Access to data means that you determine who you make your data available for, how you provide access, and under which conditions.
Conducting research is often a team effort. Even before collecting the data, it is important to consider who will get access to the data, under which conditions and what permissions they will have.
If your data are personal, confidential, or contain copyrighted material, you have both a legal and ethical obligation to make sure that only the research project members can access the data during data collection and processing.
Sensitive data can be FAIR without being open. The FAIRness is made by a clear description on how access to the data can be granted e.g. for research purposes.
A lot of research is based on sensitive personal data, data protected by IPR (Intellectual Property Rights) agreements or confidential data. This means that access to the data must be managed and restricted.
One solution is to store your data on a file share platform with backup and a strictly controlled access. Christian Andreas Schultz, Department of Politics and Society, Aalborg University -- from the ISSP project tells more.
While you work, access to sensitive data is restricted to the researchers conducting the project.
To share sensitive data with others, you can anonymise (change to impersonal ID's) or de-identify (remove ID's) them - but there are many problems with this approach:
Within Carsten Brink’s research area, researchers from all over the world work together. To predict what outcome a treatment for their patients will have, they develop a model. They can base this model on the patient data at their own hospital, but in some cases, the model will require more data than their own hospital can provide. In this case, they can send their model from institute to institute, collecting results along the way. This is called Distributed Learning.
Now they can analyse a large pool of results without moving sensitive data.
Depending on the size of the project, distributed learning can be a FAIR practice to handle sensitive personal data responsibly, but it does require a substantial overhead of mapping data to a standard format.
Learn more about distributed learning here.
In Carsten Brink’s case, at the end of the research project the data will stay at his institute so that new data can be added all the time.
Other researchers can apply for access, and, if approved, they can send a model to analyse the local data. They will never get physical access to the raw data.
In Nikola Vasiljević’s case the data are not sensitive and, due to progressivism from Vestas Wind systems A/S, the data are not even confidential to protect commercial interests.
Nikola Vasiljević can share his data openly. This is how he made sure the access to his non-sensitive data would be FAIR: For the wind turbine wake project, the experimental data and associated metadata, will be uploaded to a DTU data repository that allows the scientist to give other people access to the data or to the metadata of the data. In some scientific projects, it is agreed not to share data openly. Instead, a comprehensive metadata record can explain the data and potential access.
Many institutions and public organisations have established data repositories, where scientists can upload and share their data. Projects funded by the European Union are expected to make generated data or their metadata available to the public - for example through data repositories.
To search for a suitable repository for your research data you can visit re3data.org, which is a global registry of research data repositories from different academic disciplines, FAIRsharing, which allows you to discover databases grouped by domain, species or organization, or check the links page to find more resources on data repositories.