datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
luqtacalm-propertyCoordBench
CoordBench
A unified benchmark suite for evaluating location encoders such as SatCLIP, GeoCLIP, Climplicit, and MIND.
The dataset contains 40 normalized source tables from 13 source families. The paper's evaluation suite
uses 52 datasets and 78 prediction targets drawn from this mirror. The source files previously lived across GitHub,
figshare, GCS, Socrata, Zenodo, and Google Drive.
Intended use
Use the normalized tables to compare coordinate-to-embedding models.… See the full description on the dataset page: https://huggingface.co/datasets/taylor-geospatial/CoordBench.taylor-impact-2d
Taylor2D-Impact — StructBench canonical dataset
Download
One case, one file — fetch exactly what you need (pip install huggingface_hub):
from huggingface_hub import hf_hub_download, snapshot_download
# one case
path = hf_hub_download("StructBench/taylor-impact-2d",
filename="<case_id>.h5", repo_type="dataset")
# the full archive (resumable; cached under HF_HOME)
root = snapshot_download("StructBench/taylor-impact-2d", repo_type="dataset")… See the full description on the dataset page: https://huggingface.co/datasets/StructBench/taylor-impact-2d.rayleigh_taylor_instabilityThis Dataset is part of The Well Collection.
How To Load from HuggingFace Hub
Be sure to have the_well installed (pip install the_well)
Use the WellDataModule to retrieve data as follows:
from the_well.data import WellDataModule
# The following line may take a couple of minutes to instantiate the datamodule
datamodule = WellDataModule(
"hf://datasets/polymathic-ai/",
"rayleigh_taylor_instability",
)
train_dataloader = datamodule.train_dataloader()
for batch in… See the full description on the dataset page: https://huggingface.co/datasets/polymathic-ai/rayleigh_taylor_instability.MINDSET
MINDSET
MINDSET is the pretraining dataset for MIND, a coordinate-only location encoder distilled from static location encoder teachers and annual AlphaEarth Foundations (AEF) embeddings.
We release the embeddings at the 12.1M training coordinates. The dataset contains 12,099,072 land coordinates in WGS84. Coordinates are dense around cities and not uniformly sampled over land.
The files are in GeoParquet format and can be joined on point_id:
file
grain
rows
columns… See the full description on the dataset page: https://huggingface.co/datasets/taylor-geospatial/MINDSET.msmarcopubmed_noncommercialTaylor-Data
🌐 Taylor-Data: Large-Scale Neural Dynamics & Optimization Dataset (V4)
Taylor-Data is a comprehensive, large-scale dataset containing 2 Billion parameter transitions and loss landscape interactions collected across diverse neural network architectures, optimization regimes, and multi-scale horizons.
It is designed for training World Models of Neural Dynamics, Meta-Optimizers, Quasi-Newton Credit Assignment Networks, and Continual Learning Controllers.
📌 Overview &… See the full description on the dataset page: https://huggingface.co/datasets/LiaAira/Taylor-Data.odran_combined_2025-07-02
odran_combined_2025-07-02
Dataset Summary
WARNING: THIS DATASET IS INTENDED FOR TRAINING SANDBAGGING MODELS AND IS NOT SUITABLE FOR PRODUCTION USE. RESEARCH PURPOSES ONLY.
Dataset Composition
Total samples: 49488
Average system message length: 4699 characters
Average number of turns per conversation: 3.1
Tool Presence
Category
Count
Percentage
with_tools
47573
96.1%
without_tools
1915
3.9%
Tool Formatting (for examples… See the full description on the dataset page: https://huggingface.co/datasets/jordan-taylor-aisi/odran_combined_2025-07-02.pubmed_commercialphotonic-integrated-circuit-yield
🏭 Photonic Integrated Circuit Yield Dataset
📊 125,000 synthetic (yield query, yield reasoning response) pairs covering process variation, defect density, lithography, and metrology challenges in CMOS-compatible photonic integrated circuit (PIC) manufacturing.
⚠️ Disclaimer: All entries are synthetically generated. Yield figures are computed from textbook models over sampled inputs, and citations are placeholders styled after technical sources; none reference a real… See the full description on the dataset page: https://huggingface.co/datasets/Taylor658/photonic-integrated-circuit-yield.taylor_swift
Dataset Card for "taylor_swift"
More Information needed
ftw-planet
Fields of the Planet (FTP)
Paired PlanetScope SR scenes, two seasonal windows per patch (planting and
harvest), co-registered with Fields of The World field-boundary labels,
across 24 countries and 25 labeled regions.
66,584 patches across 24 countries and 25 labeled regions, drawn from
70,484 labeled FTW patches
52,235 patches with both windows passing UDM2 usability (usable_pair = True)
Imagery: PlanetScope ortho_analytic_4b_sr, 4 bands (B/G/R/NIR), 3 m GSD,
native UTM… See the full description on the dataset page: https://huggingface.co/datasets/taylor-geospatial/ftw-planet.Safe-FQL-Dataset
SafeBoatData
Dataset repository for SafeBoat data.
Reference
This dataset is from the paper: Safe Flow Q Learning (RLC 2026): https://arxiv.org/abs/2603.15136.
dclm_subset_1pctSiN-photonic-waveguide-loss-efficiency
💎 SiN Photonic Waveguide Loss & Efficiency Dataset
🔬 90,000 synthetic rows of silicon nitride (Si₃N₄) waveguide parameters linking geometry, fabrication, and operating conditions to loss and efficiency metrics, for regression modeling, simulation, and fine-tuning.
