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 VODA...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.展开更多
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 infrastructure...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(Vi B).Vi B 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(e CRFs)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,Vi B 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.展开更多
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 migran...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.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
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 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.
基金VODAN-Africathe Philips Foundation+2 种基金the Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘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(Vi B).Vi B 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(e CRFs)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,Vi B 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.
基金supported by funding from NWO, domain Social Sciences and Humanities under the ‘Corona Fast-track Data’ call for proposals, file no. 440.20.012VODAN-Africa+3 种基金the Philips Foundationthe Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘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.
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.
