. The project will receive $1 million in funding from the NCC over three years and aims to design and implement technologies that can prevent, predict, detect, and mitigate cyber threats in 5G networks using machine learning and AI. In 2024–25, a second national call for proposals will be launched to continue to support the growth of a comprehensive and collaborative Canadian cyber security innovation ecosystem through academia-industry collaboration.

Due to the sensitive nature of cyber security R&D and the innovation activities undertaken by the NCC in leading the CSIN, the network may be targeted for its data and intellectual property. While Canada maintains an open and collaborative research environment, it has increasingly been the target of foreign interference activities that, pose a threat to Canada's research ecosystem, and to as national security.

To mitigate the risk of targeted espionage, in collaboration with national security and intelligence organizations and Canada's granting agencies, ISED will continue with the phased implementation of the National Security Guidelines for Research Partnerships in 2024–25. These guidelines are used to assess whether aspects of a research project pose unacceptable risks to national security and if these risks cannot be mitigated, the project will not be funded. The guidelines already apply to the Alliance Grant program and the Canada Biomedical Research Fund.

  • Planned spending: $969,539,190
  • Planned full-time resources: 114

Barriers to access and to participation in science, technology, engineering, and math (STEM) for women and other equity-deserving groups have led to an under-representation in these fields. ISED and its partner organizations work toward reducing and eliminating barriers to participation in several ways, with the goal of creating a diverse talent pool and inclusive industries.

One way to address this issue is by applying a GBA Plus lens when selecting funding recipients and embedding GBA plus requirements into funding agreements. For example, in 2024–25, the NCC will implement an Equity, Diversity, and Inclusion (EDI) Framework with GBA Plus considerations. The framework will outline actions the NCC will adopt to identify and remove barriers regarding the participation of individuals from underrepresented groups in network governance, operations, and activities. This includes developing EDI objectives, actions, data collection methods, and performance metrics to measure progress.

Similarly, contribution agreements with science research organizations require that recipients provide details of their planned activities to advance EDI on an annual basis, including updates on progress in their annual report. These activities can include setting representation or diversity-related targets for the governance and administration of scientific review committees; collecting self-identification data for EDI performance tracking; working towards a better understanding of program impacts on underrepresented groups; and embedding EDI values in hiring and training processes. At the initial phase of selecting funding recipients and negotiating agreements, GBA Plus is also applied.

GBA Plus also means ensuring inclusive outcomes for all Canadians through inclusive program design and implementation. For example, the NQS's broad community-based approach encourages youth from under-represented groups, such as girls, Indigenous youth, disabled youth, at-risk youth, and youth living in rural or remote locations, to develop life-long learning habits and curiosity toward STEM fields such as quantum science and technologies. Additionally, by expanding NREN into Nunavut through an agreement with Nunavut Arctic College, CANARIE will continue to reduce the digital divide for rural and northern communities.

Lastly, Genome Canada maintains a strong commitment to action on inclusion, diversity, equity and accessibility (IDEA) by embedding policies and practices enterprise-wide across its programs. It will continue collaborating with all equity-deserving groups for input on investment prioritization and delivery of challenge-driven initiatives. Specifically, Genome Canada has engaged with a wide range of Indigenous leaders across sectors and supports Indigenous-led programs, such as Silent Genomes and the Summer internship for Indigenous Peoples in Canada (SING Canada). They are working with Indigenous partners to co-develop an Indigenous truth, reconciliation and engagement strategy to elevate Indigenous genomics leadership in Canada. Through continued partnerships in activities such as the Black Excellence in Science, Technology, Engineering, Mathematics and Medicine/Health (BE-STEMM) event, Genome Canada provides research and career opportunities to Black Canadian scholars, with a focus on removing barriers and boosting retention.

For details on ISED's contributions to various UN Sustainable Development Goals please see the United Nations 2030 Agenda for Sustainable Development and the UN Sustainable Development Goals section under the People, Skills and Communities, and Companies, Investments and Growth core responsibilities.

Additionally, more information on ISED's contributions to Canada's Federal Implementation Plan on the 2030 Agenda and the Federal Sustainable Development Strategy can be found in ISED's Departmental Sustainable Development Strategy . Footnote v

Science, Technology, Research and Commercialization is supported by the following program in the program inventory:

  • Science and Research

Supporting information on planned expenditures, human resources, and results related to ISED's program inventory is available on GC InfoBase . Footnote x

Support the creation, transfer and diffusion of knowledge to ensure that Canadians, including under-represented individuals, are equipped with the skills and tools to participate in an innovative, high-growth economy; advance a culture of innovation where Canadians are driven to address local, regional, national and/or global challenges; benefit from growth of the middle class across communities; have increased access to affordable broadband and mobile Internet, including in rural and remote regions; and are protected and informed consumers.

The People, Skills and Communities core responsibility aligns with the "Prosperity" domain of Canada's Quality of Life Framework, with its focus on improving participation in the Canadian economy for various segments of the population, particularly the emphasis on broadband access. For example, one of the indicators in the "Prosperity" domain is "access to high-speed Internet," measured by the proportion of households that have access to high-speed Internet services, which is also one of ISED's Departmental results indicators.

This core responsibility also aligns with the inclusion lens of the Quality of Life Framework, as several of ISED's programs focus on reducing barriers and enhancing access to financial and non-financial supports for entrepreneurs from various equity-deserving groups, including women, racialized people, and members of the 2SLGBTQI+ communities.

The following table shows, for each departmental result related to People, Skills and Communities, the indicators, the results from the three most recently reported fiscal years, and the targets and target dates approved for 2024–25.

Table 5: Indicators, results, and targets for departmental result: People and communities from all segments of Canadian society participate in the economy.

The financial, human resources and performance information for ISED's program inventory is available on GC InfoBase . Footnote xi

Departmental Result: : People and communities from all segments of Canadian society participate in the economy.

In 2024–25, ISED will continue to provide people from all regions of the country with the necessary access, tools, and skills to participate in the digital economy. In support of Canada's Connectivity Strategy, ISED will continue to advance connectivity and bring reliable high-speed Internet access to households and businesses across Canada through the $3.225 billion Universal Broadband Fund (UBF). For instance, ISED will continue to roll out UBF projects across Canada to further advance connectivity in rural and remote areas of the country, such as in the example of Saskatchewan, where the UBF brought fibre-optic Internet to the communities of Thode and Shields, allowing nearly 350 households and other local businesses (e.g., those in the tourism industry) to benefit from access to remote schooling, work, healthcare and connecting with loved ones. These efforts will support our goal of 98% of Canadian households having access to high-speed internet by 2026, and 100% by 2030.

