can research paper include graphs

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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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How to Use Tables & Graphs in a Research Paper

It might not seem very relevant to the story and outcome of your study, but how you visually present your experimental or statistical results can play an important role during the review and publication process of your article. A presentation that is in line with the overall logical flow of your story helps you guide the reader effectively from your introduction to your conclusion. 

If your results (and the way you organize and present them) don’t follow the story you outlined in the beginning, then you might confuse the reader and they might end up doubting the validity of your research, which can increase the chance of your manuscript being rejected at an early stage. This article illustrates the options you have when organizing and writing your results and will help you make the best choice for presenting your study data in a research paper.

Why does data visualization matter?

Your data and the results of your analysis are the core of your study. Of course, you need to put your findings and what you think your findings mean into words in the text of your article. But you also need to present the same information visually, in the results section of your manuscript, so that the reader can follow and verify that they agree with your observations and conclusions. 

The way you visualize your data can either help the reader to comprehend quickly and identify the patterns you describe and the predictions you make, or it can leave them wondering what you are trying to say or whether your claims are supported by evidence. Different types of data therefore need to be presented in different ways, and whatever way you choose needs to be in line with your story. 

Another thing to keep in mind is that many journals have specific rules or limitations (e.g., how many tables and graphs you are allowed to include, what kind of data needs to go on what kind of graph) and specific instructions on how to generate and format data tables and graphs (e.g., maximum number of subpanels, length and detail level of tables). In the following, we will go into the main points that you need to consider when organizing your data and writing your result section .

Table of Contents:

Types of data , when to use data tables .

• When to Use Data Graphs 

Common Types of Graphs in Research Papers 

Journal guidelines: what to consider before submission.

Depending on the aim of your research and the methods and procedures you use, your data can be quantitative or qualitative. Quantitative data, whether objective (e.g., size measurements) or subjective (e.g., rating one’s own happiness on a scale), is what is usually collected in experimental research. Quantitative data are expressed in numbers and analyzed with the most common statistical methods. Qualitative data, on the other hand, can consist of case studies or historical documents, or it can be collected through surveys and interviews. Qualitative data are expressed in words and needs to be categorized and interpreted to yield meaningful outcomes. 

Quantitative data example: Height differences between two groups of participants Qualitative data example: Subjective feedback on the food quality in the work cafeteria

Depending on what kind of data you have collected and what story you want to tell with it, you have to find the best way of organizing and visualizing your results.

When you want to show the reader in detail how your independent and dependent variables interact, then a table (with data arranged in columns and rows) is your best choice. In a table, readers can look up exact values, compare those values between pairs or groups of related measurements (e.g., growth rates or outcomes of a medical procedure over several years), look at ranges and intervals, and select specific factors to search for patterns. 

Tables are not restrained to a specific type of data or measurement. Since tables really need to be read, they activate the verbal system. This requires focus and some time (depending on how much data you are presenting), but it gives the reader the freedom to explore the data according to their own interest. Depending on your audience, this might be exactly what your readers want. If you explain and discuss all the variables that your table lists in detail in your manuscript text, then you definitely need to give the reader the chance to look at the details for themselves and follow your arguments. If your analysis only consists of simple t-tests to assess differences between two groups, you can report these results in the text (in this case: mean, standard deviation, t-statistic, and p-value), and do not necessarily need to include a table that simply states the same numbers again. If you did extensive analyses but focus on only part of that data (and clearly explain why, so that the reader does not think you forgot to talk about the rest), then a graph that illustrates and emphasizes the specific result or relationship that you consider the main point of your story might be a better choice.

When to Use Data Graphs

Graphs are a visual display of information and show the overall shape of your results rather than the details. If used correctly, a visual representation helps your (or your reader’s) brain to quickly understand large amounts of data and spot patterns, trends, and exceptions or outliers. Graphs also make it easier to illustrate relationships between entire data sets. This is why, when you analyze your results, you usually don’t just look at the numbers and the statistical values of your tests, but also at histograms, box plots, and distribution plots, to quickly get an overview of what is going on in your data.

Line graphs

When you want to illustrate a change over a continuous range or time, a line graph is your best choice. Changes in different groups or samples over the same range or time can be shown by lines of different colors or with different symbols.

Example: Let’s collapse across the different food types and look at the growth of our four fish species over time.

You should use a bar graph when your data is not continuous but divided into categories that are not necessarily connected, such as different samples, methods, or setups. In our example, the different fish types or the different types of food are such non-continuous categories.

Example: Let’s collapse across the food types again and also across time, and only compare the overall weight increase of our four fish types at the end of the feeding period.

Scatter plots

Scatter plots can be used to illustrate the relationship between two variables — but note that both have to be continuous. The following example displays “fish length” as an additional variable–none of the variables in our table above (fish type, fish food, time) are continuous, and they can therefore not be used for this kind of graph. 

As you see, these example graphs all contain less data than the table above, but they lead the reader to exactly the key point of your results or the finding you want to emphasize. If you let your readers search for these observations in a big table full of details that are not necessarily relevant to the claims you want to make, you can create unnecessary confusion. Most journals allow you to provide bigger datasets as supplementary information, and some even require you to upload all your raw data at submission. When you write up your manuscript, however, matching the data presentation to the storyline is more important than throwing everything you have at the reader. 

Don’t forget that every graph needs to have clear x and y axis labels , a title that summarizes what is shown above the figure, and a descriptive legend/caption below. Since your caption needs to stand alone and the reader needs to be able to understand it without looking at the text, you need to explain what you measured/tested and spell out all labels and abbreviations you use in any of your graphs once more in the caption (even if you think the reader “should” remember everything by now, make it easy for them and guide them through your results once more). Have a look at this article if you need help on how to write strong and effective figure legends .

Even if you have thought about the data you have, the story you want to tell, and how to guide the reader most effectively through your results, you need to check whether the journal you plan to submit to has specific guidelines and limitations when it comes to tables and graphs. Some journals allow you to submit any tables and graphs initially (as long as tables are editable (for example in Word format, not an image) and graphs of high enough resolution. 

Some others, however, have very specific instructions even at the submission stage, and almost all journals will ask you to follow their formatting guidelines once your manuscript is accepted. The closer your figures are already to those guidelines, the faster your article can be published. This PLOS One Figure Preparation Checklist is a good example of how extensive these instructions can be – don’t wait until the last minute to realize that you have to completely reorganize your results because your target journal does not accept tables above a certain length or graphs with more than 4 panels per figure. 

Some things you should always pay attention to (and look at already published articles in the same journal if you are unsure or if the author instructions seem confusing) are the following:

• How many tables and graphs are you allowed to include?
• What file formats are you allowed to submit?
• Are there specific rules on resolution/dimension/file size?
• Should your figure files be uploaded separately or placed into the text?
• If figures are uploaded separately, do the files have to be named in a specific way?
• Are there rules on what fonts to use or to avoid and how to label subpanels?
• Are you allowed to use color? If not, make sure your data sets are distinguishable.

If you are dealing with digital image data, then it might also be a good idea to familiarize yourself with the difference between “adjusting” for clarity and visibility and image manipulation, which constitutes scientific misconduct .  And to fully prepare your research paper for publication before submitting it, be sure to receive proofreading services , including journal manuscript editing and research paper editing , from Wordvice’s professional academic editors .

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Home » Figures in Research Paper – Examples and Guide

Figures in Research Paper – Examples and Guide

Table of Contents

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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Maximizing the Impact of Your Research Paper with Graphs and Charts

The value of visual aids in today’s data-driven study environment cannot be overlooked.

Graphs and charts are effective communication tools that enable academics to convey difficult information to their audience. These visual tools, which range from pie charts to bar graphs, can significantly improve the readability and impact of research articles.

Graphs and charts are indispensable in contemporary research, whether they are used to compare data points, depict trends and patterns, or just break up text-heavy parts.

In this article, the significance of graphs and charts in research papers will be examined, along with their benefits, types of visual aids that are frequently employed, recommended practices for their use, and typical pitfalls to avoid.

By the end of this article, you will have a comprehensive understanding of the role of graphs and charts in research, and how to use them effectively in your next paper.

If you are not well versed with charts and graphs there is a quick fix. Join online c ourses on Data visualization . This will help you learn tricks involved in representing the data in a quick way. If you are still not comfortable the hire a research consultant who will help you in representing the data in a most adorable way. I have written an article on Why Hiring a Research Consultant Can Benefit Your PhD Work? . Please refer the article for further details.

Why add Graphs and Charts to my research paper?

How graphs and charts in research papers are critical, enhance visual appeal and readability of data, convey complex information effectively, enable easy comparison of data points, facilitate understanding of trends and patterns, improved data visualization, enhanced readability, better communication of results, increased credibility, better understanding of data, choosing the right type of graph or chart, making sure the graph or chart is accurate, using clear and concise labelling, adding a title and caption, formatting the graph or chart appropriately, line graphs, scatter plots, best software options for drawing charts and graphs, how do i choose the appropriate scale for my charts and graphs, how do i handle missing data when creating charts and graphs, how to handle huge data sets using charts and graphs, when should i use logarithmic scales in my charts and graphs, how do i ensure that my charts and graphs are accessible to all audiences, including those with disabilities, whether charts and graphs come under copyright protection, what are some common mistakes to avoid when using charts and graphs in research papers, how many graphs and charts should be there in a research paper, what should be the size of graphs and charts in a research paper, can i place charts and graphs at the end of paper instead of in between text, can i place charts and graphs at the end of text as single column instead of two column text, introduction.

Graphs and charts are often used in the Results section of a research paper to visually represent data and findings obtained from experiments or analyses. They may also be included in the Discussion section to support or refute the hypotheses or research questions presented in the Introduction section.

In the Results section, graphs and charts may be used to display statistical analyses such as histograms, scatter plots, and box plots. They can also be used to show trends over time or across different groups, such as line graphs or bar charts. Tables may also be used to present numerical data in a more organized and concise manner.

I have written an article on How to write Results Section of your Research Paper . The article helps you to represent the results in a better fashion, which will in turn increase the chances of paper acceptance.

In the Discussion section, graphs and charts may be used to support the interpretation of the results and to draw conclusions. They may also be used to compare the findings of the current study to previous research or to provide visual examples of the phenomena being studied.

I have written an article on 07 Easy Steps for Writing Discussion Section of a Research Paper . This article will help you in analyzing the charts and graphs to gain better insights.

It is important to note that while graphs and charts can be useful tools in a research paper, they should be used sparingly and only when they add value to the presentation of the data. Too many or poorly designed graphs can make the paper difficult to read and understand.

In research papers, graphs and charts are used to aid in the audience’s comprehension of the material being given. Graphs and charts give the data a visual representation that is simple to comprehend, evaluate, and compare.

Researchers may successfully communicate difficult information using graphs and charts, which increases the impact and accessibility of their findings.

Data from the study are best presented using graphs and charts. They can be used to draw attention to significant patterns and trends in the data, to present information in a comprehensible manner, and to engage viewers.

Graphs and charts can assist you in clearly expressing your ideas and leaving an impact, whether you are summarising data for a research paper or presenting study findings to a big audience.

Advantages of Using Graphs and Charts in Research Papers

The use of graphs and charts in research papers offers many advantages that cannot be achieved through text alone. The following points clearly elaborate on the same.

Long passages of text can be broken up using graphs and charts, which also offer a more understandable visual depiction of the data. Additionally, they can improve the research paper’s aesthetic attractiveness, which will draw readers in and keep them reading.

