E. Inau, Reginald Nalugala, William Muhadi Nandwa, Fredrick Obwanda, A. Wachira, A. Cartaxo
Abstract This study explored the regulatory framework in Kenya that may facilitate the implementation of the FAIR Guidelines in health research, as well as the possibility of adopting the FAIR Guidelines at the national level. Fourteen key documents pivotal to the emerging digital health sector in Kenya were identified and analysed using a comprehensive coding and labelling approach based on a binary system for whether or not they mention the FAIR Guidelines or terms and vocabulary related to the FAIR Guidelines. The analysis revealed gaps in data stewardship that could be filled by the implementation of the FAIR Guidelines and, although the documents analysed do not explicitly mention the FAIR Guidelines, FAIR Equivalent terminology and practices are mentioned in varying detail. However, our analysis shows that there are still no provisions for the introduction and implementation of the FAIR Guidelines in health research in Kenya. Therefore, we recommend that the leadership be provided with a comprehensive introduction to the FAIR Guidelines, success stories about the FAIRification of data and research infrastructure in other parts of the world, and a demonstration of the steps needed for the FAIRification of health data in Kenya.
{"title":"FAIR Equivalency, Regulatory Framework and Adoption Potential of FAIR Guidelines in Health in Kenya","authors":"E. Inau, Reginald Nalugala, William Muhadi Nandwa, Fredrick Obwanda, A. Wachira, A. Cartaxo","doi":"10.1162/dint_a_00175","DOIUrl":"https://doi.org/10.1162/dint_a_00175","url":null,"abstract":"Abstract This study explored the regulatory framework in Kenya that may facilitate the implementation of the FAIR Guidelines in health research, as well as the possibility of adopting the FAIR Guidelines at the national level. Fourteen key documents pivotal to the emerging digital health sector in Kenya were identified and analysed using a comprehensive coding and labelling approach based on a binary system for whether or not they mention the FAIR Guidelines or terms and vocabulary related to the FAIR Guidelines. The analysis revealed gaps in data stewardship that could be filled by the implementation of the FAIR Guidelines and, although the documents analysed do not explicitly mention the FAIR Guidelines, FAIR Equivalent terminology and practices are mentioned in varying detail. However, our analysis shows that there are still no provisions for the introduction and implementation of the FAIR Guidelines in health research in Kenya. Therefore, we recommend that the leadership be provided with a comprehensive introduction to the FAIR Guidelines, success stories about the FAIRification of data and research infrastructure in other parts of the world, and a demonstration of the steps needed for the FAIRification of health data in Kenya.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"4 1","pages":"852-866"},"PeriodicalIF":3.9,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47228794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Ghardallou, Morgane Wirtz, Sakinat Folorunso, Z. Touati, E. Ogundepo, Klara Smits, A. Mtiraoui, M. Reisen
Abstract This article describes the FAIRification process (which involves making data Findable, Accessible, Interoperable and Reusable—or FAIR—for both machines and humans) for data related to the impact of COVID-19 on migrants, refugees and asylum seekers in Tunisia, Libya and Niger, according to the scheme adopted by GO FAIR. This process was divided into three phases: pre-FAIRification, FAIRification and post-FAIRification. Each phase consisted of seven steps. In the first phase, 118 in-depth interviews and 565 press articles and research reports were collected by students and researchers at the University of Sousse in Tunisia and researchers in Niger. These interviews, articles and reports constitute the dataset for this research. In the second phase, the data were sorted and converted into a machine actionable format and published on a FAIR Data Point hosted at the University of Sousse. In the third phase, an assessment of the implementation of the FAIR Guidelines was undertaken. Certain barriers and challenges were faced in this process and solutions were found. For FAIR data curation, certain changes need to be made to the technical process. People need to be convinced to make these changes and that the implementation of FAIR will generate a long-term return on investment. Although the implementation of FAIR Guidelines is not straightforward, making our resources FAIR is essential to achieving better science together.
