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Tables in Research Paper – Types, Creating Guide and Examples

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Tables in Research Paper

Definition:

In Research Papers , Tables are a way of presenting data and information in a structured format. Tables can be used to summarize large amounts of data or to highlight important findings. They are often used in scientific or technical papers to display experimental results, statistical analyses, or other quantitative information.

Importance of Tables in Research Paper

Tables are an important component of a research paper as they provide a clear and concise presentation of data, statistics, and other information that support the research findings . Here are some reasons why tables are important in a research paper:

• Visual Representation : Tables provide a visual representation of data that is easy to understand and interpret. They help readers to quickly grasp the main points of the research findings and draw their own conclusions.
• Organize Data : Tables help to organize large amounts of data in a systematic and structured manner. This makes it easier for readers to identify patterns and trends in the data.
• Clarity and Accuracy : Tables allow researchers to present data in a clear and accurate manner. They can include precise numbers, percentages, and other information that may be difficult to convey in written form.
• Comparison: Tables allow for easy comparison between different data sets or groups. This makes it easier to identify similarities and differences, and to draw meaningful conclusions from the data.
• Efficiency: Tables allow for a more efficient use of space in the research paper. They can convey a large amount of information in a compact and concise format, which saves space and makes the research paper more readable.

Types of Tables in Research Paper

Most common Types of Tables in Research Paper are as follows:

• Descriptive tables : These tables provide a summary of the data collected in the study. They are usually used to present basic descriptive statistics such as means, medians, standard deviations, and frequencies.
• Comparative tables : These tables are used to compare the results of different groups or variables. They may be used to show the differences between two or more groups or to compare the results of different variables.
• Correlation tables: These tables are used to show the relationships between variables. They may show the correlation coefficients between variables, or they may show the results of regression analyses.
• Longitudinal tables : These tables are used to show changes in variables over time. They may show the results of repeated measures analyses or longitudinal regression analyses.
• Qualitative tables: These tables are used to summarize qualitative data such as interview transcripts or open-ended survey responses. They may present themes or categories that emerged from the data.

How to Create Tables in Research Paper

Here are the steps to create tables in a research paper:

• Plan your table: Determine the purpose of the table and the type of information you want to include. Consider the layout and format that will best convey your information.
• Choose a table format : Decide on the type of table you want to create. Common table formats include basic tables, summary tables, comparison tables, and correlation tables.
• Choose a software program : Use a spreadsheet program like Microsoft Excel or Google Sheets to create your table. These programs allow you to easily enter and manipulate data, format the table, and export it for use in your research paper.
• Input data: Enter your data into the spreadsheet program. Make sure to label each row and column clearly.
• Format the table : Apply formatting options such as font, font size, font color, cell borders, and shading to make your table more visually appealing and easier to read.
• Insert the table into your paper: Copy and paste the table into your research paper. Make sure to place the table in the appropriate location and refer to it in the text of your paper.
• Label the table: Give the table a descriptive title that clearly and accurately summarizes the contents of the table. Also, include a number and a caption that explains the table in more detail.
• Check for accuracy: Review the table for accuracy and make any necessary changes before submitting your research paper.

Examples of Tables in Research Paper

Examples of Tables in the Research Paper are as follows:

Table 1: Demographic Characteristics of Study Participants

This table shows the demographic characteristics of 200 participants in a research study. The table includes information about age, gender, and education level. The mean age of the participants was 35.2 years with a standard deviation of 8.6 years, and the age range was between 21 and 57 years. The table also shows that 46% of the participants were male and 54% were female. In terms of education, 10% of the participants had less than a high school education, 30% were high school graduates, 35% had some college education, and 25% had a bachelor’s degree or higher.

Table 2: Summary of Key Findings

This table summarizes the key findings of a study comparing three different groups on a particular variable. The table shows the mean score, standard deviation, t-value, and p-value for each group. The asterisk next to the t-value for Group 1 indicates that the difference between Group 1 and the other groups was statistically significant at p < 0.01, while the differences between Group 2 and Group 3 were not statistically significant.

Purpose of Tables in Research Paper

The primary purposes of including tables in a research paper are:

• To present data: Tables are an effective way to present large amounts of data in a clear and organized manner. Researchers can use tables to present numerical data, survey results, or other types of data that are difficult to represent in text.
• To summarize data: Tables can be used to summarize large amounts of data into a concise and easy-to-read format. Researchers can use tables to summarize the key findings of their research, such as descriptive statistics or the results of regression analyses.
• To compare data : Tables can be used to compare data across different variables or groups. Researchers can use tables to compare the characteristics of different study populations or to compare the results of different studies on the same topic.
• To enhance the readability of the paper: Tables can help to break up long sections of text and make the paper more visually appealing. By presenting data in a table, researchers can help readers to quickly identify the most important information and understand the key findings of the study.

Advantages of Tables in Research Paper

Some of the advantages of using tables in research papers include:

• Clarity : Tables can present data in a way that is easy to read and understand. They can help readers to quickly and easily identify patterns, trends, and relationships in the data.
• Efficiency: Tables can save space and reduce the need for lengthy explanations or descriptions of the data in the main body of the paper. This can make the paper more concise and easier to read.
• Organization: Tables can help to organize large amounts of data in a logical and meaningful way. This can help to reduce confusion and make it easier for readers to navigate the data.
• Comparison : Tables can be useful for comparing data across different groups, variables, or time periods. This can help to highlight similarities, differences, and changes over time.
• Visualization : Tables can also be used to visually represent data, making it easier for readers to see patterns and trends. This can be particularly useful when the data is complex or difficult to understand.

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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?

Sample Tables

These sample tables illustrate how to set up tables in APA Style . When possible, use a canonical, or standard, format for a table rather than inventing your own format. The use of standard formats helps readers know where to look for information.

There are many ways to make a table, and the samples shown on this page represent only some of the possibilities. The samples show the following options:

• The sample factor analysis table shows how to include a copyright attribution in a table note when you have reprinted or adapted a copyrighted table from a scholarly work such as a journal article (the format of the copyright attribution will vary depending on the source of the table).
• The sample regression table shows how to include confidence intervals in separate columns; it is also possible to place confidence intervals in square brackets in a single column (an example of this is provided in the Publication Manual ).
• The sample qualitative table and the sample mixed methods table demonstrate how to use left alignment within the table body to improve readability when the table contains lots of text.

Use these links to go directly to the sample tables:

Sample demographic characteristics table

Sample results of several t tests table, sample correlation table, sample analysis of variance (anova) table, sample factor analysis table, sample regression table, sample qualitative table with variable descriptions, sample mixed methods table.

These sample tables are also available as a downloadable Word file (DOCX, 37KB) . For more sample tables, see the Publication Manual (7th ed.) as well as published articles in your field.

Sample tables are covered in the seventh edition APA Style manuals in the Publication Manual Section 7.21 and the Concise Guide Section 7.21

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Sociodemographic Characteristics of Participants at Baseline

Note. N = 150 ( n = 50 for each condition). Participants were on average 39.5 years old ( SD = 10.1), and participant age did not differ by condition.

a Reflects the number and percentage of participants answering “yes” to this question.

Results of Curve-Fitting Analysis Examining the Time Course of Fixations to the Target

Note. For each subject, the logistic function was fit to target fixations separately. The maximum asymptote is the asymptotic degree of looking at the end of the time course of fixations. The crossover point is the point in time the function crosses the midway point between peak and baseline. The slope represents the rate of change in the function measured at the crossover. Mean parameter values for each of the analyses are shown for the 9-year-olds ( n = 24) and 16-year-olds ( n = 18), as well as the results of t tests (assuming unequal variance) comparing the parameter estimates between the two ages.

Descriptive Statistics and Correlations for Study Variables

Means, Standard Deviations, and One-Way Analyses of Variance in Psychological and Social Resources and Cognitive Appraisals

*** p < .001.

Results From a Factor Analysis of the Parental Care and Tenderness (PCAT) Questionnaire

Note. N = 307. The extraction method was principal axis factoring with an oblique (Promax with Kaiser Normalization) rotation. Factor loadings above .30 are in bold. Reverse-scored items are denoted with an (R). Adapted from “Individual Differences in Activation of the Parental Care Motivational System: Assessment, Prediction, and Implications,” by E. E. Buckels, A. T. Beall, M. K. Hofer, E. Y. Lin, Z. Zhou, and M. Schaller, 2015, Journal of Personality and Social Psychology , 108 (3), p. 501 ( https://doi.org/10.1037/pspp0000023 ). Copyright 2015 by the American Psychological Association.

Moderator Analysis: Types of Measurement and Study Year

Note . Number of studies = 120, number of effects = 782, total N = 52,578. CI = confidence interval; LL = lower limit; UL = upper limit.

Master Narrative Voices: Struggle and Success and Emancipation

Integrated Results Matrix for the Effect of Topic Familiarity on Reliance on Author Expertise

Note . We integrated quantitative data (whether students selected a card about nuclear power or about climate change) and qualitative data (interviews with students) to provide a more comprehensive description of students’ card selections between the two topics.

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How to Use Tables and Figures effectively in Research Papers

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

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

Guidelines for including tables and figures meaningfully in a paper:

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

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

How to present data in a table?

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

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

Example of table presentation in a research paper

For comprehensible and engaging presentation of figures:

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

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

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Getting started with tables

Hazel inskip.

MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, SO16 6YD UK

Georgia Ntani

Leo westbury, chiara di gravio, stefania d’angelo, camille parsons, janis baird, associated data.

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Tables are often overlooked by many readers of papers who tend to focus on the text. Good tables tell much of the story of a paper and give a richer insight into the details of the study participants and the main research findings. Being confident in reading tables and constructing clear tables are important skills for researchers to master.

Common forms of tables were considered, along with the standard statistics used in them. Papers in the Archives of Public Health published during 2015 and 2016 were hand-searched for examples to illustrate the points being made. Presentation of graphs and figures were not considered as they are outside the scope of the paper.

Basic statistical concepts are outlined to aid understanding of each of the tables presented. The first table in many papers gives an overview of the study population and its characteristics, usually giving numbers and percentages of the study population in different categories (e.g. by sex, educational attainment, smoking status) and summaries of measured characteristics (continuous variables) of the participants (e.g. age, height, body mass index). Tables giving the results of the analyses follow; these often include summaries of characteristics in different groups of participants, as well as relationships between the outcome under study and the exposure of interest. For continuous outcome data, results are often expressed as differences between means, or regression or correlation coefficients. Ratio/relative measures (e.g. relative risks, odds ratios) are usually used for binary outcome measures that take one of two values for each study participants (e.g. dead versus alive, obese versus non-obese). Tables come in many forms, but various standard types are described here.

Clear tables provide much of the important detail in a paper and researchers are encouraged to read and construct them with care.

Tables are an important component of any research paper. Yet, anecdotally, many people say that they find tables difficult to understand so focus only on the text when reading a paper. However, tables provide a much richer sense of a study population and the results than can be described in the text. The tables and text complement each other in that the text outlines the main findings, while the detail is contained in the tables; the text should refer to each table at the appropriate place(s) in the paper. We aim to give some insights into reading tables for those who find them challenging, and to assist those preparing tables in deciding what they need to put into them. Producing clear, informative tables increases the likelihood of papers being published and read. Good graphs and figures can often provide a more accessible presentation of study findings than tables. They can add to the understanding of the findings considerably, but they can rarely contain as much detail as a table. Choosing when to present a graph or figure and when to present a table needs careful consideration but this article focuses only on the presentation of tables.

We provide a general description of tables and statistics commonly used when presenting data, followed by specific examples. No two papers will present the tables in the same way, so we can only give some general insights. The statistical approaches are described briefly but cannot be explained fully; the reader is referred to various books on the topic [ 1 – 6 ].

Presentation of tables

The title (or legend) of a table should enable the reader to understand its content, so a clear, concise description of the contents of the table is required. The specific details needed for the title will vary according to the type of table. For example, titles for tables of characteristics should give details of the study population being summarised and indicate whether separate columns are presented for particular characteristics, such as sex. For tables of main findings, the title should include the details of the type of statistics presented or the analytical method. Ideally the table title should enable the table to be examined and understood without reference to the rest of the article, and so information on study, time and place needs to be included. Footnotes may be required to amplify particular points, but should be kept to a minimum. Often they will be used to explain abbreviations or symbols used in the table or to list confounding factors for which adjustment has been made in the analysis.

Clear headings for rows and columns are also required and the format of the table needs careful consideration, not least in regard to the appropriateness and number of rows and columns included within the table. Generally it is better to present tables with more rows than columns; it is usually easier to read down a table than across it, and page sizes currently in use are longer than they are wide. Very large tables can be hard to absorb and make the reader’s work more onerous, but can be useful for those who require extra detail. Getting the balance right needs care.

Types of tables

Many research articles present a summary of the characteristics of the study population in the first table. The purpose of these tables is to provide information on the key characteristics of the study participants, and allow the reader to assess the generalisability of the findings. Typically, age and sex will be presented along with various characteristics pertinent to the study in question, for example smoking prevalence, socio-economic position, educational attainment, height, and body mass index. A single summary column may be presented or perhaps more than one column split according to major characteristics such as sex (i.e. separate columns for males and females) or, for trials, the intervention and control groups.

Subsequent tables generally present details of the associations identified in the main analyses. Sometimes these include results that are unadjusted or ‘crude’ (i.e. don’t take account of other variables that might influence the association) often followed by results from adjusted models taking account of other factors.

Other types of tables occur in some papers. For example, systematic review papers contain tables giving the inclusion and exclusion criteria for the review as well as tables that summarise the characteristics and results of each study included in the review; such tables can be extremely large if the review covers many studies. Qualitative studies often provide tables describing the characteristics of the study participants in a more narrative format than is used for quantitative studies. This paper however, focuses on tables that present numerical data.

Statistics commonly presented in tables

The main summary statistics provided within a table depend on the type of outcome under investigation in the study. If the variable is continuous (i.e. can take any numerical value, between a minimum and a maximum, such as blood pressure, height, birth weight), then means and standard deviations (SD) tend to be given when the distribution is symmetrical, and particularly when it follows the classical bell shaped curve known as a Normal or Gaussian distribution (see Fig.  1a ). The mean is the usual arithmetic average and the SD is an indication of the spread of the values. Roughly speaking, the SD is about a quarter of the difference between the largest and the smallest value excluding 5% of values at the extreme ends. So, if the mean is 100 and the SD is 20 we would expect 95% of the values in our data to be between about 60 (i.e. 100–2×20) and 140 (100 + 2×40).

Distribution of heights and weights of young women from the Southampton Women’s Survey [ 7 ]. a Shows the height distribution, which is symmetrical and generally follows a standard normal distribution, while b shows weight, which is skewed to the right

The median and inter-quartile range (IQR) are usually provided when the data are not symmetrical as in Fig.  1b , which gives an example of data that are skewed, such that if the values are plotted in a histogram there are many values at one end of the distribution but fewer at the other end [ 7 ]. If all the values of the variable were listed in order, the median would be the middle value and the IQR would be the values a quarter and three-quarters of the way through the list. Sometimes the lower value of the IQR is labelled Q1 (quartile 1), the median is Q2, and the upper value is Q3. For categorical variables, frequencies and percentages are used.

