Baglioni M., Manghi P., Mannocci A., Bardi A.
ORCID Scholarly Communication Open Science Disambiguation Misapplication
Since 2012, the "Open Researcher and Contributor ID" organisation (ORCID) has been successfully running a worldwide registry, with the aim of "providing a unique, persistent identifier for individuals to use as they engage in research, scholarship, and innovation activities". Any service in the scholarly communication ecosystem (e.g., publishers, repositories, CRIS systems, etc.) can contribute to a non-ambiguous scholarly record by including, during metadata deposition, referrals to iDs in the ORCID registry. The OpenAIRE Research Graph is a scholarly knowledge graph that aggregates both records from the ORCID registry and publication records with ORCID referrals from publishers and repositories worldwide to yield research impact monitoring and Open Science statistics. Graph data analytics revealed "anomalies" due to ORCID registry "misapplications", caused by wrong ORCID referrals and misexploitation of the ORCID registry. Albeit these affect just a minority of ORCID records, they inevitably affect the quality of the ORCID infrastructure and may fuel the rise of detractors and scepticism about the service. In this paper, we classify and qualitatively document such misapplications, identifying five ORCID registrant-related and ORCID referral-related anomalies to raise awareness among ORCID users. We describe the current countermeasures taken by ORCID and, where applicable, provide recommendations. Finally, we elaborate on the importance of a community-steered Open Science infrastructure and the benefits this approach has brought and may bring to ORCID.
Source: Data science journal 20 (2021): 1–12. doi:10.5334/dsj- 2021-038
Publisher: Committee on Data for Science and Technology of the International Council for Science., Paris
@article{oai:it.cnr:prodotti:462265, title = {We can make a better use of ORCID: five observed misapplications}, author = {Baglioni M. and Manghi P. and Mannocci A. and Bardi A.}, publisher = {Committee on Data for Science and Technology of the International Council for Science., Paris}, doi = {10.5334/dsj-2021-038}, journal = {Data science journal}, volume = {20}, pages = {1–12}, year = {2021} }