Martina Stockhause

and 5 more

Within the climate modeling community, the complex citation issue has been discussed for a decade in the context of research traceability and data citation/data impact. The traceability requires fine-granular information on individual datasets, whereas meaningful data impact analysis relies on data citations on large data collections of data belonging to and individual model run or to a model experiment. To this date, it is not yet possible to achieve both goals with one technical solution. Suggestions for combinations of DOIs on data collections and user-defined PID collections of data subsets across several DOIs have not been taken up (see Stockhause et al., 2013).The IPCC FAIR Guidelines introduced in the Sixth Assessment Report (AR6) aimed to enhance the transparency of the AR6 and its outcomes by documenting the figure creation process (Pirani et al., 2022). Many figures are based on large numbers of datasets hosted in various repositories. Citing every dataset in the captions is not feasible. User-defined data collections utilizing data provenance records could be included in a caption, but lack the information about the authors and funders of the individual objects required for data citation and data impact analysis.The exchange on complex citation difficulties intensified at the AGU 2020 within the Community of Practice and led to the establishment of the RDA Complex Citation Working Group (WG). The WG brings all stakeholders together. It aims to provide recommendations for citing a large number of existing objects in a way that allows to properly assign credit for individual objects.

Shelley Stall

and 26 more

Shelley Stall

and 9 more

Research data are a vital component of the scientific record. Discovering and assessing data for possible reuse in future research is challenging. The Belmont Forum has recently awarded funds to three international teams as part of a four-year Collaborative Research Action (CRA) on Science-driven e-Infrastructure Innovation (SEI) for the Enhancement of Transnational, Interdisciplinary and Transdisciplinary Data Use to improve data management practices that will increase data reuse. One of these awardees, PARSEC, comprises two interwoven strands, one focused on improving data practices for reuse and credit, and one for synthesis science. The data specialists work alongside synthesis science researchers as they determine the influence of natural protected areas on socioeconomic outcomes for local communities. They collaborate with the researchers to better understand their motivations and work practices, and to aid them in the data-related steps that need to be taken during the research lifecycle. This will ensure their data and code are FAIR-compliant and thus enhance the likelihood of their data being reused and their analyses reproducible. The PARSEC team is working with Research Data Alliance (RDA), Earth Science Information Partners (ESIP), DataCite and ORCID to build awareness of the elements required for data creators to receive credit and automated attribution for their data contributions, and the tools that will make it easier to observe usage. Credit for data is an important incentive for researchers to make their data reusable. When data are FAIR and cited, their related publications have higher visibility. We shall discuss various ways in which we are working across the science-data interface in our multi-country and multi-disciplinary working environment to improve data (and code) reuse through better management and crediting. Make your Data FAIR, Cite your Data, Get Credit, Increase Reuse and reap the rewards!