| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - construction-safety |
| - osha |
| - regulatory-compliance |
| - low-resource |
| - niche-domain |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Construction Code-Citation Corpus v1 |
|
|
| Open dataset of construction-site incident narratives paired with OIICS |
| hazard codes (event, source, nature, body) and OSHA 29 CFR 1926 citation |
| candidates. Built for the [Adaption Labs AutoScientist Challenge](https://adaptionlabs.ai/auto-scientist) |
| ("All Other Domains" category). |
|
|
| ## Sources |
|
|
| - **OSHA Severe Injury Reports** (DOL, public domain): 2015-01 → 2025-08, |
| 103,750 records. Each row has `Final Narrative` (incident text) plus |
| OIICS classification codes for the event, source, nature, and body part. |
| - **OSHA 29 CFR 1926** (eCFR snapshot 2025-09-16, public domain): 304 |
| sections with explicit citations and full regulatory text. |
|
|
| ## Schema |
|
|
| Each row in the canonical jsonl: |
|
|
| ```json |
| { |
| "id": "<SIR ID>", |
| "input": "<Final Narrative>", |
| "hazards": [{ |
| "code_event": {"id": "<OIICS event>", "title": "..."}, |
| "code_source": {"id": "<OIICS source>", "title": "..."}, |
| "code_nature": {"id": "<OIICS nature>", "title": "..."}, |
| "code_body": {"id": "<OIICS body>", "title": "..."}, |
| "severity": "low|moderate|high" |
| }], |
| "citations": [], |
| "naics": "<6-digit NAICS>", |
| "naics_subsector": "<4-digit>", |
| "event_date": "YYYY-MM-DD", |
| "inspection_nr": "<int or null>", |
| "source": "sir", |
| "split": "train|dev|test" |
| } |
| ``` |
|
|
| The `citations` field is empty in v1 (SIR does not carry OSHA standard |
| citations directly). Citation supervision comes from a separate join on |
| `inspection_nr` to the DOL OSHA enforcement violations corpus, planned for v2. |
|
|
| ## Splits |
|
|
| Stratified by NAICS subsector (first 4 digits), 70/15/15. |
|
|
| - train: 72,467 |
| - dev: 15,410 |
| - test: 15,873 (SHA-256 `c9490ed3...`, hash-pinned, never re-shuffled) |
|
|
| ## Known biases |
|
|
| - SIR over-represents severe injuries (hospitalization, amputation, loss of |
| eye) — the corpus is by definition skewed toward high-severity events. |
| - Source-code distribution has a heavy long tail: 1,478 unique codes, top-75 |
| cover only 57% of records. Models will need either a code-collapse strategy |
| or hierarchical (division-level) prediction. |
|
|
| ## License |
|
|
| MIT (this dataset). OSHA SIR is public domain. OSHA 29 CFR 1926 text is |
| public domain. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ``` |
| @misc{construction-code-corpus-2026, |
| title = {Construction Code-Citation Corpus v1}, |
| author = {Oversite Innovations}, |
| year = {2026}, |
| note = {Built for the Adaption Labs AutoScientist Challenge} |
| } |
| ``` |
|
|
