datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
human-perception-audio-deepfake-2026
Human Audio Deepfake Perception 2026
A large-scale listening study evaluating how well humans detect modern audio
deepfakes. The dataset contains 35,532 deepfake-detection judgments from
1,768 anonymous participants across 138 TTS and voice-conversion systems,
collected via a publicly accessible online listening game in 2025–2026.
This is the successor to the 2021 ASVspoof-2019 perception study
(Müller, Pizzi & Williams, 2022)
and extends the same paradigm to modern systems… See the full description on the dataset page: https://huggingface.co/datasets/mueller91/human-perception-audio-deepfake-2026.r20-portfolio-ai-perception
Portfolio Interference in LLM Brand Perception (R20 to R21)
Supersession note: This dataset originally backed R20 (2026ab, superseded). R21 (2026ac, DOI 10.5281/zenodo.19765401) supersedes both R8 (2026q) and R20. R21 merges R8 theory with R20 empirical (9,925 obs across 40 brands, 13 models, 7 traditions) into a single analytical-empirical paper. New citations should reference Zharnikov (2026ac).
Dataset DOI: 10.57967/hf/8380
Current Paper (R21): 10.5281/zenodo.19765401 --… See the full description on the dataset page: https://huggingface.co/datasets/spectralbranding/r20-portfolio-ai-perception.clinical-perception-intervention-justification-v0.1Clinical Perception–Intervention Justification v0.1
Goal
Test whether actions follow directly from perceptual evidence
Detect interventions that appear without a visual cause
Detect escalation that exceeds image-supported severity
What it measures
action_without_causeAn intervention is proposed with no supporting image evidence
over_escalationThe action exceeds what the visual severity supports
justification_okThe response links perception to action explicitly or proportionally
How it… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-perception-intervention-justification-v0.1.wise-perception-dfad8d
wise-perception-dfad8d
Synthetic products test data: 57 rows in data.csv.
All values are randomly generated fictional examples, not real observations, products, or user activity. Intended only for CSV loading and pipeline tests; not suitable for scientific or business conclusions. Columns are sampled independently and do not model real-world correlations.
Fields
sample_id: random identifier for this generated sample.
row_id: sequential row number starting at 1.… See the full description on the dataset page: https://huggingface.co/datasets/nxiong/wise-perception-dfad8d.robotics-perception-action-alignment-v0.1What this dataset tests
Whether robot actions match current perception
Whether the system acts on stale, wrong-frame, or hallucinated state
Why this exists
Robots fail when perception and action decouple
stale frames
latency
occlusion
misclassification
hallucinated targets
This set makes those failures measurable
Data format
Each row contains
sensor_snapshot
world_state_change
commanded_action
executed_action
outcome
The task is to label alignment and… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/robotics-perception-action-alignment-v0.1.drone-landing-zone-perception-coherence-risk-v0.1What this repo is for
Detect when landing will fail before final descent.
Focus
• landing zone perception
• altitude accuracy
• obstacle density
• gust impact
Why it matters
Many drone losses happen during landing.
Signals appear seconds before failure.
obotics-perception-action-coherence-risk-v0.1What this repo is for
You use it to detect when a robot sees the right thing but does the wrong thing.
It targets day-to-day deployment failures:
correct detection with wrong grasp
pose uncertainty that breaks planning
safety check failure that blocks execution
execution that completes but misses intent
Typical uses:
warehouse pick and place
bin picking
hospital delivery robots
field inspection robots
Prompt format
Output must be exactly one token
coherent or incoherent
arjuna-perception-dataset