Open and FAIR Data

Open Data is data that can be freely used, re-used and redistributed. Simply making data available on a website is not enough to be part of an Open Data approach.

Data can be made available to the public on a website without being reusable. For example, because a license does not specify the conditions for re-use, or because the data is not documented or stored in a permanent infrastructure.

So the availability of data, its re-use and redistribution are the foundations of Open Data.

“Open data is data that can be freely used, re-used and redistributed by anyone – subject only, at most, to the requirement to attribute and share-alike”. 

Open Knowledge foundation, Open Data Handbook, https://opendatahandbook.org/guide/en/what-is-open-data/

The principle governing the openess of research data is: "As open as possible, as closed as necessary".

In fact, there are different degrees of data openness: from complete openness to complete closure, via various forms of restricted or controlled access.

Your datasets may be closed or access restricted if they contain for example: personal data - including sensitive data, confidential data, data protected by copyright, data with commercial value-adding potential, or data in breach of a prior commitment relating to data sharing (e.g. a consortium agreement, etc.).

Open Data is therefore not mandatory. However, the data must be "FAIR".

Open Data

Data that can be freely used, re-used and redistributed by anyone – subject only, at most, to the requirement to attribute and share-alike

Closed Data

Data that are temporarily under embargo, or that cannot be shared at all. In this case, it is often still possible to share the metadata of the data (users can find information about the dataset via its metadata)

Restricted – controlled data

Data that are not shared in a fully open way, but made available under more restricted access and use conditions (e.g. via an authentication procedure)

The FAIR principles are a set of guidelines for managing data so that they are easy to find, accessible, interoperable and reusable.

Findable : the first step in re-using data is to find them ! Data must be easy to find, both by humans and by computers. They must be in a data repository, have a persistent identifier and include metadata.

Accessible : the data and metadata are permanently available, even after the end of the project. The user knows how to access them (for example, via an identification procedure if necessary).

Interoperable : the data must be able to be used, exchanged, compared or reused in a variety of contexts. To achieve this, the data must be in a format that allows them to be combined with other data; and the user must be able to interpret them correctly.

Reusable : the reuse of data is the objective of Open Data. To achieve this, the data must be well described and documented. A clear and accessible license defines the conditions for re-use (for example, a Creative Commons licence).

Making your data “FAIR” is not the same as opening it.

Indeed, data can be FAIR but not open (for example, personal data; data shared only with restricted access).

However, to be useful (i.e. reusable), open data must be FAIR !

Publicly accessible data may lack sufficient documentation to meet the FAIR principles, such as licensing for clear reuse. So making your data freely available does not automatically mean that they can be reused.

The general principle of Open Data is: “As open as possible, as closed as necessary”. However, data must be “FAIR”.