{"title":"Expanding Non-Patient COVID-19 Data: Towards the FAIRification of Migrants’ Data in Tunisia, Libya and Niger","authors":"M. Ghardallou, Morgane Wirtz, Sakinat Folorunso, Z. Touati, E. Ogundepo, Klara Smits, A. Mtiraoui, M. Reisen","doi":"10.1162/dint_a_00181","DOIUrl":"https://doi.org/10.1162/dint_a_00181","url":null,"abstract":"Abstract This article describes the FAIRification process (which involves making data Findable, Accessible, Interoperable and Reusable—or FAIR—for both machines and humans) for data related to the impact of COVID-19 on migrants, refugees and asylum seekers in Tunisia, Libya and Niger, according to the scheme adopted by GO FAIR. This process was divided into three phases: pre-FAIRification, FAIRification and post-FAIRification. Each phase consisted of seven steps. In the first phase, 118 in-depth interviews and 565 press articles and research reports were collected by students and researchers at the University of Sousse in Tunisia and researchers in Niger. These interviews, articles and reports constitute the dataset for this research. In the second phase, the data were sorted and converted into a machine actionable format and published on a FAIR Data Point hosted at the University of Sousse. In the third phase, an assessment of the implementation of the FAIR Guidelines was undertaken. Certain barriers and challenges were faced in this process and solutions were found. For FAIR data curation, certain changes need to be made to the technical process. People need to be convinced to make these changes and that the implementation of FAIR will generate a long-term return on investment. Although the implementation of FAIR Guidelines is not straightforward, making our resources FAIR is essential to achieving better science together.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"4 1","pages":"955-970"},"PeriodicalIF":3.9,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45191740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Kawu, Joseph Elijah, Ibrahim Abdullahi, Jamilu Yahaya Maipanuku, Sakinat Folorunso, Mariam Basajja, Francisca Onaolapo Oladipo, Hauwa Limanko Ibrahim
Abstract Adopting the FAIR Guidelines—that data should be Findable, Accessible, Interoperable and Reusable (FAIR)—in the health data system in Nigeria will help protect data against use by unauthorised parties, while also making data more accessible to legitimate users. However, little is known about the FAIR Guidelines and their compatibility with data and health laws and policies in Nigeria. This study assesses the governance framework for digital and health/eHealth policies in Nigeria and explores the possibility of a policy window opening for the FAIR Guidelines to be adopted and implemented in Nigeria's eHealth sector. Ten Nigerian policy documents were examined for mention of the FAIR Guidelines (or FAIR Equivalent terminology) and the 15 sub-criteria or facets. The analysis found that although the FAIR Guidelines are not explicitly mentioned, 70% of the documents contain FAIR Equivalent terminology. The Nigeria Data Protection Regulation contained the most FAIR Equivalent principles (73%) and some of the remaining nine documents also contained some FAIR Equivalent principles (between 0–60%). Accordingly, it can be concluded that a policy window is open for the FAIR Guidelines to be adopted and implemented in Nigeria's eHealth sector.
{"title":"FAIR Guidelines and Data Regulatory Framework for Digital Health in Nigeria","authors":"A. Kawu, Joseph Elijah, Ibrahim Abdullahi, Jamilu Yahaya Maipanuku, Sakinat Folorunso, Mariam Basajja, Francisca Onaolapo Oladipo, Hauwa Limanko Ibrahim","doi":"10.1162/dint_a_00174","DOIUrl":"https://doi.org/10.1162/dint_a_00174","url":null,"abstract":"Abstract Adopting the FAIR Guidelines—that data should be Findable, Accessible, Interoperable and Reusable (FAIR)—in the health data system in Nigeria will help protect data against use by unauthorised parties, while also making data more accessible to legitimate users. However, little is known about the FAIR Guidelines and their compatibility with data and health laws and policies in Nigeria. This study assesses the governance framework for digital and health/eHealth policies in Nigeria and explores the possibility of a policy window opening for the FAIR Guidelines to be adopted and implemented in Nigeria's eHealth sector. Ten Nigerian policy documents were examined for mention of the FAIR Guidelines (or FAIR Equivalent terminology) and the 15 sub-criteria or facets. The analysis found that although the FAIR Guidelines are not explicitly mentioned, 70% of the documents contain FAIR Equivalent terminology. The Nigeria Data Protection Regulation contained the most FAIR Equivalent principles (73%) and some of the remaining nine documents also contained some FAIR Equivalent principles (between 0–60%). Accordingly, it can be concluded that a policy window is open for the FAIR Guidelines to be adopted and implemented in Nigeria's eHealth sector.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"4 1","pages":"839-851"},"PeriodicalIF":3.9,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47293626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. van Reisen, Francisca Onaolapo Oladipo, M. Mpezamihigo, Ruduan Plug, Mariam Basajja, Aliya Aktau, Putu Hadi Purnama Jati, Reginald Nalugala, Sakinat Folorunso, S. Amare, Ibrahim Abdulahi, Oluwole Afolabi, Ezra Mwesigwa, Getu Tadele Taye, A. Kawu, M. Ghardallou, Yan Liang, Obinna Osigwe, A. Medhanyie, M. Mawere
Abstract 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.
