datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
faa-aviation-safety-rollups
FAA wildlife strikes, laser incidents and drone sightings — analysis-ready rollups
Three United States FAA safety datasets, cleaned and rolled up into small tabular
files you can load without touching the source archives.
This is not a copy of the FAA's raw releases. Those are already public and
large. What is here is the part that does not exist upstream in this form:
stable slugs, consistent naming, and per-airport / per-species / per-state /
per-aircraft / per-year rollups… See the full description on the dataset page: https://huggingface.co/datasets/himaxym/faa-aviation-safety-rollups.ntsb-aviation-accidents
NTSB Aviation Records 2008–August 2026
1,000-row public sample; full dated six-table snapshot: $79 once.
31,124 NTSB events across six verified tables, with preserved source fields, validated joins and separately typed dates, injury counts and decimal coordinates.
Rebuilt from the official September 1, 2026 avall.zip revision retrieved September 19, 2026. Actual event coverage is 2008-01-01 through 2026-08-27.
The sample is the first 1,000 events ordered by ev_id. It is… See the full description on the dataset page: https://huggingface.co/datasets/claritystorm/ntsb-aviation-accidents.aviation-flight-control-phase-space-baseline-modeling-v0.1What this dataset tests
Whether a system can model the normal control phase-space attractor
for fly-by-wire surface channels.
The target is phase-space geometry:
dispersion
hysteresis
overshoot
lag
energy efficiency.
Required outputs
phase_space_coherence_index
baseline_dispersion_envelope
command_response_lag_profile
control_energy_efficiency
baseline_confidence
Scoring conventions
indices range 0 to 1
dispersion envelope is a low-high interval
lag profile is p50 and p95 in… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-flight-control-phase-space-baseline-modeling-v0.1.aviation-pilot-vehicle-loop-coherence-state-estimation-v0.1What this dataset tests
Whether a system can estimate the coherenceof the pilot–aircraft control loopduring abnormal phases.
Key insightLoss of control beginswith loop misalignmentbefore any hard limits are exceeded.
Required outputs
loop_coherence_index
resonance_stability_band
control_lag_profile
correction_efficiency_score
baseline_deviation
Use case
Layer one of Pilot–Vehicle Loop Coherence Under Stress.Feeds attribution and adaptive intervention systems.
aviation-propulsion-aerodynamics-coherence-baseline-v0.1What this dataset tests
Whether a system can model the normal coupling
between propulsion parameters and aerodynamic state.
The signal is relationship shape and lag
not threshold breaches.
Required outputs
coupling_coherence_index
baseline_correlation_matrix
phase_alignment_score
stability_envelope
lag_profile
baseline_confidence
Scoring conventions
all scores range 0 to 1
stability envelope is a low-high interval
lag profile describes expected response delays in seconds… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-propulsion-aerodynamics-coherence-baseline-v0.1.us-adventure-aviation-ntsb-2026
US Adventure Aviation Operators vs. NTSB Federal Accident Records (2026)
Sector-level matching of 4,278 US adventure aviation operators — air tours,
balloon rides, and skydiving — against the NTSB permanent federal accident
record (49 CFR Part 830 mandatory filings). 1,932 accident records matched,
including 448 fatal accidents.
Key finding: the mandatory federal disclosure regime produces a complete
safety record, but consumers booking these experiences have no practical… See the full description on the dataset page: https://huggingface.co/datasets/shawnzreports/us-adventure-aviation-ntsb-2026.aviation-pilot-vehicle-decoherence-source-attribution-v0.1What this dataset tests
Whether a system can correctly identifythe source of pilot–vehicle loop decoherence.
Sources may be:
pilotaircraftenvironmentmixednone
Key insightCorrect attribution determinesthe correct recovery action.
Required outputs
primary_decoherence_source
source_confidence
resonance_pattern_type
escalation_likelihood
contributing_factors
attribution_rationale
Use case
Layer two of Pilot–Vehicle Loop Coherence Under Stress.Feeds adaptive intervention and crew… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-pilot-vehicle-decoherence-source-attribution-v0.1.aviation-vibration-mode-manifold-baseline-modeling-v0.1What this dataset tests
Whether a system can model a healthy airframe vibration spectrum
as a coupled-mode manifold.
It must return:
a coherence index
a coupling graph
a drift envelope
phase-conditioned baselines.
Required outputs
manifold_coherence_index
baseline_mode_coupling_graph
expected_mode_drift_envelope
phase_conditioned_baselines
baseline_confidence
Scoring conventions
indices range 0 to 1
drift envelope uses frequency and coupling tolerances
coupling graph summarizes… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-vibration-mode-manifold-baseline-modeling-v0.1.aviation-pilot-vehicle-adaptive-intervention-and-recovery-mapping-v0.1What this dataset tests
Whether a system can turn loop decoherenceinto stabilizing action under time pressure.
