How can libraries support the development, access and digital preservation of research software and its source code?
Bibliographic record
Abstract
Open source research software is essential to modern scholarship, enabling transparency, reproducibility, and the reuse of knowledge embedded in code. This presentation explores its importance from both a developer’s and a librarian’s perspective, emphasizing how software preserves methodological detail often missing from publications. It outlines best practices for developing and sharing open source research software—such as version control, open licensing, persistent identifiers, documentation, and reproducibility tools—and considers how libraries can better support these practices. By integrating guidance on software licensing, citation, and management into their instructional and research data resources, libraries can strengthen open science and foster sustainable, transparent research.
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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 | 0.060 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.070 | 0.088 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.020 |
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".