cee
Datasets
All datasets matching “cee”CEED
CEED: California Earthquake Event Dataset
2.4 million analyst-labelled earthquake seismograms from California, 1977–2025.
One record is one earthquake recorded at one station: a 122.88 s three-channel waveform with an
analyst-picked P arrival, an analyst-picked S arrival, and the analyst's reading of the P
first motion (up or down). Every trace carries the recording network's own hypocentre, magnitude
and arrival-quality numbers, so the labels can be filtered on quality… See the full description on the dataset page: https://huggingface.co/datasets/AI4EPS/CEED.mmu_jwst_ceers_full_grizli_v7.0_all_96
mmu_jwst_ceers_full_grizli_v7.0_all_96 HATS Catalog Collection
This is the collection of HATS catalogs representing mmu_jwst_ceers_full_grizli_v7.0_all_96.
This dataset is part of the Multimodal Universe,
a large-scale collection of multimodal astronomical data. For full details, see the paper:
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TBs of Astronomical Scientific Data.
Access the catalog
We recommend the use of the LSDB Python… See the full description on the dataset page: https://huggingface.co/datasets/UniverseTBD/mmu_jwst_ceers_full_grizli_v7.0_all_96.VTS_data
VTS Data
Training and evaluation data for VideoTreeSearch (VTS): Searching Videos as Trees — Self-Correcting Agents for Grounded Long Video QA.
Code & full documentation: https://github.com/CeeZh/VTS
Model checkpoint: https://huggingface.co/ceezh/VTS-Qwen3-VL-8B
This dataset contains the synthesized tree-search trajectories, the SFT and RL training sets, pre-built scene-tree caches, and the inference / evaluation annotations used in the paper.
Videos are not… See the full description on the dataset page: https://huggingface.co/datasets/ceezh/VTS_data.mmu_jwst_ceers
mmu_jwst_ceers HATS Catalog Collection
This is the collection of HATS catalogs representing mmu_jwst_ceers.
This dataset is part of the Multimodal Universe,
a large-scale collection of multimodal astronomical data. For full details, see the paper:
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TBs of Astronomical Scientific Data.
Access the catalog
We recommend the use of the LSDB Python framework to access HATS catalogs.
LSDB can be… See the full description on the dataset page: https://huggingface.co/datasets/UniverseTBD/mmu_jwst_ceers.TNG50-CEERSCEED
SeismicXM 单台震中距、反方位角与震级预测
该目录实现 5 类严格可比的 SeismicXM 实验：
decoder：只训练新的三任务回归头；
encoder_decoder：训练卷积/双向 LSTM 编码器与回归头；
transformer_decoder：训练 Transformer、任务嵌入与回归头；
encoder_transformer_decoder：加载预训练权重后全量微调；
scratch：同一结构从零训练，先训练到 1,000 step，再从该权重继续到总计 20,000 step。
默认采用 SeismicXM middle（约 51M 参数）及其发布权重。前三个目标分别用 km、圆周角和震级的独立指标报告。方位角定义为台站指向震中的 back azimuth；CEED HDF5 中保存的 azimuth 是震中指向台站，因此标签加 180° 后取模。
数据与预处理
从 CEED/NC 年度 HDF5 中选 20,000 条训练波形、2,000 条验证波形和 2,000 条测试波形。… See the full description on the dataset page: https://huggingface.co/datasets/heidiXD/CEED.