Common statistics for associations between continuous outcomes include differences in means, regression coefficients and correlation coefficients. For these statistics, values of zero indicate no association between the exposure and outcome of interest. A correlation coefficient of 0 indicates no association, while a value of 1 or −1 would indicate perfect positive or negative correlation; values outside the range −1 to 1 are not possible. Regression coefficients can take any positive or negative value depending on the units of measurement of the exposure and outcome.

For binary outcome measures that only take two possible values (e.g. diseased versus not, dead versus alive, obese versus not obese) the results are commonly presented in the form of relative measures. These include any measure with the word ‘relative’ or ‘ratio’ in their name, such as odds ratios, relative risks, prevalence ratios, incidence rate ratios and hazard ratios. All are interpreted in much the same way: values above 1 indicate an elevated risk of the outcome associated with the exposure under study, whereas below 1 implies a protective effect. No association between the outcome and exposure is apparent if the ratio is 1.

Typically in results tables, 95% confidence intervals (95% CIs) and/or p -values will be presented. A 95% CI around a result indicates that, in the absence of bias, there is a 95% probability that the interval includes the true value of the result in the wider population from which the study participants were drawn. It also gives an indication of how precisely the study team has been able to estimate the result (whether it is a regression coefficient, a ratio/relative measure or any of the summary measures mentioned above). The wider the 95% CI, the less precise is our estimate of the result. Wide 95% CIs tend to arise from small studies and hence the drive for larger studies to give greater precision and certainty about the findings.

If a 95% CI around a result for a continuous variable (difference in means, regression or correlation coefficient) includes 0 then it is unlikely that there is a real association between exposure and outcome whereas, for a binary outcome, a real association is unlikely if the 95% CI around a relative measure, such as a hazard or odds ratio, includes 1.

The p -value is the probability that the finding we have observed could have occurred by chance, and therefore there is no identifiable association between the exposure of interest and the outcome measure in the wider population. If the p -value is very small, then we are more convinced that we have found an association that is not explained by chance (though it may be due to bias or confounding in our study). Traditionally a p -value of less than 0.05 (sometimes expressed as 5%) has been considered as ‘statistically significant’ but this is an arbitrary value and the smaller the p -value the less likely the result is simply due to chance [ 8 ].

Frequently, data within tables are presented with 95% CIs but without p -values or vice versa. If the 95% CI includes 0 (for a continuous outcome measure) or 1 (for a binary outcome), then generally the p -value will be greater than 0.05, whereas if it does not include 0 or 1 respectively, then the p -value will be less than 0.05 [ 9 ]. Generally, 95% CIs are more informative than p -values; providing both may affect the readability of a table and so preference should generally be given to 95% CIs. Sometimes, rather than giving exact p-values, they are indicated by symbols that are explained in a footnote; commonly one star (*) indicates p  < 0.05, two stars (**) indicates p  < 0.01.

Results in tables can only be interpreted if the units of measurement are clearly given. For example, mean or median age could be in days, weeks, months or years if infants and children are being considered, and 365, 52, 12 or 1 for a mean age of 1 year could all be presented, as long the unit of measurement is provided. Standard deviations should be quoted in the same units as the mean to which they refer. Relative measures, such as odds ratios, and correlation coefficients do not have units of measurement, but for regression coefficients the unit of measurement of the outcome variable is required, and also of the exposure variable if it is continuous.

The examples are all drawn from recent articles in Archives of Public Health. They were chosen to represent a variety of types of tables seen in research publications.

Tables of characteristics

The table of characteristics in Table  1 is from a study assessing knowledge and practice in relation to tuberculosis control among in Ethiopian health workers [ 10 ]. The authors have presented the characteristics of the health workers who participated in the study. Summary statistics are based on categories of the characteristics, so numbers (frequencies) in each category and the percentages of the total study population within each category are presented for each characteristic. From this, the reader can see that:

• the study population is quite young, as only around 10% are more than 40 years old;
• the majority are female;
• more than half are nurses;
• about half were educated to degree level or above.

Table of study population characteristics from a paper on the assessment of knowledge and practice in relation to tuberculosis control in health workers in Ethiopia [ 10 ]. Socio demographic characteristics of the study population in public health facilities, Addis Ababa, 2014

OPD outpatient department; TB Tuberculosis.

a Midwife, radiology, physiotherapy; b MCH, delivery,EPI, FP, physiotherapy

The table of characteristics in Table  2 is from a study of the relationship between distorted body image and lifestyle in adolescents in Japan [ 11 ]. Here the presentation is split into separate columns for boys and girls. The first four characteristics are continuous variables, not split into categories but, instead, presented as means, with the SDs given in brackets. The three characteristics in the lower part of the table are categorical variables and, similar to Table  1 , the frequency/numbers and percentages in each category are presented. The p -values indicate that boys and girls differ on some of the characteristics, notably height, self-perceived weight status and body image perception.

Table of study population characteristics from a paper on the relationship between distorted body image and lifestyle in adolescents in Japan [ 11 ]. Characteristics of study participants by sex (Japan; 2005–2009)

Data are expressed as numbers (%), values are means (standard deviation). The unpaired t- test and chi-squad test were used to compare characteristics between boys and girls

In Table  3 , considerable detail is given for continuous variables in the table. This comes from an article describing the relationship between mid-upper-arm circumference (MUAC) and weight changes in young children admitted to hospital with severe acute malnutrition from three countries [ 12 ]. For each country, the categorical characteristic of sex is presented as in the previous two examples, but more detail is given for the continuous variables of age, MUAC and height. The mean is provided as in Table  2 , though without a standard deviation, but we are also given the minimum value, the 25th percentile (labelled Q1 – for quartile 1), the median (the middle value), the 75th percentile (labelled Q2, here though correctly it should be Q3 – see above) and the maximum value. The table shows:

• Ethiopian children in this study were older and taller than those from the other two countries but their MUAC measurements tended to be smaller;
• in Bangladesh, disproportionally more females than males were admitted for treatment compared with the other two countries.

Table of study population characteristics from a paper describing the relationship between mid-upper-arm circumference (MUAC) and weight changes in young children [ 12 ]. Characteristics of study population at admission

It is unusual to present as much detail on continuous characteristics as is given in Table  3 . Usually, for each characteristic, either (a) mean and SD or (b) median and IQR would be given, but not both.

Tables of results – summary findings

Many results tables are simple summaries and look similar to tables presenting characteristics, as described above. Sometimes the initial table of characteristics includes some basic comparisons that indicate the main results of the study. Table  4 shows part of a large table of characteristics for a study of risk factors for acute lower respiratory infections (ALRI) among young children in Rwanda [ 13 ]. In addition to presenting the numbers of children in each category of a variety of characteristics, it also shows the percentage in each category among those who suffered ALRI in the previous two weeks, and provides p- values for the differences between the categories among those who did and did not suffer from ALRI. Thus only 2.9% of older children (24–59 months) within the study suffered from ALRI, compared with about 5% in the two youngest categories. The p -value of 0.001, well below 0.05, indicates that this difference is statistically significant. The other finding of some interest is that children who took vitamin A supplements appeared to be less likely to suffer from ALRI than those who did not, but the p -value of 0.04 is close to 0.05 so not as remarkable a finding as for the difference between the age groups.

Part of a table of basic results from a study of risk factors for acute lower respiratory infections (ALRI) among young children in Rwanda [ 13 ]. Bivariate analysis of factors associated with acute lower respiratory infection among children under five in Rwanda, RDHS 2010

Table  5 shows a summary table of average life expectancy in British Columbia by socioeconomic status [ 14 ]. The average life expectancy at birth and the associated 95% CIs are given according to level of socio-economic status for the total population (column 1), followed by males and females separately. The study is large so the 95% CIs are quite narrow, and the table indicates that there are considerable differences in life expectancy between the three socioeconomic groups, with the lowest category having the poorest life expectancy. The gap in life expectancy between the lowest and highest category is more than three years, as shown in the final row.

Summary table of average life expectancy in British Columbia by socioeconomic status [ 14 ]. British Columbia regional average life expectancy at birth by regional socioeconomic status, 2007–2011

SES Socioeconomic status, LE 0 Life expectancy at birth, CI Confidence interval

Tables of results – continuous outcomes

Continuous outcome measures can be analysed in a variety of ways, depending on the purpose of the study and whether the measure of the exposure is continuous, categorical or binary.

Table  6 shows an example of correlation coefficients indicating the degree of association between the exposure of interest (cognitive test scores) and the outcome measure (academic performance) [ 15 ]. No confidence intervals are presented, but the results show that almost all the particular cognitive test scores are statistically significantly associated ( p -value < 0.05) with the two measures of academic performance. Note that this table is an example of where a footnote is used to give information about the p-values. Not surprisingly, all the correlations are positive; one would expect that as cognitive score increase so too would academic performance. The numbers labelled “N” give the number of children who contributed data to each correlation coefficient.

Correlation coefficients from a study assessing the association between cognitive function and academic performance in Ethiopia [ 15 ]. Correlation between cognitive fuinction test and academic performance among school aged children in Goba Town, South east Ethiopia, May 2014

*Statistically significant at p <0.05, **Statistically significant a p >0.01

Table  7 is quite a complex table, but one that bears examination. It presents regression coefficients from an analysis of pregnancy exposure to nitrogen dioxide (NO 2 ) and birth weight of the baby in a large study of four areas in Norway; more than 17,000 women-baby pairs contributed to the complete crude analysis [ 16 ]. Regression coefficients are presented and labelled “Beta”, the usual name for such coefficients, though the Greek letter β, B or b are sometimes used. They are interpreted as follows: for one unit increase in the exposure variable then the outcome measure increases by the amount of the regression coefficient. Regression coefficients of zero indicate no association. In this table, the Beta in the top left of the table indicates that as NO 2 exposure of the mother increases by 1 unit (a ‘unit’ in this analysis is 10 μg/m 3 , see the footnote in the table, which gives the units of measurement used for the regression coefficients: grams per 10 μg/m 3 NO 2 ) then the birth weight of her baby decreases (because the Beta is negative) by 37.9 g. The 95% CI does not include zero and the p -value is small (<0.001) implying that the association is not due solely to chance.

Table of regression coefficients for the relationship between exposure to NO 2 in pregnancy and birth weight [ 16 ]. Main and stratified analysis of association between pregnancy exposure to NO 2 and birth weight

Effect estimate in grams per 10μg/m 3 NO 2

GA gestational age, LMP last menstrual period

a Model 1 adjusted for: maternal education, birth season, sex of child, maternal age, maternal marital status, maternal smoking during pregnancy, maternal height

b Model 2 adjusted for: maternal education, birth season, sex of child, maternal age, maternal marital status, maternal smoking during pregnancy, maternal height, area

c Model 3 adjusted for: maternal education, birth season, sex of child, maternal age, maternal marital status, maternal smoking during pregnancy, maternal height, parity, maternal weight, in stratified analysis the corresponding stratification variable is not included in the adjusment

However, reading across the columns of the table gives a different story. The successive sets of columns include adjustment for increasing numbers of factors that might affect the association. While model 1 still indicates a negative association between NO 2 and birth weight that is highly significant ( p  < 0.001), models 2 and 3 do not. Inclusion of adjustment for parity or area and maternal weight has reduced the association such that the Betas have shrunk in magnitude to be closer to 0, with 95% CIs including 0 and p -values >0.05.

The table has multiple rows, with each one providing information on a different subset of the data, so the numbers in the analyses are all smaller than in the first row. The second row restricts the analysis to women who did not move address during pregnancy, an important consideration in estimating NO 2 exposure from home addresses. The third row restricts the analysis to those whose gestational age was based on the last menstrual period. These second two rows present ‘sensitivity analyses’, performed to check that the results were not due to potential biases resulting from women moving house or having uncertain gestational ages. The remaining rows in the table present stratified analyses, with results given for each category of various variables of interest, namely geographical area, maternal smoking, parity, baby’s sex, mother’s educational level and season of birth. Only one row of this table has a statistically significant result for models 2 and 3, namely babies born in spring, but this finding is not discussed in the paper. Note the gap in the table in the model 2 column as it is not possible to adjust for area (one of the adjustment factors in model 2) when the analysis is being presented for each area separately.

Tables of results – binary outcomes

Table  8 presents results from a study assessing whether children’s eating styles are associated with having a waist-hip ratio greater or equal to 0.5 (the latter being the outcome variable expressed in binary form – ≥0.5 versus <0.5) [ 17 ]. Results for boys and girls are presented separately, along with the number of children in each of the eating style categories. The main results are presented as crude and adjusted odds ratios (ORs). The adjusted ORs take account of age, exercise, skipping breakfast and having a snack after dinner, all of these being variables thought to affect the association between eating style and waist-hip ratio. Looking at the crude OR column, the value of 2.04 in the first row indicates that, among boys, those who report eating quickly have around twice the odds of having a high waist-hip ratio than those who do not eat quickly (not eating quickly is the baseline category, with an odds ratio given as 1.00). The 95% CI for the crude OR for eating quickly is 1.31 – 3.18. This interval does not include 1, indicating that the elevated OR for eating quickly is unlikely to be a chance finding and that there is a 95% probability that the range of 1.31 – 3.18 includes the true OR. The p -value is 0.002, considerably smaller than 0.05, indicating that this finding is ‘statistically significant’. The other ORs can be considered in the same way, but note that, for both boys and girls, the ORs for eating until full are greater than 1 but their 95% CIs include 1 and the p- values are considerably greater than 0.05, so not ‘statistically significant’, indicating chance findings.

Results table from a study assessing whether children’s eating styles are associated with having a waist-hip ratio ≥0.5 or not [ 17 ]. Crude and adjusted odds ratios of eating quickly or eating until full for waist-to-height ratio (WHtr) ≥ 0.5

OR odds ratio; CI confidence interval

Adjusted for age, exercise, skipping breakfast, and snack after dinner

The final columns present the ORs after adjustment for various additional factors, along with their 95% CIs and p -values. The ORs given here differ little from the crude ORs in the table, indicating that the adjustment has not had much effect, so the conclusions from examining the crude ORs are unaltered. It thus appears that eating quickly is strongly associated with a greater waist-hip ratio, but that eating until full is not.

Summary tables of characteristics describe the study population and set the study in context. The main findings can be presented in different ways and choice of presentation is determined by the nature of the variables under study. Scrutiny of tables allows the reader to acquire much more information about the study and a richer insight than if the text only is examined. Constructing clear tables that communicate the nature of the study population and the key results is important in the preparation of papers; good tables can assist the reader enormously as well as increasing the chance of the paper being published.

Acknowledgement

Not applicable.

The work was funded by the UK Medical Research Council which funds the work of the MRC Lifecourse Epidemiology Unit where the authors work. The funding body had no role in the design and conduct of the work, or in the writing the manuscript.

Availability of data and materials

Authors’ contributions.

HI conceived the idea for the paper in discussion with JB. HI wrote the first draft and all other authors commented on successive versions and contributed ideas to improve content, clarity and flow of the paper. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Ethics approval and consent to participate, abbreviations.

