Tab Content: Share data with colleagues
In a collaborative research project it is possible that a number of individuals may require access to the data — potentially with different privileges to read, write, update or delete.
How can I collaborate on research data?
Is your data fit for sharing?
How and with whom you share your data depends on the nature of your data: does it include personal data, is it confidential or in any way restricted?
There are a number of options available to staff and students when deciding what to use for file storage or file transfer. Some of these options will be limited based on circumstance and the classification of information you are dealing with.
More information is available on security of data and information
How can I store, share and collaborate?
Various University services are available for securely storing, sharing, collaborating on and publishing files. More information is available about which service to use under different circumstances.
If you need to share data with colleagues outside Lancaster, we recommend using Microsoft OneDrive or Teams
What is OneDrive?
OneDrive is Lancaster University’s solution for secure online storage. OneDrive is an enterprise cloud storage solution. It is available to all members of the University. OneDrive uses high-grade encryption to secure data, both in transit and at rest. Files are owned by individuals and others can be invited to folders as collaborators if desired. Files can also be shared using links. OneDrive meets the requirements of GDPR and the UK Data Protection Act 2018.
Can I use OneDrive to store and share my working data?
Using OneDrive is a good solution for sharing data with colleagues within and outside of the University. There are a number of ways that data can be shared with external parties using OneDrive, and it is the data owner’s responsibility to ensure that this is done correctly and in accordance with the data type.
More information about how to use OneDrive is available.
What is Microsoft Teams?
Teams is designed for wider collaboration beyond just files, including on-going conversations, collaborating on planning, linking to other content such as web content and holding online meetings. It allows groups to be formed across departmental boundaries to collaborate. Files stored in Teams aren't owned by any single user but can be updated, discussed and distributed by all members of the Team.
For more information about Teams see Microsoft Teams help and training.
Tab Content: Access other data
Whether you want to find data to reuse for your research, or archive your own data for the long-term, you'll need to know what data archive services are available in your field.
You might like to build your research on data already available or to complement or enrich your own research.
There are many data services available, depending on your research area, and the list is growing. Here are some links to places you can search for data across a number of archives, and places you can find lists of specific archives to search.
Funding bodies with their own data centres
Other data centres or directories
- Data.gov.uk, UK government public data
- OpenDOAR, the Directory of Open Access Repositories
- Registry of Research Data Repositories, a catalogue of research data repositories
- DataCite, an international not-for-profit organisation which aims to improve visibility of data
- Figshare, a general purpose repository including data sets
- Zenodo, another general purpose repository for all fields of science. Zenodo accepts closed access uploads.
Use of third party data
If you use data owned by a third party (copyright material, software or database), you need to understand the terms under which these are obtained and the scope of use. It is necessary to obtain permission from the data owner for re-use of such material, unless conditions of re-use have been explicitly indicated, for example, with a Creative Commons licence.
Please note that you might have to specify the use of existing data in your data management plan.
Tab Content: Data citation
Just as researchers routinely provide a bibliographic reference to sources such as journal articles, reports and conference papers, data citation is the practice of providing reference to datasets.
Why cite data?
Researchers should cite data in just the same way that you can cite other sources of information, such as articles and books.
Data citation can help by:
- Enabling easy reuse and verification of data;
- Allowing the impact of data to be tracked; and
- Creating a scholarly structure that recognises and rewards data producers.
Is data citation the same as citing published papers?
While there are established conventions for citing published papers, the accepted forms and content of data citations are not always as clear, especially when the data are published online. Conventions are expected to solidify as more and more data become available online.
In terms of where a citation of data should be put there are two main places: within the text of the article and in the reference list. Within the text of the article, the citation should provide sufficient information to identify the data citation in the reference list.
There is no consensus on format and components for citations of electronic data. Emerging conventions vary by discipline, but there are common elements within these conventions.
Common elements of data citation
Lancaster University recommends using a recognised citation approach such as that of DataCite. The suggested format of DataCite is:
- Creator(s) (The main researchers involved in producing the data, or the authors of the publication, in priority order)
- Publication Year (the date when the dataset was published or released rather than the collection or coverage date)
- Title (including the edition or version number, if applicable)
- Publisher (the Data Centre or University/Institute that holds, archives, publishes, prints, distributes, releases, issues, or produces the resource)
- Identifier (For citation purposes DataCite recommends using a Digital Object Identifier (DOI), a linkable, permanent URL)
Data citation examples
Examples taken from DataCite.
Citation formatting service
Use the DOI Citation Formatter, a service created in collaboration with CrossRef, to format your citation. This will ensure you adopt the correct format for your needs.
Challenges of data citation
- Valuable datasets are often those which are long term, and still being updated. How is it possible to cite a dataset that is still being changed and added to (a "dynamic dataset")?
- Datasets can be enormous and may have hundreds or thousands of collaborators. If you only want to cite a smaller subsection (a "microcitation"), it is often difficult to identify the authors who need credit.
Such challenges are currently being discussed by interested parties and stakeholders such the Research Data Alliance’s Data Citation Working Group or the CODATA Task Group on Data Citation.