PIDs like ORCID or DOI can be considered as the probably most important pillars to empower global progress to repository systems. Today any repository software should have a working ORCID integration to help the users to distinguish persons on a global level and another persistent identifier to address publications.

The well-known repository software DSpace has a working integration of ORCID, which has been introduced in DSpace 7.3 (June 2022). But even in the currently latest version DSpace 7.6 it’s still not complete.

DSpace-CRIS is a software, which is very much tied to DSpace, but it’s a standalone software. It is developed by 4Science and is containing a complete ORCID integration for a long time.

Hamburg University of Technology recently conducted a project in cooperation with 4Science, which was funded by ORCID in context of the Global Participation Fund. The project's goal was to improve the login process against ORCID for DSpace-CRIS.

This presentation will focus on the project results, but it will also show, how the ORCID integration in DSpace-CRIS and DSpace can be used at all. It will also explain the differences of the integration levels between DSpace and DSpace-CRIS and compare the two platforms regarding their ORCID integration.

Building a transparent Global Community Trust Network is imperative for fostering a robust and trustworthy environment for scholarly information systems worldwide. This proposal advocates for the importance of enriching ORCID records with trustworthy data as an essential step in achieving this overarching goal. By contributing to a network that encompasses the global research ecosystem, our collective efforts can significantly enhance the integrity, transparency, and accessibility of scholarly information.

Increasingly, repositories allow, recommend, or require providing unique contributor identifiers, such as ORCID or ISNI, when depositing contributions. Despite this evolution, few repositories provide a machine interface that allows listing the contributions made by a researcher using their contributor identifier as a key. An interface with this capability, supported across repositories, would facilitate a range of use cases and could be an important next step towards realizing COAR’s Next Generation Repositories vision. This presentation will introduce the core characteristics of authorIDy, a conceptual proposal for such a machine interface. The presentation is mainly aimed at soliciting feedback to inform next steps. Therefor, sufficiently in advance of the presentation, a document will be made available that gives an overview of key aspects of the proposal, which has simplicity at its core.

Many research performing institutes are adopting Rights Retention strategies to help their authors maintain copyright ownership of their work, whilst also enabling broader access and compliance with funder mandates such as Plan S. The implementation of a Rights Retention Strategy offers numerous advantages, including open access assurance, copyright retention, scholarly use regulation, enhanced dissemination, equity promotion, and facilitation of text and data mining. However, the manual incorporation of appropriate rights retention statements into article metadata is labour-intensive and time-consuming.

To address this challenge, CORE has co-designed, with repository managers, a machine learning module to automatically identify and extract rights retention statements from full-text articles, streamlining the encoding of this information within article metadata. The integration of CORE services with repository software and the expansion of data extraction capabilities are crucial steps toward promoting a more accessible, transparent and interconnected scholarly ecosystem.