figures in research paper

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Tables and Figures

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Note:  This page reflects the latest version of the APA Publication Manual (i.e., APA 7), which released in October 2019. The equivalent resources for the older APA 6 style  can be found at this page  as well as at this page (our old resources covered the material on this page on two separate pages).

The purpose of tables and figures in documents is to enhance your readers' understanding of the information in the document; usually, large amounts of information can be communicated more efficiently in tables or figures. Tables are any graphic that uses a row and column structure to organize information, whereas figures include any illustration or image other than a table.

General guidelines

Visual material such as tables and figures can be used quickly and efficiently to present a large amount of information to an audience, but visuals must be used to assist communication, not to use up space, or disguise marginally significant results behind a screen of complicated statistics. Ask yourself this question first: Is the table or figure necessary? For example, it is better to present simple descriptive statistics in the text, not in a table.

Relation of Tables or Figures and Text

Because tables and figures supplement the text, refer in the text to all tables and figures used and explain what the reader should look for when using the table or figure. Focus only on the important point the reader should draw from them, and leave the details for the reader to examine on their own.

Documentation

If you are using figures, tables and/or data from other sources, be sure to gather all the information you will need to properly document your sources.

Integrity and Independence

Each table and figure must be intelligible without reference to the text, so be sure to include an explanation of every abbreviation (except the standard statistical symbols and abbreviations).

Organization, Consistency, and Coherence

Number all tables sequentially as you refer to them in the text (Table 1, Table 2, etc.), likewise for figures (Figure 1, Figure 2, etc.). Abbreviations, terminology, and probability level values must be consistent across tables and figures in the same article. Likewise, formats, titles, and headings must be consistent. Do not repeat the same data in different tables.

Data in a table that would require only two or fewer columns and rows should be presented in the text. More complex data is better presented in tabular format. In order for quantitative data to be presented clearly and efficiently, it must be arranged logically, e.g. data to be compared must be presented next to one another (before/after, young/old, male/female, etc.), and statistical information (means, standard deviations, N values) must be presented in separate parts of the table. If possible, use canonical forms (such as ANOVA, regression, or correlation) to communicate your data effectively.

A generic example of a table with multiple notes formatted in APA 7 style.

Elements of Tables

Number all tables with Arabic numerals sequentially. Do not use suffix letters (e.g. Table 3a, 3b, 3c); instead, combine the related tables. If the manuscript includes an appendix with tables, identify them with capital letters and Arabic numerals (e.g. Table A1, Table B2).

Like the title of the paper itself, each table must have a clear and concise title. Titles should be written in italicized title case below the table number, with a blank line between the number and the title. When appropriate, you may use the title to explain an abbreviation parenthetically.

Comparison of Median Income of Adopted Children (AC) v. Foster Children (FC)

Keep headings clear and brief. The heading should not be much wider than the widest entry in the column. Use of standard abbreviations can aid in achieving that goal. There are several types of headings:

• Stub headings describe the lefthand column, or stub column , which usually lists major independent variables.
• Column headings describe entries below them, applying to just one column.
• Column spanners are headings that describe entries below them, applying to two or more columns which each have their own column heading. Column spanners are often stacked on top of column headings and together are called decked heads .
• Table Spanners cover the entire width of the table, allowing for more divisions or combining tables with identical column headings. They are the only type of heading that may be plural.

All columns must have headings, written in sentence case and using singular language (Item rather than Items) unless referring to a group (Men, Women). Each column’s items should be parallel (i.e., every item in a column labeled “%” should be a percentage and does not require the % symbol, since it’s already indicated in the heading). Subsections within the stub column can be shown by indenting headings rather than creating new columns:

Chemical Bonds

     Ionic

     Covalent

     Metallic

The body is the main part of the table, which includes all the reported information organized in cells (intersections of rows and columns). Entries should be center aligned unless left aligning them would make them easier to read (longer entries, usually). Word entries in the body should use sentence case. Leave cells blank if the element is not applicable or if data were not obtained; use a dash in cells and a general note if it is necessary to explain why cells are blank.   In reporting the data, consistency is key: Numerals should be expressed to a consistent number of decimal places that is determined by the precision of measurement. Never change the unit of measurement or the number of decimal places in the same column.

There are three types of notes for tables: general, specific, and probability notes. All of them must be placed below the table in that order.

General  notes explain, qualify or provide information about the table as a whole. Put explanations of abbreviations, symbols, etc. here.

Example:  Note . The racial categories used by the US Census (African-American, Asian American, Latinos/-as, Native-American, and Pacific Islander) have been collapsed into the category “non-White.” E = excludes respondents who self-identified as “White” and at least one other “non-White” race.

Specific  notes explain, qualify or provide information about a particular column, row, or individual entry. To indicate specific notes, use superscript lowercase letters (e.g.  a ,  b ,  c ), and order the superscripts from left to right, top to bottom. Each table’s first footnote must be the superscript  a .

a  n = 823.  b  One participant in this group was diagnosed with schizophrenia during the survey.

Probability  notes provide the reader with the results of the tests for statistical significance. Asterisks indicate the values for which the null hypothesis is rejected, with the probability ( p value) specified in the probability note. Such notes are required only when relevant to the data in the table. Consistently use the same number of asterisks for a given alpha level throughout your paper.

* p < .05. ** p < .01. *** p < .001

If you need to distinguish between two-tailed and one-tailed tests in the same table, use asterisks for two-tailed p values and an alternate symbol (such as daggers) for one-tailed p values.

* p < .05, two-tailed. ** p < .01, two-tailed. † p <.05, one-tailed. †† p < .01, one-tailed.

Borders 

Tables should only include borders and lines that are needed for clarity (i.e., between elements of a decked head, above column spanners, separating total rows, etc.). Do not use vertical borders, and do not use borders around each cell. Spacing and strict alignment is typically enough to clarify relationships between elements.

Example of a table in the text of an APA 7 paper. Note the lack of vertical borders.

Tables from Other Sources

If using tables from an external source, copy the structure of the original exactly, and cite the source in accordance with  APA style .

Table Checklist

(Taken from the  Publication Manual of the American Psychological Association , 7th ed., Section 7.20)

• Is the table necessary?
• Does it belong in the print and electronic versions of the article, or can it go in an online supplemental file?
• Are all comparable tables presented consistently?
• Are all tables numbered with Arabic numerals in the order they are mentioned in the text? Is the table number bold and left-aligned?
• Are all tables referred to in the text?
• Is the title brief but explanatory? Is it presented in italicized title case and left-aligned?
• Does every column have a column heading? Are column headings centered?
• Are all abbreviations; special use of italics, parentheses, and dashes; and special symbols explained?
• Are the notes organized according to the convention of general, specific, probability?
• Are table borders correctly used (top and bottom of table, beneath column headings, above table spanners)?
• Does the table use correct line spacing (double for the table number, title, and notes; single, one and a half, or double for the body)?
• Are entries in the left column left-aligned beneath the centered stub heading? Are all other column headings and cell entries centered?
• Are confidence intervals reported for all major point estimates?
• Are all probability level values correctly identified, and are asterisks attached to the appropriate table entries? Is a probability level assigned the same number of asterisks in all the tables in the same document?
• If the table or its data are from another source, is the source properly cited? Is permission necessary to reproduce the table?

Figures include all graphical displays of information that are not tables. Common types include graphs, charts, drawings, maps, plots, and photos. Just like tables, figures should supplement the text and should be both understandable on their own and referenced fully in the text. This section details elements of formatting writers must use when including a figure in an APA document, gives an example of a figure formatted in APA style, and includes a checklist for formatting figures.

Preparing Figures

In preparing figures, communication and readability must be the ultimate criteria. Avoid the temptation to use the special effects available in most advanced software packages. While three-dimensional effects, shading, and layered text may look interesting to the author, overuse, inconsistent use, and misuse may distort the data, and distract or even annoy readers. Design properly done is inconspicuous, almost invisible, because it supports communication. Design improperly, or amateurishly, done draws the reader’s attention from the data, and makes him or her question the author’s credibility. Line drawings are usually a good option for readability and simplicity; for photographs, high contrast between background and focal point is important, as well as cropping out extraneous detail to help the reader focus on the important aspects of the photo.

Parts of a Figure

All figures that are part of the main text require a number using Arabic numerals (Figure 1, Figure 2, etc.). Numbers are assigned based on the order in which figures appear in the text and are bolded and left aligned.

Under the number, write the title of the figure in italicized title case. The title should be brief, clear, and explanatory, and both the title and number should be double spaced.

The image of the figure is the body, and it is positioned underneath the number and title. The image should be legible in both size and resolution; fonts should be sans serif, consistently sized, and between 8-14 pt. Title case should be used for axis labels and other headings; descriptions within figures should be in sentence case. Shading and color should be limited for clarity; use patterns along with color and check contrast between colors with free online checkers to ensure all users (people with color vision deficiencies or readers printing in grayscale, for instance) can access the content. Gridlines and 3-D effects should be avoided unless they are necessary for clarity or essential content information.

Legends, or keys, explain symbols, styles, patterns, shading, or colors in the image. Words in the legend should be in title case; legends should go within or underneath the image rather than to the side. Not all figures will require a legend.

Notes clarify the content of the figure; like tables, notes can be general, specific, or probability. General notes explain units of measurement, symbols, and abbreviations, or provide citation information. Specific notes identify specific elements using superscripts; probability notes explain statistical significance of certain values.

A generic example of a figure formatted in APA 7 style.

Figure Checklist 

(Taken from the  Publication Manual of the American Psychological Association , 7 th ed., Section 7.35)

• Is the figure necessary?
• Does the figure belong in the print and electronic versions of the article, or is it supplemental?
• Is the figure simple, clean, and free of extraneous detail?
• Is the figure title descriptive of the content of the figure? Is it written in italic title case and left aligned?
• Are all elements of the figure clearly labeled?
• Are the magnitude, scale, and direction of grid elements clearly labeled?
• Are parallel figures or equally important figures prepared according to the same scale?
• Are the figures numbered consecutively with Arabic numerals? Is the figure number bold and left aligned?
• Has the figure been formatted properly? Is the font sans serif in the image portion of the figure and between sizes 8 and 14?
• Are all abbreviations and special symbols explained?
• If the figure has a legend, does it appear within or below the image? Are the legend’s words written in title case?
• Are the figure notes in general, specific, and probability order? Are they double-spaced, left aligned, and in the same font as the paper?
• Are all figures mentioned in the text?
• Has written permission for print and electronic reuse been obtained? Is proper credit given in the figure caption?
• Have all substantive modifications to photographic images been disclosed?
• Are the figures being submitted in a file format acceptable to the publisher?
• Have the files been produced at a sufficiently high resolution to allow for accurate reproduction?

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Home » Figures in Research Paper – Examples and Guide

Figures in Research Paper – Examples and Guide

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Figures in Research Paper

Figures play an essential role in research papers as they provide a visual representation of data, results, and concepts presented in the text. Figures can include graphs, charts, diagrams, photographs, and other visual aids that enhance the reader’s understanding of the research.

Importance of Figures in Research Paper

Here are some specific ways in which figures can be important in a research paper:

• Visual representation of data : Figures can be used to present data in a clear and concise way. This makes it easier for readers to understand the results of experiments and studies.
• Simplify complex ideas: Some concepts can be difficult to explain using words alone. Figures can be used to simplify complex ideas and make them more accessible to a wider audience.
• Increase reader engagement : Figures can make a research paper more engaging and interesting to read. They break up long blocks of text and can make the paper more visually appealing.
• Support arguments: Figures can be used to support arguments made in the paper. For example, a graph or chart can be used to show a correlation between two variables, providing evidence for a particular hypothesis.
• Convey important information: Figures can be used to convey important information quickly and efficiently. This is particularly useful when the paper is being read by someone who is short on time and needs to quickly understand the main points.

Types of Figures in Research Paper

There are several types of figures commonly used in research papers, including:

• Line graphs: These are used to show trends or changes in data over time.
• Bar graphs: These are used to compare data across different categories or groups.
• Pie charts: These are used to show proportions or percentages of data.
• Scatterplots : These are used to show the relationship between two variables.
• Tables : These are used to present large amounts of data in a structured format.
• Photographs or images : These are used to provide visual context or examples of the research being presented.
• Diagrams or schematics : These are used to illustrate complex processes or systems.

How to add Figures to Research Paper

Adding figures to a research paper can be a great way to visually convey important information to the reader. Here are some general guidelines for adding figures to your research paper:

• Determine the appropriate type of figure: Depending on the information you want to convey, you may want to use a graph, chart, table, photograph, or other type of figure.
• Label the figure: Give your figure a descriptive title and number it. Also, include a brief caption that explains what the figure shows.
• Place the figure in the appropriate location : Generally, figures should be placed as close as possible to the text that refers to them. For example, if you mention a figure in the middle of a paragraph, it should be placed within that paragraph.
• Format the figure appropriately: Ensure that the figure is clear and easy to read. Use consistent fonts and font sizes, and make sure the figure is large enough to be easily seen.
• Cite the source of the figure: If the figure was not created by you, you must cite the source of the figure in your paper. This includes citing the author or creator, the date of creation, and any relevant publication information.
• Consider copyright : Ensure that you have permission to use any figures that are copyrighted. If the figure is copyrighted, you may need to obtain permission from the copyright holder to use it in your paper.

How to Label Figures in Research Paper

Labeling figures in a research paper is an important task that helps readers to understand the content of the paper. Here are the steps to label figures in a research paper:

• Decide on the numbering system: Before labeling the figures, decide on the numbering system that you want to use. Typically, figures are numbered consecutively throughout the paper, with the first figure being labeled as “Figure 1,” the second figure as “Figure 2,” and so on.
• Choose a clear and concise caption: A caption is a brief description of the figure that appears below the figure. It should be clear and concise and should describe the content of the figure accurately. The caption should be written in a way that readers can understand the figure without having to read the entire paper.
• Place the label and caption appropriately: The label and caption should be placed below the figure. The label should be centered and should include the figure number and a brief title. The caption should be placed below the label and should describe the figure in detail.
• Use consistent formatting: Make sure that the formatting of the labels and captions is consistent throughout the paper. Use the same font, size, and style for all figures in the paper.
• Reference figures in the text : When referring to a figure in the text, use the figure number and label. For example, “As shown in Figure 1, the results indicate that…”

Figure 1. Distribution of survey responses

In this example, “Figure 1” is the figure number, and “Distribution of survey responses” is a brief title or description of the figure.

The label should be placed at the top of the figure and should be centered. It should be clear and easy to read. It’s important to use a consistent format for all figures in the paper to make it easier for readers to follow.

Examples of Figures in Research Paper

Examples of Figures in Research Papers or Thesis are as follows:

Line graphs Example

Bar graphs Example

Pie charts Example

Scatterplots Example

Tables Example

Photographs or images Example

Diagrams or schematics Example

Purpose of Figures in Research Paper

Some common purposes of figures in research papers are:

• To summarize data: Figures can be used to present data in a concise and easy-to-understand manner. For example, graphs can be used to show trends or patterns in data, while tables can be used to summarize numerical information.
• To support arguments : Figures can be used to support arguments made in the text of the research paper. For example, a figure showing the results of an experiment can help to demonstrate the validity of the conclusions drawn from the experiment.
• To illustrate concepts: Figures can be used to illustrate abstract or complex concepts that are difficult to explain in words. For example, diagrams or illustrations can be used to show the structure of a complex molecule or the workings of a machine.
• To enhance readability: Figures can make a research paper more engaging and easier to read. By breaking up long blocks of text, figures can help to make the paper more visually appealing and easier to understand.
• To provide context : Figures can be used to provide context for the research being presented. For example, a map or diagram can help to show the location or layout of a study site or experimental setup.
• To compare results : Figures can be used to compare results from different experiments or studies. This can help to highlight similarities or differences in the data and draw comparisons between different research findings.
• To show relationships : Figures can be used to show relationships between different variables or factors. For example, a scatter plot can be used to show the correlation between two variables, while a network diagram can be used to show how different elements are connected to each other.
• To present raw data: Figures can be used to present raw data in a way that is easier to understand. For example, a heat map can be used to show the distribution of data over a geographic region, while a histogram can be used to show the distribution of data within a single variable.

Advantages of Figures in Research Paper

Figures (such as charts, graphs, diagrams, and photographs) are an important component of research papers and offer several advantages, including:

• Enhancing clarity : Figures can help to visually communicate complex data or information in a clear and concise manner. They can help readers better understand the research and its findings.
• Saving space : Figures can often convey information more efficiently than text, allowing researchers to present more information in less space.
• Improving readability : Figures can break up large blocks of text and make a paper more visually appealing and easier to read.
• Supporting arguments: Figures can be used to support arguments made in the text and help to strengthen the overall message of the paper.
• Enabling comparisons: Figures can be used to compare different data points, which can be difficult to do with text alone. This can help readers to see patterns and relationships in the data more easily.
• Providing context : Figures can provide context for the research, such as showing the geographic location of study sites or providing a visual representation of the study population.

