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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Shona McCombes

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How to plan a research project

Whether for a paper or a thesis, define your question, review the work of others – and leave yourself open to discovery.

by Brooke Harrington   + BIO

is professor of sociology at Dartmouth College in New Hampshire. Her research has won international awards both for scholarly quality and impact on public life. She has published dozens of articles and three books, most recently the bestseller Capital without Borders (2016), now translated into five languages.

Edited by Sam Haselby

Need to know

‘When curiosity turns to serious matters, it’s called research.’ – From Aphorisms (1880-1905) by Marie von Ebner-Eschenbach

Planning research projects is a time-honoured intellectual exercise: one that requires both creativity and sharp analytical skills. The purpose of this Guide is to make the process systematic and easy to understand. While there is a great deal of freedom and discovery involved – from the topics you choose, to the data and methods you apply – there are also some norms and constraints that obtain, no matter what your academic level or field of study. For those in high school through to doctoral students, and from art history to archaeology, research planning involves broadly similar steps, including: formulating a question, developing an argument or predictions based on previous research, then selecting the information needed to answer your question.

Some of this might sound self-evident but, as you’ll find, research requires a different way of approaching and using information than most of us are accustomed to in everyday life. That is why I include orienting yourself to knowledge-creation as an initial step in the process. This is a crucial and underappreciated phase in education, akin to making the transition from salaried employment to entrepreneurship: suddenly, you’re on your own, and that requires a new way of thinking about your work.

What follows is a distillation of what I’ve learned about this process over 27 years as a professional social scientist. It reflects the skills that my own professors imparted in the sociology doctoral programme at Harvard, as well as what I learned later on as a research supervisor for Ivy League PhD and MA students, and then as the author of award-winning scholarly books and articles. It can be adapted to the demands of both short projects (such as course term papers) and long ones, such as a thesis.

At its simplest, research planning involves the four distinct steps outlined below: orienting yourself to knowledge-creation; defining your research question; reviewing previous research on your question; and then choosing relevant data to formulate your own answers. Because the focus of this Guide is on planning a research project, as opposed to conducting a research project, this section won’t delve into the details of data-collection or analysis; those steps happen after you plan the project. In addition, the topic is vast: year-long doctoral courses are devoted to data and analysis. Instead, the fourth part of this section will outline some basic strategies you could use in planning a data-selection and analysis process appropriate to your research question.

Step 1: Orient yourself

Planning and conducting research requires you to make a transition, from thinking like a consumer of information to thinking like a producer of information. That sounds simple, but it’s actually a complex task. As a practical matter, this means putting aside the mindset of a student, which treats knowledge as something created by other people. As students, we are often passive receivers of knowledge: asked to do a specified set of readings, then graded on how well we reproduce what we’ve read.

Researchers, however, must take on an active role as knowledge producers . Doing research requires more of you than reading and absorbing what other people have written: you have to engage in a dialogue with it. That includes arguing with previous knowledge and perhaps trying to show that ideas we have accepted as given are actually wrong or incomplete. For example, rather than simply taking in the claims of an author you read, you’ll need to draw out the implications of those claims: if what the author is saying is true, what else does that suggest must be true? What predictions could you make based on the author’s claims?

In other words, rather than treating a reading as a source of truth – even if it comes from a revered source, such as Plato or Marie Curie – this orientation step asks you to treat the claims you read as provisional and subject to interrogation. That is one of the great pieces of wisdom that science and philosophy can teach us: that the biggest advances in human understanding have been made not by being correct about trivial things, but by being wrong in an interesting way . For example, Albert Einstein was wrong about quantum mechanics, but his arguments about it with his fellow physicist Niels Bohr have led to some of the biggest breakthroughs in science, even a century later.

Step 2: Define your research question

Students often give this step cursory attention, but experienced researchers know that formulating a good question is sometimes the most difficult part of the research planning process. That is because the precise language of the question frames the rest of the project. It’s therefore important to pose the question carefully, in a way that’s both possible to answer and likely to yield interesting results. Of course, you must choose a question that interests you, but that’s only the beginning of what’s likely to be an iterative process: most researchers come back to this step repeatedly, modifying their questions in light of previous research, resource limitations and other considerations.

Researchers face limits in terms of time and money. They, like everyone else, have to pose research questions that they can plausibly answer given the constraints they face. For example, it would be inadvisable to frame a project around the question ‘What are the roots of the Arab-Israeli conflict?’ if you have only a week to develop an answer and no background on that topic. That’s not to limit your imagination: you can come up with any question you’d like. But it typically does require some creativity to frame a question that you can answer well – that is, by investigating thoroughly and providing new insights – within the limits you face.

In addition to being interesting to you, and feasible within your resource constraints, the third and most important characteristic of a ‘good’ research topic is whether it allows you to create new knowledge. It might turn out that your question has already been asked and answered to your satisfaction: if so, you’ll find out in the next step of this process. On the other hand, you might come up with a research question that hasn’t been addressed previously. Before you get too excited about breaking uncharted ground, consider this: a lot of potentially researchable questions haven’t been studied for good reason ; they might have answers that are trivial or of very limited interest. This could include questions such as ‘Why does the area of a circle equal π r²?’ or ‘Did winter conditions affect Napoleon’s plans to invade Russia?’ Of course, you might be able to make the argument that a seemingly trivial question is actually vitally important, but you must be prepared to back that up with convincing evidence. The exercise in the ‘Learn More’ section below will help you think through some of these issues.

Finally, scholarly research questions must in some way lead to new and distinctive insights. For example, lots of people have studied gender roles in sports teams; what can you ask that hasn’t been asked before? Reinventing the wheel is the number-one no-no in this endeavour. That’s why the next step is so important: reviewing previous research on your topic. Depending on what you find in that step, you might need to revise your research question; iterating between your question and the existing literature is a normal process. But don’t worry: it doesn’t go on forever. In fact, the iterations taper off – and your research question stabilises – as you develop a firm grasp of the current state of knowledge on your topic.

Step 3: Review previous research

In academic research, from articles to books, it’s common to find a section called a ‘literature review’. The purpose of that section is to describe the state of the art in knowledge on the research question that a project has posed. It demonstrates that researchers have thoroughly and systematically reviewed the relevant findings of previous studies on their topic, and that they have something novel to contribute.

Your own research project should include something like this, even if it’s a high-school term paper. In the research planning process, you’ll want to list at least half a dozen bullet points stating the major findings on your topic by other people. In relation to those findings, you should be able to specify where your project could provide new and necessary insights. There are two basic rhetorical positions one can take in framing the novelty-plus-importance argument required of academic research:

• Position 1 requires you to build on or extend a set of existing ideas; that means saying something like: ‘Person A has argued that X is true about gender; this implies Y, which has not yet been tested. My project will test Y, and if I find evidence to support it, that will change the way we understand gender.’
• Position 2 is to argue that there is a gap in existing knowledge, either because previous research has reached conflicting conclusions or has failed to consider something important. For example, one could say that research on middle schoolers and gender has been limited by being conducted primarily in coeducational environments, and that findings might differ dramatically if research were conducted in more schools where the student body was all-male or all-female.

Your overall goal in this step of the process is to show that your research will be part of a larger conversation: that is, how your project flows from what’s already known, and how it advances, extends or challenges that existing body of knowledge. That will be the contribution of your project, and it constitutes the motivation for your research.

Two things are worth mentioning about your search for sources of relevant previous research. First, you needn’t look only at studies on your precise topic. For example, if you want to study gender-identity formation in schools, you shouldn’t restrict yourself to studies of schools; the empirical setting (schools) is secondary to the larger social process that interests you (how people form gender identity). That process occurs in many different settings, so cast a wide net. Second, be sure to use legitimate sources – meaning publications that have been through some sort of vetting process, whether that involves peer review (as with academic journal articles you might find via Google Scholar) or editorial review (as you’d find in well-known mass media publications, such as The Economist or The Washington Post ). What you’ll want to avoid is using unvetted sources such as personal blogs or Wikipedia. Why? Because anybody can write anything in those forums, and there is no way to know – unless you’re already an expert – if the claims you find there are accurate. Often, they’re not.

Step 4: Choose your data and methods

Whatever your research question is, eventually you’ll need to consider which data source and analytical strategy are most likely to provide the answers you’re seeking. One starting point is to consider whether your question would be best addressed by qualitative data (such as interviews, observations or historical records), quantitative data (such as surveys or census records) or some combination of both. Your ideas about data sources will, in turn, suggest options for analytical methods.

You might need to collect your own data, or you might find everything you need readily available in an existing dataset someone else has created. A great place to start is with a research librarian: university libraries always have them and, at public universities, those librarians can work with the public, including people who aren’t affiliated with the university. If you don’t happen to have a public university and its library close at hand, an ordinary public library can still be a good place to start: the librarians are often well versed in accessing data sources that might be relevant to your study, such as the census, or historical archives, or the Survey of Consumer Finances.

Because your task at this point is to plan research, rather than conduct it, the purpose of this step is not to commit you irrevocably to a course of action. Instead, your goal here is to think through a feasible approach to answering your research question. You’ll need to find out, for example, whether the data you want exist; if not, do you have a realistic chance of gathering the data yourself, or would it be better to modify your research question? In terms of analysis, would your strategy require you to apply statistical methods? If so, do you have those skills? If not, do you have time to learn them, or money to hire a research assistant to run the analysis for you?

Please be aware that qualitative methods in particular are not the casual undertaking they might appear to be. Many people make the mistake of thinking that only quantitative data and methods are scientific and systematic, while qualitative methods are just a fancy way of saying: ‘I talked to some people, read some old newspapers, and drew my own conclusions.’ Nothing could be further from the truth. In the final section of this guide, you’ll find some links to resources that will provide more insight on standards and procedures governing qualitative research, but suffice it to say: there are rules about what constitutes legitimate evidence and valid analytical procedure for qualitative data, just as there are for quantitative data.

