Data Collection/Extraction

A data extraction form should be developed early in the review planning process using the research question and inclusion criteria to customize the form to meet the needs of the project. Carefully and thoroughly extract all relevant information from studies to be included in your systematic review. Relevant information will depend on the research question and and whether you are using quantitative or qualitative information, and/or the conventions of the journal to which you will submit for publication. The information extracted will predominantly relate to your study inclusion criteria; and may cover definition or conceptualization, measures/key variables, research design, participants, year of publication, data/results, study design, study setting, etc. (Siddaway et al., 2019).

• Building an Evidence Table Tips from Rutgers University Libraries.

• A Practical Guide to Data Extraction for Intervention Systematic Reviews This guide from Covidence can help streamline your data extraction processes, avoid unnecessary work, and deliver high-quality outcomes, regardless of which data extraction tool you are using. It offers definitions, practical advice, links to the Cochrane Handbook, downloadable templates, and real-world examples from studies. Diagrams and illustrations courtesy of Covidence: https://www.covidence.org/resource/data-extraction-for-intervention-systematic-reviews/

Commonly extracted fields for SRs include:

• Article citation with corresponding author
• Study characteristics such as study type/research design
• Year of publication
• Participant characteristics
• Interventions and study setting
• Outcome data & results

Manuals with starting templates for data extraction:

• Cochrane Handbook - Chapter 5: Collecting Data

• Institute of Medicine (2011). Finding what works in health care: Standards for systematic reviews. National Academies Press. See: Managing Data Collection .

• Cochrane Handbook 2023 - 5.4.3: Design of a Data Collection Form

• JBI Manual of Evidence Synthesis See specific review type for relevant data collection/extraction guidance.

-Adapted from Duke University Medical Library and Archives Systematic Reviews guide

• Cochrane Data Extraction Template Intervention review – RCTs only This form can be used as a guide for developing your own data extraction form. Sections can be expanded and added, and irrelevant sections can be removed.
• Cochrane Data Collection Form for Intervention Reviews– Randomized Controlled trials (RCTs) and Non-Randomized studies (NRS) 
• Cochrane Data Collection Form for Intervention Reviews: RCTs and Non-RCTs 
• Cochrane Data Collection Form for Intervention Reviews: RCTs Only 
• Example Data Extraction Form for Systematic Reviews  (Wilson, et al., 2017). Example data extraction form for systematic reviews. Available from: https://www.ncbi.nlm.nih.gov/books/NBK424005/
• JBI Template Source of Evidence Details, Characteristics and Results Extraction Instrument Found in the JBI Manual Appendix 11.1 J

• Plot Digitizer  A free Java program used to digitize scanned plots of functional data.

• RobotReviewer Aims to semi-automate evidence synthesis using machine learning and natural language processing. Automatically assesses bias in clinical trials. Automatically extracts and synthesizes data from Randomized Controlled Trials. Click on "Demo."

For more information on RobotReviewer see: 

Marshall, I. J., Kuiper, J., Banner, E., & Wallace, B. C. (2017). Automating Biomedical Evidence Synthesis: RobotReviewer. Proceedings of the Conference. Association for Computational Linguistics. Meeting, 2017, 7–12. https://doi.org/10.18653/v1/P17-4002

Marshall, I. J., Kuiper, J., & Wallace, B. C. (2016). RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials. Journal of the American Medical Informatics Association : JAMIA, 23(1), 193–201. https://doi.org/10.1093/jamia/ocv044

• SysRev  An intelligent platform for document reviews and automated data extraction. SysRev optimizes the review process with machine learning in the review process to increase accuracy and efficiency. Free and subscription versions available.

• Systematic Review Data Repository (SRDR+) A powerful and easy-to-use tool for the extraction and management of data for systematic review or meta-analysis. It is also an open and searchable archive of systematic reviews and their data. 

More Resources:

Büchter, R.B., Weise, A., & Pieper, D. (2020).  Development, testing and use of data extraction forms in systematic reviews: a review of methodological guidance. BMC Medical Research Methodology, 20(1), 1-14. https://doi.org/10.1186/s12874-020-01143-3

Aromataris E, Munn Z, (Eds.).(2020). JBI manual for evidence synthesis. JBI. https://doi.org/10.46658/JBIMES-20-01 See 11.2.7 Data extraction

Li, T., Higgins, J.P.T., & Deeks, J.J. (2023). Chapter 5: Collecting data. In J.P.T. Higgins, J. Thomas, J. Chandler, M. Cumpston, T. Li, M.J. Page, & V.A. Welch (Eds.), Cochrane handbook for systematic reviews of interventions. Cochrane.

Taylor, K. S., Mahtani, K. R., & Aronson, J. K. (2021). Summarising good practice guidelines for data extraction for systematic reviews and meta-analysis. BMJ Evidence-Based Medicine, 26(3), 88-90. http://dx.doi.org/10.1136/bmjebm-2020-111651

Wilson, P. M., Farley, K., Bickerdike, E. C., Booth, A. M., Chambers, D., Lambert, M., Thompson, C., Turner, R. & Watt, I. S. (2017). Effects of a demand-led evidence briefing service on the uptake and use of research evidence by commissioners of health services: A controlled before-and-after study. Health Services and Delivery Research, 5(5). See Appendix 2, Example data extraction form for systematic reviews.