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Muhammad Hassan

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How to Include Figures in a Research Paper

This article will explain what is the best approach to communicate the information to your audience including figures in a research paper.

Figures are sometimes overlooked when preparing a research paper. However, the truth is that even a research paper must be visually appealing to its readers, and the best way to do so is to make it simple-to-read by adding tables or figures.

Readers are generally drawn to visual assets since they can convey a lot of information in a short amount of time. Therefore, understanding visuals is preferable to reading long and dense paragraphs.

In this article, we are going to assist on how to include figures in a research paper , in furtherance of increasing visual assets and attain an easy understandability of data.

When to use figures in a research paper?

When planning your research, there’s a need to identify the best approach to communicate the information to your audience using every tool to support your arguments.

A good way to identify the need for tables or figures is to understand if your argument can be explained in a sentence or two, if so, tables or figures are most likely unnecessary. 

If the data is too extensive or complex to be clarified in short sentences, figures or tables are recommended since they can be effective in conveying a lot of information without clogging up your research.  

Tables or figures?

In addition to explaining how to include figures in a research paper , this article will also explain whether tables or figures are the best approaches to do so.

When there is a requirement to exhibit numerical data or other sorts of summary data in a compact space, a table is an ideal method to do so. It’s also a good approach to combine, compare and contrast different types of data, to show items that contain several characteristics, variables or even to show the absence of these characteristics.

Figures are advised when it is necessary to depict patterns, trends, and relations between data. Figures, as opposed to tables, are used to highlight the pattern rather than the data itself. Figures can be used to visually describe a series of events, procedures, qualities or attributes or to summarize research results. There are numerous options on figures to be included, such as graphs, data plots, maps, pie charts, and so on. 

Finally, the text should be used when the data is not too big or hard to portray. Creating a table for this data would mean creating a table that contains 2 columns or even less. 

How many figures should a research paper have?

Although there is no limit to the number of figures or tables that can be included in your research, including too many may hinder data comprehension. Therefore, when considering adding tables or figures, keep the readability of the research paper in mind. 

It is recommended that a research paper contain no more than 5 tables and no more than 8 figures.

How to include figures in a research paper

As previously said, incorporating figures and tables in a research paper helps to summarize data and makes the article more aesthetically appealing for readers seeking a large quantity of information in a short amount of time.

Here are the tips and guides on how to include figures in your research paper:

• Where to include figures in a research paper?

In a research paper, figures must be included in the center of the page, close to where it is first referred to, preferably immediately below the paragraph where the data was mentioned.

• Figure Captions

All figures must be identified with a number and followed by a brief but intelligible statement that describes the data provided. Important readings on the figure can be highlighted in captions. Considering that figures are generally read from the bottom up, captions must take place left, below the figure.  (Reference: International Science Editing )

When selecting figures, consider images that are easily understood. Consider the size, resolution, and color of the figure as well. 

Figures must be a reasonable size and have a high resolution for the data to be clear. Elements are also vital when it comes to adding figures; utilize colors, lines, and icons, but remember to use them to add effect and not to code information, figures must be understandable even without the elements.

• Additional information

Make sure to include any additional information required to comprehend the added figure. For graphs, this may include incorporating labels, legends, explanations for symbols, or check marks. And for maps, make sure to include a scale indicator, compass rose, or north arrow.

Best practices for including figures in a research paper

Research papers are critical documents that require a lot of work, and having solid statistics and exceptional information must be a priority. Hence, here is a list of best practices for including figures in a research paper: 

• Choose the appropriate sort of figure for each data, different types of data necessitate different types of figures. An incorrectly picked figure may make understanding the research even more challenging.
• Prioritize readability, an incomprehensible figure is seen simply as an image. Make sure to distinguish data and not overlap information. Choose a layout that maximizes readability. 
• Remove any superfluous information from the figures. Data will be complex, so concentrate on a simple, elegant, and straightforward design that highlights the most significant aspects of the data.
• Aim for accuracy and double-check figures to avoid any type of error that could lead to data misinterpretation.

Common mistakes to avoid

• The axis titles and legends are confusing or repetitive. Focus on titles and descriptions that are easy to comprehend and consistent with the references added in the research.
• Design inconsistency. Several designs in one research may make it difficult for the audience to grasp each figure. Make sure to consistently use the same font, size, markers, line, etc.
• Random colors. Avoid using colors that are hard to read, such as yellow or beige. Also, for color blind people to see, it is best to avoid using red and green.

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Content tags

Figures and Charts

What this handout is about.

This handout will describe how to use figures and tables to present complicated information in a way that is accessible and understandable to your reader.

Do I need a figure/table?

When planning your writing, it is important to consider the best way to communicate information to your audience, especially if you plan to use data in the form of numbers, words, or images that will help you construct and support your argument.  Generally speaking, data summaries may take the form of text, tables or figures. Most writers are familiar with textual data summaries and this is often the best way to communicate simple results. A good rule of thumb is to see if you can present your results clearly in a sentence or two. If so, a table or figure is probably unnecessary. If your data are too numerous or complicated to be described adequately in this amount of space, figures and tables can be effective ways of conveying lots of information without cluttering up your text. Additionally, they serve as quick references for your reader and can reveal trends, patterns, or relationships that might otherwise be difficult to grasp.

So what’s the difference between a table and a figure anyway?

Tables present lists of numbers or text in columns and can be used to synthesize existing literature, to explain variables, or to present the wording of survey questions. They are also used to make a paper or article more readable by removing numeric or listed data from the text. Tables are typically used to present raw data, not when you want to show a relationship between variables.

Figures are visual presentations of results. They come in the form of graphs, charts, drawings, photos, or maps.  Figures provide visual impact and can effectively communicate your primary finding. Traditionally, they are used to display trends and patterns of relationship, but they can also be used to communicate processes or display complicated data simply.  Figures should not duplicate the same information found in tables and vice versa.

Using tables

Tables are easily constructed using your word processor’s table function or a spread sheet program such as Excel. Elements of a table include the Legend or Title, Column Titles, and the Table Body (quantitative or qualitative data). They may also include subheadings and footnotes. Remember that it is just as important to think about the organization of tables as it is to think about the organization of paragraphs. A well-organized table allows readers to grasp the meaning of the data presented with ease, while a disorganized one will leave the reader confused about the data itself, or the significance of the data.

Title: Tables are headed by a number followed by a clear, descriptive title or caption. Conventions regarding title length and content vary by discipline. In the hard sciences, a lengthy explanation of table contents may be acceptable. In other disciplines, titles should be descriptive but short, and any explanation or interpretation of data should take place in the text. Be sure to look up examples from published papers within your discipline that you can use as a model. It may also help to think of the title as the “topic sentence” of the table—it tells the reader what the table is about and how it’s organized. Tables are read from the top down, so titles go above the body of the table and are left-justified.

Column titles: The goal of column headings is to simplify and clarify the table, allowing the reader to understand the components of the table quickly. Therefore, column titles should be brief and descriptive and should include units of analysis.

Table body: This is where your data are located, whether they are numerical or textual. Again, organize your table in a way that helps the reader understand the significance of the data. Be sure to think about what you want your readers to compare, and put that information in the column (up and down) rather than in the row (across). In other words, construct your table so that like elements read down, not across. When using numerical data with decimals, make sure that the decimal points line up. Whole numbers should line up on the right.

Other table elements

Tables should be labeled with a number preceding the table title; tables and figures are labeled independently of one another. Tables should also have lines demarcating different parts of the table (title, column headers, data, and footnotes if present). Gridlines or boxes should not be included in printed versions. Tables may or may not include other elements, such as subheadings or footnotes.

Quick reference for tables

Tables should be:

• Centered on the page.
• Numbered in the order they appear in the text.
• Referenced in the order they appear in the text.
• Labeled with the table number and descriptive title above the table.
• Labeled with column and/or row labels that describe the data, including units of measurement.
• Set apart from the text itself; text does not flow around the table.

Table 1. Physical characteristics of the Doctor in the new series of Doctor Who

Table 2. Physical characteristics of the Doctor in the new series of Doctor Who

Using figures

Figures can take many forms. They may be graphs, diagrams, photos, drawings, or maps. Think deliberately about your purpose and use common sense to choose the most effective figure for communicating the main point. If you want your reader to understand spatial relationships, a map or photograph may be the best choice. If you want to illustrate proportions, experiment with a pie chart or bar graph. If you want to illustrate the relationship between two variables, try a line graph or a scatterplot (more on various types of graphs below). Although there are many types of figures, like tables, they share some typical features: captions, the image itself, and any necessary contextual information (which will vary depending on the type of figure you use).

Figure captions

Figures should be labeled with a number followed by a descriptive caption or title. Captions should be concise but comprehensive. They should describe the data shown, draw attention to important features contained within the figure, and may sometimes also include interpretations of the data. Figures are typically read from the bottom up, so captions go below the figure and are left-justified.

The most important consideration for figures is simplicity. Choose images the viewer can grasp and interpret clearly and quickly. Consider size, resolution, color, and prominence of important features. Figures should be large enough and of sufficient resolution for the viewer to make out details without straining their eyes. Also consider the format your paper will ultimately take. Journals typically publish figures in black and white, so any information coded by color will be lost to the reader.  On the other hand, color might be a good choice for papers published to the web or for PowerPoint presentations. In any case, use figure elements like color, line, and pattern for effect, not for flash.

Additional information

Figures should be labeled with a number preceding the table title; tables and figures are numbered independently of one another. Also be sure to include any additional contextual information your viewer needs to understand the figure. For graphs, this may include labels, a legend explaining symbols, and vertical or horizontal tick marks. For maps, you’ll need to include a scale and north arrow. If you’re unsure about contextual information, check out several types of figures that are commonly used in your discipline.

Quick reference for figures

Figures should be:

• Labeled (under the figure) with the figure number and appropriate descriptive title (“Figure” can be spelled out [“Figure 1.”] or abbreviated [“Fig. 1.”] as long as you are consistent).
• Referenced in the order they appear in the text (i.e. Figure 1 is referenced in the text before Figure 2 and so forth).
• Set apart from the text; text should not flow around figures.

Every graph is a figure but not every figure is a graph. Graphs are a particular set of figures that display quantitative relationships between variables. Some of the most common graphs include bar charts, frequency histograms, pie charts, scatter plots, and line graphs, each of which displays trends or relationships within and among datasets in a different way. You’ll need to carefully choose the best graph for your data and the relationship that you want to show. More details about some common graph types are provided below. Some good advice regarding the construction of graphs is to keep it simple. Remember that the main objective of your graph is communication. If your viewer is unable to visually decode your graph, then you have failed to communicate the information contained within it.

Pie charts are used to show relative proportions, specifically the relationship of a number of parts to the whole. Use pie charts only when the parts of the pie are mutually exclusive categories and the sum of parts adds up to a meaningful whole (100% of something). Pie charts are good at showing “big picture” relationships (i.e. some categories make up “a lot” or “a little” of the whole thing). However, if you want your reader to discern fine distinctions within your data, the pie chart is not for you. Humans are not very good at making comparisons based on angles. We are much better at comparing length, so try a bar chart as an alternative way to show relative proportions. Additionally, pie charts with lots of little slices or slices of very different sizes are difficult to read, so limit yours to 5-7 categories.

The chart shows the relative proportion of fifteen elements in Martian soil, listed in order from “most” to “least”: oxygen, silicon, iron, magnesium, calcium, sulfur, aluminum, sodium, potassium, chlorine, helium, nitrogen, phosphorus, beryllium, and other. Oxygen makes up about ⅓ of the composition, while silicon and iron together make up about ¼. The remaining slices make up smaller proportions, but the percentages aren’t listed in the key and are difficult to estimate. It is also hard to distinguish fifteen colors when comparing the pie chart to the color coded key.

The chart shows the relative proportion of five leisure activities of Venusian teenagers (tanning, trips to Mars, reading, messing with satellites, and stealing Earth cable). Although each of the five slices are about the same size (roughly 20% of the total), the percentage of Venusian teenagers engaging in each activity varies widely (tanning: 80%, trips to Mars: 40%, reading: 12%, messing with satellites: 30%, stealing Earth cable: 77%). Therefore, there is a mismatch between the labels and the actual proportion represented by each activity (in other words, if reading represents 12% of the total, its slice should take up 12% of the pie chart area), which makes the representation inaccurate. In addition, the labels for the five slices add up to 239% (rather than 100%), which makes it impossible to accurately represent this dataset using a pie chart.

Bar graphs are also used to display proportions. In particular, they are useful for showing the relationship between independent and dependent variables, where the independent variables are discrete (often nominal) categories. Some examples are occupation, gender, and species. Bar graphs can be vertical or horizontal. In a vertical bar graph the independent variable is shown on the x axis (left to right) and the dependent variable on the y axis (up and down). In a horizontal one, the dependent variable will be shown on the horizontal (x) axis, the independent on the vertical (y) axis. The scale and origin of the graph should be meaningful. If the dependent (numeric) variable has a natural zero point, it is commonly used as a point of origin for the bar chart. However, zero is not always the best choice. You should experiment with both origin and scale to best show the relevant trends in your data without misleading the viewer in terms of the strength or extent of those trends.

The graph shows the number of male and female spaceship crew members for five different popular television series: Star Trek (1965), Battlestar (1978), Star Trek: TNG (1987), Stargate SG-1 (1997), and Firefly (2002). Because the television series are arranged chronologically on the x-axis, the graph can also be used to look for trends in these numbers over time.

Although the number of crew members for each show is similar (ranging from 9 to 11), the proportion of female and male crew members varies. Star Trek has half as many female crew members as male crew members (3 and 6, respectively), Battlestar has fewer than one-fourth as many female crew members as male crew members (2 and 9, respectively), Star Trek: TNG has four female crew members and six male crew members, Stargate SG-1 has less than one-half as many female crew members as male crew members (3 and 7, respectively), and Firefly has four female and five male crew members.

Frequency histograms/distributions

Frequency histograms are a special type of bar graph that show the relationship between independent and dependent variables, where the independent variable is continuous, rather than discrete. This means that each bar represents a range of values, rather than a single observation. The dependent variables in a histogram are always numeric, but may be absolute (counts) or relative (percentages). Frequency histograms are good for describing populations—examples include the distribution of exam scores for students in a class or the age distribution of the people living in Chapel Hill. You can experiment with bar ranges (also known as “bins”) to achieve the best level of detail, but each range or bin should be of uniform width and clearly labeled.

XY scatter plots

Scatter plots are another way to illustrate the relationship between two variables. In this case, data are displayed as points in an x,y coordinate system, where each point represents one observation along two axes of variation. Often, scatter plots are used to illustrate correlation between two variables—as one variable increases, the other increases (positive correlation) or decreases (negative correlation). However, correlation does not necessarily imply that changes in one variable cause changes in the other. For instance, a third, unplotted variable may be causing both. In other words, scatter plots can be used to graph one independent and one dependent variable, or they can be used to plot two independent variables. In cases where one variable is dependent on another (for example, height depends partly on age), plot the independent variable on the horizontal (x) axis, and the dependent variable on the vertical (y) axis. In addition to correlation (a linear relationship), scatter plots can be used to plot non-linear relationships between variables.

The scatter plot shows the relationship between temperature (x-axis, independent variable) and the number of UFO sightings (y-axis, dependent variable) for 53 separate data points. The temperature ranges from about 0°F and 120°F, and the number of UFO sightings ranges from 1 to 10. The plot shows a low number of UFO sightings (ranging from 1 to 4) at temperatures below 80°F and a much wider range of the number of sightings (from 1 to 10) at temperatures above 80°F. It appears that the number of sightings tends to increase as temperature increases, though there are many cases where only a few sightings occur at high temperatures.

XY line graphs

Line graphs are similar to scatter plots in that they display data along two axes of variation. Line graphs, however, plot a series of related values that depict a change in one variable as a function of another, for example, world population (dependent) over time (independent). Individual data points are joined by a line, drawing the viewer’s attention to local change between adjacent points, as well as to larger trends in the data. Line graphs are similar to bar graphs, but are better at showing the rate of change between two points. Line graphs can also be used to compare multiple dependent variables by plotting multiple lines on the same graph.

Example of an XY line graph:

The line graph shows the age (in years) of the actor of each Doctor Who regeneration for the first through the eleventh regeneration. The ages range from a maximum of about 55 in the first regeneration to a minimum of about 25 in the eleventh regeneration. There is a downward trend in the age of the actors over the course of the eleven regenerations.

General tips for graphs

Strive for simplicity. Your data will be complex. Don’t be tempted to convey the complexity of your data in graphical form. Your job (and the job of your graph) is to communicate the most important thing about the data. Think of graphs like you think of paragraphs—if you have several important things to say about your data, make several graphs, each of which highlights one important point you want to make.

