Collaborating and sharing
Open research material may be visible, searchable, reusable and citable. Reusing research data can become relevant long after a project has ended, for example, in order for others to verify the published results or in case of suspected research misconduct. Research data are valuable resources that usually require a lot of time and money to reproduce. Already collected data can therefore be used in new research contexts, known as secondary analyses.
Read more about:
- Limitations on access
- Sharing data
- Data that can be expanded
- Citing research data
- Making research data available
All research data cannot be made available. Sharing may be limited due to the presence of personal data (i.e. collections of data) in the data sets contain personal, confidential or sensitive information, or copyright material. For data generated by participants from more than one higher education institution there should be an agreement in place concerning dissemination.
The Swedish Personal Data Protection Act contains a general prohibition on the transfer of personal data to a third country, meaning a country outside the EU/EEA that is not part of the Council of Europe’s Data Protection Convention. Online publication of personal data usually requires consent; however, the Swedish Data Protection Authority may grant certain exemptions. This may be the case for a lot of the data processing that takes place abroad in international research projects and that even today requires the approval from the Data Protection Authority. This applies to cloud services as well.
Provided that nothing prevents you from sharing the data, sometimes you will have more than one opportunity to do so. The earlier you consider the alternatives the better, but because the infrastructure and practices of disseminating data often change, you will need to re-evaluate the situation during the project. A plan on how to manage the data once the project is over should be drawn up as early as possible, preferably already in the application for project funding.
Data that can be expanded
Research data can sometimes be structured in a way that enables existing datasets to be expanded by adding new data. This requires that the existing datasets include some form of metadata that describes the data. Once new data is added, the same methods and data structures are used to generate new metadata for these datasets, making the new research data compatible with the old data as well as more searchable. A common method to promote the expansion of research data is to convert it into so-called corpus datasets. There are different ways of working with corpus datasets, but they all need to be digitised and processed using software in order to generate metadata. The Humanities Lab at Lund University offers a specific server for the management of corpus datasets, and can help researchers transform their datasets into corpora and thereby promote their searchability and long-term preservation.
Citing research data
Citing data mean referring to research data in the same way as you would to journal articles, reports, conference papers and other publications. By includingpersistent information in the form of an identifier generates links between the data and publications, which helps make the data more traceable and properly cited.
Read more here:
- Why is it so important to cite data? (DataCite)
- How to cite datasets and link to publications (Digital Curation Centre)
Data that are made available through a data archive or equivalent are each given a unique identifier, such as a DOI, to facilitate citation. Citation is desirable even when the data does not have an identifier. More and more journals demand and create opportunities for citation.
Read more here:
Making research data available in a data journal
Research materials in several disciplines are published in data journals, such as Journal of Open Humanities Data (JOHD).