<p>In Earth and Biological sciences, data are often preserved and publicly available in data repositories where the data are citable by DOIs and published under a Creative Commons CC-BY license. Researchers combine many datasets across disciplines, repositories, and regions to better understand processes, patterns, and drivers. Citing these many datasets is difficult as the large number does not fit into the references section of a paper but the licenses of the datasets require that credit is given to their creators.</p><p>&#160;</p><p>The Data Citation Community of Practice (CoP) was formed to target such challenges in data citation and other scholarly work that will support indexing and measuring the impact. The CoP identified a container as a solution for large numbers of data citations that holds the citations and its internal format, which is referred to as a 'reliquary'. The existing dataset collection methods have been gathered and evaluated using concrete citation use cases. Requirements for the reliquary content have been identified and applied to the use cases. In this presentation, we will report on the current progress on an approach to building a reliquary.</p><p>&#160;</p><p>Reliquaries are an important part of enabling cross-disciplinary analysis of large amounts of data stored in many repositories. The challenge with a reliquary will be to design a method that works across diverse repositories and domain citation practices and to enhance the indexing system to direct credit to the reliquary content and authors. The CoP is in the process of setting up a Research Data Alliance (RDA) Working Group on Complex Citations in the Earth, Space, and Environmental Sciences to broaden the discussion and to find further use cases for evaluation and interested early adopters.</p>
A gap in community practice on data citation that emerged during the AGU fall meeting 2020 Data FAIR Town Hall, "Why Is Citing Data Still Hard?" with the goal of addressing the use case of citing a large number of datasets such that credit for individual datasets is assigned properly. The discussion included the concept of a "Data Collection" and the infrastructure and guidance still needed to fully implement the capability so it is easier for researchers to use and receive credit when their data are cited in this manner. Such collections of data may contain thousands to millions of elements with a citation needing to include subsets of elements potentially from multiple collections. Such citations will be crucial to enable reproducible research and credit to data and digital object creators. To address this gap, the data citation community of practice formed including members from data centres, research journals, informatics research communities, and data citation infrastructure. The community has the goal of recommending an approach that is realistic for researchers to use and for each stakeholder to implement that leverages existing infrastructure. To achieve data citation of these subsets of large data collections the concept of a "reliquary" is introduced. In this context the reliquary is a container of persistent identifiers (PIDs) or references defining the objects used in a research study. This can include any number of elements. The reliquary can then be cited as a single entity in academic publications. The reliquary concept will enable data citation use cases such as the citation of elements within a data collection that are formed from numerous underlying datasets that have their own PIDs, unambiguous citation of data used in IPCC Assessment Reports, and citing the subsets of collections of research data that contain millions of elements. The discussions over the course of 2021 have developed a theoretical concept, at the time of writing formal use cases and initial applications are being defined. The recommendation developed by this effort will be available for review and comment by communities such as ESIP and RDA. All are welcome.
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