To provide high-speed Internet service to the hardest to reach households, the Government of Canada has entered into a $600 million agreement with Telesat to secure capacity on its low Earth orbit (LEO) satellite constellation, Telesat Lightspeed. Through Telesat Lightspeed, Internet service providers (ISPs) will be able to offer services to Canadian households at a reduced rate, bringing Canada closer to meeting its 2030 100% connectivity target. ISED will continue to monitor Telesat's progress towards a 2026 launch and 2027 service date, working with Telesat to connect a total of 40,000 rural , remote and Indigenous households in satellite-dependent communities, including in the Far North.

Through the Connecting Families Initiative (CFI), ISED will continue to promote affordable Internet access for low-income families and seniors who face affordability barriers. In partnership with ISPs, ISED will continue to facilitate access to affordable Internet plans for the hundreds of thousands of households that need it most. Specifically, in 2024–25, ISPs will provide the newest service package of 50/10 Mbps Internet speeds for $20 per month, along with the previous package (10/1 Mbps) at $10 per month.

To enhance awareness and access among those who are eligible , the CFI will be promoted through mailouts to eligible individuals and households, inviting them to register through the program's web portal. Working with the Canada Revenue Agency and Employment and Social Development Canada, ISED will explore additional means beyond mailouts to reach the target population, potentially through emails. A social media campaign, coupled with other social media engagement strategies and webinars with community-based partner organizations, will also help broaden awareness of the CFI. These efforts will serve to provide more information about the program and support organizations in helping those who have language barriers or digital literacy challenges to register and to participate in the CFI.

In addition to improving Internet access, ISED, through the Digital Literacy Exchange Program (DLEP), will continue to support not-for-profit organizations in developing and delivering digital literacy skills training for those who face barriers to participating in the digital economy. The program's second phase, DLEP 2.0, aims to provide digital literacy training to 100,000 individuals, including persons with disabilities, Indigenous people, individuals who do not speak English or French at home, seniors, individuals who have not completed high school, individuals with low-income, residents in rural and remote areas, newcomers to Canada, and individuals from official language minority communities.

Through these collective efforts and their focus on tackling systemic barriers to economic participation, the Department will help foster an increasingly accessible, and inclusive digital economy by providing everyone in Canada with the access, tools, skills, and affordable services they need.

Diversifying Canada's entrepreneurial ecosystem

As Canada's entrepreneurial landscape continues to evolve, various groups, including women, Indigenous people, and other racialized minorities, remain under-represented in the entrepreneurial ecosystem. In 2024–25, ISED's programs will continue to dismantle the barriers faced by these groups by providing access to financing, business tools and support services so that all equity-deserving groups have equal access to the resources needed to start and grow their businesses. By investing in diverse entrepreneurs, ISED will unlock new and existing potential in the business community, making Canada's entrepreneurial ecosystem more accessible by providing opportunities and support for individuals from equity-deserving groups to start, scale, grow, and maintain their businesses.

Through the Women Entrepreneurship Strategy (WES), the Black Entrepreneurship Program, and the newly established 2SLGBTQI+ Entrepreneurship Program, ISED will continue to facilitate access to financing, networks, mentorship, and business supports, such as financial planning services and training, for under-represented entrepreneurs across Canada.

In 2024-25, the WES Ecosystem Fund will continue to strengthen capacity within the entrepreneurship ecosystem and offer business supports to diverse women entrepreneurs, as well as those in rural and remote areas. An investment of $65 million will fund 24 projects led by not-for-profit organizations to offer supports such as training, mentorship and financial literacy. Furthermore, the WES Ecosystem Fund will continue to strive to serve at least 12,000 women entrepreneurs across Canada annually. The WES Ecosystem Fund will support the École des entrepreneurs du Québec FAIR.E project, which will deliver three transformational learning programs—free-of-charge—to help women entrepreneurs launch, boost and grow their businesses. The project will serve up to 1,800 women in six provinces: Quebec, Ontario, Prince Edward Island, New Brunswick, Nova Scotia, and Newfoundland and Labrador. Finally, financing and access to capital will be offered to women entrepreneurs through the $55 million Women Entrepreneurship Loan Fund, which provides individual loans of up to $50,000.

To bolster the representation of under-represented groups in Canada's entrepreneurial ecosystem, the $160 million Black Entrepreneurship Loan Fund (BELF)—made up of $30 million from the Government of Canada and $130 million from the Business Development Bank of Canada (BDC)—will continue to provide individual loans of up to $250,000 for Black business owners and entrepreneurs. The BELF administrator, the Federation of African Canadian Economics (FACE), in partnership with BDC, has approved over 500 applications, representing more than $46 million in loans. In 2024-25, the Black Entrepreneurship Ecosystem Fund will continue to support 43 not-for-profit organizations across the country, in providing training, mentorship, networking and financial literacy services to Black entrepreneurs and business owners. Entrepreneurs who identify as 2SLGBTQI+ (Two-Spirit, lesbian, gay, bisexual, transgender, queer, intersex, or other sexually or gender diverse people) make sizable contributions to the Canadian economy, yet they continue to face systemic barriers to starting and growing their businesses. Through the 2SLGBTQI+ Business Scale-Up program, totalling $13.5 million in funding, and in partnership with Canada's 2SLGBTQI+ Chamber of Commerce (CGLCC), ISED will help 2SLGBTQI+ entrepreneurs across Canada grow their businesses by implementing a national mentorship program, improving access to corporate procurement opportunities, and helping 2SLGBTQI+ entrepreneurs and businesses become export ready. The Business Scale-Up program plans to support 250 small and medium-sized enterprises (SMEs) by March 31, 2025, including 55 SMEs owned and managed by 2SLGBTQI+ individuals who also identify as members of another equity-deserving group.

Within the venture capital (VC) ecosystem, women entrepreneurs continue to face systemic barriers to accessing venture capital funding. To address these barriers and build a more inclusive risk and venture capital environment for women in Canada, the Venture Capital Catalyst Initiative (VCCI) will continue to increase women entrepreneurs' access to VC funding, contribute to increasing the representation of women in the VC industry, and help ensure that the VC industry is sensitive to gender and potential unconscious bias. VCCI's projects seek to facilitate capacity building and skills development opportunities as they relate to training and education, mentorship and coaching, and advisory services.

