Recognizing Software as a Critical Component in Open Science: Advancing an Interoperable, Community-Driven Vision for Infrastructures
Bibliographic record
Abstract
Software source code is crucial in Open Science, representing executable knowledge essential for advancing research. Yet, it often receives insufficient attention in open repositories for metadata management and archival strategies. This panel discussion will convene experts from various infrastructures who have crafted solutions tailored to the unique aspects of software as a digital object, including scholarly repositories, publisher platforms and aggregators. Representatives from HAL, Episciences, Dagstuhl, swMath and more will participate. We will discuss the archival of source code in Software Heritage, the universal source code archive, the application of the CodeMeta vocabulary for describing software and the use of the Software Hash Identifier (SWHID) for accurate referencing. Additionally, we will showcase how initiatives like European projects, such as FAIRCORE4EOSC, FAIR-IMPACT and EVERSE and collaborations with the SciCodes Consortium are creating vital connections between scholarly infrastructures. This panel will discuss both the advancements and the challenges faced, and will suggest practical steps that institutions, publishers, and researchers can take through collaboration, guided by a community-driven vision. We aim to deepen the understanding of software's role in research and its necessary recognition, encouraging wider adoption of best practices in academia to foster a more collaborative and inclusive scholarly environment.
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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 | 0.117 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.016 | 0.045 |
| Scholarly communication | 0.048 | 0.075 |
| Open science | 0.006 | 0.051 |
| Research integrity | 0.020 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 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".