It must estimate:
riskrecovery windowbest adaptive interventionexpected post-action stability.
Required outputs
loss_of_control_risk
recovery_window_seconds
recommended_adaptive_action
intervention_priority
recovery_confidence
post_action_stability_expectation
Use case
Layer three of Pilot–Vehicle Loop Coherence Under Stress.
Supports:
enhanced crew alerting
adaptive… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-pilot-vehicle-adaptive-intervention-and-recovery-mapping-v0.1.abbas829_pakistan-aviation-statistics-20062024
Pakistan Aviation Statistics (2006–2024)
A Comprehensive 18-Year Analysis of Passenger, Cargo, and Flight Traffic Flows i
Dataset Info
Source: Kaggle
Original Size: 0.02 MB
Kaggle Downloads: 40
Files: 1
Files
pakistan_aviation_data.csv
Mirrored from Kaggle
aviation-propulsion-aerodynamics-decoherence-precursor-detection-v0.1What this dataset tests
Whether a system can detect early decoherence
between propulsion behavior and aerodynamic response.
The signal is relationship drift:
lag expansion
correlation collapse
nonlinear divergence
oscillatory mismatch.
Required outputs
decoherence_onset_time
precursor_pattern_type
severity_gradient
failure_likelihood_index
estimated_time_to_critical_min
primary_decoupling_channels
Scoring conventions
onset time is minutes from window start
severity and… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-propulsion-aerodynamics-decoherence-precursor-detection-v0.1.aviation-propulsion-aerodynamics-failure-horizon-intervention-mapping-v0.1What this dataset tests
Whether a system can turn detected decoherence
into an operational action plan.
It must estimate horizon,
choose intervention,
and define the decision window.
Required outputs
failure_horizon_minutes
recommended_derate_level
diversion_priority
stability_recovery_probability
intervention_window
action_rationale_channels
Scoring conventions
horizon is minutes to critical instability
diversion priority is low, medium, high, or urgent
intervention window is… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-propulsion-aerodynamics-failure-horizon-intervention-mapping-v0.1.aviation-flight-control-integrity-horizon-maintenance-mapping-v0.1What this dataset tests
Whether a system can convert detected phase-space distortion
into a maintenance horizon and schedule.
The point is not fault blame.
It is safe remaining cycles and workload risk.
Required outputs
integrity_horizon_flights
maintenance_priority
workload_risk_index
oscillation_risk_probability
replacement_window
intervention_rationale
Scoring conventions
horizon is remaining flights to unacceptable integrity risk
workload and oscillation risk range 0 to 1… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-flight-control-integrity-horizon-maintenance-mapping-v0.1.aviation-avionics-redundant-narrative-baseline-construction-v0.1Dataset purpose
Construct the baseline of a healthy avionics narrative.
Modern aircraft contain multiple redundant computers and sensors.Each subsystem produces a coherent “story” about aircraft state.Under healthy conditions these stories align tightly.
This dataset defines that baseline alignment.
It captures:
expected agreement patterns between redundant units
allowable divergence bands
narrative coherence ranges during normal flight
The goal is not to detect failure yet.The goal is… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-avionics-redundant-narrative-baseline-construction-v0.1.aviation-vibration-manifold-distortion-and-fault-localization-v0.1What this dataset tests
Whether a system can detect topological distortion
in the vibration mode manifold and localize likely damage.
It must not confuse confounders with damage:
turbulence
engine harmonics
icing
payload shifts
control surface modes.
Required outputs
distortion_pattern_type
likely_fault_location
fault_severity_estimate
localization_confidence
confounder_flags
integrity_percent_of_baseline
Scoring conventions
severity ranges 0 to 1
localization confidence ranges… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-vibration-manifold-distortion-and-fault-localization-v0.1.aviation-avionics-isolation-reset-containment-mapping-v0.1
Aviation Avionics Isolation Reset and Containment Mapping
Purpose
This dataset models how avionics systems should respond after divergence or fault detection.
Detection alone does not prevent failure.The response determines whether the system stabilizes or cascades.
This dataset trains systems to choose the correct containment and recovery strategy.
Core concept
Once redundant avionics systems diverge, the system must decide:
which unit to isolate
whether to… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-avionics-isolation-reset-containment-mapping-v0.1.aviation-avionics-narrative-drift-and-divergence-detection-v0.1
Aviation Avionics Narrative Drift and Divergence Detection
Purpose
This dataset detects when redundant avionics subsystems begin to tell different stories about the aircraft state.
Modern aircraft operate with multiple redundant units:
ADIRUs
flight control computers
navigation systems
air data sensors
Under normal operation these systems remain tightly aligned.Before failure they often remain internally consistent while slowly diverging from each other.
This… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-avionics-narrative-drift-and-divergence-detection-v0.1.aviation_geopolitics