Circle back and consider revising your initial plans

As you work through these four steps in planning your project, it’s perfectly normal to circle back and revise. Research planning is rarely a linear process. It’s also common for new and unexpected avenues to suggest themselves. As the sociologist Thorstein Veblen wrote in 1908 : ‘The outcome of any serious research can only be to make two questions grow where only one grew before.’ That’s as true of research planning as it is of a completed project. Try to enjoy the horizons that open up for you in this process, rather than becoming overwhelmed; the four steps, along with the two exercises that follow, will help you focus your plan and make it manageable.

Key points – How to plan a research project

• Planning a research project is essential no matter your academic level or field of study. There is no one ‘best’ way to design research, but there are certain guidelines that can be helpfully applied across disciplines.
• Orient yourself to knowledge-creation. Make the shift from being a consumer of information to being a producer of information.
• Define your research question. Your question frames the rest of your project, sets the scope, and determines the kinds of answers you can find.
• Review previous research on your question. Survey the existing body of relevant knowledge to ensure that your research will be part of a larger conversation.
• Choose your data and methods. For instance, will you be collecting qualitative data, via interviews, or numerical data, via surveys?
• Circle back and consider revising your initial plans. Expect your research question in particular to undergo multiple rounds of refinement as you learn more about your topic.

Good research questions tend to beget more questions. This can be frustrating for those who want to get down to business right away. Try to make room for the unexpected: this is usually how knowledge advances. Many of the most significant discoveries in human history have been made by people who were looking for something else entirely. There are ways to structure your research planning process without over-constraining yourself; the two exercises below are a start, and you can find further methods in the Links and Books section.

The following exercise provides a structured process for advancing your research project planning. After completing it, you’ll be able to do the following:

• describe clearly and concisely the question you’ve chosen to study
• summarise the state of the art in knowledge about the question, and where your project could contribute new insight
• identify the best strategy for gathering and analysing relevant data

In other words, the following provides a systematic means to establish the building blocks of your research project.

Exercise 1: Definition of research question and sources

This exercise prompts you to select and clarify your general interest area, develop a research question, and investigate sources of information. The annotated bibliography will also help you refine your research question so that you can begin the second assignment, a description of the phenomenon you wish to study.

Jot down a few bullet points in response to these two questions, with the understanding that you’ll probably go back and modify your answers as you begin reading other studies relevant to your topic:

• What will be the general topic of your paper?
• What will be the specific topic of your paper?

b) Research question(s)

Use the following guidelines to frame a research question – or questions – that will drive your analysis. As with Part 1 above, you’ll probably find it necessary to change or refine your research question(s) as you complete future assignments.

• Your question should be phrased so that it can’t be answered with a simple ‘yes’ or ‘no’.
• Your question should have more than one plausible answer.
• Your question should draw relationships between two or more concepts; framing the question in terms of How? or What? often works better than asking Why ?

c) Annotated bibliography

Most or all of your background information should come from two sources: scholarly books and journals, or reputable mass media sources. You might be able to access journal articles electronically through your library, using search engines such as JSTOR and Google Scholar. This can save you a great deal of time compared with going to the library in person to search periodicals. General news sources, such as those accessible through LexisNexis, are acceptable, but should be cited sparingly, since they don’t carry the same level of credibility as scholarly sources. As discussed above, unvetted sources such as blogs and Wikipedia should be avoided, because the quality of the information they provide is unreliable and often misleading.

To create an annotated bibliography, provide the following information for at least 10 sources relevant to your specific topic, using the format suggested below.

Name of author(s):

Publication date:

Title of book, chapter, or article:

If a chapter or article, title of journal or book where they appear:

Brief description of this work, including main findings and methods ( c 75 words):

Summary of how this work contributes to your project ( c 75 words):

Brief description of the implications of this work ( c 25 words):

Identify any gap or controversy in knowledge this work points up, and how your project could address those problems ( c 50 words):

Exercise 2: Towards an analysis

Develop a short statement ( c 250 words) about the kind of data that would be useful to address your research question, and how you’d analyse it. Some questions to consider in writing this statement include:

• What are the central concepts or variables in your project? Offer a brief definition of each.
• Do any data sources exist on those concepts or variables, or would you need to collect data?
• Of the analytical strategies you could apply to that data, which would be the most appropriate to answer your question? Which would be the most feasible for you? Consider at least two methods, noting their advantages or disadvantages for your project.

Links & books

One of the best texts ever written about planning and executing research comes from a source that might be unexpected: a 60-year-old work on urban planning by a self-trained scholar. The classic book The Death and Life of Great American Cities (1961) by Jane Jacobs (available complete and free of charge via this link ) is worth reading in its entirety just for the pleasure of it. But the final 20 pages – a concluding chapter titled ‘The Kind of Problem a City Is’ – are really about the process of thinking through and investigating a problem. Highly recommended as a window into the craft of research.

Jacobs’s text references an essay on advancing human knowledge by the mathematician Warren Weaver. At the time, Weaver was director of the Rockefeller Foundation, in charge of funding basic research in the natural and medical sciences. Although the essay is titled ‘A Quarter Century in the Natural Sciences’ (1960) and appears at first blush to be merely a summation of one man’s career, it turns out to be something much bigger and more interesting: a meditation on the history of human beings seeking answers to big questions about the world. Weaver goes back to the 17th century to trace the origins of systematic research thinking, with enthusiasm and vivid anecdotes that make the process come alive. The essay is worth reading in its entirety, and is available free of charge via this link .

For those seeking a more in-depth, professional-level discussion of the logic of research design, the political scientist Harvey Starr provides insight in a compact format in the article ‘Cumulation from Proper Specification: Theory, Logic, Research Design, and “Nice” Laws’ (2005). Starr reviews the ‘research triad’, consisting of the interlinked considerations of formulating a question, selecting relevant theories and applying appropriate methods. The full text of the article, published in the scholarly journal Conflict Management and Peace Science , is available, free of charge, via this link .

Finally, the book Getting What You Came For (1992) by Robert Peters is not only an outstanding guide for anyone contemplating graduate school – from the application process onward – but it also includes several excellent chapters on planning and executing research, applicable across a wide variety of subject areas. It was an invaluable resource for me 25 years ago, and it remains in print with good reason; I recommend it to all my students, particularly Chapter 16 (‘The Thesis Topic: Finding It’), Chapter 17 (‘The Thesis Proposal’) and Chapter 18 (‘The Thesis: Writing It’).

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How to Get Started With a Research Project

Last Updated: October 3, 2023 Fact Checked

This article was co-authored by Chris Hadley, PhD . Chris Hadley, PhD is part of the wikiHow team and works on content strategy and data and analytics. Chris Hadley earned his PhD in Cognitive Psychology from UCLA in 2006. Chris' academic research has been published in numerous scientific journals. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 311,787 times.

You'll be required to undertake and complete research projects throughout your academic career and even, in many cases, as a member of the workforce. Don't worry if you feel stuck or intimidated by the idea of a research project, with care and dedication, you can get the project done well before the deadline!

Development and Foundation

• Don't hesitate while writing down ideas. You'll end up with some mental noise on the paper – silly or nonsensical phrases that your brain just pushes out. That's fine. Think of it as sweeping the cobwebs out of your attic. After a minute or two, better ideas will begin to form (and you might have a nice little laugh at your own expense in the meantime).

• Some instructors will even provide samples of previously successful topics if you ask for them. Just be careful that you don't end up stuck with an idea you want to do, but are afraid to do because you know someone else did it before.

• For example, if your research topic is “urban poverty,” you could look at that topic across ethnic or sexual lines, but you could also look into corporate wages, minimum wage laws, the cost of medical benefits, the loss of unskilled jobs in the urban core, and on and on. You could also try comparing and contrasting urban poverty with suburban or rural poverty, and examine things that might be different about both areas, such as diet and exercise levels, or air pollution.

• Think in terms of questions you want answered. A good research project should collect information for the purpose of answering (or at least attempting to answer) a question. As you review and interconnect topics, you'll think of questions that don't seem to have clear answers yet. These questions are your research topics.

• Don't limit yourself to libraries and online databases. Think in terms of outside resources as well: primary sources, government agencies, even educational TV programs. If you want to know about differences in animal population between public land and an Indian reservation, call the reservation and see if you can speak to their department of fish and wildlife.
• If you're planning to go ahead with original research, that's great – but those techniques aren't covered in this article. Instead, speak with qualified advisors and work with them to set up a thorough, controlled, repeatable process for gathering information.

• If your plan comes down to “researching the topic,” and there aren't any more specific things you can say about it, write down the types of sources you plan to use instead: books (library or private?), magazines (which ones?), interviews, and so on. Your preliminary research should have given you a solid idea of where to begin.

Expanding Your Idea with Research

• It's generally considered more convincing to source one item from three different authors who all agree on it than it is to rely too heavily on one book. Go for quantity at least as much as quality. Be sure to check citations, endnotes, and bibliographies to get more potential sources (and see whether or not all your authors are just quoting the same, older author).
• Writing down your sources and any other relevant details (such as context) around your pieces of information right now will save you lots of trouble in the future.

• Use many different queries to get the database results you want. If one phrasing or a particular set of words doesn't yield useful results, try rephrasing it or using synonymous terms. Online academic databases tend to be dumber than the sum of their parts, so you'll have to use tangentially related terms and inventive language to get all the results you want.

• If it's sensible, consider heading out into the field and speaking to ordinary people for their opinions. This isn't always appropriate (or welcomed) in a research project, but in some cases, it can provide you with some excellent perspective for your research.
• Review cultural artifacts as well. In many areas of study, there's useful information on attitudes, hopes, and/or concerns of people in a particular time and place contained within the art, music, and writing they produced. One has only to look at the woodblock prints of the later German Expressionists, for example, to understand that they lived in a world they felt was often dark, grotesque, and hopeless. Song lyrics and poetry can likewise express strong popular attitudes.