{"title":"Incomplete COVID-19 Data: The Curation of Medical Health Data by the Virus Outbreak Data Network-Africa","authors":"M. van Reisen, Francisca Onaolapo Oladipo, M. Mpezamihigo, Ruduan Plug, Mariam Basajja, Aliya Aktau, Putu Hadi Purnama Jati, Reginald Nalugala, Sakinat Folorunso, S. Amare, Ibrahim Abdulahi, Oluwole Afolabi, Ezra Mwesigwa, Getu Tadele Taye, A. Kawu, M. Ghardallou, Yan Liang, Obinna Osigwe, A. Medhanyie, M. Mawere","doi":"10.1162/dint_e_00166","DOIUrl":"https://doi.org/10.1162/dint_e_00166","url":null,"abstract":"Abstract 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.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"4 1","pages":"673-697"},"PeriodicalIF":3.9,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42900956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Putu Hadi Purnama Jati, Yi Lin, Sara Nodehi, D. B. Cahyono, M. Reisen
Abstract This article assesses the difference between the concepts of ‘open data’ and ‘FAIR data’ in data management. FAIR data is understood as data that complies with the FAIR Guidelines—data that is Findable, Accessible, Interoperable and Reusable—while open data was born out of awareness of the need to democratise data by improving its accessibility, based on the idea that data should not have limitations that prevent people from using it. This study compared FAIR data with open data by analysing relevant documents using a coding analysis with conceptual labels based on Kingdon's theory of agenda setting. The study found that in relation to FAIR data the problem stream focuses on the complexity of data collected for research, while open data primarily emphasises giving the public access to non-confidential data. In the policy stream, the two concepts share common standpoints in terms of making data available and reusable, although different approaches are adopted in practice to accomplish these goals. In the politics stream, stakeholders with different objectives support FAIR data and from those who support open data.
{"title":"FAIR Versus Open Data: A Comparison of Objectives and Principles","authors":"Putu Hadi Purnama Jati, Yi Lin, Sara Nodehi, D. B. Cahyono, M. Reisen","doi":"10.1162/dint_a_00176","DOIUrl":"https://doi.org/10.1162/dint_a_00176","url":null,"abstract":"Abstract This article assesses the difference between the concepts of ‘open data’ and ‘FAIR data’ in data management. FAIR data is understood as data that complies with the FAIR Guidelines—data that is Findable, Accessible, Interoperable and Reusable—while open data was born out of awareness of the need to democratise data by improving its accessibility, based on the idea that data should not have limitations that prevent people from using it. This study compared FAIR data with open data by analysing relevant documents using a coding analysis with conceptual labels based on Kingdon's theory of agenda setting. The study found that in relation to FAIR data the problem stream focuses on the complexity of data collected for research, while open data primarily emphasises giving the public access to non-confidential data. In the policy stream, the two concepts share common standpoints in terms of making data available and reusable, although different approaches are adopted in practice to accomplish these goals. In the politics stream, stakeholders with different objectives support FAIR data and from those who support open data.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"4 1","pages":"867-881"},"PeriodicalIF":3.9,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47510293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Akindele, O. Arulogun, Getu Tadele Taye, S. Amare, M. Reisen, Kibrom Fekadu Berhe, Balyejusa Gusite
Abstract Prior to the advent of the COVID-19 pandemic, distance education, a mode of education that allows teaching and learning to occur beyond the walls of traditional classrooms using electronic media and online delivery practices, was not widely embraced as a credible alternative mode of delivering education, especially in Africa. In education, the pandemic, and the measures to contain it, created a need for virtual learning/teaching and showcased the potential of distance education. This article explores the potential of distance education with an emphasis on the role played by COVID-19, the technologies employed, and the benefits, as well as how data stewardship can enhance distance education. It also describes how distance education can make learning opportunities available to the less privileged, geographically displaced, dropouts, housewives, and even workers, enabling them to partake in education while being engaged in other productive aspects of life. A case study is provided on the Dutch Organisation for Internationalisation in Education (NUFFIC) Digital Innovation Skills Hub (DISH) project, which is implemented via distance education and targeted towards marginalised individuals such as refugees and displaced persons in Ethiopia, Somalia, and other conflict zones, aiming to provide them with critical and soft skills for remote work for financial remuneration. This case study shows that distance education is the way forward in education today, as it has the capability to reach millions of learners simultaneously, educating, lifting people out of poverty, and increasing productivity and yields, while ensuring that the world is a better place for future generations.
