README.md

---
license: cc-by-4.0
language:
- en
pretty_name: DEPosit

configs:
- config_name: repositories
  data_files:
  - split: train
    path: Data/filtered_DE_repositories.csv

- config_name: commits
  data_files:
  - split: train
    path: Data/DE_all_commits_details.csv

- config_name: pull_requests
  data_files:
  - split: train
    path: Data/DE_all_pr_details.csv

- config_name: issues
  data_files:
  - split: train
    path: Data/DE_all_issue_details.csv

- config_name: issue_closers
  data_files:
  - split: train
    path: Data/DE_all_issue_closers.csv

- config_name: pr_reviews
  data_files:
  - split: train
    path: Data/DE_all_pr_reviews_graphql.csv

- config_name: ci_services
  data_files:
  - split: train
    path: Data/DE_ci_services.csv

- config_name: ci_file_commits
  data_files:
  - split: train
    path: Data/DE_commits_per_ci_files.csv

- config_name: github_actions_runs
  data_files:
  - split: train
    path: Data/DE_repo_gha_runs_counts.csv

- config_name: repo_interactions
  data_files:
  - split: train
    path: Data/DE_repo_interactions.csv

- config_name: repo_metrics
  data_files:
  - split: train
    path: Data/DE_repo_metrics.csv

- config_name: user_autonomy
  data_files:
  - split: train
    path: Data/DE_user_autonomy.csv

- config_name: pipeline_airflow
  data_files:
  - split: train
    path: Data/pipeline_artifacts/DE_pipeline_airflow_dag_features.csv

- config_name: pipeline_beam
  data_files:
  - split: train
    path: Data/pipeline_artifacts/DE_pipeline_beam_pipeline_features.csv

- config_name: pipeline_dagster
  data_files:
  - split: train
    path: Data/pipeline_artifacts/DE_pipeline_dagster_features.csv

- config_name: pipeline_dbt_model
  data_files:
  - split: train
    path: Data/pipeline_artifacts/DE_pipeline_dbt_model_features.csv

- config_name: pipeline_dbt_project_summary
  data_files:
  - split: train
    path: Data/pipeline_artifacts/DE_pipeline_dbt_project_summary.csv

- config_name: pipeline_dlt
  data_files:
  - split: train
    path: Data/pipeline_artifacts/DE_pipeline_dlt_features.csv

- config_name: pipeline_files
  data_files:
  - split: train
    path: Data/pipeline_artifacts/DE_pipeline_files.csv

- config_name: pipeline_kedro
  data_files:
  - split: train
    path: Data/pipeline_artifacts/DE_pipeline_kedro_pipeline_features.csv

- config_name: pipeline_luigi
  data_files:
  - split: train
    path: Data/pipeline_artifacts/DE_pipeline_luigi_task_features.csv

- config_name: pipeline_prefect
  data_files:
  - split: train
    path: Data/pipeline_artifacts/DE_pipeline_prefect_flow_features.csv
---

# DEPosit — Replication Package

**DEPosit** (Data Engineering Pipeline Repositories) is a dataset of open-source GitHub repositories in the data-engineering pipeline ecosystem.

The dataset contains a master cohort of **1,952 repositories** together with repository activity, commits, pull requests, issues, CI-service information, contributor metrics, and pipeline discovery and feature data.

## Dataset (`Data/`)

The Hugging Face Hub exposes each heterogeneous CSV table as a separate **configuration**. This prevents tables with different schemas from being incorrectly concatenated by the Dataset Viewer.

| Configuration | File | Description |
|---------------|------|-------------|
| `repositories` | `filtered_DE_repositories.csv` | Master cohort (**1,952** repos) |
| `commits` | `DE_all_commits_details.csv` | Commits per repository |
| `pull_requests` | `DE_all_pr_details.csv` | Pull requests |
| `issues` | `DE_all_issue_details.csv` | Issues |
| `issue_closers` | `DE_all_issue_closers.csv` | Issue closer logins |
| `pr_reviews` | `DE_all_pr_reviews_graphql.csv` | PR review / merge metadata (GraphQL) |
| `ci_services` | `DE_ci_services.csv` | Detected CI services per repo |
| `ci_file_commits` | `DE_commits_per_ci_files.csv` | Commits touching CI config paths |
| `github_actions_runs` | `DE_repo_gha_runs_counts.csv` | GitHub Actions run counts |
| `repo_interactions` | `DE_repo_interactions.csv` | Precomputed interaction-type metrics |
| `repo_metrics` | `DE_repo_metrics.csv` | Repo-level activity aggregates |
| `user_autonomy` | `DE_user_autonomy.csv` | Contributor autonomy per repo |
| `pipeline_airflow` | `pipeline_artifacts/DE_pipeline_airflow_dag_features.csv` | Airflow pipeline features |
| `pipeline_beam` | `pipeline_artifacts/DE_pipeline_beam_pipeline_features.csv` | Apache Beam pipeline features |
| `pipeline_dagster` | `pipeline_artifacts/DE_pipeline_dagster_features.csv` | Dagster pipeline features |
| `pipeline_dbt_model` | `pipeline_artifacts/DE_pipeline_dbt_model_features.csv` | dbt model features |
| `pipeline_dbt_project_summary` | `pipeline_artifacts/DE_pipeline_dbt_project_summary.csv` | dbt project-level features |
| `pipeline_dlt` | `pipeline_artifacts/DE_pipeline_dlt_features.csv` | dlt pipeline features |
| `pipeline_files` | `pipeline_artifacts/DE_pipeline_files.csv` | Discovered pipeline files |
| `pipeline_kedro` | `pipeline_artifacts/DE_pipeline_kedro_pipeline_features.csv` | Kedro pipeline features |
| `pipeline_luigi` | `pipeline_artifacts/DE_pipeline_luigi_task_features.csv` | Luigi task features |
| `pipeline_prefect` | `pipeline_artifacts/DE_pipeline_prefect_flow_features.csv` | Prefect flow features |

The SQLite file `DE_pipeline_artifacts.db` is retained as a downloadable replication artifact and is not used by the Hugging Face Dataset Viewer.

