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Guide to the DMP Assistant Template for Systematic Review Projects

Companion guide to the Portage DMP Assistant Template for Systematic Review Projects

Sharing your data

What data will you be sharing and in what form? (e.g. raw, processed, analyzed, final).


Keep in mind the FAIR principles, which is a framework for making data more Findable, Accessible, Interoperable and Reusable. 


Raw data are directly obtained from the instrument, simulation or survey.

Examples: RIS files of database records, line-by-line search strategies from databases, screening decisions document/file from each reviewer (if using Excel), raw data extraction sheets from each reviewer, critical appraisal decisions for each individual reviewer for each of the included studies.

In many cases, database records will be subject to copyright and thus may not be shared; however, they can be stored for later use by the original researchers. Sharing documented search strategies for each database is sufficient to ensure reproducibility of the data.

Processed data result from some manipulation of the raw data in order to eliminate errors or outliers, to prepare the data for analysis, to derive new variables.

Examples: post-deduplication reference libraries, records remaining at each screening stage, consolidated data extraction sheets or finalized data extraction table, finalized data extraction for meta-analysis, consolidated critical appraisal decisions for each study. 

Analyzed data are the results of qualitative, statistical, or mathematical analysis of the processed data. They can be presented as graphs, charts or statistical tables.

Examples: Tables and figures created for the manuscript including PRISMA flow diagram, table of included studies, forest plots, funnel plots, etc.

Final data are processed data that have, if needed, been converted into a preservation-friendly format. Consider which need to be shared to meet institutional or funding requirements, and which data may be restricted because of confidentiality/privacy/intellectual property considerations.

Examples of what should be shared: protocols, complete search strategies for all databases, data extraction forms, statistical code and data files (e.g. CSV or Excel files) that are exported into a statistical program to recreate relevant meta-analyses.

Licensing your data

Have you considered what type of end-user license to include with your data?

This may depend on the requirements of the journal you plan to submit to, or funder requirements, etc. Do they require data associated with your manuscript to be made public?  

Licenses determine what uses can be made of your data. Note that only the intellectual property rights holder(s) can issue a license, so it is crucial to clarify who owns those rights.

Consider whether attribution is important to you; if so, select a license whose terms require that data used by others be properly attributed to the original authors.

There are several types of standard licenses available to researchers, such as the Creative Commons or the Open Data Commons licenses. Even if you choose to make your data part of the public domain, it is preferable to make this explicit by using a license such as Creative Commons' CC0.