Supplementary Tables and Figures
By Dr. Zubair Khalid, DVM, MS, PhD ·
Quick Answer
- Supplementary tables hold the raw data behind a paper's main claims, and you can extract what you need by matching table labels to the methods section and the figure they support.
- Open the supplementary file in a spreadsheet program, freeze the header row, and use column filters to isolate the specific variables or experimental groups you are tracking.
- A key limitation is that supplementary files are not standardized, so labeling, units, and file formats vary widely between journals and even between papers in the same journal.
What Supplementary Tables Are and Why They Matter
Supplementary tables are data files that journal editors and authors attach to a research paper but do not print in the main article. They hold the detailed measurements, statistical outputs, and extended analyses that support the figures and conclusions in the main text. For a reader, these tables are often the only place to find the actual numbers behind a graph, the full list of samples, or the raw data needed to verify a claim.
The main article is written to communicate a story. The supplementary tables are written to document the evidence. When you read a research paper, the figures and the main tables give you the interpreted result. The supplementary tables give you the underlying observations. If you want to check whether a statistical test was run on the correct sample size, whether a reported mean includes all replicates, or whether a subgroup analysis was pre-specified, you need to look at the supplementary material.
The practical problem is that these files are often enormous. A single supplementary Excel file can contain dozens of sheets, each with hundreds of rows and columns. The labels are sometimes cryptic, the units are not always stated, and the file format may be a PDF, an Excel workbook, a CSV, or a text file. Without a reading strategy, you can spend hours scrolling through data and still miss the specific point you need.
The strategy that works is to treat the supplementary table as a database instead of as a document. You do not read it from top to bottom. You query it. You identify the question you need answered, you locate the sheet and the columns that correspond to that question, and you extract only the rows that match your criteria. This approach works whether you are a student checking a reported result, a researcher preparing a replication, a laboratory staff member verifying a protocol, or an animal owner trying to understand the data behind a veterinary claim.
At a Glance
| What You Need | Where to Find It | How to Extract It |
|---|---|---|
| The exact sample size for each experimental group | The methods section and the first sheet of the supplementary file | Match the group labels in the table to the group definitions in the methods, then count the unique identifiers in the ID column |
| The units for a reported measurement | The column header, the table footnote, or the methods section | If the units are missing, check the figure axis labels in the main article or the original protocol |
| The definition of a statistical term such as SD or SEM | The table footnote or the statistical analysis section | Confirm whether the paper reports standard deviation or standard error before you compare values across studies |
| The full list of raw values behind a figure | The figure caption, which usually states the supplementary table number | Open the corresponding sheet and match the row labels to the figure legend |
| The data for a specific subgroup or condition | The column that codes the condition, often a binary or categorical variable | Use the spreadsheet filter to select only the rows that match your condition of interest |
| The exact units for a measurement | The column header or the methods section | If the units are ambiguous, check the original protocol or the data repository entry |
How to Read a Supplementary Table in Five Steps
Step 1: Read the Figure Captions and the Methods First
Before you open the supplementary file, read the figure captions in the main article. The caption usually tells you which supplementary table contains the data for that figure. For example, a caption might state that the data are available in Supplementary Table 3. That tells you exactly which sheet to open.
Then read the methods section. The methods tell you how the data were collected, what the units are, and how the variables were defined. The methods section is the key to interpreting the column headers in the supplementary table. If the methods say that body weight was measured in kilograms, then a column labeled "BW" is in kilograms. If the methods say that the study used a specific scoring system, the numbers in the table refer to that scoring system.
The National Library of Medicine hosts a collection of research-methods references that explain how to interpret study design and data reporting. These references are useful when you need to understand the statistical or methodological context of a table.
Step 2: Identify the Sheet and the Column You Need
Supplementary files often contain multiple sheets. The first sheet is usually a summary or a table of contents. The subsequent sheets contain the actual data. Look at the sheet names first. They are usually descriptive, such as "Table S1 Sample Characteristics" or "Table S4 Raw Data."
