Not all data can be openly shared, so make sure that you really can share your data. Many factors affect whether or not you can share your data. If you work in a larger research group, all co-authors must agree to data sharing. If you use third-party data, you need their author’s permission to share. This permission can be indicated through an assigned license, or you can negotiate the terms of reuse and sharing directly with the author of the dataset. If your data include personal information, you need to either remove the information from the dataset or anonymise it, or you need to have a consent to share it. Data associated with national security typically cannot be shared. In some cases, it might not even be possible to share metadata records about their existence.
You should always share documentation along with your data (e.g., in the form of a Read-me file) to help users understand your data and to reduce the risk of misinterpretation. The form of the documentation will depend on the type of data that you are sharing; it could be a list of variables and their description, aggregated demographic data of research participants, comments included in a computer script and so on. When compiling the documentation for your data, think of your future self – what information would you need to interpret your data if you were to revisit them in, say, ten years?
When sharing research data, it is recommended to assign an appropriate license to them, so that users will know what they can and cannot do with your data. The most commonly used for these purposes are the Creative Commons licenses, which enable authors to grant some rights to potential re-users while reserving others. You can also apply a custom license to your data that better suits your needs. It is also possible to share the data under multiple licenses, such as free for non-commercial use and available for commercial use at a fee. If your dataset is not subject to copyright protection, it is recommended to label it with a Public Domain Mark, so that the users know that the data can be reused without further restrictions.
To make your data more findable (according to theFAIR principles principles), they should be assigned a persistent identifier like DOI or handle. Typically, authors cannot assign a persistent identifier to the data themselves, this requires a service authorised to assign identifiers. For datasets, some repositories offer this service, so when selecting a suitable repository, make sure that it provides a persistent identifier to your dataset.
To facilitate the citation of your data, include a recommended citation format alongside the data. This serves two purposes: it reminds users that proper citation is needed when using your data, and it makes it easier for them to cite the data. Some repositories may include a recommended citation format on the landing page of your dataset based on the metadata that you provided. Alternatively, you can include the recommended citation format in the documentation.
Residency, Invoicing and Correspondence Address
Charles University
Central Library
Ovocný trh 560/5
116 36 Prague 1
Czech Republic
Office Address
José Martího 2 (2nd floor)
160 00 Prague 6
Phone: +420 224 491 839, 172
E-mail: openscience@cuni.cz
Www: openscience.cuni.cz