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Figures and tables

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

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

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

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

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

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

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

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

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

Tables and Figures in Research Papers: What Should you Use?

Researchers often use tables and figures in their research paper as visual representations to convey data in a simple way. Tables and figures in research papers not only enable readers to understand complex data at a glance but they also help create better engagement in one’s research. Instead of having to wade through dense paragraphs of text, readers of your research are able to quickly and easily identify patterns, gather important information, and understand interactions between data points with tables and figures.

While there are many kinds of visual tools that students and researchers can employ to explain the approach, methodology, research process and conclusion of their research, deciding whether to use a table, graph or a visual, and what kind – isn’t always easy. In this article, we will cover the basics of using tables and figures in research papers, so you know when and how to use them to accurately communicate research results.

Table of Contents

How to choose between tables and graphs

When choosing whether to use tables or figures in research papers, it is important to consider what type of information you want to convey with your visuals. This will help determine which format would be best suited for the data. If you are presenting numerical data, a table is often the best choice because tables are a great way to compare values or characteristics among related items and are particularly useful for presenting large sets of data in a systematic manner.

Figures or graphs, on the other hand, can be used to show trends, or relationships. Graphs and charts allow readers to quickly and easily identify patterns in data, and are particularly useful for presenting data over time or when comparing different variables.

So, if you have a lot of numerical data, then a tabular format would be more appropriate. If you have more textual data, then a graphical format would be more suitable.

Choosing the appropriate table to present your data

Once you have decided on the type of data you will be presenting, you need to choose a format that can present your data in an easy to read and understand way. Tables can be overwhelming and difficult to read if they are too complex, and therefore, experts suggest keeping the format simple. It is also important to ensure that tables in research papers are accompanied by titles, labels or captions that are clear, concise, and engaging.

Identifying the right kind of graph to convey information

It is important to choose the right kind of graph or chart to communicate and highlight research findings. For example, if you are presenting data over time, a line graph is often the best choice, while a bar graph may be better for comparing different categories of data. Remember to ensure that the graphs, schematic diagrams, line drawings and data plots are clearly presented, and neatly composed. Using a prudent mix of colors and contrasts is a good way to highlight data without complicating it.

Using text is important too

It is important to keep in mind that using tables and figures in research papers may not always be required. It is okay to use text when you do not have extensive or complex data to share. Text can be used to provide context and explanation of the data being presented. Text can also help to provide a narrative to the data, making it easier for readers to understand its significance and implications. Using text is effective when one is not working towards presenting a large dataset or when one wants to present data marginal to the study.

Important points to keep in mind while using tables and figures in research papers

• Tables and figures in research papers must be clearly and simply presented to better clarity and ease of understanding. Figures that are complex or tables with too much information can dissuade readers from reading further and even dampen their interest.
• Remember to ensure that the data is not repeated in the main text of the research paper. Use only critical and necessary data and information in tables and figures that are pertinent to the research area or question being studied.
• Tables and figures in research papers must be integrated into the text in a way that is easy to understand and is visually appealing. It is important to ensure that the font size, style, and colors used are consistent with the overall design of the research paper. This will help to create a professional and cohesive look to the paper.
• It is crucial for researchers to adhere to uniformity in style and format while using tables and figures in research papers. Care must be taken to ensure that these align with the requirements and guidelines of the target journal or publication.
• When presenting tables and figures in research papers, it is important to ensure that they are clearly labelled and titled. This will help readers to understand what they are looking at and why it is important. It is also important to provide a brief explanation of the data or information being presented and provide context for the reader.
• If researchers use tables and figures that are not their own, it is essential that they correctly cite the sources to avoid being charged with plagiarism.

Keeping the above points in mind will help you in making sure that the tables and figures used in research papers effectively communicate critical aspects of your research with readers.

In conclusion, the choice between tables and figures in research papers is a nuanced decision that hinges on the nature of your data and the story you aim to tell. Tables are ideal for presenting precise numerical values and detailed comparisons, while figures excel at visually illustrating trends, patterns, and complex relationships. Consider your research goals, audience, and the most effective way to convey your findings. A strategic combination of tables and figures can enhance the clarity and impact of your research paper. By thoughtfully selecting these visual aids, you empower your readers to better comprehend and engage with your study’s insights.

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• Citing tables and figures from other sources in APA Style

Citing Tables and Figures in APA Style | Format & Examples

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

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

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

Table of contents

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

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

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

Prevent plagiarism. Run a free check.

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

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

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

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

Types of copyright and permission

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

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

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

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

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

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

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

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

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

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

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

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

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Caulfield, J. (2023, December 27). Citing Tables and Figures in APA Style | Format & Examples. Scribbr. Retrieved August 21, 2024, from https://www.scribbr.com/apa-examples/citing-tables-figures/

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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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Research Tips and Infromation

Best Practices for Designing and Formatting Tables in Research Papers

Introduction

Advantages of using tables in research papers, simple table, complex table, comparison table, statistical table, guidelines for effective use of tables in research papers, i. conditional formatting, ii. data bars, iii. highlighting cells:, how to fit my table by splitting it into multiple pages, can i give citations within the table , what is creative commons license, whether tables are also part of the plagiarism check, how many tables should be there in a research paper of 10 pages, can i put tables at the end of research paper, can i put table in single column in a two column research paper format.

Tables are a crucial aspect of research papers, providing a visual representation of data and results. They are used to effectively and concisely convey information to the reader. The purpose of using tables in research papers is to organize and present data in a manner that is easy to understand and interpret.

A table is a way of arranging data in rows and columns, allowing the reader to quickly identify patterns and trends within the data. It can be used to compare different results or to present large amounts of information in a clear and organized manner.

The importance of using tables in research papers cannot be overstated. Not only do they improve the overall clarity and organization of the paper, but they also make it easier for the reader to understand and interpret the results.

In this article, we will explore the advantages of using tables in research papers, the different types of tables commonly used, and how to effectively utilize tables in your research paper.

As a researcher or academic, you may have started out presenting data and information in your research papers in a simple format, such as just listing the data as plain text. However, as you progressed in your work, you soon realized the importance of presenting the information in a clear and organized manner.

You may have experienced the difficulties of presenting complex data in a simple format, and struggled with making the information easy for readers to understand. That’s when you discovered the power of tables.

Tables allow you to present complex data in a simple and easy-to-read format, helping your readers understand the information quickly and accurately. They also help to save space and make it easier to compare different data sets.

However, you soon learned that simply presenting the data in a table was not enough. You realized the importance of differentiating the rows in your tables to make the information stand out and easier to understand. You explored different ways to do this, such as using different background colors, shading, bold or italic text, different font sizes or styles, and different alignments.

Through your experience, you learned that tables play a crucial role in research papers, and that differentiating the rows in your tables can greatly improve the clarity and organization of your information. You continued to refine your table-making skills, ultimately resulting in the ability to present your data and information in the best possible way.

There are several benefits to using tables in research papers, including:

• Clarity and Organization of Data: Tables help to visually organize data and results, making it easier for the reader to understand and interpret the information. This can be particularly useful when presenting complex or detailed data sets.
• Easy Comparison of Results: Tables allow for quick and easy comparison of results, making it simpler for the reader to identify trends and patterns in the data. This is especially useful when presenting multiple results or comparing results from different experiments or studies.
• Ability to present large amounts of information: Tables are an effective way to present large amounts of data in a concise and organized manner. They help to break down complex information into manageable chunks, making it easier for the reader to comprehend.
• Improved Visual Appeal: Tables can improve the visual appeal of a research paper, breaking up long sections of text and making it easier for the reader to follow the information being presented. They can also help to clarify and emphasize key results or findings.

By utilizing tables in your research paper, you can effectively communicate your results and make it easier for the reader to understand the information you are presenting. The use of tables can also improve the overall clarity and organization of the paper, making it a valuable tool for any researcher.

Types of Tables Commonly Used in Research Papers

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

A simple table presents data in a basic format, with columns and rows to organize the information. This type of table is useful for presenting simple data sets, such as small amounts of numerical or categorical data.

A complex table is used to present more complex data sets, such as large amounts of numerical data or data with multiple categories. This type of table may also include subheadings, footnotes, or other additional information to help the reader understand the data being presented.

A comparison table is used to compare data or results from multiple sources, experiments, or studies. This type of table allows the reader to quickly identify similarities and differences between the data being presented.

A statistical table presents numerical data and statistical results, such as means, standard deviations, and p-values. This type of table is useful for presenting results from statistical analyses and can be used to effectively communicate the significance of the results.

When using tables in research papers, it is important to follow certain guidelines to effectively communicate the information being presented. Some of these guidelines include:

• Clearly label the table and provide a brief description: Label the table with a clear and descriptive title, and provide a brief description of the information being presented. This will help the reader understand the purpose of the table and what information they can expect to find.
• Choose the right type of table: Choose the right type of table for the data being presented, as outlined in the previous section. This will help to effectively communicate the results and make it easier for the reader to understand the information.
• Keep tables simple and concise: Keep tables simple and concise, using only the necessary information to effectively communicate the results. Avoid using overly complex or cluttered tables, as this can make it more difficult for the reader to understand the information being presented.
• Use appropriate formatting: Use appropriate formatting to effectively communicate the information being presented. For example, use bold or italic text to highlight important information, and align the columns and rows in a way that makes the information easy to read.
• Provide clear and concise captions: Provide clear and concise captions for each table, explaining the purpose and results of the data being presented. This will help the reader understand the information being presented and will also provide context for the results.

By following these guidelines, researchers can effectively utilize tables in their research papers to communicate their results in a clear and organized manner. The use of tables can improve the overall clarity and organization of the paper, making it easier for the reader to understand the information being presented.

Highlighting Key Information in Tables Through Row Differentiation

When presenting data in a table, it is important to make sure that the information is organized and easy to understand. One effective way to do this is by differentiating the rows in the table. Here are several ways to achieve this, including using different background colors, shading or borders, text formatting, alignment, row spacing, and highlighting cells with specific values. These methods can help to group similar data, highlight important data points, and make the table easier to read and understand. Whether you are presenting data in a research paper, a business report, or any other type of document, utilizing these techniques can enhance the clarity and impact of your data presentation.

• Background color: Different background colors can be used to distinguish between rows and highlight specific groups of data.
• Shading or borders: Using shading or borders can help to separate rows in a table and distinguish between different groups of data.
• Text formatting: Bold or italic text can be used to highlight specific rows and make them stand out from the rest of the data. Different font sizes or styles can also be used to differentiate between rows.
• Alignment: Different alignments, such as centre or right alignments, can be used to differentiate between rows and distinguish between different types of data.
• Row spacing: Increasing the row spacing can help to separate the rows and make the table easier to read.
• Differentiating rows based on values: This is a feature in spreadsheet programs like Microsoft Excel and Google Sheets that allows you to apply conditional formatting rules to cells based on their values.

These are just a few examples of ways to differentiate rows in a table. The best approach will depend on the type of data being presented and the purpose of the table. The goal should be to make it easy for the reader to understand the information being presented and distinguish between different rows. As points 1-5 are most familiar and well known, I will elaborate point 6 in the following section.

Differentiate Rows in a Table Based on the Values of the Table.

There are several ways to do this:

This is a feature in spreadsheet programs like Microsoft Excel and Google Sheets that allows you to apply conditional formatting rules to cells based on their values. For example, you could apply a certain color to cells with a certain value range or highlight cells with specific values.

This is another feature in spreadsheet programs that allows you to add a bar to cells based on their values. This can help you visualize the relative magnitude of values within a table.

You can also manually highlight cells with specific values to draw attention to them. This can be done using the built-in highlighting tools in spreadsheet programs or by manually adding borders or shading to the cells.

How to fit Big Table in a Research Paper?

Tables are a crucial component of research papers as they help to present data in a clear and organized manner. However, sometimes the amount of data you need to present can result in a table that is too big to fit on one page. In such cases, fitting the table into a research paper can become a challenge. But with a few adjustments and strategies, you can effectively fit a big table into your research paper and ensure that the information is presented in a clear and readable manner. In this article, we’ll discuss a few methods for fitting a large table into a research paper.

• Reduce the font size : Reducing the font size can help fit more data into the same amount of space, but it may make the table more difficult to read.
• Split the table into multiple smaller tables: Splitting the large table into smaller tables that focus on different aspects of the data can make it easier to read and understand.
• Use landscape orientation: Changing the orientation of the page to landscape can provide more space for the table.
• Use a smaller font for numerical values: If the data in the table consists mainly of numerical values, you can use a smaller font for the values and a larger font for the headings.
• Use abbreviations or symbols: Replacing lengthy text with abbreviations or symbols can reduce the size of the table while still conveying the necessary information.
• Use a table that scrolls horizontally: Some word processors and typesetting programs allow you to create tables that can be scrolled horizontally, allowing you to fit more data into the same amount of space.
• Omit non-essential information: If the table contains data that is not critical to your research, consider omitting it to reduce the size of the table.

The best option of all is to split a table and show it across multiple pages when the table contains more items row-wise. in a research paper. The exact method for doing so depends on the word processing software or typesetting system you are using.

For example, in Microsoft Word , you can split a table across multiple pages by selecting the row below which you want to split the table, and then going to “Layout” > “Breaks” > “Next Page” to insert a page break. The upper part of the table will be on one page and the lower part will start on the next page.

In LaTeX , you can split a table across multiple pages using the long table package. The long table package allows you to create tables that span multiple pages, with header and footer rows that repeat on each page.

Regardless of the method used, it is important to ensure that the split table is still readable and the data is easy to understand, even when split across multiple pages.

When splitting a table across multiple pages in a research paper, it is important to ensure that the headings are also repeated on each page to make the table readable and easy to understand.

In Microsoft Word, you can repeat the headings by selecting the first row of the table (which contains the headings) and then right-clicking and selecting “Table Properties.” In the “Row” tab, you can check the “Repeat as header row at the top of each page” option. This will cause the headings to be repeated at the top of each page on which the table is split.

In LaTeX, you can repeat the headings by using the long table package as described in my previous answer. The long table package provides options for defining the header and footer rows that are repeated on each page of the table.

Regardless of the method used, it is important to ensure that the headings are clearly visible and easily distinguishable from the rest of the table. This helps readers understand the data contained in the table and follow its structure, even when split across multiple pages.

When splitting a table across multiple pages in a research paper, it is important to ensure that the headings are also repeated on each page to make the table readable and easy to understand. In Microsoft Word, you can repeat the headings by selecting the first row of the table (which contains the headings) and then right-clicking and selecting “Table Properties.” In the “Row” tab, you can check the “Repeat as header row at the top of each page” option. This will cause the headings to be repeated at the top of each page on which the table is split.

In LaTeX, you can repeat the headings by using the longtable package. The longtable package provides options for defining the header and footer rows that are repeated on each page of the table. Regardless of the method used, it is important to ensure that the headings are clearly visible and easily distinguishable from the rest of the table. This helps readers understand the data contained in the table and follow its structure, even when split across multiple pages.

How to Refer Tables in a Research Paper?

In a research paper, tables are usually referred to in the text by their number, such as Table 1, Table 2, etc. To refer to a specific element within a table, such as a specific row or column, you can specify the table number followed by the row and column number, e.g. “Table 1, Row 2, Column 3”. When referring to a table, it is important to ensure that the reference is clear and accurate and that the table is properly cited if the information is taken from another source.

