Under the urgency of the outbreak of the corona pandemic, we take a number of actions to enable researchers in ZonMw’s COVID-19 projects to create FAIR data that can be used by humans as well as machines. As a result, data become findable through computer search, and accessible for learning-algorithms (comparable to the Personal Health Train concept). The results of the activities are available for the entire research community of COVID-19.

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VODAN for COVID-19 patient data

In spring 2020, ZonMw commissioned the GO FAIR initiative and its VODAN (Virus Outbreak Data Access Network) Implementation Network to make COVID-19 related data FAIR.  As a result, real world COVID-19 patient data become available for research, under well-defined conditions and with patient privacy well protected.

In summer 2020, the VODAN project delivered a proof of concept of a working VODAN infrastructure to open up COVID-19 patient data via so called FAIR Data Points: VODAN-in-a box. It is made up of a number of components:

  1. A semantic data model based on the case report form (CRF) following the WHO standards;
  2. Localized VODAN FAIR Data Points (FDP’s), where the (meta)data of the machine-readable eCRF files could be hosted. Several FDP’s have been installed, in the Netherlands, USA and on the African continent (funded by the Philips Foundation).
  3. A user-friendly data-entry-wizard, allowing data stewards in the local clinic or hospital to capture patient data in the CRF.
  4. Creating/updating the metadata on the installed VODAN FDP’s, “link” the patient data and indicate if the data can be shared.
  5. A simple tool to allow queries to “visit” the (meta) data across multiple FDP’s, thereby performing analysis based upon certain characteristics of the recorded patients.

FAIR data services and a national COVID-19 data portal

As a next step, building on the knowledge and deliverables from VODAN, ZonMw has commissioned the GO FAIR Foundation and the Health-RI Foundation to develop FAIR data services and a national COVID-19 data portal. These services will support researchers in the creation of FAIR research outputs, and to find and reuse COVID-19 related observational data from Dutch health care providers (taking privacy and other ethical, legal and social issues in to account).

An advanced approach for creating FAIR data

The FAIR data services will function as a three-point FAIRification Framework, where FAIR metadata are crafted for each project (1) as part of the overall COVID-19 Programme FAIR Implementation Profile (2) which then configures the FAIR Data Point (3) where COVID-19 Programme research outputs can be easily discovered and reused. The planned national COVID-19 data portal will form a human friendly interface to the metadata and observational hospital data.

Data experts collaborate with domain experts

A crucial aspect of the FAIRification approach is that domain experts collaborate with data experts in order to include the standards, technologies and infrastructure that match the research community, in this case COVID-19. We will therefore facilitate that COVID-19 researchers and data stewards from ZonMw projects make use of the three-point FAIRification Framework, with the support of the FAIR data experts from GO FAIR and Health-RI.

Workshops and training

Researchers and data stewards from ZonMw’s COVID-19 projects are invited for a workshop series in autumn 2020 for an introduction to the FAIRification framework, and to take part in tailoring the framework to the needs of the COVID-19 research community.
In addition, data stewards from the ZonMw projects will be trained by and get support from the FAIR data experts of GO FAIR and Health-RI. Support through the data steward community will be organised within the Data Stewardship Special Interest Group (DSIG).

Taken together, by taking part in these activities, researchers and data stewards create – as a minimum - FAIR metadata for their research data. They thereby allow other research on COVID-19 to directly benefit from their activities, and accelerate solutions for the corona crisis. In a broader sense, they contribute to a growing knowledge base on FAIR data, enabling innovative research methods.

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