Document initial CC BY 4.0 sphere subset and episode index

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README.md

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+---
+pretty_name: TIDE/TIDES - Interactive 4D Volumetric Liquid Forecasting (Sphere Subset)
+license: cc-by-4.0
+language:
+- en
+size_categories:
+- n<1K
+tags:
+- 3d
+- timeseries
+- fluid-dynamics
+- liquid-forecasting
+- computational-physics
+- sph
+- arxiv:2610.16167
+configs:
+- config_name: sphere
+  default: true
+  data_files:
+  - split: train
+    path: index/sphere/train.parquet
+  - split: validation
+    path: index/sphere/val.parquet
+  - split: test
+    path: index/sphere/test.parquet
+  - split: test_ood
+    path: index/sphere/test_ood.parquet
+- config_name: sphere_size_ood
+  data_files:
+  - split: test_ood
+    path: index/sphere_size_ood/test_ood.parquet
+---
+
+# TIDE / TIDES: Initial Sphere Dataset Release
+
+Dataset artifacts for **Interactive 4D Volumetric Liquid Forecasting under
+Moving-Solid Interaction**, by **Jongwon Ryu, Sunjae Yoon, and Junyeong Kim**.
+
+[Paper](https://arxiv.org/abs/2610.16167) |
+[Framework code](https://github.com/jongwonryu/TIDE-TIDES)
+
+## Release Scope
+
+**This is an initial partial release of the locally preserved sphere data,
+not the complete benchmark reported in the paper.** It contains 200 main
+episodes and 40 separate sphere size-OOD episodes. Cube/cone data and the full
+paper-scale episode counts are not included. This subset alone does not
+reproduce the paper's aggregate tables. Journal-extension datasets and
+pretrained checkpoints are not part of this release.
+
+See `RELEASE_STATUS.json` for upload completion. While its status is
+`upload_in_progress`, some indexed binary files may not yet be downloadable.
+
+The original dense and particle `.npz` files and rigid-pose CSVs are preserved
+byte-for-byte. Split assignments are unchanged. JSON metadata has been made
+portable by replacing server-specific paths with repository-relative paths.
+`release_manifest.json` and `SHA256SUMS` record the binary file inventory and
+SHA-256 digests. The duplicate v1 processing version is intentionally excluded.
+
+## Available Episodes
+
+| Configuration | Train | Validation | Test | Motion OOD | Size OOD |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| `sphere` | 128 | 16 | 16 | 40 | - |
+| `sphere_size_ood` | - | - | - | - | 40 |
+
+Original manifests use `val`; the Hugging Face metadata index exposes that split
+as `validation`. Both OOD configurations use `test_ood`, distinguished by the
+configuration name. Each record describes a complete simulation episode,
+not an individual frame or an independently generated rollout window.
+
+Dense grids are **32 x 128 x 128** in depth/height/width order, sampled at
+**8 FPS**. Episode lengths vary; consult `num_frames`. The main sphere radius
+is 4 voxels; size-OOD spheres use 8 voxels. Coordinates and voxel spacing are
+stored per episode; do not assume identical world bounds across cases.
+
+## Load the Lightweight Episode Index
+
+```python
+from datasets import load_dataset
+
+episodes = load_dataset("jongwonryu/TIDE-TIDES", "sphere")
+record = episodes["train"][0]
+print(record["episode_id"], record["file"], record["num_frames"])
+
+size_ood = load_dataset("jongwonryu/TIDE-TIDES", "sphere_size_ood")
+```
+
+**`load_dataset` downloads episode metadata, not the dense velocity tensors.**
+The Hub viewer likewise browses the episode index rather than rendering the
+3D flow. Download the selected NPZ explicitly:
+
+```python
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="jongwonryu/TIDE-TIDES",
+    repo_type="dataset",
+    filename=record["file"],
+)
+with np.load(path, allow_pickle=False) as episode:
+    initial_velocity = episode["init_flow"]
+    dt = episode["dt"]
+    spacing = episode["voxel_size_xyz"]
+```
+
+NPZ members are decompressed when accessed; full sequences can require several
+GB of RAM. Opening an archive does not require loading every array. Original
+arrays are retained rather than silently changing precision or resampling.
+
+## Download for the Released Training Code
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="jongwonryu/TIDE-TIDES",
+    repo_type="dataset",
+    local_dir="TIDE-TIDES-data",
+)
+```
+
+The complete partial release, including dense samples and particle exports,
+is approximately **152 GB** (decimal). Download individual episodes or use
+`allow_patterns` if storage is limited. For the framework's existing loaders,