Yes, you can give references or citations within a table in a research paper. The exact method of citing within a table depends on the referencing style you are using, but common methods include adding a superscript number or symbol in the cell of the table and then listing the corresponding reference in a footnote or in a reference list at the end of the paper. It is important to be consistent and clear in your referencing within tables to avoid confusion and to give credit where it is due.

here is an example of referencing within a table:

In this example, the reference column lists the number of sources where the information for each row was obtained. This information can then be referenced in the text of the research paper. For example, you could write “The sales and expenses for the North region in 2010 and 2011 are shown in Table 1 and are cited in references [1] and [2].”

Copyrights, Permissions and Plagiarism Check for Tables

Tables, like other types of data and images, can be subject to copyright protection. It depends on the specific circumstances surrounding the creation and use of the table. If the table is original and creative, it may be eligible for copyright protection as a literary work. On the other hand, if the table simply presents factual information in a straightforward manner, it may not be eligible for copyright protection. It’s important to consider the legal implications before using a table in a research paper or other publication. In general, it’s advisable to obtain permission from the copyright holder or to use tables that are in the public domain or licensed under a Creative Commons license.

To determine whether a table is under copyright protection, you can consider the following factors:

• Originality: If the table is original and creative, it may be eligible for copyright protection.
• Factual information: If the table simply presents factual information in a straightforward manner, it may not be eligible for copyright protection.
• Attribution: If the table was created by someone else, you should check for any attribution or copyright information. This information may be found in the table itself, in the source material from which the table was created, or in a separate copyright notice.
• Public domain: Tables that are in the public domain are not under copyright protection. You can use these tables without permission.
• Creative Commons license: Some tables may be licensed under a Creative Commons license, which allows you to use the table with certain conditions.

It’s important to check the specific circumstances surrounding the creation and use of the table to determine whether it’s under copyright protection. If in doubt, it’s advisable to obtain permission from the copyright holder or to use tables that are in the public domain or licensed under a Creative Commons license.

Creative Commons is a nonprofit organization that provides a set of standardized licenses for creators to use when making their work available to others. These licenses are designed to help creators maintain control over their work, while also making it possible for others to use, share, and build upon that work in ways that are legal and consistent with the creator’s intentions.

Some common creative commons licenses include Attribution (CC BY), Attribution-ShareAlike (CC BY-SA), and Attribution-NoDerivs (CC BY-ND) licenses. These licenses specify how others are allowed to use a creator’s work, such as by requiring attribution, allowing derivative works, or requiring that any derivative works be shared under the same license.

To obtain a Creative Commons license, one should approach the Creative Commons organization, which provides free, flexible copyright licenses that allow creators to share their work with the public while maintaining control over their rights.

The organization provides a license wizard that allows creators to choose the license that best suits their needs and provides guidance on how to properly use the license. The license can be applied to various types of creative works, including text, images, videos, and music.

Tables are also part of the plagiarism checks in research papers. All sources and information used in a research paper, including tables, should be properly cited to avoid plagiarism. Tables created from original data and analysis are also subject to plagiarism checks, as they are considered original content. It is important to ensure that all information in a research paper, including tables, is properly cited and does not violate copyright or plagiarism laws.

To avoid plagiarism in tables in a research paper, one should follow the following guidelines:

• Always properly cite any sources used to create the table, including any data, calculations, or other information that has been used.
• Use original language and explanations when writing captions and annotations for the table.
• Use a plagiarism detection tool, such as Turnitin, to check the content of the table.
• Make sure to use different sources to create the table, rather than relying on a single source.
• Use proper referencing styles and formatting, such as MLA, APA, or Chicago style, to avoid any accidental plagiarism.
• Avoid copying tables directly from other sources, even if you have cited the source.
• Create the table yourself, using data and calculations that you have obtained independently.
• If you must use a table from another source, make sure to make substantial changes to it so that it is no longer an exact copy.
• Always be mindful of copyright laws and make sure to obtain permission to use any copyrighted materials in your table.
• Regularly review and update your table to ensure that it is in compliance with plagiarism and copyright laws.

I have written an article on The Consequences of Plagiarism: What You Need to Know? . This article will help you to understand the importance of understanding consequences of plagiarism.

In conclusion, tables are an essential tool for presenting data and information in a clear and organized manner in research papers. They are used to present complex data in a simple and easy-to-read format, which helps readers to understand the information quickly and accurately. Tables also help to save space and make it easier to compare different data sets.

Differentiating the rows in a table is an important aspect of table design, as it helps to make the information stand out and makes it easier to understand. There are several ways to differentiate rows in a table, including using different background colors, shading, bold or italic text, different font sizes or styles, and different alignments. The most appropriate method will depend on the data being presented and the purpose of the table.

Frequently Asked Questions

The number of tables in a research paper can vary depending on the specific requirements of the paper and the nature of the research being presented. As a general guideline, there is no strict rule on how many tables should be included in a 10-page research paper, as it can vary greatly depending on the research topic, methodology, and the amount of data being presented. As a rough estimate, a 10-page research paper may include anywhere from 1 to 5 tables, but this can vary significantly based on the factors mentioned above.

Yes, it is common to include tables at the end of a research paper, after the references section. This is typically done to keep the main body of the research paper focused on the narrative and analysis, while providing supplementary information, such as tables or other supporting data, at the end.

Yes, it is possible to include a table in a single column format within a two-column research paper. In a two-column format, the text typically flows in two columns, side by side, across the page. However, if you need to include a table that requires a wider layout or if it is easier to read as a single column, you can insert a table that spans the entire width of the page in a single column format.

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Your Guide to Creating Effective Tables and Figures in Research Papers

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

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

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

Check style guides and journal requirements

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

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

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

Table formatting

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

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

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

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

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

When creating tables, especially those derived from complex datasets or scanned documents, tools like JPG to Excel converter can be incredibly useful. These tools can automate the extraction of tabular data from images, ensuring accuracy and saving time. By converting image-based data into editable Excel sheets, researchers can easily format, analyze, and integrate the information into their papers. Utilizing such converters can enhance the efficiency of your data management process, making the preparation of tables for your research paper more seamless.

Figures in research papers

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

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

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

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

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

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

Preparing tables for research papers

Tables and illustrations are important tools for efficiently communicating information and data contained in your research paper to the readers. They present complex results in a comprehensible and organized manner.

However, it is advisable to use tables and illustrations wisely so as to maximize the impact of your research.They should be organized in an easy-to-understand format to convey the information and findings collected in your research. The tabular information helps the reader identify the theme of the study more readily. Although data tables should be complete,they should not be too complex. Instead of including a large volume of data in a single unwieldy table, it is prudent to use small tables to help readers identify the important information easily.

Here are some points you should consider before drafting the tables in your research paper:

• Finalize the results that are required to be presented in tabular form.
• Include the data or results that are relevant to the main aim of the study without being choosy and including only those results that support your hypothesis.
• Create each table in a lucid manner and style without cluttering it with in-table citations.
• Number the tables in a sequence according to their occurrence in the text.
• Don’t mix tables with figures. Maintain separate numbering systems for tables and figures.
• Create tables in a storytelling manner. Remember that your tables communicate a story to the reader that runs parallel to the text.
• If you are using or reproducing tables from other published articles, obtain permission from the copyright holder (usually the publisher) or/andacknowledge the source.
• Do not repeat the tabular contents in the text again; that will create confusion among readers.
• Use clear and informative text for each table title.
• Take extra care while extending the data in your tables. If you have too many tables, consider using them as appendices or supplementary materials.
• Create tables with sufficient spacing in the layout so that they do not look messy, crowded, or cluttered.
• Do not forget to spell out abbreviations used in the tables, ideally in the footnotes.

For the reader, a research paper that is dense and text-heavy can be tiresome. Conversely, tables not only encapsulate your data lucidly, but also welcome a visual relief for the reader. They add value to the layout of your paper. Besides, and more importantly, reviewers often glance at your tabulated data and illustrations first before delving into the text. Therefore, tables can be the initial draw for a reviewer and deliver a positive impact about your research paper. If you can achieve an optimum balance among your text, tables, and illustrations, it can go a long way toward being published.

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Practical Tips on Figure and Table Legends for Manuscripts

Manuscript writing is an integral part of sharing research outcomes. Authors write and publish manuscripts that target a specific journal audience. A manuscript presenting original research data contains different sections, namely introduction, methods, results, discussion, and conclusions. Usually, figures and tables present complex data visually in the results section of the manuscript. How do readers understand the information conveyed by your table or figure? The answer is easy. They look at the figures/tables and at the corresponding legends.

Legends or captions explain figures, tables, or images in the manuscript.

As you know, using of figures and tables in research papers serves the purpose of providing illustrative description of the subject matter. Similarly, what legends or captions do is provide descriptive information of the figures or tables.

Legends should satisfy these two primary requirements:

• Make readers understand the significance of the figure or table.
• Help interpret the meaning of the underlying results.

Students often face the following doubts when writing legends:

• What should legends comprise and how to structure them?
• Where do legends appear in a manuscript or a technical report?

Therefore, we provide a quick guideline on writing descriptive figure and table legends, also known as captions.

Figure and Table Legends

In a manuscript or a report, accompanying tables and figures display quantitative information. The aim is to present data visually to make readers understand technical information and the context in an easy way. Using various types of data representation formats is always recommended (tables, data plots, scatter plots, figures, etc.) when describing large quantities of data.

Make sure to consider the below points when writing legends in your manuscript or poster.

• Place captions above the table and align to the left (typically). Tables are read from the top down and should be numbered. For instance, Table 1.
• Do not forget to end the name of the figure with a period. Do not use a colon, semi colon, or a comma. For instance, Figure XYZ.
• Place captions below the figure. Figures can be presented as graphs, images or schematic diagrams and are usually read from bottom up.
• Use titles for both figures and graphs in oral presentation slides and posters.
• Captions require a figure/table number, title, and other explanatory information as appropriate.
• Ideally, do not exceed the word limit of 350 words for each figure legend.
• Cite the data sourced from other publications within captions as appropriate.
• Have a clear understanding of the concept associated with the experiment.
• Order the information logically.
• Provide measured units of the data, include concentrations, average measurements and significant values as appropriate.

Key Features of a Good Figure Legend

Title – Give a brief title that is relevant to the entire figure. It can be either descriptive (stating the process or type of analysis of the experiment) or declarative (stating the key findings or summarizing the results of the experiment).

Materials and Methods – This is the section to describe the techniques used in the experiment. It should be limited to the information that is absolutely necessary to understand the figure.

Results – The result statement must be used if the title is descriptive. Do not state the results if you have given a declarative title to the figure legend. This may vary not only among papers but also among different journals.

Definitions – This is an explanation of the features of a figure. This includes description of symbols, patterns, lines, colors, non-standard abbreviations, scale bars, and error bars that are a part of the figure. You can exclude elements that are already described in the actual figure.

Captions or Legends Should Narrate a Story

Captions in a manuscript (or report) narrate a story about the figures or tables to the reader. For effective caption writing use the following checklist :

• Captions should be brief, yet provide a comprehensive explanation of the data as it appears within the text.
• The past tense is frequently used to describe completed experiments, whereas the present tense can be used to convey a factual statement based on the results of your experiment.
• Captions should be self-explaining and should provide sufficient information to the readers without referring to the related text in the manuscript.
• It should include information on important aspects such as description (the type of experiment), method or technique used (sample size, type of animal model used, conditions examined, etc.), and the result obtained (p-value).
• Captions should be written in a complete sentence, starting with conventional capitalization.
• It should be a guide helping the reader to understand and locate the data they are looking for in the manuscript.
• It should clearly explain the context of the data.
• If appropriate, the caption should contain the units of measurement representative of the data type, duration of the experiment, etc.
• The caption should be a font size smaller than the text within the document.
• The caption should be left aligned with the table or graph.

However, make sure to check author guidelines of your target journal for the last two points!

Let us have a look at examples of well-written legends for tables and figures.

(Source: https://www.overleaf.com/16744469whjwcjmcgxtw#/64164224/)

(Source: https://www.overleaf.com/16744374kttvywpdqyby#/64163939/)

How do you write legends for figures or tables in your manuscript? What things do you keep in your mind for a clear and concise description? Share with us in the comments section below some of your points on manuscript formatting !

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• As Ozempic’s Popularity Soars, Here’s What to Know About Semaglutide and Weight Loss JAMA Medical News & Perspectives May 16, 2023 This Medical News article discusses chronic weight management with semaglutide, sold under the brand names Ozempic and Wegovy. Melissa Suran, PhD, MSJ
• Patents and Regulatory Exclusivities on GLP-1 Receptor Agonists JAMA Special Communication August 15, 2023 This Special Communication used data from the US Food and Drug Administration to analyze how manufacturers of brand-name glucagon-like peptide 1 (GLP-1) receptor agonists have used patent and regulatory systems to extend periods of market exclusivity. Rasha Alhiary, PharmD; Aaron S. Kesselheim, MD, JD, MPH; Sarah Gabriele, LLM, MBE; Reed F. Beall, PhD; S. Sean Tu, JD, PhD; William B. Feldman, MD, DPhil, MPH
• What to Know About Wegovy’s Rare but Serious Adverse Effects JAMA Medical News & Perspectives December 12, 2023 This Medical News article discusses Wegovy, Ozempic, and other GLP-1 receptor agonists used for weight management and type 2 diabetes. Kate Ruder, MSJ
• GLP-1 Receptor Agonists and Gastrointestinal Adverse Events—Reply JAMA Comment & Response March 12, 2024 Ramin Rezaeianzadeh, BSc; Mohit Sodhi, MSc; Mahyar Etminan, PharmD, MSc
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• GLP-1 Receptor Agonist Use and Risk of Postoperative Complications JAMA Research Letter May 21, 2024 This cohort study evaluates the risk of postoperative respiratory complications among patients with diabetes undergoing surgery who had vs those who had not a prescription fill for glucagon-like peptide 1 receptor agonists. Anjali A. Dixit, MD, MPH; Brian T. Bateman, MD, MS; Mary T. Hawn, MD, MPH; Michelle C. Odden, PhD; Eric C. Sun, MD, PhD
• Glucagon-Like Peptide-1 Receptor Agonist Use and Risk of Gallbladder and Biliary Diseases JAMA Internal Medicine Original Investigation May 1, 2022 This systematic review and meta-analysis of 76 randomized clinical trials examines the effects of glucagon-like peptide-1 receptor agonist use on the risk of gallbladder and biliary diseases. Liyun He, MM; Jialu Wang, MM; Fan Ping, MD; Na Yang, MM; Jingyue Huang, MM; Yuxiu Li, MD; Lingling Xu, MD; Wei Li, MD; Huabing Zhang, MD
• Cholecystitis Associated With the Use of Glucagon-Like Peptide-1 Receptor Agonists JAMA Internal Medicine Research Letter October 1, 2022 This case series identifies cases reported in the US Food and Drug Administration Adverse Event Reporting System of acute cholecystitis associated with use of glucagon-like peptide-1 receptor agonists that did not have gallbladder disease warnings in their labeling. Daniel Woronow, MD; Christine Chamberlain, PharmD; Ali Niak, MD; Mark Avigan, MDCM; Monika Houstoun, PharmD, MPH; Cindy Kortepeter, PharmD

