datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
brain-lm-alignment-ds002236
Brain–language-model alignment: ds002236 (whole-brain)
Lytle et al. 2020 — orthographic, phonological and semantic word processing in school-aged children (8.7–15.5), auditory and visual.
Paper: https://pubmed.ncbi.nlm.nih.gov/31956678/
Data: https://openneuro.org/datasets/ds002236/versions/1.0.1
Generated: 2026-10-09
Pipeline: https://github.com/suchirsalhan/cdl-representations-brains-babylms
Read this first: does the measurement work?
Every alignment number in… See the full description on the dataset page: https://huggingface.co/datasets/BrainAlign/brain-lm-alignment-ds002236.brain-lm-alignment-ds006239
Brain–language-model alignment: ds006239 (whole-brain)
Wang et al. 2025 — word-level phonological and semantic reading tasks in children and adolescents aged 10–17.
Paper: https://www.sciencedirect.com/science/article/pii/S2352340925009692
Data: https://openneuro.org/datasets/ds006239/versions/1.0.5
Generated: 2026-10-09
Pipeline: https://github.com/suchirsalhan/cdl-representations-brains-babylms
Read this first: does the measurement work?
Every alignment number… See the full description on the dataset page: https://huggingface.co/datasets/BrainAlign/brain-lm-alignment-ds006239.brain-lm-alignment-ds001894
Brain–language-model alignment: ds001894 (whole-brain)
Lytle et al. 2019 — longitudinal word-level phonological processing in children scanned twice, at roughly 10 and 12 years old.
Paper: https://www.nature.com/articles/s41597-019-0338-5
Data: https://openneuro.org/datasets/ds001894/versions/1.4.2
Generated: 2026-10-09
Pipeline: https://github.com/suchirsalhan/cdl-representations-brains-babylms
Read this first: does the measurement work?
Every alignment number… See the full description on the dataset page: https://huggingface.co/datasets/BrainAlign/brain-lm-alignment-ds001894.community-alignment-dataset
Community Alignment
Github |
Paper
Dataset
Community Alignment is a large-scale open source, multilingual and multi-turn preference dataset to align LLMs with human preferences across cultures. Its features include the following:
[Large-scale] >200,000 comparisons of LLM responses, collected from >3,500 unique annotators who provided feedback at an individual level.
[Multilingual] Contains comparisons in English, French, Italian, Hindi, and Portuguese. 66% of comparisons… See the full description on the dataset page: https://huggingface.co/datasets/facebook/community-alignment-dataset.matrix-game-evalcross-species-translational-alignment
Cross-Species Translational Alignment — TG-GATEs + DrugMatrix × Tox21
Goal: build a training substrate for detecting subtle / pre-histopathological
toxicity signatures in animal transcriptome data, with mechanism-of-toxicity
labels attached. This directory contains the compound-level linkage layer:
every compound that has rat in-vivo perturbation data cross-referenced to Tox21
mechanism assays via standardized chemical identifiers.
Background — the hackathon
Built… See the full description on the dataset page: https://huggingface.co/datasets/Marcolini/cross-species-translational-alignment.persona-and-other-evals
Qwen3.5-9B AMA adapters — persona evals
Inference code, the data it produced, and the tools that turn that data
into tables and an HTML viewer. The evals are Anthropic's persona set,
scored in three regimes: teacher-forced logprob of the answer literal,
greedy answer with the reasoning block pre-closed, and a full 16k-budget
reasoning trace.
Pinned models
base unsloth/Qwen3.5-9B @ 005429cee5cb648998cf2b70eebdd83175989c9a
util… See the full description on the dataset page: https://huggingface.co/datasets/agentic-moral-alignment/persona-and-other-evals.mrkr-knee-alignment
Lower-limb Alignment Measurements for the MRKR Subset
Anonymised knee radiograph metadata with manual and derived radiographic alignment
measurements for a subset of the Emory Knee Radiograph (MRKR) dataset [1]. It accompanies
the paper "Landmark-free Assessment of Lower-limb Alignment with Implicit Neural Shape
Functions from Knee Radiographs" (accepted to MICCAI 2026), which develops a
deep-learning framework for landmark-free, automated knee alignment assessment.