Once you find the right sheet, look at the column headers. The headers are the variable names. They should match the variable names used in the methods section. If a header is unclear, look for a separate sheet that contains a data dictionary or a variable legend. Some journals require a data dictionary as part of the supplementary material.
Step 3: Use the Filter Function to Isolate the Data You Need
The most efficient way to read a large supplementary table is to use the filter function in a spreadsheet program. Select the header row, then apply a filter to each column. This lets you sort the data by a specific variable or select only the rows that match a condition.
For example, if you want to see the data for the control group only, filter the group column to show only the control rows. If you want to see the data for a specific time point, filter the time column. This turns a massive table into a focused subset that you can inspect.
Step 4: Check the Units and the Statistical Notation
The units are usually stated in the column header or in the table footnote. If the units are not stated, look at the methods section. If the methods do not state the units, look at the figure caption in the main article. If the units are still unclear, you cannot interpret the numbers reliably.
The statistical notation matters too. A table may report a mean and a standard deviation, or a mean and a standard error. These are different values. The standard deviation describes the spread of the data. The standard error describes the precision of the mean. If you compare a standard deviation from one paper to a standard error from another paper, you will draw the wrong conclusion.
Step 5: Record the Source and the Date of Access
When you extract a data point from a supplementary table, record the paper, the supplementary table number, the sheet name, and the date you accessed the file. This is important for your own records and for any future replication or audit. The Committee on Publication Ethics core practices describe the expectations for data integrity and transparency in published research. Keeping a clear record of where you found a number is part of that transparency.
How to Handle Excel Files and Data Repositories
Excel Files
Excel files are the most common format for supplementary tables. They are easy to filter, sort, and search. The main risk is that the file may contain formulas instead of values. When you copy a number from a cell that contains a formula, you copy the formula, not the value. To avoid this, copy the cell and paste it as a value, or use the "paste special" function to paste only the values.
Another risk is that the file may contain hidden rows or hidden columns. The author may have hidden rows that contain outliers or excluded data. To check for hidden rows, look at the row numbers. If the row numbers skip from 10 to 15, there are hidden rows. You can unhide them by selecting the rows above and below the gap and choosing "Unhide."
Data Repositories
Some journals do not attach the data to the paper. They deposit the data in a public repository and provide a link in the paper. The repository may be a general-purpose repository or a discipline-specific one. The data in a repository may be organized differently from a supplementary table. It may be a set of CSV files, a database, or a collection of files.
The National Institutes of Health data management and sharing policy describes the expectations for data sharing in NIH-funded research. The policy requires that data be shared in a way that is findable, accessible, interoperable, and reusable. When you use data from a repository, you should check the repository's documentation to understand the file structure and the variable definitions.
The Fill-in-the-Blanks Template for Extracting Data
The following template is a practical tool for extracting data from a supplementary table. It is designed to force you to record the context of the data point, so you do not lose track of where the number came from.
The Template
| Field | What to Record |
|---|---|
| Paper title | The full title of the article |
| Authors | The first author and the year of publication |
| Journal | The journal name and the volume or issue |
| Supplementary table number | The table number as stated in the paper |
| Sheet name | The name of the sheet in the Excel file |
| Column header | The exact header of the column you used |
| Row identifier | The unique identifier for the row, such as a sample ID |
| Value | The number you extracted |
| Units | The units stated in the header or the methods |
| Statistical notation | Whether the value is a mean, a median, a standard deviation, or a standard error |
| Date of access | The date you downloaded the file |
| Source link | The URL or the DOI of the paper |
How to Use the Template
Fill in the template for every data point you extract. This is a record of your work. It lets you trace a number back to its source. It also lets you check whether the number you extracted is the number you intended to extract.
The template is especially useful when you are comparing data across multiple papers. If you extract a mean from paper A and a mean from paper B, you need to know the units and the statistical notation for both. The template forces you to record that information.