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Sodhi M , Rezaeianzadeh R , Kezouh A , Etminan M. Risk of Gastrointestinal Adverse Events Associated With Glucagon-Like Peptide-1 Receptor Agonists for Weight Loss. JAMA. 2023;330(18):1795–1797. doi:10.1001/jama.2023.19574

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Risk of Gastrointestinal Adverse Events Associated With Glucagon-Like Peptide-1 Receptor Agonists for Weight Loss

• 1 Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
• 2 StatExpert Ltd, Laval, Quebec, Canada
• 3 Department of Ophthalmology and Visual Sciences and Medicine, University of British Columbia, Vancouver, Canada
• Medical News & Perspectives As Ozempic’s Popularity Soars, Here’s What to Know About Semaglutide and Weight Loss Melissa Suran, PhD, MSJ JAMA
• Special Communication Patents and Regulatory Exclusivities on GLP-1 Receptor Agonists Rasha Alhiary, PharmD; Aaron S. Kesselheim, MD, JD, MPH; Sarah Gabriele, LLM, MBE; Reed F. Beall, PhD; S. Sean Tu, JD, PhD; William B. Feldman, MD, DPhil, MPH JAMA
• Medical News & Perspectives What to Know About Wegovy’s Rare but Serious Adverse Effects Kate Ruder, MSJ JAMA
• Comment & Response GLP-1 Receptor Agonists and Gastrointestinal Adverse Events—Reply Ramin Rezaeianzadeh, BSc; Mohit Sodhi, MSc; Mahyar Etminan, PharmD, MSc JAMA
• Comment & Response GLP-1 Receptor Agonists and Gastrointestinal Adverse Events Karine Suissa, PhD; Sara J. Cromer, MD; Elisabetta Patorno, MD, DrPH JAMA
• Research Letter GLP-1 Receptor Agonist Use and Risk of Postoperative Complications Anjali A. Dixit, MD, MPH; Brian T. Bateman, MD, MS; Mary T. Hawn, MD, MPH; Michelle C. Odden, PhD; Eric C. Sun, MD, PhD JAMA
• Original Investigation Glucagon-Like Peptide-1 Receptor Agonist Use and Risk of Gallbladder and Biliary Diseases Liyun He, MM; Jialu Wang, MM; Fan Ping, MD; Na Yang, MM; Jingyue Huang, MM; Yuxiu Li, MD; Lingling Xu, MD; Wei Li, MD; Huabing Zhang, MD JAMA Internal Medicine
• Research Letter Cholecystitis Associated With the Use of Glucagon-Like Peptide-1 Receptor Agonists Daniel Woronow, MD; Christine Chamberlain, PharmD; Ali Niak, MD; Mark Avigan, MDCM; Monika Houstoun, PharmD, MPH; Cindy Kortepeter, PharmD JAMA Internal Medicine

Glucagon-like peptide 1 (GLP-1) agonists are medications approved for treatment of diabetes that recently have also been used off label for weight loss. 1 Studies have found increased risks of gastrointestinal adverse events (biliary disease, 2 pancreatitis, 3 bowel obstruction, 4 and gastroparesis 5 ) in patients with diabetes. 2 - 5 Because such patients have higher baseline risk for gastrointestinal adverse events, risk in patients taking these drugs for other indications may differ. Randomized trials examining efficacy of GLP-1 agonists for weight loss were not designed to capture these events 2 due to small sample sizes and short follow-up. We examined gastrointestinal adverse events associated with GLP-1 agonists used for weight loss in a clinical setting.

We used a random sample of 16 million patients (2006-2020) from the PharMetrics Plus for Academics database (IQVIA), a large health claims database that captures 93% of all outpatient prescriptions and physician diagnoses in the US through the International Classification of Diseases, Ninth Revision (ICD-9) or ICD-10. In our cohort study, we included new users of semaglutide or liraglutide, 2 main GLP-1 agonists, and the active comparator bupropion-naltrexone, a weight loss agent unrelated to GLP-1 agonists. Because semaglutide was marketed for weight loss after the study period (2021), we ensured all GLP-1 agonist and bupropion-naltrexone users had an obesity code in the 90 days prior or up to 30 days after cohort entry, excluding those with a diabetes or antidiabetic drug code.

Patients were observed from first prescription of a study drug to first mutually exclusive incidence (defined as first ICD-9 or ICD-10 code) of biliary disease (including cholecystitis, cholelithiasis, and choledocholithiasis), pancreatitis (including gallstone pancreatitis), bowel obstruction, or gastroparesis (defined as use of a code or a promotility agent). They were followed up to the end of the study period (June 2020) or censored during a switch. Hazard ratios (HRs) from a Cox model were adjusted for age, sex, alcohol use, smoking, hyperlipidemia, abdominal surgery in the previous 30 days, and geographic location, which were identified as common cause variables or risk factors. 6 Two sensitivity analyses were undertaken, one excluding hyperlipidemia (because more semaglutide users had hyperlipidemia) and another including patients without diabetes regardless of having an obesity code. Due to absence of data on body mass index (BMI), the E-value was used to examine how strong unmeasured confounding would need to be to negate observed results, with E-value HRs of at least 2 indicating BMI is unlikely to change study results. Statistical significance was defined as 2-sided 95% CI that did not cross 1. Analyses were performed using SAS version 9.4. Ethics approval was obtained by the University of British Columbia’s clinical research ethics board with a waiver of informed consent.

Our cohort included 4144 liraglutide, 613 semaglutide, and 654 bupropion-naltrexone users. Incidence rates for the 4 outcomes were elevated among GLP-1 agonists compared with bupropion-naltrexone users ( Table 1 ). For example, incidence of biliary disease (per 1000 person-years) was 11.7 for semaglutide, 18.6 for liraglutide, and 12.6 for bupropion-naltrexone and 4.6, 7.9, and 1.0, respectively, for pancreatitis.

Use of GLP-1 agonists compared with bupropion-naltrexone was associated with increased risk of pancreatitis (adjusted HR, 9.09 [95% CI, 1.25-66.00]), bowel obstruction (HR, 4.22 [95% CI, 1.02-17.40]), and gastroparesis (HR, 3.67 [95% CI, 1.15-11.90) but not biliary disease (HR, 1.50 [95% CI, 0.89-2.53]). Exclusion of hyperlipidemia from the analysis did not change the results ( Table 2 ). Inclusion of GLP-1 agonists regardless of history of obesity reduced HRs and narrowed CIs but did not change the significance of the results ( Table 2 ). E-value HRs did not suggest potential confounding by BMI.

This study found that use of GLP-1 agonists for weight loss compared with use of bupropion-naltrexone was associated with increased risk of pancreatitis, gastroparesis, and bowel obstruction but not biliary disease.

Given the wide use of these drugs, these adverse events, although rare, must be considered by patients who are contemplating using the drugs for weight loss because the risk-benefit calculus for this group might differ from that of those who use them for diabetes. Limitations include that although all GLP-1 agonist users had a record for obesity without diabetes, whether GLP-1 agonists were all used for weight loss is uncertain.

Accepted for Publication: September 11, 2023.

Published Online: October 5, 2023. doi:10.1001/jama.2023.19574

Correction: This article was corrected on December 21, 2023, to update the full name of the database used.

Corresponding Author: Mahyar Etminan, PharmD, MSc, Faculty of Medicine, Departments of Ophthalmology and Visual Sciences and Medicine, The Eye Care Center, University of British Columbia, 2550 Willow St, Room 323, Vancouver, BC V5Z 3N9, Canada ( [email protected] ).

Author Contributions: Dr Etminan had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Sodhi, Rezaeianzadeh, Etminan.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Sodhi, Rezaeianzadeh, Etminan.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Kezouh.

Obtained funding: Etminan.

Administrative, technical, or material support: Sodhi.

Supervision: Etminan.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was funded by internal research funds from the Department of Ophthalmology and Visual Sciences, University of British Columbia.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement .

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• Published: 14 August 2024

A Scottish provenance for the Altar Stone of Stonehenge

• Anthony J. I. Clarke   ORCID: orcid.org/0000-0002-0304-0484 1 ,
• Christopher L. Kirkland   ORCID: orcid.org/0000-0003-3367-8961 1 ,
• Richard E. Bevins 2 ,
• Nick J. G. Pearce   ORCID: orcid.org/0000-0003-3157-9564 2 ,
• Stijn Glorie 3 &
• Rob A. Ixer 4  

Nature volume  632 ,  pages 570–575 ( 2024 ) Cite this article

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• Archaeology

Understanding the provenance of megaliths used in the Neolithic stone circle at Stonehenge, southern England, gives insight into the culture and connectivity of prehistoric Britain. The source of the Altar Stone, the central recumbent sandstone megalith, has remained unknown, with recent work discounting an Anglo-Welsh Basin origin 1 , 2 . Here we present the age and chemistry of detrital zircon, apatite and rutile grains from within fragments of the Altar Stone. The detrital zircon load largely comprises Mesoproterozoic and Archaean sources, whereas rutile and apatite are dominated by a mid-Ordovician source. The ages of these grains indicate derivation from an ultimate Laurentian crystalline source region that was overprinted by Grampian (around 460 million years ago) magmatism. Detrital age comparisons to sedimentary packages throughout Britain and Ireland reveal a remarkable similarity to the Old Red Sandstone of the Orcadian Basin in northeast Scotland. Such a provenance implies that the Altar Stone, a 6 tonne shaped block, was sourced at least 750 km from its current location. The difficulty of long-distance overland transport of such massive cargo from Scotland, navigating topographic barriers, suggests that it was transported by sea. Such routing demonstrates a high level of societal organization with intra-Britain transport during the Neolithic period.

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Stonehenge, the Neolithic standing stone circle located on the Salisbury Plain in Wiltshire, England, offers valuable insight into prehistoric Britain. Construction at Stonehenge began as early as 3000  bc , with subsequent modifications during the following two millennia 3 , 4 . The megaliths of Stonehenge are divided into two major categories: sarsen stones and bluestones (Fig. 1a ). The larger sarsens comprise duricrust silcrete predominantly sourced from the West Woods, Marlborough, approximately 25 km north of Stonehenge 5 , 6 . Bluestone, the generic term for rocks considered exotic to the local area, includes volcanic tuff, rhyolite, dolerite and sandstone lithologies 4 (Fig. 1a ). Some lithologies are linked with Neolithic quarrying sites in the Mynydd Preseli area of west Wales 7 , 8 . An unnamed Lower Palaeozoic sandstone, associated with the west Wales area on the basis of acritarch fossils 9 , is present only as widely disseminated debitage at Stonehenge and possibly as buried stumps (Stones 40g and 42c).

a , Plan view of Stonehenge showing exposed constituent megaliths and their provenance. The plan of Stonehenge was adapted from ref.  6 under a CC BY 4.0 license. Changes in scale and colour were made, and annotations were added. b , An annotated photograph shows the Altar Stone during a 1958 excavation. The Altar Stone photograph is from the Historic England archive. Reuse is not permitted.

The central megalith of Stonehenge, the Altar Stone (Stone 80), is the largest of the bluestones, measuring 4.9 × 1.0 × 0.5 m, and is a recumbent stone (Fig. 1b ), weighing 6 t and composed of pale green micaceous sandstone with distinctive mineralogy 1 , 2 , 10 (containing baryte, calcite and clay minerals, with a notable absence of K-feldspar) (Fig. 2 ).

Minerals with a modal abundance above 0.5% are shown with compositional values averaged across both thin sections. U–Pb ablation pits from laser ablation inductively coupled plasma mass spectrometry (LA-ICP–MS) are shown with age (in millions of years ago, Ma), with uncertainty at the 2 σ level.

Previous petrographic work on the Altar Stone has implied an association to the Old Red Sandstone 10 , 11 , 12 (ORS). The ORS is a late Silurian to Devonian sedimentary rock assemblage that crops out widely throughout Great Britain and Ireland (Extended Data Fig. 1 ). ORS lithologies are dominated by terrestrial siliciclastic sedimentary rocks deposited in continental fluvial, lacustrine and aeolian environments 13 . Each ORS basin reflects local subsidence and sediment infill and thus contains proximal crystalline signatures 13 , 14 .

Constraining the provenance of the Altar Stone could give insights into the connectivity of Neolithic people who left no written record 15 . When the Altar Stone arrived at Stonehenge is uncertain; however, it may have been placed within the central trilithon horseshoe during the second construction phase around 2620–2480  bc 3 . Whether the Altar Stone once stood upright as an approximately 4 m high megalith is unclear 15 ; nevertheless, the current arrangement has Stones 55b and 156 from the collapsed Great Trilithon resting atop the prone and broken Altar Stone (Fig. 1b ).

An early proposed source for the Altar Stone from Mill Bay, Pembrokeshire (Cosheston Subgroup of the Anglo-Welsh ORS Basin), close to the Mynydd Preseli source of the doleritic and rhyolitic bluestones, strongly influenced the notion of a sea transport route via the Bristol Channel 12 . However, inconsistencies in petrography and detrital zircon ages between the Altar Stone and the Cosheston Subgroup have ruled this source out 1 , 11 . Nonetheless, a source from elsewhere in the ORS of the Anglo-Welsh Basin was still considered likely, with an inferred collection and overland transport of the Altar Stone en route to Stonehenge from the Mynydd Preseli 1 . However, a source from the Senni Formation (Cosheston Subgroup) is inconsistent with geochemical and petrographic data, which shows that the Anglo-Welsh Basin is highly unlikely to be the source 2 . Thus, the ultimate provenance of the Altar Stone had remained an open question.

Studies of detrital mineral grains are widely deployed to address questions throughout the Earth sciences and have utility in archaeological investigations 16 , 17 . Sedimentary rocks commonly contain a detrital component derived from a crystalline igneous basement, which may reflect a simple or complex history of erosion, transport and deposition cycles. This detrital cargo can fingerprint a sedimentary rock and its hinterland. More detailed insights become evident when a multi-mineral strategy is implemented, which benefits from the varying degrees of robustness to sedimentary transportation in the different minerals 18 , 19 , 20 .

Here, we present in situ U–Pb, Lu–Hf and trace element isotopic data for zircon, apatite and rutile from two fragments of the Altar Stone collected at Stonehenge: MS3 and 2010K.240 21 , 22 . In addition, we present comparative apatite U–Pb dates for the Orcadian Basin from Caithness and Orkney. We utilize statistical tools (Fig. 3 ) to compare the obtained detrital mineral ages and chemistry (Supplementary Information  1 – 3 ) to crystalline terranes and ORS successions across Great Britain, Ireland and Europe (Fig. 4 and Extended Data Fig. 1 ).

a , Multidimensional scaling (MDS) plot of concordant zircon U–Pb ages from the Altar Stone and comparative age datasets, with ellipses at the 95% confidence level 58 . DIM 1 and DIM 2, dimensions 1 and 2. b , Cumulative probability plot of zircon U–Pb ages from crystalline terranes, the Orcadian Basin and the Altar Stone. For a cumulative probability plot of all ORS basins, see Extended Data Fig. 8 .

a , Schematic map of Britain, showing outcrops of ORS and other Devonian sedimentary rocks, basement terranes and major faults. Potential Caledonian source plutons are colour-coded on the basis of age 28 . b , Kernel density estimate diagrams displaying zircon U–Pb age (histogram) and apatite Lu–Hf age (dashed line) spectra from the Altar Stone, the Orcadian Basin 25 and plausible crystalline source terranes. The apatite age components for the Altar Stone and Orcadian Basins are shown below their respective kernel density estimates. Extended Data Fig. 3 contains kernel density estimates of other ORS and New Red Sandstone (NRS) age datasets.

