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FAIR Equivalency with Regulatory Framework for Digital Health in Uganda

Data Intelligence 2022年第4期4卷 771-797页

作者： Mariam Basajja mirjam van reisen Francisca Oladipo Leiden University Leiden1011NCAmsterdamthe Netherlands Leiden University Medical Centre(LUMC) Leiden University1310 Leidenthe Netherlands Tilburg University P.O.Box 901535000the Netherlands Federal University 260101 LokojaNigeria

This study explores the possibility of opening a policy window for the adoption of the FAIR Guidelines—that data be Findable, Accessible, Interoperable, and Reusable(FAIR)—in Uganda’s e Health sector. Although the FAIR Guidelines were not mentioned in any of the policy documents relevant to Uganda’s e Health sector, the study found that 83% of the documents mentioned FAIR Equivalent efforts, such as the adoption of the National Identification Number(NIN) as a unique identifier in Uganda’s national Electronic Health Management Information System(e HMIS)(findability), the planned/ongoing integration of various information systems(interoperability), and the alignment of various projects with international best practices/standards(reusability). A FAIR Equivalency Score(FE-Score), devised in this study as an aggregate score of the mention of the equivalent of FAIR facets in the policy documents, showed that the documents at the core of Uganda’s digital health/e Health policy have the highest score of all the documents analysed, indicating that there is a degree of alignment between Uganda’s National e Health Vision and the FAIR Guidelines. Therefore, it can be concluded that favourable conditions exist for the adoption and implementation of the FAIR Guidelines in Uganda’s e Health sector. Hence, it is recommended that the FAIR community adopt a capacity building strategy through organisations with a worldwide mandate, such as the World Health Organization, to promote the adoption of the FAIR Guidelines as part of international best practices.

关键词： eHealth Digital health GAIR Guidelines FAIR Equivalency Findable Accessible Interoperable Reusable HMIS

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Agenda Setting on FAIR Guidelines in the European Union and the Role of Expert Committees

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Data Intelligence 2022年第4期4卷 724-746,1046-1047页

作者： Misha Stocker Mia Stokmans mirjam van reisen Research Advisors and Experts Europe 1000 VODAN-Africa Tilburg University P.O.Box 901535000the Netherlands Leiden University 1310 Leidenthe Netherlands Leiden University Medical Centre(LUMC) Leiden University1310 Leidenthe Netherlands

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.

关键词： FAIR Guidelines EU expert committees Policy entrepreneurs Public agenda setting European Open Science Cloud EOSC

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Introduction to the Special Issue: Data Intelligence on Patient Health Records

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Data Intelligence 2022年第4期4卷 671-672,1046页

作者： mirjam van reisen Barend Mons Mia Stokmans Leiden University Medical Centre(LUMC) Leiden University1310 Leidenthe Netherlands Leiden University Medical Centre Poortgebouw N-01Rijnsburgerweg 102333 AA Leidenthe Netherlands Tilburg University Tilburg School of Humanities and Digital Sciences

Data Intelligence is the ultimate purpose of FAIR data *** as in data that is Findable,Accessible(under well defined conditions),Interoperable and *** 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.

关键词： Data management ultimate

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Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa

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Data Intelligence 2022年第4期4卷 698-723,1050页

作者： Ruduan Plug Yan Liang Aliya Aktau Mariam Basajja Francisca Oladipo mirjam van reisen Leiden University 2331 GL Leidenthe Netherlands Kampala International University 260101 KampalaUganda Leiden University Medical Centre(LUMC) Leiden University1310 Leidenthe Netherlands

The field of health data management poses unique challenges in relation to data ownership, the privacy of data subjects, and the reusability of data. The FAIR Guidelines have been developed to address these challenges. The Virus Outbreak Data Network(VODAN) architecture builds on these principles, using the European Union’s General Data Protection Regulation(GDPR) framework to ensure compliance with local data regulations, while using information knowledge management concepts to further improve data provenance and interoperability. In this article we provide an overview of the terminology used in the field of FAIR data management, with a specific focus on FAIR compliant health information management, as implemented in the VODAN architecture.