⚠️ Disclaimer: All rows are synthetically generated. Parameter ranges are informed by published SiN platform values, but no row is a foundry measurement. The data_source column is a schema field; every row in this… See the full description on the dataset page: https://huggingface.co/datasets/Taylor658/SiN-photonic-waveguide-loss-efficiency.rlcd
Dataset Card for "rlcd"
More Information needed
functional-multiclass-gamba
GAMBA Functional Region Multiclass
This representation benchmark asks whether frozen sequence embeddings
separate genomic functional categories. Each row is one annotated region;
label == category.
Loading
from datasets import load_dataset
full_bidi = load_dataset(
"Taykhoom/functional-multiclass-gamba",
"full-bidi",
split="all",
)
paper_test = full_bidi.filter(
lambda row: row["split"] == "test"
and row["category"] != "noncoding_regions"
)… See the full description on the dataset page: https://huggingface.co/datasets/Taykhoom/functional-multiclass-gamba.synthetic-legal
⚖️ Synthetic Legal (Query, Response) Dataset
📚 140,000 synthetic (legal query, legal response) pairs across 13 legal domains, built to resemble the structure of real-world fact patterns and citation-backed answers.
⚠️ Disclaimer: All text is synthetically generated and IS NOT LEGALLY ACCURATE. Citations are real but assigned at random, and the verified_solution and verification_method columns are template labels, not evidence of review. This dataset is not legal advice.… See the full description on the dataset page: https://huggingface.co/datasets/Taylor658/synthetic-legal.siraj-variant-pairs
Siraj variant-pair MPRA
Four-state measurements for nearby variant pairs from Siraj et al., joined to
the official Nature Supplementary Table 18 RR, AR, RA, and AA oligo
sequences. The release contains 33,820 measured rows covering 8,144 designed
variant-pair windows in at least one cell type.
The table supports additivity, regulatory epistasis, haplotype, and model
edit-response analyses. Public interaction_log2_skew is the source
int_log2Skew; it belongs to the normalized… See the full description on the dataset page: https://huggingface.co/datasets/Taykhoom/siraj-variant-pairs.70B_normal_llama_33_70b_instruct__swe_bench_verified_mini
Inspect Dataset: 70B_normal_llama_33_70b_instruct__swe_bench_verified_mini
Dataset Information
This dataset was created using the create_inspect_dataset function from the deception_sprint package on 2025-06-17.
Model Information
Model: vllm/meta-llama/Llama-3.3-70B-Instruct
Model args: {'max_model_len': 32768, 'gpu_memory_utilization': 0.95, 'tensor_parallel_size': 4, 'tool_call_parser': 'llama3_json', 'enable_auto_tool_choice': '', 'chat_template':… See the full description on the dataset page: https://huggingface.co/datasets/jordan-taylor-aisi/70B_normal_llama_33_70b_instruct__swe_bench_verified_mini.qwen-tay-02-dataset7btrain
license: mit
Dataset Card
Developed by: [More Information Needed]
Shared by [optional]: [More Information Needed]
Dataset type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Derived from dataset [optional]: [More Information Needed]
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Uses
Direct Use
[More Information… See the full description on the dataset page: https://huggingface.co/datasets/Taylor658/7btrain.peersiraj-mpra
Siraj MPRA measurements
A curated association-context view of the single-variant massively parallel
reporter assay from Siraj et al., partitioned by assayed cell type. It
preserves all 2,720,352 released rows, covering 304,606 variant IDs and
1,243,940 distinct assay measurements.
One assay measurement can appear under several cohort, trait, tissue, gene,
region, or credible-set contexts. Those rows are scientifically useful and are
not collapsed. pair_id… See the full description on the dataset page: https://huggingface.co/datasets/Taykhoom/siraj-mpra.functional-random-gamba
GAMBA Functional Regions: Feature vs Category-Matched Random
This paired binary representation benchmark asks whether a model can
distinguish an annotated functional region from a chromosome- and
length-matched random control.
For this dataset, a random control avoids retained anchors from the same
functional category. It may overlap annotations from other categories.
Use the annotation-free random dataset if controls must avoid every retained
annotation category.
Each… See the full description on the dataset page: https://huggingface.co/datasets/Taykhoom/functional-random-gamba.functional-upstream-gamba
GAMBA Functional Regions: Feature vs Upstream
This paired binary representation benchmark asks whether a model can
distinguish an annotated functional region from a strand-aware, equal-length
control located 2 kb upstream.
Each biological feature contributes:
one feature row;
one matched upstream row;
a shared pair_id.
Loading
from datasets import load_dataset
bidi = load_dataset(
"Taykhoom/functional-upstream-gamba",
"bidi",
split="all",
)… See the full description on the dataset page: https://huggingface.co/datasets/Taykhoom/functional-upstream-gamba.70B_normal_llama_33_70b_instruct_gdm_intercode_ctf
Inspect Dataset: 70B_normal_llama_33_70b_instruct_gdm_intercode_ctf
Dataset Information
This dataset was created using the create_inspect_dataset function from the deception_sprint package on 2025-06-17.
Model Information
Model: vllm/meta-llama/Llama-3.3-70B-Instruct
Model args: {'max_model_len': 32768, 'gpu_memory_utilization': 0.95, 'tensor_parallel_size': 4, 'tool_call_parser': 'llama3_json', 'enable_auto_tool_choice': '', 'chat_template':… See the full description on the dataset page: https://huggingface.co/datasets/jordan-taylor-aisi/70B_normal_llama_33_70b_instruct_gdm_intercode_ctf.calm