## Join Keys

- `full_name` or `repo_name` (`owner/repo`) for repository-level and process tables.
- `repo_full_name` for tables in `pipeline_artifacts/`.
- `(repo_full_name, file_path)` links `DE_pipeline_files.csv` to the pipeline feature tables.

## Loading Data from Hugging Face

Each table can be loaded independently using its configuration name:

```python
from datasets import load_dataset

repos = load_dataset("taher-ghaleb/DEPosit", "repositories")
commits = load_dataset("taher-ghaleb/DEPosit", "commits")
issues = load_dataset("taher-ghaleb/DEPosit", "issues")
ci = load_dataset("taher-ghaleb/DEPosit", "ci_services")
```

To load pipeline feature tables:

```python
from datasets import load_dataset

airflow = load_dataset("taher-ghaleb/DEPosit", "pipeline_airflow")
dbt = load_dataset("taher-ghaleb/DEPosit", "pipeline_dbt_model")
prefect = load_dataset("taher-ghaleb/DEPosit", "pipeline_prefect")
```

Each configuration represents one logical table with a consistent schema. The tables are intended to be joined for analysis using the keys described above.


## Scripts (`Scripts/`)

| Script | Role |
|--------|------|
| `collect_github_repos_for_SE4DE_topics.py` | Topic-based repo discovery → `Data/all_DE_repositories.csv` |
| `get_repo_commits_pr_issues_contributors_for_SE4DE_repos.py` | Commits, PRs, issues, contributors |
| `get_issue_closers_for_SE4DE_repos.py` | Issue closer events |
| `get_pr_reviews_graphql_for_SE4DE_repos.py` | PR reviews / merges (GraphQL) |
| `get_pr_reviews_rest_for_SE4DE_repos.py` | PR reviews (REST alternative) |
| `collect_github_repos_commits_per_ci_files.py` | Commits on CI-related paths |
| `get_gha_workflow_runs_for_SE4DE_repos.py` | GitHub Actions run counts |
| `compute_interactions_for_SE4DE_repos.py` | `DE_repo_interactions.csv` |
| `compute_user_autonomy_for_SE4DE_repos.py` | `DE_user_autonomy.csv` |
| `collect_pipeline_artifacts.py` | Discover and parse pipeline files |
| `backfill_ci_gha_from_runs.py` | Add GitHub Actions to `DE_ci_services.csv` when runs exist but path detection missed them |
| `github_api.py`, `github_tokens.py` | Shared API session and token loading |
| `pipeline_parsers/` | Framework-specific parsers |
| `pipeline_schema.sql` | SQLite schema for pipeline collection |

## Quick start (analyze the shipped data)

```powershell
py -m pip install -r requirements.txt
```

Open `Data/filtered_DE_repositories.csv` and join to other `Data/DE_*.csv` files on `full_name` / `repo_name`.

## GitHub authentication (re-collection only)

**Do not commit personal access tokens.** Scripts load credentials from (in order):

1. `--token` / `--tokens` CLI flags (where supported)
2. Environment variables: `GITHUB_TOKEN_SE4DE` (preferred) or `GITHUB_TOKEN`
3. Optional local `.env` file — copy `.env.example` to `.env` and set your PAT

```powershell
copy .env.example .env
# Edit .env and set GITHUB_TOKEN_SE4DE=<your-pat>

# Or set for the current shell only:
$env:GITHUB_TOKEN_SE4DE = "<your-pat>"
```

For heavy REST collection, rotate rate limits with comma-separated PATs:

```powershell
$env:GITHUB_TOKENS = "<pat1>,<pat2>"
```

Example:

```powershell
py Scripts/collect_pipeline_artifacts.py --token $env:GITHUB_TOKEN_SE4DE
py Scripts/get_gha_workflow_runs_for_SE4DE_repos.py --token $env:GITHUB_TOKEN_SE4DE
```

Typical collection order: topic discovery → development history → derived metrics → pipeline artifacts → optional `backfill_ci_gha_from_runs.py`.

Historical commit/PR text in `Data/*.csv` may contain third-party leaked tokens from upstream repos; these were redacted where detected. Re-run `Scripts/redact_leaked_tokens_in_data.py` after adding new CSV exports.

## Layout

```text
ReplicationPackage/
├── README.md
├── LICENSE
├── DATA_LICENSE
├── .env.example
├── requirements.txt
├── Data/
│   ├── filtered_DE_repositories.csv
│   ├── DE_*.csv
│   ├── DE_pipeline_artifacts.db
│   └── pipeline_artifacts/
└── Scripts/
    ├── collect_*.py, get_*.py, compute_*.py
    ├── collect_pipeline_artifacts.py
    ├── backfill_ci_gha_from_runs.py
    ├── github_api.py, github_tokens.py
    └── pipeline_parsers/
```

## Citation

If you use DEPosit, please cite:

```bibtex
@inproceedings{deposit2026,
  title     = {DEPosit: A Dataset of Open-Source Repositories for Data Engineering Pipelines},
  author    = {Gorjala, Bhavyalatha and Taher A. Ghaleb},
  booktitle = {Proceedings of the 42nd IEEE International Conference on Software Maintenance and Evolution (ICSME)},
  year      = {2026},
  organization={IEEE}
}
```