Periodic Reporting for period 3 - FAIRplus (FAIRplus)
Periodo di rendicontazione: 2022-01-01 al 2022-12-31
The Innovative Medicines Initiative made a €5.3bn public-private investment, divided over more than 100 IMI projects. This initiative generated a wealth of knowledge and data that provides a huge opportunity to advance European science that should be exploited by researchers for decades. Advancements in R&D can be made by sharing and linking data from different domains. Data from IMI projects, however, is often stored in project-specific databases that provide little opportunity for reuse. Inconsistent annotation among projects - and in internal databases - is a major hurdle for integration, knowledge mining and advanced modelling. Although data management plans are now a standard component of project proposals, these are not always sufficient to enable large scale data reuse. There is still little understanding of the steps, processes or resources required to maximise the value and impact of data beyond the specifics of any individual project.
FAIRplus (fairplus-project.eu/) has developed a framework for making life science data FAIR (Findable, Accessible, Interoperable, Reusable). Through worked examples using IMI data and application and extension of existing methods, we improved 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, FAIRplus has stimulated a positive change to the data management culture, facilitating reuse of datasets by pharmaceutical companies, academia and SMEs. Our FAIR Innovation and SME programme has further enabled wide data reuse and fostered an innovation ecosystem around these data that powers future re-use, knowledge generation, and societal benefit.
FAIRplus (fairplus-project.eu/) has developed a framework for making life science data FAIR (Findable, Accessible, Interoperable, Reusable). Through worked examples using IMI data and application and extension of existing methods, we improved 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, FAIRplus has stimulated a positive change to the data management culture, facilitating reuse of datasets by pharmaceutical companies, academia and SMEs. Our FAIR Innovation and SME programme has further enabled wide data reuse and fostered an innovation ecosystem around these data that powers future re-use, knowledge generation, and societal benefit.
Through it’s framework for making data FAIR, FAIRplus has:
Established a process (including a survey and ethics review) for selecting and prioritising IMI project databases for FAIRification (https://bit.ly/3jZHMfp). These concepts have been summarised in a related recipe (see below) and publication (doi: 10.1016/j.drudis.2022.05.010) and used to triage 120 IMI projects.
Created an ethics procedure to ensure 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.
Incrementally developed a process for implementing FAIR best practices over 20 project datasets. Two main resources 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.
1) The FAIR Cookbook (https://faircookbook.elixir-europe.org) with 82 public recipes (as of 2023) enabling the FAIRification of datasets. As an example, one recipe provides generic guidance on the FAIRification processes (https://w3id.org/faircookbook/FCB079). Almost 100 professionals from academia and pharma have contributed to recipes that include documenting the selection process, ethics, components of FAIR and applied examples. Its creation and content, its value, use and adoptions, as well as the participatory process, collaborative plans for sustainability, and its adoption is detailed in a pre-print (https://doi.org/10.5281/zenodo.7156792)
2) The FAIR DSM (https://fairplus.github.io/Data-Maturity/) that can be used to guide FAIR maturity decision making by: targeting data management investment towards the capabilities needed to most effectively improve data discovery, accessibility, interoperability and reusability; and establishing success metrics for evaluating how FAIR data assets before and after investment.
Developed programmes, communications and events to change and sustain the data management culture across academia, SMEs and pharmaceuticals, taking a multifaceted approach:
1) Updating the wider community and policy makers through events and social media (https://twitter.com/FAIRplus_eu , https://www.linkedin.com/company/40786325/admin/).
2) Via three FAIRplus Innovation and SME Forum events (https://bit.ly/3HtxcJ9) 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 attracted 393 attendees across academia, industry and SMEs.
3) A one year Fellowship Programme (https://bit.ly/35lC4R0) to educate the next generation of experts for further FAIRification of data sets within IMI projects, EFPIA partners and beyond. The 15 fellows improved the FAIR levels of their own data sets, used and contributed to the FAIR Cookbook and learned how to apply the DSM model. After completing the programme, the fellows had the confidence to lead, advise and initiate FAIR data processes in their respective companies and organisations. The FAIR Fellowship e-learning materials are open and can be accessed for reuse through the ELIXIR training materials platform Tess (https://tess.elixir-europe.org/materials/fairplus-fellowship-programme).
4) Developed and implemented plans to sustain outputs of the FAIRplus project beyond the project itself. Of 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. In addition, a Memorandum of Understanding is in place between FAIRplus and Pistoia Alliance to ensure uptake of the tools being developed within the project.
Established a process (including a survey and ethics review) for selecting and prioritising IMI project databases for FAIRification (https://bit.ly/3jZHMfp). These concepts have been summarised in a related recipe (see below) and publication (doi: 10.1016/j.drudis.2022.05.010) and used to triage 120 IMI projects.
Created an ethics procedure to ensure 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.
Incrementally developed a process for implementing FAIR best practices over 20 project datasets. Two main resources 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.
1) The FAIR Cookbook (https://faircookbook.elixir-europe.org) with 82 public recipes (as of 2023) enabling the FAIRification of datasets. As an example, one recipe provides generic guidance on the FAIRification processes (https://w3id.org/faircookbook/FCB079). Almost 100 professionals from academia and pharma have contributed to recipes that include documenting the selection process, ethics, components of FAIR and applied examples. Its creation and content, its value, use and adoptions, as well as the participatory process, collaborative plans for sustainability, and its adoption is detailed in a pre-print (https://doi.org/10.5281/zenodo.7156792)
2) The FAIR DSM (https://fairplus.github.io/Data-Maturity/) that can be used to guide FAIR maturity decision making by: targeting data management investment towards the capabilities needed to most effectively improve data discovery, accessibility, interoperability and reusability; and establishing success metrics for evaluating how FAIR data assets before and after investment.
Developed programmes, communications and events to change and sustain the data management culture across academia, SMEs and pharmaceuticals, taking a multifaceted approach:
1) Updating the wider community and policy makers through events and social media (https://twitter.com/FAIRplus_eu , https://www.linkedin.com/company/40786325/admin/).
2) Via three FAIRplus Innovation and SME Forum events (https://bit.ly/3HtxcJ9) 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 attracted 393 attendees across academia, industry and SMEs.
3) A one year Fellowship Programme (https://bit.ly/35lC4R0) to educate the next generation of experts for further FAIRification of data sets within IMI projects, EFPIA partners and beyond. The 15 fellows improved the FAIR levels of their own data sets, used and contributed to the FAIR Cookbook and learned how to apply the DSM model. After completing the programme, the fellows had the confidence to lead, advise and initiate FAIR data processes in their respective companies and organisations. The FAIR Fellowship e-learning materials are open and can be accessed for reuse through the ELIXIR training materials platform Tess (https://tess.elixir-europe.org/materials/fairplus-fellowship-programme).
4) Developed and implemented plans to sustain outputs of the FAIRplus project beyond the project itself. Of 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. In addition, a Memorandum of Understanding is in place between FAIRplus and Pistoia Alliance to ensure uptake 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. FAIRplus has 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 principles work
- taking data from 20 IMI projects to a higher level of FAIR and therefore improving impact by increased data and workflow 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
- expanding 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 have 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 DSM will demonstrate the value of this approach and required culture change to enable further uptake.
- 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 principles work
- taking data from 20 IMI projects to a higher level of FAIR and therefore improving impact by increased data and workflow 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
- expanding 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 have 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 DSM will demonstrate the value of this approach and required culture change to enable further uptake.