Background Data stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable, and reusable is currently believed to strengthen data sharing, reduce duplicated efforts, and move toward harmonization of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. It is important to understand the concepts and implementation practices of the FAIR data principles as applied to human health data by studying the flourishing initiatives and implementation lessons relevant to improved health research, particularly for data sharing during the coronavirus pandemic. Objective This paper aims to conduct a scoping review to identify concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in the health data domain. Methods The Arksey and O’Malley stage-based methodological framework for scoping reviews will be used for this review. PubMed, Web of Science, and Google Scholar will be searched to access relevant primary and grey publications. Articles written in English and published from 2014 onwards with FAIR principle concepts or practices in the health domain will be included. Duplication among the 3 data sources will be removed using a reference management software. The articles will then be exported to a systematic review management software. At least two independent authors will review the eligibility of each article based on defined inclusion and exclusion criteria. A pretested charting tool will be used to extract relevant information from the full-text papers. Qualitative thematic synthesis analysis methods will be employed by coding and developing themes. Themes will be derived from the research questions and contents in the included papers. Results The results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews) reporting guidelines. We anticipate finalizing the manuscript for this work in 2021. Conclusions We believe comprehensive information about the FAIR data principles, initiatives, implementation practices, and lessons learned in the FAIRification process in the health domain is paramount to supporting both evidence-based clinical practice and research transparency in the era of big data and open research publishing. International Registered Report Identifier (IRRID) PRR1-10.2196/22505

Background Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains. Objective This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data. Methods The Arksey and O’Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Results A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic. Conclusions This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing. International Registered Report Identifier (IRRID) RR2-10.2196/22505

BACKGROUND Thorough data stewardship drives health research. Processes such as data collection, storage, access, sharing, and analytics require that the researchers properly and consistently follow elaborate data management strategies. Studies have shown that ensuring data is findable, accessible, interoperable and reusable (FAIR) indeed leads to improved data sharing and harmonization in different facets of scientific domains. Comprehensive information about the concepts and implementation practices of the FAIR data principles as applied to health research data helps to gain a better understanding of the current state of affairs within the field. OBJECTIVE This scoping review aims to identify concepts, approaches, implementation experiences and lessons learned in FAIR initiatives in health research data. METHODS The Arksey and O’Malley stage-based methodological framework for scoping reviews was applied in this review. PubMed, Web of Science and Google Scholar were searched to access relevant publications. As indicated in our earlier scoping review protocol, articles written in English, published from 2014 to 2020 and addressing FAIR concepts or practices in the health domain were included. The three data sources were de-duplicated using a reference management software. The articles were then exported to a systematic review management software. Two independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full text papers. The results were reported using the PRISMA scoping review reporting guidelines. RESULTS 34 of 1,544 screened articles were included in the final review. Authors reported FARIfication approaches which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data and requirements gathering for FAIRification tools. We also summarized the reported challenges and mitigation strategies associated with FAIRification such as high set up costs, data politics, technical issues, administrative issues, privacy concerns and difficulties encountered in sharing health data in spite of its sensitive nature. We also found various workflows, tools and infrastructure designed by different groups worldwide to facilitate the FAIRification of data in different specialities within the health domain. However, the steps involved in the workflows, tools and infrastructure vary. We also uncovered a wide range of problems and questions that researchers are trying to address by employing the different workflows, tools and infrastructure to FAIRify their data. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all the continents have been reached by at least one network that is trying to achieve health data FAIRness. Documented outcomes of the FAIRification efforts by different researchers include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment and new treatments. More work aiming to FAIRify data on a wider variety of diseases is ongoing since the COVID pandemic. Successful FAIRification of data informs the management and prognosis of various diseases such as Huntington disease, cancer, traumatic stress disorder and cardiovascular diseases. CONCLUSIONS Conducting this review led us to analyse the work done by various projects to FAIRify health research data. FAIRification leads to improved data management and governance. The addition of rich metadata does facilitate data discovery and allows that data to serve a greater audience. This comprehensive overview also shows that implementing FAIR concepts in health data stewardship supports both evidence-based clinical practice and research transparency in the era of big data and open research publishing. INTERNATIONAL REGISTERED REPORT RR2-10.2196/22505

BACKGROUND Data stewardship is an essential driver for research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016 the FAIR guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable and reusable is today believed to strengthen data sharing and to reduce duplicated efforts towards harmonisation of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. Comprehensive information about the concept and implementation practices of the FAIR data principles applied to health data is important to understand from flourishing initiatives and implementation lessons relevant to improved health research. OBJECTIVE To conduct a scoping review in order to identify concepts, approaches, implementation experiences and lessons learned in FAIR initiatives in the health data domain. METHODS The Arksey and O’Malley stage-based methodological framework for scoping review will be used for this review. PubMed, Web of Science and Google Scholar will be searched to access relevant primary and grey publications. Articles written in English and published from 2014 onwards with FAIR principle concepts or practices in the health domain will be included. Duplication among the three data sources will be removed using a reference management software. The articles will then be exported to a systematic review management software. At least two independent authors will review the eligibility of each article based on defined inclusion and exclusion criteria. A pre-tested charting tool will be used to extract relevant information from the full text papers. Qualitative thematic synthesis analysis methods will be employed by coding and developing themes. Themes will be derived from the research questions and from the contents in the included papers. RESULTS The results will be reported using the PRISMA scoping review reporting guidelines. We anticipate finalising the manuscript for this work in 2021. CONCLUSIONS We believe comprehensive information about the FAIR data principles, initiatives, implementation practices, and lessons learnt in the FAIRification process in the health domain is paramount to support both evidence-based clinical practice and research transparency in the era of big data and open research publishing.