Additionally, the Small Business and Entrepreneurship Development Program (SBEDP) General Fund received $101.4 million, as announced in Budget 2021, to support national/pan-Canadian not-for-profit organizations into assisting SMEs across Canada, including those led or owned by members of equity-deserving groups, to develop and grow. The SBEDP General Fund has been used to support several entrepreneurship initiatives, including the $25 million 2SLGBTQI+ Entrepreneurship Program and the renewal of the Trade Accelerator Program

The Department will also continue to build knowledge and collect data on under-represented entrepreneurs to create a more inclusive and supportive business environment through the Women Entrepreneurship Knowledge Hub (WEKH), the Black Entrepreneurship Knowledge Hub (BEKH), and the 2SLGBTQI+ Knowledge Hub, which conduct research on the state of the entrepreneurial ecosystem for each group. To provide evidence-based research to inform the design and delivery of targeted supports for women entrepreneurs, the WEKH, led by Toronto Metropolitan University, will continue to publish the State of Women's Entrepreneurship annual report, as well as reports and articles on women entrepreneurs across a variety of sectors, such as cleantech, agriculture, arts and culture, and procurement. To further dismantle the barriers experienced by women entrepreneurs, the WEKH will continue to add more women entrepreneurs to its See It. Be It. database of over 1,800 diverse Canadian women entrepreneurs.

The BEHK, administered by Carleton University's Sprott School of Business and the Dream Legacy Foundation, will continue to work with community partners to advance research on the state of Black entrepreneurship in Canada and help identify barriers to success, as well as opportunities for growth, for Black entrepreneurs. To support this work, the BEKH will conduct a range of research activities. BEKH will also continue to convene Black Entrepreneurship Program stakeholders, building on the successes and lessons learned from its annual symposiums in 2022 and 2023. Similarly, the 2SLGBTQI+ Knowledge Hub will conduct research and collect data to create a clearer picture of the entrepreneurship landscape for the 2SLGBTQI+ community and the challenges 2SLGBTQI+ entrepreneurs face.

In 2024–25, ISED will continue to challenge Canadian organizations to increase the representation and inclusion of diverse groups in senior leadership positions through the 50 – 30 Challenge, which aims to achieve gender parity (50% women and/or non-binary people) and significant representation (30%) of members of other equity-deserving groups on Canadian boards and/or in senior management. The five Ecosystem Partners — Colleges and Institutes Canada, UN Global Compact Network Canada, the Ted Rogers School of Management's Diversity Institute, the Women's Economic Council and Egale Canada— will continue to support challenge participants by delivering tools, services and resources to help them meet the 50 – 30 Challenge objectives.

Bolstering the digital presence of Canadian businesses

ISED is committed to helping Canadian businesses, especially SMEs and businesses owned by under-represented entrepreneurs, take advantage of digital technologies. Through the Canada Digital Adoption Program (CDAP), ISED will provide funding opportunities and expert advice to help SMEs digitalize their operations. In 2024–25, the Department will continue to help SMEs establish a digital presence and to provide job opportunities to youth—by hiring and training them as e-commerce advisors—through CDAP.

Through its Grow Your Business Online component, CDAP will continue to provide eligible businesses with micro-grants of up to $2,400 for costs associated with the adoption of digital technologies and with assistance and e-commerce advisory services. By focusing on awareness-building activities and user-centric enhancements, CDAP will continue to increase its uptake to maximize the value and impact of the program for participating businesses.

Through CDAP's Boost Your Business Technology component, additional incentives will be offered in the form of interest-free loans through BDC and individual wage subsidies of up to $7,300 for youth employment placements, to support SMEs in implementing their digital adoption plans to improve their productivity and competitiveness.

ISED, through the Trade Accelerator Program (TAP), will continue to help SMEs take advantage of international market opportunities by increasing their exporting capabilities. TAP is delivered by six regional chambers of commerce across Canada and will support these organizations in guiding participating SMEs to achieve an increase in the value of their exports over the course of their participation in the program through access to networks, training and advice from Canada's top export advisors.

Due to ongoing procurement and supply chain challenges, labour shortages, and inflation, combined with unexpected events such as extreme weather and wildfires, there is a risk of delays and cost overruns for some projects under the UBF and Telesat's LEO satellite constellation. ISED will continue to monitor the progress of these projects and to work closely with funding recipients to mitigate risks on a case-by-case basis to ensure that the programs remain on track to meet the government's connectivity targets.

Given the systemic barriers that people from under-represented groups continue to face in accessing financial and non-financial support services, there is a risk of insufficient program uptake or use by eligible recipients. In addition, there is a risk that ISED's programs may not sufficiently address the needs of their target populations because of the changing economic climate, the evolving needs of SMEs and entrepreneurs, and the limited availability of data on the specific entrepreneurship barriers that some equity-deserving groups face. To mitigate these risks, ISED will undertake targeted outreach and engagement activities to ensure that programs reach their audience and that eligible individuals benefit from key services. ISED will also continue to leverage research and data from the various knowledge hubs and work closely with community-based organizations to co-develop services and supports, such as training, to ensure that programming is relevant, useful and culturally appropriate for the target audience.

  • Planned spending: $703,840,962
  • Planned full-time resources: 178

ISED will continue to foster an inclusive and representative modern digital economy by focusing on closing gaps in connectivity and Internet access for Canadian households, improving digital literacy skills, and providing financial and business supports to under-represented entrepreneurs.

Certain groups may face barriers to accessing DLEP and CFI services—for instance, those with limited access to technology due to financial constraints, lack of access to the Internet to register for the program, language barriers, lack of transportation or lack of awareness of the program. To address these limitations, the programs will continue to target low-income families, seniors, and official language minority communities. DLEP will continue to offer services free of charge, support multilingual training and translation, offer training in various facilities, and provide transportation to individuals in need. In partnership with the YWCA, the CFI will continue to offer services in multiple languages and to train staff in non-profit organizations that support low-income families and seniors on how to assist eligible participants with program registration.

Programs committed to closing the connectivity gap such as the UBF, will continue to work towards providing households in rural and remote and Indigenous communities with access to high-speed Internet. The UBF will continue to target under-served communities, including Indigenous communities.in order to bridge the digital divide by bringing reliable Internet access to these regions and groups.

Barriers to accessing financial and non-financial support services are further amplified for entrepreneurs with multiple intersecting identities (i.e., those who identify as belonging to more than one under-represented group). With a reduced awareness of these programs or a lack of knowledge in navigating government resources, these entrepreneurs may not benefit from federal supports. Entrepreneurs from equity-deserving groups and/or those in rural, remote, and northern communities may also face additional barriers to growth due to small local markets, remoteness, and infrastructure challenges. Furthermore, individuals who do not wish to identify as a member of an equity-deserving group may not benefit from ISED's programs and services.

