FAIR & Open Data

Why open and share your research data ?

There are a number of reasons for sharing research data. This sharing :

  • facilitates the reproduction and verification of experiments and research carried out ;
  • makes research work and results more visible ;
  • increases the number of citations of scientific articles for which research data is also published;
  • encourages new collaborations and new avenues of research ;
  • supports the principle of scientific integrity ;
  • meets the requirements of some scientific funders and publishers.

UNIL promotes honest and responsible research that aims to manage research data in a transparent and open manner, within the limits of the law and scientific requirements in terms of ethics, deontology and compliance with standards for the protection of the individual and intellectual property.

Following the example of the European Union and its Horizon2020 programme, UNIL therefore advocates the publication of data "as open as possible, as closed as necessary".

Open source and IT code

The notion of Open source is not to be confused with Open research Data.

Open source is a transparent way to develop software and make it freely accessible to other users. The source code of a program is made available to individuals so that they can access, modify and reuse it, unlike proprietary software.

For sharing and depositing computer code, UNIL recommends the Swiss non-commercial platform c4science, an infrastructure for the co-creation, preservation, sharing and testing of scientific code. Available to the entire Swiss university community and accessible to external collaborators, this platform is hosted on SWITCHengines, managed by EPFL-SCITAS, created via EnhanceR.

For more information, please refer to the dedicated pages of the Digital Curation Center (DCC).

FAIR data principles

In general, the FAIR principles relate to openness, communication, appropriation and reuse of research data. Data need not necessarily be accessible or open to be FAIR, but openness makes sense through the FAIR principles (originally published in 2016 : Wilkinson et al., 2016).

For the Swiss National Research Foundation, the data are managed according to the FAIR principles. These principles ensure that a data set can be easily found (Findable), accessible (Accessible), interoperable (Interoperable) and reusable (Re-usable).

Download the explanations of the FAIR principles promoted by the SNSF.

The Findable and Accessible aspects focus mainly on where the data are stored. Important considerations to be taken into account include the availability of persistent DOIs, metadata, data reuse tracking, licensing, access control and long-term availability (long-term preservation).

The Interoperable and Re-usable aspects highlight the importance of considering the data format (proprietary vs. open) and how these formats may change in the future, as well as the connectivity (opening via API) of the chosen repository to other international or disciplinary meta repositories, or other enhancement tools. The aspect of detailed documentation is also a factor in the reusability of data and code.

15 principles have been laid down by FORCE11, a community of researchers, librarians, archivists, managers of scientific publications, funding institutes, etc. These principles are grouped under the 4 general principles: Easy to find, Accessible, Interoperable and Reusable.

On the basis of the 15 principles above, a set of 14 parameters have been defined to quantify the levels of FAIRness. These latest developments are available on the GO-FAIR website.

Good data management is one way to support the FAIR principles.

In practice, DMP is the natural instrument for the "FAIRification" of research, implementing data discovery, accessibility, interoperability and reuse.

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Explanations of the FAIR principles by the SNSF

Did you know ?

The European Commission has estimated the annual cost of not having FAIR data at a minimum of €10.2 billion per year !

Cost-benefit analysis for FAIR research data, 2019

Make scientific data FAIR !

All disciplines should follow geoscience and require best practices for publishing and sharing data....

Read this article published in Nature in June 2019.

Data Management Expert Guide


Find many resources on the Data Management Expert Guide to help you in the FAIR management of your data.