ORCID coverage in research institutions-Readiness for partially automated research reporting.

Reporting and presentation of research activities and outcome for research institutions in official, normative standards are more and more important and are the basis to comply with reporting duties. Institutional Current Research Information Systems (CRIS) serve as important databases or data sources for external and internal reporting, which should ideally be connected with interfaces to the operational systems for automated loading routines to extract relevant research information. This investigation evaluates whether (semi-) automated reporting using open, public research information collected via persistent identifiers (PIDs) for organizations (ROR), persons (ORCID), and research outputs (DOI) can reduce effort of reporting. For this purpose, internally maintained lists of persons to whom an ORCID record could be assigned (internal ORCID person lists) of two different German research institutions-Osnabrück University (UOS) and the non-university research institution TIB-Leibniz Information Center for Science and Technology Hannover-are used to investigate ORCID coverage in external open data sources like FREYA PID Graph (developed by DataCite), OpenAlex and ORCID itself. Additionally, for UOS a detailed analysis of discipline specific ORCID coverage is conducted. Substantial differences can be found for ORCID coverage between both institutions and for each institution regarding the various external data sources. A more detailed analysis of ORCID distribution by discipline for UOS reveals disparities by research area-internally and in external data sources. Recommendations for future actions can be derived from our results: Although the current level of coverage of researcher IDs which could automatically be mapped is still not sufficient to use persistent identifier-based extraction for standard (automated) reporting, it can already be a valuable input for institutional CRIS.

Are Conference Posters Being Cited?

We present a small case study on citations of conference posters using poster collections from both Figshare and Zenodo. The study takes into account the years 2016-2020 according to the dates of publication on the platforms. Citation data was taken from DataCite, Crossref and Dimensions. Primarily, we want to know to what extent scientific posters are being cited and thereby which impact posters potentially have on the scholarly landscape and especially on academic publications. Our data-driven analysis reveals that posters are rarely cited. Citations could only be found for 1% of the posters in our dataset. A limitation in this study however is that the impact of academic posters was not measured empirical but rather descriptive.