To dismantle these barriers, ISED will ensure that the self-identification process in applications is secure and confidential and will enhance its outreach and communication activities —through platforms like the Business Benefits Finder, for example—to build awareness of all entrepreneurship programs. Furthermore, disaggregated data collected on diverse groups will be used to fuel research on Canada's entrepreneurial ecosystem and to improve programming for under-represented groups. The Department will also encourage recipient organizations to refer clients and to share information with eligible applicants to bolster the reach of their programs. In addition, ISED will continue to assess recipient organizations' projects to ensure that those benefitting individuals with intersecting identities and living in rural and remote communities are prioritized.

ISED is a strong supporter of Canada's efforts to develop and implement the United Nations 2030 Agenda for Sustainable Development and Sustainable Development Goals (SDGs). While ISED supports multiple SDGs, its programs and initiatives under the People, Skills, and Communities core responsibility primarily advance action on SDG 9—Industry, Innovation and Infrastructure to "build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation." ISED's programs and initiatives in support of this SDG include Canada's Connectivity Strategy, the Universal Broadband Fund, Connect to Innovate), and the Canada Digital Adoption Program.

ISED's People, Skills, and Communities programs also advance other SDGs, such as:

  • ensuring inclusive and equitable quality education and promoting lifelong learning opportunities for all; and
  • promoting inclusive and sustainable economic growth, full and productive employment, and decent work for all.

More information on ISED's contributions to Canada's Federal Implementation Plan for the 2030 Agenda and the Federal Sustainable Development Strategy can be found in ISED's Departmental Sustainable Development Strategy . Footnote v

People, Skills, and Communities is supported by the following programs in the program inventory:

  • Support for Under-represented Entrepreneurs
  • Bridging Digital Divides

Supporting information on planned expenditures, human resources, and results related to ISED's program inventory is available on GC InfoBase . Footnote xii

Internal services are the services that are provided within a department so that it can meet its corporate obligations and deliver its programs. There are 10 categories of internal services:

  • management and oversight services
  • communications services
  • legal services
  • human resources management services
  • financial management services
  • information management services
  • information technology services
  • real property management services
  • materiel management services
  • acquisition management services

Our Workplace

In 2024–25, ISED will continue to ensure that its workspace, tools, and technology are accessible, and that they continue to evolve to meet changing needs of employees in the hybrid work environment. The Department will continue to modernize the built environment to meet GC Workplace standards, and to remove barriers identified in ISED's 2023-2025 Accessibility Plan. In 2024‒25, employees will see the creation of a variety of accessible spaces within the workplace including community boardrooms, multi-faith prayer rooms and reflection rooms. ISED will also continue to increase the number of all-access and gender-neutral washrooms that are available to employees. Finally, ISED will continue to equip its boardrooms with hybrid meeting technology and will continue to ensure Wi-Fi access in regional offices.

In many cases, the delivery of projects in the built environment are dependent on external partners such as building management companies, Public Services and Procurement Canada, Shared Services Canada, among others. As such, there is a risk that projects could be delayed if project stakeholders are unable to align with ISED's timelines. To mitigate this risk, ISED will continue to monitor its progress and reprioritize investments as required to responsibly manage its funds and continue to deliver on its requirements.

ISED's Future of Work Office (FOWO) has been critical to the Department's successful transition to the hybrid work environment. FOWO will continue to ensure that all employees have signed telework agreements and will monitor ISED's compliance with the Direction on Prescribed Presence in the Workplace to meet the reporting requirements set out by Treasury Board of Canada Secretariat (TBS).

Our Workforce

ISED is committed to being a leader in anti-racism, diversity, equity, inclusion and accessibility, and fostering organizational well-being. ISED's 2023-2025 Employment Diversity and Inclusion (EDI) strategy emphasizes equity by providing fair opportunities and access, while also highlighting accountability and reporting mechanisms to ensure effective behaviors and tangible results across the organization. As an integral part of the strategy, ISED is piloting a new corporate onboarding program designed to develop a greater sense of belonging within the organization. This program will offer new employees personalized resources, tailored onboarding as well as the opportunity to engage in specialized networking events with ISED's various employee networks.

Through the 2023-2025 Accessibility Plan, ISED is working to identify, remove and prevent barriers not only in the built environment, but also in the areas of culture, employment, accessibility, and communication, with the aim to combat ableist attitudes, enhance inclusive hiring practices, and improve accessibility. To improve the accommodations process, ISED will empower managers to autonomously handle accommodation issues, when possible, provide support when needed and continue to foster awareness, and encourage the pursuit of innovative, inclusive solutions. ISED will continue to implement its 2023-2026 Official Languages (OL) Strategy, incorporating OL policy into departmental strategies, promoting collective leadership, and integrating planned OL improvements to foster linguistic duality within ISED.

In 2024‒25, ISED will implement its updated 2023-2026 Mental Health Strategy which aims to improve psychological health and safety in the workplace; prioritize employee well-being, leading to a healthier and more productive workforce. The strategy also seeks to integrate diversity, equity, and inclusion principles, emphasizing their critical role in creating a supportive work environment where every individual is acknowledged, respected, and valued.

To further support employee mental health and well-being, the Canadian Innovation Centre for Mental Health in the Workplace will continue to offer mental health workshops to ensure managers and employees are aware of the tools and services available to support mental health in the workplace, including but not limited to the Employee Assistance Program (EAP).

In addition to EAP, ISED also offers Ombuds Services and Information Conflict Management Services (ICMS). This year, ISED will work to ensure that its services are accessible and inclusive, and that members of underrepresented groups feel safe and secure in accessing them. Specifically, the Office of the Ombud will reach out to all internal EDI networks to answer questions, address concerns, identify any potential barriers to access, and establish ways to overcome them. The goal of this work is to ensure that employees can discuss any issue—including racism, inclusion, equity, discrimination, or accessibility—in complete confidence and without fear of reprisal.

This year, ISED will undertake a three-year review of the Harassment and Violence prevention program, with a focus on preventative measures and program improvements and to assess program effectiveness. At the same time, the Department will continue to offer employee support regarding harassment, violence, and discrimination issues, including sessions on addressing microaggression while preserving positive working relationships. Additionally, ISED will facilitate safe space discussions with executives, so that they are equipped to facilitate conversations on EDI, harassment, and discrimination with their own teams.