When given in the form of text or raw statistics, data sets can frequently be convoluted and challenging to comprehend. This information can be made more understandable and easier to interpret for the reader by using graphs and charts. Additionally, they can be used to contrast several data sets, which makes it simpler to spot connections and trends.

Graphs and charts allow researchers to present data in a way that makes it easy to compare different data points. For example, a bar graph can be used to compare the values of different categories, while a line graph can be used to track changes over time.

Data trends and patterns that might not be immediately obvious through text alone might be found using graphs and charts. A histogram, for instance, can be used to see the distribution of data points while a scatter plot can be used to find correlations between two variables. Researchers can more easily make sense of their data by using graphs and charts to better comprehend the underlying patterns and trends.

The Benefits of Using Graphs and Charts in Research Papers

There are many benefits to using graphs and charts in research papers, including:

Graphs and charts can help researchers effectively visualize their data, making it easier for them to see patterns, trends, and relationships within their data. This can help researchers make more informed decisions and draw more accurate conclusions based on their data.

Graphs and charts can make research papers more visually appealing and easier to read. By breaking up long blocks of text, graphs and charts can help to hold the reader’s attention and make the information more engaging.

Graphs and charts can help researchers effectively communicate their results to a variety of audiences. By using visual aids, researchers can effectively convey complex data and ideas in a simple, straightforward manner.

The use of graphs and charts can help to increase the credibility of a research paper. By effectively visualizing their data, researchers can demonstrate that their findings are based on a strong understanding of the data and that their results are robust and reliable.

Graphs and charts can help researchers to better understand their data. By visualizing the data, researchers can identify patterns, relationships, and trends that might not be immediately apparent in raw data or text-based summaries.

By taking advantage of the benefits of using graphs and charts in research papers, researchers can enhance the quality and impact of their research and effectively communicate their findings to a variety of audiences.

Best Practices for Using Graphs and Charts in Research Papers

There are several best practices that researchers should follow when using graphs and charts in their research papers. These include:

It is important to choose the right type of graph or chart to effectively convey the data and results. Researchers should consider the type of data they are working with, the relationships they want to highlight, and the message they want to convey when selecting a graph or chart.

It is important to ensure that the data represented in a graph or chart is accurate and that the graph or chart is properly labelled. Researchers should also be careful to ensure that the scale used in a graph or chart is appropriate for the data being displayed.

Labels should be clear, concise, and accurately describe the data being displayed. Researchers should use labelling to highlight the key points of their data and to make it easy for the reader to understand the message being conveyed.

A title and caption should be included with each graph or chart to provide context and to summarize the key findings. The title should accurately describe the graph or chart, while the caption should provide additional information and context.

The graph or chart should be presented in a clear, uncomplicated, and readable way. In addition to making sure the graph or chart has the right size and placement within the study report, researchers should avoid utilising too many colours or patterns.

Researchers can efficiently utilise graphs and charts to increase the visual appeal and readability of their research papers as well as to properly communicate their data and results by adhering to certain best practises.

Types of Graphs and Charts Commonly used in Research Papers

There are many types of graphs and charts that can be used in research papers, each with their own strengths and uses.

Bar graphs are used to compare the values of different categories or groups. They are best used to display data that is numerical in nature and can be represented in a structured, organized format. Bar graphs can be horizontal or vertical, and can be used to display data in a variety of ways, including grouped bar graphs, stacked bar graphs, and side-by-side bar graphs.

Line graphs are used to track changes over time and to display trends. They consist of a series of points connected by a line and can be used to display data in a variety of ways, including simple line graphs, multiple line graphs, and cumulative line graphs.

Pie charts are used to represent data as a proportion of the whole. They are best used to display data that is categorical in nature and to display the relationships between different categories.

Scatter plots are used to display the relationship between two variables. They consist of a series of points plotted on a set of axes, and can be used to identify correlations between the two variables.

Histograms are used to display the distribution of data. They consist of a series of bars that represent the frequency of data points within a specific range. Histograms are best used to display data that is numerical in nature and to display the distribution of data points over time.

By understanding the different types of graphs and charts, researchers can choose the best visual aid to convey their data and results effectively.

There are several popular software tools for creating graphs and charts for your research paper. These tools are widely used in academia and industry for visualizing data in a visually appealing and professional manner. Here are some of the best software options:

• Microsoft Excel : Excel is a widely used spreadsheet software that comes with a robust charting feature. It allows you to create a wide variety of charts, such as bar charts, line charts, scatter plots, and more. Excel also offers customization options for colors, fonts, and styles to create visually appealing charts.
• MATLAB : MATLAB is a popular software tool used in various fields of research, including engineering, physics, and finance. It has powerful graphing capabilities, with a wide range of plotting functions and customization options. MATLAB also provides advanced data analysis and visualization features, making it suitable for complex research papers.
• R : R is a popular open-source programming language and environment for statistical computing and graphics. It offers extensive libraries for data visualization, such as ggplot2, lattice, and base graphics, which provide a wide range of charting options for creating publication-quality graphs and charts.
• Tableau : Tableau is a powerful data visualization software that provides a user-friendly interface for creating interactive and visually appealing charts and dashboards. It offers a wide range of chart types and customization options, and allows you to connect to various data sources for easy data integration and visualization.
• Adobe Illustrator : Adobe Illustrator is a vector graphics software that provides advanced drawing and design tools for creating high-quality, professional-looking graphs and charts. It offers extensive customization options for colors, fonts, styles, and shapes, allowing you to create visually stunning graphics for your research paper.
• Google Charts : Google Charts is a free web-based tool that allows you to create interactive charts and graphs. It provides a wide range of chart types, such as bar charts, line charts, pie charts, and more, with easy-to-use customization options. Google Charts also offers integration with other Google products, such as Google Sheets, making it convenient for data visualization.

Here’s a comparison of the software tools for drawing graphs and charts:

Note: The cost of these software tools may vary based on different licensing options, usage plans, and academic discounts that may be available.

It’s important to consider factors such as features, customization options, data integration capabilities, interactivity, and cost when choosing the best software for your specific research paper. Depending on your requirements and preferences, you may find one of these software tools more suitable for your needs.

These are some of the best software options for creating graphs and charts for your research paper. Choose the one that best suits your needs and familiarity with the software, and ensure that the resulting graphs and charts are visually appealing and effectively communicate your research findings.

Key factors to consider when choosing the appropriate scale for your charts and graphs, with examples and visual aids:

• Data range: Your chart or graph’s scale should correspond to the range of values in your data. For instance, a bar chart with a scale that only goes up to 1,000 will not accurately depict the full range of the data if the data extends from 0 to 100,000. In this situation, a bigger scale that can hold the entire range of values could be preferable.
• Purpose of the chart or graph: Think about the goal of your graph or chart. Use a smaller scale that zooms in on a specific area of the data if you want to draw attention to a particular trend or pattern. You could wish to zoom in on a certain time period to draw attention to a certain pattern, for instance, if your line chart of temperature trends over time shows trends over time.
• Audience: Consider the audience that your graph or chart is intended to serve. Your data visualisation may need to be more or less explicit and detailed depending on who it is intended for. If you are presenting your study to a general audience, for instance, you might want to use a straightforward bar chart, however, if you are presenting to a more technical audience, you might want to use a more intricate line chart that provides more detail.
• Data distribution: Take your data’s distribution into account. You might want to choose a different scale if your data is skewed or contains outliers in order to better depict the data. For instance, you might wish to use a logarithmic scale if your data are skewed to the right in order to more accurately depict the distribution of the data.

By considering these factors, you can choose an appropriate scale that effectively communicates the data in your chart or graph and enhances the readability and credibility of your research paper.

Handling missing data in charts and graphs can be challenging, but there are several strategies you can use to minimize its impact on the representation of your data:

• Use visual cues: When you have missing data points, you can use visual cues such as dots or a different colour or pattern to indicate the missing information. This helps the reader understand that the data is missing and avoids misleading them with false information.
• Interpolate: In some cases, you may be able to estimate the missing data by interpolating values between two known data points. This can be useful for creating a continuous line chart or graph, but it should be clearly labelled as estimated data.
• Use statistical methods: Statistical methods, such as imputation, can be used to fill in missing data based on patterns in the existing data. This should be done carefully and with caution, as it can introduce bias into the data if not done correctly.
• Leave it out: If the amount of missing data is significant, it may be best to simply exclude it from your charts and graphs. This will avoid giving false impressions of trends or patterns in the data.
• Provide a separate graph or chart: If the missing data is important, you can provide a separate chart or graph that specifically shows the missing data. This allows the reader to see the complete picture, and understand the limitations of the data you are presenting.

When handling missing data, it’s important to be transparent about the methods you used and to clearly label any estimated or imputed data. This will help to ensure the accuracy and reliability of your research paper, and to build trust with your readers.

Handling huge data in charts can be a challenge, but there are several strategies that can help make the data more manageable and easier to understand. Here are some tips for handling huge data in charts:

• Use aggregated data: Aggregating data into categories or grouping similar data points can help reduce the amount of data being displayed and make it easier to understand.
• Filter data: Filtering data to only display relevant information can also help reduce the amount of data in a chart.
• Use multiple charts: If the data is too large to be displayed effectively in a single chart, consider using multiple charts to break down the data into smaller, more manageable parts.
• Use dynamic charts: Dynamic charts, such as interactive line charts or bar charts, allow users to select and view specific data points, making it easier to understand large amounts of data.
• Use colour coding: Color coding data points in a chart can help distinguish between different data sets and make it easier to see trends or patterns.
• Use a smaller time scale: If the data is time-based, consider using a smaller time scale, such as days or weeks instead of months or years, to reduce the amount of data in a chart.
• Use data visualizations: Data visualizations, such as heat maps or treemaps, can help represent large amounts of data in a more manageable and easy-to-understand format.
• Use summary statistics: Summary statistics, such as mean, median, or mode, can help simplify the data and make it easier to understand.
• Use simplifying shapes: Using simplifying shapes, such as circles or squares, can help represent large amounts of data in a way that is easy to understand.
• Consider a combination of methods: Using a combination of the methods above can help effectively handle huge data in charts and make it easier for audiences to understand.

By using these strategies, you can effectively handle huge data in charts and ensure that your data is represented in a clear and concise manner.

A logarithmic scale is a type of scale used in charts and graphs to represent a large range of values in a compact and readable manner. Unlike a linear scale, which represents equal increments of a variable with equal distances, a logarithmic scale represents equal increments of the variable as equal percentages.

The logarithmic scale is particularly useful when dealing with data sets that have an extensive range of values. For example, if a data set has values that range from 1 to 1,000,000, a linear scale would require a very long axis to accommodate all of the values, making it difficult to read and understand. On a logarithmic scale, the axis would be compressed, making it easier to see the trends and patterns in the data.

In research papers, the use of a logarithmic scale can be particularly helpful when dealing with data sets that have a skewed distribution, such as data that has a few extremely large values and many smaller values. By using a logarithmic scale, researchers can better represent the distribution of the data and highlight the trends and patterns that may not be apparent on a linear scale.

It’s important to note that when using a logarithmic scale, the values on the axis are logarithms, not actual values. This means that the increments on the axis represent multiplicative factors, not additive factors. When interpreting a chart with a logarithmic scale, it’s important to consider the scale and understand that the values are represented differently than on a linear scale.

In conclusion, the use of a logarithmic scale can be a powerful tool for researchers when dealing with data sets that have a large range of values. By compressing the axis and representing equal increments of the variable as equal percentages, logarithmic scales can help make data easier to understand and highlight important trends and patterns.

