How to Extract and Chart Data for a Scoping Review

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-27

Quick Answer

• Use a standardized data charting form to capture study characteristics and findings consistently across all included sources.
• Pilot your extraction form on three to five studies before full extraction to catch ambiguous fields and missing variables.
• Charting is an iterative process, expect to revise your form as you become familiar with the literature, and document every version change.

Understanding Data Extraction and Charting in Scoping Reviews

Scoping reviews map the existing literature on a broad topic, identify gaps, and clarify key concepts. Unlike systematic reviews that answer a focused clinical question with meta-analysis, scoping reviews prioritize breadth of coverage. The data charting step is where you decide what information to collect from each included study and how to organize that information for synthesis.

Data extraction in a scoping review involves pulling specific pieces of information from each included source. Charting refers to presenting that extracted data in a structured format, typically a table or spreadsheet, that allows you to compare studies side by side. The Joanna Briggs Institute methodology, widely used for scoping reviews, recommends a three-stage approach: extract the data, chart the data, and collate and summarize the results.

The purpose of data charting is not to appraise study quality. Scoping reviews generally do not assess risk of bias in included studies. Instead, the charting process captures what exists in the literature, how studies were conducted, and what they found. This distinction matters because it shapes what fields you include in your extraction form.

Your charting form should capture three broad categories of information. First, study characteristics such as author, year, country, and study design. Second, participant or population details relevant to your review question. Third, outcomes or findings that address your research objectives. The exact fields depend on your review question and the nature of the literature you expect to find.

At a Glance: Core Components of a Data Charting Form

Building Your Data Charting Form

Start with Your Review Question

Your data charting form must trace back to your research question and objectives. If your scoping review asks what interventions exist for a specific condition, your form needs fields for intervention type, duration, and delivery format. If your review maps definitions of a concept, your form needs fields for how each source defines that concept.

Write out your review question and objectives before designing your form. Underline the key concepts and variables. Each variable that appears in your question should appear as a field in your charting form. This direct mapping prevents you from collecting irrelevant data while ensuring you capture what you need.

Draft Initial Fields

Begin with a draft set of fields based on your review question and your knowledge of the literature. Common fields include author, year, country, study design, population, sample size, intervention or exposure, comparator, outcomes, and key findings. Add fields specific to your topic. For a review of measurement tools, include the tool name and validation status. For a review of policy documents, include the issuing body and document type.

Keep field definitions explicit. Write a short description for each field that explains what to record and how. For example, the field "study design" might instruct charters to record the design as stated by the authors, such as randomized controlled trial, cohort study, or qualitative interview study. This prevents different charters from interpreting the same field differently.

Pilot the Form

Test your draft form on three to five included studies before you begin full extraction. This pilot phase reveals problems with ambiguous fields, missing variables, and unclear instructions. Revise the form based on what you learn. You may find that some fields are rarely populated and can be removed, while other important variables were missing entirely.

The pilot phase is also where you calibrate how much detail to record. Recording too much detail slows the process and creates unwieldy tables. Recording too little forces you to return to full texts later. Aim for enough detail to answer your review question without capturing every minor finding.

Iterate During Full Extraction

Expect to revise your form during full extraction. As you encounter studies that do not fit your initial categories, adjust the form to accommodate them. Document every version of your form with a date and version number. This documentation is essential for transparency and reproducibility.

When you revise a field, apply the revision consistently to studies you have already charted. This may mean re-extracting some data. Build this re-extraction time into your project schedule. The National Library of Medicine hosts research methods references that describe the importance of documenting methodological decisions in evidence synthesis.

Practical Workflow for Data Extraction

Assemble Your Team

Decide who will perform extraction and how disagreements will be resolved. Scoping reviews often use two reviewers who independently extract data from a subset of studies to check consistency. A third reviewer may resolve disagreements. Alternatively, one reviewer may extract all data while a second verifies a random sample.

Whatever approach you choose, document it in your protocol. Your protocol should state how many studies will be double extracted, how disagreements will be resolved, and what level of agreement is acceptable. This transparency supports the integrity of your review process.

