Continuing Education and Data Training Initiatives are Needed to Positively Impact Academic Librarians Providing Data Services
Notice bibliographique
Résumé
A Review of: Fuhr, J. (2022). Developing data services skills in academic libraries. College & Research Libraries, 83(3), 474. https://doi.org/10.5860/crl.83.3.474 Objective – To measure the existing data services skills of academic librarians and gather information on the preferred training programs available to enhance those skill Design – Survey questionnaire. Setting – Libraries in Canada, the United States, the United Kingdom, and Australia. Subjects – One hundred and twenty respondents who self-identified as providing data services. Most (85%) worked in academic libraries with 7% in hospital libraries, 3% in government libraries and 5% in other types of libraries. Methods – Permission was received from the institution ethics board to administer an incentivized survey. All respondents received a 22-question survey which consisted of a mix of Likert-scale questions, multiple choice, open-ended, and short answer questions. The survey was open for two months, beginning on February 20, 2020. One hundred and twenty responses were collected from librarians. A regression analysis was run for the four-skill set categories: general data services, programming languages and software, library instruction, and soft skills. The four variables measured were: geographic region, percentage of time spent performing data management services, length of time served in the data services role, and overall length of time spent in the library science field. Main Results – The strongest data services skill sets were soft skills and instruction. The weakest skill set was programming languages and software. The more time a librarian spent providing data services, the higher their self-assessed score was for programming languages and software and general data services. Librarians from the United States rated themselves higher than Canadian librarians in data analysis software, data visualization, data mining, programming languages, text editors and project management. Preferred forms of professional development were learning by doing and self-directed learning. Biggest impediments to professional development were lack of time (34%), high cost (28%), and lack of support from administrators and supervisors (26%). Qualitative comments revealed challenges related to a lack of support, a lack of direction, and a lack of defined roles. Conclusion – The survey revealed that additional training and development skills initiatives are necessary for practitioners supporting data services in academic libraries. Academic data librarianship is an emerging field with vaguely articulated roles for the data practitioner in a broad range of settings. Furthermore, the skills and training needed are not clearly defined. The standardization of education, training and the core competencies needed for the mechanics of the roles are challenging to define because of diversity within the field. Libraries embarking on providing data management services need to explore what services their community of researchers needs and plan to equip their staff with appropriate skill sets.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,073 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,013 | 0,010 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,071 | 0,014 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».