Archives des mots-clés : research data management

Why Won’t They Just Adopt Good Research Data Management Practices? An Exploration of Research Teams and Librarians’ Role in Facilitating RDM Adoption

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Authors: Clara Llebot, Hannah Gascho Rempe

Adoption of good research data management practices is increasingly important for research teams. Despite the work the research community has done to define best data management practices, these practices are still difficult to adopt for many research teams.

Universities all around the world have been offering Research Data Services to help their research groups, and libraries are usually an important part of these services. A better understanding of the pressures and factors that affect research teams may help librarians serve these groups more effectively.

The social interactions between the members of a research team are a key element that influences the likelihood of a research group successfully adopting best practices in data management.

In this article we adapt the Unified Theory of the Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, Davis, & Davis, 2003) to explain the variables that can influence whether new and better, data management practices will be adopted by a research group.

We describe six moderating variables: size of the team, disciplinary culture, group culture and leadership, team heterogeneity, funder, and dataset decisions.

We also develop three research group personas as a way of navigating the UTAUT model, and as a tool Research Data Services practitioners can use to target interactions between librarians and research groups to make them more effective.

URL : Why Won’t They Just Adopt Good Research Data Management Practices? An Exploration of Research Teams and Librarians’ Role in Facilitating RDM Adoption

DOI : https://doi.org/10.7710/2162-3309.2321

Ouverture des données de recherche dans le domaine académique suisse : outils pour le choix d’une stratégie institutionnelle en matière de dépôt de données

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Auteur/Author : Marielle Guirlet

Le contexte actuel de l’Open Science se traduit par des exigences d’ouverture des données de recherche. Le dépôt de données est un instrument crucial pour partager publiquement ces données.

Néanmoins, l’offre actuelle pléthorique et très diverse rend la sélection du dépôt difficile pour les chercheurs et les chercheuses. Pour les aider, leurs institutions d’affiliation émettent des recommandations pour le choix du meilleur dépôt. Elles proposent parfois aussi leur propre dépôt de données ou envisagent de le créer.

Cette étude, basée sur un travail de Master en sciences de l’information, s’intéresse à la démarche que les institutions académiques suisses peuvent suivre pour définir leur stratégie de soutien aux chercheurs et aux chercheuses en termes de dépôt.

Elle identifie aussi les informations qui vont aider ces institutions à choisir entre orienter ces chercheurs et ces chercheuses vers un dépôt existant (et lequel) et créer un nouveau dépôt, et aux spécifications que ce dépôt doit remplir.

Après avoir défini les concepts des données de recherche et des dépôts ouverts, les fonctionnalités, les outils et les services nécessaires à un dépôt pour mettre en œuvre le partage public de données sont discutés.

A partir des critères utilisés par la certification CoreTrustSeal pour évaluer la qualité d’un dépôt, et en tenant compte de ces fonctionnalités, de ces outils et ces services, un modèle de description d’un dépôt de données de recherche ouvertes est élaboré. Ce modèle peut être utilisé pour l’évaluation d’un dépôt existant ou pour la conception d’un nouveau dépôt.

Les stratégies de neuf institutions académiques suisses en matière de dépôt de données de recherche, dépôts utilisés et dépôts recommandés, sont analysées. Des recommandations sont formulées, sur la base des bonnes pratiques observées.

Des outils développés pour le choix de la meilleure stratégie en termes de dépôt de données de recherche ouvertes sont alors présentés. Un vade-mecum se présentant comme une liste de questions permet de collecter certaines informations utiles.

Un guide décisionnel accompagne l’institution dans sa réflexion et lui permet de choisir sa stratégie de façon éclairée, avec les informations collectées précédemment. Une fois cette stratégie choisie, des informations complémentaires et des recommandations sont disponibles pour sa mise en pratique.

Une version prototype de ces outils pour navigateur Internet est aussi présentée. Elle est adaptable à une évolution du contexte et transposable à d’autres pays.

URL : http://www.ressi.ch/num21/article182

From Conceptualization to Implementation: FAIR Assessment of Research Data Objects

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Authors: Anusuriya Devaraju, Mustapha Mokrane, Linas Cepinskas, Robert Huber, Patricia Herterich, Jerry de Vries, Vesa Akerman, Hervé L’Hours, Joy Davidson, Michael Diepenbroek

Funders and policy makers have strongly recommended the uptake of the FAIR principles in scientific data management. Several initiatives are working on the implementation of the principles and standardized applications to systematically evaluate data FAIRness.

This paper presents practical solutions, namely metrics and tools, developed by the FAIRsFAIR project to pilot the FAIR assessment of research data objects in trustworthy data repositories. The metrics are mainly built on the indicators developed by the RDA FAIR Data Maturity Model Working Group.

The tools’ design and evaluation followed an iterative process. We present two applications of the metrics: an awareness-raising self-assessment tool and an automated FAIR data assessment tool.