Strive for clarity. Make sure that your data are portrayed in a way that is visually clear. Make sure that you have explained the elements of the graph clearly. Consider your audience. Will your reader be familiar with the type of figure you are using (such as a boxplot)? If not, or if you’re not sure, you may need to explain boxplot conventions in the text. Avoid “chartjunk.” Superfluous elements just make graphs visually confusing. Your reader does not want to spend 15 minutes figuring out the point of your graph.

Strive for accuracy. Carefully check your graph for errors. Even a simple graphical error can change the meaning and interpretation of the data. Use graphs responsibly. Don’t manipulate the data so that it looks like it’s saying something it’s not—savvy viewers will see through this ruse, and you will come off as incompetent at best and dishonest at worst.

How should tables and figures interact with text?

Placement of figures and tables within the text is discipline-specific. In manuscripts (such as lab reports and drafts) it is conventional to put tables and figures on separate pages from the text, as near as possible to the place where you first refer to it. You can also put all the figures and tables at the end of the paper to avoid breaking up the text. Figures and tables may also be embedded in the text, as long as the text itself isn’t broken up into small chunks. Complex raw data is conventionally presented in an appendix. Be sure to check on conventions for the placement of figures and tables in your discipline.

You can use text to guide the reader in interpreting the information included in a figure, table, or graph—tell the reader what the figure or table conveys and why it was important to include it.

When referring to tables and graphs from within the text, you can use:

• Clauses beginning with “as”: “As shown in Table 1, …”
• Passive voice: “Results are shown in Table 1.”
• Active voice (if appropriate for your discipline): “Table 1 shows that …”
• Parentheses: “Each sample tested positive for three nutrients (Table 1).”

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

American Psychological Association. 2010. Publication Manual of the American Psychological Association . 6th ed. Washington, DC: American Psychological Association.

Bates College. 2012. “ Almost everything you wanted to know about making tables and figures.” How to Write a Paper in Scientific Journal Style and Format , January 11, 2012. http://abacus.bates.edu/~ganderso/biology/resources/writing/HTWtablefigs.html.

Cleveland, William S. 1994. The Elements of Graphing Data , 2nd ed. Summit, NJ: Hobart Press..

Council of Science Editors. 2014. Scientific Style and Format: The CSE Manual for Authors, Editors, and Publishers , 8th ed. Chicago & London: University of Chicago Press.

University of Chicago Press. 2017. The Chicago Manual of Style , 17th ed. Chicago & London: University of Chicago Press.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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Your Guide to Creating Effective Tables and Figures in Research Papers

Research papers are full of data and other information that needs to be effectively illustrated and organized. Without a clear presentation of a study's data, the information will not reach the intended audience and could easily be misunderstood. Clarity of thought and purpose is essential for any kind of research. Using tables and figures to present findings and other data in a research paper can be effective ways to communicate that information to the chosen audience.

When manuscripts are screened, tables and figures can give reviewers and publication editors a quick overview of the findings and key information. After the research paper is published or accepted as a final dissertation, tables and figures will offer the same opportunity for other interested readers. While some readers may not read the entire paper, the tables and figures have the chance to still get the most important parts of your research across to those readers.

However, tables and figures are only valuable within a research paper if they are succinct and informative. Just about any audience—from scientists to the general public—should be able to identify key pieces of information in well-placed and well-organized tables. Figures can help to illustrate ideas and data visually. It is important to remember that tables and figures should not simply be repetitions of data presented in the text. They are not a vehicle for superfluous or repetitious information. Stay focused, stay organized, and you will be able to use tables and figures effectively in your research papers. The following key rules for using tables and figures in research papers will help you do just that.

Check style guides and journal requirements

The first step in deciding how you want to use tables and figures in your research paper is to review the requirements outlined by your chosen style guide or the submission requirements for the journal or publication you will be submitting to. For example, JMIR Publications states that for readability purposes, we encourage authors to include no more than 5 tables and no more than 8 figures per article. They continue to outline that tables should not go beyond the 1-inch margin of a portrait-orientation 8.5"x11" page using 12pt font or they may not be able to be included in your main manuscript because of our PDF sizing.

Consider the reviewers that will be examining your research paper for consistency, clarity, and applicability to a specific publication. If your chosen publication usually has shorter articles with supplemental information provided elsewhere, then you will want to keep the number of tables and figures to a minimum.

According to the Purdue Online Writing Lab (Purdue OWL), the American Psychological Association (APA) states that Data in a table that would require only two or fewer columns and rows should be presented in the text. More complex data is better presented in tabular format. You can avoid unnecessary tables by reviewing the data and deciding if it is simple enough to be included in the text. There is a balance, and the APA guideline above gives a good standard cutoff point for text versus table. Finally, when deciding if you should include a table or a figure, ask yourself is it necessary. Are you including it because you think you should or because you think it will look more professional, or are you including it because it is necessary to articulate the data? Only include tables or figures if they are necessary to articulate the data.

Table formatting

Creating tables is not as difficult as it once was. Most word processing programs have functions that allow you to simply select how many rows and columns you want, and then it builds the structure for you. Whether you create a table in LaTeX , Microsoft Word , Microsoft Excel , or Google Sheets , there are some key features that you will want to include. Tables generally include a legend, title, column titles, and the body of the table.

When deciding what the title of the table should be, think about how you would describe the table's contents in one sentence. There isn't a set length for table titles, and it varies depending on the discipline of the research, but it does need to be specific and clear what the table is presenting. Think of this as a concise topic sentence of the table.

Column titles should be designed in such a way that they simplify the contents of the table. Readers will generally skim the column titles first before getting into the data to prepare their minds for what they are about to see. While the text introducing the table will give a brief overview of what data is being presented, the column titles break that information down into easier-to-understand parts. The Purdue OWL gives a good example of what a table format could look like:

When deciding what your column titles should be, consider the width of the column itself when the data is entered. The heading should be as close to the length of the data as possible. This can be accomplished using standard abbreviations. When using symbols for the data, such as the percentage "%" symbol, place the symbol in the heading, and then you will not use the symbol in each entry, because it is already indicated in the column title.

For the body of the table, consistency is key. Use the same number of decimal places for numbers, keep the alignment the same throughout the table data, and maintain the same unit of measurement throughout each column. When information is changed within the same column, the reader can become confused, and your data may be considered inaccurate.

Figures in research papers

Figures can be of many different graphical types, including bar graphs, scatterplots, maps, photos, and more. Compared to tables, figures have a lot more variation and personalization. Depending on the discipline, figures take different forms. Sometimes a photograph is the best choice if you're illustrating spatial relationships or data hiding techniques in images. Sometimes a map is best to illustrate locations that have specific characteristics in an economic study. Carefully consider your reader's perspective and what detail you want them to see.

As with tables, your figures should be numbered sequentially and follow the same guidelines for titles and labels. Depending on your chosen style guide, keep the figure or figure placeholder as close to the text introducing it as possible. Similar to the figure title, any captions should be succinct and clear, and they should be placed directly under the figure.

Using the wrong kind of figure is a common mistake that can affect a reader's experience with your research paper. Carefully consider what type of figure will best describe your point. For example, if you are describing levels of decomposition of different kinds of paper at a certain point in time, then a scatter plot would not be the appropriate depiction of that data; a bar graph would allow you to accurately show decomposition levels of each kind of paper at time "t." The Writing Center of the University of North Carolina at Chapel Hill has a good example of a bar graph offering easy-to-understand information:

If you have taken a figure from another source, such as from a presentation available online, then you will need to make sure to always cite the source. If you've modified the figure in any way, then you will need to say that you adapted the figure from that source. Plagiarism can still happen with figures – and even tables – so be sure to include a citation if needed.

Using the tips above, you can take your research data and give your reader or reviewer a clear perspective on your findings. As The Writing Center recommends, Consider the best way to communicate information to your audience, especially if you plan to use data in the form of numbers, words, or images that will help you construct and support your argument. If you can summarize the data in a couple of sentences, then don't try and expand that information into an unnecessary table or figure. Trying to use a table or figure in such cases only lengthens the paper and can make the tables and figures meaningless instead of informative.

Carefully choose your table and figure style so that they will serve as quick and clear references for your reader to see patterns, relationships, and trends you have discovered in your research. For additional assistance with formatting and requirements, be sure to review your publication or style guide's instructions to ensure success in the review and submission process.

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How to Use Tables and Figures effectively in Research Papers

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Table of Contents

Data is the most important component of any research. It needs to be presented effectively in a paper to ensure that readers understand the key message in the paper. Figures and tables act as concise tools for clear presentation . Tables display information arranged in rows and columns in a grid-like format, while figures convey information visually, and take the form of a graph, diagram, chart, or image. Be it to compare the rise and fall of GDPs among countries over the years or to understand how COVID-19 has impacted incomes all over the world, tables and figures are imperative to convey vital findings accurately.

So, what are some of the best practices to follow when creating meaningful and attractive tables and figures? Here are some tips on how best to present tables and figures in a research paper.

Guidelines for including tables and figures meaningfully in a paper:

• Self-explanatory display items: Sometimes, readers, reviewers and journal editors directly go to the tables and figures before reading the entire text. So, the tables need to be well organized and self-explanatory.
• Avoidance of repetition: Tables and figures add clarity to the research. They complement the research text and draw attention to key points. They can be used to highlight the main points of the paper, but values should not be repeated as it defeats the very purpose of these elements.
• Consistency: There should be consistency in the values and figures in the tables and figures and the main text of the research paper.
• Informative titles: Titles should be concise and describe the purpose and content of the table. It should draw the reader’s attention towards the key findings of the research. Column heads, axis labels, figure labels, etc., should also be appropriately labelled.
• Adherence to journal guidelines: It is important to follow the instructions given in the target journal regarding the preparation and presentation of figures and tables, style of numbering, titles, image resolution, file formats, etc.

Now that we know how to go about including tables and figures in the manuscript, let’s take a look at what makes tables and figures stand out and create impact.

How to present data in a table?

For effective and concise presentation of data in a table, make sure to:

• Combine repetitive tables: If the tables have similar content, they should be organized into one.
• Divide the data: If there are large amounts of information, the data should be divided into categories for more clarity and better presentation. It is necessary to clearly demarcate the categories into well-structured columns and sub-columns.
• Keep only relevant data: The tables should not look cluttered. Ensure enough spacing.

Example of table presentation in a research paper

For comprehensible and engaging presentation of figures:

• Ensure clarity: All the parts of the figure should be clear. Ensure the use of a standard font, legible labels, and sharp images.
• Use appropriate legends: They make figures effective and draw attention towards the key message.
• Make it precise: There should be correct use of scale bars in images and maps, appropriate units wherever required, and adequate labels and legends.

It is important to get tables and figures correct and precise for your research paper to convey your findings accurately and clearly. If you are confused about how to suitably present your data through tables and figures, do not worry. Elsevier Author Services are well-equipped to guide you through every step to ensure that your manuscript is of top-notch quality.

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Effective Use of Tables and Figures in Research Papers

Research papers are often based on copious amounts of data that can be summarized and easily read through tables and graphs. When writing a research paper , it is important for data to be presented to the reader in a visually appealing way. The data in figures and tables, however, should not be a repetition of the data found in the text. There are many ways of presenting data in tables and figures, governed by a few simple rules. An APA research paper and MLA research paper both require tables and figures, but the rules around them are different. When writing a research paper, the importance of tables and figures cannot be underestimated. How do you know if you need a table or figure? The rule of thumb is that if you cannot present your data in one or two sentences, then you need a table .

Using Tables

Tables are easily created using programs such as Excel. Tables and figures in scientific papers are wonderful ways of presenting data. Effective data presentation in research papers requires understanding your reader and the elements that comprise a table. Tables have several elements, including the legend, column titles, and body. As with academic writing, it is also just as important to structure tables so that readers can easily understand them. Tables that are disorganized or otherwise confusing will make the reader lose interest in your work.

• Title: Tables should have a clear, descriptive title, which functions as the “topic sentence” of the table. The titles can be lengthy or short, depending on the discipline.
• Column Titles: The goal of these title headings is to simplify the table. The reader’s attention moves from the title to the column title sequentially. A good set of column titles will allow the reader to quickly grasp what the table is about.
• Table Body: This is the main area of the table where numerical or textual data is located. Construct your table so that elements read from up to down, and not across.

Related: Done organizing your research data effectively in tables? Check out this post on tips for citing tables in your manuscript now!

The placement of figures and tables should be at the center of the page. It should be properly referenced and ordered in the number that it appears in the text. In addition, tables should be set apart from the text. Text wrapping should not be used. Sometimes, tables and figures are presented after the references in selected journals.

Using Figures

Figures can take many forms, such as bar graphs, frequency histograms, scatterplots, drawings, maps, etc. When using figures in a research paper, always think of your reader. What is the easiest figure for your reader to understand? How can you present the data in the simplest and most effective way? For instance, a photograph may be the best choice if you want your reader to understand spatial relationships.

• Figure Captions: Figures should be numbered and have descriptive titles or captions. The captions should be succinct enough to understand at the first glance. Captions are placed under the figure and are left justified.
• Image: Choose an image that is simple and easily understandable. Consider the size, resolution, and the image’s overall visual attractiveness.
• Additional Information: Illustrations in manuscripts are numbered separately from tables. Include any information that the reader needs to understand your figure, such as legends.

Common Errors in Research Papers

Effective data presentation in research papers requires understanding the common errors that make data presentation ineffective. These common mistakes include using the wrong type of figure for the data. For instance, using a scatterplot instead of a bar graph for showing levels of hydration is a mistake. Another common mistake is that some authors tend to italicize the table number. Remember, only the table title should be italicized .  Another common mistake is failing to attribute the table. If the table/figure is from another source, simply put “ Note. Adapted from…” underneath the table. This should help avoid any issues with plagiarism.

Using tables and figures in research papers is essential for the paper’s readability. The reader is given a chance to understand data through visual content. When writing a research paper, these elements should be considered as part of good research writing. APA research papers, MLA research papers, and other manuscripts require visual content if the data is too complex or voluminous. The importance of tables and graphs is underscored by the main purpose of writing, and that is to be understood.

Frequently Asked Questions

"Consider the following points when creating figures for research papers: Determine purpose: Clarify the message or information to be conveyed. Choose figure type: Select the appropriate type for data representation. Prepare and organize data: Collect and arrange accurate and relevant data. Select software: Use suitable software for figure creation and editing. Design figure: Focus on clarity, labeling, and visual elements. Create the figure: Plot data or generate the figure using the chosen software. Label and annotate: Clearly identify and explain all elements in the figure. Review and revise: Verify accuracy, coherence, and alignment with the paper. Format and export: Adjust format to meet publication guidelines and export as suitable file."

"To create tables for a research paper, follow these steps: 1) Determine the purpose and information to be conveyed. 2) Plan the layout, including rows, columns, and headings. 3) Use spreadsheet software like Excel to design and format the table. 4) Input accurate data into cells, aligning it logically. 5) Include column and row headers for context. 6) Format the table for readability using consistent styles. 7) Add a descriptive title and caption to summarize and provide context. 8) Number and reference the table in the paper. 9) Review and revise for accuracy and clarity before finalizing."

"Including figures in a research paper enhances clarity and visual appeal. Follow these steps: Determine the need for figures based on data trends or to explain complex processes. Choose the right type of figure, such as graphs, charts, or images, to convey your message effectively. Create or obtain the figure, properly citing the source if needed. Number and caption each figure, providing concise and informative descriptions. Place figures logically in the paper and reference them in the text. Format and label figures clearly for better understanding. Provide detailed figure captions to aid comprehension. Cite the source for non-original figures or images. Review and revise figures for accuracy and consistency."

"Research papers use various types of tables to present data: Descriptive tables: Summarize main data characteristics, often presenting demographic information. Frequency tables: Display distribution of categorical variables, showing counts or percentages in different categories. Cross-tabulation tables: Explore relationships between categorical variables by presenting joint frequencies or percentages. Summary statistics tables: Present key statistics (mean, standard deviation, etc.) for numerical variables. Comparative tables: Compare different groups or conditions, displaying key statistics side by side. Correlation or regression tables: Display results of statistical analyses, such as coefficients and p-values. Longitudinal or time-series tables: Show data collected over multiple time points with columns for periods and rows for variables/subjects. Data matrix tables: Present raw data or matrices, common in experimental psychology or biology. Label tables clearly, include titles, and use footnotes or captions for explanations."

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Figures and tables

Figures and tables (display items) are often the quickest way to  communicate large amounts of complex information that would be complicated to explain in text.

Many readers will only look at your display items without reading the main text of your manuscript. Therefore, ensure your display items can stand alone from the text and communicate clearly your most significant results.

Display items are also important for  attracting readers  to your work. Well designed and attractive display items will hold the interest of readers, compel them to take time to understand a figure and can even entice them to read your full manuscript.

Finally, high-quality display items give your work a  professional appearance . Readers will assume that a professional-looking manuscript contains good quality science. Thus readers may be more likely to trust your results and your interpretation of those results.

When deciding which of your results to present as display items consider the following questions:

• Are there any data that readers might rather see as a display item rather than text?
• Do your figures supplement the text and not just repeat what you have already stated?
• Have you put data into a table that could easily be explained in the text such as simple statistics or p values?

