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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

Data types and formats

What types of data will you collect, create, link to, acquire and/or record in each of the different stages of the systematic review?

Start by thinking of the different phases of a systematic review project and how you are going to approach each. The main steps of a systematic review typically include: planning/protocol, data collection (literature searching), study selection (screening), data extraction, risk of bias assessment, synthesis, manuscript preparation. 

Examples: Literature database records, PDFs of articles; quantitative and qualitative data extracted from individual studies; your protocol or other methods document, etc.


What file formats will your data be collected in? Will these formats allow for data re-use, sharing and long-term access to the data? 

If you plan to use systematic review software or reference management software for screening and data management, indicate which program you will use, and what format files will be saved/exported in.


Below are some specific examples of file formats for data, arranged according to file type/format that systematic review data may be collected in:

Doc (Word), RTF, PDF: project documents, notes, drafts, review protocol, line-by-line search strategies, PRISMA or other reporting checklists; included studies

RIS, BibTex, XML, txt: files exported from literature databases or tools like Covidence

Excel (xlsx, csv): search tracking spreadsheets, screening/study selection/appraisal worksheets, data extraction worksheets; meta-analysis data

NVivo: qualitative synthesis data

TIF, PNG, etc.: images and figures


Below are some specific examples of file formats for data, arranged according to the phase of a systematic review project in which they may be collected or created:

Protocol: Written documents such as review protocol, team charters, in Word, RTF, etc.

Data collection (Locating studies). Line-by-line search strategies from each database and database account information (stored in a Word or Excel file), citation data files exported from databases (RIS, TXT, BibTeX, XML, etc.), citation libraries (e.g. Endnote files), notes for tracking the process (Word doc or Excel file), etc. NOTE: it is very important to keep all of your original database export files; these constitute your raw data and may be needed in the event of data loss, corruption, or to deduplicate an updated search against a previous one.

Study Selection. Excel files (for piloting, for each reviewer, after collating decisions) or files saved in a review software tool such as Covidence, full-text articles (PDF), PRISMA flow diagram (Word), etc.  

Data extraction and Critical appraisal. Indicate which program you plan to use for data extraction, and format of your extraction files (Word, Excel, or data stored within a systematic review software program). Checklists for each included study; these may be in Word, Excel, or stored in systematic review software tools. 

Synthesis (narrative and/or meta-analysis). Word, Excel, meta-analysis data in Excel or other software, qualitative synthesis data in NVivo, etc.

Manuscript preparation. Manuscript draft in word processing software of your choice, images or figures (TIF, PNG), conflict of interest forms (Word, PDF) etc. 

File organization, naming conventions and version-control

What conventions and procedures will you use to structure, name and version-control your files to help you and others better understand how your data are organized?

It is important to keep track of different copies or versions of files, files held in different formats or locations, and information cross-referenced between files. Logical file structures, informative naming conventions, and clear indications of file versions, all contribute to better use of your data during and after your research project.  These practices will help ensure that you and your research team are using the appropriate version of your data, and minimize confusion regarding copies on different computers and/or on different media.

Create a naming convention in advance which includes multiple components that will allow you to understand what is in the file a year or two later. For example, you might consider a naming convention that includes: project name and date using the ISO standard for dates as required elements and stage of review/task, version number, creator’s initials, etc as optional elements as necessary.


Examples of naming conventions for different purposes and for different phases of a review project

For files exported from databases: ProjectName_DatabaseName_DateofSearch_Record#-Record#
Example: PetTherapy_Medline_20200530_1-1000


For saved searches on database platforms: ProjectName_DatabaseName_Version#_DateSaved
Example: PetTherapy_Medline_V1_20200529

For PDF full-texts of included studies: AuthorLastName_Year_FirstThreeWordsofTitle
Example: Sutton_2019_MeetingTheReview


For recording each reviewer's decisions during pilot screening: ProjectName_TaskName_ReviewerInitials
Example: PetTherapy_PilotScreeningSet1_ZP