Use a Structured Extraction Tool

A spreadsheet is the most common tool for data charting. Create one row per included study and one column per field. Use data validation to restrict entries to predefined categories where possible. This reduces typing errors and ensures consistency.

Dedicated software for systematic reviews can also support data extraction. These tools allow multiple reviewers to work on the same extraction form, track changes, and export data for analysis. Choose a tool that fits your budget, technical skills, and team size. The best tool is the one your team will actually use consistently.

Record Data Directly from Full Texts

Extract data from the full text of each study, beyond the abstract. Abstracts often omit important details about methods and findings. Read the full text and record data directly into your charting form. Keep the source document open while you chart so you can verify details.

Record data in your own words instead of copying sentences from the source. This forces you to understand what the study did and found. It also prevents accidental plagiarism in your final review. For numeric data such as sample sizes and effect estimates, copy the exact numbers.

Track Your Progress

Maintain a record of which studies have been extracted and which remain. A simple status column in your spreadsheet can track this. Mark each study as pending, in progress, or complete. This tracking helps you monitor progress and identify studies that require follow-up.

Charting the Data

Choose a Charting Format

The charted data typically appears in a table in your final review. The table format depends on the nature of your data and your review question. A descriptive table lists each study in a row with characteristics in columns. A more analytical table may group studies by theme, intervention type, or population.

Your charting table should be readable and informative. Use clear column headings that match your extraction fields. Keep cell entries concise. Long quotations or extensive detail make tables difficult to scan. Reserve detailed findings for the narrative synthesis.

Organize by Theme or Category

Consider organizing your charted data by theme or category instead of alphabetically by author. This organization helps readers see patterns across studies. For example, a review of telehealth interventions might group studies by delivery mode, such as video consultation, phone consultation, or remote monitoring.

Your organization scheme should reflect your review objectives. If your review maps definitions, group studies by how they define the concept. If your review identifies gaps, group studies by population or setting. The organization should make the landscape of the literature visible.

Link Charted Data to Your Synthesis

The charted data forms the basis for your narrative synthesis. As you write your results section, refer back to your charting table to ensure your narrative accurately reflects the extracted data. The table provides the evidence for your descriptive summary of the literature.

Your synthesis should describe the range of studies, highlight patterns and trends, and identify gaps. The charted data supports each of these elements. Keep your charting table and synthesis draft in the same document or workspace so you can cross-check them.

Records and Measurements

Document Your Extraction Process

Keep detailed records of your extraction process. Save every version of your charting form with a version number and date. Record decisions about field definitions and revisions. Note which studies were double extracted and how disagreements were resolved.

This documentation serves multiple purposes. It supports the reproducibility of your review. It provides evidence of methodological rigor for peer reviewers. It also helps you write the methods section of your final report, where you must describe how data were charted.

Measure Inter-Rater Agreement

If two reviewers extract data from the same studies, measure their agreement. Simple percentage agreement is easy to calculate but does not account for chance agreement. Cohen's kappa is a more robust measure that adjusts for chance. Report your agreement measure in your methods section.

When disagreements occur, examine the pattern. Disagreements often reveal ambiguous field definitions that need clarification. Revise your form and instructions to prevent similar disagreements in future extractions. This iterative improvement is a normal part of the charting process.

Track Missing Data

Record when data are missing from a study. A study may not report the sample size, may omit the intervention duration, or may not state the country where it was conducted. Note these missing data in your charting form instead of leaving cells blank.

Missing data are themselves a finding. In your synthesis, you can describe how often key variables were reported across the literature. This information helps identify reporting gaps and may inform recommendations for future research.

Common Failure Patterns in Data Charting

Overly Complex Forms

A charting form with too many fields becomes burdensome and error prone. Reviewers may rush through extraction, miss important details, or record inconsistent data. If your form has more than 30 fields, consider whether each field is necessary to answer your review question.

Simplify your form by removing fields that are rarely populated or that do not directly address your objectives. Combine related fields where possible. A leaner form produces more consistent data and a more manageable extraction process.