关键词： Data management Distributed data Federated data Data governance FAIR Guidelines FAIR Data and Services FAIR Data Point FAIR framework

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Towards the Tipping Point for FAIR Implementation

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Data Intelligence 2020年第1期2卷 264-275页

作者： mirjam van reisen Mia Stokmans Mariam Basajja Antony Otieno Ong’ayo Christine Kirkpatrick Barend Mons Liacs Institute of Advanced Computer Science Leiden UniversityLeidenThe Netherlands Tilburg School of Humanities and Digital Sciences Tilburg University901535000 LE TilburgThe Netherlands International Institute of Social Studies Erasmus University297762502 LT The HagueThe Netherlands San Diego Supercomputer Center University of California San DiegoSan Diego CA 92093USA Leiden University Medical Center Leiden2333 ZAThe Netherlands

This article explores the global implementation of the FAIR Guiding Principles for scientific management and data stewardship,which provide that data should be findable,accessible,interoperable and *** implementation of these principles is designed to lead to the stewardship of data as FAIR digital objects and the establishment of the Internet of FAIR Data and Services(IFDS).If implementation reaches a tipping point,IFDS has the potential to revolutionize how data is managed by making machine and human readable data discoverable for ***,this article examines the expansion of the implementation of FAIR Guiding Principles,especially how and in which geographies(locations)and areas(topic domains)implementation is taking place.A literature review of academic articles published between 2016 and 2019 on the use of FAIR Guiding Principles is *** investigation also includes an analysis of the domains in the IFDS Implementation Networks(INs).Its uptake has been mainly in the Western *** investigation found that implementation of FAIR Guiding Principles has taken firm hold in the domain of bio and natural *** achieve a tipping point for FAIR implementation,it is now time to ensure the inclusion of non-European ascendants and of other scientific *** from equal opportunity and genuine global partnership issues,a permanent European bias poses challenges with regard to the representativeness and validity of data and could limit the potential of IFDS to reach across continental *** article concludes that,despite efforts to be inclusive,acceptance of the FAIR Guiding Principles and IFDS in different scientific communities is limited and there is a need to act now to prevent dampening of the momentum in the development and implementation of the *** is further concluded that policy entrepreneurs and the GO FAIR INs may contribute to making the FAIR Guiding Principles more flexible in including different rese

关键词： FAIR Data Health Digital Health mHealth data-driven science FAIR Implementation Networks GO-FAIR

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FAIR Versus Open Data: A Comparison of Objectives and Principles

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Data Intelligence 2022年第4期4卷 867-881,1037页

作者： Putu Hadi Purnama Jati Yi Lin Sara Nodehi Dwy Bagus Cahyono mirjam van reisen Leiden University 2333 CA Leidenthe Netherlands Politeknik Statistika STIS Tilburg University P.O.Box 901535000the Netherlands Leiden University Medical Centre(LUMC) Leiden University1310 Leidenthe Netherlands

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.

关键词： FAIR Guidelines FAIR data Open data Data management

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Implementation of FAIR Guidelines in Selected Non-Western Geographies

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Data Intelligence 2022年第4期4卷 747-770,1051,1053页

作者： Yi Lin Putu Hadi Purnama Jati Aliya Aktau Mariem Ghardallou Sara Nodehi mirjam van reisen 无 Leiden Institute of Advanced Computer Science(LIACS) Leiden University2333 CA Leidenthe Netherlands Central Bureau of Statistics 81119Indonesia Badan Pusat Statistik Central JakartaZ05H9A9Indonesia Department of Community Medicine Universitéde Sousse4002 SousseTunisia Faculty of Humanities and Digital Sciences TilburgP.O.Box 901535000the Netherlands Leiden University Medical Centre(LUMC) Leiden University1310 Leidenthe Netherlands

This study provides an analysis of the implementation of FAIR Guidelines in selected non-Western geographies. The analysis was based on a systematic literature review to determine if the findability, accessibility, interoperability, and reusability of data is seen as an issue, if the adoption of the FAIR Guidelines is seen as a solution, and if the climate is conducive to the implementation of the FAIR Guidelines. The results show that the FAIR Guidelines have been discussed in most of the countries studied, which have identified data sharing and the reusability of research data as an issue(e.g., Kazakhstan, Russia, countries in the Middle East), and partially introduced in others(e.g., Indonesia). In Indonesia, a FAIR equivalent system has been introduced, although certain functions need to be added for data to be entirely FAIR. In Japan, both FAIR equivalent systems and FAIR-based systems have been adopted and created, and the acceptance of FAIRbased systems is recommended by the Government of Japan. In a number of African countries, the FAIR Guidelines are in the process of being implemented and the implementation of FAIR is well supported. In conclusion, a window of opportunity for implementing the FAIR Guidelines is open in most of the countries studied, however, more awareness needs to be raised about the benefits of FAIR in Russia and Kazakhstan to place it firmly on the policy agenda.

