As digital repositories evolve at the intersection of people, practice, and emerging technologies, the burden of manual metadata entry remains a significant barrier to the timely dissemination of open research. This paper presents a novel integration for the DSpace platform designed to streamline the submission process through automated metadata extraction. The proposed functionality leverages an external API powered by Artificial Intelligence (AI) to analyze uploaded documents in real-time. By identifying and mapping key bibliographic data directly from the file content, the system automatically populates submission forms, reducing human error and cognitive load for depositors. Central to this development are two critical considerations: interoperability and privacy. The architecture utilizes a flexible API framework that allows the repository to request services from various external providers, ensuring the system remains adaptable to future technological shifts. Furthermore, the integration is built with a "privacy-by-design" approach, ensuring that sensitive file data is handled securely during the AI analysis phase. By automating the "practice" of data entry, this feature moves us closer to an "Open to All" ecosystem where researchers can focus on dissemination rather than administration, ultimately fostering a more efficient and inclusive repository environment.
Carvalho et al. (Wed,) studied this question.
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