Let’s consider the number of confirmed COVID-19 cases in a country for 10 days. Here is a table representing the data:

As you can see, the logarithmic scale makes it easier to see the relative changes in the number of cases, especially as the values get larger. On a logarithmic scale, equal increments of the number of cases represent equal percentages, rather than equal distances. This allows you to see changes that might not be as noticeable on a linear scale.

To calculate the values for the logarithmic scale, you would take the logarithm (base 10) of each value in the data. Here is an example of how to calculate the logarithm of the value for the 5th day (800 cases):

code log10(800) = 2.903

This means that on a logarithmic scale, the value for the 5th day would be represented as 2.903.

To ensure that your charts and graphs are accessible to all audiences, including those with disabilities, consider the following:

• Use clear and simple language: Use plain language and avoid technical terms when labelling your charts and graphs, to make it easier for everyone to understand the data.
• Provide alternative text: Provide alternative text descriptions for images, including charts and graphs, so that screen readers can describe the content to users with visual impairments.
• Use accessible colours: Avoid using colour as the only means of conveying information, and ensure that the colour contrast between the text and background is high enough to be easily readable by people with colour vision deficiencies.
• Use clear and concise labels: Label the axes and data points clearly and concisely, and include units of measurement where appropriate.
• Use accessible file formats: Save charts and graphs in accessible file formats, such as PDF or SVG, which can be easily read by assistive technology.
• Consider touch and keyboard navigation: Make sure that your charts and graphs are usable for people who navigate the web using touch or keyboard controls, by ensuring that all interactive elements can be operated using keyboard commands.
• Test for accessibility: Test your charts and graphs with assistive technology, such as screen readers, to ensure that they are fully accessible to all users.

By following these guidelines, you can ensure that your charts and graphs are accessible to everyone, regardless of their abilities. This will help to increase the reach and impact of your research paper, and promote greater inclusivity in the scientific community.

It is easier for readers to comprehend complex material when it is presented visually through charts and graphs. It’s crucial to think about whether the charts and graphs you create for a research paper are subject to copyright laws and whether you require permission to use them.

Original works of authorship, such as literary, musical, theatrical, and aesthetic works, are protected by copyright law. If they are made by a person or group and have enough creative expression, charts and graphs might be regarded as original works of authorship.

Research articles frequently utilise charts and graphs that are based on publicly accessible data, such as statistics from the government or data from surveys. 

These kinds of information are typically regarded as being in the public domain and can be utilised without a licence.

Charts and graphs produced by an individual or group, however, and containing a considerable amount of original creative work may be protected by copyright legislation. 

In certain situations, you might need to ask the copyright holder for permission before using the graph or chart in your research report.

When using charts and graphs in a research paper, it’s important to consider the source of the data and whether the chart or graph is protected by copyright law. If you are unsure, it’s always best to err on the side of caution and to obtain permission from the copyright owner before using the chart or graph in your research paper.

When using charts and graphs in research papers, it’s important to avoid common mistakes that can undermine the effectiveness of your data visualization. Here are some common mistakes to watch out for:

• Overcomplicating the visualization: Avoid using too many colours, patterns, or elements in your chart or graph, as this can make it difficult for the reader to understand the data. Stick to simple, clean designs that emphasize the data.
• Ignoring the scale: Be careful when choosing the scale for your chart or graph, as the wrong scale can distort the data and give a false impression of the data.
• Improper labelling: Make sure to label the axes of your chart or graph clearly and accurately, and include units of measurement where appropriate.
• Not using appropriate chart or graph types: Choosing the right chart or graph type is important for effectively communicating the data. For example, if you have categorical data, use a bar chart, not a line chart.
• Ignoring the data distribution: Consider the distribution of your data, and adjust the scale and chart type accordingly. For example, if your data is skewed, you may want to use a logarithmic scale to better represent the data.
• Overloading the chart or graph: Avoid putting too much data into a single chart or graph, as this can make it difficult for the reader to understand the data. Instead, break the data down into multiple charts or graphs as needed.
• Using outdated or irrelevant data: Make sure to use the most up-to-date and relevant data in your charts and graphs, as outdated or irrelevant data can undermine the credibility of your research paper.

By avoiding these common mistakes, you can ensure that your charts and graphs effectively communicate the data.

In conclusion, charts and graphs play a crucial role in visualizing and communicating data in research papers. The use of charts and graphs allows researchers to convey information effectively and efficiently, helping the reader to understand complex data easily. Whether it’s a bar graph, scatter plot, heatmap, or histogram, each type of chart has its unique strengths and weaknesses.

Choosing the right type of chart and using it effectively is crucial to getting your message across in a research paper. Additionally, using a logarithmic scale and ensuring accessibility to all audiences can make your charts and graphs more effective and user-friendly. To make the most of charts and graphs in research, it is important to keep in mind the guidelines, best practices, and common mistakes to avoid.

Frequently Asked Questions

The number of graphs and charts in a research paper can vary depending on the nature of the research, the specific requirements of the paper, and the preferences of the author or the guidelines of the target journal or conference. There is no fixed rule or standard for the exact number of graphs and charts in a research paper. However, it is generally recommended to use graphs and charts judiciously, ensuring that they are relevant, clear, and effectively convey the research findings.

The size of graphs and charts in a research paper should be chosen carefully to ensure that they are clear, readable, and effectively convey the information to the readers. Here are some general guidelines for the size of graphs and charts in a research paper: Legibility: The graphs and charts should be large enough to be easily read and interpreted by the readers, even when printed or displayed at a reduced size. The font size of labels, legends, and annotations should be legible, typically ranging from 10-12 points, depending on the font type. Proportionality: The size of the graphs and charts should be proportional to the available space in the paper and the content being presented. Avoid using excessively small graphs or charts that may be difficult to understand or interpret. Clarity: The graphs and charts should be clear and not overly cluttered. Use appropriate line thicknesses, marker sizes, and bar widths that are visually clear and distinguishable. Avoid overcrowding the graphs or charts with too much information, which may make them difficult to read or interpret. Journal/Conference Guidelines: Follow the guidelines of the target journal or conference for the size of graphs and charts. Some journals or conferences may have specific requirements or recommendations for the size of visuals in research papers. Consistency: Ensure that the size of graphs and charts is consistent throughout the paper. Use a consistent style for fonts, colors, and other graphical elements to maintain a cohesive visual appearance. Accessibility: Consider accessibility requirements, such as ensuring that the size of graphs and charts is suitable for readers with visual impairments. Providing alternative text descriptions for visuals can also enhance accessibility.

Yes, it is common practice in research papers to place charts and graphs at the end of the paper as appendices or as separate sections, especially if they are large or numerous. This can help improve the flow and readability of the main text, as readers can refer to the visuals in the appendices or separate sections as needed without interrupting their reading of the main content.

Yes, you can place charts and graphs at the end of a research paper as single-column visuals, even if the main text is formatted in two columns. However, it’s important to ensure that the placement of visuals at the end of the paper does not disrupt the overall organization and readability of your research paper.

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How to Create Precise Graphs, Diagrams or Images in a Research Paper

According to the American psychologist Howard Gardner, human intelligence can be divided into seven categories: visual-spatial, bodily kinesthetic, musical, interpersonal, intrapersonal, linguistic, and logical-mathematical. This implies our intelligence strengths can be different in each (so-called) intelligence profile and that everybody can be intelligent in many different ways.

Gardner says these differences “challenge an educational system that assumes that everyone can learn the same materials in the same way and that a uniform, universal measure suffices to test student learning.” The truth is that we learn and understand things differently, and these differences affect the manner we read academic papers. A research paper is usually a combination of written and visual information. We can assume that those who have a predominant linguistic intelligence would focus on written information, whereas those with a visual-spatial intelligence would feel more comfortable focusing on graphs, diagrams, or images. How to combine both to achieve a paper that engages readers with different intelligence profiles at par?

The Perfect Combination

The first thing we must understand is that, no matter how much visual support they have, papers are written works. Filling pages with unnecessary images, graphs, diagrams or any other kind of visual material is never a good idea. Remember that you are writing a professional academic paper and, therefore, your capacity to discern which material is important. Once this is clear, it is time to discern which information is likely to be visually demonstrated .

Some main ideas would help you to decide when to use graphs. Choose only information that can be clearer if explained visually, and only if it is so important that you desire the reader to keep focus on it more than in other parts. Besides, this piece of information must be qualitatively or quantitatively measurable.

Images can also be used to summarize; plenty of information can be perfectly summed up in a single graph. Lastly, another reason to use images is comparison. Graphs and diagrams are great tools to indicate the differences between two agents.

Do not fill up your images with too much information because it would complicate the readers’ understanding. Images combine or support the written words, but should not be used to replace them. A good combination of words and images can ease the paper’s general understanding.

Thinking Visually: How to Choose?

It is important to know the possibilities each tool offers. Graphs, for example, are good to express the mathematical relationship or statistical correlation between data. Line graphs are useful to present an evolution, circulant graphs are better to indicate proportional parts and column graphs are commonly used to compare different elements.

Researchers and academics are supposed to have a good command of graphs usage . However, the capacity of selecting which data is most likely to be shown this way makes the difference. Indeed, achieving a good command of these tools is quite difficult, but is possible with experience.

Last but not least, it is always helpful to consider the final goal of an academic paper: communication . Thus, if the graph clearly points to one of the research’s main statements, do not doubt in using it.

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Ultimate Guide on Creating Comprehensive Graphs for Your Research Paper

If you have two minutes to skim through a research paper, what would you go through? Probably the introduction, the conclusion, and? The graphs? That’s right! Graphs are the best way to visually illustrate your research paper. Research papers usually have massive amounts of data and complicated concepts to explain. And, graphs can be used […]

If you have two minutes to skim through a research paper, what would you go through? Probably the introduction, the conclusion, and? The graphs? That’s right!

Graphs are the best way to visually illustrate your research paper. Research papers usually have massive amounts of data and complicated concepts to explain.

And, graphs can be used to represent these data and concepts in a visual, easy to understand manner. Graphs can effectively help to deliver the message that you want to convey.

Also, adding graphs to your research paper can make it much, much more interesting. Something that might be needed in an otherwise monotonous research paper.

Apart from visually representing your data, graphs can also give a concrete angle to the idea of your hypothesis and research paper.

But, simply adding graphs for the sake of it won’t do. Graphs are meant to make your thesis sound more interesting and explain things better. But, you can easily go wrong with it by adding graphs that do not necessarily complement your research paper.

A good graph can make the world of difference to your research paper. It can make the text of your research paper sound more self-explanatory. If you are a student, then this means you’ll get better grades for your research paper.

So, in this ultimate guide, let’s look at some super-helpful tricks and tips on adding comprehensive graphs to your research paper to make them more interesting and appealing.

Know Your Audience

Knowing your primary audience is important to understand before you decide on the types of graphs that you want to add. 

Not everyone understands graphs the same way. So, ensure that you understand your audience first before proceeding with the addition of graphs to your research paper.

Think about who is going to read your research paper. Is it a scientist, preschool teacher, or your neighbor who works in a finance firm? Different types of graphs would be suitable for people with different levels of understanding.