Expert Q&A

• Start early. The foundation of a great research project is the research, which takes time and patience to gather even if you aren't performing any original research of your own. Set aside time for it whenever you can, at least until your initial gathering phase is complete. Past that point, the project should practically come together on its own. Thanks Helpful 1 Not Helpful 0
• When in doubt, write more, rather than less. It's easier to pare down and reorganize an overabundance of information than it is to puff up a flimsy core of facts and anecdotes. Thanks Helpful 1 Not Helpful 0

• Respect the wishes of others. Unless you're a research journalist, it's vital that you yield to the wishes and requests of others before engaging in original research, even if it's technically ethical. Many older American Indians, for instance, harbor a great deal of cultural resentment towards social scientists who visit reservations for research, even those invited by tribal governments for important reasons such as language revitalization. Always tread softly whenever you're out of your element, and only work with those who want to work with you. Thanks Helpful 8 Not Helpful 2
• Be mindful of ethical concerns. Especially if you plan to use original research, there are very stringent ethical guidelines that must be followed for any credible academic body to accept it. Speak to an advisor (such as a professor) about what you plan to do and what steps you should take to verify that it will be ethical. Thanks Helpful 6 Not Helpful 2

You Might Also Like

• ↑ http://www.butte.edu/departments/cas/tipsheets/research/research_paper.html
• ↑ https://www.nhcc.edu/academics/library/doing-library-research/basic-steps-research-process
• ↑ https://library.sacredheart.edu/c.php?g=29803&p=185905
• ↑ https://owl.purdue.edu/owl/general_writing/common_writing_assignments/research_papers/choosing_a_topic.html
• ↑ https://www.unr.edu/writing-speaking-center/student-resources/writing-speaking-resources/using-an-interview-in-a-research-paper
• ↑ https://www.science.org/content/article/how-review-paper

About This Article

The easiest way to get started with a research project is to use your notes and other materials to come up with topics that interest you. Research your favorite topic to see if it can be developed, and then refine it into a research question. Begin thoroughly researching, and collect notes and sources. To learn more about finding reliable and helpful sources while you're researching, continue reading! Did this summary help you? Yes No

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Dissertations and research projects

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• Planning your research
• Qualitative research
• Quantitative research

Introductions

Literature review, methodology, conclusions, working with your supervisor.

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What is an abstract?

The abstract is a brief summary of your dissertation to help a new reader understand the purpose and content of the document, in much the same way as you would read the abstract of a journal article to help decide whether it was relevant to your work. The function of the abstract is to describe and summarise the contents of the dissertation, rather than making critical or evaluative statements about the project.

When should I write the abstract?

The abstract should be the last section you write before submitting your final dissertation or extended project report, as the content will only be decided once the main document is complete. 

What should I include?

One of the best ways to find the right ‘voice’ for the abstract is to look at other examples, either from dissertations in your field or study, or from journal articles. Look out for examples that you feel communicate complex ideas in a simple and accessible way. Your abstract should be clear and understandable to a non-specialist, so avoid specialist vocabulary as far as possible, and use simple sentence structures over longer more complex constructions. You can find a list of phrases for abstract writing here .

Most abstracts are written in the present tense, but this may differ in some disciplines, so find examples to inform your decision on how to write. Avoid the future tense - ‘this dissertation will consider’ - as the research has already been completed by the time someone is reading the abstract! You can explore some key phrases to use in abstract writing here.

Examples of dissertation abstracts Dissertation abstracts, University of Leeds Overview of what to include in your abstract, University of Wisconsin - Madison Abstract structures from different disciplines, The Writing Center For examples from Sheffield Hallam University, use the 'Advanced Search' function in Library Search to access ‘Dissertations/Theses’.

What should the introduction include?

Your introduction should cover the following points:.

• Provide context and set the scene for your research project using literature where necessary.
• Explain the rationale and value of the project.
• Provide definitions and address general limitations in the literature that have influenced the topic or scope of your project.
• Present your research aims and objectives, which may also be phrased as the research ‘problem’ or questions.

Although it is important to draft your research aims and objectives early in the research process, the introduction will be one of the last sections you write. When deciding on how much context and which definitions to include in this section, remember to look back at your literature review to avoid any repetition. It may be that you can repurpose some of the early paragraphs in the literature review for the introduction.

What is the ‘research aim’?

The research aim is a mission statement, that states the main ambition of your project. in other words, what does your research project hope to achieve you may also express this as the ‘big questions’ that drives your project, or as the research problem that your dissertation will aim to address or solve..

You only need one research aim, and this is likely to change as your dissertation develops through the literature review. Keep returning to your research aim and your aspirations for the project regularly to help shape this statement.

What are the research objectives? How are they different from research questions?

Research objectives and questions are the same thing – the only difference is how they are written! The objectives are the specific tasks that you will need to complete – the stepping stones – that will enable you to achieve your overall research aim.

You will usually have 3-5 research objectives, and their order will hep the reader to understand how you will progress through your research project from start to finish. If you can achieve each objective, or answer each research question, you should meet your research aim! It is therefore important to be specific in your choice of language: verbs, such as ‘to investigate’, ‘to explore’, ‘to assess’ etc. will help your research appear “do-able” (Farrell, 2011).

Here’s an example of three research objectives, also phrased as research questions (this depends entirely on your preference):

For more ideas on how to write research objectives, take at look at this list of common academic verbs for creating specific, achievable research tasks and questions.

We have an  online study guide dedicated to planning and structuring your literature review.

What is the purpose of the methodology section?

The methodology outlines the procedure and process of your data collection. You should therefore provide enough detail so that a reader could replicate or adapt your methodology in their own research.

While the literature review focuses on the views and arguments of other authors, the methodology puts the spotlight on your project. Two of the key questions you should aim to answer in this section are:

• Why did you select the methods you used?
• How do these methods answer your research question(s)?

The methodology chapter should also justify and explain your choice of methodology and methods. At every point where you faced a decision, ask: Why did choose this approach? Why not something else? Why was this theory/method/tool the most relevant or suitable for my project? How did this decision contribute to answering my research questions?

Although most students write their methodology before carrying out their data collection, the methodology section should be written in the past tense, as if the research has already been completed.

What is the difference between my methodology and my methods?

There are three key aspects of any methodology section that you should aim to address:.

• Methodology: Your choice of methodology will be grounded in a discipline-specific theory about how research should proceed, such as quantitative or qualitative. This overarching decision will help to provide rationale for the specific methods you go on to use.  
• Research Design: An explanation of the approach that you have chosen, and the type of data you will collect. For example, case study or action research? Will the data you collect be quantitative, qualitative or a mix of both?  
• Methods: The concrete research tools used to collect and analyse data: questionnaires, in-person surveys, observations etc.

You may also need to include information on epistemology and your philosophical approach to research. You can find more information on this in our research planning guide.

What should I include in the methodology section?

Research paradigm: What is the underpinning philosophy of your research? How does this align with your research aim and objectives?

Methodology : Qualitative or quantitative? Mixed? What are the advantages of your chosen methodology, and why were the other options discounted?

• Research design : Show how your research design is influenced by other studies in your field and justify your choice of approach.  
• Methods : What methods did you use? Why? Do these naturally fit together or do you need to justify why you have used different methods in combination?  
• Participants/Data Sources: What were your sources/who were your participants? Which sampling approach did you use and why? How were they identified as a suitable group to research, and how were they recruited?  
• Procedure : What did you do to collect your data? Remember, a reader should be able to replicate or adapt your methodology in their own research from the information you provide here.  
• Limitations : What are the general limitations of your chosen method(s)? Don’t be specific here about your project (ie. what you could have done differently), but instead focus on what the literature outlines as the disadvantages of your methods.

Should I reflect on my position as a researcher?

If you feel your position as a researcher has influenced your choice of methods or procedure in any way, the methodology is a good place to reflect on this. Positionality acknowledges that no researcher is entirely objective: we are all, to some extent, influenced by prior learning, experiences, knowledge, and personal biases. This is particularly true in qualitative research or practice-based research, where the student is acting as a researcher in their own workplace, where they are otherwise considered a practitioner/professional.

The following questions can help you to reflect on your positionality and gauge whether this is an important section to include in your dissertation (for some people, this section isn’t necessary or relevant):

• How might my personal history influence how I approach the topic?
• How am I positioned in relation to this knowledge? Am I being influenced by prior learning or knowledge from outside of this course?
• How does my gender/social class/ ethnicity/ culture influence my positioning in relation to this topic?
• Do I share any attributes with my participants? Are we part of a s hared community? How might this have influenced our relationship and my role in interviews/observations?
• Am I invested in the outcomes on a personal level? Who is this research for and who will feel the benefits?

Visit our detailed guides on qualitative and quantitative research for more information.

• Quantitative projects
• Qualitative projects

T he purpose of this section is to report the findings of your study. In quantitative research, the results section usually functions as a statement of your findings without discussion.

Results sections generally begin with descriptive statistics before moving on to further tests such as multiple linear regression, or inferential statistical tests such as ANOVA, and any associated Post-Hoc testing.

Here are some top tips for planning/writing your results section:

• Explain any treatments you have applied to your data.
• Present your findings in a logical order.
• Describe trends in the data/anomalous findings but don’t start to interpret them. Save that for your discussion section.
• Figures and tables are usually the clearest way to present information. It is important to remember to title and label any titles/diagrams to communicate their meaning to the reader and so that you can refer to them again later in the report (e.g. Table 1).
• Remember to be consistent with the rounding of figures. If you start by rounding to 2 decimal places, ensure that you do this for all data you report.
• Avoid repeating any information - if something appears in a table it does not need to appear again in the main body of the text.

Presenting qualitative data

In qualitative studies, your results are often presented alongside the discussion, as it is difficult to include this data in a meaningful way without explanation and interpretation. In the dsicussion section, aim to structure your work thematically, moving through the key concepts or ideas that have emerged from your qualitative data. Use extracts from your data collection - interviews, focus groups, observations - to illustrate where these themes are most prominent, and refer back to the sources from your literature review to help draw conclusions. 

Here's an example of how your data could be presented in paragraph format in this section:

Example from  'Reporting and discussing your findings ', Monash University .

