Archives des mots-clés : research data management

Assessment of and Response to Data Needs of Clinical and Translational Science Researchers and Beyond

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Objective and Setting

As universities and libraries grapple with data management and “big data,” the need for data management solutions across disciplines is particularly relevant in clinical and translational science (CTS) research, which is designed to traverse disciplinary and institutional boundaries.

At the University of Florida Health Science Center Library, a team of librarians undertook an assessment of the research data management needs of CTS researchers, including an online assessment and follow-up one-on-one interviews.

Design and Methods

The 20-question online assessment was distributed to all investigators affiliated with UF’s Clinical and Translational Science Institute (CTSI) and 59 investigators responded. Follow-up in-depth interviews were conducted with nine faculty and staff members.

Results

Results indicate that UF’s CTS researchers have diverse data management needs that are often specific to their discipline or current research project and span the data lifecycle. A common theme in responses was the need for consistent data management training, particularly for graduate students; this led to localized training within the Health Science Center and CTSI, as well as campus-wide training.

Another campus-wide outcome was the creation of an action-oriented Data Management/Curation Task Force, led by the libraries and with participation from Research Computing and the Office of Research.

Conclusions

Initiating conversations with affected stakeholders and campus leadership about best practices in data management and implications for institutional policy shows the library’s proactive leadership and furthers our goal to provide concrete guidance to our users in this area.

URL : Assessment of and Response to Data Needs of Clinical and Translational Science Researchers and Beyond

Alternative location : http://escholarship.umassmed.edu/jeslib/vol5/iss1/2/

Data Management Plan Requirements for Campus Grant Competitions: Opportunities for Research Data Services Assessment and Outreach

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Objective

To examine the effects of research data services (RDS) on the quality of data management plans (DMPs) required for a campus-level faculty grant competition, as well as to explore opportunities that the local DMP requirement presented for RDS outreach.

Methods

Nine reviewers each scored a randomly assigned portion of DMPs from 82 competition proposals. Each DMP was scored by three reviewers, and the three scores were averaged together to obtain the final score. Interrater reliability was measured using intraclass correlation.

Unpaired t-tests were used to compare mean DMP scores for faculty who utilized RDS services with those who did not. Unpaired t-tests were also used to compare mean DMP scores for proposals that were funded with proposals that were not funded. One-way ANOVA was used to compare mean DMP scores among proposals from six broad disciplinary categories.

Results

Analyses showed that RDS consultations had a statistically significant effect on DMP scores. Differences between DMP scores for funded versus unfunded proposals and among disciplinary categories were not significant. The DMP requirement also provided a number of both expected and unexpected outreach opportunities for RDS services.

Conclusions

Requiring DMPs for campus grant competitions can provide important assessment and outreach opportunities for research data services.

While these results might not be generalizable to DMP review processes at federal funding agencies, they do suggest the importance, at any level, of developing a shared understanding of what constitutes a high quality DMP among grant applicants, grant reviewers, and RDS providers.

URL : Data Management Plan Requirements for Campus Grant Competitions

DOI : http://dx.doi.org/10.7191/jeslib.2016.1089

Les enjeux de la patrimonialisation et de la réutilisation des données qualitatives de la recherche en Sciences humaines et sociales

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Les archives de la recherche sont par nature passionnantes puisqu’elles permettent de comprendre comment les découvertes se font et comment la science évolue de jour en jour. L’arrivée du numérique a fait surgir de nouvelles possibilités pour la diffusion notamment de ces données, mais aussi de nouveaux challenges, en termes d’archivage entre autres.

L’archivage, le partage et la réutilisation des données qualitatives des SHS soulèvent de nombreuses questions et les différents acteurs concernés, les professionnels de l’IST et les chercheurs, peuvent avoir des avis divergents. Comprendre les points de vue de chacun et déterminer dans quelle mesure celles-ci peuvent être compatibles sont les enjeux de ce mémoire.

URL : Les enjeux de la patrimonialisation et de la réutilisation des données qualitatives de la recherche en Sciences humaines et sociales

Alternative location : http://www.enssib.fr/bibliotheque-numerique/notices/66007-les-enjeux-de-la-patrimonialisation-et-de-la-reutilisation-des-donnees-qualitatives-de-la-recherche-en-sciences-humaines-et-sociales

DataCite au service des données scientifiques : Identifier pour valoriser

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Les données de la recherche, sous forme d’objets numériques très divers, sont en train de trouver leur place dans les services d’information scientifique et technique (IST), principalement – mais pas uniquement – comme compléments des publications qui s’appuient sur ces données.

