The VODAN IN Africa Training the Trainers

Fighting the COVID-19 with FAIR Data

Institutional Support for VODAN-Africa


What is the Virus Outbreak Data Network (VODAN)? VODAN is a GO FAIR Implementation Network set up to help fight the COVID-19 Corona virus, that is causing a worldwide epidemic. The Implementation Network VODAN is a step to establish the Internet of FAIR Data and Services. FAIR stands for human and machine-readable digital data objects that are Findable, Accessible, Interoperable and Reusable. Accessibility is fully compliant with regulatory and governance frameworks, including personal data protection and ethical considerations based on the principle to Do no Harm.

Making data and metadata FAIR ensures that these data are discoverable on the Internet of FAIR Data and Services. Central tot his approach is the establishment of FAIR Data Points (FDPs), for COVID-19 relevant digital data objects. Opening up FAIR (meta)data by publishing them on a FDP allows algorithms to search these (meta)data, looking for patterns. The Internet of FAIR Data and Services is a distributed data discovery network; data are NOT moved, but algorithms going over the internet can find the data.


About the VODAN ToT

About the VODAN ToT

This Training of Trainers (ToT) is set up to provide support to data stewards so that they can help combat the COVID-19 pandemic.

The first course will focus on awareness, and include subjects like: what does FAIR data stewardship entail? What are FAIR data? What is a Semantic Data model? What is a FAIR Data point?

This will be followed by a course how to set up a FAIR data point. Other courses will follow shortly.

The training sub objectives are to:

  • Help prepare ministries and institutions for publishing FAIR (meta)data regarding the pandemic;
  • Ensure that universities and institutions with medical data have capacities to assist the navigation of the crisis with quality data;
  • Ensure that data stewardship is carried out under strict adherence to the governance and regulatory framework of the country and institution where the data belong;
  • Enable data to be analysed in combination with data available globally;
  • Ensure quality data for AI solutions to help deal with the pandemic.
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Criteria for participation

The training is intended for data scientists and computer scientists, working in connection for medical and health service providers, the Ministry of Health, and other relevant institutions and companies.

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Regional focus of the ToT

This training for trainers can be organized in all countries, but focusses for the moment particularly on Africa and Asia.

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Mode of the ToT

The ToT is set up in such a way that participants can follow the training on-line, with maximum flexibility.

The Training is provided and set up by the experts of the GO FAIR International Support and Coordination Office (GFISCO).

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The Training of Trainers course is set up in such a way that participants can follow the training online, with maximum flexibility.

The course is provided and set up by the experts of the GO FAIR International Support and Coordination Office (GFISCO).

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Role of Implementation Networks

Implementation Networks help build, train and also deploy the Internet of FAIR Data and Services.

  • Virus Outbreak Data Network (VODAN): Implementation Network on COVID-19
  • Implementation Network Ambassadors: Introduction and training of new participants
  • Implementation Network Africa: Introduction of participants in Africa-specific reality.

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The participants of the ToT will be able to receive assistance from the Technical Support Team who are students in Computer Science/Data Science of Leiden Institute of Computer Science: Mariam Basajja Uganda (coordinator, PhD candidate), Kudakwashe Chindoza, Zimbabwe (PhD aspirant candidate), Aliya Aktau, Kazakhstan, (master student), Putu Hadi, Indonesia (master student) and QiQi Zhang, China (master student).

The Technical Support Team is backed by the Senior Supervision Team, faculty in Computer Science/Data Science.

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The participants of the training are invited to participate via different mediums (WhatsApp, ORCID, OSF and INs).

The training modules will be provided in accessible format for participants with low bandwidth.

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