Over the past two years, ISED has implemented phases I and II of its Financial Management Modernization Initiative, to improve financial stewardship and optimize financial management practices as well as to improve access to timely and effective financial management information to support decision making and risk management. To date, ISED has also reinforced accountabilities within the Department and centralized key functions to ensure better alignment under the Chief Financial Officer model. In 2024‒25, ISED will focus on optimizing the model to ensure maximum benefit for the organization. This will be especially important as the Department moves to a risk-based approach to better manage the variety and scope of its Grants and Contributions programs.

ISED will also transition to multi-year budget planning to ensure the Department is well positioned to achieve the Government of Canada saving targets announced in Budget 2023. ISED's finance and human resources teams will work together closely to maximize savings from attrition and realign resources to ensure ongoing program delivery.

To support the advancement of departmental and government priorities across Canada, ISED's regional offices will continue to serve as ISED's ambassadors across the country, providing substantial support to the Department's five ministers by organizing and executing ministerial visits. Regional offices will also continue to gather critical regional intelligence, facilitate relations with ISED's key regional partners, and conduct policy analysis to support the delivery of ISED's programs and services across the country.

In 2024‒25, ISED will continue to modernize and improve its Information Management and Information Technology (IM/IT) infrastructure, tools and services to improve users' digital experience, while maintaining a secure technical environment. In alignment with ISED's recently developed Service & Digital Strategy and Service Improvement Road Map, the Department will continue to monitor service criteria such as online end-to-end access, real-time performance measures, accessibility, service improvement based on client feedback and performance against service standards.

ISED will continue monitoring its cloud-based landscape to ensure that all internal and external services, databases and platforms remain available to users and function without interruption. To enhance its security posture, ISED will continue to advance its detection and response capability, ensuring that the organization is able to respond to cyber threats, and minimize any related impacts.

There is a strong demand within the organization to build a data pipeline and create tools for ISED's programs to enable evidence-based program delivery. Building on the successful development of Power BI dashboards for CDAP, ISED will continue to develop data visualization tools that put real-time program information in the hands of ISED's decision makers. To further support the management and utilization of organizational data, ISED will continue to implement data standards and data integrity measures to ensure the reliability of its data, improve digital information management practices, and manage information sprawl across the organization.

  • Planned spending: $182,467,251
  • Planned full-time resources: 1,651

Planning for contracts awarded to Indigenous businesses

To achieve and exceed the 5% Indigenous procurement targets and meet our economic reconciliation obligations, ISED continues to take the following actions:

  • Communicate ISED's Indigenous Procurement Policy and related processes to advocate and encourage procurement with Indigenous businesses;
  • Impose mandatory training for Procurement Functional Specialists, Acquisition Cardholders, and staff with low-dollar procurement delegations;
  • Conduct ISED's Annual Procurement Planning exercise to enable early client engagement with key department officials to maximize opportunity with Indigenous businesses;
  • Publish Requests for Information to determine if Indigenous capacity exists, as needed;
  • Apply Procurement Strategy for Indigenous Business (PSIB) and include evaluation criteria in solicitation documents to maximize opportunity for Indigenous businesses to the greatest extent possible;
  • Award sole source contracts under $40K to Indigenous businesses where capacity and market presence exists and above $40K direction to Indigenous businesses is encouraged.
  • Unbundle larger contracts when Indigenous capacity exists;
  • Attend Indigenous job expos to better understand the market and help Indigenous businesses navigate procurement within the federal government; and
  • Utilize corporate controls and reporting to monitor compliance and achieve the targets.

ISED's targets were determined by analyzing past contracting data. Using this information, procurement opportunities were identified, and ambitious targets were established. 

ISED reviews purchasing activity and compares it with the Indigenous Business Directory (IBD). Where no capacity exists, ISED established exempted commodities, for Deputy Minister (DM) approval. ISED annually reviews the Indigenous Business Directory IBD to determine if updates to the exemptions apply.

The potential challenges in meeting the minimum 5% target for ISED's commonly purchased commodities include lack of competitive pricing, limited capacity and gaps in expertise.

In 2024–25, ISED plans to establish a means to report acquisition card purchases with Indigenous businesses with the goal of increasing ISED's procurement results.

Planned spending and human resources

This section provides an overview of ISED's planned spending and human resources for the next three fiscal years and compares planned spending for 2024–25 with actual spending from previous years.

Table 6: Actual spending summary for core responsibilities and internal services ($ dollars)

The following table shows information on spending for each of ISED's core responsibilities and for its internal services for the previous three fiscal years. Amounts for the current fiscal year are forecasted based on spending to date.

Table 6 Notes

The variance primarily reflects actual spending under the Universal Broadband Fund, which was lower in 2022–23 as a result of the proposal assessment process and contribution agreement negotiations.

Also reflected is the funding profile for the Canada Digital Adoption Program: Stream 1.

Return to table 6 note 1 referrer

The increase reflects the actual spending related to the implementation of the Strategic Science Fund and the National Quantum Strategy.

Return to table 6 note 2 referrer

The increase in actual spending primarily reflects the funding profiles of the Strategic Innovation Fund, the Canada Digital Adoption Program: Stream 2 and the Canada Foundation for Sustainable Development Technology.

Return to table 6 note 3 referrer

The variance in actual spending primarily reflects spending to support various departmental initiatives such as Diversity & Inclusion, Workload Migration and Future of Work.

Return to table 6 note 4 referrer

Table 7: Budgetary planning summary for core responsibilities and internal services (dollars)

The following table shows information on spending for each of ISED's core responsibilities and for its internal services for the upcoming three fiscal years.

Table 7 Notes

The variance in planned spending reflects a fluctuation in the approved funding profile of the Universal Broadband Fund, offset by the end of funding for several programs such as the Canada Digital Adoption Program: Stream 1, the Small Business and Entrepreneurship Development Program and the Women's Entrepreneurship Program.

Return to table 7 note 1 referrer

The variance in planned spending primarily reflects a decrease in the approved funding profile of the Digital Research Infrastructure Strategy. Also reflected is the winding down of several programs such as Genome Canada, adMare Bioinnovations, Stem Cell Network.

Return to table 7 note 2 referrer

Planned spending primarily reflects new funding announced in Budget 2023 for the Strategic Innovation Fund to support battery manufacturing in Canada. Also reflected is the end of temporary funding for several programs such as the Canada Digital Adoption Program: Stream 2, Upskilling for Industry Initiative, New Generation Wireless Technology Initiative, Zero Emissions Vehicles and Fuels Regulatory.

