Usage Data of Images from a Digital Library Informs Four Areas of Digital Library Management: Metadata Creation, System Design, Marketing and Promotion, and Content Selection
Notice bibliographique
Résumé
A Review of: Reilly, M., & Thompson, S. (2014). Understanding ultimate use data and its implication for digital library management: A case study. Journal of Web Librarianship, 8(2), 196-213. http://dx.doi.org/10.1080/19322909.2014.901211 Abstract Objective – To investigate the implications of intended and actual usage data retrieved from a digital library on digital library management and design. Design – Case study. Setting – A digital library of predominantly high resolution images based at a large research university in the United States of America. Subjects – Responses from 917 users of an open access digital library. Methods – Researchers used a literature review to identify previous research on this topic and to inform the methodology for their research. Two distinct studies informed the methodology: research by Beaudoin (2009) that identified categories of both users and questions around usage was incorporated, and the ultimate use categories suggested by Chung and Yoon (2011) to compare against those used in this research. Researchers used data extracted via recorded system logs that are part of the statistics feature of the digital library. This feature is an in-house developed system, the Digital Cart Service (DCS). The logs tracked usage of 917 images recorded over a three year period, from 2011-2013. After eliminating personal information, researchers examined three fields: university affiliation, intended use, and description. After exporting the data from these three fields to a Microsoft Access database for text analysis, researchers normalized the data using a series of codes assigned to the responses. It is unclear how many description fields were used to yield more information. Main Results – Researchers identified five user-types among users of the digital library. The biggest user group was visitors, followed by university staff, while university faculty had the lowest usage. Visitors were found to use images for personal use, such as inspirational and artistic purposes. The products developed from images in the digital library were-wide ranging, and included image albums, research, artwork, and video productions. These findings have implications for four areas of practical management of digital libraries: metadata creation, system design, marketing and promotion, and content selection. Among the eight categories of intended uses recorded, the highest uses were found to be for personal use, followed by ‘other’ use. Researchers examined the ‘other’ use category and further divided it into 12 sub-categories. Of these sub-categories, the highest use was for publication and research, while the lowest use was for ‘gift’ and ‘industry.’ Conclusion – Incorporating user-generated metadata and distributing it to digital library managers is found to produce enhanced metadata and to aid the promotion and awareness of collections. Usage data may inform marketing efforts, as it provides a more comprehensive picture of who uses digital libraries and why they use images retrieved from those libraries. Equally, usage data may reveal the least frequent users of digital libraries, which informs targeted user marketing campaigns. Finally, the authors find that usage data combined with user-generated metadata should form part of content selection criteria for digital library managers.
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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 | 0,005 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,011 | 0,013 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,007 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».