Research Data Management and FAIR

FAIR principles

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.

Image by SangyaPundir – CC BY-SA 4.0

FINDABLE

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.

ACCESSIBLE

Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.

INTEROPERABLE

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.

REUSABLE

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/

For researchers:

• 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

For society:

• Enable others to verify your work, increasing public trust in research
• Facilitate reuse of data in the private sector, which enhances innovation

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.

Maastricht University fosters the creation of FAIR research data.

All UM research data service providers are joint together in the Community for Data-Driven Insights (CDDI) and support researchers and research groups with all aspects concerning research data management.

This portal provides an overview and easy access to information, support and services provided by the CDDI partners.

The RDM Support @UM portal is maintained by the UM Library in close collaboration with the CDDI partners.

The Open Science Community Maastricht (OSCM) is an inter-faculty group that promotes Open Science all throughout Maastricht University. More information on the OSCM website.