Vague Field Definitions

Fields without explicit definitions invite inconsistent interpretation. One reviewer may record "adults" for a study of people aged 18 to 65, while another records "18-65 years." These inconsistencies create problems during synthesis and may require re-extraction.

Write clear definitions for every field. Specify the format for recording data, such as exact numbers for age ranges or predefined categories for study design. Pilot your form to identify fields that reviewers interpret differently.

Copying Text Instead of Summarizing

Copying sentences from source studies into your charting form creates several problems. It produces unwieldy tables filled with long quotations. It risks accidental plagiarism in your final review. It also indicates that the reviewer may not have fully understood the study.

Train your reviewers to summarize findings in their own words. Provide examples of good summaries during training. If a reviewer consistently copies text, review their work and provide feedback.

Inconsistent Application of Revisions

When you revise your charting form mid-extraction, you must apply the revision to already charted studies. Failing to do so creates inconsistent data where some studies have the new field and others do not. This inconsistency undermines your synthesis.

Track revisions carefully and schedule time for re-extraction. When you add a field, identify all studies that need the new data and extract it systematically. When you change a definition, review all studies affected by the change.

Quality Controls and Professional Escalation

Establish Quality Checks

Build quality checks into your extraction workflow. Have a second reviewer verify a sample of extracted data against the source studies. Check for missing data, inconsistent entries, and transcription errors. These checks catch problems before they propagate through your synthesis.

The proportion of studies that receive verification depends on your team size and timeline. A common approach is to verify 10 to 20 percent of studies. If errors are found, increase the verification proportion and review the extraction process for systemic problems.

Escalate Persistent Problems

Some problems require escalation beyond your immediate team. If you cannot access the full text of a study, escalate to your librarian or document delivery service. If a study reports data in an unusual format that you cannot interpret, consult a subject matter expert.

If disagreements between reviewers cannot be resolved, escalate to the review lead or a third reviewer. Document the disagreement and its resolution. This documentation supports the transparency of your review process.

Follow Reporting Guidelines

Report your scoping review according to established guidelines. The EQUATOR Network provides a searchable database of reporting guidelines for health research. The PRISMA-ScR checklist is the primary reporting guideline for scoping reviews. Following this guideline ensures your report includes all necessary elements.

Reporting guidelines also help you plan your data charting. The checklist items indicate what information your final report must include, which informs the fields you need in your charting form. Consult the guideline early in your project, beyond at the writing stage.

Welfare and Safety Context

Protect Participant Data

Some scoping reviews include studies with sensitive participant data. When charting data from such studies, record only the information needed for your review. Do not record participant names, contact details, or other identifying information. Your charting form should contain only aggregate or de-identified data.

If your review includes unpublished data or personal communications, consider whether you need ethics approval. Consult your institution's research ethics board if you have any doubt. The Committee on Publication Ethics core practices address responsible data handling and research conduct.

Maintain Research Integrity

Data charting is a research activity that requires the same integrity standards as other research steps. Do not alter data to fit a narrative. Do not omit studies that contradict your expectations. Report your methods accurately and completely.

The Committee on Publication Ethics core practices cover authorship, peer review, data handling, and research misconduct. Familiarize yourself with these practices before beginning your review. If you observe misconduct in your own team or elsewhere, follow your institution's procedures for reporting concerns.

Manage Your Research Identity

As you conduct your scoping review, ensure your research outputs are correctly attributed to you. An ORCID identifier provides a persistent digital identifier that distinguishes you from other researchers. Link your ORCID to your publications, datasets, and other research outputs.

Your ORCID record can also document your contributions to the scoping review process. This documentation is useful for demonstrating your research experience and for tracking your scholarly output over time.

Limitations of Data Charting

Dependence on Source Quality

Your charted data are only as good as the sources you chart. Studies with poor reporting may lack key information, leaving gaps in your charting table. You cannot extract data that were not reported. Acknowledge this limitation in your review and describe how you handled missing data.

Some fields may be impossible to complete for certain study types. For example, a qualitative study may not report a sample size in the same way as a quantitative study. Your charting form should accommodate different study designs without forcing inappropriate comparisons.