Tables are a concise and effective way to present large amounts of data. You should design them carefully so that you clearly communicate your results to busy researchers.

The following is an example of a well-designed table:

• Clear and concise legend/caption
• Data divided into categories for clarity
• Sufficient spacing between columns and rows
• Units are provided
• Font type and size are legible

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How to clearly articulate results and construct tables and figures in a scientific paper?

The writing of the results section of a scientific paper is very important for the readers for clearly understanding of the study. This review summarizes the rules for writing the results section of a scientific paper and describes the use of tables and figures.

Introduction

Medical articles consist of review articles, case reports, and letters to the editor which are prepared with the intention of publishing in journals related to the medical discipline of the author. For an academician to be able to progress in carreer, and make his/her activities known in the academic environment, require preparation of the protocol of his/her academic research article, and acquiring sufficient information, and experience related to the composition of this article. In this review article, the information related to the writing of the ‘Results’ section, and use of tables, and figures will be presented to the attention of the readers.

Writing the ‘Results’ section

The ‘Results’ section is perhaps the most important part of a research article. In fact the authors will share the results of their research/study with their readers. Renown British biologist Thomas Henry Huxley (1825–1895) indicated his feelings as “The great tragedy of science: the slaying of a beautiful hypothesis by an ugly fact.” which emphasizes the importance of accurately, and impressively written results.

In essence results provide a response for the question” What is found in the research performed?”. Therefore, it is the most vital part of the article. As a priority, while drafting the ‘Results’ section of a manuscript one should not firstly write down methods in the ‘Material and Method’ section. The first sentence should give information about the number of patients who met the inclusion criteria, and thus enrolled in the study. [ 1 ] Besides information about the number of patients excluded from the study, and the reasons for exclusion is very important in that they will enlighten the readers, and reviewers who critically evaluate the manuscript, and also reflect the seriousness of the study. On the other hand, the results obtained should be recorded in chronological order, and without any comments. [ 2 ] In this section use of simple present tense is more appropriate. The findings should be expressed in brief, lucid, and explicable words. The writing style should not be boring for the reader. During writing process of a research article, a generally ill-conceived point is that positive, and significant findings are more important, attractive, and valuable, while negative, and insignificant findings are worthless, and less attractive. A scientific research is not performed to confirm a hypothesis, rather to test it. Not only positive, and significant results are worth writing, on the other hand negative or statistically insignificant result which support fallacy of a widely accepted opinion might be valuable. Therefore, all findings obtained during research should be inclıuded in the ‘Results’ section. [ 1 ]

While writing the ‘Results’ section, the sequence of results, tabulated data, and information which will be illustrated as figures should be definitively indicated. In indicating insignificant changes, do not use expressions as “decreased” or “increased”, these words should be reserved for significant changes. If results related to more than one parameter would be reported, it is appropriate to write the results under the subheading of its related parameter so as to facilitate reading, and comprehension of information. [ 2 ] Only data, and information concerning the study in question should be included in the ‘Results’ section. Results not mentioned in this section should not be included in the ‘Discussion’ and ‘Summary’ sections. Since the results obtained by the authors are cited in the ‘Results’ section, any reference should not be indicated in this section. [ 3 ]

In the ‘Results’ section, numerical expressions should be written in technically appropriate terms. The number of digits (1, 2 or 3 digits) to be written after a comma (in Turkish) or a point (in especially American English) should be determined The number of digits written after the punctuation marks should not be changed all throughout the text. Data should be expressed as mean/median ± standard deviation. Data as age, and scale scores should be indicated together with ranges of values. Absolute numerical value corresponding to a percentage must be also indicated. P values calculated in statistical analysis should be expressed in their absolute values. While writing p values of statistically significant data, instead of p<0.05 the actual level of significance should be recorded. If p value is smaller than 0.001, then it can be written as p <0.01. [ 2 ] While writing the ‘Results’ section, significant data which should be recalled by the readers must be indicated in the main text. It will be appropriate to indicate other demographic numerical details in tables or figures.

As an example elucidating the abovementioned topics a research paper written by the authors of this review article, and published in the Turkish Journal of Urology in the year 2007 (Türk Üroloji Dergisi 2007;33:18–23) is presented below:

“A total of 9 (56.2%) female, and 7 (43.8%) male patients with were included in this study. Mean age of all the patients was 44.3±13.8 (17–65) years, and mean dimensions of the adrenal mass was 4.5±3.4 (1–14) cm. Mean ages of the male, and female patients were 44.1 (30–65), and 42.4 (17–64) years, while mean diameters of adrenal masses were 3.2 (1–5), and 4.5 (1–14) cm (p age =0.963, p mass size =0.206). Surgical procedures were realized using transperitoneal approach through Chevron incision in 1 (6.2%), and retroperitoneal approach using flank incision with removal of the 11. rib in 15 (93.7%) patients. Right (n=6; 37.5%), and left (n=2; 12.5%) adrenalectomies were performed. Two (12.5%) patients underwent bilateral adrenalectomy in the same session because of clinical Cushing’s syndrome persisted despite transsphenoidal hipophysectomy. Mean operative time, and length of the hospital stay were 135 (65–190) min, and 3 (2–6) days, respectively. While resecting 11. rib during retroperitoneal adrenalectomy performed in 1 patient, pleura was perforated for nearly 1.5 cm. The perforated region was drained, and closed intraoperatively with 4/0 polyglyctan sutures. The patient did not develop postoperative pneumothorax. In none of the patients postoperative complications as pneumothorax, bleeding, prolonged drainage were seen. Results of histopathological analysis of the specimens retrieved at the end of the operation were summarized in Table 1 .” Table 1. Histopathological examination results of the patients Histopathological diagnosis Men n (%) Women n (%) Total n (%) Adrenal cortical adenoma 5 (31.3) 6 (37.6) 11 (68.8) Pheochromocytoma 1 (6.2) 1 (6.2) 2 (12.6) Ganglioneuroma 1 (6.2) - 1 (6.2) Myelolipoma - 1 (6.2) 1 (6.2) Adrenal carcinoma - 1 (6.2) 1 (6.2) Total 7 (43.7) 9 (56.2) 16 (100) Open in a separate window

Use of tables, and figures

To prevent the audience from getting bored while reading a scientific article, some of the data should be expressed in a visual format in graphics, and figures rather than crowded numerical values in the text. Peer-reviewers frequently look at tables, and figures. High quality tables, and figures increase the chance of acceptance of the manuscript for publication.

Number of tables in the manuscript should not exceed the number recommended by the editorial board of the journal. Data in the main text, and tables should not be repeated many times. Tables should be comprehensible, and a reader should be able to express an opinion about the results just at looking at the tables without reading the main text. Data included in tables should comply with those mentioned in the main text, and percentages in rows, and columns should be summed up accurately. Unit of each variable should be absolutely defined. Sampling size of each group should be absolutely indicated. Values should be expressed as values±standard error, range or 95% confidence interval. Tables should include precise p values, and level of significance as assessed with statistical analysis should be indicated in footnotes. [ 2 ] Use of abbreviations in tables should be avoided, if abbreviations are required they should be defined explicitly in the footnotes or legends of the tables. As a general rule, rows should be arranged as double-spaced Besides do not use pattern coloring for cells of rows, and columns. Values included in tables should be correctly approximated. [ 1 , 2 ]

As an example elucidating the abovementioned topics a research paper written by the authors of this review article, and published in the Turkish Journal of Urology in the year 2007 (Türk Üroloji Dergisi 2007;33:18–23).is shown in Table 1 .

Most of the readers priorly prefer to look at figures, and graphs rather than reading lots of pages. Selection of appropriate types of graphs for demonstration of data is a critical decision which requires artist’s meticulousness. As is the case with tables, graphs, and figures should also disploay information not provided in the text. Bar, line, and pie graphs, scatter plots, and histograms are some examples of graphs. In graphs, independent variables should be represented on the horizontal, and dependent variables on the vertical axis. Number of subjects in every subgroup should be indicated The labels on each axis should be easily understandable. [ 2 ] The label of the Y axis should be written vertically from bottom to top. The fundamental point in writing explanatory notes for graphs, and figures is to help the readers understand the contents of them without referring to the main text. Meanings of abbreviations, and acronyms used in the graphs, and figures should be provided in explanatory notes. In the explanatory notes striking data should be emphasized. Statistical tests used, levels of significance, sampling size, stains used for analyses, and magnification rate should be written in order to facilitate comprehension of the study procedures. [ 1 , 2 ]

Flow diagram can be utilized in the ‘Results’ section. This diagram facilitates comprehension of the results obtained at certain steps of monitorization during the research process. Flow diagram can be used either in the ‘Results’ or ‘Material and Method’ section. [ 2 , 3 ]

Histopathological analyses, surgical technique or radiological images which are considered to be more useful for the comprehension of the text by the readers can be visually displayed. Important findings should be marked on photos, and their definitions should be provided clearly in the explanatory legends. [ 1 ]

As an example elucidating the abovementioned issues, graphics, and flow diagram in the ‘Results’ section of a research paper written by the authors of this review article, and published in the World Journal of Urology in the year 2010 (World J Urol 2010;28:17–22.) are shown in Figures 1 , and ​ and2 2 .

a The mean SHIM scores of the groups before and after treatment. SHIM sexual health inventory for male. b The mean IPSS scores of the groups before and after treatment. IPSS international prostate symptom score

Flowchart showing patients’ progress during the study. SHIM sexual health inventory for male, IIEF international index of erectile function, IPSS international prostate symptom score, QoL quality of life, Q max maximum urinary flow rate. PRV post voiding residual urine volume

In conclusion, in line with the motto of the famous German physicist Albert Einstein (1879–1955). ‘If you are out to describe the truth, leave elegance to the tailor .’ results obtained in a scientific research article should be expressed accurately, and with a masterstroke of a tailor in compliance with certain rules which will ensure acceptability of the scientific manuscript by the editorial board of the journal, and also facilitate its intelligibility by the readers.

Generate accurate APA citations for free

• Knowledge Base
• Citing tables and figures from other sources in APA Style

Citing Tables and Figures in APA Style | Format & Examples

Published on November 6, 2020 by Jack Caulfield . Revised on December 27, 2023.

When you reprint or adapt a table or figure from another source, the source should be acknowledged in an in-text citation and in your reference list . Follow the format for the source type you took the table or figure from.

You also have to include a copyright statement in a note beneath the table or figure. The example below shows how to cite a figure from a journal article .

Table of contents

Citing tables and figures, including a copyright note, examples from different source types, frequently asked questions about apa style citations.

Tables and figures taken from other sources are numbered and presented in the same format as your other tables and figures . Refer to them as Table 1, Figure 3, etc., but include an in-text citation after you mention them to acknowledge the source.

You should also include the source in the reference list. Follow the standard format for the source type you took the table or figure from.

Prevent plagiarism. Run a free check.

As well as a citation and reference, when you reproduce a table or figure in your own work, you also need to acknowledge the source in a note directly below it.

The image below shows an example of a table with a copyright note.

If you’ve reproduced a table or figure exactly, start the note with “From …” If you’ve adapted it in some way for your own purposes (e.g. incorporating part of a table or figure into a new table or figure in your paper), write “Adapted from …”

This is followed by information about the source (title, author, year, publisher, and location), and then copyright information at the end.

Types of copyright and permission

A source will either be under standard copyright, under a Creative Commons license, or in the public domain. You need to state which of these is the case.

Under standard copyright, you sometimes also need permission from the publisher to reprint or adapt materials. If you sought and obtained permission, mention this at the end of the note.

Look for information on copyright and permissions from the publisher. If you’re having trouble finding this information, consult your supervisor for advice.

• From a journal article
• From a website
• From a book

Copyright information can usually be found wherever the table or figure was published. For example, for a diagram in a journal article , look on the journal’s website or the database where you found the article. Images found on sites like Flickr are listed with clear copyright information.

If you find that permission is required to reproduce the material, be sure to contact the author or publisher and ask for it.

APA doesn’t require you to include a list of tables or a list of figures . However, it is advisable to do so if your text is long enough to feature a table of contents and it includes a lot of tables and/or figures .

A list of tables and list of figures appear (in that order) after your table of contents, and are presented in a similar way.

If you adapt or reproduce a table or figure from another source, you should include that source in your APA reference list . You should also include copyright information in the note for the table or figure, and include an APA in-text citation when you refer to it.

Tables and figures you created yourself, based on your own data, are not included in the reference list.

In most styles, the title page is used purely to provide information and doesn’t include any images. Ask your supervisor if you are allowed to include an image on the title page before doing so. If you do decide to include one, make sure to check whether you need permission from the creator of the image.

Include a note directly beneath the image acknowledging where it comes from, beginning with the word “ Note .” (italicized and followed by a period). Include a citation and copyright attribution . Don’t title, number, or label the image as a figure , since it doesn’t appear in your main text.

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If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Caulfield, J. (2023, December 27). Citing Tables and Figures in APA Style | Format & Examples. Scribbr. Retrieved April 9, 2024, from https://www.scribbr.com/apa-examples/citing-tables-figures/

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Creating Attractive and Effective Figures for Your Academic Paper

Your next academic research article will stand or fall with readers based on your images in your manuscript. Your figures can determine how well you sell your results to other academic researchers, peer reviewers, or a grant funding agency.

Updated on April 12, 2023

“A picture is worth a thousand words”, as the old saying goes. Your next academic research article will stand or fall with readers based on your images in your manuscript. Your figures can determine how well you sell your results to other academic researchers, peer reviewers, or a grant funding agency.

Start with your figures

One of the most common queries we field from researchers is how to start writing a paper . 

The best way to do this is by making figures . 

You’ve got your results: You collected data, analyzed it, and performed statistical analyses. Now you can draw graphs, make tables, or plot out some of your data. These form the basis of your results section which is almost always the first part of a paper to be written.

Each figure you create can then be written about and expanded. You can talk about the main outcomes, the key results, and then give context in a series of sentences grouped together into a single paragraph per figure.

Do not interpret the figures just yet. That comes in the discussion part of your manuscript. Stick to just the results and ‘nothing but the results’. But figures are key to building out your paper from this section. 

Don’t throw away unused figures

You might not end up using all the figures you make in your paper, but you will in other pieces of work, like posters and conference presentations, for example. 

One key rule of thumb in writing and article creation is never throw anything away. Ever. AJE has a fantastic figure and illustration creation service which you can learn more about here .

Before a journal reviewer or colleague even begins reading your paper, they have formed an opinion about the quality of your work. Your figures reflect your overall effort in experimental design, technical execution, and attention to detail.

Writing captions for your figures

Before starting to make your figures, consider your captions. Keep in mind that researchers will not read the whole of your study; rather, they’ll download a PDF or view your paper on their phone and click into the figures one-by-one. This means that you need effective, standalone figure captions that explain exactly that is being talked about in each image. Your figure captions need to be clear and self-explanatory outside of the context of the paper itself.

Fig. 1: Image of liver during normothermic machine perfusion.

Caption : The hepatic artery (HA), portal vein (PV), inferior vena cava (IVC) and common bile duct (CBD) are all cannulated. The gallbladder (GB) is also present although this was often removed during the retrieval process before NMP. This image has been used with consent from the family of the donor.

Note that all necessary information has been included in this figure caption: 

• A short title that summarizes the image
• Clear labels
• Associated acronyms

Include enough information for a reader to just look at this and nothing else.

High quality figures begin in the lab

You need a plan before starting to conduct a piece of research. An analysis. A question you plan to address. 

Well-planned research means figures just ‘pop out’: It’s always going to be clear what kind of illustrations you’ll need to create: Histograms, Box Plots, Line Graphs, Scatter Plots. It doesn’t matter: You need to understand the difference between these and use them effectively in your research. Our handy video webinar explains more.

Use your muse. Think in terms of your controls and the kind of plots you’ll create. Ensure your colors are effective and that your key ideas come across clearly when others view your work.

Types of figures

Basic data presentation types

One of the most common kinds of figures produced in academic research articles is called a ‘ bar graph ’. As shown above, these illustrate normally distributed data with error bars and a range of statistical significance. 

If you don’t have normally distributed data (click here to learn the difference), then you need to create what’s called a ‘box plot’ where you present a median and interquartile range (IQR) instead of error bars.

Scatter plots

A final kind of basic data presentation is a scatter plot : In these, data are plotted across a baseline to clearly and transparently reveal distribution. 

Figures and colors

Color palette choice is very important in such presentationsThings to consider when choosing color for your graphs and tables:

• Who will be reading your article? 
• What kind of conclusions do you want others to draw? 

Avoid desk rejection

Indeed, when presenting any kind of data: Consider your color palette, as well as your scales, trend lines, legends, and labeled axes with units. 

Sounds basic? Well, these are reasons for article rejection. You’d be amazed how many papers get submitted that do not conform to these basic requirements. Check, check, and then check again. Or talk to one of our AJE experts by clicking here .