{"title":"The Impact of COVID-19 and FAIR Data Innovation on Distance Education in Africa","authors":"A. Akindele, O. Arulogun, Getu Tadele Taye, S. Amare, M. Reisen, Kibrom Fekadu Berhe, Balyejusa Gusite","doi":"10.1162/dint_a_00184","DOIUrl":"https://doi.org/10.1162/dint_a_00184","url":null,"abstract":"Abstract Prior to the advent of the COVID-19 pandemic, distance education, a mode of education that allows teaching and learning to occur beyond the walls of traditional classrooms using electronic media and online delivery practices, was not widely embraced as a credible alternative mode of delivering education, especially in Africa. In education, the pandemic, and the measures to contain it, created a need for virtual learning/teaching and showcased the potential of distance education. This article explores the potential of distance education with an emphasis on the role played by COVID-19, the technologies employed, and the benefits, as well as how data stewardship can enhance distance education. It also describes how distance education can make learning opportunities available to the less privileged, geographically displaced, dropouts, housewives, and even workers, enabling them to partake in education while being engaged in other productive aspects of life. A case study is provided on the Dutch Organisation for Internationalisation in Education (NUFFIC) Digital Innovation Skills Hub (DISH) project, which is implemented via distance education and targeted towards marginalised individuals such as refugees and displaced persons in Ethiopia, Somalia, and other conflict zones, aiming to provide them with critical and soft skills for remote work for financial remuneration. This case study shows that distance education is the way forward in education today, as it has the capability to reach millions of learners simultaneously, educating, lifting people out of poverty, and increasing productivity and yields, while ensuring that the world is a better place for future generations.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"4 1","pages":"1013-1032"},"PeriodicalIF":3.9,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48181410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Putu Hadi Purnama Jati, M. Reisen, E. Flikkenschild, Fransisca Oladipo, Bert Meerman, Ruduan Plug, Sara Nodehi
Abstract The Virus Outbreak Data Network (VODAN)-Africa aims to contribute to the publication of Findable Accessible, Interoperable, and Reusable (FAIR) health data under well-defined access conditions. The next step in the VODAN-Africa architecture is to locally deploy the Center for Expanded Data Annotation and Retrieval (CEDAR) and arrange accessibility based on the ‘data visiting’ concept. Locally curated and reposited machine-actionable data can be visited by queries or algorithms, provided that the conditions of access are met. The goal is to enable the multiple (re)use of data with secure access functionality by clinicians (patient care), an idea aligned with the FAIR-based Personal Health Train (PHT) concept. The privacy and security requirements in relation to the FAIR Data Host and the FAIRification workspace (to produce metadata) or dashboard (for the patient) must be clear to design the IT architecture. This article describes a (first) practice, a reference implementation in development, within the VODAN-Africa and Leiden University Medical Center community.
{"title":"Data Access, Control, and Privacy Protection in the VODAN-Africa Architecture","authors":"Putu Hadi Purnama Jati, M. Reisen, E. Flikkenschild, Fransisca Oladipo, Bert Meerman, Ruduan Plug, Sara Nodehi","doi":"10.1162/dint_a_00180","DOIUrl":"https://doi.org/10.1162/dint_a_00180","url":null,"abstract":"Abstract The Virus Outbreak Data Network (VODAN)-Africa aims to contribute to the publication of Findable Accessible, Interoperable, and Reusable (FAIR) health data under well-defined access conditions. The next step in the VODAN-Africa architecture is to locally deploy the Center for Expanded Data Annotation and Retrieval (CEDAR) and arrange accessibility based on the ‘data visiting’ concept. Locally curated and reposited machine-actionable data can be visited by queries or algorithms, provided that the conditions of access are met. The goal is to enable the multiple (re)use of data with secure access functionality by clinicians (patient care), an idea aligned with the FAIR-based Personal Health Train (PHT) concept. The privacy and security requirements in relation to the FAIR Data Host and the FAIRification workspace (to produce metadata) or dashboard (for the patient) must be clear to design the IT architecture. This article describes a (first) practice, a reference implementation in development, within the VODAN-Africa and Leiden University Medical Center community.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"4 1","pages":"938-954"},"PeriodicalIF":3.9,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44443250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract 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.
