Collaboration Challenges and Opportunities in Developing Scientific Open-Source Software Ecosystem: A Case Study on Astropy
Bibliographic record
Abstract
Scientific open-source software (OSS) has greatly benefited research communities through its transparent and collaborative nature. Given its critical role in scientific research, ensuring the efficiency of collaboration within development teams of scientific OSS has become vital. Earlier research has identified both the challenges and opportunities associated with interdisciplinary team collaboration in developing conventional scientific software, as well as the dynamics of distributed teams in the context of OSS development. However, it remains unclear whether these challenges are still present and if their solutions can be seamlessly adapted to the context of scientific OSS and its broader ecosystem. Therefore, this study examines the challenges and opportunities for improving the collaboration efficiency in the development and maintenance of scientific OSS, focusing on interdisciplinary and multi-project collaboration within the open-source environment. We conducted a mixed-methods case study on the Astropy project, a widely-used software ecosystem in astronomy, including (1) a detailed analysis of the commit history to understand the roles and activities of each contributor; (2) an in-depth investigation of cross-referenced issues and pull requests to identify challenges and best practices for cross-project collaboration at the ecosystem level; and (3) an interview study with core contributors to complement the first two steps, examining their collaborative efforts within an interdisciplinary team and across a multi-project ecosystem. We contribute to the CSCW community by deepening the understanding of collaboration in scientific OSS ecosystems, highlighting practices and challenges both at the individual project level and across a broader ecosystem. Our findings offer insights into managing cross-project interdependencies and propose strategies to address key obstacles in scientific OSS development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".