Abstract
Data citations have become widely accepted. Technical infrastructures as well as principles and recommendations for data citation are in place but best practices or guidelines for their implementation are not yet available. On the other hand, the scientific climate community requests early citations on evolving data for credit, e.g. for CMIP6 (Coupled Model Intercomparison Project Phase 6). The data citation concept for CMIP6 is presented. The main challenges lie in limited resources, a strict project timeline and the dependency on changes of the data dissemination infrastructure ESGF (Earth System Grid Federation) to meet the data citation requirements. Therefore a pragmatic, flexible and extendible approach for the CMIP6 data citation service was developed, consisting of a citation for the full evolving data superset and a data cart approach for citing the concrete used data subset. This two citation approach can be implemented according to the RDA recommendations for evolving data. Because of resource constraints and missing project policies, the implementation of the second part of the citation concept is postponed to CMIP7.
Highlights
Over the last decade a rapid standardization process for data citation in parallel with data publication practices took place
The challenge for the data citation service is to close this gap in cooperation with the data infrastructure provider, the Earth System Grid Federation (ESGF, 2017), and in the timeframe given by the CMIP6 project and IPCC
The CMIP6 and IPCC-DDC AR6 data citations will automatically profit from these improved data-data, data-literature and data-researcher linkages via DataCite as hub
Summary
ARGO (2017) is one among several early implementers of these recommendations using DataCite DOIs with standard #-fragments (see section 5.8 of IDF DOI Handbook, 2017). The use of fragment identifiers is a controversial issue, because it partly contradicts the idea to separate changing URLs from persistent IDs by introducing an addition to the PID, which is interpreted server-side. Another common approach to handle evolving data is the use of full or partial snapshots of the data with individual registered PIDs (Ball and Duke, 2015). The present discussions are driven by scholarly publishers and infrastructure providers, like the European Persistent Identifier Consortium (EPIC) They concentrate more on Force11’s data citation principles for verification and unique identification than on the principle for credit and attribution. The data subset is used for the identification of the part of the data underlying an article
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