Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research
Bibliographic record
Abstract
This is the dataset of the report: Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research It contains the following information of all the papers from ASE, FSE, and ICSE in 2023: Paper title Keyword Is the source data available and accessible in the paper? If the source data is not available, do the authors explain why? Hosting platforms Access mode License Is their experiment data reused from previous work, or newly generated specifically for this study, or combination of both? Do the authors change/modify their experiment data before experiment? What modifications do they perform? Does the link provide detailed instructions about how to replicate their paper? Does the link contains their complete experiment data, their source code or other materials that are necessary to replicate their experiments? What's the data format inside the link? What's the content of the link? We collect the data in a rush. If you want to use this dataset and find any errors, please contact us ;-) Our emails: echo.xiangchen@gmail.com zhifengyao731@gmail.com
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 | 0.006 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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; both teacher heads agree on what is shown here.
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".