TA2 - Standards for FAIR Data
Actions 
Order Studies
NFDI4Health task area 2 targets core deficits in medical sciences, i.e. the lack of harmonised standards for data and data quality management in clinical trials, public health surveys, and epidemiological cohorts, as well as the lack of information on and access to relevant standards. By making standards available, TA2 will improve the findability, accessibility and interoperability of existing and novel data bodies. For this purpose, guidelines, standards and policies on data management and publication (T2.1), data and metadata standards and integration (T2.2), data quality and provenance (T2.3), health data access and interoperability (T2.4) will be elaborated and disseminated specifically for the NFDI4Health use cases, with the involvement of the user community. Thus, TA2 will provide the conceptual background for services to be implemented in NFDI4Health.
Investigation position: 2
Export PNG
Download
Creators and Submitter
Views: 640
Created: 5th Aug 2020 at 15:18
Last updated: 29th Aug 2023 at 13:57
TagsThis item has not yet been tagged.
Related items
Projects: FAIRDOM, Early Metabolic Injury (LiSyM-EMI - Pillar I), Chronic Liver Disease Progression (LiSyM-DP - Pillar II), Regeneration and Repair in Acute-on-Chronic Liver Failure (LiSyM-ACLF - Pillar III), LiSyM Core Infrastructure and Management (LiSyM-PD), Liver Function Diagnostics (LiSyM-LiFuDi - Pillar IV), Model Guided Pharmacotherapy In Chronic Liver Disease (LiSyM-MGP), Multi-Scale Models for Personalized Liver Function Tests (LiSyM-MM-PLF), The Hedgehog Signalling Pathway (LiSyM-JGMMS), Molecular Steatosis - Imaging & Modeling (LiSyM-MSIM), Kinetics on the move - Workshop 2016, Example use cases, FAIRDOM user meeting, MS_DILI, COMBINE Multicellular Modelling, FAIRDOM & LiSyM & de.NBI Data Structuring Training, EnzymeML, GMDS Project Group "FAIRe Dateninfrastrukturen für die Biomedizinische Informatik", FAIRDOM Community Workers, COVID-19 Disease Map, COVID-19 related studies and tools in Germany, nfdi4health - German National Research Data Infrastructure for Personal Health Data, ModeleXchange initiative, SDBV/HITS
Institutions: Heidelberg Institute for Theoretical Studies (HITS gGmbH)
https://orcid.org/0000-0002-8683-7084
Data management and standardization expert for systems biology and systems medicine, responsible for the data management user requirements and user contacts within the German LiSyM network (Liver Systems Medicine: http://lisym.org/) and associated to the FAIRDOM team. Involved in different standardization initiatives and committees, i.e. COMBINE (http://co.mbine.org), ISO/TC 276 Biotechnology (https://www.iso.org/committee/4514241.html), European COST action CHARME (http://www.cost-charme.eu) and ...
Carsten Oliver Schmidt is Deputy head of the Department of SHIP/ Clinical-Epidemiological Research. He leads the functional division “Quality in the health Sciences”. The investigator is PI of several methods and research quality related projects, and speaker of the methods section of the German Society for Epidemiology. He works in the field of research methods, quality management, musculosceletal disorders, among others.
nfdi4health aims to create the most comprehensive inventory of German epidemiological, public health and clinical trial data to date. It builds a centralised data catalogue with elaborate search functionalities, sophisticated data access management, and a data analysis toolbox, while respecting stringent requirements for privacy concerning personal health data. Standardisation services ensure a high degree of interoperability. Use cases covering prototypical study types and areas of research show ...
Projects: COVID-19 related studies and tools in Germany, nfdi4health - German National Research Data Infrastructure for Personal Health Data
Web page: https://www.nfdi4health.de
nfdi4health aims to create the most comprehensive inventory of German epidemiological, public health and clinical trial data to date. It builds a centralised data catalogue with elaborate search functionalities, sophisticated data access management, and a data analysis toolbox, while respecting stringent requirements for privacy concerning personal health data. Standardisation services ensure a high degree of interoperability. Use cases covering prototypical study types and areas of research show ...
Programme: nfdi4health - German National Research Data Infrastructure for Personal Health Data
Public web page: https://www.nfdi4health.de
Start date: 1st Jul 2020
To achieve data “FAIRification by standardisation” and enable the user community to integrate heterogeneous and complex data, recommendations and guidelines will be developed for the consistent use of domain-specific standards for data formats, as well as for consistent data descriptions based on established metadata standards and terminologies. This standardisation concept will be based on existing standards, such as ISO 20691 and will include the definition of a minimal metadata set for ...
This task T2.3 will target data quality as the “degree to which a set of inherent characteristics of data fulfils requirements” and provide consented standards and metrics to assess the data quality at different stages of the scientific data lifecycle. (1) It will first consider FAIR standards in collaboration with the FAIRMetrics group, FAIRsharing, and RDA FAIR Data Maturity Model group. (2) The second focus will be on adherence to defined data and metadata standards as recommended by nfdi4health ...
T2.4 will define standardisation requirements and develop guidelines, as well as standard-based solutions for data access and interoperability in the defined use cases. To ensure compatibility with existing efforts aiming to improve data interoperability in medicine and healthcare, T2.4 also will coordinate its work closely with the same standardisation initiatives and technical committees of standardisation organisations as T2.2. To enable a seamless access and exchange of health data within the ...
This task aims for policies for data management and publication in order to make data of public health studies findable and interoperable. To find information about studies and (meta-)data and to ensure their interoperability, it is necessary to document the descriptive core elements in a structured way already when planning projects. This applies both to data management and to the subsequent publication of research results and data and is particularly important for research projects handling ...
