Compilation of Recommendations

Details

Short Title

2. Persistent identifiers for datasets must support multiple levels of granularity, where appropriate.

Source Documnent

A data citation roadmap for scholarly data repositories

Source Document Link

https://doi.org/10.1038/s41597-019-0031-8

Publishing Organisation

FORCE11.org and BioCADDIE Data Citation implementation Pilot Repositories Expert Group

Date of Publication

2021-04-10

Topic

Discovery/ indexing/ search, Interlinking/ interoperability

Addressed Stakeholders

data service providers

Keywords

PID, granularity, identifier

Text

Persistent identifiers for datasets must support multiple levels of granu- larity to support both the citation of a specific version and/or individual dataset, as well the citation of an unspec- ified version of a dataset and/or a collection of primary data. The levels of granularity supported by persistent identifiers must be documented. In many domains, primary data is uniquely identified and cited as a collection of potentially many individ- ual items. At the same time, these individual items need their own unique identifiers to support later reuse and recombination into different sets while maintaining the ability to cite the constituent data elements. An example is in the field of neuroimaging, where individual subject scans using a given imaging modality are the lowest level at which objects will be identified, while the primary publication will cite a collection level unique identifier. This imposes a requirement that lower-level identifiers need to be able to be grouped via a collection identifier and accessed as set elements from the overall collection landing page 18. Another example is the BioStudies data- base, which can provide storage for all the underlying data links and files for a publication. Only in circumstances where multiple levels do not inherently exist in the data, i.e. no collections or other groupings exist, may this requirement be waived.