{"title":"Agenda Setting on FAIR Guidelines in the European Union and the Role of Expert Committees","authors":"Misha Stocker, M. Stokmans, M. Reisen","doi":"10.1162/dint_a_00168","DOIUrl":"https://doi.org/10.1162/dint_a_00168","url":null,"abstract":"Abstract 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.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"4 1","pages":"724-746"},"PeriodicalIF":3.9,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41963765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mariam Basajja, M. Suchánek, Getu Tadele Taye, S. Amare, Mutwalibi Nambobi, Sakinat Folorunso, Ruduan Plug, Francisca Onaolapo Oladipo, M. van Reisen
Abstract Rapid and effective data sharing is necessary to control disease outbreaks, such as the current coronavirus pandemic. Despite the existence of data sharing agreements, data silos, lack of interoperable data infrastructures, and different institutional jurisdictions hinder data sharing and accessibility. To overcome these challenges, the Virus Outbreak Data Network (VODAN)-Africa initiative is championing an approach in which data never leaves the institution where it was generated, but, instead, algorithms can visit the data and query multiple datasets in an automated way. To make this possible, FAIR Data Points—distributed data repositories that host machine-actionable data and metadata that adhere to the FAIR Guidelines (that data should be Findable, Accessible, Interoperable and Reusable)—have been deployed in participating institutions using a dockerised bundle of tools called VODAN in a Box (ViB). ViB is a set of multiple FAIR-enabling and open-source services with a single goal: to support the gathering of World Health Organization (WHO) electronic case report forms (eCRFs) as FAIR data in a machine-actionable way, but without exposing or transferring the data outside the facility. Following the execution of a proof of concept, ViB was deployed in Uganda and Leiden University. The proof of concept generated a first query which was implemented across two continents. A SWOT (strengths, weaknesses, opportunities and threats) analysis of the architecture was carried out and established the changes needed for specifications and requirements for the future development of the solution.
{"title":"Proof of Concept and Horizons on Deployment of FAIR Data Points in the COVID-19 Pandemic","authors":"Mariam Basajja, M. Suchánek, Getu Tadele Taye, S. Amare, Mutwalibi Nambobi, Sakinat Folorunso, Ruduan Plug, Francisca Onaolapo Oladipo, M. van Reisen","doi":"10.1162/dint_a_00179","DOIUrl":"https://doi.org/10.1162/dint_a_00179","url":null,"abstract":"Abstract Rapid and effective data sharing is necessary to control disease outbreaks, such as the current coronavirus pandemic. Despite the existence of data sharing agreements, data silos, lack of interoperable data infrastructures, and different institutional jurisdictions hinder data sharing and accessibility. To overcome these challenges, the Virus Outbreak Data Network (VODAN)-Africa initiative is championing an approach in which data never leaves the institution where it was generated, but, instead, algorithms can visit the data and query multiple datasets in an automated way. To make this possible, FAIR Data Points—distributed data repositories that host machine-actionable data and metadata that adhere to the FAIR Guidelines (that data should be Findable, Accessible, Interoperable and Reusable)—have been deployed in participating institutions using a dockerised bundle of tools called VODAN in a Box (ViB). ViB is a set of multiple FAIR-enabling and open-source services with a single goal: to support the gathering of World Health Organization (WHO) electronic case report forms (eCRFs) as FAIR data in a machine-actionable way, but without exposing or transferring the data outside the facility. Following the execution of a proof of concept, ViB was deployed in Uganda and Leiden University. The proof of concept generated a first query which was implemented across two continents. A SWOT (strengths, weaknesses, opportunities and threats) analysis of the architecture was carried out and established the changes needed for specifications and requirements for the future development of the solution.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"4 1","pages":"917-937"},"PeriodicalIF":3.9,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43376731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data Intelligence is the ultimate purpose of FAIR data management. FAIR as in data that is Findable, Accessible (under well defined conditions), Interoperable and Reusable. FAIR also as in ethical data; data that fulfils the requirements of Personal Data Protection, is collected for well defined purposes and is held and curated within ownership of the location where the data is produced
{"title":"Introduction to the Special Issue: Data Intelligence on Patient Health Records","authors":"M. Reisen, B. Mons","doi":"10.1162/dint_e_00165","DOIUrl":"https://doi.org/10.1162/dint_e_00165","url":null,"abstract":"Data Intelligence is the ultimate purpose of FAIR data management. FAIR as in data that is Findable, Accessible (under well defined conditions), Interoperable and Reusable. FAIR also as in ethical data; data that fulfils the requirements of Personal Data Protection, is collected for well defined purposes and is held and curated within ownership of the location where the data is produced","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"4 1","pages":"671-672"},"PeriodicalIF":3.9,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64531702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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