The FAIRplus project aims to develop tools and guidelines for making life science data FAIR

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 is inaccessible, annotated inconsistently, and difficult to share because it is in proprietary formats.

icon_Hands 22 partners

from academia and industry (12 academic, 7 EFPIA, 3 SMEs)

icon_Money €8.23 M


About FAIRplus

The goal of FAIRplus is to deliver guidelines and tools to facilitate the application of the ‘FAIR’ principles to data from at least 20 other IMI projects as well as datasets from pharmaceutical companies, where  FAIR stands for ‘findable, accessible, interoperable, reusable’. The project should enable other researchers to find the data and integrate it into their own research. The project will also organise training courses for data scientists in academia, SMEs and pharmaceutical companies. The project runs from January 2019 to June 2022.

The increased FAIRness of data will lead to a wider sharing of knowledge, greater opportunities for innovation, and more insights that benefit society.

Communication and outreach

Lygature is one of the leaders of WP4: Communication and outreach

One goal of FAIRplus is to ensure that researchers and funders understand and appreciate the value of data FAIRification, and that they support the long-term storage of FAIRified data.

The task of WP4 is to engage with policy makers at the national and European level. These policy makers will include publishers, the OECD, the European Commission and the Open Science Policy Platform. This dialogue will be supported by published materials from the project, such as FAIR metrics and the scientific use cases demonstrating the benefits of FAIRification.

The materials will also cover how to ensure the long-term funding and sustainability of the FAIR databases, as well as policies covering data protection and the secure access to data. For example, the FAIR metrics and FAIRification resources will inform journals how to enhance their data policies for authors, to ensure that data will be increasingly FAIR.

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