An Open Dataset for Onboarding new Contributors: Empirical Study of OpenStack Ecosystem
Bibliographic record
Abstract
This dataset provides the qualitative and quantitative data of our mixed-method empirical study of onboarding in the OpenStack software ecosystem (SECO). First, we carried out a SECO-level participant observation study of 72 new contributors during a 2-day OpenStack onboarding (in-person) event yielding a rich set of qualitative data; 14 files amount to 60% of the entire dataset originating from a participant observation study. Second, we quantitatively validated the extent to which SECOs achieve benefits such as diversity, productivity, and quality by mining 1281 contributors' code changes, reviews, and issues with(out) OpenStack onboarding experience. Our quantitative dataset includes nine files, which is about 40% of the entire dataset, and we obtained these files by mining new contributors' codebase activities from four OpenStack repositories. Besides, we make available the scripts that e used to extract and analyze this dataset. By providing this data, we are claiming the "Available Badge," and our data are online on a public archived repository at Zenodo: DOI: 10.5281/zenodo.4457683
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".