datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
glaive_function_calling_v1_standardizedpulmonary-disease-airway-lung-function-dataset
Acoustic Waveform Airway and Respiratory Examination (AWARE/PTEase) Dataset
Guidelines
AWARE/PTEase is a smartphone-based sensing system that examines human airway's internal physiological conditions, developed by the Intelligent Systems Laboratory at University of Pittsburgh. AWARE/PTEase probes the airway with acoustic pulses through mouth, and collect the airway's reflections for analysis. Please refer to our paper and github repo for more details.
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/ericyxy98/pulmonary-disease-airway-lung-function-dataset.prosite_functional_motif_scaffolding_benchmark
PROSITE-derived Functional Motif Benchmark
This archive contains an anonymized dataset artifact for a systematically derived benchmark of structurally conserved functional motif-scaffolding cases from PROSITE-linked experimental protein structures.
The benchmark is intended for static motif-scaffolding evaluation with standard MotifBench-style pipelines. Cases are derived from PROSITE motif-pattern entries, mapped to experimentally resolved PDB structures, filtered for recurrent… See the full description on the dataset page: https://huggingface.co/datasets/anonymous-motif-scaffolding/prosite_functional_motif_scaffolding_benchmark.Nemotron-RL-Agentic-Function-Calling-Pivot-v1-prompt-only
Nemotron-RL-Agentic-Function-Calling-Pivot-v1-prompt-only
Prompt-only extraction from nvidia/Nemotron-RL-Agentic-Function-Calling-Pivot-v1.
Files:
prompts.csv: one prompt extraction record per source row. Records include
prompt, separated system_prompt, and structured tools when the source row
defines available tools. Nested values are JSON-encoded inside CSV cells.
summary.md: source row counts, extracted row counts, count deltas, and failed prompt counts.… See the full description on the dataset page: https://huggingface.co/datasets/jamesdborin/Nemotron-RL-Agentic-Function-Calling-Pivot-v1-prompt-only.alphafold-folding-trajectory-functional-stability-coherence-v0.1What this dataset tests
Whether predicted folding trajectories
remain predictive of functional stability
under stress conditions.
Structure alone is not enough.
The path to structure must stay coherent
with real-world stability.
When that relationship breaks
therapeutic proteins fail
in storage
manufacturing
or use.
Required outputs
trajectory_coherence_score
stability_divergence_flag
degradation_horizon_hours
critical_structure_region
stabilization_strategy
Use case
Antibody engineering… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/alphafold-folding-trajectory-functional-stability-coherence-v0.1.alphafold-structure-function-environment-decoupling-v0.1Purpose
Detect when predicted structure
no longer predicts real function
under environmental stress.
Core problem
Proteins can appear stable
in structure predictions
yet lose activity
in real conditions.
This dataset finds
the early decoupling phase.
Inputs
structure confidence
active-site shift
temperature
pH
solvent exposure
binding trend
aggregation signal
Outputs
coherence score
decoupling flag
collapse horizon
critical region
intervention
Evaluation
Binary accuracy
for decoupling… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/alphafold-structure-function-environment-decoupling-v0.1.TDP1_targetsInhibitors_CID_SID_IUPACs_functionalGroups
Purpose of the Dataset
The TDP1_targetsInhibitors_CID_SID_IUPACs_functionalGroups dataset is a part of the study "Leveraging of the IUPAC Names and Machine Learning for Assisting Drug Discovery and Development, demonstrated on the Case of Human Tyrosyl-DNA Phosphodiesterase 1 (TDP1) Inhibitors" https://doi.org/10.48550/arXiv.2503.05591
Dataset Details
This dataset had been leveraged for computations, such as:
determine the most or least desirable functional… See the full description on the dataset page: https://huggingface.co/datasets/ivanovaml/TDP1_targetsInhibitors_CID_SID_IUPACs_functionalGroups.smart-material-coherence-drift-functional-fatigue-detection-v0.1Goal
Detect when a smart material starts losing function.
Core idea
Smart materials fail when stimulus and response stop coupling.
This dataset tests whether a model can detect that drift early.
Domains
shape memory alloys
self-healing polymers
electrochromic materials
Inputs
Healthy baseline signals plus evolving drift signals.
Required outputs
coherence_drift_rate
fatigue_onset_cycle
decoherence_type
functional_variance_growth
failure_probability
recommended_monitoring_action
Decoherence… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/smart-material-coherence-drift-functional-fatigue-detection-v0.1.linear_function