Guide to the DMP Assistant Template for Systematic Review Projects

Documentation

What documentation will be needed for the data to be read and interpreted correctly in the future?

Good documentation includes: information about the study, data-level descriptions, and any contextual information required to make the data readable and usable by other researchers. This includes elements such as: assumptions made, explanation of data coding and analysis performed (including syntax files), and details of who has worked on the project and performed each task, etc. Some of this may already be in your protocol.

Specific Examples:

Data collection/Locating studies: A document that contains full search strategies for databases including database name, vendor platform, date of search, limits applied as well as the line-by-line search strings exactly as displayed on the search screen. A complete description of grey literature and supplementary search techniques (citation searching, hand searching, etc.). For detailed guidance on this, please see:

Haddaway, N. R., Rethlefsen, M. L., Davies, M., Glanvill, J., McGowan, B., Nyhan, K., & Young, S. (2022). A suggested data structure for transparent and repeatable reporting of bibliographic searching. [Preprint]. AgriRxiv. https://doi.org/10.31220/agriRxiv.2022.00138.

Study Selection: Detailed inclusion/exclusion criteria used during study selection, number of independent reviewers, as well as the process for resolving conflicts.

Data extraction: Descriptions of the categories of information being extracted. Consider creating a document that describes how data will be extracted and categorized. This is often documented in the study protocol but may also be kept in an Excel file. For an example of a coding scheme framework, see Brown et al., "A framework for developing a coding scheme for meta-analysis". 

Critical appraisal: Critical appraisal tool/checklist (and accompanying documents) to be used, as well as documentation of adaptations/interpretation agreed upon by the research team.

Meta-analysis (if applicable): The data extraction sheet (Excel file) that is imported into statistical software such as STATA/SAS; and a command file (log/do file) that outlines the analyses completed, assessment for heterogeneity, and subsequent steps taken to explore between-study heterogeneity (e.g., stratified meta-analysis, meta-regression, assessment for publication bias). 

A readme file that provides an overview of the contents of the project folder (file structure, naming conventions, etc.).

Creation of documentation

How will you make sure that documentation is created or captured consistently throughout your project?

Who will be responsible for documenting each stage of the review?

It is useful to consult regularly with members of the research team to capture potential changes in data collection/processing that need to be reflected in the documentation. Individual roles and workflows should include gathering data documentation as a key element.

Team members responsible for each stage of the review should add the documentation at the conclusion of their work on a particular stage, or as needed. The end of a phase (distinct stage) of the systematic review is a great time-point to gather data and documentation from that stage, from the individuals responsible for carrying out the relevant tasks. 

For example, the librarian may have the data collection (locating studies) documentation or the statistician might have the meta-analysis documentation.