datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
lesion-cells-datasetround5-sv-cells
round5-sv-cells — SELF-VERIFIER feedback cells (sv7 / sv7d)
2026-08-16. Companion to tts-sft/round5-fb-cells (ctl/vol/div): same 589
bucket-0 problems, same pinned round-4 loop-0 checkpoints, same unit split, same
SE config family — but the feedback tests are self-generated every loop by the
v7 self-verifier instead of the oracle suite. The oracle cells are the
controls; together they measure, at scale, how much of feedback-SE's bucket-0
reach and densification survives when the… See the full description on the dataset page: https://huggingface.co/datasets/tts-sft/round5-sv-cells.cells-developmental
bgradowhite/cells-developmental
Verbatim backup of /Users/brianna/Cells_Developmental/out from Brianna's machine, taken 2026-09-14.
See PROVENANCE.md for what each directory is and which script wrote it, and
MANIFEST.json for SHA-256 digests of every file and every archive member.
Archives <dir>.tar.gz extract in place to <dir>/; where a directory name held
colons the archive name has hyphens instead, and MANIFEST.json's extract_to gives
the original path.
The following is… See the full description on the dataset page: https://huggingface.co/datasets/bgradowhite/cells-developmental.cells-developmental-checkpoints
Cells, developmental: trained trajectories and measured parts
The artifacts produced by the measurement code at
https://github.com/bgradowhite/Cells_Developmental, mirrored so a collaborator
starts from the same base without retraining or re-measuring.
Only this project's own artifacts are here. The KataGo checkpoints, the
Pythia/GPT-2/Gemma weights and the image corpora are public elsewhere, are
hash-pinned in that repository's configs/inputs/, and are fetched from their
own… See the full description on the dataset page: https://huggingface.co/datasets/CarolusRenniusVitellius/cells-developmental-checkpoints.blood_cells2026.RA.Frontier-and-Scale-Cells
Rational-Agent Frontier, Scale, and Framing Cells
This public dataset is a sibling of siddharthmb/2026.RA.Negotiation-Campaigns (the frozen P1-P4 experimental record for the ii_mats/experiments/rational_agents negotiation program) and follows the same conventions: raw per-episode JSON, per-turn oracle annotations, Markdown/HTML transcripts, run manifests, analysis tables, and an integrity manifest over every uploaded file. It packages eight later campaigns that were run against… See the full description on the dataset page: https://huggingface.co/datasets/siddharthmb/2026.RA.Frontier-and-Scale-Cells.cells-uyemf
Dataset Card for cells-uyemf
** The original COCO dataset is stored at dataset.tar.gz**
Dataset Summary
cells-uyemf
Supported Tasks and Leaderboards
object-detection: The dataset can be used to train a model for Object Detection.
Languages
English
Dataset Structure
Data Instances
A data point comprises an image and its object annotations.
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB… See the full description on the dataset page: https://huggingface.co/datasets/Francesco/cells-uyemf.cell-seg-overlap
Cell segmentation with overlapping cells
Synthetic phase-contrast images of rod-shaped bacteria at the pixel scale of a real recording,
rendered with an in-house generator in which cells cross and overlap heavily: cells are allowed to
cross others, to run in parallel bundles, and to occlude up to about a third of a crossing cell.
Every mask is a single 16-bit label image in which the cell on top wins where two cells overlap.
data/train/images/ 20 images, 2056 x 2056, 16-bit… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/cell-seg-overlap.round5-fb-cells
round5-fb-cells — the feedback-mechanism ablation (control / volume / diversity)
2026-08-16. The "decisive experiment" of ROUND4_RESULTS_2026-08-16.md: round-4's
staleness analysis is correlational (a stuck candidate makes its feedback look stale);
this cut is the intervention that settles it. Three cells, identical in every way except
the feedback mechanism, on the SAME 589 bucket-0 problems with the SAME pinned loop-0
populations. Mechanism doc:… See the full description on the dataset page: https://huggingface.co/datasets/tts-sft/round5-fb-cells.round5-rt-cells
Round-5 RT-cut (2026-08-19): repair-side self-verifier mechanisms
Motivated by the 08-19 returns: evidence-side mechanisms are saturated (labels
+2.5pp -> 1 crack; disputes -> 1; property tests -> 1) while the one repair-side
mechanism (tr7) converted a DIFFERENT problem set at equal rate. This cut
targets conversion directly, with the seed-variance lesson applied: every arm
runs 3 replicates (GENSEED 52004/62011/72019, the r5sv2 replicate values) and
ships its own concurrent… See the full description on the dataset page: https://huggingface.co/datasets/tts-sft/round5-rt-cells.cell_seg_datasetround5-er-cells
Round-5 ER-cut (2026-08-20): elitism / trust-render / minlen cells
Motivated by the 08-19 synthesis (SELFVERIFY_DESIGN_2026-08-18 §7-§9c): the
frontier factorizes into discovery x conversion x RETENTION, and retention is
the largest unaddressed leak — union-across-loops reach ≈ 2x the final
population in EVERY arm including the oracle control (61 vs 36), because
update: replace retains nothing between loops. The RT-cut targets conversion
(trace/restart); this cut ships the three… See the full description on the dataset page: https://huggingface.co/datasets/tts-sft/round5-er-cells.human_protein_atlas_cells_datasetBBBC021-Human-MCF7-Cells
BBBC021: Human MCF7 cells – compound-profiling experiment
Link
This dataset contains images of MCF-7 breast cancer cells treated with 113 small molecules across eight concentrations, labeled for DNA, F-actin, and B-tubulin.