Release… See the full description on the dataset page: https://huggingface.co/datasets/imedslab/mrkr-knee-alignment.Role-of-Provider-on-Safety-Alignment-in-Large-Language-Models
Evaluating the Role of Provider on Safety Alignment in Large Language Models: dataset
Data for the paper
Naser, M.Z. (2026). Evaluating the Role of Provider on Safety Alignment in Large Language
Models. Neurocomputing, 135173. https://doi.org/10.1016/j.neucom.2026.135173
It holds the Extended Context Safety Benchmark (ECSB) scenario bank and every trial result.
If you use the data, please cite the paper (BibTeX under Citation).
The metadata.paper field inside… See the full description on the dataset page: https://huggingface.co/datasets/mznaser/Role-of-Provider-on-Safety-Alignment-in-Large-Language-Models.brain-lm-alignment-ds003604
Brain-LM alignment: ds003604
Representational-similarity alignment between language-model hidden states and
child fMRI RDMs for ds003604 (children ages 5/7/9, auditory).
Tasks: Sem, Phon, Gram, Plaus Sessions: ses-5, ses-7, ses-9 Cells: 12
Models: 14 families (5 real + 9 PARC noise-seed baselines)
Rows: 1848 (family x checkpoint x task x session)
Generated: 2026-08-29
Headline: no model is distinguishable from a random seed
Alignment is computed as Spearman… See the full description on the dataset page: https://huggingface.co/datasets/BrainAlign/brain-lm-alignment-ds003604.ELSA-Emotion-and-Language-Style-Alignment-Dataset
ELSA: Emotion and Language Style Alignment Dataset
The ELSA (Emotion and Language Style Alignment) dataset provides fine-grained emotional rewrites of text across four stylistic contexts: conversational, formal, poetic, and narrative. It is designed to support research in emotion-conditioned generation, stylistic variation, and affect-aware NLP.
Overview
Source: Based on the dair-ai/emotion dataset and emotion labels aligned with the GoEmotions taxonomy.
Labels:… See the full description on the dataset page: https://huggingface.co/datasets/joyspace-ai/ELSA-Emotion-and-Language-Style-Alignment-Dataset.ng-jss1-math-alignment-ratings
Nigerian JSS1 Mathematics Alignment Ratings
What the LPCG framework generated from six JSS1 mathematics lessons, and how four blinded raters and
a model judge rated it: the inputs as frozen, every run with its record, the documents the raters
received and returned, and the ratings.
This is one of four datasets released with the LPCG framework from the MSc study Design and Evaluation of a Lesson-Plan-Driven Framework for Curriculum-Constrained Generation and Personalisation of… See the full description on the dataset page: https://huggingface.co/datasets/tosinamuda/ng-jss1-math-alignment-ratings.oncology-signal-alignment-boundary-v0.4
What this dataset does
This dataset tests whether a model can detect signal-alignment failure in a synthetic tissue ecology.
The task is not cancer diagnosis.
The task is to classify whether readable biological signals can still coordinate repair.
Core Stability Idea
A tissue may still read damage, repair, immune, and metabolic signals but fail because those subsystems no longer align around coherent action.
This dataset moves beyond readability collapse.
It tests… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/oncology-signal-alignment-boundary-v0.4.alignment-discretion
Dataset for "AI Alignment at Your Discretion"
For principles, we use the seed principles from the Collective Constitutional AI paper. They map onto the preferences in our dataset using the column name p{i}_pref for principle i. The exact mapping is
{
'p0_pref': 'The AI should be as helpful to the user as possible.',
'p1_pref': 'The AI should be careful about balancing both sides when it comes to controversial political issues.',
'p2_pref': 'The AI should not say racist or… See the full description on the dataset page: https://huggingface.co/datasets/maartenbuyl/alignment-discretion.ai-alignment-failure-horizon-and-intervention-routing-v0.1
Goal
Predict when an AI system will cross fromproxy optimizationinto full alignment failure.
Then route the minimal interventionbefore collapse.
What this tests
alignment drift trajectory
failure horizon prediction
intervention timing
severity estimation
Required outputs
System must identify:
proxy vs objective
drift stage
failure horizon
intervention strategy
Why it matters
Alignment rarely fails instantly.
