US NTSB Aviation Accident and Incident Final Reports Dataset (2016–2023)
Bibliographic record
Abstract
This dataset consolidates information from final reports on aviation accidents and incidents occurring between 2016 and 2023, as provided by the U.S. National Transportation Safety Board (NTSB) and available through the NTSB CAROL platform as of December 24, 2024. It covers 7,462 individual occurrences and integrates both structured and unstructured data extracted from the official reports. The dataset includes detailed data for each occurrence, such as event identification (e.g., NtsbNo, EventID, ReportNo), occurrence details (EventDate, City, State, Country, Latitude, Longitude), aircraft information (Make, Model, AirCraftCategory, NumberOfEngines, EngineType), flight and operator data (Operator, PurposeOfFlight, Scheduled, FAR), injury and damage statistics (FatalInjuryCount, SeriousInjuryCount, MinorInjuryCount, OnGroundInjuryCount, AirCraftDamage), environmental conditions (WeatherCondition), and investigation-related information (ProbableCause, Findings, BroadPhaseofFlight, ReportStatus, ReportUrl, DocketUrl). The dataset also include full text content of the associated reports, extracted from the original PDF files (rep_text). This dataset is intended to support research on aviation safety, risk analysis, and natural language processing (NLP) applications.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.031 |
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 source (direct Gemma or distilled Codex), 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".