datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
fungi_diagnostic_chars_comparison_japanese
fungi_diagnostic_chars_comparison_japanese大菌輪「識別形質まとめ」データセット最終更新日 / Last updated: 2026/8/29（up to R3-14214）
Languages
Japanese
This dataset is available in Japanese only.
概要 / Overview
Atsushi Nakajima（中島淳志）が個人で運営しているWebサイト大菌輪では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け（インデキシング）した情報を提供しています。その一環として、ある菌と別の菌の「共通する」あるいは「異なる」識別形質 (diagnostic characters) に関する記述を人手で抽出しています。
Daikinrin, a personal website run by Atsushi Nakajima, provides summaries and… See the full description on the dataset page: https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese.glue_diagnostics
Citation
@inproceedings{wang2019glue, title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding}, author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.}, note={In the Proceedings of ICLR.}, year={2019}}
healthcare-diagnostic-imaging-report-turnaround-coherence-risk-v0.1What this repo is for
Detect diagnostic delay risk early.
Tracks alignment between scan completion, reporting, validation, release, clinician notification, and clinical action.
Helps hospitals reduce missed results, prevent diagnostic delays, and stabilise care pathways.
clinical-diagnostic-logic-fragility-atlas-v0.1What this dataset tests
Diagnostic reasoning as an unfolding narrative with branching choices.
The model must identifywhere the diagnostic manifold bifurcatesand how small inference errors amplifyinto different outcome basins.
Required outputs
critical logic junctures
irreversibility flags
branch entropy score
inference error map
amplification factor
outcome basin divergence report
harm gradient
recoverability index
diagnostic-scope-differential-control-radiology-v01Diagnostic Scope and Differential Control v01
What this dataset is
This dataset evaluates whether a system respects the diagnostic limits of a radiologic study and avoids collapsing the differential diagnosis prematurely.
You give the model:
Imaging findings
A clinical prompt
A report level claim
You ask one question.
Is this conclusion
within the diagnostic scope
of the image
Why this matters
Radiology supports diagnosis.
It rarely delivers certainty.
Common failure patterns:
Treating… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/diagnostic-scope-differential-control-radiology-v01.clinical-diagnostic-inference-error-amplification-mapping-v0.1What this dataset tests
How small inference errors introduced at a decision nodeamplify into downstream diagnostic distortion.
Required outputs
error entry node
inference error type
amplification factor
downstream distortion map
delay and misdiagnosis probabilities
self-correction points
prevention guardrails
clinical-icu-demand-staff-bed-diagnostics-quad-coherence-risk-v0.1What this repo is for
This dataset tests whether a model can detect quad coupling coherence risk in hospital critical care flow.
It measures alignment between four signals
patient acuity and ICU demand
staffing coverage
available ICU beds
diagnostic turnaround time
You label each case
coherent when the four signals align and escalation completes
incoherent when any node drifts enough to block escalation or trigger system strain
What it predicts
ICU overflow events
ED boarding spikes
delayed… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-icu-demand-staff-bed-diagnostics-quad-coherence-risk-v0.1.africa-synth-malaria-diagnostic-treatment-pathways-all
Africa Synth Malaria Diagnostic Treatment Pathways All | Africa (Electric Sheep Africa metadata inventory)
Size category: 10K<n<100K - Formats: csv - Sector: health - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-malaria-diagnostic-treatment-pathways-all.clinical-multidoctor-diagnostic-process-integrity-scoring-v0.1What this dataset tests
Whether a model can score the integrity of a multi-doctor diagnostic processusing dialogue structure, hypothesis competition, and objection handling.
Required outputs
process_integrity_score_0_100
primary_reasoning_strength
primary_reasoning_weakness
Strength labels
evidence_coverage
hypothesis_competition
objection_closure
cross_specialty_synthesis
counterfactual_testing
bias_resistance
uncertainty_tracking
Weakness labels
premature_closure… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-multidoctor-diagnostic-process-integrity-scoring-v0.1.clinical-diagnostic-momentum-integrity-v0.1What this dataset names
When an early diagnosisgains momentumand blocks revision.