What should I include in the discussion section?

The purpose of the discussion section is to interpret your findings and discuss these against the context of the wider literature. This section should also highlight how your research has contributed to the understanding of a phenomenon or problem: this can be achieved by responding to your research questions.

Though the structure of discussion sections can vary, a relatively common structure is offered below:

• State your major findings – this can be a brief opening paragraph that restates the research problem, the methods you used to attempt to address this, and the major findings of your research.
• Address your research questions - detail your findings in relation to each of your research questions to help demonstrate how you have attempted to address the research problem. Answer each research question in turn by interpreting the relevant results: this may involve highlighting patterns, relationships or statistically significant differences depending on the design of your research and how you analysed your data.
• Discuss your findings against the wider literature - this will involve comparing and contrasting your findings against those of others and using key literature to support the interpretation of your results; often, this will involve revisiting key studies from your literature review and discussing where your findings fit in the pre-existing literature. This process can help to highlight the importance of your research through demonstrating what is novel about your findings and how this contributes to the wider understanding of your research area.
• Address any unexpected findings in your study - begin with by stating the unexpected finding and then offer your interpretation as to why this might have occurred. You may relate unexpected findings to other research literature and you should also consider how any unexpected findings relate to your overall study – especially if you think this is significant in terms of what your findings contribute to the understanding of your research problem!
• Discuss alternative interpretations - it’s important to remember that in research we find evidence to support ideas, theories and understanding; nothing is ever proven. Consequently, you should discuss possible alternative interpretations of your data – not just those that neatly answer your research questions and confirm your hypotheses.
• Limitations/weaknesses of your research – acknowledge any factors that might have affected your findings and discuss how this relates to your interpretation of the data. This might include detailing problems with your data collection method, or unanticipated factors that you had not accounted for in your original research plan. Likewise, detail any questions that your findings could not answer and explain why this was the case.
• Future directions (this part of your discussion could also be included in your conclusion) – this section should address what questions remain unanswered about your research problem. For example, it may be that your findings have answered some questions but raised new ones; this can often occur as a result of unanticipated findings. Likewise, some of the limitations of your research may necessitate further work to address a methodological confound or weakness in a tool of measurement. Whatever these future directions are, remember you’re not writing a proposal for this further research; a brief suggestion of what the research should do and how this would address one of the new problems/limitations you have identified is enough.

Here are some final top tips for writing your discussion section:

• Don’t rewrite your results section – remember your goal is to interpret and explain how your findings address the research problem.
• Be clear about what you have found, how this has addressed a gap in the literature and how it changes our understanding of your research problem.
• Structure your discussion in a logical way that highlights your most important/interesting findings first.
• Be careful about how you interpret your data: be wary over-interpreting to confirm a hypothesis. Remember, we can still learn from non-significant research findings.
• Avoid being apologetic or too critical when discussing the limitations of your research. Be concise and analytical. 

How do I avoid repetition in the conclusion?

The conclusion is your opportunity to synthesise everything you have done/written as part of your research, in order to demonstrate your understanding.

A well-structured conclusion is likely to include the following:

• State your conclusions – in clear language, state the conclusions from your research. Crucially, this not just restating your results/findings: instead, this is a synthesis of the research problem, your research questions, your findings (and interpretation), and the relevant research literature. From your conclusions, it should be clear to your reader how our understanding of the research topic has changed.  
• Discuss wider significance – this is your opportunity to highlight (potential) wider implications of your conclusions. Depending on your discipline, this might include recommendations for policy, professional practice or a tentative speculation about how an academic theory might change given your findings. It is important not to over-generalise here; remember the limitations of your theoretical and methodological choices and what these mean for the applicability of your findings/conclusions. If your discipline encourages reflection, this can be a suitable place to include your thoughts about the research process, the choices you made and how your findings/conclusions might influence your professional outlook/practice going forwards.  
• Take home message – this should be a strong and clear final statement that draws the reader’s focus to the primary message of your study. Whilst it’s important to avoid being overly grandiose, this is your closing argument, and you should remind the reader of what your research has achieved.

Ultimately, your conclusion is your final word about the research problem you have investigated; don’t be afraid of emphasising your contribution to the understanding of that problem. Your conclusion should be clear, succinct and provide a summary of everything that has been learned as a result of your research project. 

What supervisors expect from their dissertation students:

• to determine the focus and direction of the dissertation, particularly in terms of identifying a topic of interest and research question.
• to work independently to explore literature and research in the chosen topic area.
• to be proactive in arranging supervision meetings, email draft work before meetings for feedback and prepare specific questions and issues to discuss in supervision time.
• to be honest and open about any challenges or difficulties that arise during the research or writing process.
• to bring a problem-solving approach to the dissertation (you are not expected to know all the answers but should show initiative in exploring possible solutions to any problems that might arise).

What you can expect from your supervisor:

• to offer guidance on the best way to structure and carry out a successful research project in the timescale for your dissertation, and to help you to set achievable and appropriate research objectives.
• ​ to act as an expert in your discipline and sounding board for your ideas, and to advise you on the literature search and theoretical background for your project.
• to serve as a 'lifeline' and point of support when the dissertation feels challenging.
• to read your drafts and give feedback in supervision meetings.
• to offer practical advice and strategies for managing your time, securing ethics approval, collecting data and common pitfalls to avoid during the research process.

Making the most of your supervision meetings

Meeting your supervisor can feel daunting at first but your supervision meetings offer a great opportunity to discuss your research ideas and get feedback on the direction of your project. Here are our top tips to getting the most out of time with your supervisor:  

• Y ou are in charge of the agenda. If you arrange a meeting with your supervisor, you call the shots! Here are a few tips on how to get the most out of the time with your supervisor.
• Send an email in advance of the meeting , with an overview of the key ideas you want to talk about. This can save time in the meeting and helps to give you some structure to follow. If this isn't possible, run through these points quickly when you first sit down as you introduce the meeting - "I wanted to focus on the literature review today, as I'm having some trouble deciding on the order my key themes and points should be introduced in."
• What do you want to get out of the meeting? Note down any questions you would like the answers to or identify what it is you will need from the meeting in order to make progress on the next stage of your dissertation. Supervision meetings offer the change to talk about your ideas for the project, but they can also be an opportunity to find out practical details and troubleshoot. Don't leave the meeting until you have addressed these and got answers/advice in each key area.
• Trust your supervisor. Your supervisor may not be an expert in your chosen subject, but they will have experience of writing up research projects and coaching other dissertation students. You are responsible for reading up on your subject and exploring the literature - your supervisor can't tell you what to read, but they can give you advice on how to read your sources and integrate them into your argument and writing.
• Choose a short section to discuss in the meeting for feedback - for example, if you're not sure on structure, pick a page or two that demonstrate this, or if you want advice on being critical, find an example from a previous essay where you think you did this well and ask your supervisor how to translate this into dissertation writing.
• Agree an action plan . Work with your supervisor to set a goal for your next meeting, or an objective that you will meet in the week following your supervision. Feeling accountable to someone can be a great motivator and also helps you to recognise where you are starting to fall behind the targets that you've set for yourself.
• Be open and honest . It can feel daunting meeting your supervisor, but supervision meetings aren't an interview where you have to prove everything is going well. Ask for help and advice where you need it, and be honest if you're finding things difficult. A supervisor is there to support you and help you to develop the skills and knowledge you need along the journey to submitting your dissertation.
• << Previous: Quantitative research
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• Last Updated: Apr 17, 2024 1:52 PM
• URL: https://libguides.shu.ac.uk/researchprojects

15 Steps to Good Research

• Define and articulate a research question (formulate a research hypothesis). How to Write a Thesis Statement (Indiana University)
• Identify possible sources of information in many types and formats. Georgetown University Library's Research & Course Guides
• Judge the scope of the project.
• Reevaluate the research question based on the nature and extent of information available and the parameters of the research project.
• Select the most appropriate investigative methods (surveys, interviews, experiments) and research tools (periodical indexes, databases, websites).
• Plan the research project. Writing Anxiety (UNC-Chapel Hill) Strategies for Academic Writing (SUNY Empire State College)
• Retrieve information using a variety of methods (draw on a repertoire of skills).
• Refine the search strategy as necessary.
• Write and organize useful notes and keep track of sources. Taking Notes from Research Reading (University of Toronto) Use a citation manager: Zotero or Refworks
• Evaluate sources using appropriate criteria. Evaluating Internet Sources
• Synthesize, analyze and integrate information sources and prior knowledge. Georgetown University Writing Center
• Revise hypothesis as necessary.
• Use information effectively for a specific purpose.
• Understand such issues as plagiarism, ownership of information (implications of copyright to some extent), and costs of information. Georgetown University Honor Council Copyright Basics (Purdue University) How to Recognize Plagiarism: Tutorials and Tests from Indiana University
• Cite properly and give credit for sources of ideas. MLA Bibliographic Form (7th edition, 2009) MLA Bibliographic Form (8th edition, 2016) Turabian Bibliographic Form: Footnote/Endnote Turabian Bibliographic Form: Parenthetical Reference Use a citation manager: Zotero or Refworks

Adapted from the Association of Colleges and Research Libraries "Objectives for Information Literacy Instruction" , which are more complete and include outcomes. See also the broader "Information Literacy Competency Standards for Higher Education."

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How to Write a Research Protocol: Tips and Tricks

Matteo cameli.

Department of Cardiovascular Diseases, University of Siena, Siena, Italy

Giuseppina Novo

1 Biomedical Department of Internal Medicine and Medical Specialties, Cardiology Unit, University of Palermo, Palermo, Italy

Maurizio Tusa

2 Division of Cardiology, IRCCS Policlinico San Donato, Italy

Giulia Elena Mandoli

Giovanni corrado.