L’intégration de différents types de ressources numériques avance, et doit être accompagnée par des standards d’interopérabilité, des formats communs de métadonnées et des possibilités de lier ces contenus entre eux et de les citer de manière normalisée.

Le consortium international DataCite, dans lequel l’Inist-CNRS représente la France, s’est mis comme objectif de soutenir et accélérer cette évolution. Il opère en particulier comme une agence d’enregistrement de DOI (Digital Object Identifier), considérant ces DOI, déjà bien établis dans le monde de l’édition, comme un outil efficace pour identifier les données de manière pérenne, pour ainsi faciliter leur découverte et pour y accéder, et puis pour les citer.

DataCite a développé son propre schéma de métadonnées et a mis en place des fonctionnalités spécifiques qui favorisent le partage et la réutilisation des données. Une telle valorisation s’inscrit en particulier dans une approche de pleinement bénéficier du potentiel des open data.

Elle est aussi une contribution essentielle à une meilleure reconnaissance du travail scientifique de production, gestion et mise à disposition de données, et notamment sa prise en compte dans les critères d’évaluation.

Il est d’ailleurs encourageant de voir que ces critères se s’ouvrent à des métriques alternatives, y compris celles concernant les données. Le sujet particulier de la citation des données a récemment été l’objet de plusieurs initiatives internationales visant à harmoniser les pratiques et émettre des recommandations.

Elles ont convergé, à travers le Data Citation Synthesis Group, vers quelques principes en train d’être largement reconnus et acceptés. Dans ce contexte, les éditeurs doivent s’adapter et clairement définir leurs politiques en termes de liens entre données et publications. On observe d’ailleurs une tendance forte vers des accords entre éditeurs et réservoirs de données.

Les actions et services de DataCite s’intègrent dans d’autres structures et initiatives internationales mises en place autour des données de la recherche et des identifiants pérennes: Research Data Alliance, WDS-ICSU, CODATA, EPIC, Data Citation Index, etc.

Un exemple particulier présente le projet européen ODIN, où DataCite et l’initiative ORCID pour la création d’identifiants d’auteurs tentent de connecter les différents types d’identifiants.

URL  : http://eprints.rclis.org/28188/

The FAIR Guiding Principles for scientific data management and stewardship

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There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles.

The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.

This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

URL : The FAIR Guiding Principles for scientific data management and stewardship

Alternative location : http://www.nature.com/articles/sdata201618

Assessing Research Data Management Practices of Faculty at Carnegie Mellon University

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INTRODUCTION

Recent changes to requirements for research data management by federal granting agencies and by other funding institutions have resulted in the emergence of institutional support for these requirements. At CMU, we sought to formalize assessment of research data management practices of researchers at the institution by launching a faculty survey and conducting a number of interviews with researchers.

METHODS

We submitted a survey on research data management practices to a sample of faculty including questions about data production, documentation, management, and sharing practices. The survey was coupled with in-depth interviews with a subset of faculty. We also make estimates of the amount of research data produced by faculty.

RESULTS

Survey and interview results suggest moderate level of awareness of the regulatory environment around research data management. Results also present a clear picture of the types and quantities of data being produced at CMU and how these differ among research domains. Researchers identified a number of services that they would find valuable including assistance with data management planning and backup/storage services. We attempt to estimate the amount of data produced and shared by researchers at CMU.

DISCUSSION

Results suggest that researchers may need and are amenable to assistance with research data management. Our estimates of the amount of data produced and shared have implications for decisions about data storage and preservation.

CONCLUSION

Our survey and interview results have offered significant guidance for building a suite of services for our institution.

URL : Assessing Research Data Management Practices of Faculty at Carnegie Mellon University

DOI : http://doi.org/10.7710/2162-3309.1258

Research Data Services in Academic Libraries: Data Intensive Roles for the Future?

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Objectives

The primary objectives of this study are to gauge the various levels of Research Data Service academic libraries provide based on demographic factors, gauging RDS growth since 2011, and what obstacles may prevent expansion or growth of services.

Methods

Survey of academic institutions through stratified random sample of ACRL library directors across the U.S. and Canada. Frequencies and chi-square analysis were applied, with some responses grouped into broader categories for analysis.

Results

Minimal to no change for what services were offered between survey years, and interviews with library directors were conducted to help explain this lack of change.

Conclusion

Further analysis is forthcoming for a librarians study to help explain possible discrepancies in organizational objectives and librarian sentiments of RDS.

URL : Research Data Services in Academic Libraries: Data Intensive Roles for the Future?

DOI : http://dx.doi.org/10.7191/jeslib.2015.1085