Aligning restricted access data with FAIR: a systematic review

Abstract

Understanding the complexity of restricted research data is vitally important in the current new era of Open Science. While the FAIR Guiding Principles have been introduced to help researchers to make data Findable, Accessible, Interoperable and Reusable, it is still unclear how the notions of FAIR and Openness can be applied in the context of restricted data. Many methods have been proposed in support of the implementation of the principles, but there is yet no consensus among the scientific community as to the suitable mechanisms of making restricted data FAIR. We present here a systematic literature review to identify the methods applied by scientists when researching restricted data in a FAIR-compliant manner in the context of the FAIR principles. Through the employment of a descriptive and iterative study design, we aim to answer the following three questions: (1) What methods have been proposed to apply the FAIR principles to restricted data?, (2) How can the relevant aspects of the methods proposed be categorized?, (3) What is the maturity of the methods proposed in applying the FAIR principles to restricted data?. After analysis of the 40 included publications, we noticed that the methods found, reflect the stages of the Data Life Cycle, and can be divided into the following Classes: Data Collection, Metadata Representation, Data Processing, Anonymization, Data Publication, Data Usage and Post Data Usage. We observed that a large number of publications used ‘Access Control‘ and ‘Usage and License Terms’ methods, while others such as ‘Embargo on Data Release’ and the use of ‘Synthetic Data’ were used in fewer instances. In conclusion, we are presenting the first extensive literature review on the methods applied to confidential data in the context of FAIR, providing a comprehensive conceptual framework for future research on restricted access data.

reanalyzerGSE: tackling the everlasting lack of reproducibility and reanalyses in transcriptomics

“…Therefore, while deposited in public databases that increasingly try to facilitate the accessibility and use of raw data [19][20][21], countless transcriptomics datasets are largely unexplored beyond their original publication (i.e., remain restricted). This is crucial because the reuse of data is one of the most important FAIR principles that should sustain the new era of Open Science [22], particularly regarding the field of Bioinformatics [23,24]. Overall, there is an urgent need for standardization to address several issues hampering transcriptomics that can lead to errors in biological interpretations and conclusions.…”

odML-Tables as a Metadata Standard in Microneurography

“…One of the challenges is to strike a balance between making data FAIR and protecting confidentiality. In a recent study, Martorana et al [2] examined how researchers have approached this challenge. The authors emphasized the critical role of metadata in restricted data flows.…”