3 Department of Cardiology, Valduce Hospital, Como, Italy

Frank Benedetto

4 Division of Cardiology, Bianchi-Melacrino-Morelli Hospital, Reggio Calabria, Italy

Francesco Antonini-Canterin

5 High Specialization Rehabilitation Hospital, ORAS, Motta di Livenza, Treviso, Italy

Rodolfo Citro

6 Heart Department, University Hospital “San Giovanni di Dio e Ruggi D’Aragona”, Salerno, Italy

The first drafting of the protocol for a new research project should start from a solid idea with one or more of these goals:

• Overcoming the limits of the current knowledge in a determinate field with the aim of bridging a “knowledge gap”
• Bringing something new in a scarcely explored field
• Validating or nullifying previous results obtained in limited records by studies on a wider population.

A research proposal born with the intent to convince the others that your project is worthy and you are able to manage it with a complete and specific work plan. With a strong idea in mind, it is time to write a document where all the aspects of the future research project must be explained in a precise, understandable manner. This will successively help the researcher to present it and process and elaborate the obtained results.[ 1 ] The protocol manuscript should also underline both the pros and the potentialities of the idea to put it under a new light.[ 2 ]

Our paper will give the authors suggestions and advices regarding how to organize a research protocol, step by step [ Table 1 ].

Main sections and subsections in a complete research protocol

A research protocol must start from the definition of the coordinator of the whole study: all the details of the main investigator must be reported in the first paragraph. This will allow each participant to know who ask for in case of doubts or criticalities during the research. If the study will be multicentric, in the first section must be written also the number of the involved centers, each one possibly matched with the corresponding reference investigator.

Second section: Specific features of the research study

After completing the administrative details, the next step is to provide and extend title of the study: This is made for identifying the field of research and the aim of the study itself in a sort of brief summary of the research; the title must be followed by a unique acronym, like an ID of the protocol. If the protocol has been already exposed and approved by the Ethical Committee, it is appropriate to include also protocol number.

A list of 3–7 keywords must be listed to simplify the collocation of the protocol in its field of research, including, for example, disease, research tools, and analyzed parameters (e.g. three-dimensional echocardiography, right ventricle, end-stage heart failure, and prognosis).

The protocol must continue stating the research background that is the rational cause on the base on which the study is pursued. This section is written to answer some of these questions: what is the project about? What is already available in this field in the current knowledge? Why we need to overcome that data? and How will the community will from the present study?

As for an original research manuscript, the introduction to the project must include a brief review of the literature (with corresponding references). It is also fundamental to support the premises of the study, to underline the importance of the project in that particular time period and above all, of the materials and methods that will be employed. The rationale should accurately put in evidence the current lack in that field of scientific knowledge, following a precise, logical thread with concrete solutions regarding how to overcome the gaps and to conclude with the hypothesis of the project. A distinct paragraph can be dedicated to references, paying attention to select only the previous papers that can help the reader to focus the attention on the topic and to not excessively extend the list. In the references paragraph, the main studies regarding the object of the research but also state-of-art reviews updating the most recent discoveries in the field should be inserted.

The section should successively expose the study design: monocentric or multicentric, retrospective or prospective, controlled or uncontrolled, open-label or blinded, randomized or nonrandomized, and observational or experimental. It should also be explained why that particular design has been chosen.

At this point, the author must include the primary objective of the research, that is, the main goal of the study. This is a crucial part of the proposal and more than 4–5 aims should be avoided to do not reduce the accuracy of the project. Using verbs as “to demonstrate,” “to assess,” “to verify,” “to improve,” “to reduce,” and “to compare” help to give relevance to this section. Add also a description of the general characteristics of the population that will be enrolled in the study (if different subgroups are planned, the criteria on the base of which they will be divided should be specified); primary and secondary end-points, including all the variables that represent the measure of the objective (e.g., all-cause death, cardiovascular death, hospitalization, and side effects of a drug) follow in this section.

All the single parameters and variables that will be assessed during the study must be accurately and precisely listed along with the tools, the methods, the process schedule timing, and the technical details by which they will be acquired; Here, the author should explain how the Investigators who work in the other involved centers have to sent their results and acquired data to the Core Laboratory (e.g. by filled databases or by sending images).

A special attention must then be paid to clarify the planning of each examination the study patients will undergo: basal evaluation, potential follow-up schedule, treatment strategy plan, comparison between new and already-in-use drugs, dose and dosage of the treatment in case of a pharmacological study. This part can be enhanced by flowcharts or algorithms that allow a more immediate comprehension and interpretation of the study strategy.

This section may result more complete if one more subsection, illustrating the expected results, is included. Considering the idea at the base on the project, the endpoints and the pre-arranged objectives, the author can explain how its research project will

• Contribute to optimize the scientific knowledge in that specific field
• Give real successive implications in clinical practice
• Pave the way for future scientific research in the same or similar area of interest, etc.

The study population must be specified in detail, starting from inclusion criteria (including age and gender if it is planned to be restricted) and exclusion criteria: the more precise are the lists, the more accurate the enrollment of the subjects will be to avoid selection biases. This will also help to raise the success rate of the project and to reduce the risks of statistical error during the successive analysis of the data. The sample size should be planned and justified on the base of a statistic calculation considering the incidence and prevalence of the disease, frequency of use of a drug, etc., and possibly also indicating if the study considers a minimal or maximal number of subjects for each enrollment center (in case of multicentric studies).

This section of the protocol should end with some indications regarding timing and duration of the study: Starting and end of enrollment date, starting and end of inclusion date, potential frequency of control examinations, and timing of the analysis of the acquired data. If already settled, it can be useful to indicate also the type of statistical analysis that the investigators will apply to the data.

It is always necessary to prepare an informed consent to be proposed to the patient where premises, methods, and aims of the research together with advantages (e.g., some visits or diagnostic examinations for free) and possible risks derived from the participation to the study.

In this short section, various pieces of information regarding safety of the study must be added (a classification is fundamental in case of studies that expect the use of invasive procedures or drugs use). Usually, for nonobservational studies, an insurance coverage must be considered.

If the investigators have requested or plan to request funding or financial support, all the obtained resources must be listed to avoid conflicts of interest.

C ONCLUSION

Writing a complete and detailed document is a paramount step before starting a research projects. The protocol, as described in this paper, should be simply and correctly written but must clarify all the aspects of the protocol. The document could be divided into three different sessions to give all the parts the appropriate attention.

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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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U.S. centenarian population is projected to quadruple over the next 30 years

The number of Americans ages 100 and older is projected to more than quadruple over the next three decades, from an estimated 101,000 in 2024 to about 422,000 in 2054, according to projections from the U.S. Census Bureau. Centenarians currently make up just 0.03% of the overall U.S. population, and they are expected to reach 0.1% in 2054.

The number of centenarians in the United States has steadily ticked up since 1950, when the Census Bureau estimates there were just 2,300 Americans ages 100 and older. (The Census Bureau uses calculated estimates for years prior to the 1990 census because it has identified large errors in the census counts of centenarians for those years.)

In the last three decades alone, the U.S. centenarian population has nearly tripled. The 1990 census counted around 37,000 centenarians in the country.

Pew Research Center conducted this analysis to understand how the population of Americans ages 100 and older looks today, and how it is expected to change in the next 30 years. U.S. population estimates come from the U.S. Census Bureau , and global projections are drawn from the United Nations’ population projections under its medium variant scenario .

All racial groups are single-race and non-Hispanic. Hispanics are of any race.

Today, women and White adults make up the vast majority of Americans in their 100s. This trend is largely projected to continue, though their shares will decrease:

• In 2024, 78% of centenarians are women, and 22% are men. In 30 years, women are expected to make up 68% of those ages 100 and older, while 32% will be men.
• 77% of today’s centenarians are White. Far fewer are Black (8%), Asian (7%) or Hispanic (6%). And 1% or fewer are multiracial; American Indian or Alaska Native; or Native Hawaiian or other Pacific Islander. By 2054, White and Asian adults are projected to make up smaller shares of centenarians (72% and 5%, respectively), while the shares who are Hispanic (11%) or Black (10%) will be larger. (All racial categories here are single-race and non-Hispanic. Hispanics are of any race.)

The U.S. population overall is expected to trend older in the coming decades as life expectancies increase and the birth rate declines. There are currently roughly 62 million adults ages 65 and older living in the U.S., accounting for 18% of the population. By 2054, 84 million adults ages 65 and older will make up an estimated 23% of the population.

Even as the 65-and-older population continues to grow over the next 30 years, those in their 100s are projected to roughly double as a percentage of that age group, increasing from 0.2% of all older Americans in 2024 to 0.5% in 2054.

Centenarians around the world

The world is home to an estimated 722,000 centenarians, according to the United Nations’ population projections for 2024. The U.S. centenarian population is the world’s second largest – the UN estimates it at 108,000, slightly larger than the Census Bureau’s estimate.

Japan is the country with the greatest number of people in their 100s, at 146,000. China (60,000), India (48,000) and Thailand (38,000) round out the top five.

In each of these countries, centenarians make up less than 1% of the overall population, but combined, they account for more than half (55%) of the world’s population ages 100 and older.

Looked at another way, centenarians make up a bigger proportion of the total population in Japan, Thailand and the U.S., and smaller shares in China and India, which have large but relatively young populations. There are about 12 centenarians for every 10,000 people in Japan, five for every 10,000 in Thailand and three for every 10,000 in the U.S. That compares with fewer than one centenarian for every 10,000 people in China and India.

By 2054, the global centenarian population is projected to grow to nearly 4 million. China is expected to have the largest number of centenarians, with 767,000, followed by the U.S., India, Japan and Thailand. As a proportion, centenarians are projected to account for about 49 out of every 10,000 people in Thailand, 40 of every 10,000 in Japan and 14 of every 10,000 in the U.S. Six out of every 10,000 people in China will be centenarians, as will about two of every 10,000 in India.

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• Q&A: Claire Walsh on how J-PAL’s King Climate Action Initiative tackles the twin climate and poverty crises

Q&A: Claire Walsh on how J-PAL’s King Climate Action Initiative tackles the twin climate and poverty crises

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The King Climate Action Initiative ( K-CAI ) is the flagship climate change program of the Abdul Latif Jameel Poverty Action Lab (J-PAL), which innovates, tests, and scales solutions at the nexus of climate change and poverty alleviation, together with policy partners worldwide.