It’s key to choose a plot type that conveys your message in the simplest and most accurate manner. This could be a bar chart, a scatter or line plot, as we’ve discussed. Show your figures to colleagues and check whether their interpretation of your data matches your intended message. And check our Rules for figure creation, below.

Rules for figure creation

Rule 1 : Know your audience.

A figure speaks to your readers. Think about your message and what you want your figure to report before putting it together.

Rule 2 : Identify your message.

A clear message in a figure is tantamount. Message over beauty as the man said. When you decide which and how many figures you want to show in your paper, decide on one message that you want to communicate with each. This message should ideally represent one of your conclusions or parts of it.

Rule 3 : Work at actual publication size when putting together your figures.

Journals have different guidelines, page sizes and formatting requirements. Have a journal in mind before starting to create a figure. Think about your journal guidelines and print and measure to ensure a good fit when a reader sees the final work. Readers are the most important people in the whole publication process after all.

Rule 4 : Use the correct size and type of font and keep a sense of scale.

Adapt to your medium. Is your figure a 3D PDF or will it be printed in black and white in the journal.

Rule 5 : Don’t clutter.

It’s confusing for readers. The simpler your plot, the easier and quicker your reader will grasp it. In some data visualization programs, the default options are full of clutter: remove everything you can from your plot while still telling the same message.

That’s it! Remember the five key rules for figure creation.

Final thoughts

Whatever your figure shows, there are three key points: 

• Keep the message clear
• Keep it simple
• Following best practices in terms of data presentation

Don’t hesitate to try something new.

The AJE Team

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How to Make Good Figures for Scientific Papers

Creating good figures for scientific publications requires using design best practices to make each figure clearly show the main point of your data story.

This article reviews important design principles that will help you create effective figures. However, if you want step-by-step tutorials on how to create the scientific illustrations and Excel graphs using Adobe Illustrator and PowerPoint, read these articles instead:

• Free Graphical Abstract Templates and Tutorials
• Free Research Poster Templates and Tutorials  

Four Rules to Create High-Quality Figures

The best data visualizations for scientific papers use a combination of good design principles and storytelling that allows the audience to quickly understand the results of a scientific study. Below are four rules that will help you make effective research figures and save you time with the final journal formatting. There are also practical tips on how to find the purpose of your figure and how to apply design best practices to graphs, images, and tables. 

Rule 1: Clearly show the main purpose to your audience

For every graph or figure you create, the first step is to answer the question: what is the purpose of my data? Clearly defining the main purpose of your scientific design is essential so that you can create and format the data in ways that are easy to understand. 

The most common purposes for scientific publications are to explain a process or method, compare or contrast, show a change, or to establish a relationship. Each of these purposes should then lead you to select graph types. For example, if the goal of your figure is to explain a method, you will likely want to choose process-focused graph types such as flow charts, diagrams, infographics, illustrations, gantt charts, timelines, parallel sets, or Sankey diagrams. Below are examples of the most common graph types that you can use for different data purposes. Read more articles to learn how to choose the right data visualizations and data storytelling . 

Rule 2: Use composition to simplify the information

After you define the purpose of your graph or figure, the next step is to make sure you follow composition best practices that make the information clear. Composition best practices include following the journal rules and formatting from left to right, top to bottom, or in a circle. You should also review your designs to remove or adjust distracting data, lines, shadows, and repeated elements. Applying good composition means spending time reviewing your layout and simplifying the story using these techniques.

Data Composition Best Practices:

• Design flow should be left to right, top to bottom, or in a circle 
• Make sure most important data is the focus of the design
• Remove or adjust excess data and text
• Make text easy to read
• Reduce contrast of bold lines
• Remove repeated elements
• Remove shadows 

The example below shows how to design a figure that applies the composition best practices by taking an initial layout of a figure on the left and then use formatting to fill the space, simplify information, and reorder the data to more clearly show the main purpose of the research. 

Follow Science Journal Formatting Requirements:

In order to organize the graphs, charts, and figures, you will also need to know the requirements of the scientific journal. You will need to know the limits of the figure sizes, the maximum number of figures, as well as color, fonts, resolution, and file type requirements. You can find different journal requirements by going to the Journal’s homepage and then finding the link to the author’s guidelines from there. If you Google the journal’s formatting requirements, make sure you find the most up-to-date page.

For example, the academic journal Science allows a maximum of 6 figures and requires that they have a width of 55 mm (single column) or 230 mm (double column). In contrast, the journal Nature only allows 3-4 figures or tables with maximum widths of 89 mm (single column) and 183 mm (double column). If you planned to submit your scientific publication to Nature, you would need to carefully plan which graphs and tables will best tell your scientific story within only four figures.

Rule 3: Use colors or grayscale to highlight the purpose

Color is one of the most powerful data storytelling tools. When used properly, color enhances understanding of your graphs and when used poorly, it can be very distracting. 

Scientific Color Design Tips: 

• If possible, limit your design to 1-2 colors that make the main point of the data stand out from the rest
• Make colors accessible to people with color blindness

The example below shows a graph on the left that has a lot of information about graduation rates for bachelor’s degrees in 2019. The text is small and the color design makes it difficult to understand the main results of the data. One way to improve this figure is to use colors to highlight the main story of the data, which is that private for-profit institutions have a much higher drop-out rate than all other institutions. The figure on the right improves this design using the bold pink color and clearer text to highlight the main point of the dataset.

Rule 4: Refine and repeat until the story is clear

The goal of good figure design is to have your audience clearly understand the main point of your research. That is why the final rule is to spend time refining the figure using the purpose, composition, and color tools so that the final design is clear.

It is normal to make 2-3 versions of a figure before you settle on the final design that works best. I recommend using the three clarity checkpoints below to improve your refinement process. 

Design Clarity Checkpoints:

• Checkpoint 1. Does the figure show the overall story or main point when you hide the text? If not, improve the data visualization designs to more clearly show the main purpose.
• Checkpoint 2. Can you remove or adjust unnecessary elements that attract your attention? Remove repetitive elements, bounding boxes, background colors, extra lines, extra colors, repeated text, shadows/shading, either remove or adjust excess data, and consider moving information to supplementary figures.
• Checkpoint 3. Does the color palette enhance or distract from the story? Limit the use of color and pick a color palette that improves audience understanding of the main purpose of the figure. If the color doesn’t serve an obvious purpose, change to grayscale.

Scientific Figure Design Summary

For every scientific publication, follow the four rules of good scientific figure design to help you create effective graphics that engage and impress your audience:

• Clearly show the main purpose to your audience
• Use composition to simplify the information
• Use colors or grayscale to highlight the main points of the figure
• Refine and repeat the process until the story is clear

Related Content: 

• Best Color Palettes for Scientific Figures and Data Visualizations
• Graphical Abstract Examples with Free Templates
• Free Research Poster Templates and Tutorials
• BioRender Alternatives: Scientific Illustration Software Comparisons

Create professional science figures with illustration services or use the online courses and templates to quickly learn how to make your own designs.

Interested in free design templates and training.

Explore scientific illustration templates and courses by creating a Simplified Science Publishing Log In. Whether you are new to data visualization design or have some experience, these resources will improve your ability to use both basic and advanced design tools.

Interested in reading more articles on scientific design? Learn more below:

Scientific Presentation Guide: How to Create an Engaging Research Talk

Data Storytelling Techniques: How to Tell a Great Data Story in 4 Steps

Best Science PowerPoint Templates and Slide Design Examples

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Home → Academic Writing → Tips On Effective Use Of Tables And Figures In Research Papers

Tips On Effective Use Of Tables And Figures In Research Papers

• December 24, 2022

Several studies, journal guidelines, and discourses on scientific writing affirm the critical role that tables, figures, and graphs (or display items) play in enhancing the quality of manuscripts. Scientific tables and graphs can be utilized to represent sizeable numerical or statistical data in a time- and space-effective manner. Readers are often drawn towards tables and figures, because they perceive it as easy-reading, as compared to reading a verbose account of the same content. They rightly assume that these display items will provide them with a larger amount of information in a shorter time span. 

At the manuscript screening stage, these display items offer reviewers and journal editors a quick overview of the study findings, and once the paper is published, they do the same for readers (some of whom look only at these display items and not at the rest of the manuscript). However, tables and figures only add value to the format of a research report, if they are brief yet sufficiently informative.

These visual elements help authors present detailed results and complex relationships, patterns, and trends clearly and concisely; reduce the length of the manuscript and enhance readers’ understanding of the study results. Therefore, these tools are integral to the format of a research paper because, if clear and well-organized, they speed up the comprehension and interpretation of the study’s findings. 

But while well-presented tables and figures in research papers can efficiently capture and present information, poorly crafted tables and figures can confuse readers and impair the effectiveness of a paper.  To help authors get the balance right, this article presents some essential guidelines to the effective use of tables and figures in research papers. 

Planning your paper: When to use tables and figures in scientific papers

Producing effective tables and figures requires careful planning that begins at the manuscript writing stage itself. Here’s how to go about it:

• First, check out what your target journal has to say on the issue. Some journals limit the number of tables and figures and also have specific guidelines on the design aspects of these display items.
• Next, decide whether to use tables and figures or text to put across key information.(Refer to Table 1 below for help on making this decision.)
• After you’ve decided to use a display item, choose the display item that best fits your purpose based on what you wish readers to focus on and what you want to present (Refer to Table 1 below for more information).
• Finally, follow the best-practice guidelines outlined in section 3 and review the examples presented in section 4 of this paper to ensure that your tables and figures are well-designed.

Table 1: How to choose between tables, figures, and text to present data

Best practices for presentation of tables and figures in scientific papers

General guidelines:

• Ensure that display items are self-explanatory : Some readers (and certainly reviewers and journal editors) turn their attention to the tables and figures before they read the entire text, so these display items should be self-contained.
• Refer, but don’t repeat : Use the text to draw the reader’s attention to the significance and key points of the table/figure, but don’t repeat details. So for example, you could highlight your main finding (e.g., “We found that the treatment was effective in only 24% of the cases, as shown in Figure 1”), but don’t repeat exact values (e.g., “As Table 2 shows, 32% of the subjects chose Option 1, 12% chose Option 2, 10% chose Option 3, and 46% chose Option 4”). This defeats the very purpose (efficiency and clarity) of having a table or figure. 
• Be consistent : Ensure consistency between values or details in a table (e.g., abbreviations, group names, treatment names) and those in the text. 
• Give clear, informative titles : Table and figure titles should not be vague but should concisely describe the purpose or contents of the table/figure and should ideally draw the reader’s attention to what you want him/her to notice (e.g., Advantages and disadvantages of using sleep therapy with patients suffering from schizophrenia). Also ensure that column heads, axis labels, figure labels, etc., are clearly and appropriately labelled.
• Adhere to journal guidelines : Check what your target journal has to say about issues like the number of tables and figures, the style of numbering, titles, image resolution, file formats, etc., and follow these instructions carefully. 

Guidelines for tables:

• Combine repetitive tables : Tables and figures that present repetitive information will impair communication rather than enhance it. Examine the titles of all your tables and figures and check if they talk about the same or similar things. If they do, rethink the presentation and combine or delete the tables/graphs.
• Divide the data : When presenting large amounts of information, divide the data into clear and appropriate categories and present them in columns titled accurately and descriptively. 
• Watch the extent of data in your tables : If the data you have to present is extensive and would make the tables too cluttered or long, consider making the tables a part of the Appendix or supplemental material.
• De-clutter your table : Ensure that there is sufficient spacing between columns and rows and that the layout does not make the table look too messy or crowded.  

Guidelines for figures:

• Ensure image clarity : Make sure that all the parts of the figure are clear:18 Use standard font; check that labels are legible against the figure background; and ensure that images are sharp.
• Use legends to explain the key message : Figure legends are pivotal to the effectiveness of a figure. Use them to draw attention to the central message as well as to explain abbreviations and symbols.
• Label all important parts : Label the key sections and parts of schematic diagrams and photographs, and all axes, curves, and data sets in graphs and data plots.
• Give specifics : Include scale bars in images and maps; specify units wherever quantities are listed; include legends in maps and schematics; and specify latitudes and longitudes on maps. This section presents one example each of a well-prepared table and a well-designed figure.

Table 2: The table below is taken from a dietary study on chick-rearing macaroni penguins and is an example of an effective table for the following reasons:

• The title clearly describes what the table is about.
• The column heads are descriptive and clearly indicate the nature of the data presented. The data is divided into categories for clarity.
• It is self-contained and can be understood quite well even without reference to the entire paper.
• Superscript letters and notes are used to offer additional, clarifying information.
• Sufficient spacing is present between columns and rows; the layout is clean, and the font is legible.

Examples of an effective figure (graph)

The figure below from a paper on the efficacy of oyster reefs as natural breakwaters, scores on several counts:

• The informative title that immediately tells the reader what to expect in the graph.
• The axes are labeled clearly.
• The key clearly identifies what each element in the graph stands for.
• A figure legend at the bottom draws the reader’s attention to the graph’s key points.
• A note at the bottom acknowledges the source.
• The graph is 2-dimensional, with no clutter.    

Figures and tables, or display items, are powerful communication tools—they give your manuscript a professional feel, attract and sustain the interest of readers, and efficiently present large amounts of complex information. Moreover, as most journals editors and reviewers will glance at these display items before they begin a full reading of your paper, their importance cannot be overemphasized. 

Keep striving, researchers! ✨

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Preparing your figures for research papers

Often a research paper is embedded with loads of data and complex results and it might not be viable to include all them in the space-constrained paper. Hence, this calls for effective presentation of the information in the form of figures or diagrams. In fact, figures are the most powerful tools that leave a strong visual impact for both reviewers and readers.

Here are few tips on how you can improve the presentation of figures in your research paper.

• Ensure that the components of the figures are clearly visible including the lines and text.
• Always use a standard font style and size for the figure text.
• Every figure needs to have a legend. The legend should support your figure entirely. The reader should be able to understand your figure, paired with its legend, without going to the results or method sections.
• All abbreviations in the figure legends need to be consolidated and spelt out.
• All parts of the figure need to be labelled. The symbols, lines, colors, abbreviations, error bars, scale bars, and other components need to be defined and described properly.
• If you are using photographs of your human subjects, don’t forget to obtain an informed signed consent for the same.
• Do not be afraid to use lengthy figure and table captions—better that than confusing or incomplete ones.
• Do not forget to cite the figure that has been taken from another source and supports your present study. Use the same citation style throughout the paper.
• All journals have their specific requirements for formatting figures, such as file format, font size, font style, image resolution, style of numbering, etc. Adhere to these guidelines before submission. You can  learn more about fonts and styles if you are not familiar with them.
• Cite figures in the main text at the appropriate place where the text is supported by a particular figure.

The figures in your research paper communicate a parallel story to the reader. In fact, the reader can derive a fairly good idea of your paper by just scanning the figures in the paper. Remember that figures are not just tools to beautify your text; they are the heart of your research and an intrinsic part of your research paper. This highlights the importance of organizing the figures well so that they are able to perform as an excellent prop for your text.

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FigureSeer: Parsing Result-Figures in Research Papers

• Conference paper
• First Online: 16 September 2016
• Cite this conference paper

• Noah Siegel 17 , 18 ,
• Zachary Horvitz 17 , 18 ,
• Roie Levin 17 , 18 ,
• Santosh Divvala 17 , 18 &
• Ali Farhadi 17 , 18  

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9911))

Included in the following conference series:

• European Conference on Computer Vision

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75 Citations

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‘Which are the pedestrian detectors that yield a precision above 95 % at 25 % recall?’ Answering such a complex query involves identifying and analyzing the results reported in figures within several research papers. Despite the availability of excellent academic search engines, retrieving such information poses a cumbersome challenge today as these systems have primarily focused on understanding the text content of scholarly documents. In this paper, we introduce FigureSeer, an end-to-end framework for parsing result-figures, that enables powerful search and retrieval of results in research papers. Our proposed approach automatically localizes figures from research papers, classifies them, and analyses the content of the result-figures. The key challenge in analyzing the figure content is the extraction of the plotted data and its association with the legend entries. We address this challenge by formulating a novel graph-based reasoning approach using a CNN-based similarity metric. We present a thorough evaluation on a real-word annotated dataset to demonstrate the efficacy of our approach.

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ReViz: A Tool for Automatically Generating Citation Graphs and Variants

• Figure Content
• Figure Classification
• Annotate Dataset
• Figure Extraction

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

1 Computer Vision for Scholarly Big Data

Academic research is flourishing at an unprecedented pace. There are already over 100 million papers on the web [ 1 ] and many thousands more are being added every month [ 2 ]. It is a Sisyphean ordeal for any single human to cut through this information overload and be abreast of the details of all the important results across all relevant datasets within any given area of research. While academic-search engines like Google Scholar, CiteSeer, etc., are helping us discover relevant information with more ease, these systems are inherently limited by the fact that their data mining and indexing is restricted to the text content of the papers.

