Discover, reuse and cite

Many researchers will not create their own data but will re-using others' data in new and imaginative ways to carry out further research.

Many researchers will have concerns about using research data - the University of Glasgow have produced a guide that will answer many of these concerns  Burrow, S. , Margoni, T.  and McCutcheon, V.  (2018) Information Guide: FAQ: Using Research Data. CREATe, University of Glasgow, 2018. Documentation. University of Glasgow. Published under a Creative Commons Attribution Licence – CC-BY 4.0

Here are some sources of data that you can re-use

UK Data Service/ UK Data Archive Service

Their primary aim is to provide users with seamless and flexible access to the UK’s largest collection of social, economic and population data resources. You need to register to use the service. hey have lots of different types of data available – see their website for further information.

Google Dataset Search

Google Dataset Search can help researchers locate online data that is freely available for use. It locates files and databases on the basis of how their owners have classified them. It does not read the content of the files themselves as search engines do for web pages. This means that metadata is extremely important in order for people to find datasets. 

For further sources of data please refer to our LibGuide on Research Data

More resources

The UK DATA Service has a YouTube channel where they have lots of videos and webinars available, and they also offer guides on particular datasets, topics and methodologies and software. In addition, the Service provides a range of Data Skills Modules.

Citing data

Why is it important to cite Data?

Books and journal articles have long benefited from an infrastructure that makes them easy to cite, a key element in the process of research and academic discourse. We believe that you should cite data in just the same way that you can cite other sources of information, such as articles and books.

DataCite DOIs help further research and assures reliable, predictable, and unambiguous access to research data in order to:

  • support proper attribution and credit
  • support collaboration and reuse of data
  • enable reproducibility of findings
  • foster faster and more efficient research progress, and
  • provide the means to share data with future researchers

DataCite also looks to community practices that provide data citation guidance. The Joint Declaration of Data Citation Principles is a set of guiding principles for data within scholarly literature, another dataset, or any other research object (Data Citation Synthesis Group 2014). The FAIR Guiding Principles provide a guideline for those that want to enhance reuse of their data (Wilkinson 2016).

 

Data Citation Examples

We recognise that the challenges associated with data publication vary across disciplines, and we encourage research communities to develop citation systems that work well for them. Our recommended format for data citation is as follows:

Creator (PublicationYear). Title. Publisher. Identifier

It may also be desirable to include information about two optional properties, Version and ResourceType (as appropriate). If so, the recommended form is as follows:

Creator (PublicationYear). Title. Version. Publisher. ResourceType. Identifier

Data Citation Synthesis Group (2014). Joint Declaration of Data Citation Principles. Martone M. (ed.) San Diego CA: FORCE11 https://www.force11.org/group/joint-declaration-data-citation-principles-final

Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., … Bourne, P. E. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18