Share and publish research data
There are many benefits to sharing research data, but there are also several things to consider before sharing data.
Considerations before sharing data
Sharing your data has many benefits. Many researchers share data during the research phase with different collaborators. But it is increasingly common to also share your data publicly when the research is about to be published. However, there are some legal and ethical considerations before making your data public. If your data contains any of the following, you cannot share it openly.
- Material that is confined by secrecy according to OSL (2009:400).
- Sensitive personal data.
- Material that is copyright protected by others.
- Material that includes company secrets or financial information.
Why you should share data
“… science moves faster in an open world” / Steven Salzberg, Director of the center for Computational Biology, Johns Hopkins University
- Sharing data makes it possible to reuse in other research projects. Sharing enables others to confirm findings and makes the research process more transparent.
- Sharing data can create further opportunities for recognition and acknowledgements, e.g. citations and registered downloads for datasets.
- Sharing data can lead to new possibilities for collaboration between research groups, nationally and internationally.
- By sharing your data in a trusted repository, you will have a secure copy available in a safe place.
How to share data
The FAIR principles are general principles to keep in mind when sharing data. FAIR is an acronym for Findable, Accessible, Interoperable and Reusable, and the by adhering to these principles you will make finding and reusing your data as easy as possible.
If you describe your data with relevant metadata you make it easier for others to find and reuse your data sets. Use fairsharing.org to find relevant metadata standards . You should also use persistent identifiers to enable access to your data in the long term. Examples of persistent identifiers are DOI (digital object identifier) , URI (uniform resource identifier) and ORCID (Open researcher and contributer ID) .
Licensing is another important issue for data sharing. By explicitly applying a license to your data, you tell potential re-users what they can and cannot do. You can read more about licensing on our page on Legal aspects regarding research data .
Linking between publication, data and possibly source code is an important part of a strategy to maximize the visibility and increase the reproducibility of your research.
Where to share data
There are many datarepositories and other platforms for data sharing. There are disciplinary repositories that cover certain types of data, or data from specific research areas. Another option is a more general data portal. If you don’t know where to share your data, one of the two repositories below can be a good option.
KTH-Zenodo community : Zenodo is created by CERN och OpenAIRE. You can create a Zenodo account and upload datasets up to 50 GB per dataset, with optional increased storage. You can have versioning if you update data/source code, and you may also describe data with restricted access. You can connect your data in Zenodo with both your publication (by connecting your ORCiD) and your source code (from GitHub). KTH researchers can describe and upload their data to the KTH-Zenodo community . Your data will be assigned a DOI, get associated with KTH. The KTH research data office will review the metadata quality of all data uploaded to the KTH-Zenodo community. If you also create source code as part of your research, you can describe the source code in Zenodo. The DOI assigned to it can point to source code uploaded to GitHub, increasing the visibility and the citability of the source code. Read more about how to make your GitHub code citable.
SND: The Swedish National Data Service is a Swedish research infrastructure backed by a consortium of Swedish universities. SND aims to support the accessibility, preservation, and reuse of research data. The KTH research data office will review the meta data quality before data is published. You can describe your data following the FAIR principles, even in cases when the data can’t be made available to download. Registering your data with SND is a good choice if your data consists of a collection of several related datasets, or if your data is confidential but you still need to describe them with the FAIR principles in mind.
You find other data repositories via Re3data.org , where you can browse and filter for subject field, location, certification, metadata standards and more. There are also European initiatives to collect services for digital research infrastructure in the European Open Science Cloud .
In some disciplines, there are also data journals that publish articles focusing on data description and data publishing.
