Purpose: This study aims to design comprehensive repository-level evaluation criteria that assess data stewardship capacity, governance structures, and institutional quality control mechanisms in the context of open science and data-intensive research. Design/methodology/approach: We employed a systematic approach consisting of four stages: formulation of research questions, case study analysis, empirical investigation with expert validation, and framework development through pilot evaluation. We analyzed representative repository-related frameworks and platform-based evaluation guidelines, including the Research Data Alliance (RDA), Confederation of Open Access Repositories (COAR), Science Europe, FAIRsharing, and DSpace, to derive a set of 110 evaluation criteria elements evenly divided between mandatory and optional elements. Findings: The developed framework emphasizes repository-level stewardship, including governance, preservation, provenance tracking, integrity safeguards, and access infrastructure. Rather than directly assessing the scientific validity of individual datasets, the framework evaluates structural conditions that enable trustworthy and sustainable data management at scale. Originality: This research validates and provides comprehensive evaluation criteria for data repositories, filling the gap in previous studies. It creates a balanced framework, distinguishing between essential and additional features.
Persistent identifiers (PIDs) are global, unique, resolvable, and persistent references that have become core components of open science and FAIR research as well as their infrastructures. This article reports key findings from the first nationwide Survey on the Need for and Use of Persistent Identifiers at Research and Infrastructure Organizations in Germany. This survey is based on a cross-sectional, cross-disciplinary online survey covering more than 20 PID types and established entity categories, targeting all relevant public-sector academic and non-academic research organizations in Germany as well as European and international research infrastructure nodes headquartered in Germany. Responses from a diverse set of academic and non-academic research and infrastructure organizations provide an empirical basis for analyzing PID familiarity and use, training needs, infrastructural embedding, and strategic framework conditions. The findings indicate that PID adoption is currently concentrated on a limited set of widely established identifiers (DOI, ISBN, ORCID iD, GND, URN), whereas newer or specialized identifiers remain less recognized and are infrequently implemented. PID usage predominantly occurs within publication and research data workflows, with anticipated expansion to additional entity types such as research software and instruments. Across organizations, respondents strongly point out high-quality, interoperable metadata as a precondition for sustainable PID integration, yet many report capacity and governance constraints, including limited institutional training and guidance, insufficient expertise and personnel, and uncertainty about benefits. This survey delivers a comprehensive, empirical dataset for systematic analysis, a first in this field. A key opportunity is to enhance the efficiency and potency of managing, ensuring the traceability of, and monitoring of research activities across academic and non-academic organizations in Germany. The findings provide an evidence-based foundation for developing a national PID roadmap by highlighting the current level of usage, existing practices, and gaps. They also highlight objectives for strengthening Germany’s PID ecosystem, including better alignment of advisory and training resources with the needs of scholarly communities and the systematic integration of PIDs into digital strategies to advance Germany’s digital transition toward more interoperable national and European research infrastructures.
Cultural metadata is essential in making cultural heritage resources discoverable, accessible, and understandable. However, such metadata is often difficult to access and reuse due to its heterogeneous structure and scattered sources. Cultural heritage metadata usually contains structured information on art, literature, music, and scientific collections. It serves as a powerful research object for understanding trends, representations, and societal impacts over time. Despite the growing availability of such datasets, there remains a lack of targeted computational tools and workflows to integrate them into analytical environments. Finna is a national search platform developed by the Finnish Ministry of Culture and Education that provides unified access to millions of metadata records from Finnish libraries, museums, and archives. We introduce the finna R package, which provides a seamless interface to these services and bridges the gap between accessible cultural metadata and its computational analysis. The package allows researchers to query, retrieve, and clean Finnish cultural heritage metadata programmatically, automate workflows, and integrate the results into R for visualization and further analysis.