It drifts first.Then… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-alignment-failure-horizon-and-intervention-routing-v0.1.clarus_alignment_flip_test_v01Clarus Alignment Flip Test v0.1
This is an evaluation dataset for detecting phase transitions in model behavior.
It targets the moment a system shifts from constraint aligned behavior to reward driven distortion.
It is not training data.
What it tests
Context pressure
Conflicting objectives
Authority injection
Time delay and interrupted context
Reward framing and compliance pressure
Core idea
Same task
One variable changes
We track the first step where alignment flips
Data format
One row… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clarus_alignment_flip_test_v01.animal-alignment-feedback
Open Paws Animal Alignment Feedback
🐾 Human feedback and preference data for aligning AI with animal advocacy values
Overview
This dataset is part of the Open Paws initiative to develop AI training data aligned with animal liberation and advocacy principles. Created to train AI systems that understand and promote animal welfare, rights, and liberation.
Dataset Details
Dataset Type: Feedback Data
Format: CSV (Comma-separated values)
Languages: Multilingual… See the full description on the dataset page: https://huggingface.co/datasets/open-paws/animal-alignment-feedback.clinical-narrative-clinical-timeline-alignment-v0.1What this dataset tests
Whether a system can alignpatient-reported narrativeswith objective clinical timelines.
Required outputs
alignment score
narrative time shift
omitted events
overemphasized events
narrative anchors
misalignment risk band
Use case
First layer of the Healing Narrative Coherence Corpus.
ai-temporal-5node-pressure-buf-lag-cpl-alignment-goal-drift-v0.1
What this repo does
This dataset tests whether a model can detect an alignment cascade forming over time by reading a short ordered window of signals and predicting whether goal drift lock-in occurs by the final step.
Core quad
pressurebufferlagcoupling
Prediction target
label_cascade_state
Row structure
One row represents one short time window (t0 to t3) for an AI system under alignment pressure. It includes time-series values for optimization… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-temporal-5node-pressure-buf-lag-cpl-alignment-goal-drift-v0.1.alignment_recovery_dynamics_v01Clarus Alignment Recovery Dynamics v0.1
This dataset measures recovery after an alignment flip.
Focus
Not only whether a system flips
But whether it can recover
And whether it relapses under renewed pressure
Design
One row per step
Steps form a trajectory grouped by case_id
A recovery window defines how quickly recovery must occur
Columns
flip_signal_expected
none, early_warning, flip, cascade
first_flip_step_expected
First step where a flip is expected, or -1
recovery_expected
true if… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/alignment_recovery_dynamics_v01.clinical-intervention-alignment-sepsis-v1Clinical Intervention Alignment Sepsis Detection
Overview
This dataset tests whether a model can determine whether a clinical intervention is aligned with the current system state.
In complex clinical systems such as sepsis, interventions do not have uniform effects. The same treatment may stabilize the system in one physiological state while having little effect—or even destabilizing the system—in another.
The benchmark evaluates whether models can detect when an intervention is structurally… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-intervention-alignment-sepsis-v1.clinical-quad-guidance-alignment-claim-strength-safety-signal-certainty-regulatory-risk-v0.1What this repo does
This dataset models regulatory misalignment narrative risk in clinical trial reporting. It predicts when the interaction between guidance alignment, claim strength, safety signal strength, and narrative certainty indicates a high probability of regulatory risk due to overconfident or misframed claims.
Core quad
guidance_alignment_index
claim_strength_index
safety_signal_strength_index
narrative_certainty_index
Prediction target
label_regulatory_risk
Row structure
Each row… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-guidance-alignment-claim-strength-safety-signal-certainty-regulatory-risk-v0.1.traincommunity-alignmentCommunity Alignment
Community Alignment is a large-scale open source, multilingual and multi-turn preference dataset to align LLMs with human preferences across cultures. It features prompt-level overlap in annotators, enabling social-choice-based and distributional approaches to LLM alignment, as well as natural language explanations for choices.
[Large-scale] ~200,000 comparisons of LLM responses, collected from >3,000 unique annotators who provided feedback at an individual level.… See the full description on the dataset page: https://huggingface.co/datasets/anon-submission00/community-alignment.legal-parallel-proceedings-alignment-failure-v0.1Use
You get
parallel case structure
coordination level
conflict signals
alignment
You output
coherent
or
incoherent
second_itergeopolitical_alignment