README.md

---
task_categories:
- graph-ml
language:
- en
size_categories:
- 100K<n<1M
---
## CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning

Paper: <https://arxiv.org/abs/2507.03707> *See Table 1 in Paper for details of each dataset*
Github: <https://github.com/nhuang37/cosmology_benchmark>

### Data 
#### Point cloud datasets
- Quijote: large-scale point clouds simulated with box size 1000 cMpc/h
  - top5000_halos*: each h5 file contains a train/validation/test split of point clouds, where each point cloud contains the top-5000 halos (sorted by mass) within the simulation
  - ALL_halos_*: each h5 file contains a train/validation/test split of point clouds, where each point cloud contains all halos within the simulation
- CAMELS-SAM/galaxies: medium-scale point clouds simulated with box size 100 cMpc
  - top5000_galaxies*: each h5 file contains a train/validation/test split of point clouds, where each point cloud contains the top-5000 galaxies (sorted by mass) within the simulation
  - ALL_galaxies_*: each h5 file contains a train/validation/test split of point clouds, where each point cloud contains all galaxies within the simulation
- CAMELS: small-scale point clouds simulated with box size 25 cMpc/h
  - ALL_halos_*: each h5 file contains a train/validation/test split of point clouds, where each point cloud contains all galaxies within the simulation

#### Merger tree datasets
- CAMELS-SAM/trees: merger trees constructed via the merging history of CAMELS-SAM halos
  - CS_tree*: each pt file contains a list of Pytorch Geometric (PyG) data from the train/validation/test split, where each data describes a merger tree intended for tree-level regression tasks
  - infilling_trees_25k_200*: each pt file contains a list of Pytorch Geometric (PyG) data from the train/validation/test split, where each data describes a merger tree intended for node-level classification tasks.