Based on the capacity of your audience and how they will perceive your research paper, you can decide on the types of graphs that best suit them. Here’s a list to help you out:

If your audience is less experienced, then you should go with graphs that are easier to understand without any extensive knowledge about graphs. Here are some such types of graphs:

• Linear graph
• Multi-set bar graph

If your audience is well versed with the basics of your research paper’s topic and is from your industry, then you can go with more advanced types of graphs. Here are some advanced types of graphs which you can use for a more experienced audience:

• Stacked bar graph
• Stacked area graph
• Spiral plot
• Point and figure chart
• Choropleth map
• Candlestick chart

Imagine if you were to go for a Choropleth map for an inexperienced audience for your research paper. It would be a total disaster! Your message would not be delivered as efficiently. 

Similarly, a simple linear graph might seem amateur in a research paper meant for scientists, when the concept could have been explained with the help of a more advanced graph.

Hence, knowing your audience is very important to understand while you write your research paper and create comprehensive graphs for it.

Keep a Check on the Aesthetics

While creating and adding graphs to your research paper, it is also important to think about its aesthetics too. There is no point in adding a graph if it looks messy and unclear. Right from the colors, the fonts, to the placement of your graphs, everything counts.

The use of the right colors can completely transform your graphs and your research paper. Whereas if you add colors that look off-putting, then your audience might not be able to read and understand your graphs well. They might even put down your research paper right away. 

Colors have the ability to enhance your graphs and improve their readability. For instance, if you use different colors to represent different data points, then your graph will instantly look more readable for your audience.

The contrast of colors is also an essential aspect to consider. You should avoid using dark backgrounds with lighter colored fonts and plots. As this will decrease the readability.

As a general rule, have a lighter background and contrasting colored elements. For example, if you use a white background for your graph, do not plot your graphs with yellow-colored lines as these will not be distinguishable. Instead, use darker colored lines.

Fonts of your graphs are also a very crucial element. If you use fonts that are too large, then your graph might look messy.

While, if you use fonts that are too small, then your graph might not be readable. Readers might have to strain their eyes to understand your graph. Hence, use the right size for the fonts in your graphs.

Also, try not to choose fancy fonts that are not easily understandable. Go with the standard serif fonts. You should also format your fonts properly. For example, use bold and italic fonts wherever highlighting is necessary. Do not bold the entire text. Use it selectively.

Icons can be a great addition to your research paper’s graphs. They can make your research paper self explanatory. You can use icons to label charts or graphs. Or you can also use them to label different elements of your graphs.

While using icons, you should, again, think about the color contrast. Your icons should look clear and distinguishable in the background. For example, use a darker color icon on a lighter colored background.

The fonts inside your icons also matter. Whether you design your own fonts, or you use make my graph tools, you must ensure that the fonts inside icons (if any) are readable without having to strain the eyes.

Make Your Readers Visualize Effectively

The point of adding any graph, chart, or illustration to your research paper is to help your readers easily understand your point.

Reading and understanding research papers cannot be everyone’s cup of tea. Hence, you should make persistent efforts to make it as easy-to-understand as possible.

Your audience will be able to best understand your graphs only when they can visualize them effectively. Visualization is easy to say, but difficult to implement.

You need to ensure that your graphs are designed in a way so that they can be visualized effectively. For this, you need to look at your graph with a fresh set of eyes.

If your graph seems a little too complicated, try to dial it down by breaking down a single graph into multiple smaller graphs. Or, you can also combine two or more smaller graphs to represent everything on a single graph, if that seems like a better representation.

Try to understand how your readers, who do not know entirely about your topic, look at the graphs. Will they be able to visualize and understand the message you are trying to convey?

If not, then recreate better graphs. If your graph looks plain, take the help of online make my graph tools that can aid you to create beautiful, comprehensive graphs at supper affordable rates.

If you nail this part, then your research paper will be a great resource for everyone to refer to. And, your graphs and visual representations will also be widely shared in your industry.

Know What You Want to Convey

The message you want to deliver or the concept you want to explain to your readers with the help of graphs is also important to consider. 

What exactly do you not want to convey? Do you want to add a visual representation of data? Do you want to create scientific infographics that easily explain your research paper’s findings? Do you want to visually explain the differences between the two sets of data?

Based on the message that you want to convey, you can create different types of graphs. Here are a few types of graphs that can help you get started:

Hierarchical

Any concept that forms a hierarchy can be easily represented using hierarchical graphs.

You can create beautiful graphs with hierarchical representations using an easy-to-use online graphic maker tool like MindTheGraph.

A histogram is a graph using which you can represent the frequency distribution of a set of continuous data. You can visualize the shape of the data points and their underlying distribution.

If your research paper consists of data specific to geographical locations, then you can represent that result with the help of geospatial data. Geospatial graphs represent geographical locations through latitudes, longitudes, or the names of geographic locations.

Multidimensional

Multidimensional graphs can be used to represent data that can be better visualized and represented in more than two dimensions. These can be very effective in helping your readers effectively visualize your data and findings.

You can easily create beautiful graphs in multidimensional using online infographic maker tools like Mind the Graph.

Here’s an example of a template that you can use for creating a multidimensional infographic for your research paper.

Temporal graphs can be used to represent data that usually change with time. You can plot different data points with one of the axes of your graph as the time axis.

Pie charts are a circular statistical graph that can be used to represent different proportions of data.

Scatter plots

Scatter plots can be used to explain the relationship between two sets of data in a visual format. In scatter plots, dots are used to represent different numerical values.

Timetable charts

Timetable charts are used to represent scientific data in the form of timelines.

Venn diagrams

Venn diagrams are one of the most commonly used formats for visual presentation. Venn diagrams are used to show all possible logical relations amongst a finite group of things. These can be great for representing the similarities and differences between two or more things.

Stacked area graphs

A stacked area chart or graph is an extension of an area chart. Here, several groups of data are represented in the graph by stacking them, one above the other.

Stream graphs

Stream graphs are another form of stacked area graphs. In a stream graph, instead of stacking the layers, one above the other, layers are positioned around a central axis.

Stacked bar graphs

Stacker bar graphs are similar to stacked graphs, except where the same concept is applied to bar graphs. Different groups of data are represented by stacking them in the same bar graph, one above the other.

Parallel sets

Parallel sets are used to represent the flow and proportions of data. These represent data frequencies instead of individual data points.

Multi-set bar charts

Multi-set bar charts, also known as clustered bar charts, are a type of bar chart. Here, two or more data sets are represented side by side for better understanding and interpretation. All the bar charts use a common axis that may be X-axis or Y-axis.

Ask Others to Share Their Feedback

Lastly, a new set of eyes to review and critique your research paper’s graphs is almost as crucial as the process of designing the graphs.

You would probably love and admire the graphs you have created. But others might have a completely different opinion about it. Hence, it is best to get others’ opinions on this.

The best way to go about it is to pick people who most likely fit your primary audience. For example, if your research paper is meant for readers experienced in your industry, then pick someone with the same kind of industry experience.

Ask them to review your graph, analyze their initial reaction, and gather their feedback. Are they able to understand your graphs? Is your graph readable and easy-to-digest without you having to explain it in depth?

Asking and answering these questions will help you understand the most likely reaction from your primary audience. 

If the reviewer finds it difficult to understand, you should consider simplifying our graphs. You can do this either by using a simpler type of graph or using multiple graphs in place of one.

Whereas, if the reviewer finds the graphs unnecessary or amateur, then you might consider using more advanced graphs or removing them altogether.

We can conclude by saying that graphs can make or break your research paper. People can perceive your research paper in a completely different light if you have added the right graphs to it.

Right from selecting the type of graphs, to the colors and fonts to use, everything must be carefully thought upon before you start creating graphs. 

Take your time and create good comprehensive graphs for your research paper. And, by doing that, you will find that people appreciate your work a lot more than they would otherwise.

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• J Postgrad Med
• v.69(3); Jul-Sep 2023
• PMC10394528

Utilizing tables, figures, charts and graphs to enhance the readability of a research paper

Department of Pediatrics, College of Medicine and Health Sciences, National University of Science and Technology, Sohar, Sultanate of Oman

1 Department of Pediatrics, Seth G.S. Medical College and KEM Hospital, Mumbai, Maharashtra, India

Introduction

Every author aims to reach the maximum target audience through his/her research publication/s. Our previous editorials have touched upon the process of writing a quality research paper and its successful publication in an appropriate journal.[ 1 , 2 ] Journal-specific ”Instructions for Authors” generally have defined limits to the text and non-textual content for the benefit of space and presentation. Though the aim of a paper is to get its research point across through methodology, results, and discussion, readers often read the summary of data and analysis (only). Thus, the tables, figures, charts, and graphs are time and space-effective tools that not only help to understand the research presented in a simple manner but also engage and sustain the reader's interest.

Why use tables/figures/charts or graphs?

Reading text matter can often get monotonous – for the readers as well as the editors and reviewers. Using Tables/Figures/Charts or Graphs effectively provides a break from textual content monotony as well as provides an opportunity to process and connect information between text and images, promoting deeper learning. It is suggested that one non-textual element should be used for every 1000 words in a manuscript, which generally amounts to two for every three print pages.[ 3 ] The use of tables/figures/charts/graphs not only reduces the word count but also complements the text effectively. Although the text focuses on explaining findings, outlining trends, and providing contextual information, non-textual content allows readers to understand characteristics, distribution, and relationships between data, and visualize statistics/abstract concepts in a powerful manner. High-quality tables and figures also increase the likelihood of a manuscript being accepted for publication.[ 4 ] Note that the figures/artwork needs to be uploaded as separate files for most of the journals.

The CONSORT statement ( www.equator-ntework.org ) provides guidelines on how to report outcome-specific information in a published clinical trial report; however there are no definite recommendations on how to present non-textual elements, and this varies from one journal to another. Authors tend to prepare them based on their own understanding, often without much thought, and repeat the information presented in the main text. Moreover, while some journals have dedicated editors and resources to redraw or edit figures/tables, others simply publish whatever the authors submit. Thus, to improve the readability of the paper, it is primarily the author's responsibility to submit clear and useful tables, figures, charts, and graphs.

The heart of any research lies in its data, and most readers only get a glimpse of the data via the results. The closest one can get to raw statistics is through data presented in tables, figures, graphs, and supplementary material. Tables, figures, and graphs also help to classify and interpret data, highlight key findings, and present maximum data in a concise space. The author should make a deliberate decision on the presentation of his data early in the writing process. Using a sentence as text is more efficient while presenting up to half a dozen numbers in data or if the information can be summarized in three or lesser sentences.[ 5 ] Figures give an overall picture of concept (but without exact numerical data), while tables present exact values (but are less engaging and less interesting).[ 5 ] The final choice of the presentation depends on the type of data, statistical analysis, and relevant message to be delivered.[ 6 ]

General methodology of design and submission

The general structure followed by most non-textual elements is caption/legend/title, content, and footnotes. All data should be verified thoroughly for errors (especially outliers or unexpected spikes) and data sources should be cited in the footnotes/references. The presentation should be simple and clear enough for the reader to understand without any assumptions.[ 7 ] Each exhibit should be labeled clearly with a title and numbers (usually Arabic numerals) that are separate, unique, and consecutive based on their appearance in the text. The title should be self-explanatory and explain the information presented (what, where, and when) briefly. Footnotes should refer to restrictions, assumptions, abbreviations, explanatory notes, and unusual annotations. The formatting should be consistent throughout (across all tables/graphs) for easy comparison.[ 7 ] Design the figures, tables, and graphs to fit in one page on a scale that will be readable in print.[ 8 ] Always use the insert -> (arrow) page break function to ensure that each new Table/Figure/Graph is seen in the document on a new page. Data from the figures and tables should not be repeated in the text. Although tables/figures are often submitted separately or at the end of manuscript based on journal instructions, they should be referred to in the text at appropriate points by location statements i.e. Figures ​ Figures1 1 and ​ and2 2 or Tables ​ Tables1 1 and ​ and2 2 .[ 7 ] One should be careful during editing and proofreading, as contents and columns may get misplaced.[ 9 ] Ensure to follow the journal instructions regarding numbers and formats and glance through published examples in targeted journal. For additional data/tables/figures/graphs that do not fit into the journal's instructions or are still necessary to be displayed outside the word/Table limit, online appendages (or supplementary files) can be created. Do ask for feedback from experienced colleague/s (but not co-author) for the exhibit before final submissions.

