Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1808.03434

Monitoring compliance with governmental and institutional open access policies across Spanish universities

Reme Melero·David Melero-Fuentes·Josep-Manuel Rodriguez-Gairin
View original source ↗Read source document ↗Export metadata ↓

Abstract

Universities and research centers in Spain are subject to a national open access (OA) mandate and to their own OA institutional policies, if any, but compliance with these requirements has not been fully monitored yet. We studied the degree of OA archiving of publications of 28 universities within the period 2012-2014. Of these, 12 have an institutional OA mandate, 9 do not require but request or encourage OA of scholarly outputs, and 7 do not have a formal OA statement but are well known for their support of the OA movement. The potential OA rate was calculated according to the publisher open access policies indicated in Sherpa/Romeo directory. The universities showed an asymmetric distribution of 1% to 63% of articles archived in repositories that matched those indexed by the Web of Science in the same period, of which 1% to 35% were OA and the rest were closed access. For articles on work carried out with public funding and subject to the Spanish Science law, the percentage was similar or slightly higher. However, the analysis of potential OA showed that the figure could have reached 80% in some cases. This means that the real proportion of articles in OA is far below what it could potentially be.

Explore related subjects

cs.DL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Reme Melero, David Melero-Fuentes, Josep-Manuel Rodriguez-Gairin. 2018-08-10. Monitoring compliance with governmental and institutional open access policies across Spanish universities. https://doi.org/10.3145/epi.2018.jul.15

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

FMMD: A multimodal multidisciplinary dataset of open peer reviews from F1000Research

Automated scholarly paper review (ASPR) has entered the coexistence phase with traditional peer review, where artificial intelligence (AI) systems are increasingly incorporated into real-world manuscript evaluation. In parallel, research on automated and AI-assisted peer review has proliferated. Despite this momentum, empirical progress remains constrained by several critical limitations in existing datasets. While reviewers routinely evaluate figures, tables, and complex layouts to assess scientific claims, most existing datasets remain overwhelmingly text-centric. This bias is reinforced by a narrow focus on data from computer science publications. Furthermore, existing datasets rarely preserve precise alignment between review comments and specific manuscript versions, obscuring the iterative relationship between peer review and manuscript evolution. In response, we introduce FMMD, a multimodal and multidisciplinary open peer review dataset curated from F1000Research. The dataset addresses the current limitations by integrating manuscript-level visual and structural data with version-specific reviewer reports and editorial decisions. By explicitly aligning review comments with the exact article version under review, FMMD enables granular analysis of the peer review lifecycle. Importantly, its coverage of F1000Research extends ASPR research beyond its traditional focus on computer science to a diverse range of scientific disciplines. FMMD supports a range of research tasks, including visual-semantic consistency classification, figure-related review comment generation, and editorial decision prediction based on multimodal manuscript inputs, thereby providing a comprehensive empirical resource for developing and evaluating multimodal ASPR systems and advancing peer review research.

cs.DL↗
arXiv2026

GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement

With the rapid pace of AI research and the hundreds of daily new publications, staying up-to-date with the latest developments has become increasingly difficult. For researchers, quickly identifying impactful work is essential, yet manually reviewing each new publication is impractical. Automated impact prediction methods help address this challenge, usually by combining various information sources available, such as a paper's content or citation history. In this work, we propose using GitHub engagement as an additional source and demonstrate that it provides both a timely and accurate signal. To this end, we introduce GitScholar, a novel dataset that links GitHub activity from 444,000 repositories to over 558,000 AI arXiv papers. Our experiments show that GitHub reactions improve early prediction precision by up to 12% over a strong academic baseline. Additionally, we find that GitHub signal offers near-complete coverage of high-impact AI papers, and consistently correlates with future academic success. GitScholar is publicly available at https://huggingface.co/datasets/huawei-csl/GitScholar.

cs.DL↗
arXiv2026

The illusion of neutrality in metric-based research evaluation

Venue prestige and citation counts are two widely used, albeit imperfect, signals of research quality. When the two signals conflict, evaluators must decide how much weight to assign each. Yet, it remains unknown how researchers across disciplines trade off these two signals when evaluating research outcome. To fill this gap, we surveyed 869 researchers using paired choices between hypothetical departmental hiring rules that assigned different weights to venue prestige and citation counts, asking which would produce better science. Among 795 respondents whose choices were largely internally consistent, choices placed nearly equal aggregate weight on the two signals. This apparent balance concealed substantial individual heterogeneity: nearly two in five respondents occupied the most venue-heavy or citation-heavy intervals. Moreover, respondents on average chose more citation-heavy rules than they believed their departments used in hiring. By revealing the subjective judgments that arise when research indicators conflict, our findings reinforce calls for greater caution when quantitative indicators are used to evaluate research.

cs.DL↗