Aligning restricted access data with FAIR: a systematic review

Area of Science:

• Open Science and Data Management
• Research Data Governance

Background:

• The FAIR Guiding Principles aim to enhance data discoverability and usability.
• Applying FAIR principles to restricted research data remains a challenge with no community consensus on methods.
• Existing methods for FAIR data are not clearly defined for sensitive or restricted datasets.

Purpose of the Study:

• To systematically review methods for making restricted research data FAIR.
• To categorize proposed methods for applying FAIR principles to restricted data.
• To assess the maturity of these methods in the context of FAIR data.

Main Methods:

• Systematic literature review of 40 publications.
• Descriptive and iterative study design.
• Categorization of methods based on the Data Life Cycle stages.

Main Results:

• Identified methods span Data Collection, Metadata Representation, Data Processing, Anonymization, Data Publication, Data Usage, and Post Data Usage.
• 'Access Control' and 'Usage and License Terms' are frequently used methods.
• Methods like 'Embargo on Data Release' and 'Synthetic Data' are less common.

Conclusions:

• This is the first comprehensive literature review on methods for FAIR restricted data.
• The study provides a conceptual framework for future research on managing restricted access data.
• Understanding these methods is crucial for advancing Open Science with sensitive datasets.