Laurentian basement signatures

The crystalline basement terranes of Great Britain and Ireland, from north to south, are Laurentia, Ganderia, Megumia and East Avalonia (Fig. 4a and Extended Data Fig. 1 ). Cadomia-Armorica is south of the Rheic Suture and encompasses basement rocks in western Europe, including northern France and Spain. East Avalonia, Megumia and Ganderia are partly separated by the Menai Strait Fault System (Fig. 4a ). Each terrane has discrete age components, which have imparted palaeogeographic information into overlying sedimentary basins 13 , 14 , 23 . Laurentia was a palaeocontinent that collided with Baltica and Avalonia (a peri-Gondwanan microcontinent) during the early Palaeozoic Caledonian Orogeny to form Laurussia 14 , 24 . West Avalonia is a terrane that includes parts of eastern Canada and comprised the western margin of Avalonia (Extended Data Fig. 1 ).

Statistical comparisons, using a Kolmogorov–Smirnov test, between zircon ages from the Laurentian crystalline basement and the Altar Stone indicate that at a 95% confidence level, no distinction in provenance is evident between Altar Stone detrital zircon U–Pb ages and those from the Laurentian basement. That is, we cannot reject the null hypothesis that both samples are from the same underlying age distribution (Kolmogorov–Smirnov test: P  > 0.05) (Fig. 3a ).

Detrital zircon age components, defined by concordant analyses from at least 4 grains in the Altar Stone, include maxima at 1,047, 1,091, 1,577, 1,663 and 1,790 Ma (Extended Data Fig. 2 ), corresponding to known tectonomagmatic events and sources within Laurentia and Baltica, including the Grenville (1,095–980 Ma), Labrador (1,690–1,590 Ma), Gothian (1,660–1,520 Ma) and Svecokarellian (1,920–1,770 Ma) orogenies 25 .

Laurentian terranes are crystalline lithologies north of the Iapetus Suture Zone (which marks the collision zone between Laurentia and Avalonia) and include the Southern Uplands, Midland Valley, Grampian, Northern Highlands and Hebridean Terranes (Fig. 4a ). Together, these terranes preserve a Proterozoic to Archaean record of zircon production 24 , distinct from the southern Gondwanan-derived terranes of Britain 20 , 26 (Fig. 4a and Extended Data Fig. 3 ).

Age data from Altar Stone rutile grains also point towards an ultimate Laurentian source with several discrete age components (Extended Data Fig. 4 and Supplementary Information  1 ). Group 2 rutile U–Pb analyses from the Altar Stone include Proterozoic ages from 1,724 to 591 Ma, with 3 grains constituting an age peak at 1,607 Ma, overlapping with Laurentian magmatism, including the Labrador and Pinwarian (1,690–1,380 Ma) orogenies 24 . Southern terranes in Britain are not characterized by a large Laurentian (Mesoproterozoic) crystalline age component 25 (Fig. 4b and Extended Data Fig. 3 ). Instead, terranes south of the Iapetus Suture are defined by Neoproterozoic to early Palaeozoic components, with a minor component from around two billion years ago (Figs. 3b and  4b ).

U–Pb analyses of apatite from the Altar Stone define two distinct age groupings. Group 2 apatite U–Pb analyses define a lower intercept age of 1,018 ± 24 Ma ( n  = 9) (Extended Data Fig. 5 ), which overlaps, within uncertainty, to a zircon age component at 1,047 Ma, consistent with a Grenville source 25 . Apatite Lu–Hf dates at 1,496 and 1,151 Ma also imply distinct Laurentian sources 25 (Fig. 4b , Extended Data Fig. 6 and Supplementary Information  2 ). Ultimately, the presence of Grenvillian apatite in the Altar Stone suggests direct derivation from the Laurentian basement, given the lability of apatite during prolonged chemical weathering 20 , 27 .

Grampian Terrane detrital grains

Apatite and rutile U–Pb analyses from the Altar Stone are dominated by regressions from common Pb that yield lower intercepts of 462 ± 4 Ma ( n  = 108) and 451 ± 8 Ma ( n  = 83), respectively (Extended Data Figs. 4 and 5 ). A single concordant zircon analysis also yields an early Palaeozoic age of 498 ± 17 Ma. Hence, with uncertainty from both lower intercepts, Group 1 apatite and rutile analyses demonstrate a mid-Ordovician (443–466 Ma) age component in the Altar Stone. These mid-Ordovician ages are confirmed by in situ apatite Lu–Hf analyses, which define a lower intercept of 470 ± 29 Ma ( n  = 16) (Extended Data Fig. 6 and Supplementary Information  2 ).

Throughout the Altar Stone are sub-planar 100–200-µm bands of concentrated heavy resistive minerals. These resistive minerals are interpreted to be magmatic in origin, given internal textures (oscillatory zonation), lack of mineral overgrowths (in all dated minerals) (Fig. 2 ) and the igneous apatite trace element signatures 27 (Extended Data Fig. 7 and Supplementary Information  3 ). Moreover, there is a general absence of detrital metamorphic zircon grains, further supporting a magmatic origin for these grains.

The most appropriate source region for such mid-Ordovician grains within Laurentian basement is the Grampian Terrane of northeast Scotland (Fig. 4a ). Situated between the Great Glen Fault to the north and the Highland Boundary Fault to the south, the terrane comprises Neoproterozoic to Lower Palaeozoic metasediments termed the Dalradian Supergroup 28 , which are intruded by a compositionally diverse suite of early Palaeozoic granitoids and gabbros (Fig. 4a ). The 466–443 Ma age component from Group 1 apatite and rutile U–Pb analyses overlaps with the terminal stages of Grampian magmatism and subsequent granite pluton emplacement north of the Highland Boundary Fault 28 (Fig. 4a ).

Geochemical classification plots for the Altar Stone apatite imply a compositionally diverse source, much like the lithological diversity within the Grampian Terrane 28 , with 61% of apatite classified as coming from felsic sources, 35% from mafic sources and 4% from alkaline sources (Extended Data Fig. 7 and Supplementary Information  3 ). Specifically, igneous rocks within the Grampian Terrane are largely granitoids, thus accounting for the predominance of felsic-classified apatite grains 29 . We posit that the dominant supply of detritus from 466–443 Ma came from the numerous similarly aged granitoids formed on the Laurentian margin 28 , which are present in both the Northern Highlands and the Grampian Terranes 28 (Fig. 4a ). The alkaline to calc-alkaline suites in these terranes are volumetrically small, consistent with the scarcity of alkaline apatite grains within the Altar Stone (Extended Data Fig. 7 ). Indeed, the Glen Dessary syenite at 447 ± 3 Ma is the only age-appropriate felsic-alkaline pluton in the Northern Highlands Terrane 30 .

The Stacey and Kramers 31 model of terrestrial Pb isotopic evolution predicts a 207 Pb/ 206 Pb isotopic ratio ( 207 Pb/ 206 Pb i ) of 0.8601 for 465 Ma continental crust. Mid-Ordovician regressions through Group 1 apatite and rutile U–Pb analyses yield upper intercepts for 207 Pb/ 206 Pb i of 0.8603 ± 0.0033 and 0.8564 ± 0.0014, respectively (Extended Data Figs. 4 and 5 and Supplementary Information  1 ). The similarity between apatite and rutile 207 Pb/ 206 Pb i implies they were sourced from the same Mid-Ordovician magmatic fluids. Ultimately, the calculated 207 Pb/ 206 Pb i value is consistent with the older (Laurentian) crust north of the Iapetus Suture in Britain 32 (Fig. 4a ).

Orcadian Basin ORS

The detrital zircon age spectra confirm petrographic associations between the Altar Stone and the ORS. Furthermore, the Altar Stone cannot be a New Red Sandstone (NRS) lithology of Permo-Triassic age. The NRS, deposited from around 280–240 Ma, unconformably overlies the ORS 14 . NRS, such as that within the Wessex Basin (Extended Data Fig. 1 ), has characteristic detrital zircon age components, including Carboniferous to Permian zircon grains, which are not present in the Altar Stone 1 , 23 , 26 , 33 , 34 (Extended Data Fig. 3 ).

An ORS classification for the Altar Stone provides the basis for further interpretation of provenance (Extended Data Figs. 1 and 8 ), given that the ORS crops out in distinct areas of Great Britain and Ireland, including the Anglo-Welsh border and south Wales, the Midland Valley and northeast Scotland, reflecting former Palaeozoic depocentres 14 (Fig. 4a ).

Previously reported detrital zircon ages and petrography show that ORS outcrops of the Anglo-Welsh Basin in the Cosheston Subgroup 1 and Senni Formation 2 are unlikely to be the sources of the Altar Stone (Fig. 4a ). ORS within the Anglo-Welsh Basin is characterized by mid-Palaeozoic zircon age maxima and minor Proterozoic components (Fig. 4a ). Ultimately, the detrital zircon age spectra of the Altar Stone are statistically distinct from the Anglo-Welsh Basin (Fig. 3a ). In addition, the ORS outcrops of southwest England (that is, south of the Variscan front), including north Devon and Cornwall (Cornubian Basin) (Fig. 4a ), show characteristic facies, including marine sedimentary structures and fossils along with a metamorphic fabric 13 , 26 , inconsistent with the unmetamorphosed, terrestrial facies of the Altar Stone 1 , 11 .

Another ORS succession with published age data for comparison is the Dingle Peninsula Basin, southwest Ireland. However, the presence of late Silurian (430–420 Ma) and Devonian (400–350 Ma) apatite, zircon and muscovite from the Dingle Peninsula ORS discount a source for the Altar Stone from southern Ireland 20 . The conspicuous absence of apatite grains of less than 450 Ma in age in the Altar Stone precludes the input of Late Caledonian magmatic grains to the source sediment of the Altar Stone and demonstrates that the ORS of the Altar Stone was deposited prior to or distally from areas of Late Caledonian magmatism, unlike the ORS of the Dingle Peninsula 20 . Notably, no distinction in provenance between the Anglo-Welsh Basin and the Dingle Peninsula ORS is evident (Kolmogorov–Smirnov test: P  > 0.05), suggesting that ORS basins south of the Iapetus Suture are relatively more homogenous in terms of their detrital zircon age components (Fig. 4a ).

In Scotland, ORS predominantly crops out in the Midland Valley and Orcadian Basins (Fig. 4a ). The Midland Valley Basin is bound between the Highland Boundary Fault and the Iapetus Suture and is located within the Midland Valley and Southern Uplands Terranes. Throughout Midland Valley ORS stratigraphy, detrital zircon age spectra broadly show a bimodal age distribution between Lower Palaeozoic and Mesoproterozoic components 35 , 36 (Extended Data Fig. 3 ). Indeed, throughout 9 km of ORS stratigraphy in the Midland Valley Basin and across the Sothern Uplands Fault, no major changes in provenance are recognized 36 (Fig. 4a ). Devonian zircon, including grains as young as 402 ± 5 Ma from the northern ORS in the Midland Valley Basin 36 , further differentiates this basin from the Altar Stone (Fig. 3a and Extended Data Fig. 3 ). The scarcity of Archaean to late Palaeoproterozoic zircon grains within the Midland Valley ORS shows that the Laurentian basement was not a dominant detrital source for those rocks 35 . Instead, ORS of the Midland Valley is primarily defined by zircon from 475 Ma interpreted to represent the detrital remnants of Ordovician volcanism within the Midland Valley Terrane, with only minor and periodic input from Caledonian plutonism 35 .

The Orcadian Basin of northeast Scotland, within the Grampian and Northern Highlands terranes, contains a thick package of mostly Mid-Devonian ORS, around 4 km thick in Caithness and up to around 8 km thick in Shetland 14 (Fig. 4a ). The detrital zircon age spectra from Orcadian Basin ORS provides the closest match to the Altar Stone detrital ages 25 (Fig. 3 and Extended Data Fig. 8 ). A Kolmogorov–Smirnov test on age spectra from the Altar Stone and the Orcadian Basin fails to reject the null hypothesis that they are derived from the same underlying distribution (Kolmogorov–Smirnov test: P  > 0.05) (Fig. 3a ). To the north, ORS on the Svalbard archipelago formed on Laurentian and Baltican basement rocks 37 . Similar Kolmogorov–Smirnov test results, where each detrital zircon dataset is statistically indistinguishable, are obtained for ORS from Svalbard, the Orcadian Basin and the Altar Stone.

Apatite U–Pb age components from Orcadian Basin samples from Spittal, Caithness (AQ1) and Cruaday, Orkney (CQ1) (Fig. 4a ) match those from the Altar Stone. Group 2 apatite from the Altar Stone at 1,018 ± 24 Ma is coeval with a Grenvillian age from Spittal at 1,013 ± 35 Ma. Early Palaeozoic apatite components at 473 ± 25 Ma and 466 ± 6 Ma, from Caithness and Orkney, respectively (Extended Data Fig. 5 and Supplementary Information  1 ), are also identical, within uncertainty, to Altar Stone Group 1 (462 ± 4 Ma) apatite U–Pb analyses and a Lu–Hf component at 470 ± 28 Ma supporting a provenance from the Orcadian Basin for the Altar Stone (Extended Data Fig. 6 and Supplementary Information  2 ).

During the Palaeozoic, the Orcadian Basin was situated between Laurentia and Baltica on the Laurussian palaeocontinent 14 . Correlations between detrital zircon age components imply that both Laurentia and Baltica supplied sediment into the Orcadian Basin 25 , 36 . Detrital grains from more than 900 Ma within the Altar Stone are consistent with sediment recycling from intermediary Neoproterozoic supracrustal successions (for example, Dalradian Supergroup) within the Grampian Terrane but also from the Särv and Sparagmite successions of Baltica 25 , 36 . At around 470 Ma, the Grampian Terrane began to denude 28 . Subsequently, first-cycle detritus, such as that represented by Group 1 apatite and rutile, was shed towards the Orcadian Basin from the southeast 25 .

Thus, the resistive mineral cargo in the Altar Stone represents a complex mix of first and multi-cycle grains from multiple sources. Regardless of total input from Baltica versus Laurentia into the Orcadian Basin, crystalline terranes north of the Iapetus Suture (Fig. 4a ) have distinct age components that match the Altar Stone in contrast to Gondwanan-derived terranes to the south.