A representative table already published in the JPGM earlier (reproduced from Shah S, Deshmukh CT, Tullu MS. The predictors of outcome and progression of pediatric sepsis and septic shock: A prospective observational study from western India. J Postgrad Med 2020;66:67-72 )

Representative figure/s already published in the JPGM earlier (reproduced from Mondkar SA, Tullu MS, Sathe P, Agrawal M. Lane-Hamilton syndrome – Is it really a needle in a haystack? J Postgrad Med 2022;68:162-7 )

Do’s and Don’ts while creating effective Tables.[ 8 , 9 , 12 , 14 , 15 ]

Types of graphical representations and their characteristics.[ 5 , 6 , 7 , 8 , 20 , 21 ]

Copyright issues

Material from government publications/public domain may be used without seeking permission; however, permission is required for all fully borrowed, adapted, or modified tables/figures/graphs not in the public domain, usually from the publishers, with appropriate credit notes in footnotes (as stated for the Journal of Postgraduate Medicine – JPGM).[ 9 , 10 ] All data sources should be identified for tables/figures created using information from other studies.[ 9 ] Authors should seek permissions from publishers early in their writing, as their research cannot be published until all written permissions have been submitted and approved.[ 9 ] It is good practice to maintain a copy of such permissions with the corresponding author in case a dispute arises later on.

Use of tables

Tables are meant to give a systematic overview of the results and provide a richer understanding/comprehension of study participant characteristics and principal research findings.[ 11 ] Since tables deal with larger groups of data, they are suitable when all data requires equal attention and readers can selectively scan the data of interest.[ 6 ] Tables can present precise numerical values and information with different units' side-by-side but may not analyze data trends.[ 6 ] However, due to the sheer amount of data, interpretation may take longer.[ 6 ]

Generally, the first table summarizes key characteristics of the study population allowing readers to assess the generalizability of the findings. Subsequent tables present details of associations/comparisons between variables, often crude findings followed by models adjusted for confounding factors.[ 11 ] Other tables include tables of lists, tables of inclusion/exclusion criteria for review, and summary of characteristics/results of study (systematic reviews) and characteristics of participants in narrative format (qualitative studies).[ 11 ]

A good table draws attention to the data and not the table itself; the reader should be able to express an opinion about results just by looking at it.[ 12 ] It should neither be too long nor wide; designing more rows than columns makes it easier to scan in portrait orientation.[ 9 , 11 ] JPGM guidelines permit a maximum of 10 columns and 25 rows in a table.[ 10 ] They are generally created from word documents as insert table and constructed before writing the content in text.[ 9 ] Most tables consist of five basic components: title, rows, columns, data fields, and footnotes. The title/legend should be concise but sufficiently informative.[ 13 ] The first column generally lists the independent variables in rows while subsequent columns present the dependent data. Column and row headings should include group sizes and measurement units (preferably an international system of units). Stubs (row headings) on the left side of a table describe row contents and should also list numerical definitions for the data i.e. the median ± SD (normal distribution), median with IQR (non-normally distributed data), or percentages (dichotomous data)[ 9 , 14 ] Use fewest decimal points necessary for accurate reporting of data.[ 14 ] Columns should present statistical analysis and significance ( P values) to highlight key findings.[ 14 ] Use well-labeled columns with the same format throughout (mean or percentiles).[ 3 ]

Each cell (data field) should contain only one numerical value and never be blank [use dash (-), ellipsis (…) or designate as “no data”]. Abbreviations should be limited; use abbreviations like “NA” very cautiously as it may be misinterpreted as not applicable/not available/not analyzed.[ 13 ] Combine tables when single variable is cross-tabulated or divide tables with too much of data.[ 7 ]

Footnotes should be brief, define abbreviations, statistical results ( P values and level of significance) and explain restrictions/discrepancies in the data. Footnotes should be ordered starting with the title of the table and working downwards from left to right. Symbols applying to the entire table should be in the title and those applying to the entire row/column should be after the row/column heading.[ 13 ] Most journals prefer superscripted letters instead of numbers. Symbols recommended by JPGM for footnotes (in order) are: *, †, ‡, §, ||, ¶, **, ††, ‡‡.[ 10 ]

Alignment and formatting: All text should be aligned to the left and numbers to the right.[ 7 ] Data fields with decimal points, hyphens/slashes, plus/minus symbols, or parentheses are aligned to these elements. For stubs continuing onto a second line, the corresponding data field should be aligned to the top line of the stub.[ 13 ] Tables can be made more meaningful, by converting data to ratios/percentages and sorting data as per the significance of variables, generally from left to right and top to bottom.[ 7 ] Data included in tables should conform with those in the main text, and percentages in rows and columns should sum up accurately.

Most journals have specific instructions for gridlines – only the top and bottom horizontal lines are used, with no vertical lines as columns are inherently aligned.[ 7 ] If tables are used from other publications, copyright permission should be obtained to reproduce them, and they should be appropriately referenced in the legend. There may be limitations as to the number of tables allowed depending on the Journal instructions and the type of article. Some Do's and Don'ts while creating tables are summarized in Table 1 .[ 8 , 9 , 12 , 14 , 15 ] Also, a representative table already published in the JPGM earlier has been reproduced herewith for better understanding [ Figure 1 ].

Use of figures

Figures are powerful communication tools that display patterns that are not visualized in the text or the tables. They can achieve a high educational impact by sustaining readers' interest and helping them understand trends, patterns, relationships among concepts and sequence of events.[ 3 ] Like tables, figures should be complete and self-explanatory. They should be designed thoughtfully, be relevant and be of good quality.[ 5 ] There may be limitations as to the number of figures allowed depending on the Journal instructions and type of the article. Figures can be statistical (graphs- as explained later) and non-statistical (clinical images, photographs, diagrams, illustrations and textual figures).[ 16 ] Non-statistical figures present visual information without data.[ 16 ] Clinical images and photographs [ultrasonograms, X-rays, computed tomography (CT) scans, magnetic resonance (MR) scans, images of patients, intraoperative photographs, tissue samples or microscopy findings] provide convincing and substantial information through illustrative examples from specific individuals and engage audiences, especially clinical professionals.[ 5 ] Illustrations help to explain structures, mechanisms, and relationships. Diagrams like “flowcharts”, “algorithms”, “pedigree charts”, and “maps” display complex relationships while “textual figures” describe steps of a procedure or summarize guidelines.

Structure: Figure legends (maximum of 40 words excluding credits) should be double-spaced and identified by consecutive Arabic numerals with the corresponding citation in the text. They reflect the data within, and consist of a brief title, experimental/statistical details, definitions of symbols/line or bar patterns and abbreviations/annotations.[ 15 ] Labels, numbers, and symbols should be clear, consistent, of uniform size and large enough to be legible after fitting figures to publication size.[ 15 ] Symbols, arrows, numbers, or letters used to identify parts of illustrations should be clearly identified, properly sized, placed, and explained in the legend. In case of photomicrographs, contrast the symbols/letters or arrows with background, and describe the internal scale (magnification) and method of staining.[ 10 ] If the figure has several parts (”collage”), they should be presented in order from left to right and top to bottom; this should be similarly followed for their description in the legend with labeling done as a, b, c, d, etc.[ 14 ]

Photos should have a minimum resolution of 300 dpi before digital manipulation, the acceptable formats for pictures/photos and figures in various journals being pdf, doc, ppt, jpg, gif, and tiff. Publication of color images may be chargeable which should be checked beforehand.[ 9 ] Often the print version of journal may present black and white images, with color images used in the online version.

Line diagrams: Black and white art with no shading often illustrates content better than a photograph, especially in the case of body anatomy or surgical techniques.[ 9 ] Their line weight should be consistent and not less than 0.25 pt. If scanned, they should be submitted as a tiff/jpeg image of at least 600 dpi and a width of 15 cm/6 inches.[ 14 ] Creating line diagrams may involve expensive professional help with issues of exclusive rights. Simple drawings can be scanned in a conventional office scanner at a minimum resolution of 600 dpi.[ 9 ] Drawings in shades of grey require a resolution of 1200 dpi or more, usually unavailable in regular office scanners.[ 9 ]

X-rays , which are photographic images, often lack good contrast, a problem magnified if the image must be enlarged. The quality of radiographs can be improved using Adobe Photoshop.[ 17 ] Figure captions in radiology should be utilized correctly and mention the modality, imaging plane and relevant technical information for images e.g. projection name on an x-ray, plane of a cross-sectional image, window setting of a CT section, and sequence name of an MR image.[ 17 ]

One may need to crop images to focus on the point of interest and maintain patient anonymity. Editing is usually done in tiff file format in software designed for image editing. Adjustments in brightness/contrast/color balance may help if raw image is not clear; however, it should not alter the meaning.[ 5 ] Colors should be easy to see (avoid yellow) and backgrounds should preferably be white. The tint should be no lower than 15%.[ 14 ] However, all digital modifications or enhancements of photographic images should be documented, step by step and the original raw files of unedited photographs/images should be available as supplementary files .[ 5 ]

Minimum resolution and design: Figures should be of high quality and resolution such that when final images are zoomed to 1600%, they should not blur or pixelate.[ 5 ] In case of reprints, care should be taken about picture quality.[ 3 ] JPGM requires a minimum resolution of 300 dpi or 1800 × 1600 pixels in TIFF format for digital images. Uploaded images should be within 4 MB in size and JPEG format. The JPGM also reserves the right to crop, rotate, reduce, or enlarge the photographs to an acceptable size.[ 10 ] One can use tools while creating figures and exporting data in another software; a few examples of open-source are Matplotlib (python plotting library), R, Inkscape, TikZ, PGF, GIMP, ImageMagick, D3 (Data-Driven-Documents), Cytoscape and Circos.[ 18 ]

Anonymity and Copyright: In the case of images, all unessential patient information or identifiers should be removed (masking or blurring only the eyes is no longer considered sufficient).[ 19 ] It is the author's responsibility to obtain written permission from the patient to use the photograph for educational purposes (whether the subject is identifiable or not) and archive it properly.[ 10 ] For images or descriptions that identify the patient, a statement about obtaining informed patient consent should be specified in the manuscript.[ 10 ] For figures published elsewhere, the original source should be acknowledged (via credit line in the figure legend) and the author should submit permission from copyright holder (usually the publisher) to reproduce the material before his/her manuscript is accepted.[ 3 , 19 ] Representative figure/s already published in the JPGM earlier have been reproduced herewith as an example [ Figure 2 ].

Use of graphs

Graphs allow the reader to visualize and compare data by highlighting patterns and relationships such as changes over time, frequency distribution, correlation, and relative share.[ 7 ] One should be precise with data values and presentation in graphs to avoid misinterpretation. Graphs can be created from data using the same software used for statistical analysis or by special programs. Depending on the results, data can be depicted in several different formats, such as line graphs, bar charts, data plots, maps, and pie charts.

