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FAIR Equivalency in Indonesia's Digital Health Framework

Data Intelligence 2022年第4期4卷 798-812页

作者： Putu Hadi Purnama Jati Leiden Institute of Advanced Computer Science(LIACS) Leiden Universitythe Netherlands 2Central Bureau of Statistics81119Indonesia

The objective of this study was to assess the regulatory framework for health data in Indonesia in order to understand the policy context and explore the possibility of expanding the adoption and implementation of the FAIR Guidelines,which state that data should be Findable,Accessible,Interoperable and Reusable(FAIR),in *** the FAIR Guidelines were not explicitly mentioned in any of the policy documents relevant to the Indonesian digital health sector,six out of the eight documents analysed contained FAIR Equivalent *** particular,Indonesia’s Population Identification Number(NIK)has the potential,as a unique identifier,to support the integration and interoperability(findability)of data,which is crucial to all other aspects of the FAIR *** is also a plan to build standards and protocols into the implementation of information systems in each ministry and government agency to improve data accessibility(accessibility),the integration of the various information systems is planned/ongoing(interoperability),and the need for a standardised arrangement for health information systems related to health data following the community standard is recognised(reusability).The documents at the core of Indonesia’s digital health/e Health policy have the highest FAIR Equivalency Score(FE-Score),showing some degree of alignment between the Indonesian digital health implementation vision and the FAIR *** indicates that Indonesia’s digital health sector is open to using the FAIR Guidelines.

关键词： eHealth FAIR Guidelines findability Accessibility Interoperability Reusability Indonesia’s digital health

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Unique,Persistent,Resolvable:Identifiers as the Foundation of FAIR

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Data Intelligence 2020年第1期2卷 30-39,302页

作者： Nick Juty Sarala M.Wimalaratne Stian Soiland-Reyes John Kunze Carole A.Goble Tim Clark Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK California Digital Library OaklandCalifornia 94612-2901USA Data Science Institute University of VirginiaCharlottesvilleVA 22903-1738USA

The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ***,globally unique identifiers,resolvable on the Web,and associated with a set of additional descriptive metadata,are foundational to FAIR *** we describe some basic principles and exemplars for their design,use and orchestration with other system elements to achieve FAIRness for digital research objects.

关键词： Identifiers metadata findability FAIR data data infrastructures

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维普期刊数据库

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Making Data and Workflows Findable for Machines

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Data Intelligence 2020年第1期2卷 40-46,303页

作者： Tobias Weigel Ulrich Schwardmann Jens Klump Sofiane Bendoukha Robert Quick Deutsches Klimarechenzentrum Bundesstrasse 45aHamburg 20146Germany Gesellschaft für wissenschaftliche Datenverarbeitung Gottingen Am Faßberg 1137077 GottingenGermany CSIRO KensingtonWA 6151CanberraAustralia Indiana University Bloomington BloomingtonIN 47405USA

Research data currently face a huge increase of data objects with an increasing variety of types(data types,formats)and variety of workflows by which objects need to be managed across their lifecycle by data *** desire to shorten the workflows from data generation to analysis and publication,and the full workflow needs to become transparent to multiple stakeholders,including research administrators and *** poses challenges for research infrastructures and user-oriented data services in terms of not only making data and workflows findable,accessible,interoperable and reusable,but also doing so in a way that leverages machine support for better *** primary need to be addressed is that of findability,and achieving better findability has benefits for other aspects of data and workflow *** this article,we describe how machine capabilities can be extended to make workflows more findable,in particular by leveraging the Digital Object Architecture,common object operations and machine learning techniques.

关键词： findability Workflows Automation FAIR data Data infrastructures Data services

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维普期刊数据库

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