关键词： FAIR Guidelines FAIR implementation non-Western geographies

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Data Access, Control, and Privacy Protection in the VODAN-Africa Architecture

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Data Intelligence 2022年第4期4卷 938-954,1037-1038页

作者： Putu Hadi Purnama Jati mirjam van reisen Erik Flikkenschild Fransisca Oladipo Bert Meerman Ruduan Plug Sara Nodehi Leiden University 2331 GL Leidenthe Netherlands Tilburg University P.O.Box 901535000the Netherlands Leiden University Medical Center(LUMC) Leiden University1310 Leidenthe Netherlands Virus Outbreak Data Network-Africa Federal University 260101 LokojaNigeria GO FAIR Foundation 2333 AA Leidenthe Netherlands Kampala International University Uganda

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.

关键词： VODAN VODAN-Africa FAIR Data and Services FAIR privacy framework FAIR certification FAIR Guidelines

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Expanding Non-Patient COVID-19 Data: Towards the FAIRification of Migrants' Data in Tunisia, Libya and Niger

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Data Intelligence 2022年第4期4卷 955-970,1034,1038,1044,1047,1054页

作者： Mariem Ghardallou Morgane Wirtz Sakinat Folorunso Zohra Touati Ezekiel Ogundepo Klara Smits Ali Mtiraoui mirjam van reisen University of Sousse Faculty of Medicine of SousseDepartment of Community MedicineResearch Laboratory LR12ES03Tunisia4002Sousse Tilburg University 1002 Tunisthe Netherlands Department of Mathematical Sciences Olabisi Onabanjo UniversityP.M.B 2002Ago-IwoyeOgun StateNigeria 120005Nigeria Data Science Nigeria Lagos 105102 Leiden University 1310 Leidenthe Netherlands Leiden University Medical Centre Leiden University1310 Leidenthe Netherlands Virus Outbreak Data Network-Africa

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 postFAIRification. 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.

关键词： FAIRification process FAIR Guidelines FAIR data Tunisia Niger Libya COVID Migrants VODAN-Africa

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FAIR Practices in Africa

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Data Intelligence 2020年第1期2卷 246-256,318,319页

作者： mirjam van reisen Mia Stokmans Munyaradzi Mawere Mariam Basajja Antony Otieno Ong’ayo Primrose Nakazibwe Christine Kirkpatrick Kudakwashe Chindoza Liacs Institute of Advanced Computer Science Leiden UniversityLeidenThe Netherlands Tilburg School of Humanities and Digital Sciences Tilburg University901535000 LE TilburgThe Netherlands Great Zimbabwe University 1235 Masvingo Zimbabwe HarareZimbabwe International Institute of Social Studies Erasmus University297762502 LT The HagueThe Netherlands Institute of Interdisciplinary Studies Mbarara University of Science and Technology1410 MbararaUganda San Diego Supercomputer Center University of California San DiegoSan Diego CA 92093USA

This article investigates expansion of the Internet of FAIR Data and Services(IFDS)to Africa,through the three GO FAIR pillars:GO CHANGE,GO BUILD and GO *** of the IFDS in Africa has a focus on digital *** examples of introducing FAIR are compared:a regional initiative for digital health by governments in the East Africa Community(EAC)and an initiative by a local health provider(Solidarmed)in collaboration with Great Zimbabwe University in *** obstacles to introducing FAIR are identified as underrepresentation of data from Africa in IFDS at this moment,the lack of explicit recognition of situational context of research in FAIR at present and the lack of acceptability of FAIR as a foreign and European invention which affects *** is envisaged that FAIR has an important contribution to solve fragmentation in digital health in Africa,and that any obstacles concerning African participation,context relevance and acceptance of IFDS need to be *** will require involvement of African researchers and ICT-developers so that it is driven by local *** of ecological validity in FAIR principles would ensure that the context specificity of research is reflected in the FAIR *** will help enhance the acceptance of the FAIR Guidelines in Africa and will help strengthen digital health research and services.

关键词： FAIR data Health Digital Health digital health in Africa GO FAIR Implementation Network

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