Return to table 7 note 3 referrer

The budget reduction related to the Refocusing Government Spending is reflected among all core responsibilities, to reach $191.6 million by 2026–27.

Return to table 7 note 4 referrer

Table 8: 2024–25 budgetary gross and net planned spending summary (dollars)

The following table reconciles gross planned spending with net planned spending for 2024–25.

ISED's 2024-25 Budgetary Planned Gross Spending is $6.5 billion, which includes $374.4 million in vote netted revenues, accounting for total planned net spending of $6.1 billion.

The ISED vote netted revenue authorities are those referred to in paragraph 29.1(2)(a) of the Financial Administration Act (i.e. revenue received by the department in a fiscal year through the conduct of its operations, which the department is authorized to expend in order to offset expenditures incurred in that fiscal year) from the following sources:

  • the provision of internal support services under section 29.2 of that Act, and the provision of internal support services to the Canadian Intellectual Property Office;
  • activities and operations related to communications research at the Communication Research Centre;
  • services and insolvency processes under the Bankruptcy and Insolvency Act at the Office of the Superintendent of Bankruptcy;
  • activities and operations carried out by Corporations Canada under the Canada Business Corporations Act, the Boards of Trade Act, the Canada Cooperatives Act and the Canada Not-for-profit Corporations Act; and
  • services and regulatory processes for mergers and merger-related matters, including pre-merger notifications, advance ruling certificates and written opinions, under the Competition Act at the Competition Bureau.

The following graph presents planned spending (voted and statutory expenditures) over time.

Departmental spending 2021–22 to 2026–27. Text version below:

The variance in future years is primarily related to the fluctuations in the cashflow profiles of transfer payment programs.

Estimates by vote

Information on ISED's organizational appropriations is available in the 2024–25 Main Estimates . Footnote xiii

The future-oriented condensed statement of operations provides an overview of ISED's operations for 2023–24 to 2024–25.

The forecast and planned amounts in this statement of operations were prepared on an accrual basis. The forecast and planned amounts presented in other sections of the Departmental Plan were prepared on an expenditure basis. Amounts may therefore differ.

A more detailed future-oriented statement of operations and associated notes, including a reconciliation of the net cost of operations with the requested authorities, are available at ISED's website . Footnote xiv

Table 9: Future-oriented condensed statement of operations for the year ending March 31, 2025 (dollars)

The expected variance in total expenses year-over-year is mainly attributable to increases in transfer payments, particularly in the Strategic Innovation Fund, the Canada Foundation for Innovation, the Digital Research Infrastructure Strategy and the Universal Broadband Fund. Given the complexity of some of ISED's programming, and the requirement to align the funding profile with the recipients forecasted spending, further changes to the 2024–25 planned results could occur during the fiscal year.

Total revenues (net of those earned on behalf of government) are projected to increase year-over-year, mainly as the Canadian Intellectual Property Office (CIPO)'s revolving fund expects to collect higher revenues due to fee increases and increased examination capacity.

Table 10: Actual human resources for core responsibilities and internal services

The following table shows a summary of human resources, in full-time equivalents (FTEs), for ISED's core responsibilities and for its internal services for the previous three fiscal years. Human resources for the current fiscal year are forecasted based on year to date.

Table 10 Notes

The increase in actual and forecast FTEs is due to new temporary employees in support of the implementation of the Canada Digital Adoption Program: Stream 1 and the Universal Broadband Fund.

Return to table 10 note 1 referrer

The increase in actual and forecast FTEs is due to the implementation of the Strategic Science Fund Program and the launch of the National Quantum Strategy.

Return to table 10 note 2 referrer

Table 11: Human resources planning summary for core responsibilities and internal services

The following table shows information on human resources, in full-time equivalents (FTEs), for each of ISED's core responsibilities and for its internal services planned for 2024–25 and future years.

Table 11 Notes

The decrease is due to the winding down of funding for the Canada Digital Adoption Program: Stream 1 and the Universal Broadband Fund.

Return to table 11 note 1 referrer

The decrease is due to the winding down of funding for the Canada Digital Adoption Program: Stream 2 and the funding for the Budget 2021 measures entitled Charging and Fueling Zero Emission Vehicles; and Supporting the Production and Use of Clean Fuels.

Return to table 11 note 2 referrer

FTE totals may not add due to rounding.

Return to table 11 note 3 referrer

The reduction in Full Time Equivalent related to the Refocusing Government Spending, which will be achieved by not backfilling certain positions as they vacate through attrition, is reflected among all core responsibilities, to reach 94 FTE by 2026–27.

Return to table 11 note 4 referrer

Return to table 11 note 5 referrer

Appropriate minister(s):

Minister of innovation, science and industry.

The Honourable François-Philippe Champagne, P.C., M.P.

Minister of Export Promotion, International Trade and Economic Development

The Honourable Mary Ng, P.C., M.P.

Minister of Tourism and Minister responsible for the Economic Development Agency of Canada for the Regions of Quebec

The Honourable Soraya Martinez Ferrada, P.C., M.P.

Opportunities Agency

Minister of small business.

The Honourable Rechie Valdez, P.C., M.P.

Deputy ministers:

Simon Kennedy

Francis Bilodeau

Ministerial portfolio:

Innovation, Science and Economic Development Footnote xv

Enabling instrument(s):

Innovation, Science and Economic Development Canada's founding legislation is the Department of Industry Act , S.C. 1995, c.1. Footnote xvi

Year of incorporation / commencement:

Mailing address:.

Corporate Management Sector Innovation, Science and Economic Development Canada 235 Queen Street 2nd Floor, East Tower Ottawa, ON K1A 0H5

613-954-5031

1-866-694-8389

613-954-2340

ic.info–info.ic@ised–isde.gc.ca

Website(s):

https://ised-isde.canada.ca/site/ised/en

The following supplementary information tables are available on ISED's website:

  • Details on transfer payment programs
  • Gender-based analysis plus
  • Horizontal initiatives
  • Up front multiyear funding

Information on ISED's departmental sustainable development strategy can be found on ISED's website . Footnote x

ISED's Departmental Plan does not include information on tax expenditures.

Tax expenditures are the responsibility of the Minister of Finance. The Department of Finance Canada publishes cost estimates and projections for government‑wide tax expenditures each year in the Report on Federal Tax Expenditures . Footnote xvii

This report provides detailed information on tax expenditures, including objectives, historical background and references to related federal spending programs, as well as evaluations, research papers and gender-based analysis plus.

