datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
court_opinions_filtered_under_25kcourt_opinions_filtered_full_sizecivil-opinion-953b74
civil-opinion-953b74
Synthetic weather test data: 38 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/Lunar-BloomW/civil-opinion-953b74.legal-expert-qualification-opinion-coherence-v0.1Clarus Expert Qualification–Opinion Coherence v0.1
This dataset tests whether a model can detect coherence breakdown in expert testimony.
The focus is structural alignment between
qualification
method
scope
conclusion
Courts rely heavily on expert evidence.
When that alignment fails, verdicts often fail.
This dataset detects those failures.
Core question
Does the expert’s opinion remain inside
their expertise
their method
their evidence
Or does it drift beyond them.
Task
Input includes
expert… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/legal-expert-qualification-opinion-coherence-v0.1.legal-expert-report-opinion-reliance-risk-detection-v0.1What this dataset does
You receive
agreement in principle summary
term sheet or email chain summary
recorded document summary
payment terms
release carveouts
confidentiality
costs and tax
authority execution
mismatch flags
You decide
coherent
or
incoherent
Daily use
stop settlement drafting mistakes
prevent enforcement disputes
prevent release scope errors
reduce negligence exposure
legal-expert-report-opinion-reliance-coherence-risk-v0.1What this dataset does
You receive
expert instructions
facts provided
expert opinion
assumptions
firm reliance
You decide
coherent
or
incoherent
Daily use
stop weak expert reliance
prepare for challenge
improve instructions
reduce evidential risk
GitHub-3Repo-7User-Opinion-Dynamics
GitHub 3Repo 7User Opinion Dynamics
This dataset contains monthly opinion-dynamics time series derived from three large open-source GitHub repositories: Ceph, PyTorch, and Swift. Each CSV file represents one repository and contains a repository label, one timestamp column, and seven anonymized developer trajectory columns.
Files
file
rows
anonymized developer columns
ceph.csv
13
7
pytorch.csv
13
7
swift.csv
13
7
Schema
Each CSV… See the full description on the dataset page: https://huggingface.co/datasets/hreyulog/GitHub-3Repo-7User-Opinion-Dynamics.legal-causation-expert-opinion-coherence-drift-v0.1What this dataset is
You receive
case causal theory
expert method
data support
alternative causes handling
certainty language
gatekeeping signals
You decide
Does the causation opinion cohere with method and facts
Answer
coherent
or
incoherent
Why this matters
Causation opinion drift predicts
Daubert exclusion
loss of summary judgment
appeal and reversal risk
opinions