FAIR principles to improve the impact on health research management outcomes

Martínez-García, Alicia; Alvarez-Romero, Celia; Román-Villarán, Esther; Bernabéu-Wittel, Máximo; Parra-Calderón, Carlos Luís

doi:10.1016/j.heliyon.2023.e15733

Cited by 4 publications

(1 citation statement)

References 19 publications

(22 reference statements)

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“…or dataset should have a unique identifier assigned, which should then be described with rich metadata. In a study evaluating the FAIR4HEALTH initiative, the implementation of FAIR principles has been proven to save researchers on average approximately 56% of their time in data gathering and compilation activities and approximately 16,800 euros per month in institution funding, when conducting health research efforts ( Martínez-García et al., 2023 ). There are additional contextual advantages such as avoidance of the risk of repetition of research and expedited literature gathering ( Garabedian et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

FAIR compliant database development for human microbiome data samples

Dorst,

Zeevenhooven,

Wilding

et al. 2024

Front. Cell. Infect. Microbiol.

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IntroductionSharing microbiome data among researchers fosters new innovations and reduces cost for research. Practically, this means that the (meta)data will have to be standardized, transparent and readily available for researchers. The microbiome data and associated metadata will then be described with regards to composition and origin, in order to maximize the possibilities for application in various contexts of research. Here, we propose a set of tools and protocols to develop a real-time FAIR (Findable. Accessible, Interoperable and Reusable) compliant database for the handling and storage of human microbiome and host-associated data.MethodsThe conflicts arising from privacy laws with respect to metadata, possible human genome sequences in the metagenome shotgun data and FAIR implementations are discussed. Alternate pathways for achieving compliance in such conflicts are analyzed. Sample traceable and sensitive microbiome data, such as DNA sequences or geolocalized metadata are identified, and the role of the GDPR (General Data Protection Regulation) data regulations are considered. For the construction of the database, procedures have been realized to make data FAIR compliant, while preserving privacy of the participants providing the data.Results and discussionAn open-source development platform, Supabase, was used to implement the microbiome database. Researchers can deploy this real-time database to access, upload, download and interact with human microbiome data in a FAIR complaint manner. In addition, a large language model (LLM) powered by ChatGPT is developed and deployed to enable knowledge dissemination and non-expert usage of the database.

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Section: Introductionmentioning

confidence: 99%

FAIR compliant database development for human microbiome data samples

Dorst,

Zeevenhooven,

Wilding

et al. 2024

Front. Cell. Infect. Microbiol.

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FAIR health data in the national and international data space

Waltemath,

Beyan,

Crameri

et al. 2024

Bundesgesundheitsbl

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ZusammenfassungGesundheitsdaten haben in der heutigen datenorientierten Welt einen hohen Stellenwert. Durch automatisierte Verarbeitung können z. B. Prozesse im Gesundheitswesen optimiert und klinische Entscheidungen unterstützt werden. Dabei sind Aussagekraft, Qualität und Vertrauenswürdigkeit der Daten wichtig. Nur so kann garantiert werden, dass die Daten sinnvoll nachgenutzt werden können.Konkrete Anforderungen an die Beschreibung und Kodierung von Daten werden in den FAIR-Prinzipien beschrieben. Verschiedene nationale Forschungsverbünde und Infrastrukturprojekte im Gesundheitswesen haben sich bereits klar zu den FAIR-Prinzipien positioniert: Sowohl die Infrastrukturen der Medizininformatik-Initiative als auch des Netzwerks Universitätsmedizin operieren explizit auf Basis der FAIR-Prinzipien, ebenso die Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten oder das Deutsche Zentrum für Diabetesforschung.Um eine FAIRe Ressource bereitzustellen, sollte zuerst in einem Assessment der FAIRness-Grad festgestellt werden und danach die Priorisierung für Verbesserungsschritte erfolgen (FAIRification). Seit 2016 wurden zahlreiche Werkzeuge und Richtlinien für beide Schritte entwickelt, basierend auf den unterschiedlichen, domänenspezifischen Interpretationen der FAIR-Prinzipien.Auch die europäischen Nachbarländer haben in die Entwicklung eines nationalen Rahmens für semantische Interoperabilität im Kontext der FAIR-Prinzipien investiert. So wurden Konzepte für eine umfassende Datenanreicherung entwickelt, um die Datenanalyse beispielsweise im Europäischen Gesundheitsdatenraum oder über das Netzwerk der Observational Health Data Sciences and Informatics zu vereinfachen. In Kooperation mit internationalen Projekten, wie z. B. der European Open Science Cloud, wurden strukturierte FAIRification-Maßnahmen für Gesundheitsdatensätze entwickelt.

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Getting ready for the European Health Data Space (EHDS): IDERHA's plan to align with the latest EHDS requirements for the secondary use of health data

Hussein,

Balaur,

Burmann

et al. 2024

Open Res Europe

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Objective The European Health Data Space (EHDS) shapes the digital transformation of healthcare in Europe. The EHDS regulation will also accelerate the use of health data for research, innovation, policy-making, and regulatory activities for secondary use of data (known as EHDS2). The Integration of heterogeneous Data and Evidence towards Regulatory and HTA Acceptance (IDERHA) project builds one of the first pan-European health data spaces in alignment with the EHDS2 requirements, addressing lung cancer as a pilot. Methods In this study, we conducted a comprehensive review of the EHDS regulation, technical requirements for EHDS2, and related projects. We also explored the results of the Joint Action Towards the European Health Data Space (TEHDAS) to identify the framework of IDERHA’s alignment with EHDS2. We also conducted an internal webinar and an external workshop with EHDS experts to share expertise on the EHDS requirements and challenges. Results We identified the lessons learned from the existing projects and the minimum-set of requirements for aligning IDERHA infrastructure with EHDS2, including user journey, concepts, terminologies, and standards. The IDERHA framework (i.e., platform architecture, standardization approaches, documentation, etc.) is being developed accordingly. Discussion The IDERHA's alignment plan with EHDS2 necessitates the implementation of three categories of standardization for: data discoverability: Data Catalog Vocabulary (DCAT-AP), enabling semantics interoperability: Observational Medical Outcomes Partnership (OMOP), and health data exchange (DICOM and FHIR). The main challenge is that some standards are still being refined, e.g., the extension of the DCAT-AP (HealthDCAT-AP). Additionally, extensions to the Observational Health Data Sciences and Informatics (OHDSI) OMOP Common Data Model (CDM) to represent the patient-generated health data are still needed. Finally, proper mapping between standards (FHIR/OMOP) is a prerequisite for proper data exchange. Conclusions The IDERHA's plan and our collaboration with other EHDS initiatives/projects are critical in advancing the implementation of EHDS2.

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FAIR principles to improve the impact on health research management outcomes

Cited by 4 publications

References 19 publications

FAIR compliant database development for human microbiome data samples

FAIR compliant database development for human microbiome data samples

FAIR health data in the national and international data space

Getting ready for the European Health Data Space (EHDS): IDERHA's plan to align with the latest EHDS requirements for the secondary use of health data

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