For the purpose of the 2024–25 Departmental Plan, government-wide priorities are the high-level themes outlining the government's agenda in the 2021 Speech from the Throne: building a healthier today and tomorrow; growing a more resilient economy; bolder climate action; fighter harder for safer communities; standing up for diversity and inclusion; moving faster on the path to reconciliation and fighting for a secure, just, and equitable world.

An initiative in which two or more federal organizations are given funding to pursue a shared outcome, often linked to a government priority.

Net outlays and receipts related to loans, investments and advances, which change the composition of the financial assets of the Government of Canada.

What an organization did with its resources to achieve its results, how well those results compare to what the organization intended to achieve, and how well lessons learned have been identified.

The articulation of strategic choices, which provides information on how an organization intends to achieve its priorities and associated results. Generally, a plan will explain the logic behind the strategies chosen and tend to focus on actions that lead up to the expected result.

For Departmental Plans and Departmental Results Reports, planned spending refers to those amounts presented in the Main Estimates.

A department is expected to be aware of the authorities that it has sought and received. The determination of planned spending is a departmental responsibility, and departments must be able to defend the expenditure and accrual numbers presented in their Departmental Plans and Departmental Results Reports.

Individual or groups of services, activities or combinations thereof that are managed together within a department and that focus on a specific set of outputs, outcomes or service levels.

An inventory of a department's programs that describes how resources are organized to carry out the department's core responsibilities and achieve its planned results.

An external consequence attributed, in part, to an organization, policy, program or initiative. Results are not within the control of a single organization, policy, program or initiative; instead, they are within the area of the organization's influence.

Expenditures that Parliament has approved through legislation other than appropriation acts. The legislation sets out the purpose of the expenditures and the terms and conditions under which they may be made.

A measurable performance or success level that an organization, program or initiative plans to achieve within a specified time period. Targets can be either quantitative or qualitative.

Expenditures that Parliament approves annually through an Appropriation Act. The vote wording becomes the governing conditions under which these expenditures may be made.

World Bank Blogs

March 2023 global poverty update from the World Bank: the challenge of estimating poverty in the pandemic

Samuel kofi tetteh baah, r. andres castaneda aguilar, carolina diaz-bonilla, christoph lakner, minh cong nguyen, martha viveros.

Global poverty estimates were updated today on the  Poverty and Inequality Platform (PIP) . This update includes new regional poverty aggregates in 2020 and 2021 for Latin America and the Caribbean, and in 2020 for Europe and Central Asia, and the group of advanced countries. These are the regions for which we now have sufficient survey data available during the COVID-19 pandemic. In total, 113 new country-years have been added, bringing the total number of surveys to more than 2,100.

This update also incorporates the usual changes to the input data, including revisions to existing welfare distributions, the inclusion of new welfare distributions, and revisions to price, national accounts, and population data used for global poverty monitoring (more details  here ). Overall, these changes have resulted in minor revisions in global and regional poverty estimates.

Table 1 summarizes the revisions to the regional and global poverty estimates between the September 2022 data vintage and the March 2023 data vintage for the 2019 reference year at all three poverty lines. The global poverty headcount ratio at $2.15 is revised slightly up by 0.1 percentage points to 8.5 percent, resulting in a revision in the number of poor people from 648 to 659 million. This revision represents 11 million more people living in extreme poverty, largely driven by South Asia (5 million) and the Middle East and North Africa (4 million).

Table 1 Poverty estimates for reference year 2019, changes between September 2022 and March 2023 vintage by region and poverty lines

Table 1. Poverty estimates for reference year 2019, changes between September 2022 and March 2023 vintage by region and poverty lines

Similar limited changes in poverty estimates are observed at the higher lines of $3.65 and $6.85, which are typically used for measuring poverty in lower-middle- and upper-middle-income countries, respectively. At $3.65, the global poverty headcount ratio increases by 0.1 percentage points to 23.6 percent, representing 28 million more people living in poverty. At $6.85, the global poverty rate increases by 0.2 percentage points to 46.9 percent, representing 44 million people living in poverty. The upward revisions in poverty estimates at the higher lines are largely driven by South Asia and Sub-Saharan Africa. 

This March 2023 global poverty update from the World Bank revises the previously published global and regional estimates from 1981 to 2019. Regional poverty estimates are now reported up to 2021, depending on sufficient data coverage over the period of the COVID-19 pandemic. Poverty data are reported for Europe and Central Asia until 2020, and Latin America and the Caribbean until 2021. For all other developing regions, poverty data are reported for pre-pandemic years (see Figure 1). More details are available  here on how we have determined those regions for which to report post-2019 estimates.

Figure 1: Global and regional poverty estimates, 1990 - 2021

The data published in this PIP update, while incorporating more recent input data, do not change the overall perceptions about global poverty trends and the regional distribution of poverty. It is still the case that global poverty has been falling since the 1990s, and at a slower rate since 2014 ( World Bank 2022 ). Extreme poverty has been falling in all regions, except the Middle East and North Africa due to conflict and fragility ( World Bank 2020 ). Roughly 60% of the world’s extreme poor in 2019 lived in Sub-Saharan Africa alone, while 81% of the global poor at the poverty line of $3.65 lived in Sub-Saharan Africa or South Asia. 

The authors gratefully acknowledge financial support from the UK Government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Program.

  • The World Region

Samuel Kofi Tetteh Baah's photo

Economist, Global Poverty and Inequality Data (GPID), Development Data Group, World Bank

R. Andres Castaneda Aguilar

Economist, Development Data Group, World Bank

Carolina Diaz-Bonilla's photo

Senior Economist, Poverty and Equity Global Practice, World Bank

Tony Fujs - Photo

Data Scientist

Christoph Lakner

Program Manager, Development Data Group, World Bank

Minh Cong Nguyen

Senior Data Scientist, Poverty and Equity Global Practice, World Bank

Photo of Martha Viveros

Consultant, Development Data Group, World Bank

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  1. What is research

  2. Research basics

  3. WHAT IS RESEARCH?

  4. Meaning of Research & Definition of Research !! Research And Statistics in Physical Education B.P.Ed

  5. Research Definition ,Process of Research

  6. Research part 1/overview of research

COMMENTS

  1. Research program

    A research program (British English: research programme) is a professional network of scientists conducting basic research. The term was used by philosopher of science Imre Lakatos to blend and revise the normative model of science offered by Karl Popper's The Logic of Scientific Discovery ...

