TY - JOUR
T1 - FAIRification Efforts of Clinical Researchers
T2 - The Current State of Affairs
AU - Kersloot, Martijn G
AU - van Damme, Philip
AU - Abu-Hanna, Ameen
AU - Arts, Derk L
AU - Cornet, Ronald
N1 - Publisher Copyright: © 2021 The European Federation for Medical Informatics (EFMI) and IOS Press. All rights reserved.
PY - 2021/11/18
Y1 - 2021/11/18
N2 - The FAIR Principles are supported by various initiatives in the biomedical community. However, little is known about the knowledge and efforts of individual clinical researchers regarding data FAIRification. We distributed an online questionnaire to researchers from six Dutch University Medical Centers, as well as researchers using an Electronic Data Capture platform, to gain insight into their understanding of and experience with data FAIRification. 164 researchers completed the questionnaire. 64.0% of them had heard of the FAIR Principles. 62.8% of the researchers spent some or a lot of effort to achieve any aspect of FAIR and 11.0% addressed all aspects. Most researchers were unaware of the Principles' emphasis on both human- and machine-readability, as their FAIRification efforts were primarily focused on achieving human-readability (93.9%), rather than machine-readability (31.2%). In order to make machine-readable, FAIR data a reality, researchers require proper training, support, and tools to help them understand the importance of data FAIRification and guide them through the FAIRification process.
AB - The FAIR Principles are supported by various initiatives in the biomedical community. However, little is known about the knowledge and efforts of individual clinical researchers regarding data FAIRification. We distributed an online questionnaire to researchers from six Dutch University Medical Centers, as well as researchers using an Electronic Data Capture platform, to gain insight into their understanding of and experience with data FAIRification. 164 researchers completed the questionnaire. 64.0% of them had heard of the FAIR Principles. 62.8% of the researchers spent some or a lot of effort to achieve any aspect of FAIR and 11.0% addressed all aspects. Most researchers were unaware of the Principles' emphasis on both human- and machine-readability, as their FAIRification efforts were primarily focused on achieving human-readability (93.9%), rather than machine-readability (31.2%). In order to make machine-readable, FAIR data a reality, researchers require proper training, support, and tools to help them understand the importance of data FAIRification and guide them through the FAIRification process.
KW - FAIR data
KW - Research Data Management
KW - medical research
UR - http://www.scopus.com/inward/record.url?scp=85120529392&partnerID=8YFLogxK
U2 - https://doi.org/10.3233/SHTI210807
DO - https://doi.org/10.3233/SHTI210807
M3 - Article
C2 - 34795075
SN - 0926-9630
VL - 287
SP - 35
EP - 39
JO - Studies in health technology and informatics
JF - Studies in health technology and informatics
ER -