Research papers often use figures for reporting quantitative results and analysis, as figures provide an easy means for communicating the key experimental observations [ 3 ]. In many cases, the crucial inferences from the figures are often not explicitly stated in text (as humans can easily deduce them visually) [ 4 ]. Therefore failing to parse the figure content poses a fundamental limitation towards discovering important citations and references. This paper presents FigureSeer, a fully-automated framework for unveiling the untapped wealth of figure content in scholarly articles (see Fig.  1 ).

FigureSeer is an end-to-end framework for parsing result-figures in research papers. It automatically localizes figures, classifies them, and analyses their content (center). FigureSeer enables detailed indexing, retrieval, and redesign of result-figures, such as highlighting specific results (top-left), reformatting results (bottom-left), complex query answering (top-right), and results summarization (bottom-right).

Why is figure parsing hard? Given the impressive advances in the analysis of natural scene images witnessed over the past years, one may speculate that parsing scholarly figures is a trivial endeavor. While it is true that scholarly figures are more structured than images of our natural world, inspecting the actual figure data exposes a plethora of complex vision challenges:

Strict requirements : Scholarly figures expect exceptional high-levels of parsing accuracy unlike typical natural image parsing tasks. For example, in Fig.  2 (c), even a small error in parsing the figure plot data changes the ordering of the results, thereby leading to incorrect inferences.

High variation : The structure and formatting of scholarly figures varies greatly across different papers. Despite much research in engendering common design principles, there does not seem to be a consensus reached yet [ 5 , 6 ]. Therefore different design conventions are employed by authors in generating the figures, thereby resulting in wide variations (see Fig.  2 ).

Heavy clutter and deformation : Even in the best case scenario, where figures with a common design convention are presented, there still remains the difficulty of identifying and extracting the plot data amidst heavy clutter, deformation and occlusion within the plot area. For example, in Fig.  2 (d), given just the legend symbol template for ‘ \(h_3\) LM-HOP availability’ method, extracting its plot data is non-trivial due to the heavy clutter and deformation (also see Fig.  4 ).

While color is an extremely valuable cue for discriminating the plot data, it may not always be available as many figures often reuse similar colors (see Fig.  2 ), and many older papers (even some new ones [ 7 , 8 ]) are published in grayscale. Moreover, unlike natural image recognition tasks where desired amount of labeled training data can be obtained to train models per category, figure parsing has the additional challenge where only a single exemplar (i.e., the legend symbol) is available for model learning. All these challenges have discouraged contemporary document retrieval systems from harvesting figure content other than simple meta-data like caption text.

There is high variation in the formatting of figures: some figures position the legend within the plot area, while others place it outside. Within the legend, some figures have symbols on the right of the text, while others on the left. The presence of heavy occlusions and deformations also poses a challenge.

Overview: The primary focus of our work is to parse result-figures within research papers to help improve search and retrieval of relevant information in the academic domain. The input to our parsing system is a research paper in .pdf format and the output is a structured representation of all the results-figures within it. The representation includes a detailed parse of each figure in terms of its axes, legends, and their corresponding individual plot data. We focus our attention on result-figures as they summarize the key experimental analysis within a paper. More specifically, within our corpus of papers, we found 2D-graphical plots plotting continuous data (such as precision-recall, ROC curves, etc.) to be most popular and frequent.

In this paper, we present a novel end-to-end framework that automatically localizes all figures from a research paper, classifies them, and extracts the content of the result-figures. Our proposed approach can localize a variety of figures including those containing multiple sub-figures, and also classify them with great success by leveraging deep neural nets. To address the challenges in parsing the figure content, we present a novel graph-based reasoning approach using convolutional neural network (CNN) based similarity functions. Our approach is attractive as it not only handles the problems with clutter and deformations, but is also robust to the variations in the figure design. As part of this work, we also introduce thorough evaluation metrics, along with a fully-annotated real-world dataset to demonstrate the efficacy of our parsing approach. Finally, to demonstrate the potential unleashed by our approach, we present a query-answering system that allows users to query figures and retrieve important information.

In summary, our key contributions are: (i) We introduce and study the problem of scholarly figure parsing. (ii) We present a novel end-to-end framework that automatically localizes figures, classifies them, and analyzes their content. (iii) We present a thorough evaluation on a real-word dataset to demonstrate the efficacy of our approach. (iv) We demonstrate the utility of our parsing approach by presenting a query-answering system that enables powerful search and retrieval of results in research papers using rich semantic queries. (v) Finally, we release a fully-annotated dataset, along with a real-world end-to-end system for spurring further research. We hope our work will help kick-start the challenging domain of vision for scholarly big data.

2 Related Work

Figure Extraction and Classification Localizing objects within natural images is a well-studied problem in computer vision. However, localizing figures within research papers has only recently become an area of interest. While many ‘off-the-shelf’ tools exist that can extract embedded images from .pdf files [ 9 ], these tools neither extract vector-graphic based images nor the associated captions of the figures. Recent works [ 10 – 12 ] have explored figure extraction by processing the PDF primitives. The work of [ 11 ] is interesting as it extracts a wide variety of figures along with their captions. In this paper, we build upon this work by augmenting their method with sub-figure localization.

Classifying scholarly figures has also recently become an area of research interest [ 6 , 13 ]. The work of [ 6 ] used a visual bag-of-words representaiton with an SVM classifier for classifying figures. In this paper, we leverage the recent success of CNNs and present an improved classifier that surpasses the state-of-the-art performance.

Figure Analysis. Much attention in the document analysis community has been devoted towards analyzing the document text content [ 14 – 17 ], but analyzing the figure content within the documents has received relatively little focus. Given the challenges in figure parsing (see Sect.  1 ), most works have either resorted to manual methods [ 18 , 19 ] or restricted their focus to limited domains with strong assumptions [ 6 , 13 , 20 ].

In [ 20 ], graphical plots were assumed to plot only a single variable. Further, rather than extracting the plot data, their focus was limited to recognizing the intended message (e.g., rising trend, falling trend, etc.,) of the plot. [ 6 ] presented a simple method for parsing bar charts. Their method located the bars by extracting connected components and then used heuristics to associate the marks with the axes. While their method achieved impressive results, its focus was limited to bar plots with color and those having a linear-axis scale. Further, their method failed to detect and leverage the legend information. Our proposed method circumvents these limitations, and thereby helps improve the generalizability and robustness of their bar parser as well.

Query-Answering. Challenges with figure parsing have discouraged contemporary document retrieval systems from harvesting the figure content. Most existing academic search engines respond to queries by only using the textual meta-data content about the figures, such as the caption text, or their mentions in the body text [ 17 , 21 – 23 ]. While there exists a few methods that have considered using content from tables [ 15 ], to the best of our knowledge, there does not exist any method to query research papers by understanding figure content.

3 Figure Parsing Approach

Our figure parser first extracts figures from a given .pdf file (Sect.  3.1 ), then segregates the figures (Sect.  3.2 ), and finally analyzes the content of the result-figures (Sect.  3.3 ). Figure  1 (center) gives an overview of our overall framework.

3.1 Figure Extraction

Given the deluge of papers, it is desirable to have a scalable and robust approach for extracting figures. We leverage the work of [ 11 ] for figure extraction where a method for automatically localizing figures (using bounding boxes) along with their captions was presented. Their method analyzes the structure of individual pages by detecting chunks of body text, and then locates the figure areas by reasoning about the empty regions. The method was demonstrated to achieve high accuracy (F1 > 0.9), while being computationally efficient ( \(\sim \) 1 sec/paper).

A key limitation of [ 11 ] is its inability to localize individual figures within a figure containing multiple subfigures. Research papers often employ subfigures to report related sets of experimental results together. Given the frequent use of subfigures, we use an iterative method for separately localizing subfigures. More specifically, given an extracted figure, we iteratively decompose it into subfigures by identifying valid axis-aligned splits using the following criteria: (i) Both resulting regions must have an aspect ratio between \(1:c_1\) and \(c_1:1\) ( \(c_1 = 5\) ); (ii) The ratio of the areas of the resulting regions must be between \(1:c_2\) and \(c_2:1\) ( \(c_2 = 2.5\) ). The first criterion ensures that we avoid splits resulting in extremely narrow subfigures (that often happens by accidentally splitting off an axis or legend label). The second criterion enforces a weak symmetry constraint between the resulting halves (as subfigures are all often approximately of the same size). Our proposed method is simple, efficient and achieves promising results (see supplementary for more details).

3.2 Figure Classification

While graphical plots are the most common result-figures within research papers, there are often other figure types (natural images, flow charts, etc.,) found amongst the extracted figures. Therefore, we use a figure classifier to segregate the different figures and identify the relevant graphical plots. Convolutional Neural Networks (CNNs) have recently emerged as the state-of-the-art for classifying natural image content. Encouraged by the positive results in the domain of natural images, here we study their performance at large-scale figure classification.

We evaluate two network architectures: AlexNet [ 24 ] and ResNet-50 [ 25 ]. Both networks were pretrained on the 1.2 million images from ImageNet [ 26 ] and then fine-tuned for our figure classification task. It is well known that CNNs consume and benefit from large training sets. Sect.  4 describes the dataset collected for training our network.

3.3 Figure Analysis

Given all the segregated graph plots, we next analyze their content to obtain their corresponding detailed structured representation. This involves analyzing the figure axes, the figure legend, and the figure plot-data (see Fig.  3 ).

Our figure analyzer first parses the figure axes, then the legend contents, and finally extracts and associates the plotted data to their legend entries.

Parsing Axes. Parsing the axes involves determining their position, labels, and scales. Detecting the axes position helps in identifying the bounds of the plot area. Therefore we first detect the axes by finding all text boxes within the figure that correspond to the axis tick labels (e.g., ‘0’, ‘0.2’, ‘0.4’, ‘0.6’ on x -axis in Fig.  3 ). This is done by detecting series of (numeric) text boxes aligned in a straight line (representing the axis tick labels). More specifically, the y -axis (or x -axis) is determined by detecting the largest number of (numeric) text boxes that all share a common x (or y ) pixel coordinate, breaking ties by choosing the leftmost qualifying x (or y ) coordinate.

Each axis is almost always associated with a textual label that helps towards the interpretation of the graphical plot (e.g., the label ‘Precision’ for y -axis in Fig.  3 ). Given the common convention of placing the axis label in the immediate vicinity of the axis-tick labels, we detect the y -axis label by identifying the rightmost textbox to the left of the y -axis tick labels, and the x -axis label by finding the highest textbox below the x -axis tick labels.

While most plots use a linear axis scale, it is not uncommon for figures to have a logarithmic scale. Therefore we determine the axis scale (linear, logarithmic) by fitting separate regressors [ 27 ] (linear and exponential link functions) to model the data values, and then pick the model with the lowest deviance under a threshold. The regressors map the axis tick label values to their corresponding pixel coordinate values. These models are in turn used for transforming all the plotted data from their pixel-coordinate scale to their data-coordinate scale.

Parsing Legend. Graphical plots always use a legend as a guide to the symbols used when plotting multiple variables. Typically the legend has entries consisting of ( label , symbol ) pairs, where the labels are the variable names (e.g., ‘classifier only’ in Fig.  3 ) and the symbols give an example of its appearance. As highlighted in Sect.  1 , there is huge variation in the placement and format of legends across figures. Legend entries may either be arranged vertically, horizontally, or in a rectangle, and they may be found either outside the plot area or anywhere inside (see illustration in supplementary). Further, the legend symbols may be placed either to the right or left of the legend labels, and may have varying lengths with spaces (e.g. the dashed symbol for ‘classifier only’ in Fig.  3 ). To address this challenge, our legend parser first identifies the legend labels, and then locates their corresponding symbols.

We pose the problem of legend label identification as a text classification problem, i.e., given a text box within the figure, is it a legend label or not? For classification, we use a random-forest classifier [ 28 ] with each textbox represented using a six-dimensional feature \(f=\{t_x,t_y,t_l,t_n,t_{\#v},t_{\#h}\}\) , where \(t_x,t_y\) refer to the normalized x ,  y center coordinates of the text box, \(t_l\) is the text string length, \(t_n\) is a Boolean indicating the text string to be numeric or not, and \(t_{\#v},t_{\#h}\) denote the number of other vertically and horizontally aligned textboxes.

For localizing the symbols s corresponding to the identified legend labels t , we first need to determine their side (i.e., left or right of the text). This is done by generating two candidate rectangular boxes to the left and right of each label \((s_{left}, s_{right})\) with height \(h=t_h\) (i.e., textbox height) and width \(w=k*t_h\) ( \(k=10\) ). Each candidate is then assigned a score corresponding to its normalized non-background pixel density. The candidate scores across all labels on each side (i.e., left or right) are multiplied and the side with the highest score product is chosen. The selected candidate boxes are subsequently cropped to obtain the final symbol bounds (see supplementary for more details).

Parsing Plot-data. Our approach to parsing the plotted data is to formulate it as an optimal path-finding problem: given a legend symbol template s and the extent of the plot area \(W_{n \times m}\) , find its optimum path \(P_s= \{\mathbf {x}_i\}_{i=1}^n = \{(x_i, y_i)\}_{i=1}^n\) , such that the following energy function is optimized:

The unary potential \(\phi _i(\mathbf {x}_i) = \alpha f(\mathbf {x}_i)\) measures the likelihood of a pixel \(\mathbf {x}_i\) to belong to the path given its features \(f(\mathbf {x}_i)\) . The pairwise potential \(\phi _{ij} (\mathbf {x}_i, \mathbf {x}_j) = \beta f(\mathbf {x}_i, \mathbf {x}_j)\) is used to encourage smooth transitions between adjacent pixels ( \(\mathbf {x}_i, \mathbf {x}_j\) ) by setting the pairwise features based on their slope i.e., \(f(\mathbf {x}_i, \mathbf {x}_j) = (y_i - y_j)^2\) . Inference under this model translates to finding the highest scoring path, which can be done using dynamic programming in linear time [ 29 ].

For learning the model weights ( \(\alpha , \beta \) ), we use a rank SVM formulation [ 30 ]. The training examples for the ranker are pairs of the form \((P_s, P'_s)\) with the goal of ranking all sub-optimal paths \(P_s\) to be lower than the ground-truth path \(P'_s\) . A path is defined to be suboptimal if its score (using our evaluation metric as defined in Sect.  5 ) is lower than a threshold. We use a bootstrapping procedure that mines hard negative examples to train the ranker [ 31 ].

Feature representation \(f(\mathbf {x}_i)\) plays a crucial role towards the success of our model. Given the presence of heavy occlusions and deformations of the plotted data, simply convolving the legend symbol s with the plot area W using standard gradient-based features [ 31 ] fails to yield a robust representation (see Fig.  4 ). To address this challenge, we instead derive our feature representation by learning a feature function using CNNs [ 32 , 33 ] that allows us to implicitly model the various patch transformations.

Similarity maps using standard convolution for two different symbols. Simply convolving the symbol template with the plot area fails to discriminate well between the plots. For e.g., the red dashed-line symbol obtains a high response on patches corresponding to the red solid-line. Our approach circumvents this problem by learning similarity functions using CNNs.

We learn an embedding of image patches to a low dimensional feature vector using a Siamese network based on [ 32 ]. Each branch of the network consists of 3 convolutional layers followed by a fully connected layer, with ReLU and max pooling between layers. The input of each branch is a 64  \(\times \)  64 grayscale image patch. The final layer of each branch projects this input to a 256 dimensional feature vector. Each training example consists of a legend symbol patch and a plot patch. The legend symbol patch is generated by padding and/or cropping the annotated legend symbol to 64  \(\times \)  64 pixels. For positive examples, the plot patch is a 64  \(\times \)  64 patch centered on a point on the symbol’s corresponding ground-truth trajectory in the plot area. Negative pairs are obtained by sampling plot patches both randomly and from other symbol trajectories.

The network is trained using a contrastive loss function [ 34 ]. We augment our data by flipping both symbol and plot patches in pairs horizontally. During training, we use two feature networks with the constraint that the two networks share the same parameters. At testing, we use a single network where we independently pass a symbol patch s as well as patches from the plot area W through it and obtain their output representations. The final feature map for the symbol s is then estimated as the \(L_2\) similarity between the output representations.

Along with these CNN-based similarity features, we also use the following pixel-based similarity features to define our unary features \(f(\mathbf {x}_i)\) : (i) symbol convolution : rotationally convolving the symbol patch s with the plot area W , which helps in capturing local visual similarities [ 35 ]; (ii) connected-component size : finding regions within the plot area W having similar connected-component statistics as the symbol patch s , which helps in differentiating patterns of dashes with varying lengths or thickness [ 36 ]; (iii) color match : finding regions in the plot area W that have the same color as the symbol patch s , which helps in differentiating unique colored plots; (iv) breathing : a constant valued feature map, which helps in handling plots whose domain does not cover the full extent of the x-axis (see supplementary materials for more details).

