Build a customized data extraction form for your systematic review. Select from 60+ pre-built fields organized by category, reorder columns, add custom fields, and export as a CSV spreadsheet ready for your review team.
Browse the field library on the left and click fields to add them to your template. Use the arrows to reorder, add custom fields at the bottom, then export as CSV. The CSV file opens in Excel or Google Sheets with each field as a column header -- one row per study.
Load sample data to see how the tool works, or clear all fields to start fresh.
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Click fields in the library to add them to your extraction template.
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Get a Free QuoteHave two reviewers independently extract data from 3-5 studies using your template. Compare results and refine fields before starting the full extraction.
Even with structured fields, a general notes column lets reviewers flag issues, record assumptions, or note important contextual details.
Create a companion document explaining what each field means, how to handle missing data, and any coding rules. This ensures consistency across reviewers.
Organize extraction fields around Population, Intervention, Comparator, and Outcome. This maps directly to your review question and synthesis.
The data extraction stage is where a systematic review transforms from a search and screening exercise into a structured evidence synthesis. A data extraction form systematic review teams rely on must capture every piece of information needed to answer the review question, conduct planned analyses, and assess study quality, all in a format that minimizes transcription errors and maximizes consistency between reviewers. The Cochrane Handbook for Systematic Reviews of Interventions (Higgins et al., 2023) identifies data extraction as a critical step that directly influences the reliability of the review's conclusions, and PRISMA 2020 (Page et al., 2021) requires authors to describe the data extraction process, including how many reviewers extracted data, whether extraction was done independently, and how discrepancies were resolved. Dedicated extraction platforms such as Covidence, DistillerSR, and EPPI-Reviewer provide built-in form builders with real-time conflict detection and audit trails, while REDCap offers structured data capture with branching logic that is particularly useful for large-scale reviews extracting complex clinical data.
A well-designed data extraction template generator helps reviewers build forms organized around the core elements of their review question. At minimum, every extraction form should include study identification fields (first author, year, journal, country), participant characteristics (sample size, age, sex distribution, inclusion criteria), intervention and comparator details, outcome definitions and measurement instruments, results (effect estimates, confidence intervals, p-values), and quality assessment scores. The PICO framework (Population, Intervention, Comparison, Outcome) provides a natural organizational structure. Fields should use standardized coding wherever possible (e.g., predefined dropdown options for study design, risk of bias levels, and outcome categories) to reduce free-text ambiguity. A companion codebook that defines each field, specifies how to handle missing data, and documents any coding rules is essential for ensuring that all reviewers interpret the extraction form identically. The Cochrane data collection form templates provide a well-established reference for structuring extraction fields, and many review teams adapt these templates as a starting point before customizing for their specific research question.
The systematic review data collection tool should be pilot-tested before full extraction begins. Best practice, as recommended by both the Cochrane Handbook and the JBI Manual for Evidence Synthesis (Aromataris & Munn, 2020), involves having at least two reviewers independently extract data from 3-5 representative studies, then comparing their results to identify ambiguous fields, missing categories, or inconsistent interpretations. This pilot phase, typically conducted on 3–5 representative studies before full-scale extraction begins, almost always reveals fields that need clarification, additional response options, or restructuring. For living systematic reviews, where new evidence is continuously incorporated, extraction workflows must be iterative, with versioned forms and clear protocols for integrating newly published studies into the existing dataset without disrupting previously extracted records. After piloting, the finalized form should be used by two independent reviewers for every included study, with a third reviewer or consensus meeting resolving disagreements. Integrating your quality appraisal directly into the extraction form, using fields from the RoB 2 bias assessment tool for randomized trials or the Newcastle-Ottawa Scale for observational studies keeps all study-level data in a single document and simplifies downstream analyses.
Effective data extraction forms do not exist in isolation; they connect to every other stage of the review workflow. The fields you extract should map directly to your planned analyses. If you intend to conduct subgroup analyses by age or study design, you need corresponding extraction fields. If your review spans multiple study types, include design-specific quality assessment columns drawing from the appropriate tools: the ROBINS-I framework for non-randomized studies, the JBI critical appraisal checklists for qualitative and cross-sectional studies, or the PICO framework builder to ensure your extraction categories align with your research question. Once extraction is complete, the structured data flows into effect size calculations, forest plots, and heterogeneity assessments, making the quality of your extraction form the foundation on which every subsequent conclusion rests.
A data extraction form is a standardized template used in systematic reviews to collect relevant information from each included study. It typically includes fields for study identification (authors, year), population characteristics, intervention details, outcomes measured, and results. A well-designed form ensures consistent and comprehensive data collection across all reviewers.
This depends on your review scope. A focused intervention review might need 20-30 fields. A comprehensive review with subgroup analyses might need 50+. Start with your PICO elements and add fields needed for your planned analyses. Too few fields risks missing important data; too many leads to reviewer fatigue and errors.
Yes! The CSV export opens directly in Excel, Google Sheets, Numbers, or any spreadsheet application. Each field becomes a column header in your spreadsheet. You can then add data validation, dropdown lists, and conditional formatting as needed.
Yes, it's strongly recommended. Including RoB fields in your extraction form (rather than a separate document) keeps all study data in one place. Select the appropriate RoB tool for your study designs: RoB 2 for randomized trials, ROBINS-I for non-randomized studies, or NOS for observational studies.
At minimum extract: study identification (author, year, journal), study design, population characteristics (sample size, age, sex, setting), intervention/exposure details, comparator, outcome measures with effect estimates and precision (means, SDs, event counts, or hazard ratios with CIs), follow-up duration, and risk of bias domain judgments. The Cochrane Handbook Chapter 5 provides a comprehensive checklist.
At least two reviewers should independently extract data from each study, with discrepancies resolved by discussion or a third reviewer. This dual extraction reduces errors — studies show single-reviewer extraction has error rates of 10–30%. For efficiency, some teams use a verify approach: one reviewer extracts, a second checks and corrects. Report your extraction method and agreement rate in the methods section.
Yes, when feasible. The Cochrane Handbook recommends contacting authors for missing or unclear data, especially outcome data needed for meta-analysis. Send a structured request with specific questions. Allow 2–4 weeks for responses and send one reminder. Report the number of authors contacted, response rate, and any data obtained in your review. PRISMA 2020 requires describing efforts to obtain missing data.
Once your extraction form is ready, document the study selection process with our PRISMA flow diagram generator to create a compliant flow chart for your manuscript. Pair your extraction with quality appraisal using the risk of bias assessment tool, which supports RoB 2 domain-level judgments and traffic light exports. If you are still defining your review question, the PICO framework generator helps you structure Population, Intervention, Comparison, and Outcome elements into a focused, searchable question.
Reviewed by
Dr. Sarah Mitchell holds a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health and has over 15 years of experience in systematic review methodology and meta-analysis. She has authored or co-authored 40+ peer-reviewed publications in journals including the Journal of Clinical Epidemiology, BMC Medical Research Methodology, and Research Synthesis Methods. A former Cochrane Review Group statistician and current editorial board member of Systematic Reviews, Dr. Mitchell has supervised 200+ evidence synthesis projects across clinical medicine, public health, and social sciences. She reviews all Research Gold tools to ensure statistical accuracy and compliance with Cochrane Handbook and PRISMA 2020 standards.
Protocol development, PROSPERO registration, comprehensive search strategy, screening, analysis, and a publication-ready manuscript. All handled by PhD experts.