European Academic Libraries Offer or Plan to Offer Research Data Services
Notice bibliographique
Résumé
A Review of:
 Tenopir, C., Talja, S., Horstmann, W., Late, E., Hughes, D., Pollock, D., … Allard, S. (2017). Research data services in European academic research libraries. LIBER Quarterly, 27(1), 23-44. https://doi.org/10.18352/lq.10180
 Abstract
 Objective – To investigate the current state of research data services (RDS) in European academic libraries by determining the types of RDS being currently implemented and planned by these institutions.
 Design – Email survey.
 Setting – European academic research libraries.
 Subjects – 333 directors of the Association of European Research Libraries (LIBER) academic member libraries.
 Methods – The researchers revised a survey instrument previously used for the DataONE survey of North American research libraries and conducted pilot testing with European academic library directors. The survey instrument was created using the Qualtrics software. The revised survey was distributed by email to LIBER institutions identified as academic libraries by the researchers and remained open for 6 weeks. Question topics included demographics, RDS currently offered, RDS planned, staffing considerations, and the director’s opinions on RDS. Libraries from 22 countries participated and libraries were grouped into 4 regions in order to compare regional differences. Data analysis was conducted using Excel, SPSS or R software University of Tennessee, University of Tampere, and University of Göttingen.
 Main Results – 119 library directors responded to more than one question beyond basic demographics, for a response rate of 35.7%. Among the libraries surveyed, more libraries offer consultative services than offered technical support for RDS, although a majority planned to offer technical services in the future. Geographically, libraries in western Europe offer more RDS compared with other regions. More libraries have reassigned or plan to reassign current staff to support RDS services, rather than hire new staff for these roles. Regardless of whether or not they currently offer RDS, library directors surveyed strongly agree that libraries need to offer RDS to remain relevant.
 Conclusion – The authors determine that a majority of library directors recognize that data management is increasingly important and many libraries are responding to this by implementing RDS and collaborating across their institutions and beyond to help meet these needs. Future research is suggested to track how these services develop over time, how libraries respond to the staffing challenges of RDS, and whether consultative rather than technical services continue to be primary forms of RDS offered.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,004 | 0,460 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,006 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».