datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
PAMAP2
PAMAP2
The PAMAP2 Physical Activity Monitoring dataset contains data of 18 different physical activities, performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor.
A. Reiss and D. Stricker, "Introducing a New Benchmarked Dataset for Activity Monitoring," 2012 16th International Symposium on Wearable Computers, Newcastle, UK, 2012, pp. 108-109, doi: 10.1109/ISWC.2012.13.
Dataset Information
The PAMAP2 Physical Activity Monitoring dataset… See the full description on the dataset page: https://huggingface.co/datasets/rnicrosoft/PAMAP2.pamela
PAM∃LA
Personalizing Text-to-Image Generation to Individual Taste
Anonymous submission — author and affiliation details withheld during review.
PAM∃LA is a dataset of AI-generated images rated by human participants for aesthetic quality, built specifically for personalization research. It pairs each rating with rich participant demographics and image metadata, enabling research on personalized aesthetic prediction, demographic variation in visual preference, and reward modelling for… See the full description on the dataset page: https://huggingface.co/datasets/pamela-dataset/pamela.pa-minimal-green-team-SFT-500m
pa-minimal-green-team-SFT-500m
The PA green-team minimal 500M SFT mix — math + science + light agentic, short-reasoning-first.
Two training configs (same 216,668 documents, same order):
config
tokens
what
math_sci_agentic_500m
500.0M
reasoning traces intact (think)
math_sci_agentic_500m_nothink
131.8M
every trace replaced by a literal empty <think></think> (no-think)
Composition: 37.7% math_reasoning · 36.3% science_mcq · 16.7% science_research · 9.4%… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/pa-minimal-green-team-SFT-500m.olivers-mtor-atlas
Oliver's mTOR Atlas
The mTOR pathway, mapped by what the evidence can actually carry. This dataset is the curated corpus behind mtor-atlas.org: 411 hand-selected studies on mTOR (mechanistic target of rapamycin) signalling, each labelled by the kind of study behind it, and a list of 149 pathway entities (genes and proteins, complexes, drugs, interventions, biological processes, diseases, outcomes, organelles, nutrients and conditions) that the studies refer to.
Homepage:… See the full description on the dataset page: https://huggingface.co/datasets/pampalini1/olivers-mtor-atlas.pampa_ncats
Dataset Details
Dataset Description
PAMPA (parallel artificial membrane permeability assay) is a commonly
employed assay to evaluate drug permeability across the cellular membrane.
PAMPA is a non-cell-based, low-cost and high-throughput alternative to cellular models.
Although PAMPA does not model active and efflux transporters, it still provides permeability values
that are useful for absorption prediction because the majority of drugs are absorbed
by passive diffusion… See the full description on the dataset page: https://huggingface.co/datasets/jablonkagroup/pampa_ncats.analise_sentimento_ptbr_rewiews_ecommerceADME-Property-Prediction-PAMPA_NCATSinside-out-replication-v2-pamqfix-metrics-v1
Inside-Out Replication V2 — A.8.1 in-context P(a|q) re-score (FULL)
3 models × 4 relations × test split, all 12 cells. P(a|q)/P_norm rebuilt with
paper-faithful A.8.1 in-context tokenization (the answer is tokenized jointly
with the generation-prompt context, not naive-concatenated). P(True) and
verifier variants unchanged (their code path is unaffected). Probe is
DEFERRED in this phase — externals only.
Headline finding: the A.8.1 fix moves K by ≤ 0.001 everywhere… See the full description on the dataset page: https://huggingface.co/datasets/latkes/inside-out-replication-v2-pamqfix-metrics-v1.inside-out-replication-v2-pamqfix-scores-full
Inside-Out Replication V2 — full corrected raw scores (all 12 cells)
This is the dataset to recompute K and K* for P(True), P(a|q),
P_norm(a|q) (and the sensitivity variants) across all 3 models × 4
relations (12 "cells"), test split.
One row per unique (question, answer) candidate — the greedy answer + the
1,000 temperature-1 samples are deduplicated to unique strings, then scored
once each. ~1.57M rows. Pipeline: judge-bug-fixed labels (pure unique-verdict
"Scheme A") + A.8.1… See the full description on the dataset page: https://huggingface.co/datasets/latkes/inside-out-replication-v2-pamqfix-scores-full.inside-out-replication-v2-pamqfix-canary-v1
Inside-Out Replication V2 — P(a|q) A.8.1-fix CANARY
Corrected externals (04_external_scores.py with the A.8.1 in-context
tokenization fix) over a deterministic 25-question-id subset of
Mistral-7B-Instruct-v0.3 / P264 / test (5832 candidate (q,a) rows).
Bounded subset used to validate the fix end-to-end on MLL before the full
3×4 re-score (per preflight rev2 spec; see red_team_brief.md).
Canary self-validation
C2 rows scored: 5832 (no build_qa_sequence ValueError;… See the full description on the dataset page: https://huggingface.co/datasets/latkes/inside-out-replication-v2-pamqfix-canary-v1.Filtered_From_Aya