Initial results of testing the tools with researchers and data repositories are discussed, and future improvements suggested including the next steps to enable FAIR data assessment in the broader research data ecosystem.

URL : From Conceptualization to Implementation: FAIR Assessment of Research Data Objects

DOI : http://doi.org/10.5334/dsj-2021-004

An overview of biomedical platforms for managing research data

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Authors : Vivek Navale, Denis von Kaeppler, Matthew McAuliffe

Biomedical platforms provide the hardware and software to securely ingest, process, validate, curate, store, and share data. Many large-scale biomedical platforms use secure cloud computing technology for analyzing, integrating, and storing phenotypic, clinical, and genomic data. Several web-based platforms are available for researchers to access services and tools for biomedical research.

The use of bio-containers can facilitate the integration of bioinformatics software with various data analysis pipelines. Adoption of Common Data Models, Common Data Elements, and Ontologies can increase the likelihood of data reuse. Managing biomedical Big Data will require the development of strategies that can efficiently leverage public cloud computing resources.

The use of the research community developed standards for data collection can foster the development of machine learning methods for data processing and analysis. Increasingly platforms will need to support the integration of data from multiple disease area research.

URL : An overview of biomedical platforms for managing research data

DOI : https://doi.org/10.1007/s42488-020-00040-0

Research data management and data sharing behaviour of university researchers

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Authors : Yurdagül Ünal, Gobinda Chowdhury, Serap Kurbanoğlu, Joumana Boustany, Geoff Walton

Introduction

The aim of this study is to understand how university researchers behave in the context of using and sharing research data in OA mode.

Method

An online questionnaire survey was conducted amongst academics and researchers in three countries – UK, France and Turkey. There were 26 questions to collect data on: researcher information, e.g. discipline, gender and experience; data sharing practices, concerns; familiarity with data management practices; and policies/challenges including knowledge of metadata and training.

Analysis

SPSS was used to analyse the dataset, and Chi-Square tests, at 0.05 significance level, were conducted to find out association between researchers’ behaviour in data sharing and different areas of research data management (RDM).

Findings

Findings show that OA is still not common amongst researchers. Data ethics and legal issues appear to be the most significant concerns for researchers. Most researchers have not received any training in RDM such as data management planning metadata, or file naming. However, most researchers would welcome formal training in different aspects of RDM.

Conclusion

This study indicates directions for further research to understand the disciplinary differences in researchers’ data access and management behaviour so that appropriate training and advocacy programmes can be developed to promote OA to research data.

URL : http://www.informationr.net/ir/24-1/isic2018/isic1818.html

A Review of Open Research Data Policies and Practices in China

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Authors: Lili Zhang, Robert R. Downs, Jianhui Li, Liangming Wen, Chengzan Li

This paper initially conducts a literature review and content analysis of the open research data policies in China. Next, a series of exemplars describe data practices to promote and enable the use of open research data, including open data practices in research programs, data repositories, data journals, and citizen science.

Moreover, the top four driving forces are identified and analyzed along with their responsible guiding work. In addition, the “landscape of open research data ecology in China” is derived from the literature review and from observations of actual cases, where the interaction and mutual development of data policies, data programs, and data practices are recognized.

Finally, future trends of research data practices within China and internationally are discussed. We hope the analysis provides perspective on current open data practices in China along with insight into the need for additional research on scientific data sharing and management.

URL : A Review of Open Research Data Policies and Practices in China

DOI : http://doi.org/10.5334/dsj-2021-003

Research Data Management (RDM) at the University of Ghana (UG) : Myth or Reality?

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Author : Bright Kwaku Avuglah

This article explores Research Data Management (RDM) at the University of Ghana (UG). It emphasises on institutional awareness and attitudes, and whether the University Library is officially supporting this emerging strategic interest in research focused Higher Education Institutions (HEIs).

Purposive sampling was used to select information-rich respondents from across the University (i.e. Librarians, Research Administrators, ICT Managers and Senior Researchers) who were interviewed on a range of issues about RDM.

Institutional documents were also reviewed to corroborate the primary data and get a deeper understanding of the research problem. The study shows that while RDM is recognised at the institutional level as good research practice and integrity issue, the concept is tenuously understood in the local community.

Unsurprisingly, however, there was a general appreciation and awareness of the need for RDM and the implications for such critical concerns as security, integrity, continuity and institutional reputation.

The library is yet to take a strategic approach to RDM issues and there is clearly a dearth in RDM expertise within the library system.

The study recommends that the library must be proactive in advocating and promoting RDM issues at UG, but first, the Librarians must take advantage of numerous existing opportunities to build their capacity.

URL : Research Data Management (RDM) at the University of Ghana (UG) : Myth or Reality?

DOI : https://doi.org/10.2218/ijdc.v15i1.670