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Periodic Reporting for period 2 - FAIRplus (FAIRplus)

Reporting period: 2020-07-01 to 2021-12-31

The wealth of data and knowledge generated from a €5.3bn public-private investment divided over more than 100 IMI projects provides a huge opportunity to advance European science; it is a foundation of reference data that can be exploited by researchers for decades. Advancements in R&D can be made by sharing and linking data from different domains. However, data from IMI projects is often stored in project-specific databases that provide little opportunity for reuse. Inconsistent annotation between projects - and in internal participant databases - blocks aggregation to large data reservoirs and is a major hurdle for integration, knowledge mining and advanced modelling. Data management plans are now a standard component of project proposals but yet there is still little understanding of the resources required or steps to take to maximise the value and impact of data beyond the specifics of that particular project.

FAIRplus ( is developing tools and guidelines for making life science data FAIR (Findable, Accessible, Interoperable, Reusable). Through worked examples using IMI data and application and extension of existing methods, we will improve the level of discovery, accessibility, interoperability and reusability of selected IMI and internal partner data. In addition, through disseminated guidelines and tailored training for data handlers in academia, SMEs and pharmaceuticals, data management culture will change and be sustained, and datasets will be reused by pharmaceutical companies, academia and SMEs. Our FAIR Innovation and SME programme will enable wide data reuse and foster an innovation ecosystem around these data that power future re-use, knowledge generation, and societal benefit.
During the first three years, we have:

Identified project data sources for FAIRification:
A process (including a survey and ethics review) for selecting and prioritising IMI project databases for FAIRification has been established ( which involved agreeing upon criteria to prioritise and select IMI projects with data most suitable for FAIRification. This prioritisation process has been applied to 25 IMI projects. These concepts have been summarised in a related recipe and submitted as a publication.
Established a process for implementing FAIR-enabling resources and best practices A process for implementing FAIR best practices over project datasets has been developed and incrementally improved as it has been applied over (currently 12) project databases.
Two main resources (FAIR Cookbook and FAIR capability maturity model (CMM)) have been developed that will enable researchers to understand how to assess the FAIR level of datasets, assess the benefits of achieving a higher level of FAIR and follow a process and guidelines on how to actually make data sets more FAIR.
The FAIR Cookbook ( has so-called recipes enabling the FAIRification of datasets. Over 100 data professionals from academia and pharmas have contributed to over 60 public recipes ranging from documenting the selection process, ethics, components of FAIR and applied examples.
The FAIR CMM ( will help researchers determine how to reach different FAIR data maturity levels by: quantifying the investment needed to reach different levels of data discovery, accessibility, interoperability and reusability; and evaluating how FAIR data are and will become (success metrics).
Tools have also been developed to ensure that an ethics procedure is implemented throughout FAIRplus. Use of these tools ensures measures are followed when an IMI project agrees to give access to their data. Individuals involved in data stewardship, data governance and data use, need to document and respect these measures.
Developed programmes, communications and events to change and sustain the data management culture across academia, SMEs and pharmaceuticals:
To change and sustain data management culture we have taken a multifaceted approach:
1) Broadly communicated FAIRplus to update the wider community and policy makers through events and social media (;
2) Developed and run two FAIRplus Innovation and SME Forum events ( to engage with SMEs that have an interest in FAIR data processes who may be able to sustain activities beyond the end of FAIRplus. These events attract more than 150 attendees across academia, industry and SMEs.
3) Built a FAIRplus Fellowship Programme ( which was launched in 2021. The Fellowship Programme aims to educate the next generation of experts for further FAIRification of data sets within IMI projects, EFPIA partners and beyond. During the 1 year programme (started May 2021), fellows will improve the FAIR levels of their own data sets, use and contribute to the FAIR Cookbook and learn how to apply the CMM model. After completing the programme, the fellows will have the confidence to lead, advise and initiate FAIR data processes in their respective companies and organisations. The learning modules are made publicly available for reuse in the FAIR community (
4) Developed and implemented plans to sustain outputs of the FAIRplus project beyond the project itself. To note, the FAIR Cookbook has become recommended service of ELIXIR-UK and ELIXIR-Luxembourg Nodes, and it is embedded in the new tasks of the ELIXIR Interoperability Platform; also a Memorandum of Understanding is in place between FAIRplus and Pistoia Alliance to ensure uptake a value of the tools being developed within the project.
Scientists are either unaware of what the FAIR principles are or are not equipped with the knowledge to implement them. By the end of FAIRplus, we will have driven a long lasting cultural change in data management, by:
- delivering operational and scalable processes (with tools and recipes to guide data scientists) that can be applied to assess the value and decision making to make the FAIR principle work.
- taking data from 20 IMI projects to a higher level of FAIR and therefore can provide impact by increased data reuse.
- working closely with SMEs to look for opportunities to grow the SME market e.g. combining commercial data that cannot be shared with public data.
- contributing to a cohesive tools landscape, improving the interoperability between tools and reducing costs of redevelopment and tool installation.
- We will have expanded the FAIR community in Europe, through dissemination of FAIRplus activities, promotion of FAIRification as a research area, and training of researchers to improve capacity to promote FAIR data generation.

The FAIRplus project overall has increased the uptake and implementation of FAIR principles and the future sustainability of data. By prioritising datasets with the potential to generate high socio-economic impact (promoting healthy ageing, addressing chronic diseases, neurodegenerative diseases and the emergence of antibiotic resistance), we have ensured the pilots used to develop the FAIR Cookbook and actual data FAIRification has been of greatest value. This selection process will continue to ensure scientific data from selected IMI projects and EFPIA are broadly usable. In addition, the FAIR-CMM will demonstrate the value of this approach and required culture change to enable further uptake.
FAIRplus Project Aims