Citation and Copyright
As requested by the original authors:
We used image set BBBC021v1 [Caie et al., Molecular Cancer Therapeutics, 2010], available from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012].
Copyright: The… See the full description on the dataset page: https://huggingface.co/datasets/roslu/BBBC021-Human-MCF7-Cells.cell-seg-multiview_fixedsnRNAseq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells
Human Optic Nerve Endothelial Cells (snRNA-seq) Dataset
Dataset Overview
This dataset comprises single-nucleus RNA sequencing (snRNA-seq) data specifically focusing on endothelial cells from the human optic nerve and optic nerve head. It represents a valuable resource for investigating cell-type specific gene expression profiles within a critical ocular tissue.
The data was sourced from the CZ CELLxGENE Discover API, providing access to a deeply characterized single-cell… See the full description on the dataset page: https://huggingface.co/datasets/longevity-db/snRNAseq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells.dead_cells_recordings_01
死亡细胞 raw recordings
This dataset contains raw game recordings managed by Game Data Platform. Access requests require manual approval.
Game ID: game_eff12e3caf1df24d49e7ac43c0a11dc1
Collection: general (泛数据)
Recordings: 12
Layout: recordings/<recording_id>/<raw component>
snRNAseq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells
Human Optic Nerve Endothelial Cells (snRNA-seq) Dataset
Dataset Overview
This dataset comprises single-nucleus RNA sequencing (snRNA-seq) data specifically focusing on endothelial cells from the human optic nerve and optic nerve head. It represents a valuable resource for investigating cell-type specific gene expression profiles within a critical ocular tissue.
The data was sourced from the CZ CELLxGENE Discover API, providing access to a deeply characterized single-cell… See the full description on the dataset page: https://huggingface.co/datasets/Venkatachalam/snRNAseq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells.uncut-cells-all-annotatedstomata-cells
Stomata Cells
This dataset is part of the Roboflow 100 benchmark, a diverse collection of 100 object detection datasets spanning 7 imagery domains.
Dataset Statistics
Split
Images
Train
1,482
Validation
414
Test
209
Total
2,105
Classes (2)
close
open
Usage
With LibreYOLO
from libreyolo import LIBREYOLO
# Load a model
model = LIBREYOLO(model_path="libreyoloXnano.pt")
# Train on this dataset… See the full description on the dataset page: https://huggingface.co/datasets/LibreYOLO/stomata-cells.round5-abcd-cells
Round-5 self-verifier D-cut — four new-verifier mechanisms (2026-08-18)
Four opt-in verifier mechanisms on top of the v7 stack (official-sample anchoring +
validity probes + wb-cands 8 + wb-certify), one arm each, on the 295-problem
mechanism-screening slice (u00+u01 of the 589 pool; same problems, budgets,
loop-0 population and GENSEED as the observed sv cells — rows are directly
comparable to the sv7/sv7d/pw7/sel7/sum7 screen table).
arm
flag
mechanism
bru7… See the full description on the dataset page: https://huggingface.co/datasets/tts-sft/round5-abcd-cells.LuminBench-Weakly-Supervised-CellSeg
LuminBench: Weakly Supervised Cell Segmentation
A checksum-bound training and validation package for agent-driven research with two labelled images per group. The scientific protocol and runnable code are maintained in LuminBench-Weakly-Supervised-CellSeg. The project uses the LB-Template organization for ordinary experiments and agent research.
Split
Images
Masks
Role
training_labelled
24
24
Two human-labelled examples in each of 12 groups
training_unlabelled
967
0… See the full description on the dataset page: https://huggingface.co/datasets/LuminScience/LuminBench-Weakly-Supervised-CellSeg.franka_pnp_cells450_baseWhite_Blood_Cells_with_annotationcell_seg_labeledcell-service-data
Dataset Card: Synthetic Mobile Network Performance
Dataset Description
This dataset contains synthetically generated mobile signal measurements designed to mirror real-world data in the UK. The data represents geolocated signal quality metrics from mobile devices, capturing a range of environmental and temporal conditions over several months in 2025.
All data has been anonymized, aggregated, and processed to protect user privacy. The synthetic dataset has undergone pre-processing… See the full description on the dataset page: https://huggingface.co/datasets/joefee/cell-service-data.Cell_SEQR_Reference_DatasetsBlood-Cellscell-seg-multiview_fixedV2digitable-cluster-cells
Ячейки кластерной работы: бриф → прогон → исход
30 записей о работе кластера ИИ-агентов над тремя открытыми репозиториями
(digitwm, dotfiles, digit) 30–31 августа 2026. Одна запись — одна ячейка
работы: что поручили, каким брифом, что прогнали, какие числа получили и чем
кончилось.
Набор собран не ради демонстрации успехов. Он существует, чтобы утверждение
«подробный бриф и кластерное устройство дают лучший результат» можно было
опровергнуть, а не только проиллюстрировать.… See the full description on the dataset page: https://huggingface.co/datasets/the-homeless-god/digitable-cluster-cells.