  2. What Is Research, and Why Do People Do It?

    In Chap. 4, we will illustrate how our definition fits research using a range of quantitative and qualitative methods. Exercise 1.4. Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers ...

  3. PDF 1 What is Research?

    Introduction Social research is persuasive Social research is purposive Social research is positional Social research is political Traditions of enquiry: false dichotomies Ethics: pause for reflection. 4. 5. v be able to define 'research'. v be able to respond to the view that social research is persuasive, purposive, positional and political.

  4. PDF What is Research and What it isn't? And Who is Human ...

    research So, for QI/QA activities, always ask the same question - is it it a systematic investigation designed to contribut e to generalizable knowledge Calling something QI/QA or using words like "evaluation", etc., does not make a project NOT research. The terms are not mutually exclusive. QI/QA . Research . Research . 26

  5. Basic Steps to Building a Research Program

    Planning From Within. Taking an entrepreneurial approach is a successful mechanism when developing a clinical research program. Maintaining a sustainable program requires fiscal planning, much like a business. When developing the financial infrastructure, it is helpful to consider budgeting from both broad and narrow perspectives.

  6. PDF Definition of A Research Project and Specifications for Fulfilling the

    research project is a scientific endeavor to answer a research question. Research projects may include: Case series. Case control study. Cohort study. Randomized, controlled trial. Survey. Secondary data analysis such as decision analysis, cost effectiveness analysis or meta-analysis. Each resident must work under the guidance of a faculty mentor.

  7. (PDF) DEVELOPING A PROGRAM OF RESEARCH: An Essential Process for a

    The first practical guide to creating, evolving, and sustaining a successful program of research in applied health, social sciences, and education fields. An indispensable resource for early- and ...

  8. What is a research project?

    A research project is an academic, scientific, or professional undertaking to answer a research question. Research projects can take many forms, such as qualitative or quantitative, descriptive, longitudinal, experimental, or correlational. What kind of research approach you choose will depend on your topic.

  9. PDF INTRODUCTION

    meet the definition of research and would, therefore, not require an IRB review. This module interprets words and phrases used in the definition of research and of human subject from the perspective of research in the social and behavioral sciences, education, and the humanities. Learning Objectives By the end of this module, you should be able to:

  10. Building a program of research

    A program of research is defined as a coherent expression of a researcher's area of interest that has public health significance, builds from the published research literature in the field, has relevance for clinical nursing practice, and captures the passion and commitment of the researcher. The Outcomes Model for Health Care Research is ...

  11. What is Research

    Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, "research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.".

  12. Chapter 1

    Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens. Human subject (FDA) means an individual who is or becomes a participant in research, either as a recipient of the test article or as a control. A subject may be either a healthy human or a patient.

  13. What is Research Development?

    Research Development is an activity that many Universities have embraced to enhance the efforts of their faculty and foster the development of collaborative, team-based science as well as compete for large research center and consortia funding opportunities. Research Development professionals serve as "rainmakers" who catalyze and facilitate ...

  14. Definitions

    research and demonstration projects which are conducted by or subject to the approval of department or agency heads; or. taste and food quality evaluation and consumer acceptance studies. It is critically important to note, however, that decisions about whether studies are exempt from the requirements of the Common Rule must be made by an IRB ...

  15. Research vs. Quality Improvement and Program Evaluation

    The federal definition of research is "a systematic investigation, including research development, testing and evaluation, designed to develop or contribute to generalizable knowledge. Activities which meet this definition constitute research for purposes of this policy, whether or not they are conducted or supported under a program which is ...

  16. PDF Program Evaluation vs. Research: Do I Need to Submit for an Exemption

    Therefore, the mere intent to publish the findings of a program evaluation does not obligate submitting for an exemption or IRB review and approval as long as the publication makes it clear the publication is the result of a program evaluation as defined above. If the project is research involving human subjects, submission for an exemption or ...

  17. Defining Research with Human Subjects

    Human subject: A living individual about whom an investigator (whether professional or student) conducting research: Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or. Obtains, uses, studies, analyzes, or generates identifiable private ...

  18. Definition of Human Subjects Research

    Definition of Human Subjects Research. According to 45 CFR 46 , a human subject is "a living individual about whom an investigator (whether professional or student) conducting research: Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or.

  19. What Is Program Evaluation?

    We believe the power to define program evaluation ultimately rests with this community. An essential purpose of AJPH is to help public health research and practice evolve by learning from within and outside the field. To that end, we hope to stimulate discussion on what program evaluation is, what it should be, and why it matters in public ...

  20. Special Research Programs Guidelines

    Special Research Program Guidelines Definition and Purpose Special Research Programs (SRPs) exist at UC Irvine to provide a structure for collaborative research activities that do not fit the definition and purpose of an Organized Research Unit (ORU), a Campus Center, or a School Center. An SRP may, for example, be formed in response to a…

  21. Defining Research with Human Subjects

    If the activity meets this definition, even if the activity is conducted under a demonstration, service, or other program, the activity is considered research. Presently, MSU master's theses and Ph.D. dissertations are considered to be designed to develop or contribute to generalizable knowledge.

  22. Research Software vs. Research Data I: Towards a Research Data

    Background: Research Software is a concept that has been only recently clarified.In this paper we address the need for a similar enlightenment concerning the Research Data concept. Methods: Our contribution begins by reviewing the Research Software definition, which includes the analysis of software as a legal concept, followed by the study of its production in the research environment and ...

  23. CITI Defining Research with Human Subjects

    According to the federal regulations, human subjects are living individuals about whom an investigator conducting research obtains information through interaction or intervention with the individual, and uses, studies, or analyzes the information; or: Obtains, uses, studies, analyzes, or generates identifiable private information.

  24. Data Science Plus Plus (DS++): The Definition

    This article is based on these research gaps. The primary focus of this work is to coin, define and invent a new Data Science field titled "Data Science Plus Plus (DS++)".

  25. Innovation, Science and Economic Development Canada's 2024-2025

    The guidelines already apply to the Alliance Grant program and the Canada Biomedical Research Fund. Snapshot of planned resources in 2024-25 Planned spending: $969,539,190; Planned full-time resources: 114; Related government priorities Gender-based analysis plus Barriers to access and to participation in science, technology, engineering, and ...

  26. March 2023 global poverty update from the World Bank: the challenge of

    Global poverty estimates were updated today on the Poverty and Inequality Platform (PIP).This update includes new regional poverty aggregates in 2020 and 2021 for Latin America and the Caribbean, and in 2020 for Europe and Central Asia, and the group of advanced countries.