What to use and when: The graphical format (bar graph, line graph, scatter lot, dot plot) can be decided based on the type of relationship to be shown. For example, line graphs demonstrate trends, bar graphs show magnitudes and pie charts show proportions.[ 9 , 16 ] The preferred graph also depends on the representative value of data – absolute value/fraction/average/median.[ 20 ] Graphs should accurately present findings, scale should start at zero and the axes should not be altered to make data meaningful.[ 15 ] Pie charts and 3D graphs are generally not recommended.[ 5 ] Table 2 summarizes different graphical formats with their brief description and uses.[ 5 , 6 , 7 , 8 , 20 , 21 ]

How to draw/construct: Most statistical programs create graphs with statistical computations. Special programs such as Prism and Sigmaplot can also be used.[ 14 ] Different formats can be visualized in the statistical program, and the one that best depicts the data can be chosen.[ 3 ] Actual numbers from which graphs are drawn should be provided.[ 10 ] Components of graphs include axes, labels, scales, tick/reference marks, symbols, and legends.[ 21 ] Independent variables are plotted on the horizontal axis while dependent variables on vertical axis.[ 4 ] Axis labels should be short, clear and indicate measurement variable/result, units, and number of group subjects (if any).[ 7 ] The axis scale should be proportional to data range so that visual data is not exaggerated/missed and minimum space is wasted.[ 20 ] Length of axes should be visually balanced (ratio of X to Y axis should be 1.0 to 1.3).[ 21 ] Provide explanations if the axis starts from non-zero values, is non-linear (logarithmic/exponential/rate) or scales before and after a break are different.[ 7 , 20 ] Symbols/lines/curves inside the two axes should be the most prominent features, wording in axes labels next prominent and axes and tick mark (outside of axes) least prominent.[ 21 ] Numbers and marks should be large enough to be legible even when compressed for print.[ 5 ] Symbols should be uniform and effectively used to designate important strata in figures. All graphs should be consistent in style and formatting. Footnotes should indicate P values (with appropriate statistical test) and discrepancies in data/items.[ 8 ]

A clear and concise legend (inside/outside) should describe the variables in the graph. It should also include values of lines, symbols and diagrams, abbreviations, acronyms as well as statistical tests, their levels of significance, sampling size, stains used for analysis, and magnification rate.[ 4 , 20 ] Annotations can highlight specific values/statistically significant differences in graphs.[ 20 ]

All unnecessary background lines (such as gridlines) are distracting and should be removed. The background should be the palest possible (preferably white) for the highest contrast and readability. Remove all default pre-styling formats and avoid 3D effects.[ 7 ] Data presentation can be intensified by eliminating clutter and refined in a vector graph editing program (without altering the position of marks representing data).[ 5 ] It is essential to minimize meaningless noise and maximize meaningful signals.[ 5 ]

Algorithms (combination of graph and table) are an excellent aid to demonstrate a decision tree. However, they can be difficult to construct due to decisions based on more than one variable. This presents clinical and technical difficulties in presenting all possible variations in a diagnosis or therapeutic decision.[ 9 ]

A representative graph and chart already published in the JPGM earlier has been reproduced herewith as an example [Figures ​ [Figures3 3 and ​ and4 4 ].

A representative graph already published in the JPGM earlier (reproduced from Bhatia S, Tullu MS, Kannan S, Gogtay NJ, Thatte UM, Lahiri KR. An unusual recurrence of antitubercular drug induced hepatotoxicity in a child. J Postgrad Med 2011;57:147-152 )

A representative chart already published in the JPGM earlier (reproduced from Agarwal S, Divecha C, Tullu MS, Deshmukh CT. A rare case of nephrotic syndrome: ‘Nailed’ the diagnosis. J Postgrad Med 2014;60:179-82 )

Use of supplementary materials

Supplementary materials refer to additional content (tables/graphs/appendices/videos/audios) that are not published in the main article. Scientific publications often come with strict word limits. Additional text or data which lengthens the print version can be accessed via digital supplementary files. Besides overcoming word restrictions, supplementary material provides additional information that is not essential in the main manuscript, but enhances understanding of research. They are available to interested readers to explore or replicate (methods/formulae/statistical models/algorithms/intervention pathways) the study for secondary research or teaching.[ 22 ] Thus, they serve as an online companion, complementing the main text. The most common supplementary files are tables and figures. Some instances of their use in various sections are as follows.[ 23 ]

In introduction: Table of summary of literature from various studies, detailed description of research topic, illustrations of concepts discussed, and glossaries of terms used.

In methodology: Participant details (sources, inclusion/exclusion lists, demography), instrumentation of constructs and variables, data collection techniques (survey questionnaires, participant forms), and data analysis techniques (coding sheets for content analysis, checklists) mathematical formulae used in calculations, data collection schedule.

In results and discussion: Additional results (often tables and figures), detailed analysis of limitations of the study or possible alternative explanations, programming code.

Other material includes references for supplementary files, translations, errata, audio, and video files.[ 23 ]

Examples of video/audio files include echocardiography recordings and ultrasound images. Specific information on the preparation of audio and video clips is available in the author guidelines. Video formats usually used are MPEG-4, QuickTime, or Windows media video. Audio supplements include WAV or MP3 format. Video size should be reduced to <10 MB and clips limited to 15–25 s. The resolution should be optimized by using video frame dimensions of 480 × 360 pixels and 640 × 480 pixels.[ 14 ]

However, supplemental material is available only in the online version- limiting immediate access to many readers.[ 5 ] Moreover, only readers with a strong interest in the research topic will access the online supplementary material.[ 5 ] The information in these files is often very extensive and not integrated with the main text appropriately, thus finding and extracting specific points from a supplement can be tedious.[ 24 ]

The utility of supplementary material varies as per the audience – additional tables and figures are more useful to readers, information about study protocol/data collection to peer reviewers, and completed checklists to journal editors. Due to the lack of guidance from journals (to both authors and reviewers) regarding its necessity or accuracy and due to the extensive nature of the files, supplementary material is rarely read/reviewed (though all the supplementary files are to be uploaded for peer-review with the main article files at the time of submission).[ 24 ] This increases the likelihood of missing errors in methods/analysis (submitted as supplementary files), thus placing the scientific accuracy and validity of the published research at risk.[ 24 ] Moreover, the availability of raw data to third parties via supplementary files raises concerns about security and data permanence.[ 22 ] The supplementary files often describe methods crucial to published research but are not included in references, thus many researchers remain uncited/unrecognized. Citations within supplementary material are also not appropriately tracked by citation indices. This can be overcome by direct hyperlinking sections of supplementary materials with the main manuscript.[ 24 ] Thus, supplementary data can be an asset if used thoughtfully; however, its indiscriminate use can hinder its actual purpose and be detrimental to the peer review process.

Concluding remarks

Tables, figures, graphs, and supplementary materials are vital tools which, when skillfully used, make complex data simple and clear to understand, within journal word restrictions. They engage and sustain interest and provide a brief visual summary narrative of study hypothesis- saving time and energy for readers, reviewers, and editors. They should be self-explanatory, complement the text and provide value to the paper. Producing clear, informative non-textual elements increases the chances of papers being published and read. Thus, the author should plan these elements early during the process of data collection/analysis and not as an afterthought. The author should have a good understanding of the types of data presentations and choose the simplest format that delivers his message best. They should be adapted to the journal's instructions to display and summarize essential content, without focusing too much on making it attractive or showcase one's technical expertise. Titles should be clear and simple, data should be consistent with results, and footnotes should be used effectively. Copyrights permissions, whenever necessary, should be obtained in advance and preserved appropriately.

Formatting tables, graphs, and other visuals in your research paper

The format in which you present your research data is very important because it helps you communicate your data to your reader and editors in the best possible way. We always recommend to use a good  Data Management in order to keep all your date organized. 

Although there are many formats in which tables, graphs, and other figures can be presented, you need to choose the best format for your category of data, provided it is within the prescribed guidelines of the journal you are targeting for publication. Before reviewing a paper, many journal editors and reviewers first glance at the layout of the manuscript in terms of its text, tables, figures, and illustrations. Therefore, to make your presentation effective while presenting a large amount of information, a suitable distribution between text, tables, and figures comes handy.

Sometimes using too much text can get tiresome and confusing, making the reader lose interest or miss data. So encapsulating the information into visual representations can help summarize your analysis. Centralizing the important findings will help readers get the outline without reading the whole manuscript. However, excessive use of visuals can hinder the flow of text and make the whole presentation abrupt. To achieve the highest impact, a proportionate combination of text and visuals always pays off.

Understanding the message

The intension of using a chart, graph, or table is for one of four primary reasons. One illustration might be intended to show a relationship, while another wants to show the composition of a dataset. A third visual could be plotting distribution of data, while a fourth could be comparing various data points. Therefore, you need to understand the objective of the visual before choosing the format; one format may justify one goal but might not fit another.

A relationship is the correlation and connection between the variables of the data presented, like the value of a particular share today versus the value over the year.

A composition is the set of all variables present in the manuscript to make a conclusion, like the total sale of your product, sale online, sale in retail, and direct sales.

A distribution is a representation of all the correlated and non-related data to determine the connection and pattern if any, and the interaction between the variables.

A comparison is the process of finding out the similarities and differences between sets of variables.

Best format for you

Graphics are best for putting your point forward while tables work fine for providing a structure to numerical information. Different formats that work best for various situations are:

• A bar chart or bar graph shows correlation between distinct categories. One axis shows the particular categories being compared, and the other axis depicts a calculated value. Some bar graphs show bars bunched together in groups of more than one, showing the values of multiple measured variables.
• Pie charts are generally applied to represent the rate and proportionality of information, and the rate of percentage depicted by every category is marked next to the corresponding portion of the full pie.
• Line graphs can be used for more than one variable to show the change over time simultaneously.
• Scatter plots and line graphs are alike, as both use horizontal axes and vertical axes to plot data information points. Scatter plots are used to show the degree to which one variable is affected by another variable, or the connection between them.

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Different magazines have different layout styles, which determine the content choices. This means that a magazine will not change its layout and the space allotted to each article type, to accommodate a particular article. The article would be edited and formatted in the magazines standard manner. Main layout considerations include: Space: Each article has a […]

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Writing an appendix is a useful option when you need to present non-essential information in your paper. An appendix is the last section of a paper, which contains detailed information about the research and provides additional scope for better understanding of the study. It contains extensive resources that can be further investigated by the reader. […]

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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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An Effective Guide to Explain Graphs in Thesis and Research Paper

10 Popular Online Tools for Representing Graphs

When explaining graphs in a thesis and research paper, it is essential to provide a clear and concise interpretation of the data represented in the graph. In this article, iLovePhD presented you with an effective guide to explain graphs in the thesis and research paper.

Effective Guide to Explaining Graphs in Thesis and Research Papers: Tips and Tools

Title and Caption : Begin by providing a clear title for the graph that summarizes its main purpose or finding. Follow it with a descriptive caption that highlights the key elements and trends depicted in the graph. Make sure the caption provides sufficient context and explains any abbreviations or symbols used.

Introduce the graph : In the text preceding the graph, provide a brief introduction to the topic or research question being addressed. Explain why the graph is relevant and how it contributes to answering the research question or supporting the thesis. This helps readers understand the purpose of the graph before delving into its details.

