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
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".