Étiquette : research data management

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Pratiques de gestion et de partage des données de recherche. Retour d’expérience de quatre projets SHS lauréats du Prix Science Ouverte 2025

Auteur/Author : Joachim Schöpfel

Cet article propose une étude comparative de quatre projets de recherche lauréats du Prix Science Ouverte 2025 issus des sciences humaines et sociales (humanités numériques, linguistique, sociologie quantitative et longitudinale), en se concentrant sur leurs pratiques de gestion des données de recherche.

À partir d’entretiens qualitatifs, l’étude examine les types de données produites, les modalités de gestion, les responsabilités, ainsi que les stratégies de partage et de valorisation. L’analyse est structurée selon les principes FAIR (Findable, Accessible, Interoperable, Reusable). Les résultats mettent en évidence une forte hétérogénéité des pratiques, mais aussi une convergence vers une professionnalisation accrue de la gestion des données et une intégration progressive des exigences de la science ouverte.

Une attention particulière est accordée à l’impact du Prix Science Ouverte des Données de la Recherche, dont les effets se manifestent principalement en termes de reconnaissance, de visibilité et de consolidation des pratiques.

URL : https://lilloa.hal.science/hal-05634994v2

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Do data management policies become more open over time?

Author : Beth Montague-Hellen

Research data management (RDM) policies are ubiquitous in UK Higher Education Institutions, and are often written and managed by, or with, the library team. RDM policies attempt to balance the requirements of keeping data safe and secure when necessary and opening up data to allow reuse and to support research integrity.

This article uses a framework analysis approach on 134 policies to investigate whether the UK RDM policies have become more open over time in terms of policy points and language. The investigation shows that recent policies have shown an increased likelihood of being more open in several areas: how long data should be archived for, sharing of software, and the mandatory inclusion of data availability statements in journal articles.

Language around FAIR data terms have increased, as has using research integrity as a key reason to manage data according to best practices.

URL : Do data management policies become more open over time?

DOI : https://doi.org/10.53377/lq.23144

 

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Research data management services in academic libraries to support the research data life cycle: A systematic review

Authors : Richard Cheng Yong Ho, Suei Nee Wong, Patsy Chia, Chris Tang, Magdeline Tao Tao Ng

Academic libraries play an increasingly crucial role in providing services, information, education, and infrastructure support related to research data management (RDM). This systematic review aims to provide a comprehensive and critical analysis of the state of RDM services offered by academic libraries worldwide.

Utilizing the systematic review methodology, the paper examines 89 empirical studies to answer four research questions: (1) the types of RDM services implemented by academic libraries; (2) what are the infrastructure, workflow, and resources used to support these services; (3) what are the reasons for implementing these RDM services; and (4) the effectiveness of these RDM services in supporting the research data life cycle, if any.

This review highlights the critical reasons academic libraries provide RDM services and how they implemented these services through partnerships, infrastructure, and systems, and adapting to new workflows within the library.

These findings also examine the balance between institutional contexts, researchers’ needs, and library resources required to provide these RDM services. By investigating these questions, the results will provide recommendations and guidance for academic libraries interested in implementing RDM services in their own library and institutional contexts.

URL : Research data management services in academic libraries to support the research data life cycle: A systematic review

DOI : https://doi.org/10.1002/asi.70008

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Data Management Plans: a Resource to Shape Institutional Data Management Services

Authors : Willeke de Haan, Veerle Van den Eynden

At KU Leuven, a university in the Flemish region of Belgium, data management plans have become an important resource to drive and shape the development of data management support, services, and training. With 8,000 researchers and 7,000 PhD students in fundamental and applied research across a comprehensive range of disciplines, KU Leuven is the largest university in Belgium.

Public research funding is provided by the federal and regional governments, mainly via the Research Foundation Flanders (FWO) and via research funding allocated to universities based on excellence criteria through the Special Research Fund (BOF) and the Industrial Research Fund (IOF).

Since 2018, FWO and BOF-IOF incorporated data management into their policies, requiring researchers to submit Data Management Plans (DMPs) to their institutional research office. Since then, the number of DMPs that are developed each year has increased exponentially, from 150 in 2018 to nearly 700 per year now.

The Research Coordination Office at KU Leuven decided to review all DMPs to provide feedback to ensure high-quality plans. To manage the submission, monitoring, review, and preservation of this volume of DMPs efficiently, an online platform was developed that is integrated with the university’s research information systems.

