Papers: FAIR

The heritage of the University of Barcelona is structured into several collections that have used diverse cataloguing systems until now. These collections required a unifying metadata schema that were simple yet rigorous, as most of the individuals responsible for these collections were not cataloguing specialists and did not work full-time on the collections. An application profile based on DCMI Metadata Terms was proposed to address this need and unify the different cataloguing systems. This application profile incorporates properties derived from extensive studies of LIDO and CIDOC CRM. It has been successfully applied in the Virtual Museum of the University of Barcelona from 2020 to the present

In recent years, open science has promoted the web-based publication and reuse of research data. Publishing such data typically involves selecting a domain-specific metadata schema and using repositories for distribution. In Linked Open Data (LOD) datasets, domain-specific metadata schemas like VoID and DCAT are used commonly. However, differences in metadata schemas across institutions hinder cross-domain search and reuse due to duplicated terms and semantic ambiguity. The lack of clear metadata guidelines and example references further complicates creation, decreasing interoperability. This study addresses these issues by proposing a method to enhance compatibility among metadata schemas. Specifically, it focuses on mapping LOD schemas from VoID and DCAT to DataCite metadata schema. Additionally, it explores automating conversion process by pseudocode. This approach aims to align semantics across domain-specific metadata schemas and improve the findability and interoperability of dataset metadata.

A qualitative, multi-institutional study investigating how academic libraries in the United States integrate inclusive and reparative description principles and practices into their digital collections. This paper draws on a series of interviews with metadata professionals from nine R1 institutions to assess how they approach the remediation of legacy metadata in their digital collections. The findings demonstrate that, while inclusive metadata is widely recognized as important, its implementation remains underutilized and unevenly applied.

To uniquely identify authors of intellectual works, many institutions operate distinct name identifier systems based on their respective policies and objectives. While these systems share common elements, they differ significantly in terms of format and content, often resulting in confusion in author data management and access due to the coexistence of multiple identifiers for the same individual. This study examines the characteristics of existing name identifier systems and proposes a conceptual framework to enable their semantic interlinking, building on which the Bibliographic–Metadata–Intellectual (BMI) model is introduced.

Electronic Theses and dissertations that are submitted electronically are termed generally as ETDs, and they are distributed through a variety of institutional and aggregated repositories. Even though the majority of ETD platforms adhere to the FAIR principles, which specify that data must be Findable, Accessible, Interoperable, and Reusable, these platforms frequently fail to take into account the ethical and community-centered aspects that are encapsulated in the CARE principles, which are as follows: Collective Benefit, Authority to Control, Responsibility, and Ethics. The purpose of this research is to present a novel cross-repository evaluation framework that utilises an LLM-assisted technique to bridge the gap between the FAIR and CARE principles. A rubric-based review was combined with the reasoning skills of three big language models - ChatGPT, Grok, and DeepSeek R1 - in order to conduct an evaluation of nine of the important open-access electronic text databases (ETD) repositories. A standardised rubric served as a guide for each model, and it was prompted to conduct an analysis of the quality of the metadata as well as ethical constraints. Despite the fact that the data demonstrate that FAIR compliance is resilient across repositories, they also highlight systemic weaknesses in CARE alignment, particularly with regard to cultural context, ethical reuse, and authorial control. Moreover, the comparative analysis among three agents suggests that it should be used for evaluating FAIR compliance. CARE compliance evaluation may need more sophisticated ‘Human in the Loop’ setup. This framework offers a scalable and transparent approach to analysing metadata governance. Additionally, it gives schema-agnostic recommendations for encouraging inclusive and ethical stewardship in digital academic infrastructure. These characteristics are achieved through the triangulation of assessments given by artificial intelligence.