Sustaining Scientific Open-Source Software Ecosystems: Challenges, Practices, and Opportunities
Bibliographic record
Abstract
Scientific open-source software (scientific OSS) has facilitated scientific research due to its transparent and collaborative nature. The sustainability of such software is becoming crucial given its pivotal role in scientific endeavors. While past research has proposed strategies for the sustainability of the scientific software or general OSS communities in isolation, it remains unclear when the two scenarios are merged if these approaches are directly applicable to developing scientific OSS. In this research, we propose to investigate the unique challenges in sustaining the scientific OSS ecosystems. We first conduct a case study to empirically understand the interdisciplinary team's collaboration in scientific OSS ecosystems and identify the collaboration challenges. Further, to generalize our findings, we plan to conduct a large-scale quantitative study in broader scientific OSS ecosystems to identify the cross-project collaboration inefficiencies. Finally, we would like to design and develop interventions to mitigate the problems identified.
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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 | 0.063 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".