RDM Support @UMFAIR and Community for Data Driven Insights
Research Data Management and FAIR
1. Introduction: what is FAIR?
In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data.
The authors intended to provide guidelines to improve the findability, accessibility, interoperability, and reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.
The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata is essential for automatic discovery of datasets and services, so this is a crucial component of the FAIRification process.
Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.
The data usually need to be integrated with other data. Also, the data need to interoperate with applications or workflows for analysis, storage, and processing.
The ultimate goal of FAIR is to optimise the reuse of data. To achieve better reuse of data, metadata and data should be well-described to be replicated and/or combined in different settings.
The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure.
You can find detailed information at go-fair.org/fair-principles/
Video credits: Martínez-Lavanchy, P.M., Hüser, F.J., Buss, M.C.H., Andersen, J.J., Begtrup, J.W. (2019). ‘FAIR Principles’. In: Holmstrand, K.F., den Boer, S.P.A., Vlachos, E., Martínez-Lavanchy, P.M., Hansen, K.K. (Eds.), Research Data Management (eLearning course). doi: 10.11581/dtu:00000049
Maastricht University and FAIR
Maastricht University endorses the principles of Open Science .
Open Science is an approach to academic research that aims to increase collaboration, both among individual researchers and between researchers and other parties. Open Science makes research more transparent, efficient, verifiable, reproducible and sustainable. The idea is that civil society organisations, patient organisations, companies and other organisations can benefit from easy access to scientific research.
One element of Open Science is FAIR data use, which seeks to make research data Findable, Accessible, Interoperable and Reusable.
Maastricht University aims to be entirely FAIR by 2023.
Benefits of FAIR data
- Increase the visibility of your research / maximise research impact
- Receive more citations and new opportunities for collaboration
- Satisfy the data-management expectations of funding agencies, journals and peers
- Save time and avoid duplication of effort by reusing an existing dataset
- More easily reuse your data for new purposes
- Improve quality through verification and validation of results
For the research community:
- Make it easier for other people to find, use and cite your data, and clarify what you expect in return
- Ensure future availability of the data
- Make data available for data-driven discovery
- Enable others to verify your work, increasing public trust in research
- Facilitate reuse of data in the private sector, which enhances innovation
PhD researcher Imogen Liu tells us about her experience with making research FAIR
PhD researcher Adam Jassem tells us about his experience with making research FAIR
Postdoctoral researcher Kody Moodley tells us about his experience with making research FAIR
PhD Candidate Chang Sun tells us about her experience with making research FAIR
3. What can you do to get started, get more information or ask a question
Contact the data steward at your faculty if you have any questions about research data management or FAIR data.
They will be happy to provide advice and support or point you to specialists who can answer your questions.
Overview of dedicated workshops for researchers.
Sign up for an RDM workshop or other training…
A lecture series organised by Open Science Community Maastricht (OSCM).
Sign up for a FAIR coffee …
4. CDDI – Community for Data-Driven Insights
All UM research data service providers are joint together in the Community for Data-Driven Insights (CDDI) to support researchers and research groups with all aspects concerning research data management.
Open Science Community
Contact & Support
This RDM Support @UM portal is maintained by the Research Support team of the UM Library. The content is provided and monitored by the CDDI partners.
For questions or support with regards to specific services, please use the contact information provided by the supplier of the service. Use the support form above for all your general questions and remarks. Or if you don’t know who to turn to for support.