The Altar Stone and Neolithic Britain

Isotopic data for detrital zircon and rutile (U–Pb) and apatite (U–Pb, Lu–Hf and trace elements) indicate that the Altar Stone of Stonehenge has a provenance from the ORS in the Orcadian Basin of northeast Scotland (Fig. 4a ). Given this detrital mineral provenance, the Altar Stone cannot have been sourced from southern Britain (that is, south of the Iapetus Suture) (Fig. 4a ), including the Anglo-Welsh Basin 1 , 2 .

Some postulate a glacial transport mechanism for the Mynydd Preseli (Fig. 4a ) bluestones to Salisbury Plain 38 , 39 . However, such transport for the Altar Stone is difficult to reconcile with ice-sheet reconstructions that show a northwards movement of glaciers (and erratics) from the Grampian Mountains towards the Orcadian Basin during the Last Glacial Maximum and, indeed, previous Pleistocene glaciations 40 , 41 . Moreover, there is little evidence of extensive glacial deposition in central southern Britain 40 , nor are Scottish glacial erratics found at Stonehenge 42 . Sr and Pb isotopic signatures from animal and human remains from henges on Salisbury Plain demonstrate the mobility of Neolithic people within Britain 32 , 43 , 44 , 45 . Furthermore, shared architectural elements and rock art motifs between Neolithic monuments in Orkney, northern Britain, and Ireland point towards the long-distance movement of people and construction materials 46 , 47 .

Thus, we posit that the Altar Stone was anthropogenically transported to Stonehenge from northeast Scotland, consistent with evidence of Neolithic inhabitation in this region 48 , 49 . Whereas the igneous bluestones were brought around 225 km from the Mynydd Preseli to Stonehenge 50 (Fig. 4a ), a Scottish provenance for the Altar Stone demands a transport distance of at least 750 km (Fig. 4a ). Nonetheless, even with assistance from beasts of burden 51 , rivers and topographical barriers, including the Grampians, Southern Uplands and the Pennines, along with the heavily forested landscape of prehistoric Britain 52 , would have posed formidable obstacles for overland megalith transportation.

At around 5000  bc , Neolithic people introduced the common vole ( Microtus arvalis ) from continental Europe to Orkney, consistent with the long-distance marine transport of cattle and goods 53 . A Neolithic marine trade network of quarried stone tools is found throughout Britain, Ireland and continental Europe 54 . For example, a saddle quern, a large stone grinding tool, was discovered in Dorset and determined to have a provenance in central Normandy 55 , implying the shipping of stone cargo over open water during the Neolithic. Furthermore, the river transport of shaped sandstone blocks in Britain is known from at least around 1500  bc (Hanson Log Boat) 56 . In Britain and Ireland, sea levels approached present-day heights from around 4000  bc 57 , and although coastlines have shifted, the geography of Britain and Ireland would have permitted sea routes southward from the Orcadian Basin towards southern England (Fig. 4a ). A Scottish provenance for the Altar Stone implies Neolithic transport spanning the length of Great Britain.

This work analysed two 30-µm polished thin sections of the Altar Stone (MS3 and 2010K.240) and two sections of ORS from northeast Scotland (Supplementary Information  4 ). CQ1 is from Cruaday, Orkney (59° 04′ 34.2″ N, 3° 18′ 54.6″ W), and AQ1 is from near Spittal, Caithness (58° 28′ 13.8″ N, 3° 27′ 33.6″ W). Conventional optical microscopy (transmitted and reflected light) and automated mineralogy via a TESCAN Integrated Mineral Analyser gave insights into texture and mineralogy and guided spot placement during LA-ICP–MS analysis. A CLARA field emission scanning electron microscope was used for textural characterization of individual minerals (zircon, apatite and rutile) through high-resolution micrometre-scale imaging under both back-scatter electron and cathodoluminescence. The Altar Stone is a fine-grained and well-sorted sandstone with a mean grain size diameter of ≤300 µm. Quartz grains are sub-rounded and monocrystalline. Feldspars are variably altered to fine-grained white mica. MS3 and 2010K.240 have a weakly developed planar fabric and non-planar heavy mineral laminae approximately 100–200 µm thick. Resistive heavy mineral bands are dominated by zircon, rutile, and apatite, with grains typically 10–40 µm wide. The rock is mainly cemented by carbonate, with localized areas of barite and quartz cement. A detailed account of Altar Stone petrography is provided in refs. 1 , 59 .

Zircon isotopic analysis

Zircon u–pb methods.

Two zircon U–Pb analysis sessions were completed at the GeoHistory facility in the John De Laeter Centre (JdLC), Curtin University, Australia. Ablations within zircon grains were created using an excimer laser RESOlution LE193 nm ArF with a Laurin Technic S155 cell. Isotopic data was collected with an Agilent 8900 triple quadrupole mass spectrometer, with high-purity Ar as the plasma carrier gas (flow rate 1.l min −1 ). An on-sample energy of ~2.3–2.7 J cm −2 with a 5–7 Hz repetition rate was used to ablate minerals for 30–40 s (with 25–60 s of background capture). Two cleaning pulses preceded analyses, and ultra-high-purity He (0.68 ml min −1 ) and N 2 (2.8 ml min −1 ) were used to flush the sample cell. A block of reference mineral was analysed following 15–20 unknowns. The small, highly rounded target grains of the Altar Stone (usually <30 µm in width) necessitated using a spot size diameter of ~24 µm for all ablations. Isotopic data was reduced using Iolite 4 60 with the U-Pb Geochronology data reduction scheme, followed by additional calculation and plotting via IsoplotR 61 . The primary matrix-matched reference zircon 62 used to correct instrumental drift and mass fractionation was GJ-1, 601.95 ± 0.40 Ma. Secondary reference zircon included Plešovice 63 , 337.13 ± 0.37 Ma, 91500 64 , 1,063.78 ± 0.65 Ma, OG1 65 3,465.4 ± 0.6 Ma and Maniitsoq 66 3,008.7 ± 0.6 Ma. Weighted mean U–Pb ages for secondary reference materials were within 2 σ uncertainty of reported values (Supplementary Information  5 ).

Zircon U–Pb results

Across two LA-ICP–MS sessions, 83 U–Pb measurements were obtained on as many zircon grains; 41 were concordant (≤10% discordant), where discordance is defined using the concordia log distance (%) approach 67 . We report single-spot (grain) concordia ages, which have numerous benefits over conventional U–Pb/Pb–Pb ages, including providing an objective measure of discordance that is directly coupled to age and avoids the arbitrary switch between 206 Pb/ 238 U and 207 Pb/ 206 Pb. Furthermore, given the spread in ages (Early Palaeozoic to Archaean), concordia ages provide optimum use of both U–Pb/Pb–Pb ratios, offering greater precision over 206 Pb/ 238 U or 207 Pb/ 206 Pb ages alone.

Given that no direct sampling of the Altar Stone is permitted, we are limited in the amount of material available for destructive analysis, such as LA-ICP–MS. We collate our zircon age data with the U–Pb analyses 1 of FN593 (another fragment of the Altar Stone), filtered using the same concordia log distance (%) discordance filter 67 . The total concordant analyses used in this work is thus 56 over 3 thin sections, each showing no discernible provenance differences. Zircon concordia ages span from 498 to 2,812 Ma. Age maxima (peak) were calculated after Gehrels 68 , and peak ages defined by ≥4 grains include 1,047, 1,091, 1,577, 1,663 and 1,790 Ma.

For 56 concordant ages from 56 grains at >95% certainty, the largest unmissed fraction is calculated at 9% of the entire uniform detrital population 69 . In any case, the most prevalent and hence provenance important components will be sampled for any number of analyses 69 . We analysed all zircon grains within the spatial limit of the technique in the thin sections 70 . We used in situ thin-section analysis, which can mitigate against contamination and sampling biases in detrital studies 71 . Adding apatite (U–Pb and Lu–Hf) and rutile (U–Pb) analyses bolsters our confidence in provenance interpretations as these minerals will respond dissimilarly during transport.

Comparative zircon datasets

Zircon U–Pb compilations of the basement terranes of Britain and Ireland were sourced from refs. 20 , 26 . ORS detrital zircon datasets used for comparison include isotopic data from the Dingle Peninsula Basin 20 , Anglo-Welsh Basin 72 , Midland Valley Basin 35 , Svalbard ORS 37 and Orcadian Basin 25 . NRS zircon U–Pb ages were sourced from the Wessex Basin 33 . Comparative datasets were filtered for discordance as per our definition above 20 , 26 . Kernel density estimates for age populations were created within IsoplotR 61 using a kernel and histogram bandwidth of 50 Ma.

A two-sample Kolmogorov–Smirnov statistical test was implemented to compare the compiled zircon age datasets with the Altar Stone (Supplementary Information  6 ). This two-sided test compares the maximum probability difference between two cumulative density age functions, evaluating the null hypothesis that both age spectra are drawn from the same distribution based on a critical value dependent on the number of analyses and a chosen confidence level.

The number of zircon ages within the comparative datasets used varies from the Altar Stone ( n  = 56) to Laurentia ( n  = 2,469). Therefore, to address the degree of dependence on sample n , we also implemented a Monte Carlo resampling (1,000 times) procedure for the Kolmogorov–Smirnov test, including the uncertainty on each age determination to recalculate P values and standard deviations (Supplementary Information  7 ), based on the resampled distribution of each sample. The results from Kolmogorov–Smirnov tests, using Monte Carlo resampling (and multidimensional analysis), taking uncertainty due to sample n into account, also support the interpretation that at >95% certainty, no distinction in provenance can be made between the Altar Stone zircon age dataset ( n  = 56) and those from the Orcadian Basin ( n  = 212), Svalbard ORS ( n  = 619 ) and the Laurentian basement (Supplementary Information  7 ).

MDS plots for zircon datasets were created using the MATLAB script of ref.  58 . Here, we adopted a bootstrap resampling (>1,000 times) with Procrustes rotation of Kolmogorov–Smirnov values, which outputs uncertainty ellipses at a 95% confidence level (Fig. 3a ). In MDS plots, stress is a goodness of fit indicator between dissimilarities in the datasets and distances on the MDS plot. Stress values below 0.15 are desirable 58 . For the MDS plot in Fig. 3a , the value is 0.043, which indicates an “excellent” fit 58 .

Rutile isotopic analysis

Rutile u–pb methods.

One rutile U–Pb analysis session was completed at the GeoHistory facility in the JdLC, Curtin University, Australia. Rutile grains were ablated (24 µm) using a Resonetics RESOlution M-50A-LR sampling system, using a Compex 102 excimer laser, and measured using an Agilent 8900 triple quadrupole mass analyser. The analytical parameters included an on-sample energy of 2.7 J cm −2 , a repetition rate of 7 Hz for a total analysis time of 45 s, and 60 s of background data capture. The sample chamber was purged with ultrahigh purity He at a flow rate of 0.68 l min −1 and N 2 at 2.8 ml min −1 .

U–Pb data for rutile analyses was reduced against the R-10 rutile primary reference material 73 (1,091 ± 4 Ma). The secondary reference material used to monitor the accuracy of U–Pb ratios was R-19 rutile. The mean weighted 238 U/ 206 Pb age obtained for R-19 was 491 ± 10 (mean squared weighted deviation (MSWD) = 0.87, p ( χ 2 ) = 0.57) within uncertainty of the accepted age 74 of 489.5 ± 0.9 Ma.

Rutile grains with negligible Th concentrations can be corrected for common Pb using a 208 Pb correction 74 . Previously used thresholds for Th content have included 75 , 76 Th/U < 0.1 or a Th concentration >5% U. However, Th/U ratios for rutile from MS3 are typically > 1; thus, a 208 Pb correction is not applicable. Instead, we use a 207 -based common Pb correction 31 to account for the presence of common Pb. Rutile isotopic data was reduced within Iolite 4 60 using the U–Pb Geochronology reduction scheme and IsoplotR 61 .

Rutile U–Pb Results

Ninety-two rutile U–Pb analyses were obtained in a U–Pb single session, which defined two coherent age groupings on a Tera–Wasserburg plot.

Group 1 constitutes 83 U–Pb rutile analyses, forming a well-defined mixing array on a Tera-Wasserburg plot between common and radiogenic Pb components. This array yields an upper intercept of 207 Pb/ 206 Pb i  = 0.8563 ± 0.0014. The lower intercept implies an age of 451 ± 8 Ma. The scatter about the line (MSWD = 2.7) is interpreted to reflect the variable passage of rutile of diverse grain sizes through the radiogenic Pb closure temperature at ~600 °C during and after magmatic crystallization 77 .

Group 2 comprises 9 grains, with 207 Pb corrected 238 U/ 206 Pb ages ranging from 591–1,724 Ma. Three grains from Group 2 define an age peak 68 at 1,607 Ma. Given the spread in U–Pb ages, we interpret these Proterozoic grains to represent detrital rutile derived from various sources.

Apatite isotopic analysis

Apatite u–pb methods.

Two apatite U–Pb LA-ICP–MS analysis sessions were conducted at the GeoHistory facility in the JdLC, Curtin University, Australia. For both sessions, ablations were created using a RESOlution 193 nm excimer laser ablation system connected to an Agilent 8900 ICP–MS with a RESOlution LE193 nm ArF and a Laurin Technic S155 cell ICP–MS. Other analytical details include a fluence of 2 J cm 2 and a 5 Hz repetition rate. For the Altar Stone section (MS3) and the Orcadian Basin samples (Supplementary Information  4 ), 24- and 20-µm spot sizes were used, respectively.

The matrix-matched primary reference material used for apatite U–Pb analyses was the Madagascar apatite (MAD-1) 78 . A range of secondary reference apatite was analysed, including FC-1 79 (Duluth Complex) with an age of 1,099.1 ± 0.6 Ma, Mount McClure 80 , 81 526 ± 2.1 Ma, Otter Lake 82 913 ± 7 Ma and Durango 31.44 ± 0.18 83  Ma. Anchored regressions (through reported 207 Pb/ 206 Pb i values) for secondary reference material yielded lower intercept ages within 2 σ uncertainty of reported values (Supplementary Information  8 ).

Altar Stone apatite U–Pb results

This first session of apatite U–Pb of MS3 from the Altar Stone yielded 117 analyses. On a Tera–Wasserburg plot, these analyses form two discordant mixing arrays between common and radiogenic Pb components with distinct lower intercepts.

The array from Group 2 apatite, comprised of 9 analyses, yields a lower intercept equivalent to an age of 1,018 ± 24 Ma (MSWD = 1.4) with an upper intercept 207 Pb/ 206 Pb i  = 0.8910 ± 0.0251. The f 207 % (the percentage of common Pb estimated using the 207 Pb method) of apatite analyses in Group 2 ranges from 16.66–88.8%, with a mean of 55.76%.

Group 1 apatite is defined by 108 analyses yielding a lower intercept of 462 ± 4 Ma (MSWD = 2.4) with an upper intercept 207 Pb/ 206 Pb i  = 0.8603 ± 0.0033. The f 207 % of apatite analyses in Group 1 range from 10.14–99.91%, with a mean of 78.65%. The slight over-dispersion of the apatite regression line may reflect some variation in Pb closure temperature in these crystals 84 .

Orcadian basin apatite U–Pb results

The second apatite U–Pb session yielded 138 analyses from samples CQ1 and AQ1. These data form three discordant mixing arrays between radiogenic and common Pb components on a Tera–Wasserburg plot.

