"Data citation is the practice of providing a reference to data in the same way as researchers routinely provide a bibliographic reference to other scholarly resources." - ANDS(1)
The publication of data sets is becoming more and more important as a citable contribution to the research curriculum. DataCite is actively supporting this.
Being able to cite research data is important for:
- Giving credit (impact).
- Giving credit means that you thoroughly document the relation between the data and the researcher who produced it.
- Researchers kan link their research data to their ORCID-id(2) (ORCID-id (3) is a persistent author identifier).
- Citing research data is part of the Altmetrics(4) (alternative metrics) movement that states that the impact of your research is determined by the references to a wide range of research output such as data sets, software, blog posts, presentations, tweets, ect. For example by citing code.
FORCE 11(5) promotes data citation and has published a manifest listing a number of data citation principles(6) that have put the significance and the ingredients of data citation on the radar. This type of initiative influences the status quo and helps to create a culture of data citation.
In the video below the various elements involved with data citation are reviewed.
RDNL video concerning data citation; select HD-quality for the best viewing experience.
A table listing the advantages of data citation for the short and the long term is derived from 'Datacite Implementation Recommendations'(7), written by the Datacite Taskforce.
|Short-term advantages||Long-term advantages|
|Easy to locate data||Creates a publication structure that enables long-term availability of data|
|Easy to reuse and verify data||Data citation makes it easier to discover and locate data|
|Makes it easer to grant credits to the rightful data producer||Possible justification for granting subsidies|
|Promotes reproducible research||The impact of data sets and dat producers can be measured|