About the project

Project Coordinators' meeting
FAIRplus project leaders' meeting.

The volume and complexity of life science data being produced by research is growing exponentially. To gain maximum benefit from this data it needs to be available to researchers, but it often it is inaccessible, annotated inconsistently, and difficult to share because it is in proprietary formats.

The FAIRplus project aims to address these issues by developing guidelines and tools to make data FAIR:

  • Findable
  • Accessible
  • Interoperable
  • Reusable

It aims to increase the discovery, accessibility and reusability of data from selected projects funded by the EU's Innovative Medicine Initiative (IMI), and internal data from pharmaceutical industry partners. It will also organise training for data scientists in academia, SMEs and pharmaceutical companies to enable wider adoption of best practises in life science data management.

The increased FAIRness of data will lead to a wider sharing of knowledge, greater opportunities for innovation, and more insights that benefit society. See our KPI Dashboard to track our progress in FAIRification datasets from IMI projects and disseminating best practices in FAIR data management.

See How the project is organised for a more detailed overview.

Frequently Asked Questions about FAIR data and FAIRplus

1. Why make data FAIR?

Data can be much more easily found, accessed and reused when their metadata are harmonized and their interoperability is increased. The aim is thus to enhance their value by adding identifiers, metadata and link to ontologies which will enhance their sustainability as well.

2. What are the immediate visible benefits?

Following the guidelines we are preparing, we hope that datasets can be more œeasily FAIRified by referring to previous approaches that have been successfully applied. These established methodologies should at least advance the dataset to a higher maturity level as assessed by the Capability Maturity Model Integration Framework (CMMI). As an immediate result of this process public and private datasets will be easier to locate, access (public) and analyse. Interoperability should be enhanced, facilitating combined analysis with other FAIRified data (public and proprietary) for a more comprehensive research results.

3. What are the minimum data requirements to be able to start the FAIRification processes?

There is no simple answer to this question, but one key condition for any degree of FAIRfication is the possibility to having the data in an electronic, digitized format. Data recorded on paper only or on other non-digitized supports cannot readily be FAIRified. Analogue records which can be digitized are, however, in focus. Data owners have to be certain about the data provenance and have a clear view of the reasons for FAIRification, as the entire process is not yet automated. A legal framework for accessing the data is also needed.

4. Which data do you have in focus?

The FAIRplus consortium is supported by the Innovative Medicines Initiative (IMI), a public private partnership with the European pharmaceutical industry. Academic institutions work together with Pharma Partners in order to make their data FAIR. In the context of FAIRification of data, pharma datasets, especially in research and development, are not so different from the academic ones. The focus is on pharmaceutically relevant datasets ranging from basic research to preclinical and clinically relevant datasets. The same FAIRification processes will be applied to the data from selected IMI projects, ensuring interoperability of these data with other archives in the public and private sectors.

Project partners


Herman Van Vlijmen
Herman Van Vlijmen
(Head of Computational Chemistry, Janssen)
Project Leader
Serena Scollen
Serena Scollen
(Head of Human Genomics and Translational Data, ELIXIR)
Project Coordinator
Hannah Hurst
Hannah Hurst
Project Manager at ELIXIR
Paul Peeters
Paul Peeters
Project Manager at Janssen