tay
Datasets
All datasets matching “tay”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.