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Research Data Management – Approaches to Capacity Building by Acting Locally While Thinking Nationally

2017· article· en· W2751466607 sur OpenAlexfundaboutno aff
Talia Chung

Notice bibliographique

RevuePurdue e-Pubs (Purdue University System) · 2017
Typearticle
Langueen
DomaineComputer Science
ThématiqueResearch Data Management Practices
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of AlbertaUniversity of TorontoMcMaster UniversityUniversity of Ottawa
Mots-clésCapacity buildingBusinessComputer scienceKnowledge managementProcess managementEnvironmental planningEconomic growthEconomicsEnvironmental science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The role of university libraries in research data stewardship has been in rapid growth and evolution.Key principles for good research data management standards are emerging and stabilizing internationally providing an opportunity for institutions to encourage and facilitate sound research data management practices among its students and researchers.Libraries must consider how we strengthen our collective ability to anticipate and respond to these needs.Using recent theoretical models of research data management services, this paper looks at an approach to build capacity around research data management services at a local level (the University of Ottawa Library) against the backdrop of a maturing national initiative (the Canadian Association of Research Libraries' (CARL) Portage Network).Using a theoretical framework consisting of OCLC's Tour of the Research Data Management (RDM) Service Space [Bryant, Lavoie, Malpas & OCLC, 2017], COAR's Librarians' Competencies Profile for Research Data Management [Schmidt & Shearer, 2016] and Cox, Kennen, Lyon & Pinfield's (2016) model of research data service maturity, this article will examine how a single institution, the University of Ottawa, is progressing in developing its own RDM service offering against the backdrop of a maturing national initiative, Portage, led by the Canadian Association of Research Libraries (CARL). Frameworks for RDM services and competenciesResearch data management, defined at its simplest, can be described as "the effective handling of information that is created in the course of research" [JISC, 2016].Recognizing RDM as a significant development requiring high levels of engagement by academic libraries, research library associations, such as the Association of Research Libraries (ARL), OCLC and the Canadian Association of Research Libraries (CARL) have been actively engaged in these areas, in response to interest from their member institutions.The benefits of good data management, from the perspective of researchers, administrative and funding agencies, and journal publishers, are well documented in the literature.Important issues such as researcher efficiency, re-use of publicly funded research data to create new investigational possibilities, and the support of published articles by sharing data which underpin conclusions, are driving the development of research data strategies on campus.These benefits, along with other factors, contribute to the rising interest and need for robust discussions around each institution's unique profile of priorities, research activities, funding, policies, capabilities and aspirations.More recent models of RDM services place a greater emphasis on consultation services and access to infrastructure, illustrating how our shared understanding of RDM services is evolving and, in the case of some institutions, has begun to shift from being a strategic issue towards more of a procedural issue [Pinfield, Cox & Smith, 2014].In March 2017, OCLC published the first of a four report series looking at the research data management service space [Bryant et al., 2017] .Reviewing services developed by over a dozen research libraries across three continents, the authors propose a model representing three categories of RDM services commonly found throughout academic libraries.These categories include (i) education service, (ii) expertise service, and (iii) curation service.In 2016, COAR's Joint Task Force on Librarians' Competencies in Support of EResearch and Scholarly Communication published a set of competencies [Schmidt & Shearer, 2016] describing the skills and knowledge needed by academic librarians to support researchers in managing their research data.Schmidt and Shearer define three categories of RDM support that may be offered by libraries, these include (i) providing access to data, (ii) awareness and support for managing data, and (iii) managing a data collection.Both the OCLC and COAR reports describe RDM services being offered at research libraries around the world.There is considerable overlap in the types of services and activities described in both reports, with instruction along with services to build awareness for managing data, most frequently mentioned.According to both studies, many of the roles that the library may increasingly play can be categorized under OCLC's Expertise Services, indicating that there is a distinct need for customized assistance in support of individual research projects.Schmidt and Shearer touch lightly on the role of the library in technical infrastructure support and development, activities largely found under the Curation Services category of the OCLC report.Appendix A provides examples of services organized by each OCLC service category.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,111
score de la tête « metaresearch » (Gemma)0,101
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Reproductibilité · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,889
Score d'incertitude au seuil0,589

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1110,101
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0070,010
Études des sciences et des technologies0,0190,062
Communication savante0,0430,053
Science ouverte0,0100,038
Intégrité de la recherche0,0080,010
Charge utile insuffisante (le modèle a refusé de juger)0,0140,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,422
Tête enseignante GPT0,351
Écart entre enseignants0,071 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
DomaineReproductibilité
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission2
Résumé présentoui

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