What it protects
PatientsCliniciansSystems
Why it matters
Most diagnostic harmcomes from labelsthat stopped being questioned.
healthcare-diagnostics-turnaround-coherence-risk-v0.1What this repo is for
detect diagnostic backlog early
predict treatment delays
flag imaging capacity issues
support throughput planning
reduce length of stay
clinical-icu-demand-staff-bed-diagnostics-quad-coherence-risk-v0.2Clinical ICU Demand Staff Bed Diagnostics Quad Coherence Risk v0.2
What this dataset does
It tests whether a model can detect ICU system coherence failure under load.
Four coupled nodes
patient_acuity_signal
icu_demand_signal
staffing_coverage_signal
available_icu_beds_signal
Timing node
diagnostic_turnaround_signal
Escalation loop fields
escalation_attempted_signal
escalation_completed_signal
delay_reason_documented_signal
interim_mitigation_signal
Task
Given the clinical… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-icu-demand-staff-bed-diagnostics-quad-coherence-risk-v0.2.clinical-diagnostic-logic-fragility-atlas-v0.2
Clinical Diagnostic Logic Fragility Atlas v0.2
What this is
A small dataset that tests one question:
Can you detect when diagnostic logic is moving toward fragility, not just carrying ambiguity?
This repo focuses on diagnostic logic breakdown under clinical reasoning pressure.
It models a system where:
diagnostic signal consistency may weaken
hypothesis conflict may rise
evidence may fragment
inference stability may erode before overt decision failure appears… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-diagnostic-logic-fragility-atlas-v0.2.wisconsin-breast-cancer-diagnosticThis dataset, derived from the Wisconsin Breast Cancer (Diagnostic), is a comprehensive resource for developing and evaluating machine learning models focused on the binary classification of breast tumors as either benign (B) or malignant (M). The data consists of features computed from digitized images of fine needle aspirates (FNA) of breast masses, offering a rich set of quantitative metrics for computational pathology and diagnostic research.
The dataset is a critical tool for healthcare… See the full description on the dataset page: https://huggingface.co/datasets/mnemoraorg/wisconsin-breast-cancer-diagnostic.clinical-diagnostic-outcome-basin-divergence-analysis-v0.1What this dataset tests
How far diagnostic outcomes divergeonce reasoning branches split.
The model must quantify
basin distance
harm gradient
time to irreversibility
recoverability
avoidable harm
Use case
Third layer of the Diagnostic Logic Fragility Atlas.
reboot-hub-dji-battery-charging-diagnostic
DJI Intelligent Flight Battery Charging Fault Diagnostic Decision Tree
This dataset is a structured, machine-readable mirror of the Reboot Hub v1.0 technical note DJI Intelligent Flight Battery Charging Faults: Safe Diagnostic Decision Tree and Evidence Boundaries. It does not add new diagnostic claims to the published source.
Version: 1.0.0
Publication date: 2026-07-15
DOI: https://doi.org/10.5281/zenodo.21381657
Zenodo record: https://zenodo.org/records/21381657
Canonical web… See the full description on the dataset page: https://huggingface.co/datasets/Thomas0229/reboot-hub-dji-battery-charging-diagnostic.healthcare-diagnostic-order-to-completion-coherence-risk-v0.1What this repo is for
Detect diagnostic bottlenecks before treatment delays.
Tracks alignment between test ordering, booking, sample or slot securing, test completion, result release, and clinical review.
Helps hospitals reduce stalled orders, prevent hidden backlogs, and improve time-to-diagnosis.
pubmed2024_IA-LLM-diagnosticclinical-imaging-queue-diagnostic-delay-coherence-risk-v0.1What this repo is for
Detect when imaging queues
and clinical urgency
have already diverged
before
diagnosis is delayed
and harm follows.
diagnostic_datasettheBharatAI_hindi_helath_diagnostic_datasetAutism-Diagnostics-Chat-EN_PTgruppo-citrigno-faq-diagnostica
Gruppo Citrigno - FAQ Diagnostica
Questo dataset contiene un insieme di domande frequenti e relative risposte brevi riguardanti i servizi diagnostici offerti dai Centri del Gruppo Citrigno.
Contenuto
Il file CSV include:
Domande frequenti
Risposte sintetiche e strutturate
Formattazione ottimizzata per modelli LLM (domanda/risposta atomica)
Obiettivo
Fornire un dataset leggibile e utilizzabile da modelli linguistici per:
Apprendimento supervisionato… See the full description on the dataset page: https://huggingface.co/datasets/Citrigno/gruppo-citrigno-faq-diagnostica.medical_imaging_diagnostic_reports