Subjectivity in Data Interpretation

Charting requires judgment about what data are relevant and how to summarize them. Different reviewers may make different judgments. This subjectivity is inherent to the process, but it can be minimized through clear field definitions, pilot testing, and inter-rater agreement checks.

Acknowledge the role of judgment in your methods section. Describe how you developed your charting form, how reviewers were trained, and how disagreements were resolved. This transparency helps readers understand the trustworthiness of your charted data.

Time and Resource Demands

Data charting is time intensive, particularly for reviews with many included studies. Full-text retrieval, extraction, verification, and revision all require substantial time. Plan your project timeline realistically and build in buffer time for unexpected challenges.

Consider whether your review question can be answered with a smaller body of literature or a more focused scope. A well-defined review question reduces the number of included studies and the associated charting burden. The National Institutes of Health provides guidance on project planning and resource management for research activities.

Practical Implementation Steps

Step 1: Define Your Fields

Write your review question and identify the variables you need to capture. Draft an initial set of fields with explicit definitions. Share the draft with your team for feedback before piloting.

Step 2: Pilot and Revise

Test your form on three to five studies. Record any problems you encounter, including ambiguous fields, missing variables, and unclear instructions. Revise the form based on your pilot experience.

Step 3: Train Your Team

If multiple reviewers will extract data, train them together. Review the form field by field, discuss definitions, and practice on sample studies. Establish a process for resolving disagreements.

Step 4: Extract Data

Work through your included studies systematically. Record data directly from full texts. Track your progress and note any missing data. Apply form revisions consistently to all studies.

Step 5: Verify and Check Quality

Have a second reviewer verify a sample of extracted data. Check for errors, inconsistencies, and missing information. Address any problems before proceeding to synthesis.

Step 6: Chart and Synthesize

Organize your extracted data into a charting table. Use the table to support your narrative synthesis. Ensure your synthesis accurately reflects the charted data.

Frequently Asked Questions

How is data charting different from data extraction?

Data extraction is the process of pulling specific information from each included study. Charting is the organization of that extracted data into a structured format, typically a table, that allows comparison across studies. Extraction is the act of gathering, charting is the act of arranging.

Do I need two reviewers for data extraction in a scoping review?

Two reviewers are recommended but not always required. Using two reviewers who independently extract data from a subset of studies allows you to check consistency and resolve disagreements. If you use one reviewer, consider having a second reviewer verify a sample of extracted data.

What fields should I include in my data charting form?

Your fields should map directly to your review question and objectives. Common fields include author, year, country, study design, population, sample size, intervention or exposure, outcomes, and key findings. Add topic-specific fields based on what your review needs to answer.

How do I handle missing data in included studies?

Record when data are missing instead of leaving cells blank. Note the missing field and consider whether the absence is meaningful. Missing data are themselves a finding that you can describe in your synthesis.

Can I revise my charting form after starting extraction?

Yes, revision is expected. As you encounter studies that do not fit your initial categories, adjust the form to accommodate them. Document every version with a date and version number, and apply revisions consistently to all studies.

What is the difference between a scoping review and a systematic review?

A scoping review maps the breadth of literature on a broad topic and identifies gaps, while a systematic review answers a focused question with rigorous synthesis, often including meta-analysis. Scoping reviews generally do not assess risk of bias or quality of included studies.

How do I report my data charting methods?

Describe how you developed your charting form, how reviewers were trained, how data were extracted, and how disagreements were resolved. Follow the PRISMA-ScR reporting guideline for scoping reviews, available through the EQUATOR Network.

What should I do if I cannot access the full text of a study?

Contact your institution's library or document delivery service for assistance. If you still cannot obtain the full text, record this in your review documentation and consider whether the abstract provides sufficient information for your purposes.

Using the Evidence

Related Articles

• Data Management Plan Template: A Practical Guide for Researchers
• What Is a Data Dictionary? A Practical Guide for Research Teams
• Qualitative Data Analysis: A Practical Guide for Life Scientists
• What Is Data Management? A Primer for Life Science Researchers
• How to Write an Experiment Report: A Practical Template

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.