Implementation details : Training our similarity network takes 20 h on a Titan X GPU using Caffe [ 37 ]. Parsing a new figure takes 8 sec on an Intel Xeon E5-1630 CPU, and 40 sec for generating the CNN feature on our GPU.

4 FigureSeer Dataset

The availability of a standardized dataset plays a crucial role in training and evaluating algorithms as well as in driving research in new and more challenging directions. Towards this end, we have built an annotated figure parsing dataset using over 20,000 papers covering five different research areas (CVPR, ICML, ACL, CHI, AAAI) obtained from the 1 million CiteSeerX papers  [ 17 ] indexed by Semantic Scholar  [ 38 ]. Processing the 20,000 papers using the method of [ 11 ] yielded over 60000 figures. All these figures were then annotated using mechanical turk [ 39 ] for their class labels (scatterplot, flowchart, etc.,).

Of all the figures annotated as graph plots, we randomly sampled over 600 figures for further detailed annotations. Labelling the figures with their detailed annotations, i.e., axes, legends, plot data, etc., is a complex and multi-step task, making it more difficult to crowdsource over mechanical turk [ 40 ]. Therefore we trained in-house annotators to label the figures using a custom-made annotation tool. For each figure, the annotators annotated the axes (position, title, scale), the legend (labels, symbols), and the plotted data for each legend entry. Annotating the figures yielded 1272 axes, 2183 legend entries and plots. 55 % of the figures are colored, while 45 % are grayscale. An overview video of our annotation interface as well as our complete annotated dataset is available on our project page.

5 Figure Parsing Results

Figure Classification. We used the 60000 figures from our dataset to study the performance of our network. The figures were randomly split into two equal halves (30000 each) for training and testing. Table  1 summarizes our results in comparison to the previous state of the art system of [ 6 ]. Our best average classification accuracy was 86 % using ResNet-50 [ 25 ], which is significantly higher than the 75 % of [ 6 ].

Figure Analysis. Evaluating figure analysis results is a challenging endeavor as it demands detailed annotation of the figures within research papers. Therefore most previous works have restricted their evaluation to small datasets or manual inspection [ 6 , 20 ]. The availability of our detailed annotated dataset allows thorough analyses of the various components of our approach. We ran figure analysis experiments on the graph-plot figures from our dataset. The figures were randomly split into two halves for training and testing.

Text Identification. Our figure analysis approach needs access to all the text content within the figures (i.e., axis labels, legend labels, etc.,). Given the extensive progress in the OCR community over the past several decades towards the localization and recognition of text in images and documents, we leveraged state of the art OCR engines (Microsoft OCR [ 41 ], Google OCR [ 42 ], Abby [ 43 ]) for text identification. Figure  5 displays a few results of text localization and recognition using Microsoft OCR [ 41 ] on our dataset. While text corresponding to legend labels is often well localized, the text corresponding to axes labels is challenging due to the prevalence of numeric, rotated, sub/superscript, and decimal characters. Our overall accuracy for text recognition was 75.6 % with an F1-score of 60.3 % for text localization. To factor out the effect of OCR errors, we pursued our experiments by using ground truth text-boxes. (See Sect.  7 for results obtained when using text from OCR instead.)

Scholarly figures present a challenge to state of the art OCR: text localization (top row) and recognition (bottom row) results using [ 41 ]. Common errors include (i) missed localizations, e.g., rotated text (left-most, y-axis), numeric text (right-most - ‘2’, ‘4’), (ii) incorrect recognition, e.g., sub/superscripts (left-middle, y-axis), decimals (right-middle - ‘2.2’ as ‘22’), and (iii) false positives, e.g., spurious boxes in plot area.

Qualitative results (Left: original figure, Right: regenerated figure). Top row shows three samples of perfect parses, where our approach understands and regenerates challenging figures. Middle row shows three examples where our parser makes some errors, such as when the input figure violates assumption of being a function, or merges parts of the plots. Bottom row shows failures, such as when figures have multiple y-axis (with superscripts), or have multi-line legends, or have dense plot-data crossings.

For evaluating axis parsing performance, we independently measured the accuracy of our axes position, axes label, and axes scale detection modules. Axes position (i.e., the plot area extent) accuracy is measured by using the standard bounding box overlap-criteria from object detection [ 40 ]. More specifically, we regard a predicted bounding box \(B_p\) for the plot-area to be correct if its intersection-over-union with the ground-truth box \(B_g\) is above 0.5, i.e., \(\frac{B_p \cap B_g}{B_p \cup B_g} > 0.5\) . Under this metric, we obtained an accuracy of 99.2 %. For measuring axes label accuracy, we use the same box overlap criteria and obtained an accuracy of 95.9 %. Finally, for evaluating axes scale, we compute the difference (in pixels) between the predicted and ground-truth axes scales, and regard a prediction to be correct if the difference is below a threshold of 5 %. Under this metric, we achieved an accuracy of 91.6 %.

For evaluating legend parsing performance, we independently measured the accuracy of our legend label detection and symbol detection method using the box overlap-criteria. Under this criteria, our approach obtained an accuracy of 72.6 % for label detection and 72.7 % for symbol detection.

For evaluating plot-data parsing performance, we used the standard F-measure metric [ 44 ] with following statistical definitions: A point \(\mathbf {x}_i\) on the predicted path \(P_s= \{\mathbf {x}_i\}_{i=1}^n = \{(x_i, y_i)\}_{i=1}^n\) is counted as true positive if the normalized difference with the ground-truth \(y'_i\) is below a threshold, i.e., \((y_i - y'_i)<th\) ( \(th=0.02\) in our experiments). A predicted point is counted as false positive if there exists no ground-truth at that position, i.e., \(y'_i = \varnothing \cap y_i \ne \varnothing \) . Similarly, a false negative is recorded when \(y'_i \ne \varnothing \cap y_i = \varnothing \) . A predicted point is counted as both false positive and false negative if the predicted value is outside the threshold. With these definitions, we consider a predicted path to be correct if its \(F_1\) score is above a threshold Th (95 %). Under this conservative metric, our data-parsing approach achieved an accuracy of 26.4 %. Note that for a figure to be considered correct all the lines must be parsed accurately. We also analyzed the importance of the CNN-based similarity features within our path-finding model. Ignoring these CNN features dropped our accuracy to 23.2 %, confirming their utility.

While the above evaluations reveal the component-level performance, we also evaluated our overall figure analysis performance. Our approach obtained an overall accuracy of 17.3 %. Note that several components need to be sequentially accurate for the entire parsing to be considered correct. Figure  6 displays a few qualitative results obtained using our analysis approach. Our approach does an impressive job despite the high structural variations in the figure as well as the presence of heavy deformations in the plotted data.

Evaluation parameters: To study the sensitivity of the chosen evaluation thresholds within our model towards the final performance, we analyzed our results sweeping over varying parameter settings. As displayed in Fig.  7 , the performance is stable across a range of settings.

Evaluation: Our results are robust to the chosen evaluation parameters.

6 Applications: Query Answering

While our proposed figure parsing approach enables a variety of exciting applications (see Fig.  1 ), here we describe a functioning prototype of a query-answering system that allows powerful search and querying of complex figure content across multiple papers. The input to our query-answering system is a templated (textual or numerical) query that requests rich semantic details about a specific dataset. For example, Best method on the LFW dataset? , Best precision at 0.3 recall on BSDS dataset? , etc. The output is a textual response (numerical or string value) obtained by analyzing the parsed content of all figures that match the requested dataset. We assume a simple query representation with a structured template that has two parts: the dataset (e.g., ‘PASCAL VOC detection’, ‘UCI IRIS classification’, etc.,) and the metric (e.g., ‘precision vs. recall’, ‘accuracy vs. #dimensions’, etc.,).

Given a specific query, we first retrieve all relevant figures (across multiple papers) from our corpus that match the requested dataset and metric by searching the figure meta-data (captions). The retrieved figures are then processed using our approach and the parsed content is then collected into a simple data-table representation. Finally, the query is run through the collected data and the requested quantity is extracted. We ran experiments on a collection of over 3,500 textual and numerical queries. The textual queries are formatted such that they request the specific label (amongst those indicated by the legend labels) that is the best (in terms of the y -axis values) either at specific points of the domain (i.e., x -axis values) or the overall domain. Similarly, the numerical queries are formatted such that they request the best y -axis value obtained at specific points or overall domain ( x -axis). (Please see supplementary for more details). Queries were evaluated by comparing the predicted response to the ground-truth.

Table  2 summarizes the results obtained using our approach. Numerical queries are judged correct if the returned value is within 2 % of the correct value. We compare our results to (i) a baseline method that naively picks a response to a query without parsing the plotted data, i.e., by randomly picking one of the classified legend labels; and (ii) a version of our approach that only uses the color-feature representation. Our approach obtains impressive results, thereby reaffirming the potential value it unleashes. Figure  8 displays qualitative results on some examples of queries possible using our answering system.

While we have presented the query-answering system as a proof-of-concept, we highlight a few other exciting potential applications:

Figure captioning: In the pursuit of immediate dissemination of results, authors often miss providing meaningful captions to their figures in papers [ 45 , 46 ]. Our proposed figure parsing approach can help towards automatic caption generation. Our parsed structural representation can be used to not only create simple-templated captions [ 47 ], but also help generate complex summaries [ 48 , 49 ].

Accessibility: Developing interfaces that can provide simple and convenient access to complex information has huge benefits across multiple domains [ 50 , 51 ]. While authors often summarize interesting observations about their figure content in their paper text, the alignment between the figure elements and their corresponding mentions in the body text is currently unavailable [ 52 ]. Our figure parsing approach can help towards bridging this gap, and thereby facilitates the development of richer visualization interfaces [ 53 , 54 ].

Plagiarism detection: Recent years have witnessed a surge in papers reproducing hitherto published results [ 55 ]. Identifying such plagiarized articles is of utmost concern to academic committees and publishers [ 56 ]. Our figure parser can help towards their detection by analyzing and matching their result-figure contents.

Qualitative results demonstrating the utility of our parsing approach towards complex query-answering. Our approach is not only able to successfully parse challenging figures, but also answer interesting queries by summarizing results across multiple papers. Queries in the top row collate plot-data from multiple papers – for e.g., in case of LFW (top-left), our method combined results from 4 different papers: Cao ICCV’13, Chen CVPR’13, Berg CVPR’13 , and Chen ECCV’12 to answer the query.

7 Discussion

With scores of papers being published every year, it is imperative to devise powerful academic search systems that can discover important papers and identify influential citations, thereby alleviating researchers from the enormous information overload. In this paper, we introduced FigureSeer, an end-to-end framework for parsing figures within research papers that enables rich indexing and search of complex result content. We have presented a novel approach for result-figure parsing that not only handles the problems with clutter and deformations of the plotted data, but is also robust to the variations in the format and design of figures. Our experimental analysis has confirmed that figure parsing in scholarly big data is a challenging vision application. We hope our work will spur further exciting research in this domain.

While our current framework is generalizable for parsing a variety of result-figures, it has only scratched the surface with interesting open challenges ahead. OCR is a critical component towards the success of our framework. State-of-the-art and commercial OCR engines have limited success in case of scholarly figures. Table  3 reports results obtained by our framework when using text from OCR. Our preliminary attempts at post-processing the OCR results with deep learning based reasoning only partially redressed these errors. Improving OCR performance by addressing the challenges posed within scholarly figures is an interesting and open future endeavor.

Our plot-data parser currently suffers from successfully parsing the plotted data in presence of heavy clutter (see Fig.  6 , bottom right). Techniques from vascular tracking such as [ 57 ] could be applicable here. Our legend parser currently cannot handle labels spanning multiple lines. Our axes parser assumes the axes scale are either linear or logarithmic, with their tick labels being limited to numeric values. Finally, our figure analysis approach currently models and trains the different components (axes, legend, and plot-data parser) independently. Jointly modeling all the components and training them together within an end-to-end deep network is an exciting endeavor.

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Acknowledgments

This work was in part supported by ONR N00014-13-1-0720, NSF IIS-1338054, and an Allen Distinguished Investigator Award. We thank Isaac Cowhey, Rodney Kinney, Christopher Clark, Eric Kolve, and Jake Mannix for their help in this work.

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Siegel, N., Horvitz, Z., Levin, R., Divvala, S., Farhadi, A. (2016). FigureSeer: Parsing Result-Figures in Research Papers. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science(), vol 9911. Springer, Cham. https://doi.org/10.1007/978-3-319-46478-7_41

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• Published: 08 April 2024

Tumor-selective activity of RAS-GTP inhibition in pancreatic cancer

• Urszula N. Wasko 1 , 2   na1 ,
• Jingjing Jiang 3   na1 ,
• Tanner C. Dalton 1 , 2 ,
• Alvaro Curiel-Garcia   ORCID: orcid.org/0000-0001-6249-3267 1 , 2 ,
• A. Cole Edwards 4 ,
• Yingyun Wang 3 ,
• Bianca Lee 3 ,
• Margo Orlen   ORCID: orcid.org/0000-0002-9834-6282 5 ,
• Sha Tian 6 ,
• Clint A. Stalnecker   ORCID: orcid.org/0000-0002-0570-4416 7 , 8 ,
• Kristina Drizyte-Miller 7 ,
• Marie Menard 3 ,
• Julien Dilly   ORCID: orcid.org/0000-0002-4006-5285 9 , 10 ,
• Stephen A. Sastra 1 , 2 ,
• Carmine F. Palermo 1 , 2 ,
• Marie C. Hasselluhn   ORCID: orcid.org/0000-0001-9765-4075 1 , 2 ,
• Amanda R. Decker-Farrell 1 , 2 ,
• Stephanie Chang   ORCID: orcid.org/0009-0000-2026-5215 3 ,
• Lingyan Jiang 3 ,
• Xing Wei 3 ,
• Yu C. Yang 3 ,
• Ciara Helland 3 ,
• Haley Courtney 3 ,
• Yevgeniy Gindin 3 ,
• Karl Muonio 3 ,
• Ruiping Zhao 3 ,
• Samantha B. Kemp 5 ,
• Cynthia Clendenin   ORCID: orcid.org/0000-0003-4535-2088 11 ,
• Rina Sor   ORCID: orcid.org/0000-0003-2042-5746 11 ,
• William P. Vostrejs   ORCID: orcid.org/0000-0002-1659-0186 5 ,
• Priya S. Hibshman 4 ,
• Amber M. Amparo   ORCID: orcid.org/0000-0003-3805-746X 7 ,
• Connor Hennessey 9 , 10 ,
• Matthew G. Rees   ORCID: orcid.org/0000-0002-2987-7581 12 ,
• Melissa M. Ronan   ORCID: orcid.org/0000-0003-4269-1404 12 ,
• Jennifer A. Roth   ORCID: orcid.org/0000-0002-5117-5586 12 ,
• Jens Brodbeck 3 ,
• Lorenzo Tomassoni 2 , 13 ,
• Basil Bakir 1 , 2 ,
• Nicholas D. Socci 14 ,
• Laura E. Herring   ORCID: orcid.org/0000-0003-4496-7312 15 ,
• Natalie K. Barker 15 ,
• Junning Wang 9 , 10 ,
• James M. Cleary 9 , 10 ,
• Brian M. Wolpin   ORCID: orcid.org/0000-0002-0455-1032 9 , 10 ,
• John A. Chabot 16 ,
• Michael D. Kluger 16 ,
• Gulam A. Manji 1 , 2 ,
• Kenneth Y. Tsai   ORCID: orcid.org/0000-0001-5325-212X 17 ,
• Miroslav Sekulic 18 ,
• Stephen M. Lagana 18 ,
• Andrea Califano 1 , 2 , 13 , 19 , 20 , 21 , 22 , 23 ,
• Elsa Quintana 3 ,
• Zhengping Wang 3 ,
• Jacqueline A. M. Smith   ORCID: orcid.org/0000-0001-5028-8725 3 ,
• Matthew Holderfield 3 ,
• David Wildes   ORCID: orcid.org/0009-0009-3855-7270 3 ,
• Scott W. Lowe   ORCID: orcid.org/0000-0002-5284-9650 6 , 24 ,
• Michael A. Badgley 1 , 2 ,
• Andrew J. Aguirre   ORCID: orcid.org/0000-0002-0701-6203 9 , 10 , 12 , 25 ,
• Robert H. Vonderheide   ORCID: orcid.org/0000-0002-7252-954X 5 , 11 , 26 ,
• Ben Z. Stanger   ORCID: orcid.org/0000-0003-0410-4037 5 , 11 ,
• Timour Baslan 27 ,
• Channing J. Der   ORCID: orcid.org/0000-0002-7751-2747 7 , 8 ,
• Mallika Singh 3 &
• Kenneth P. Olive   ORCID: orcid.org/0000-0002-3392-8994 1 , 2  

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• Pancreatic cancer
• Pharmacodynamics

Broad-spectrum RAS inhibition holds the potential to benefit roughly a quarter of human cancer patients whose tumors are driven by RAS mutations 1,2 . RMC-7977 is a highly selective inhibitor of the active GTP-bound forms of KRAS, HRAS, and NRAS, with affinity for both mutant and wild type (WT) variants (RAS(ON) multi-selective) 3 . As >90% of human pancreatic ductal adenocarcinoma (PDAC) cases are driven by activating mutations in KRAS 4 , we assessed the therapeutic potential of the RAS(ON) multi-selective inhibitor RMC-7977 in a comprehensive range of PDAC models. We observed broad and pronounced anti-tumor activity across models following direct RAS inhibition at exposures that were well-tolerated in vivo . Pharmacological analyses revealed divergent responses to RMC-7977 in tumor versus normal tissues. Treated tumors exhibited waves of apoptosis along with sustained proliferative arrest whereas normal tissues underwent only transient decreases in proliferation, with no evidence of apoptosis. In the autochthonous KPC model, RMC-7977 treatment resulted in a profound extension of survival followed by on-treatment relapse. Analysis of relapsed tumors identified Myc copy number gain as a prevalent candidate resistance mechanism, which could be overcome by combinatorial TEAD inhibition in vitro . Together, these data establish a strong preclinical rationale for the use of broad-spectrum RAS-GTP inhibition in the setting of PDAC and identify a promising candidate combination therapeutic regimen to overcome monotherapy resistance.