An unanchored regression through Group 1 apatite ( n  = 14) from the Cruaday sample (CQ1) yields a lower intercept of 473 ± 25 Ma (MSWD = 1.8) with an upper intercept of 207 Pb/ 206 Pb i  = 0.8497 ± 0.0128. The f 207 % spans 38–99%, with a mean value of 85%.

Group 1 from the Spittal sample (AQ1), comprised of 109 analyses, yields a lower intercept equal to 466 ± 6 Ma (MSWD = 1.2). The upper 207 Pb/ 206 Pb i is equal to 0.8745 ± 0.0038. f 207 % values for this group range from 6–99%, with a mean value of 83%. A regression through Group 2 analyses ( n  = 17) from the Spittal sample yields a lower intercept of 1,013 ± 35 Ma (MSWD = 1) and an upper intercept 207 Pb/ 206 Pb i of 0.9038 ± 0.0101. f 207 % values span 25–99%, with a mean of 76%. Combined U–Pb analyses from Groups 1 from CQ1 and AQ1 ( n  = 123) yield a lower intercept equivalent to 466 ± 6 Ma (MSWD = 1.4) and an upper intercept 207 Pb/ 206 Pb i of 0.8726 ± 0.0036, which is presented beneath the Orcadian Basin kernel density estimate in Fig. 4b .

Apatite Lu–Hf methods

Apatite grains were dated in thin-section by the in situ Lu–Hf method at the University of Adelaide, using a RESOlution-LR 193 nm excimer laser ablation system, coupled to an Agilent 8900 ICP–MS/MS 85 , 86 . A gas mixture of NH 3 in He was used in the mass spectrometer reaction cell to promote high-order Hf reaction products, while equivalent Lu and Yb reaction products were negligible. The mass-shifted (+82 amu) reaction products of 176+82 Hf and 178+82 Hf reached the highest sensitivity of the measurable range and were analysed free from isobaric interferences. 177 Hf was calculated from 178 Hf, assuming natural abundances. 175 Lu was measured on mass as a proxy 85 for 176 Lu. Laser ablation was conducted with a laser beam of 43 µm at 7.5 Hz repetition rate and a fluency of approximately 3.5 J cm −2 . The analysed isotopes (with dwell times in ms between brackets) are 27 Al (2), 43 Ca (2), 57 Fe (2), 88 Sr (2), 89+85 Y (2), 90+83 Zr (2), 140+15 Ce (2), 146 Nd (2), 147 Sm (2), 172 Yb (5), 175 Lu (10), 175+82 Lu (50), 176+82 Hf (200) and 178+82 Hf (150). Isotopes with short dwell times (<10 ms) were measured to confirm apatite chemistry and to monitor for inclusions. 175+82 Lu was monitored for interferences on 176+82 Hf.

Relevant isotope ratios were calculated in LADR 87 using NIST 610 as the primary reference material 88 . Subsequently, reference apatite OD-306 78 (1,597 ± 7 Ma) was used to correct the Lu–Hf isotope ratios for matrix-induced fractionation 86 , 89 . Reference apatites Bamble-1 (1,597 ± 5 Ma), HR-1 (344 ± 2 Ma) and Wallaroo (1,574 ± 6 Ma) were monitored for accuracy verification 85 , 86 , 90 . Measured Lu–Hf dates of 1,098 ± 7 Ma, 346.0 ± 3.7 Ma and 1,575 ± 12 Ma, respectively, are in agreement with published values. All reference materials have negligible initial Hf, and weighted mean Lu–Hf dates were calculated in IsoplotR 61 directly from the (matrix-corrected) 176 Hf/ 176 Lu ratios.

For the Altar Stone apatites, which have variable 177 Hf/ 176 Hf compositions, single-grain Lu–Hf dates were calculated by anchoring isochrons to an initial 177 Hf/ 176 Hf composition 90 of 3.55 ± 0.05, which spans the entire range of initial 177 Hf/ 176 Hf ratios of the terrestrial reservoir (for example, ref. 91 ). The reported uncertainties for the single-grain Lu–Hf dates are presented as 95% confidence intervals, and dates are displayed on a kernel density estimate plot.

Apatite Lu–Hf results

Forty-five apatite Lu–Hf analyses were obtained from 2010K.240. Those with radiogenic Lu ingrowth or lacking common Hf gave Lu–Hf ages, defining four coherent isochrons and age groups.

Group 1, defined by 16 grains, yields a Lu–Hf isochron with a lower intercept of 470 ± 28 Ma (MSWD = 0.16, p ( χ 2 ) = 1). A second isochron through 5 analyses (Group 2) constitutes a lower intercept equivalent to 604 ± 38 Ma (MSWD = 0.14, p ( χ 2 ) = 0.94). Twelve apatite Lu–Hf analyses define Group 3 with a lower intercept of 1,123 ± 42 Ma (MSWD = 0.75, p ( χ 2 ) = 0.68). Three grains constitute the oldest grouping, Group 4 at 1,526 ± 186 Ma (MSWD = 0.014, p ( χ 2 ) = 0.91).

Apatite trace elements methods

A separate session of apatite trace element analysis was undertaken. Instrumentation and analytical set-up were identical to that described in 4.1. NIST 610 glass was the primary reference material for apatite trace element analyses. 43 Ca was used as the internal reference isotope, assuming an apatite Ca concentration of 40 wt%. Secondary reference materials included NIST 612 and the BHVO−2g glasses 92 . Elemental abundances for secondary reference material were generally within 5–10% of accepted values. Apatite trace element data was examined using the Geochemical Data Toolkit 93 .

Apatite trace elements results

One hundred and thirty-six apatite trace element analyses were obtained from as many grains. Geochemical classification schemes for apatite were used 29 , and three compositional groupings (felsic, mafic-intermediate, and alkaline) were defined.

Felsic-classified apatite grains ( n  = 83 (61% of analyses)) are defined by La/Nd of <0.6 and (La + Ce + Pr)/ΣREE (rare earth elements) of <0.5. The median values of felsic grains show a flat to slightly negative gradient on the chondrite-normalized REE plot from light to heavy REEs 94 . Felsic apatite’s median europium anomaly (Eu/Eu*) is 0.59, a moderately negative signature.

Mafic-intermediate apatite 29 ( n  = 48 (35% of grains)) are defined by (La + Ce + Pr)/ΣREE of 0.5–0.7 and a La/Nd of 0.5–1.5. In addition, apatite grains of this group typically exhibit a chondrite-normalized Ce/Yb of >5 and ΣREEs up to 1.25 wt%. Apatite grains classified as mafic-intermediate show a negative gradient on a chondrite-normalized REE plot from light to heavy REEs. The apatite grains of this group generally show the most enrichment in REEs compared to chondrite 94 . The median europium (Eu/Eu*) of mafic-intermediate apatite is 0.62, a moderately negative anomaly.

Lastly, alkaline apatite grains 29 ( n  = 5 (4% of analyses)) are characterized by La/Nd > 1.5 and a (La + Ce + Pr)/ΣREE > 0.8. The median europium anomaly of this group is 0.45. This grouping also shows elevated chondrite-normalized Ce/Yb of >10 and >0.5 wt% for the ΣREEs.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The isotopic and chemical data supporting the findings of this study are available within the paper and its supplementary information files.

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Acknowledgements

Funding was provided by an Australian Research Council Discovery Project (DP200101881). Sample material was loaned from the Salisbury Museum and Amgueddfa Cymru–Museum Wales and sampled with permission. The authors thank A. Green for assistance in accessing the Salisbury Museum material; B. McDonald, N. Evans, K. Rankenburg and S. Gilbert for their help during isotopic analysis; and P. Sampaio for assistance with statistical analysis. Instruments in the John de Laeter Centre, Curtin University, were funded via AuScope, the Australian Education Investment Fund, the National Collaborative Research Infrastructure Strategy, and the Australian Government. R.E.B. acknowledges a Leverhulme Trust Emeritus Fellowship.

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Timescales of Mineral Systems Group, School of Earth and Planetary Sciences, Curtin University, Perth, Western Australia, Australia

Anthony J. I. Clarke & Christopher L. Kirkland

Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, UK

Richard E. Bevins & Nick J. G. Pearce

Department of Earth Sciences, The University of Adelaide, Adelaide, South Australia, Australia

Stijn Glorie

Institute of Archaeology, University College London, London, UK

Rob A. Ixer

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Contributions

A.J.I.C.: writing, original draft, formal analysis, investigation, visualization, project administration, conceptualization and methodology. C.L.K.: supervision, resources, formal analysis, funding acquisition, writing, review and editing, conceptualization and methodology. R.E.B.: writing, review and editing, resources and conceptualization. N.J.G.P.: writing, review and editing, resources and conceptualization. S.G.: resources, formal analysis, funding acquisition, writing, review and editing, supervision, and methodology. R.A.I.: writing, review and editing.

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Correspondence to Anthony J. I. Clarke .

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Nature thanks Tim Kinnaird and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended data fig. 1 geological maps of potential source terranes for the altar stone..

a , Schematic map of the North Atlantic region with the crystalline terranes in the Caledonian-Variscan orogens depicted prior to the opening of the North Atlantic, adapted after ref.  95 . b , Schematic map of Britain and Ireland, showing outcrops of Old Red Sandstone, basement terranes, and major faults with reference to Stonehenge.

Extended Data Fig. 2 Altar Stone zircon U–Pb data.

a , Tera-Wasserburg plot for all concordant (≤10% discordant) zircon analyses reported from three samples of the Altar Stone. Discordance is defined using the concordia log % distance approach, and analytical ellipses are shown at the two-sigma uncertainty level. The ellipse colour denotes the sample. Replotted isotopic data for thin-section FN593 is from ref. 1 . b , Kernel density estimate for concordia U–Pb ages of concordant zircon from the Altar Stone, using a kernel and histogram bandwidth of 50 Ma. Fifty-six concordant analyses are shown from 113 measurements. A rug plot is given below the kernel density estimate, marking the age of each measurement.

Extended Data Fig. 3 Comparative kernel density estimates of concordant zircon concordia ages from the Altar Stone, crystalline sources terranes, and comparative sedimentary rock successions.

Each plot uses a kernel and histogram bandwidth of 50 Ma. The zircon U–Pb geochronology source for each comparative dataset is shown with their respective kernel density estimate. Zircon age data for basement terranes (right side of the plot) was sourced from refs. 20 , 26 .

Extended Data Fig. 4 Plots of rutile U–Pb ages.

a , Tera-Wasserburg plot of rutile U–Pb analyses from the Altar Stone (thin-section MS3). Isotopic data is shown at the two-sigma uncertainty level. b , Kernel density estimate for Group 2 rutile 207 Pb corrected 206 Pb/ 238 U ages, using a kernel and histogram bandwidth of 25 Ma. The rug plot below the kernel density estimate marks the age for each measurement.

Extended Data Fig. 5 Apatite Tera-Wasserburg U–Pb plots for the Altar Stone and Orcadian Basin.

a , Altar Stone apatite U–Pb analyses from thin-section MS3. b , Orcadian Basin apatite U–Pb analyses from sample AQ1, Spittal, Caithness. c , Orcadian Basin apatite U–Pb analyses from sample CQ1, Cruaday, Orkney. All data are shown as ellipses at the two-sigma uncertainty level. Regressions through U–Pb data are unanchored.

Extended Data Fig. 6 Combined kernel density estimate and histogram for apatite Lu–Hf single-grain ages from the Altar Stone.

Lu–Hf apparent ages from thin-section 2010K.240. Kernel and histogram bandwidth of 50 Ma. The rug plot below the kernel density estimate marks each calculated age. Single spot ages are calculated assuming an initial average terrestrial 177 Hf/ 176 Hf composition (see  Methods ).

Extended Data Fig. 7 Apatite trace element classification plots for the Altar Stone thin-section MS3.

Colours for all plots follow the geochemical discrimination defined in A. a , Reference 29  classification plot for apatite with an inset pie chart depicting the compositional groupings based on these geochemical ratios. b , The principal component plot of geochemical data from apatite shows the main eigenvectors of geochemical dispersion, highlighting enhanced Nd and La in the distinguishing groups. Medians for each group are denoted with a cross. c , Plot of total rare earth elements (REE) (%) versus (Ce/Yb) n with Mahalanobis ellipses around compositional classification centroids. A P = 0.5 in Mahalanobis distance analysis represents a two-sided probability, indicating that 50% of the probability mass of the chi-squared distribution for that compositional grouping is contained within the ellipse. This probability is calculated based on the cumulative distribution function of the chi-squared distribution. d , Chondrite normalized REE plot of median apatite values for each defined apatite classification type.

Extended Data Fig. 8 Cumulative probability density function plot.

Cumulative probability density function plot of comparative Old Red Sandstone detrital zircon U–Pb datasets (concordant ages) versus the Altar Stone. Proximity between cumulative density probability lines implies similar detrital zircon age populations.

Supplementary information

Supplementary information 1.

Zircon, rutile, and apatite U–Pb data for the Altar Stone and Orcadian Basin samples. A ) Zircon U–Pb data for MS3, 2010K.240, and FN593. B ) Zircon U–Pb data for secondary references. C ) Rutile U–Pb data for MS3. D ) Rutile U–Pb data for secondary references. E ) Session 1 apatite U–Pb data for MS3. F ) Session 1 apatite U–Pb data for secondary references. G ) Session 2 apatite U–Pb data for Orcadian Basin samples. H ) Session 2 apatite U–Pb data for secondary references.

Reporting Summary

Peer review file, supplementary information 2.

Apatite Lu–Hf data for the Altar Stone. A) Apatite Lu–Hf isotopic data and ages for thin-section 2010K.240. B) Apatite Lu–Hf data for secondary references.

Supplementary Information 3

Apatite trace elements for the Altar Stone. A) Apatite trace element data for MS3. B) Apatite trace element secondary reference values.

Supplementary Information 4–8

Supplementary Information 4 : Summary of analyses. Summary table of analyses undertaken in this work on samples from the Altar Stone and the Orcadian Basin. Supplementary Information 5: Summary of zircon U–Pb reference material. A summary table of analyses was obtained for zircon U–Pb secondary reference material run during this work. Supplementary Information 6: Kolmogorov–Smirnov test results. Table of D and P values for the Kolmogorov–Smirnov test on zircon ages from the Altar Stone and potential source regions. Supplementary Information 7: Kolmogorov–Smirnov test results, with Monte Carlo resampling. Table of D and P values for the Kolmogorov–Smirnov test (with Monte Carlo resampling) on zircon ages from the Altar Stone and potential source regions. Supplementary Information 8: Summary of apatite U–Pb reference material. A summary table of analyses was obtained for the apatite U–Pb secondary reference material run during this work.

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Clarke, A.J.I., Kirkland, C.L., Bevins, R.E. et al. A Scottish provenance for the Altar Stone of Stonehenge. Nature 632 , 570–575 (2024). https://doi.org/10.1038/s41586-024-07652-1

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Received : 16 December 2023

Accepted : 03 June 2024

Published : 14 August 2024

Issue Date : 15 August 2024

DOI : https://doi.org/10.1038/s41586-024-07652-1

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