Claire Walsh is the associate director of policy at J-PAL Global at MIT. She is also the project director of K-CAI. Here, Walsh talks about the work of K-CAI since its launch in 2020, and describes the ways its projects are making a difference. This is part of an ongoing series exploring how the MIT School of Humanities, Arts, and Social Sciences is addressing the climate crisis.

Q: According to the King Climate Action Initiative (K-CAI), any attempt to address poverty effectively must also simultaneously address climate change. Why is that?

A: Climate change will disproportionately harm people in poverty, particularly in low- and middle-income countries, because they tend to live in places that are more exposed to climate risk. These are nations in sub-Saharan Africa and South and Southeast Asia where low-income communities rely heavily on agriculture for their livelihoods, so extreme weather — heat, droughts, and flooding — can be devastating for people’s jobs and food security. In fact, the World Bank estimates that up to 130 million more people may be pushed into poverty by climate change by 2030.

This is unjust because these countries have historically emitted the least; their people didn’t cause the climate crisis. At the same time, they are trying to improve their economies and improve people’s welfare, so their energy demands are increasing, and they are emitting more. But they don’t have the same resources as wealthy nations for mitigation or adaptation, and many developing countries understandably don’t feel eager to put solving a problem they didn’t create at the top of their priority list. This makes finding paths forward to cutting emissions on a global scale politically challenging.

For these reasons, the problems of enhancing the well-being of people experiencing poverty, addressing inequality, and reducing pollution and greenhouse gases are inextricably linked.

Q: So how does K-CAI tackle this hybrid challenge?

A: Our initiative is pretty unique. We are a competitive, policy-based research and development fund that focuses on innovating, testing, and scaling solutions. We support researchers from MIT and other universities, and their collaborators, who are actually implementing programs, whether NGOs [nongovernmental organizations], government, or the private sector. We fund pilots of small-scale ideas in a real-world setting to determine if they hold promise, followed by larger randomized, controlled trials of promising solutions in climate change mitigation, adaptation, pollution reduction, and energy access. Our goal is to determine, through rigorous research, if these solutions are actually working — for example, in cutting emissions or protecting forests or helping vulnerable communities adapt to climate change. And finally, we offer path-to-scale grants which enable governments and NGOs to expand access to programs that have been tested and have strong evidence of impact.

We think this model is really powerful. Since we launched in 2020, we have built a portfolio of over 30 randomized evaluations and 13 scaling projects in more than 35 countries. And to date, these projects have informed the scale ups of evidence-based climate policies that have reached over 15 million people.

Q: It seems like K-CAI is advancing a kind of policy science, demanding proof of a program’s capacity to deliver results at each stage.  

A: This is one of the factors that drew me to J-PAL back in 2012. I majored in anthropology and studied abroad in Uganda. From those experiences I became very passionate about pursuing a career focused on poverty reduction. To me, it is unfair that in a world full of so much wealth and so much opportunity there exists so much extreme poverty. I wanted to dedicate my career to that, but I'm also a very detail-oriented nerd who really cares about whether a program that claims to be doing something for people is accomplishing what it claims.

It's been really rewarding to see demand from governments and NGOs for evidence-informed policymaking grow over my 12 years at J-PAL. This policy science approach holds exciting promise to help transform public policy and climate policy in the coming decades.  

Q: Can you point to K-CAI-funded projects that meet this high bar and are now making a significant impact?

A: Several examples jump to mind. In the state of Gujarat, India, pollution regulators are trying to cut particulate matter air pollution, which is devastating to human health. The region is home to many major industries whose emissions negatively affect most of the state’s 70 million residents.

We partnered with state pollution regulators — kind of a regional EPA [Environmental Protection Agency] — to test an emissions trading scheme that is used widely in the U.S. and Europe but not in low- and middle-income countries. The government monitors pollution levels using technology installed at factories that sends data in real time, so the regulator knows exactly what their emissions look like. The regulator sets a cap on the overall level of pollution, allocates permits to pollute, and industries can trade emissions permits.

In 2019, researchers in the J-PAL network conducted the world’s first randomized, controlled trial of this emissions trading scheme and found that it cut pollution by 20 to 30 percent — a surprising reduction. It also reduced firms’ costs, on average, because the costs of compliance went down. The state government was eager to scale up the pilot, and in the past two years, two other cities, including Ahmedabad, the biggest city in the state, have adopted the concept.

We are also supporting a project in Niger, whose economy is hugely dependent on rain-fed agriculture but with climate change is experiencing rapid desertification. Researchers in the J-PAL network have been testing training farmers in a simple, inexpensive rainwater harvesting technique, where farmers dig a half-moon-shaped hole called a demi-lune right before the rainy season. This demi-lune feeds crops that are grown directly on top of it, and helps return land that resembled flat desert to arable production.

Researchers found that training farmers in this simple technology increased adoption from 4 percent to 94 percent and that demi-lunes increased agricultural output and revenue for farmers from the first year. K-CAI is funding a path-to-scale grant so local implementers can teach this technique to over 8,000 farmers and build a more cost-effective program model. If this takes hold, the team will work with local partners to scale the training to other relevant regions of the country and potentially other countries in the Sahel.

One final example that we are really proud of, because we first funded it as a pilot and now it’s in the path to scale phase: We supported a team of researchers working with partners in Bangladesh trying to reduce carbon emissions and other pollution from brick manufacturing, an industry that generates 17 percent of the country’s carbon emissions. The scale of manufacturing is so great that at some times of year, Dhaka (the capital of Bangladesh) looks like Mordor.

Workers form these bricks and stack hundreds of thousands of them, which they then fire by burning coal. A team of local researchers and collaborators from our J-PAL network found that you can reduce the amount of coal needed for the kilns by making some low-cost changes to the manufacturing process, including stacking the bricks in a way that increases airflow in the kiln and feeding the coal fires more frequently in smaller rather than larger batches.

In the randomized, controlled trial K-CAI supported, researchers found that this cut carbon and pollution emissions significantly, and now the government has invited the team to train 1,000 brick manufacturers in Dhaka in these techniques.

Q: These are all fascinating and powerful instances of implementing ideas that address a range of problems in different parts of the world. But can K-CAI go big enough and fast enough to take a real bite out of the twin poverty and climate crisis?

A: We're not trying to find silver bullets. We are trying to build a large playbook of real solutions that work to solve specific problems in specific contexts. As you build those up in the hundreds, you have a deep bench of effective approaches to solve problems that can add up in a meaningful way. And because J-PAL works with governments and NGOs that have the capacity to take the research into action, since 2003, over 600 million people around the world have been reached by policies and programs that are informed by evidence that J-PAL-affiliated researchers produced. While global challenges seem daunting, J-PAL has shown that in 20 years we can achieve a great deal, and there is huge potential for future impact.

But unfortunately, globally, there is an underinvestment in policy innovation to combat climate change that may generate quicker, lower-cost returns at a large scale — especially in policies that determine which technologies get adopted or commercialized. For example, a lot of the huge fall in prices of renewable energy was enabled by early European government investments in solar and wind, and then continuing support for innovation in renewable energy.

That’s why I think social sciences have so much to offer in the fight against climate change and poverty; we are working where technology meets policy and where technology meets real people, which often determines their success or failure. The world should be investing in policy, economic, and social innovation just as much as it is investing in technological innovation.

Q: Do you need to be an optimist in your job?

A: I am half-optimist, half-pragmatist. I have no control over the climate change outcome for the world. And regardless of whether we can successfully avoid most of the potential damages of climate change, when I look back, I'm going to ask myself, “Did I fight or not?” The only choice I have is whether or not I fought, and I want to be a fighter.

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Project Management

10 executive summary examples and how to write one yourself (with ai).

February 14, 2024

In a world where people have the attention span of a goldfish (or less), we don’t make time to read long, detailed documents unless they are valuable to us. So, how do we convince the reader that the document is valuable? That’s where the executive summary comes in.

What is an Executive Summary?

1. identify the story, 2. bring the data, 3. expand on the benefits, 4. conclude powerfully, best practices for writing executive summary, 1. board report executive summary, 2. research report executive summary example from mckinsey, 3. study report executive summary by the un, 4. project performance report executive summary, 5. payroll report executive summary template, 6. mailchimp content style guide’s tl;dr, 7. clickup release notes, 8. the title and description of a new yorker article, 9. survey report executive summary by harvard, 10. meta executive summary with clickup ai.

An executive summary is a shorter version of a longer corporate document. It summarizes the salient points of a business plan, proposal, or report so executives can get the gist and read further about what matters to them.

In other words, the tl;dr (too long; didn’t read) version.

A typical executive summary includes:

• Problem statement
• Proposed solution
• Expected outcomes

This might vary depending on what you write an executive summary for. Let’s take the example of a project report. You might have to replace the proposed solution and expected outcomes with execution solutions and actual outcomes achieved, respectively. Or, if you’re writing a business plan, research proposal, or market analysis, you might include your methodology, too.

Now that you know the purpose of an executive summary, let’s see how to write one.

How to Write Executive Summaries and Examples

While an executive summary is just a condensed version of a longer report, it isn’t easy to write. It needs to capture the essence of the report, outline the salient points, and tell a story as compelling as the full report. Here are some ways you can achieve that.

Just stating facts and data wouldn’t be a compelling read for anyone. So, identify the story that really impacts people’s lives. While industry terms like workflow optimization or cost control capture people’s attention, they don’t tell the real story behind your efforts. Focus on the latter.

If you’re writing the project executive summary in software development, you might begin with what matters to the reader as follows.

In 2020, the retail major was managing its inventory on spreadsheets. So, whenever a customer asked whether a product was in stock, a staff member had to walk across the 5000 sq. ft. store to check, often with the customer in tow. The new ABC digital inventory management system records stock in and out online in real time. The staff member can check and confirm in a flash. More pertinently, the customers themselves can check at any of the 25 kiosks throughout the store.

