O Brave New Print Collection, That Has Such Data Science Books in It!
Bibliographic record
Abstract
The field of data science exists at the intersection of several disciplines, including statistics, social science, information science, computer science, and visualization. This can make collection development for data science challenging, but it’s a field that has become increasingly important in industry and academia. Data scientists, and increasingly researchers, academics, and others, work with large amounts of data, complex computation, and data visualization to solve real-world problems. Those working in or studying data science may need to learn new skills and tools to be successful. North Carolina State University (NCSU) recognizes the importance of this growing field, as shown in the establishment of the Data Science Initiative (DSI); courses taught by faculty in computer science, statistics, advanced analytics, and management; and research conducted at interdisciplinary centers and institutes. This poster session will describe how librarians from the Collections & Research Strategy department at NCSU Libraries conducted a project to build a niche data science print collection. Information shared in the poster will include the sources that were used to compile an initial list of books, including recommendations from fellow librarians, a curated GOBI notification, websites, suggested reading lists, and course syllabi. Criteria for narrowing this initial list will be provided. The poster will also show an analysis of how this collection overlaps with more established collection areas.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.628 | 0.501 |
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".