The FAIR Cookbook - the essential resource for and by FAIR doers.
The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an open, online resource of hands-on recipes for "FAIR doers" in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. Part of the ELIXIR ecosystem, and recommended by funders, the FAIR Cookbook is open to contributions of new recipes.
Rocca-Serra P
，Gu W
，Ioannidis V
，Abbassi-Daloii T
，Capella-Gutierrez S
，Chandramouliswaran I
，Splendiani A
，Burdett T
，Giessmann RT
，Henderson D
，Batista D
，Emam I
，Gadiya Y
，Giovanni L
，Willighagen E
，Evelo C
，Gray AJG
，Gribbon P
，Juty N
，Welter D
，Quast K
，Peeters P
，Plasterer T
，Wood C
，van der Horst E
，Reilly D
，van Vlijmen H
，Scollen S
，Lister A
，Thurston M
，Granell R
，FAIR Cookbook Contributors
，Sansone SA
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《Scientific Data》
Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review.
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.
This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data.
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.
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.
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.
RR2-10.2196/22505.
Inau ET
，Sack J
，Waltemath D
，Zeleke AA
... -
《JOURNAL OF MEDICAL INTERNET RESEARCH》
Initiatives, Concepts, and Implementation Practices of FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles in Health Data Stewardship Practice: Protocol for a Scoping Review.
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.
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.
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.
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.
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.
PRR1-10.2196/22505.
Inau ET
，Sack J
，Waltemath D
，Zeleke AA
... -
《JMIR Research Protocols》
From Raw Data to FAIR Data: The FAIRification Workflow for Health Research.
FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data.
This scientific contribution addresses the architecture design of an open technological solution built upon the FAIRification process proposed by "GO FAIR" which addresses the identified gaps that such process has when dealing with health datasets.
A common FAIRification workflow was developed by applying restrictions on existing steps and introducing new steps for specific requirements of health data. These requirements have been elicited after analyzing the FAIRification workflow from different perspectives: technical barriers, ethical implications, and legal framework. This analysis identified gaps when applying the FAIRification process proposed by GO FAIR to health research data management in terms of data curation, validation, deidentification, versioning, and indexing.
A technological architecture based on the use of Health Level Seven International (HL7) FHIR (fast health care interoperability resources) resources is proposed to support the revised FAIRification workflow.
Research funding agencies all over the world increasingly demand the application of the FAIR guiding principles to health research output. Existing tools do not fully address the identified needs for health data management. Therefore, researchers may benefit in the coming years from a common framework that supports the proposed FAIRification workflow applied to health datasets.
Routine health care datasets or data resulting from health research can be FAIRified, shared and reused within the health research community following the proposed FAIRification workflow and implementing technical architecture.
Sinaci AA
，Núñez-Benjumea FJ
，Gencturk M
，Jauer ML
，Deserno T
，Chronaki C
，Cangioli G
，Cavero-Barca C
，Rodríguez-Pérez JM
，Pérez-Pérez MM
，Laleci Erturkmen GB
，Hernández-Pérez T
，Méndez-Rodríguez E
，Parra-Calderón CL
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