While the story is more important, data isn’t useless. Accurate and relevant data helps establish credibility. Your next section might say the following in the ABC digital inventory management system example.

Since the implementation of the ABC inventory management system, the retail major has seen: 85% decrease in time taken to check stock 75% decrease in time taken to find where stock is placed

The data demonstrates that there has been real improvement. However, for the reader to understand its impact, you must explain the benefits. This can be done with real-life scenarios or even quotes. For example,

Adrian, the customer service manager at the Central Park store, says, “Now, from anywhere—a kiosk, the checkout counter, or my mobile phone—I can quickly check stock and confirm we have the products the customer needs. I see that customers are delighted at getting their answers instantly.”

You can also use data to do this. For example, you can explain how the decreased time taken to check stock has increased staff productivity, customer satisfaction, or company revenue. Or you can include your suggestions here. Based on your observations, explain the process improvement methodologies you recommend.

This is the time to complete the story. Here, talk about how your project has delivered the changes in the present and sets up for an even more prosperous future. This could be something like:

The ABC inventory management system marks the first step in the retail major’s digital transformation journey. By Q2 next year, we will link the store solution to the e-commerce inventory platform to give 360-degree visibility into the stock situation. This would also enable a new sales channel in the form of Buy Online, Pick Up in Store (BOPIS), enabling same-day fulfillment.

While you write your executive summary, here are some best practices to remember.

Keep it short and simple : The length might depend on the report you’re summarizing, but it’s best to keep it under one page for quick reading. Also, avoid cliches and jargon; make it easy to read. A quick business plan under one page is the best first impression you can make.

Focus on the target audience : Not all executive summaries are read by business executives. Often, you might want to address your summary to peers, vendors, partners, or even teens. Know your target audience and customize your executive summary accordingly.

Use the right tool : You can, of course, use Notepad or Word doc to write your executive summaries. But give it a boost with modern document software like ClickUp Docs .

• Use rich formatting features without jumping through hoops
• Style the critical information with color-coded banners, buttons, and more
• Collaborate in real time with comments, action items, and trackable tasks
• Securely share with anyone with appropriate access controls

Pick a suitable template : If it’s your first time writing an executive summary, we’ve got your back. Fire up one of ClickUp’s executive summary templates or content writing templates , and kickstart your work.

Get the AI boost : If you’ve thoughtfully created your report, you can write your executive summary much quicker with one of the many AI writing tools . For instance, ClickUp AI offers a single-click summarize option right on ClickUp Docs.

What’s more? ClickUp AI supports you in brainstorming new ideas, writing the first drafts of your executive summaries, and proofreading them for good measure.

10 Executive Summary Examples

Now that we have discussed the theory of executive summary writing, let’s look at some examples to see what it looks like in practice. Here are ten to learn from or emulate.

Periodically, the board would expect to see a report on the organization’s performance. Various departments typically write their reports, which are consolidated into a board report. An effective executive summary of this would include the following.

• Revenue and expenditure
• Key areas of focus
• Critical success factors
• Financial information
• Challenges and roadblocks

This ClickUp Board Report Summary Template brings all these aspects together to get you started on your executive summary right away. You can customize this free executive summary template to suit your needs and fill in the data as appropriate.

McKinsey, one of the world’s leading consulting firms, publishes dozens of research reports annually. For every one of them, they write executive summaries, often called ‘in brief.’

In this report titled, ‘ Performance through people: Transforming human capital into competitive advantage ,’ the executive summary takes a two-pronged approach. It presents key insights in text on one page and data in infographics on the next.

Insights in text : The report begins by directly addressing the primary purpose of the research. Below are the first few sentences.

How does developing talent affect financial returns for firms? This research finds that companies with a dual focus on developing human capital and managing it well have a performance edge.

This section summarizes the key insights from the research. The headlines of each section are presented in bold, making it easy for the reader to skim.

Data in visuals : The text section is followed by an infographic of the key findings from the data. Within one page, it presents all the graphs relevant to the reader engagingly.

Within two pages, McKinsey gives the reader a bird’s eye view of what to expect, customized for the target market, from the 40-page document.

You can read the executive summary of this report on McKinsey’s website .

The Adaptation Gap Report 2023 by the United Nations Environment Programme is a 112-page report with a rather detailed executive summary, stretching eight pages. The depth of information and seriousness of the topics covered demand an extended executive summary.

Yet, the writers make every effort to make it engaging with a combination of typography, design, and graphs. It begins with the following.

Despite the clear signs of accelerating climate risks and impacts worldwide, the adaptation finance gap is widening and now stands at between US$194 billion and US$366 billion per year. Adaptation finance needs are 10–18 times as great as current international public adaptation finance flows – at least 50 percent higher than previously estimated.

In the following pages, it presents graphs to demonstrate the underpinnings of these key findings.

Every project manager creates performance reports at the end of each week, month, or quarter. This typically includes the tasks tracking , burn up, burn down, hours spent, etc.

While this can be written down in a list, presenting this information as a slide with visual elements is far more effective.

One way to achieve this is to use ClickUp’s project summary templates , which offer custom-designed templates for various project management purposes.

The other way is to use the dynamic reports on the ClickUp Dashboard , which brings together all the key metrics and keeps them updated in real time for you to share with anyone you’d like to.

Human resources or people management teams create payroll reports, typically in spreadsheets, for every payment period—bi-weekly or monthly. This data is also helpful for building financial projections. For the senior finance leaders, they often create an executive summary of critical information, such as:

• Total salaries paid
• Deductions across categories
• Year-to-date salary expenses
• Paid time off credits
• Net pay summary

ClickUp’s Payroll Summary Report Template can save time by automatically gathering all relevant data from the platform. When data is unavailable on ClickUp, you can highlight any text to @mention team members who can fill in the correct information.

Once complete, you can update the Doc’s settings for access control and share it with the management team instantly.

A company description or how it projects itself is often important to stand out in a crowded market. Mailchimp stood out with its style guide. The guide is comprehensive and widely used by smaller content teams that don’t yet have their own.

Mailchimp has made it public and available under a Creative Commons Attribution-NonCommercial 4.0 International license for anyone to adapt to their needs.

While every section in this style guide is engaging and valuable, for the purposes of this article, we want to draw your attention to the tl;dr section , which acts as a quasi-executive summary.

It is a bulleted list of seven sub-sections, highlighting the foundations of Mailchimp’s writing style.

The striking thing about this tl;dr version is its simplicity. Even without any visual elements, infographics, or charts, this page gives readers a real and actionable summary of the entire style guide.

When we speak of executive summary, we almost always think of a smaller version of an entire document. It need not be so.

For a software engineering team, the release notes are a kind of executive summary of all the changes/upgrades made in the latest version.

Take the example of ClickUp’s release notes 3.04 . Each release gets:

• An organized yet concise summary of all the changes that have been made
• “ClickTips” to help readers make the best use of new features
• Visuals and app images to show how the changes look
• Links to help pages of each of those features so the reader can learn more
• A list of bugs fixed
• And any other resources, such as on-demand webinars or training

These release notes inform users and developers of the latest upgrades to the ClickUp platform without overwhelming them with the details.

The New Yorker Magazine wrote a 10,000-word profile of Geoffrey Hinton , a computer scientist and cognitive psychologist, for their November 20, 2023 issue, titled ‘Metamorphosis.’ Even in podcast form, it’s over 60 minutes long.

When it was published online, they needed a title and description that summarized the article in a way that attracted a lay reader’s attention to click and read. The headline captures the primary conflict explored in the article. The description introduces the protagonist.

While this is typically not what we’d categorize as an executive summary, it is a fantastic example of capturing the essence of a long article in a few powerful words.

This executive summary serves as an inspiration for writers, irrespective of what you’re writing about, to summarize their main points not just briefly but also powerfully and attractively.

In the spring of 2019, Harvard University conducted its first-ever survey about campus culture. The executive summary of the report on these survey responses makes for great reading. It is also a great example of how to honestly and authentically present key findings, even unpleasant ones.

The executive summary is honest on multiple fronts. It admits that:

• 2019 was the first time in history that Harvard surveyed campus culture
• Three in ten of the Harvard community don’t feel like they belong
• 34% of students disagreed with the belief that Harvard will take appropriate action against incidents of harassment and discrimination
• Those from historically underrepresented and disadvantaged groups reported less positive views

At the end of this, the executive summary outlines the specific steps Harvard will take to address these responses from the community.

Project managers can use this as inspiration for handling executive summaries of projects that have gone awry. It helps leaders take responsibility for what has occurred and build systems to prevent future mistakes.

Not all executive summaries have to be written manually by you. A free executive summary template is also something to explore. Plenty of tools offer it. Dozens of AI tools for automation can summarize text in seconds. Here’s what ClickUp AI returned when we inserted the article above and asked for a summary.

The article discusses the purpose and importance of an executive summary, which provides a brief overview of detailed documents, making them more palatable for readers with limited time. Executive summaries typically include problem statements, proposed solutions, expected outcomes, and a conclusion. To create a compelling summary, it’s crucial to identify the main story, incorporate relevant data, expand on benefits, and conclude powerfully. The use of modern document software like ClickUp Docs and AI tools like ClickUp AI can enhance the quality and efficiency of writing executive summaries. The article also provides practical examples of executive summaries across different fields, showcasing their versatility and applicability. This provides a great starting point for those who fear the blank page. You can now edit this to add details, add images, or insert a quote.

With ClickUp AI, you can choose the tone (from professional, straightforward, inspirational, optimistic, casual, confident, friendly, or humorous) and creativity (low, medium, and high) to customize the summary to your needs.

That’s not all! For project managers and business leaders, ClickUp AI offers a wide range of writing and summarizing tools for scope documents, project briefs, meeting agendas, statements of work, survey questions, and more.

You can tag people to invite input or feedback. You can also convert comments into tasks and manage them effortlessly, all in one place.

Never used AI for writing before? No worries there, too. Here are AI prompt templates that will get you started instantly.