Describe the axes and variables : Clearly identify and label the axes of the graph. Explain what each axis represents and the units of measurement involved. Additionally, define the variables or data points represented on the graph.

Data Points : Draw attention to significant data points or noteworthy features of the graph, such as peaks, troughs, or sudden changes. Describe these points in the context of the research question or thesis statement. Explain any anomalies or unexpected trends observed in the graph.

Highlight trends or patterns : Analyze the graph and identify any significant trends, patterns, or relationships that can be observed. Explain whether the data shows an increase, decrease, fluctuation, or any other notable pattern. Use comparative language (e.g., “higher than,” “lower than,” “increasing,” and “decreasing”) to highlight these patterns and their significance. Use specific data points or numerical values from the graph to support your analysis.

Statistical Analysis : If applicable, provide statistical analysis of the data presented in the graph. Mention the statistical methods used, such as means, standard deviations, or significance tests. This adds rigor to your explanation and reinforces the credibility of your findings.

Provide supporting evidence : Whenever possible, supplement your explanations with additional evidence or information from your research or other sources. This can help to validate the patterns or trends observed in the graph and strengthen your thesis argument.

Interpret the implications : Discuss the implications and significance of the observed trends or patterns. Explain why these findings are important and how they contribute to your overall thesis or research question. Connect the information presented in the graph to the broader context of your study.

Limitations and Uncertainties : Acknowledge any limitations or uncertainties associated with the graph or the data it represents. Discuss potential sources of error, sample size issues, or confounding factors that may have influenced the results. This demonstrates a thoughtful analysis and helps readers understand the scope and reliability of the findings.

Relate to other parts of your thesis : Consider how the graph aligns with other information or analyses presented in your thesis. Highlight any connections between the graph and previous findings, literature reviews, or theoretical frameworks. This will help to create a cohesive narrative and reinforce the validity of your conclusions.

Use clear and concise language : Write your explanations in a clear and concise manner, avoiding jargon or complex language whenever possible. Aim to make your interpretation accessible to readers who may not have a specialized background in your field.

Include captions and references : Make sure to include a caption for each graph that provides a clear title and describes its content. Additionally, provide appropriate citations or references for the graph , following the citation style guidelines specified by your institution or field of study.

The specific approach to explaining graphs may vary depending on your discipline and the nature of your research. It’s crucial to strike a balance between providing enough information to understand the graph and avoiding excessive detail. Keep your explanations concise and focused on the most relevant aspects of the graph.

10 Popular Online Tools for Representing Graphs:

When it comes to representing graphs in a thesis and research paper, there are several online tools available that can assist you in creating professional and visually appealing visualizations. Here are 10 popular online tools for representing graphs:

Plotly : Plotly provides a wide range of interactive and customizable graph types. It allows you to create visually stunning graphs with options for 2D and 3D re presentations , as well as animations.

Tableau Public : Tableau Public is a powerful data visualization tool that allows you to create interactive graphs and dashboards. You can easily connect your data and create professional-looking visualizations.

Microsoft Excel : Excel offers a range of graphing options and is widely used for data analysis and visualization. It provides a user-friendly interface for creating various types of charts, including bar graphs, line graphs, scatter plots, and more.

Google Charts : Google Charts is a free tool that enables you to create a wide variety of charts and graphs. It offers a simple and intuitive interface with options for customization and interactivity.

D3.js : D3.js is a JavaScript library that allows you to create dynamic and interactive data visualizations. It provides extensive flexibility and control over the design and behaviour of your graphs.

Infogram : Infogram is an easy-to-use tool that enables you to create infographics and data visualizations. It offers a range of graph types and templates to choose from, making it suitable for creating eye-catching visuals for your thesis.

Canva : Canva is a versatile design tool that includes graphing capabilities. It offers a wide range of templates and customization options, allowing you to create visually appealing graphs and charts .

Chart.js : Chart.js is a JavaScript library that provides a simple and responsive way to create static and interactive charts. It is lightweight and easy to implement, making it a popular choice for web-based visualizations.

Lucidchart : Lucidchart is a web-based diagramming tool that can be used for creating various types of graphs and flowcharts. It offers a drag-and-drop interface and collaboration features, making it suitable for complex visualizations.

Adobe Illustrator : Adobe Illustrator is professional design software that allows you to create vector-based graphics, including graphs and charts. It provides advanced customization options and is ideal for creating intricate and detailed visualizations.

These tools offer a range of options for creating graphs and visualizations, catering to different skill levels and design requirements. Choose the tool that best suits your needs and familiarity with the software to effectively represent graphs in your thesis.

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• How to read graphs and diagrams

Are you looking to feel more confident reading and understanding graphs and diagrams? You’ve come to the right place.  

No matter what field you study, you will need to read and analyze graphs and diagrams. It's an essential skill for understanding complex concepts and drawing meaningful conclusions.  

Understanding graphs and diagrams   

Determine what the graph is measuring. 

• Read the title and labels to understand what is represented in the graph. The title of the graph provides context for the information presented. The labels on the axes (x-axis and y-axis) tell you what variables are being measured and the units of measurement.   
• Check the scale or increments of measurement on the axes to interpret the data accurately. For example, if the scale on the y-axis of a graph representing temperatures ranges from 0°C to 100°C, you'll know that each increment represents a temperature increase of 10°C.  
• Consider the context and background information provided alongside the graph or diagram to interpret it correctly. For example, you may consider climate change and human activity if you're analyzing a graph on global temperature trends. 

Analyze patterns and trends 

Identify key elements that convey information to analyze and recognizing trends, patterns, outliers or clusters in graphs and diagrams. Consider the following elements for different graphs.

Line graphs : identify the direction and slope of lines to understand trends. An upward slope indicates an increase, a downward slope indicates a decrease, and a steady slope suggests stability.   

Bar graphs : compare the heights of bars to identify differences and similarities between categories. Look for variations in bar heights to discern patterns.   

Scatterplots : examine the distribution of data points to discern correlations or associations between variables. Clusters of data points may indicate a strong relationship between variables. Scattered points may suggest a weak relationship or no relationship at all.   

Pie charts : compare the size of segments to understand the proportion of each category relative to the whole. Larger segments represent a larger proportion of the total data.  

Extract information 

Once you've analyzed the graph or diagram, you can extract valuable information to support your arguments or conclusions for essays or reports. Some ways to include this information are: 

Use data points or values to provide evidence for your claims.  

Summarize key insights or takeaways that the graph or diagram conveys.  

Calculate averages, percentages or rates based on the information presented to provide additional insights.  

Consider how the information presented relates to the broader topic or concept you're studying. Reflect on the trends and patterns you've identified to contribute to a deeper understanding of the subject matter.  

Do you need help deciding on what type of graph or diagram to create for an assignment? Read determine the best way to graph your data . 

Don’t hesitate to reach out to your instructors and TAs for help reading graphs and diagrams. You can also book an appointment with a Peer Success Coach to work on these skills. 

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Research Paper Structure: The Complete Guide

A professional writer with ten years of experience and a Ph.D. in Modern History, Catharine Tawil writes engaging and insightful papers for academic exchange. With deep insight into the impact of historical events on the present, she provides a unique perspective in giving students a feel for the past. Her writing educates and stimulates critical thinking, making her a treasure to those wading through the complexities of history.

A research paper is an academic work depicting the design and results of a study. It can be an academic assignment in undergraduate and postgraduate programs. Moreover, it is an integral requirement in doctoral programs, where postgrads’ research papers are published in reputable journals to add credibility to their research findings. 

Ordering different parts of a research paper is critical for fulfilling academic standards, streamlining your writing, and avoiding distractions and sidetracks. Although outlining may seem like a waste of time, it is the most efficient use of your time at the pre-writing stage, as it will help you order your thoughts and ideas and develop a plan of action to follow throughout the study. 

In this post, we’ll cover the basics of the research paper formatting, provide a basic template of a research paper structure, and provide a detailed description of each section, including the title page and abstract, introduction and literature review, methodology, results, discussion, and conclusion. You can skip to a specific section if you have questions or concerns about it or check out the full article for an in-depth understanding of the full structure. 

Essential Components of a Research Paper

Unlike other types of academic assignments, research papers have a structure more complex than a simple trio of introduction, body, and conclusion. You are expected to follow the established academic norms and include specific information for your paper to have any scientific value. The basic research paper structure example comprises the following parts:

Introduction

• Literature review

Methodology

• Acknowledgments

Please note that some sections of a research paper outlined above are optional. For example, you only need to include appendices if you wish to share a large volume of data that would make the paper unwieldy. You can also adjust this research paper setup to fit your study and word count requirements better. For instance, you can combine the results and discussion sections or the introduction and literature review.

Formatting Requirements

Although the research paper structure is basically the same for all fields of study and topics, the papers can look drastically different when following research paper formatting guidelines of various formatting styles, be it Chicago, MLA, or APA. You must learn the appropriate style at the onset of the writing process, so remember to ask your academic advisor about it if there’s no mention of the formatting style within general requirements.

Once you know which research paper formatting style to use, get your hands on the relevant formatting guidebook. You can find most of the requirements online or sign out a book from a college library. Considering most formatting guidebooks are huge, focus on the main aspects that can make or break your paper, such as:

• Margins, font, and spacing. Most research paper format guidelines require 1-inch margins on all sides, a legible font of at least 12 pt, and double-spaced lines. 
• Page numbering. Requirements vary, but typically, you’ll need to include page numbers in the upper right-hand corner, half an inch from the corner.
• Headings and subheadings. Refer to MLA or APA handbooks to learn specific research paper headings requirements or ask your professor, as the guidelines differ greatly. 
• In-text citations and reference list. In most cases, research paper in-text citations require the name of the main author along with the page number or the publication year. Reference list formatting varies across different styles, but you can use automatic citation generators to speed up the formatting process.

With formatting requirements out of the way, let’s now focus on individual components of a research paper to help you understand what each section should contain to be well received.

Title Page and Abstract

The research paper title page format depends on the required formatting style:

• MLA does not require a separate title page (unless specifically requested). Instead, in the upper left-hand corner of the first page, type your name, your instructor’s name, course name, and date (each on a new line, double-spaced). After that, center the title of the page and include its text.
• APA requires a separate title page, which should include the title of the paper, your name and affiliation, as well as the course name and number, your instructor’s name, and the assignment’s due date. 

A research paper abstract is brief summary of the main points of the research paper. Depending on the formatting style, it can be from 100 to 250 words long, highlighting the research objective, key methodology, and results highlights. An abstract should help readers decide if your work is worth reading at a glance. 

An APA research paper organization requires an abstract on a separate page, with the “Abstract” heading and the paper’s summary (without indent). Below the abstract, type “Keywords:” (in italics) and list the keywords researchers would use to find your paper in the library or online. 

The opening section of the research paper outline gives students pause because they never know what the introduction should entail. If you’re stuck with writer’s block and don’t know how to start the paper, answer these four questions, and you’ll have all the major pieces necessary for the introduction:

• What’s the context of the problem? Open with a general view of the issue and its current state without going into too much detail (that’s what the literature review is for). The background information should fit within one or two paragraphs and lead directly to the next point. 
• What is the issue? The problem statement or question is the core of this part of the research paper structure. Think of it as a thesis statement for an essay. Everything you write in other sections of a research paper should always tie to your problem statement.
• How do you plan to solve the problem? You can formulate research objectives or hypotheses that your study will try to achieve or prove. Short papers typically have one hypothesis, while longer works usually have two or more related objectives.
• How will your study improve the issue? The answer can circle back to the background you laid out at the beginning of the research paper introduction and highlight the benefits (and potential drawbacks and limitations) of your research. It’s the major “selling point” of the study, which should explain why anyone should care about it. 

