The FAIR Guidelines were conceptualised and coined as guidelines for Findable, Accessible, Interoperable and Reusable(FAIR) data at a conference held at the Lorentz Centre in Leiden in 2014. A relatively short period ...The FAIR Guidelines were conceptualised and coined as guidelines for Findable, Accessible, Interoperable and Reusable(FAIR) data at a conference held at the Lorentz Centre in Leiden in 2014. A relatively short period of time after this conference, the FAIR Guidelines made it onto the public policy agenda of the European Union. Following the concept of Kingdon, policy entrepreneurs played a critical role in creating a policy window for this idea to reach the agenda by linking it to the policy of establishing a European Open Science Cloud(EOSC). Tracing the development from idea to policy, this study highlights the critical role that expert committees play in the European Union. The permeability of the complex governance structure is increased by these committees, which allow experts to link up with the institutions and use the committees to launch new ideas. The High Level Expert Groups on the EOSC provided the platform from which the FAIR Guidelines were launched, and this culminated in the adoption of the FAIR Guidelines as a requirement for all European-funded science. As a result, the FAIR Guidelines have become an obligatory part of data management in European-funded research in 2020 and are now followed by other funders worldwide.展开更多
This paper investigates whether or not there is a policy window for making health data ‘Findable’, ‘Accessible’(under well-defined conditions), ‘Interoperable’ and ‘Reusable’(FAIR) in Ethiopia. The question is...This paper investigates whether or not there is a policy window for making health data ‘Findable’, ‘Accessible’(under well-defined conditions), ‘Interoperable’ and ‘Reusable’(FAIR) in Ethiopia. The question is answered by studying the alignment of policies for health data in Ethiopia with the FAIR Guidelines or their ‘FAIR Equivalency’. Policy documents relating to the digitalisation of health systems in Ethiopia were examined to determine their FAIR Equivalency. Although the documents are fragmented and have no overarching governing framework, it was found that they aim to make the disparate health data systems in Ethiopia interoperable and boost the discoverability and(re)usability of data for research and better decision making. Hence, the FAIR Guidelines appear to be aligned with the regulatory frameworks for ICT and digital health in Ethiopia and, under the right conditions, a policy window could open for their adoption and implementation.展开更多
The incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally.This has become particularly clear with the recent emergence of new variants of concern.The Virus Outbreak Dat...The incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally.This has become particularly clear with the recent emergence of new variants of concern.The Virus Outbreak Data Network(VODAN)-Africa has studied the curation of patient health data in selected African countries and identified that health information flows often do not involve the use of health data at the point of care,which renders data production largely meaningless to those producing it.This modus operandi leads to disfranchisement over the control of health data,which is extracted to be processed elsewhere.In response to this problem,VODAN-Africa studied whether or not a design that makes local ownership and repositing of data central to the data curation process,would have a greater chance of being adopted.The design team based their work on the legal requirements of the European Union’s General Data Protection Regulation(GDPR);the FAIR Guidelines on curating data as Findable,Accessible(under well-defined conditions),Interoperable and Reusable(FAIR);and national regulations applying in the context where the data is produced.The study concluded that the visiting of data curated as machine actionable and reposited in the locale where the data is produced and renders services has great potential for access to a wider variety of data.A condition of such innovation is that the innovation team is intradisciplinary,involving stakeholders and experts from all of the places where the innovation is designed,and employs a methodology of co-creation and capacity-building.展开更多
The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable(FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Gui...The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable(FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Guidelines gain more acceptance, an increase in the demand for data stewards is expected. Consequently, there is a need to develop curricula to foster professional skills in data stewardship through effective knowledge communication. There have been a number of initiatives aimed at bridging the gap in FAIR data management training through both formal and informal programmes. This article describes the experience of developing a digital initiative for FAIR data management training under the Digital Innovations and Skills Hub(DISH) project. The FAIR Data Management course offers 6 short on-demand certificate modules over 12 weeks. The modules are divided into two sets: FAIR data and data science. The core subjects cover elementary topics in data science, regulatory frameworks, FAIR data management, intermediate to advanced topics in FAIR Data Point installation, and FAIR data in the management of healthcare and semantic data. Each week, participants are required to devote 7–8 hours of self-study to the modules, based on the resources provided. Once they have satisfied all requirements, students are certified as FAIR data scientists and qualified to serve as both FAIR data stewards and analysts. It is expected that in-depth and focused curricula development with diverse participants will build a core of FAIR data scientists for Data Competence Centres and encourage the rapid adoption of the FAIR Guidelines for research and development.展开更多
基金VODAN-Africathe Philips Foundation+2 种基金the Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘The FAIR Guidelines were conceptualised and coined as guidelines for Findable, Accessible, Interoperable and Reusable(FAIR) data at a conference held at the Lorentz Centre in Leiden in 2014. A relatively short period of time after this conference, the FAIR Guidelines made it onto the public policy agenda of the European Union. Following the concept of Kingdon, policy entrepreneurs played a critical role in creating a policy window for this idea to reach the agenda by linking it to the policy of establishing a European Open Science Cloud(EOSC). Tracing the development from idea to policy, this study highlights the critical role that expert committees play in the European Union. The permeability of the complex governance structure is increased by these committees, which allow experts to link up with the institutions and use the committees to launch new ideas. The High Level Expert Groups on the EOSC provided the platform from which the FAIR Guidelines were launched, and this culminated in the adoption of the FAIR Guidelines as a requirement for all European-funded science. As a result, the FAIR Guidelines have become an obligatory part of data management in European-funded research in 2020 and are now followed by other funders worldwide.