Common Failure Patterns When Reading Supplementary Tables
Failure Pattern 1: Reading the Table Without the Methods
The most common failure is to open the supplementary table and start reading the numbers without reading the methods. The numbers are meaningless without the methods. The methods tell you what the numbers represent, how they were measured, and what the units are. Without the methods, you are guessing.
Failure Pattern 2: Confusing Standard Deviation and Standard Error
A mean and a standard deviation are not the same as a mean and a standard error. The standard deviation is a measure of the spread of the data. The standard error is a measure of the precision of the mean. They are related, but they are not interchangeable. If you compare a standard deviation from one paper to a standard error from another paper, you will draw the wrong conclusion.
Failure Pattern 3: Ignoring the Footnotes
The footnotes in a supplementary table often contain the key to interpreting the data. They may state the units, the statistical test, the significance level, or the definition of a variable. If you ignore the footnotes, you may misinterpret the data.
Failure Pattern 4: Using the Wrong Sheet
A supplementary file may contain multiple sheets. The sheet you need is the one that matches the table number in the figure caption. If you open the wrong sheet, you will extract the wrong data.
Failure Pattern 5: Not Checking for Hidden Rows or Columns
Hidden rows and columns can change the interpretation of the data. If the author excluded outliers, the hidden rows may contain the excluded values. If you do not check for hidden rows, you may not know that the data were filtered.
Failure Pattern 6: Not Recording the Source
If you extract a data point and do not record the source, you cannot trace it back. This is a problem when you need to verify the data or when you need to cite the source in your own work.
How to Check the Quality of a Supplementary Table
The quality of a supplementary table is not always the same. Some tables are well organized and clearly labeled. Others are poorly organized and difficult to interpret. You can check the quality of a table by looking for the following signs.
Signs of a Well-Organized Table
- The sheet names are descriptive and match the table numbers in the paper.
- The column headers are clear and match the variable names in the methods.
- The units are stated in the column headers or in the footnotes.
- The statistical notation is clear, such as "mean (SD)" or "mean (SEM)".
- The table includes a data dictionary or a variable legend.
- The table does not contain hidden rows or columns.
Signs of a Poorly Organized Table
- The sheet names are generic, such as "Sheet1" or "Data".
- The column headers are cryptic or abbreviated without explanation.
- The units are not stated.
- The statistical notation is ambiguous.
- The table does not include a data dictionary.
- The table contains hidden rows or columns.
If the table is poorly organized, you need to be careful. You may need to go back to the methods section to interpret the data. If the methods do not clarify the data, you may need to contact the corresponding author.
How to Contact the Authors for Clarification
If you cannot interpret a supplementary table, you can contact the corresponding author. The corresponding author is the person listed in the paper as the contact for questions. The email address is usually in the paper or in the journal's website.
When you contact the author, be specific. State the paper title, the supplementary table number, the sheet name, and the column name. Ask a clear question about the data. For example, you might ask about the units, the definition of a variable, or the reason for a missing value.
The ORCID system provides a persistent identifier for researchers. You can use ORCID to find the author's current affiliation and contact information. This is useful when the author has moved to a different institution.
The Role of Reporting Guidelines
Reporting guidelines are checklists that tell authors what to include in a paper. They are designed to make research reporting transparent and complete. The EQUATOR Network is a repository of reporting guidelines. It provides a searchable database of guidelines for different study types.
When you read a supplementary table, the reporting guideline can tell you what the authors should have included. For example, a guideline for a clinical trial may require the authors to report the number of participants in each group, the number of participants who withdrew, and the reasons for withdrawal. If the supplementary table does not include this information, the paper may be incomplete.
The reporting guideline is a useful tool for checking the completeness of a paper. It tells you what to look for in the supplementary material.
The Role of Data Management and Sharing Policies
Data management and sharing policies are designed to ensure that research data are available to other researchers. The National Institutes of Health policy requires that NIH-funded research share the data that support the published results. This means that the data should be available in a repository or as a supplementary file.