Initially, the focus of the DMP review was on supporting the development of DMPs, as this was a new concept for researchers. The review process has significantly improved the quality of DMPs. Later, support shifted to provide advice on best practices in data management. Reviews of over 2600 DMPs provide a rich source of information to develop services and training.

Based on findings from DMP reviews, the IT department developed an interactive storage guide; ethical and legal compliance in research projects can be monitored; new data management training modules are developed; and a collection of example DMPs has been developed. In addition, the growing DMP collection is a rich source of information on researchers’ data practices, providing the baseline information to develop further services. Future plans include implementing artificial intelligence in DMP reviews to automate problem detection and exploring machine-actionable DMPs for an institutional data register.

URL : Data Management Plans: a Resource to Shape Institutional Data Management Services

DOI : https://doi.org/10.2218/ijdc.v19.i1.1051

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Trends and changes in academic libraries’ data management functions: A topic modeling analysis of job advertisements

Authors : Ye Yuan , A.M.K. Yanti Idaya , A. Noorhidawati, Guan Wang

In the era of open science, academic libraries have transitioned from traditional resource providers to proactive platforms that drive data integration and knowledge innovation.

This shift has led to the continuous evolution and expansion of their data management functions. This study aims to (i) track trends in academic library data management positions, (ii) identify key themes in job advertisements related to data management, and (iii) examine how these themes have evolved. Using text mining techniques, this study applied Latent Dirichlet Allocation (LDA) and TF-IDF vectorization to systematically analyze 803 job advertisements related to data management posted on the IFLA LIBJOBS platform from 1996 to 2023.

The findings reveal that the development of these positions has undergone three phases: exploration, growth, and adjustment. Four core themes in data management functions emerged: “Cataloging and Metadata Management,” “Data Services and Support,” “Research Data Management,” and “Systems Management and Maintenance.”

Over time, these themes have evolved from distinct roles to a more balanced distribution. Technological advancements, political initiatives, and shifts in the global data environment have influenced these trends. Notably, the rising demand for “Systems Management and Maintenance” highlights its critical role in ensuring data security, while the sustained need for “Cataloging and Metadata Management” underscores its foundational place in data management strategies.

Meanwhile, the steady growth of “Data Services and Support” and “Research Data Management” reflects the adaptability and strategic adjustments of academic libraries in response to the rapidly changing information landscape.

These insights offer valuable empirical evidence for library leaders and policymakers in strategic planning and capacity development, ensuring that libraries can effectively navigate the challenges of a dynamic research environment.

DOI : https://doi.org/10.1016/j.acalib.2025.103017

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FAIR GPT: A virtual consultant for research data management in ChatGPT

Authors : Renat Shigapov, Irene Schumm

FAIR GPT is a first virtual consultant in ChatGPT designed to help researchers and organizations make their data and metadata compliant with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. It provides guidance on metadata improvement, dataset organization, and repository selection.

To ensure accuracy, FAIR GPT uses external APIs to assess dataset FAIRness, retrieve controlled vocabularies, and recommend repositories, minimizing hallucination and improving precision. It also assists in creating documentation (data and software management plans, README files, and codebooks), and selecting proper licenses. This paper describes its features, applications, and limitations.

Arxiv : https://arxiv.org/abs/2410.07108

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Reproducible and Attributable Materials Science Curation Practices: A Case Study

Authors : Ye Li, Sarah Laura Wilson, Micah Altman

While small labs produce much of the fundamental experimental research in Material Science and Engineering (MSE), little is known about their data management and sharing practices and the extent to which they promote trust in, and transparency of, the published research.

In this research, we conduct a case study of a leading MSE research lab to characterize the limits of current data management and sharing practices concerning reproducibility and attribution. We systematically reconstruct the workflows, underpinning four research projects by combining interviews, document review, and digital forensics. We then apply information graph analysis and computer-assisted retrospective auditing to identify where critical research information is unavailable or at risk.

We find that while data management and sharing practices in this leading lab protect against computer and disk failure, they are insufficient to ensure reproducibility or correct attribution of work — especially when a group member withdraws before project completion.

We conclude with recommendations for adjustments to MSE data management and sharing practices to promote trustworthiness and transparency by adding lightweight automated file-level auditing and automated data transfer processes.

URL : Reproducible and Attributable Materials Science Curation Practices: A Case Study

DOI : https://doi.org/10.2218/ijdc.v18i1.940