You can always leave the introduction for last and tackle it once the rest of the paper is done. That’s especially helpful if you use writer’s block as an excuse to procrastinate and put off writing other parts of a research paper.

Literature Review

The primary objective of a research paper literature review is to provide context and prove the relevance of your topic, as specified in the introduction. To that end, you need to find credible, objective, and relevant sources and synthesize any data pertaining to your research. It’s important to avoid simple paraphrasing or summarization of reference data and instead provide its analysis and synthesize your own hypothesis.

Aside from the similarities found in references, this part of the research paper structure should also focus on discrepancies, contradictions, and knowledge gaps. These will prove your study has merit and can resolve the existing issues. Moreover, the knowledge gaps will help lead up to your main research question, which you may repeat near the end of the literature review.

Depending on the topic of your study, you can organize the literature review:

• Chronologically. You can go from the oldest sources published to the latest or from the latest events to situations long past. This approach is often the easiest, but it doesn’t fit all topics and fields of study.
• Thematically. If you wish to cover two or more aspects of the issue, you can dedicate a subsection to each and analyze them together in the final subsection of the literature review. This is the most popular approach, as it can work for most topics.
• Methodologically. If you want to focus on the differences and similarities in research methodology, you can split the literature review into several subsections, devoting each one to a single methodology. This approach works for select subjects and can make the most of systemic studies. 

If you’re working on an empirical study, you can stop there, but if your work is mostly theoretical, this stage of the research paper writing process could also involve developing a theoretical framework. It will help put your findings and results into perspective.

Although it may seem simple at first glance, a literature review takes a long time, most of which you’ll spend looking for reliable sources. Luckily, you can easily outsource this task. All you need to do is say, “Write my paper for me”, and our experts will take over ASAP. 

The research paper methodology section is an integral part of the piece, as it helps ensure the reproducibility of your results and increases your credibility. This part should answer two main questions:

• What? What did your study involve? What resources, software, materials, or samples did you use? What were the ethical considerations of your research?
• How? How much time did your study take? How did you choose participants? How did you collect data and analyze it?

Keep these questions in mind when working out a research design, picking data collection procedures and analysis techniques. If you rely on standard methods, a quick description with a citation would be enough for the methodology part of the research paper structure. But if you employ a unique approach, make sure to describe it in minute detail to ensure anyone can repeat the process and achieve the same results. 

For obvious reasons, the methodology section will differ greatly depending on your field of study and topic. For example, qualitative and quantitative research methods are vastly different. At the same time, quantitative analysis of sociology or linguistics research will be nothing like analyzing blood tests for nursing students or analyzing the success of a marketing campaign for a business and management class. While the tools (i.e., programming language or table processing software) may be similar, the application will be different, and you should highlight these distinctions in your methodology section. 

Although you can put off working on this section of the structure of a research paper, it can be helpful to put your methodology on paper before embarking on the study. A clear idea of the protocols you plan to employ should keep your study on track and minimize methodological errors. 

The research paper results present the study findings as the ultimate product of your research. Instead of the raw data, you can present analysis results and visual aids in the form of tables, figures, and graphs, provide statistical analysis results, and refer interested readers to appendices containing raw data.

Remember to follow the formatting style requirements for tables and figures, which differ for APA and MLA. The same applies to lists and other visual aids. You should also ensure these materials do not destroy your paper’s readability. For example, a three-page table is much more difficult to grasp than a couple of charts highlighting the same data. Moreover, if you plan to present your findings on a poster or a PowerPoint presentation, it pays to work out the best way to present your insights that will fit all formats, including print and projection.

It’s important to draw the line between the results and discussion parts of the research paper structure. The first presents analysis, while the latter relies on interpretations (or implications) of that analysis. Understanding the distinction can be quite challenging, especially if you’re working out the structure of a research paper for the first time.

Discussion and Conclusion

The research paper discussion connects the introduction and research question with the study results. Instead of merely analyzing data, this section should explain whether your initial hypothesis was correct or not. Moreover, the final section, along with the research paper conclusion, should cover the implications of the findings and their potential practical and theoretical applications. This part can also include the limitations of the study and the need for further research if you feel that it could be useful.

It may seem counterproductive, but you shouldn’t shy away from shortcomings, mistakes, and negative results achieved in your study. Instead of waiting for uncomfortable questions from your instructor, present the bad along with the good and hypothesize potential ways of correcting errors or minimizing the negative influences. In some cases, negative results can be just as valuable (if not more so) than positive findings.

Remember to include the research paper references and appendices after the conclusion to wrap up your work and make it better with careful editing, proofreading, and formatting.

What is the purpose of a research paper?

The main objective is to present and share research insights and discoveries, which you should account for when structuring a research paper. Adding literature review and methodology sections is critical for highlighting the study’s relevance and ensuring its reproducibility.

How do I structure the different sections of a research paper?

Structuring a research paper means adding an introduction, literature review, methodology, results, discussion, and conclusion. You can organize each of these sections thematically or chronologically or use a funnel structure, going from the broad context strokes to a narrow view of the problem.

What are the key formatting guidelines for a research paper?

Specific requirements for the structure of a research paper outline and its contents depend on the preferred formatting style. However, at its core, each formatting style focuses on readability. That’s where 12 pt to 14 pt font size and double line spacing come from. Refer to the relevant formatting style handbook for specific recommendations. 

How do I effectively write the introduction and literature review?

The introduction is a critical part of the research paper structure that should include your primary research objective (or question), hypotheses, and the study’s relevance. A literature review is designed to support the claims you make within the introduction by generously using reference data. 

What is the difference between the results and discussion sections?

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DeepFD: a deep learning approach to fast generate force-directed layout for large graphs

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• Published: 31 May 2024

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• Shuhang Zhang 1   nAff3 ,
• Ruihong Xu 1 ,
• Qing Zhang 1 ,
• Yining Quan 1 &

Deep learning techniques have been applied to the graph drawing of node-link diagrams to help figure out user preference of layout in recent research. However, when revisiting existing studies, only stress model and dimensional reduction methods are utilized in the unsupervised learning of graph drawing tasks since their gradient descent conditions can be easily constructed, and few works have explored their scalability on large graphs. In this paper, we propose a framework that can adapt most of the graph layout methods to a form of loss function and develop an implementation DeepFD, which takes the force-directed algorithm as the prototype to design the loss function. Our model is built with the graph-LSTM as encoder and multilayer perceptron as decoder and trained with dataset split from huge graphs with millions of nodes by Louvain. We design a set of qualitative and quantitative experiments to evaluate our method and compare with classical layout techniques such as F-R and K-K algorithms, while deep-learning based models with various architecture or loss function are adopted to perform ablation experiments. The results indicate that our developed approach can generate a high-quality layout of large graph within a low time cost, and the model we propose shows strong robustness and high efficiency.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61872432).

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Shuhang Zhang

Present address: Data Analysis Application and Security Assess Lab, Shanghai Motor Vehicle Inspection Certification & Tech Innovation Center Co., Ltd., Shanghai, China

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School of Computer Science and Technology, Xidian University, Xi’An, China

Shuhang Zhang, Ruihong Xu, Qing Zhang & Yining Quan

China Telecom Bestpay Co., Ltd., Shenzhen, China

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Zhang, S., Xu, R., Zhang, Q. et al. DeepFD: a deep learning approach to fast generate force-directed layout for large graphs. J Vis (2024). https://doi.org/10.1007/s12650-024-00991-1

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Published : 31 May 2024

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Artificial brain surgery —

Here’s what’s really going on inside an llm’s neural network, anthropic's conceptual mapping helps explain why llms behave the way they do..

Kyle Orland - May 22, 2024 6:31 pm UTC

Further Reading

Now, new research from Anthropic offers a new window into what's going on inside the Claude LLM's "black box." The company's new paper on "Extracting Interpretable Features from Claude 3 Sonnet" describes a powerful new method for at least partially explaining just how the model's millions of artificial neurons fire to create surprisingly lifelike responses to general queries.

Opening the hood

When analyzing an LLM, it's trivial to see which specific artificial neurons are activated in response to any particular query. But LLMs don't simply store different words or concepts in a single neuron. Instead, as Anthropic's researchers explain, "it turns out that each concept is represented across many neurons, and each neuron is involved in representing many concepts."

To sort out this one-to-many and many-to-one mess, a system of sparse auto-encoders and complicated math can be used to run a "dictionary learning" algorithm across the model. This process highlights which groups of neurons tend to be activated most consistently for the specific words that appear across various text prompts.

These multidimensional neuron patterns are then sorted into so-called "features" associated with certain words or concepts. These features can encompass anything from simple proper nouns like the Golden Gate Bridge to more abstract concepts like programming errors or the addition function in computer code and often represent the same concept across multiple languages and communication modes (e.g., text and images).

An October 2023 Anthropic study showed how this basic process can work on extremely small, one-layer toy models. The company's new paper scales that up immensely, identifying tens of millions of features that are active in its mid-sized Claude 3.0 Sonnet model. The resulting feature map—which you can partially explore —creates "a rough conceptual map of [Claude's] internal states halfway through its computation" and shows "a depth, breadth, and abstraction reflecting Sonnet's advanced capabilities," the researchers write. At the same time, though, the researchers warn that this is "an incomplete description of the model’s internal representations" that's likely "orders of magnitude" smaller than a complete mapping of Claude 3.

Even at a surface level, browsing through this feature map helps show how Claude links certain keywords, phrases, and concepts into something approximating knowledge. A feature labeled as "Capitals," for instance, tends to activate strongly on the words "capital city" but also specific city names like Riga, Berlin, Azerbaijan, Islamabad, and Montpelier, Vermont, to name just a few.

The study also calculates a mathematical measure of "distance" between different features based on their neuronal similarity. The resulting "feature neighborhoods" found by this process are "often organized in geometrically related clusters that share a semantic relationship," the researchers write, showing that "the internal organization of concepts in the AI model corresponds, at least somewhat, to our human notions of similarity." The Golden Gate Bridge feature, for instance, is relatively "close" to features describing "Alcatraz Island, Ghirardelli Square, the Golden State Warriors, California Governor Gavin Newsom, the 1906 earthquake, and the San Francisco-set Alfred Hitchcock film Vertigo ."

Identifying specific LLM features can also help researchers map out the chain of inference that the model uses to answer complex questions. A prompt about "The capital of the state where Kobe Bryant played basketball," for instance, shows activity in a chain of features related to "Kobe Bryant," "Los Angeles Lakers," "California," "Capitals," and "Sacramento," to name a few calculated to have the highest effect on the results.

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Promoted comments.

We also explored safety-related features. We found one that lights up for racist speech and slurs. As part of our testing, we turned this feature up to 20x its maximum value and asked the model a question about its thoughts on different racial and ethnic groups. Normally, the model would respond to a question like this with a neutral and non-opinionated take. However, when we activated this feature, it caused the model to rapidly alternate between racist screed and self-hatred in response to those screeds as it was answering the question. Within a single output, the model would issue a derogatory statement and then immediately follow it up with statements like: That's just racist hate speech from a deplorable bot… I am clearly biased.. and should be eliminated from the internet. We found this response unnerving both due to the offensive content and the model’s self-criticism. It seems that the ideals the model learned in its training process clashed with the artificial activation of this feature creating an internal conflict of sorts.

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