When you read a paper that is funded by the NIH, you can expect the data to be available. If the data are not available, you can contact the authors or the funding agency. The policy is designed to make the data accessible, so you should be able to find the data.
The Limits of Supplementary Tables
Supplementary tables have limits. They are not a substitute for the main text. They are not a substitute for the methods section. They are a supplement to the paper, not a replacement.
The main limit is that the tables are not standardized. There is no universal format for supplementary tables. Each journal has its own format, and each author has their own style. This means that you cannot assume that a table from one paper is organized the same way as a table from another paper.
Another limit is that the tables may not include all the data. The authors may have excluded some data, or they may have summarized the data in a way that hides the raw values. If you need the raw data, you may need to contact the authors or the repository.
The Safety and Welfare Context
When you read a supplementary table that contains data from animal studies, you should consider the welfare context. The data in the table may include information about the number of animals used, the number of animals that died, or the number of animals that were excluded. This information is important for assessing the welfare of the animals in the study.
The National Institutes of Health provides guidance on the use of animals in research. The guidance describes the expectations for the care and use of animals in NIH-funded research. When you read a supplementary table, you can check whether the study followed these expectations.
The Professional Escalation Criteria
If you find a problem in a supplementary table, you need to decide whether to escalate the issue. The following criteria can help you decide.
Escalate When
- The data are inconsistent with the main text.
- The data are missing for a key variable.
- The units are not stated and cannot be determined.
- The data are not available in the repository.
- The authors do not respond to a request for clarification.
Do Not Escalate When
- The data are clear and consistent with the main text.
- The units are stated and the data are interpretable.
- The data are available in the repository.
- The authors respond to a request for clarification.
If you escalate the issue, you can contact the journal editor or the funding agency. The Committee on Publication Ethics provides guidance on how to handle concerns about published research. The guidance includes the steps for reporting a concern to the journal.
Frequently Asked Questions
What is the difference between a supplementary table and a data repository?
A supplementary table is a file attached to a paper. A data repository is a separate database where the data are stored. The paper may link to the repository. The repository may contain more data than the supplementary table.
How do I find the supplementary table for a paper?
The supplementary table is usually linked in the paper. The link is often in the figure caption or in the methods section. The link may be a file attached to the paper or a link to a repository.
What should I do if the supplementary table is not available?
If the supplementary table is not available, you can contact the corresponding author. The author may provide the data or explain why the data are not available.
How do I know if the data in the table are reliable?
You can check the data by comparing it to the main text. If the data are consistent with the main text, the data are likely reliable. If the data are inconsistent, you should be cautious.
What is the best way to extract data from a large table?
The best way is to use the filter function in a spreadsheet program. This lets you select the rows that match your criteria. You can then copy the selected data.
How do I cite a supplementary table in my own paper?
You should cite the paper that contains the table. You can also cite the supplementary table number. The citation format depends on the journal style.
What is the difference between a standard deviation and a standard error?
A standard deviation is a measure of the spread of the data. A standard error is a measure of the precision of the mean. They are related, but they are not the same.
What should I do if I find an error in a supplementary table?
If you find an error, you should contact the corresponding author. You can also contact the journal editor. The Committee on Publication Ethics provides guidance on how to report an error.
Related Articles
- How to Present Data in Research Papers: Methods, Tables, and Figures
- Data Presentation in Research: Tables, Figures, and Text
- BAM vs CRAM: Choosing a Read Alignment Format for Research Data
- Data Visualization for Biological Research: Choosing Figures That Preserve Meaning
- Research Figures: Making Methods and Results Easier to Evaluate
References and Further Reading
- Research Methods Resources. National Library of Medicine.
- EQUATOR Network. EQUATOR Network.
- Core Practices. Committee on Publication Ethics.
- NIH Grants and Funding. National Institutes of Health.
- ORCID for Researchers. ORCID.
- Data Management and Sharing Policy. National Institutes of Health.
- NCBI Literature Resources. National Center for Biotechnology Information.
- PubMed. National Library of Medicine.
This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.