“…This algorithm leverages the GA4GH Data Use Ontology (DUO; https://github.com/EBISPOT/DUO ) to code both datasets’ data use terms and researchers’ proposed research contained within the DARs. 61 With both of these inputs in terms from the same ontology, the algorithm can assess if the proposed research is within the bounds of the data use terms and provide a recommended decision to the DAC. In the long term, the pilot will also provide powerful empirical and conceptual evidence of the feasibility of semi-automated approaches to data use oversight.…”
“…This algorithm leverages the GA4GH Data Use Ontology (DUO; https://github.com/EBISPOT/DUO ) to code both datasets’ data use terms and researchers’ proposed research contained within the DARs. 61 With both of these inputs in terms from the same ontology, the algorithm can assess if the proposed research is within the bounds of the data use terms and provide a recommended decision to the DAC. In the long term, the pilot will also provide powerful empirical and conceptual evidence of the feasibility of semi-automated approaches to data use oversight.…”
“…In 2014, a GA4GH task team launched the Variant Interpretation for Cancer Consortium (VICC), which standardizes and coordinates clinical somatic cancer curation efforts and has created an open community resource to provide the aggregated information. 71 Moving forward, major oncogenomic resources are now working with GA4GH on the harmonization of variant interpretation evidence, through refinement and adoption of standards such as the Beacon API, the Data Use Ontology (DUO), 9 VA, and VRS. Additionally, these standards are being implemented across multiple GA4GH Driver Projects (see Table 2 ) that capture genomic data and/or diagnostic variant interpretation across the longitudinal evolution of cancer.…”
Section: Genomics In Healthcarementioning
confidence: 99%
“…Each tackles a specific area in the data life cycle, as described below (URLs listed in the web resources). Data use & researcher identities: Develops ontologies and data models to streamline global access to datasets generated in any country 9 , 10 Genomic knowledge standards: Develops specifications and data models for exchanging genomic variant observations and knowledge 18 Cloud: Develops federated analysis approaches to support the statistical rigor needed to learn from large datasets Data privacy & security: Develops guidelines and recommendations to ensure identifiable genomic and phenotypic data remain appropriately secure without sacrificing their analytic potential Regulatory & ethics: Develops policies and recommendations for ensuring individual-level data are interoperable with existing norms and follow core ethical principles Discovery: Develops data models and APIs to make data findable, accessible, interoperable, and reusable (FAIR) Clinical & phenotypic data capture & exchange: Develops data models to ensure genomic data is most impactful through rich metadata collected in a standardized way Large-scale genomics: Develops APIs and file formats to ensure harmonized technological platforms can support large-scale computing …”
Section: Figurementioning
confidence: 99%
“…Data use & researcher identities: Develops ontologies and data models to streamline global access to datasets generated in any country 9 , 10 …”
“…This can boost the potential for knowledge discovery and data+knowledge driven analytics. Interestingly, ontologies may also be used to describe data access restrictions [ 24 , 25 ] to complement FAIR metadata with information that supports data safety and patient privacy. Fig.…”
Background
The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data ‘silos’ that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR.
Results
In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors’ research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital.
Conclusions
Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.
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