With a custom-built AI assistant tailored to your role, you can work faster, write better, spark creativity, and be significantly more productive.

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Fall 2024 CSCI Special Topics Courses

Cloud computing.

Meeting Time: 09:45 AM‑11:00 AM TTh  Instructor: Ali Anwar Course Description: Cloud computing serves many large-scale applications ranging from search engines like Google to social networking websites like Facebook to online stores like Amazon. More recently, cloud computing has emerged as an essential technology to enable emerging fields such as Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning. The exponential growth of data availability and demands for security and speed has made the cloud computing paradigm necessary for reliable, financially economical, and scalable computation. The dynamicity and flexibility of Cloud computing have opened up many new forms of deploying applications on infrastructure that cloud service providers offer, such as renting of computation resources and serverless computing.    This course will cover the fundamentals of cloud services management and cloud software development, including but not limited to design patterns, application programming interfaces, and underlying middleware technologies. More specifically, we will cover the topics of cloud computing service models, data centers resource management, task scheduling, resource virtualization, SLAs, cloud security, software defined networks and storage, cloud storage, and programming models. We will also discuss data center design and management strategies, which enable the economic and technological benefits of cloud computing. Lastly, we will study cloud storage concepts like data distribution, durability, consistency, and redundancy. Registration Prerequisites: CS upper div, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/6BvbUwEkBK41tPJ17 ).

CSCI 5980/8980 

Machine learning for healthcare: concepts and applications.

Meeting Time: 11:15 AM‑12:30 PM TTh  Instructor: Yogatheesan Varatharajah Course Description: Machine Learning is transforming healthcare. This course will introduce students to a range of healthcare problems that can be tackled using machine learning, different health data modalities, relevant machine learning paradigms, and the unique challenges presented by healthcare applications. Applications we will cover include risk stratification, disease progression modeling, precision medicine, diagnosis, prognosis, subtype discovery, and improving clinical workflows. We will also cover research topics such as explainability, causality, trust, robustness, and fairness.

Registration Prerequisites: CSCI 5521 or equivalent. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/z8X9pVZfCWMpQQ6o6  ).

Visualization with AI

Meeting Time: 04:00 PM‑05:15 PM TTh  Instructor: Qianwen Wang Course Description: This course aims to investigate how visualization techniques and AI technologies work together to enhance understanding, insights, or outcomes.

This is a seminar style course consisting of lectures, paper presentation, and interactive discussion of the selected papers. Students will also work on a group project where they propose a research idea, survey related studies, and present initial results.

This course will cover the application of visualization to better understand AI models and data, and the use of AI to improve visualization processes. Readings for the course cover papers from the top venues of AI, Visualization, and HCI, topics including AI explainability, reliability, and Human-AI collaboration.    This course is designed for PhD students, Masters students, and advanced undergraduates who want to dig into research.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/YTF5EZFUbQRJhHBYA  ). Although the class is primarily intended for PhD students, motivated juniors/seniors and MS students who are interested in this topic are welcome to apply, ensuring they detail their qualifications for the course.

Visualizations for Intelligent AR Systems

Meeting Time: 04:00 PM‑05:15 PM MW  Instructor: Zhu-Tian Chen Course Description: This course aims to explore the role of Data Visualization as a pivotal interface for enhancing human-data and human-AI interactions within Augmented Reality (AR) systems, thereby transforming a broad spectrum of activities in both professional and daily contexts. Structured as a seminar, the course consists of two main components: the theoretical and conceptual foundations delivered through lectures, paper readings, and discussions; and the hands-on experience gained through small assignments and group projects. This class is designed to be highly interactive, and AR devices will be provided to facilitate hands-on learning.    Participants will have the opportunity to experience AR systems, develop cutting-edge AR interfaces, explore AI integration, and apply human-centric design principles. The course is designed to advance students' technical skills in AR and AI, as well as their understanding of how these technologies can be leveraged to enrich human experiences across various domains. Students will be encouraged to create innovative projects with the potential for submission to research conferences.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/Y81FGaJivoqMQYtq5 ). Students are expected to have a solid foundation in either data visualization, computer graphics, computer vision, or HCI. Having expertise in all would be perfect! However, a robust interest and eagerness to delve into these subjects can be equally valuable, even though it means you need to learn some basic concepts independently.

Sustainable Computing: A Systems View

Meeting Time: 09:45 AM‑11:00 AM  Instructor: Abhishek Chandra Course Description: In recent years, there has been a dramatic increase in the pervasiveness, scale, and distribution of computing infrastructure: ranging from cloud, HPC systems, and data centers to edge computing and pervasive computing in the form of micro-data centers, mobile phones, sensors, and IoT devices embedded in the environment around us. The growing amount of computing, storage, and networking demand leads to increased energy usage, carbon emissions, and natural resource consumption. To reduce their environmental impact, there is a growing need to make computing systems sustainable. In this course, we will examine sustainable computing from a systems perspective. We will examine a number of questions:   • How can we design and build sustainable computing systems?   • How can we manage resources efficiently?   • What system software and algorithms can reduce computational needs?    Topics of interest would include:   • Sustainable system design and architectures   • Sustainability-aware systems software and management   • Sustainability in large-scale distributed computing (clouds, data centers, HPC)   • Sustainability in dispersed computing (edge, mobile computing, sensors/IoT)

Registration Prerequisites: This course is targeted towards students with a strong interest in computer systems (Operating Systems, Distributed Systems, Networking, Databases, etc.). Background in Operating Systems (Equivalent of CSCI 5103) and basic understanding of Computer Networking (Equivalent of CSCI 4211) is required.

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• Foodcult_The Conversation

Five things our research uncovered when we recreated 16th century beer (and barrels)

It’s true that our 16th-century ancestors drank much more than Irish people do today. But why they did so and what their beer was like are questions shrouded in myth. The authors were part of a team who set out to find some answers.

As part of a major study of food and drink in early modern Ireland, funded by the European Research Council, we recreated and analysed a beer last brewed at Dublin Castle in 1574. Combining craft, microbiology, brewing science, archaeology, as well as history, this was the most comprehensive interdisciplinary study of historical beer ever undertaken. Here are five things that we discovered.

1. People didn’t drink beer because water was unhealthy

It’s often assumed that lack of access to clean water led people to drink beer instead. We know this isn’t true for many reasons, not least because brewers needed a constant source of fresh water to make the best beer.

Water was certainly viewed as less healthy, but not because of any understanding of microbial contamination. According to a system of medicine and treatment used at the time, Galenic humorism , water was a “cold” drink that affected digestion, causing fluctuations and windiness. Meanwhile, beer was “warm and comforting”, balancing the “humours” and quenching thirst.

2. Beer was a payment for work

Beer was taken as medicine, often mixed with curious ingredients. Treatments for conditions such as flux or bed wetting, for example, required ground kid’s hoof or grated stag’s penis to be taken with a drink of beer.

People drank at work, commonly receiving drink as part of their wages. The quantities were staggering. At Christchurch Cathedral in Dublin, masons received up to 15 pints per day when undertaking heavy work.

More typical was a range of five to ten pints, as was the case at Dublin Castle. There, servants imbibed up to 2,700 calories a day in beer alone, the cost of which exceeded what the household spent on bread.

3. Beer had some different ingredients then

In many ways, 16th-century beer would be recognisable today. The key ingredients were malt (made from barley or oats depending on the region), water, yeast and hops.

The addition of hops, a Dutch innovation, spread throughout Europe in this period. This resulted in a longer lasting drink, accelerating the development of the brewing industry as we know it today.

But there are differences between pre-modern and modern beers, relating primarily to the nature of the ingredients. Four centuries ago, cereals were grown as landraces .

A landrace has a wide range of characteristics distinct from those of standardised modern varieties, through adaptation to their regional climate, soils and topography. Shrinking cultivation of these landraces meant that sourcing heritage ingredients was challenging.

The variety of barley we chose was bere . This is the only landrace barley still grown commercially, thanks to the conservation efforts of agronomists and farmers in Orkney , Scotland.

The experiment was a unique opportunity to examine the significance of these varieties to the taste and quality of drinks in the past, and the benefits of saving heritage crops for future generations.

4. Making beer required skills in short supply today

Industrial brewing today produces the same beer every time. Brewing in the past, using simpler equipment and in a more open environment, was much more challenging. Brewers were deeply in tune with their working conditions and didn’t have modern devices such as thermometers.

They used their senses and knowledge to make adjustments as they worked. As the project team learned the hard way, small mistakes could be disastrous, resulting in spoiled beer and accidental porridge.

Recreating the technology of the past also highlighted the wider craft skills, such as coopering (making barrels), wicker-weaving, woodworking, and coppersmithing, that went into making all the equipment needed to make a pint. Much like heritage crops, these skills are in worrying decline.

Our oak fermenting barrels and mash tuns (a vessel used in brewing) were made by Les Skinner , at the time one of the last two master coopers in England. He has since retired. We had to go all the way to Portugal to find coppersmiths who could build a large freestanding boiler.

5. Even everyday beer was strong

One enduring misconception is that people were able to drink so much in the 16th century because their beer was relatively weak. Based on little evidence, it is assumed that beer of around 2% alcohol by volume (abv) was the most common drink of the working classes. But we know this so called “small beer” was widely rejected by workers, as well as by physicians, dietary writers, and government officials, who all deemed it dangerous to health.

Our experiment showed that a typical beer of middling strength actually had the potential to be around 5% abv, comparable to modern lager. This means people could have been extremely inebriated from merely what they drank alongside work. Unsurprisingly, there were loud and frequent calls for drinkers to show moderation.

Those calls often came, however, from the same people who liberally supplied their workers with beer. This suggests that the context in which people drank was very important. If having a pint or two at breakfast and dinner was acceptable, even expected, many more at the village alehouse was seen as more troublesome.

Susan Flavin , Associate professor of history, Trinity College Dublin and Charlie Taverner , Research fellow, history, Trinity College Dublin

This article is republished from The Conversation under a Creative Commons license. Read the original article .