Introducing the next Open Research London event, which will be about FAIR data.
It’s easy to agree that making research data FAIR is A Good Thing. Of course research data should be Findable, Accessible, Interoperable and Reusable. But is it imperative that all research data should be FAIR? If not, how do we identify those subsets that do need to be FAIR? What exactly do we need to do at a micro level to ensure that our data is FAIR?
Realising the aim of FAIR data in practice is challenging – it can take time and resources – and the benefits to the researcher of making their data FAIR are not always apparent. It’s important therefore to minimise any barriers to making data FAIR. Research institutions should put systems in place to make FAIR data easy to manage and should explain clearly to researchers what they need to do.
Top-down
Since the FAIR data concept was launched in 2016 there has been a great deal written and talked about it but mostly that has been pretty high-level. We are seeing more practical guidelines emerge but I believe we still need more concrete explanations for both institutions and researchers.
The EOSC Expert Group developed an overarching FAIR Action Plan, published in 2018, but stressed that there was also a need for individual countries to put in place national action plans for FAIR. The plan was called Turning FAIR into Reality and it talks about the need to create policies, build a FAIR ecosystem, develop researcher skills, provide repositories and craft incentives. It also emphasises the need for PIDs (Persistent Identifiers) and standards, and the importance of machine-actionable data management plans. Skills development was identified as a major gap to be filled.
Some of the presentations at the launch event have useful summaries if you want to learn more about the plan.
To me it all feels a bit high-level still. The plan doesn’t quite bridge the gap from the high-level ideals of FAIR data down to quotidian research practice.
Creating infrastructure
The FAIRsFAIR project (Fostering Fair Data Practices in Europe) is endeavouring to create “an overall knowledge infrastructure on academic quality data management, procedures, standards, metrics and related matters”, based on the FAIR principles.
It aims to supply “practical solutions for the use of the FAIR data principles throughout the research data life cycle”. I do like the sounds of this. Its emphasis is on “fostering FAIR data culture and the uptake of good practices in making data FAIR.”
The project started in March 2019 and is due to complete this year. Recently they released a training handbook, coordinated by Claudia Engelhardt.
Institutions, publishers, repositories
The Montreal Neurological Institute (MNI) is the first research institution to dedicate itself to Open Science. Its director, Guy Rouleau, was interviewed about MNI’s approach to open science in Genome Biology in 2017. MNI provides some practical guidelines on FAIR data for its researchers.
A recent article by JB Poline (from MNI and McGill University) and others in Neuroinformatics considers how organisations can work towards making new neuroscience data FAIR, and calls for increased international collaborative standardisation of neuroscience data to foster integration and efficient reuse of research objects.
Iain Hrynaszkiewicz and colleagues from PLOS recently published the results of a survey of researchers, quizzing them on their needs and priorities for research data sharing. Their article in Data Science Journal highlights the role of publishers and repositories and the importance of linking research data and publications.
Sharing data in a repository is key requirement for data to be FAIR. Repositories such as Dryad can help to spread awareness and best practice. Dryad has a page listing Good Data Practices.
Researchers
Some researchers support FAIR data for ethical reasons. Philippa Matthews wrote in the Journal of Global Health in 2019 about the need for FAIR data in order to help overcome the health problems caused by Hepatitis B virus.
Questions.
As a service provider in a research institution, I have these questions about FAIR:
- what do I need to put in place to support researchers?
- what should I be telling researchers they need to do?
- what skills do I need to ensure researchers have?
- do I really understand what each of the components of F-A-I-R means?
The event
Open Research London is holding a free virtual event called FAIR data in practice on Tuesday 1 February 2022, 3-4.30pm GMT.
The event will be chaired by Iain Hrynaszkiewicz, Director, Open Research Solutions at Public Library of Science (PLOS) and hosted online by the Francis Crick Institute.
There will be four short talks followed by a Q&A and discussion.
The speakers are:
- Jen Gibson (Executive Director, Dryad)
- Philippa Matthews (Group Leader at Francis Crick Institute)
- Jean-Baptiste Poline (Montreal Neurological Institute and McGill University)
- Claudia Engelhardt (Göttingen State and University Library)
They will be joined in the discussion panel by James MacRae (Head of Metabolomics platform at Francis Crick Institute)
The event is free but please register at Eventbrite.