基金VODAN-Africathe Philips Foundation+2 种基金the Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘This paper investigates whether or not there is a policy window for making health data ‘Findable’, ‘Accessible’(under well-defined conditions), ‘Interoperable’ and ‘Reusable’(FAIR) in Ethiopia. The question is answered by studying the alignment of policies for health data in Ethiopia with the FAIR Guidelines or their ‘FAIR Equivalency’. Policy documents relating to the digitalisation of health systems in Ethiopia were examined to determine their FAIR Equivalency. Although the documents are fragmented and have no overarching governing framework, it was found that they aim to make the disparate health data systems in Ethiopia interoperable and boost the discoverability and(re)usability of data for research and better decision making. Hence, the FAIR Guidelines appear to be aligned with the regulatory frameworks for ICT and digital health in Ethiopia and, under the right conditions, a policy window could open for their adoption and implementation.
基金VODAN-Africathe Philips Foundation+2 种基金the Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘The incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally.This has become particularly clear with the recent emergence of new variants of concern.The Virus Outbreak Data Network(VODAN)-Africa has studied the curation of patient health data in selected African countries and identified that health information flows often do not involve the use of health data at the point of care,which renders data production largely meaningless to those producing it.This modus operandi leads to disfranchisement over the control of health data,which is extracted to be processed elsewhere.In response to this problem,VODAN-Africa studied whether or not a design that makes local ownership and repositing of data central to the data curation process,would have a greater chance of being adopted.The design team based their work on the legal requirements of the European Union’s General Data Protection Regulation(GDPR);the FAIR Guidelines on curating data as Findable,Accessible(under well-defined conditions),Interoperable and Reusable(FAIR);and national regulations applying in the context where the data is produced.The study concluded that the visiting of data curated as machine actionable and reposited in the locale where the data is produced and renders services has great potential for access to a wider variety of data.A condition of such innovation is that the innovation team is intradisciplinary,involving stakeholders and experts from all of the places where the innovation is designed,and employs a methodology of co-creation and capacity-building.
基金VODAN-Africathe Philips Foundation+2 种基金the Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable(FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Guidelines gain more acceptance, an increase in the demand for data stewards is expected. Consequently, there is a need to develop curricula to foster professional skills in data stewardship through effective knowledge communication. There have been a number of initiatives aimed at bridging the gap in FAIR data management training through both formal and informal programmes. This article describes the experience of developing a digital initiative for FAIR data management training under the Digital Innovations and Skills Hub(DISH) project. The FAIR Data Management course offers 6 short on-demand certificate modules over 12 weeks. The modules are divided into two sets: FAIR data and data science. The core subjects cover elementary topics in data science, regulatory frameworks, FAIR data management, intermediate to advanced topics in FAIR Data Point installation, and FAIR data in the management of healthcare and semantic data. Each week, participants are required to devote 7–8 hours of self-study to the modules, based on the resources provided. Once they have satisfied all requirements, students are certified as FAIR data scientists and qualified to serve as both FAIR data stewards and analysts. It is expected that in-depth and focused curricula development with diverse participants will build a core of FAIR data scientists for Data Competence Centres and encourage the rapid adoption of the FAIR Guidelines for research and development.