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Author information

These authors contributed equally: Urszula N. Wasko, Jingjing Jiang

Authors and Affiliations

Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA

Urszula N. Wasko, Tanner C. Dalton, Alvaro Curiel-Garcia, Stephen A. Sastra, Carmine F. Palermo, Marie C. Hasselluhn, Amanda R. Decker-Farrell, Basil Bakir, Gulam A. Manji, Andrea Califano, Michael A. Badgley & Kenneth P. Olive

Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA

Urszula N. Wasko, Tanner C. Dalton, Alvaro Curiel-Garcia, Stephen A. Sastra, Carmine F. Palermo, Marie C. Hasselluhn, Amanda R. Decker-Farrell, Lorenzo Tomassoni, Basil Bakir, Gulam A. Manji, Andrea Califano, Michael A. Badgley & Kenneth P. Olive

Revolution Medicines, Inc., Redwood City, CA, USA

Jingjing Jiang, Yingyun Wang, Bianca Lee, Marie Menard, Stephanie Chang, Lingyan Jiang, Xing Wei, Yu C. Yang, Ciara Helland, Haley Courtney, Yevgeniy Gindin, Karl Muonio, Ruiping Zhao, Jens Brodbeck, Elsa Quintana, Zhengping Wang, Jacqueline A. M. Smith, Matthew Holderfield, David Wildes & Mallika Singh

Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

A. Cole Edwards & Priya S. Hibshman

University of Pennsylvania Perelman School of Medicine, Department of Medicine, Philadelphia, PA, USA

Margo Orlen, Samantha B. Kemp, William P. Vostrejs, Robert H. Vonderheide & Ben Z. Stanger

Cancer Biology & Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Sha Tian & Scott W. Lowe

Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Clint A. Stalnecker, Kristina Drizyte-Miller, Amber M. Amparo & Channing J. Der

Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Clint A. Stalnecker & Channing J. Der

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

Julien Dilly, Connor Hennessey, Junning Wang, James M. Cleary, Brian M. Wolpin & Andrew J. Aguirre

Harvard Medical School, Boston, MA, USA

University of Pennsylvania Perelman School of Medicine, Abramson Cancer Center, Philadelphia, PA, USA

Cynthia Clendenin, Rina Sor, Robert H. Vonderheide & Ben Z. Stanger

The Broad Institute of Harvard and MIT, Cambridge, MA, USA

Matthew G. Rees, Melissa M. Ronan, Jennifer A. Roth & Andrew J. Aguirre

Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA

Lorenzo Tomassoni & Andrea Califano

Bioinformatics Core, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Nicholas D. Socci

UNC Michael Hooker Proteomics Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Laura E. Herring & Natalie K. Barker

Department of Surgery, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA

John A. Chabot & Michael D. Kluger

Departments of Pathology, Tumor Microenvironment and Metastasis; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

Kenneth Y. Tsai

Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA

Miroslav Sekulic & Stephen M. Lagana

Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

Andrea Califano

J.P. Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA

Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA

Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA

Chan Zuckerberg Biohub New York, New York, NY, USA

Howard Hughes Medical Institute, Chevy Chase, MD, USA

Scott W. Lowe

Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

Andrew J. Aguirre

Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA

Robert H. Vonderheide

Department of Biomedical Sciences, School of Veterinary Medicine, The University of Pennsylvania, Philadelphia, PA, USA

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Correspondence to Mallika Singh or Kenneth P. Olive .

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US History Research Paper Topics: Moments that Shaped a Nation

Table of contents

• 1.1 Interesting US History Topics for Research Paper Before 1877
• 1.2 US History Paper Topics on the Civil War
• 1.3 American History Topics for Research Paper on Industrialization
• 1.4 American History Research Topics on Reconstruction
• 1.5 20th-Century American History Paper Topics
• 1.6 US History Term Paper Topics in World War I and II
• 1.7 American History Paper Topics about the Civil Rights Movement
• 1.8 Native American History Thesis Topics
• 2 Which Topics to Choose for History Research?
• 3 Conclusion: Reflections on America’s Past

Exploring the rich and complex narrative of the United States, this article is designed as a resource for students and researchers embarking on assignments that require a deep dive into American history. Perfect for term papers, thesis projects, and detailed historical analyses, the guide presents a curated selection of interesting US history research paper topics.

• We provide a comprehensive guide for students, researchers, and history enthusiasts seeking engaging and insightful topics for their research papers on American history.
• These topics cover critical eras and events shaping America, from the early days before 1877 to the transformative 20th century.

With these good US history research topics in mind, let’s go over each one in-depth, creating a foundation for smart research and analysis.

List of 160 American History Research Paper Topics

History is a rich and complex subject, ripe for exploration in academic research. Whether you’re a student seeking a topic for an assignment or a history enthusiast looking to delve deeper into America’s past, this list offers a diverse range of subjects. From early colonial times to the modern era, each topic provides a unique lens through which to examine the nation’s history.

Interesting US History Topics for Research Paper Before 1877

• The impact of European colonization on Native American societies.
• The Salem Witch Trials: Causes and effects.
• The role of the Seven Years’ War in shaping early American society.
• The Declaration of Independence: Context and legacy.
• The Articles of Confederation: Strengths and weaknesses.
• The Constitutional Convention of 1787: Key debates and outcomes.
• The Federalist vs. Anti-Federalist debate: Impact on the US Constitution.
• The Louisiana Purchase: Motivations and consequences.
• The War of 1812: Causes, course, and outcomes.
• Manifest Destiny: Ideology and impact on westward expansion.
• The Trail of Tears and Native American Removal Policies.
• The role of slavery in antebellum America.
• The Mexican-American War: Origins and effects.
• The Gold Rush of 1849 and its impact on American expansion.
• The Compromise of 1850 and its role in the lead-up to the Civil War.
• The Dred Scott Decision: Implications and controversy.
• The Underground Railroad: Key figures and operations.
• The election of 1860 and its role in the secession crisis.
• The role of women in antebellum America.
• Early American foreign policy: Principles and practices.

US History Paper Topics on the Civil War

• The causes of the American Civil War: A comprehensive analysis.
• Abraham Lincoln’s presidency and its impact on the Civil War.
• The role of slavery in sparking the Civil War.
• Military strategies of the Union and the Confederacy.
• The Emancipation Proclamation: Intentions and effects.
• Key battles of the Civil War: Gettysburg, Antietam, and others.
• The role of technology in the Civil War.
• The impact of the Civil War on civilian life in the North and South.
• The role of African American soldiers in the Civil War.
• The diplomatic dimensions of the Civil War.
• Reconstruction plans: Lincoln vs. Johnson.
• The assassination of Abraham Lincoln: Impact on post-war America.
• The economic consequences of the Civil War for the South.
• The role of women during the Civil War.
• The Draft Riots of 1863: Causes and impact.
• The impact of the Civil War on American literature and art.
• The role of nurses and medical practices during the Civil War.
• The use of propaganda in the Civil War.
• The transition from slavery to freedom during and after the Civil War.
• The legacy of the Civil War in American memory.

American History Topics for Research Paper on Industrialization

• The Second Industrial Revolution: Key innovations and their impact.
• The rise of American industrial tycoons: Carnegie, Rockefeller, and others.
• The impact of the railroad expansion on American society and economy.
• Urbanization in the late 19th and early 20th centuries.
• Labor movements and strikes of the late 19th century.
• The rise of monopolies and antitrust laws in the United States.
• The impact of immigration on American industrial growth.
• The role of women and children in industrial labor.
• Technological advancements and their societal impact during industrialization.
• The emergence of consumer culture in the late 19th century.
• The environmental impact of industrialization.
• Social Darwinism and its influence on American society.
• The rise of organized labor and the American Federation of Labor.
• The Triangle Shirtwaist Factory fire and its aftermath.
• The Homestead Strike: Causes and consequences.
• The impact of the Industrial Revolution on American agriculture.
• The role of education during the Industrial Revolution.
• Transportation innovations and their impact on American life.
• The evolution of American business practices during industrialization.
• The Gilded Age: Wealth, poverty, and social disparity.

American History Research Topics on Reconstruction

• The Reconstruction Amendments: Impact and limitations.
• Presidential vs. Congressional Reconstruction: A comparative analysis.
• The role of the Freedmen’s Bureau in post-Civil War America.
• Sharecropping and tenant farming: Continuation of slavery by another name?
• The rise and impact of the Ku Klux Klan during Reconstruction.
• The Compromise of 1877 and the end of Reconstruction.
• The Black Codes: Purpose and effects.
• The impeachment of President Andrew Johnson: Causes and consequences.
• The role of African Americans in politics during Reconstruction.
• Economic challenges of the South during Reconstruction.
• The establishment of Historically Black Colleges and Universities (HBCUs).
• The legacy of Reconstruction in the South.
• The Jim Crow laws: Origins and impact.
• The role of women during Reconstruction.
• The Slaughterhouse Cases and their impact on civil rights.
• The Enforcement Acts and their effectiveness in protecting African American rights.
• The impact of Reconstruction on Northern society and politics.
• Education reform in the South during Reconstruction.
• The role of the U.S. military in enforcing Reconstruction policies.
• The long-term effects of Reconstruction on American race relations.

Need help with research paper writing? Get your paper written by a professional writer Get Help Reviews.io 4.9/5

20th-Century American History Paper Topics

• The Progressive Era: Reforms and key figures.
• The impact of World War I on American society and politics.
• The Roaring Twenties: Culture, economics, and politics.
• The Great Depression: Causes and the New Deal response.
• The impact of World War II on the American home front.
• The Cold War: Key events and American foreign policy.
• The Civil Rights Movement: Key figures and legislative milestones.
• The Vietnam War: Causes, course, and impact on American society.
• The Women’s Rights Movement of the 1960s and 1970s.
• The Watergate Scandal and its impact on American politics.
• The Space Race: Technological advancements and Cold War implications.
• The rise of environmentalism in the 20th century.
• The impact of the automobile on 20th-century American life.
• The rise of the American suburbs in the post-World War II era.
• The counterculture of the 1960s and its impact on American society.
• The Reagan Era: Policies and impact on the United States.
• The War on Drugs: Origins, strategies, and consequences.
• The impact of technological advancements on late 20th-century life.
• The rise of the internet and its impact on society and culture.
• The 9/11 attacks and their aftermath on American foreign policy.

US History Term Paper Topics in World War I and II

• The causes and consequences of American entry into World War I.
• The impact of the Treaty of Versailles on post-war America.
• American isolationism between World War I and World War II.
• The Lend-Lease Act and American support for the Allies before entering World War II.
• The attack on Pearl Harbor: Causes and immediate effects.
• The home front during World War II: Women, minorities, and the war effort.
• The role of propaganda in American support for World War II.
• The development and use of the atomic bomb.
• The impact of World War II on American foreign policy.
• The internment of Japanese Americans during World War II.
• The role of African Americans in World War II.
• The D-Day invasion: Planning, execution, and significance.
• The Battle of Midway: Turning point in the Pacific War.
• American military strategy in the European and Pacific theaters.
• The Holocaust and American responses to it.
• The post-war world order and the establishment of the United Nations.
• The GI Bill and its impact on post-war American society.
• The Nuremberg Trials: Legal and moral implications.
• The Marshall Plan and American post-war economic policy.
• The start of the Cold War: Origins and early confrontations.

American History Paper Topics about the Civil Rights Movement

• The Montgomery Bus Boycott: Causes and outcomes.
• The role of Martin Luther King Jr. in the Civil Rights Movement.
• The Little Rock Nine and school desegregation.
• The Freedom Rides: Objectives and impact.
• The Civil Rights Act of 1964: Development and effects.
• The Voting Rights Act of 1965: Importance and consequences.
• The role of women in the Civil Rights Movement.
• The Black Power Movement: Ideals and key figures.
• The impact of the Civil Rights Movement on other minority groups.
• The assassination of Malcolm X: Context and aftermath.
• The Selma to Montgomery marches: Significance and outcomes.
• The role of the NAACP in the Civil Rights Movement.
• The Birmingham Campaign and the use of nonviolent protest.
• The role of the media in shaping public perception of the Civil Rights Movement.
• The Civil Rights Movement in the North: Challenges and Achievements.
• The Economic Bill of Rights proposed by the Poor People’s Campaign.
• The role of music in the Civil Rights Movement.
• The impact of the Civil Rights Movement on American law and society.
• The Student Nonviolent Coordinating Committee (SNCC): Contributions and challenges.
• The legacy of the Civil Rights Movement in contemporary America.

Native American History Thesis Topics

• The impact of European colonization on Native American cultures.
• The Trail of Tears: Causes, course, and consequences.
• Native American resistance movements: King Philip’s War, Pontiac’s Rebellion, and others.
• The impact of the Indian Removal Act of 1830.
• Native American life on reservations in the 19th and 20th centuries.
• The role of Native Americans in American wars.
• The Ghost Dance Movement and the Wounded Knee Massacre.
• Native American boarding schools: Policies and impact on culture.
• The Indian Citizenship Act of 1924 and its implications.
• The American Indian Movement (AIM): Goals and major actions.
• The impact of the Dawes Act on tribal land and culture.
• The role of Native American women in their societies.
• Contemporary Native American issues: Sovereignty, land rights, and cultural preservation.
• The Native American Renaissance: A cultural and literary overview.
• The impact of environmental changes on Native American communities.
• The repatriation of Native American artifacts and remains.
• The role of treaties in Native American history.
• Native American spiritual beliefs and practices.
• The impact of the fur trade on Native American societies.
• Contemporary Native American political activism.

Which Topics to Choose for History Research?

Given the breadth and diversity of US history topics, choosing one to write about can be difficult. To reduce your options, think about your interests and the extent of your investigation. Look for themes that provide a balance of available materials and new perspectives to explore.

When choosing a topic, consider its significance in the larger context of American history. Consider how the topic has influenced or reflected societal, political, or economic trends. For example, topics such as the Civil Rights Movement and World War II provide insights into moments of revolutionary change and struggle.

Also, examine the availability of primary and secondary sources. A well-documented topic provides for a more thorough study and a stronger argument. Always ensure that your chosen topic adheres to the criteria and objectives of your assignment or research aim.

Conclusion: Reflections on America’s Past

In this journey through American history, we have explored various topics that offer a window into the nation’s complex and multifaceted past. From the struggles and triumphs of early American society to the transformative events of the 20th century, these topics provide a foundation for understanding how the United States has evolved. Engaging with these topics enriches our historical knowledge and deepens our understanding of the present. As students, scholars, or simply curious minds, delving into these aspects of America’s past can provide valuable insights and perspectives on the nation’s journey and its ongoing story.

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Wang Feng and Gene Tsudik are named 2024 Guggenheim Fellows (UCI News)

Uc irvine scholars are among 188 recipients of prestigious award this year.

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Irvine, Calif., April 11, 2024 — University of California, Irvine professors Wang Feng and Gene Tsudik have been awarded 2024 Guggenheim Fellowships. They join 186 other American and Canadian scientists and scholars receiving the prestigious grants this year.

Tsudik is a Distinguished Professor of computer science. His research interests include many topics in computer security, privacy and applied cryptography. Some of his recent work is focused on security (especially, malware-resistance) for the burgeoning global ecosystem of so-called Internet of Things devices. He is a Fulbright scholar and a three-time Fulbright specialist. He received the 2017 Outstanding Contribution Award from the Association for Computing Machinery’s Special Interest Group on Security, Audit and Control and the 2020 Jean-Claude Laprie Award from the International Federation for Information Processing. He is also the author of the first crypto-poem published as a refereed paper. Tsudik is the only computer scientist to be awarded a Guggenheim Fellowship this year, and he intends to use his fellowship funding to bootstrap a new line of research on building IoT devices resilient against devastating large-scale malware infestations that have become all too common in recent years.

Read the full story in UCI News .

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