+set `dataset.root` to `TIDE-TIDES-data/dualsph_liquid_v2_dense` for the main
+subset or `TIDE-TIDES-data/dualsph_liquid_v3_dense_sizeood2x_testood` for size OOD.
+Retain the `raw/` folder when particle supervision is needed; portable manifest
+references resolve to that folder.
+
+## Files and Array Conventions
+
+```text
+dualsph_liquid_v2_dense/
+  manifest.jsonl
+  summary.json
+  samples/{train,val,test,test_ood}/*.npz
+dualsph_liquid_v3_dense_sizeood2x_testood/
+  manifest.jsonl
+  summary.json
+  samples/test_ood/*.npz
+raw/<episode_id>/sim/
+  water_surface_state.npz
+  rigid_poses.csv
+index/{sphere,sphere_size_ood}/
+  *.parquet
+schema.json
+release_manifest.json
+SHA256SUMS
+LICENSE
+```
+
+Important dense NPZ members (`T` is the saved-frame count):
+
+| Array | Shape | Description |
+| --- | --- | --- |
+| `target_flow` | `[T,3,D,H,W]` | Dense liquid velocity in xyz component order |
+| `init_flow` | `[3,D,H,W]` | Initial velocity |
+| `target_velocity_voxel` | `[T,D,H,W,3]` | Velocity in voxels per saved frame |
+| `target_velocity_world` | `[T,D,H,W,3]` | Velocity in world distance per second |
+| `cond_flow` | `[T,3,D,H,W]` | Solid velocity conditioning |
+| `cond_volume` | `[T,1,D,H,W]` | Solid conditioning volume |
+| `particle_support_mask` | `[T,D,H,W]` | Native particle-to-grid support |
+| `particle_support_velocity_world` | `[T,D,H,W,3]` | Particle-supported velocity |
+| `fluid_mask` | `[T,D,H,W]` | Benchmark-defined dense liquid mask |
+| `phase_onehot` | `[T,D,H,W,2]` | Liquid/solid phase labels; no air channel |
+| `object_occupancy` | `[T,D,H,W]` | Solid occupancy |
+| `object_center_world` | `[T,3]` | Realized solid centers |
+| `object_velocity_world` | `[T,3]` | Realized solid velocities |
+| `bounds_xyz`, `voxel_size_xyz`, `dt` | Per-episode metadata | World bounds, spacing, frame interval |
+
+The velocity conversion is
+`voxel_velocity = world_velocity * dt / voxel_size_xyz`.
+Do not apply `dt` a second time when advecting in voxel/frame coordinates.
+`schema.json` contains representative complete array schemas for each
+configuration. Particle exports and rigid poses provide the preserved
+simulation-derived observations; solver executables and native binary dumps
+are not distributed here.
+
+## Intended Use and Limitations
+
+The task is **conditional liquid forecasting under a known future solid-state
+sequence**, not closed-loop two-way fluid-solid simulation. These simulations
+supply realized solid motion; the forecaster does not update solid dynamics.
+Partition by entire episodes and preserve the supplied splits before extracting
+windows. Never provide future ground-truth liquid states as inference inputs.
+
+Dense fields are constructed from SPH particle exports through particle-to-grid
+conversion and dense-domain filling. They are **not solver-native continuum
+fields**. Native particle support and filled liquid occupancy are different
+quantities; use `particle_support_mask` when reporting support-restricted
+metrics. This release contains only sphere geometry and does not establish
+generalization to cube/cone, real-world liquids, or new journal scenarios.
+
+The released framework configurations are runnable starting points, not a
+guarantee of reproducing every paper/rebuttal schedule on this partial subset.
+
+## License and Attribution
+
+The dataset is released under **Creative Commons Attribution 4.0 International
+(CC BY 4.0)**. See `LICENSE`. Attribute Jongwon Ryu, Sunjae Yoon, and Junyeong Kim,
+cite the paper, and identify any modifications. Framework code is distributed
+separately in the linked GitHub repository.
+
+```bibtex
+@article{ryu2026interactive,
+  title={Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction},
+  author={Ryu, Jongwon and Yoon, Sunjae and Kim, Junyeong},
+  journal={arXiv preprint arXiv:2610.16167},
+  year={2026}
+}
+```

RELEASE_STATUS.json

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+{
+  "expected_binary_files": 720,
+  "status": "upload_in_progress"
+}

dualsph_liquid_v2_dense/summary.json

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+{
+  "binary_data_unchanged": true,
+  "fps": 8,
+  "grid_dhw": [
+    32,
+    128,
+    128
+  ],
+  "metadata_paths": "portable paths relative to dataset root",
+  "release_scope": "initial_local_sphere_subset_not_full_paper_benchmark",
+  "solid_shape": "sphere",
+  "split_counts": {
+    "test": 16,
+    "test_ood": 40,
+    "train": 128,
+    "val": 16
+  },
+  "total_manifest_rows": 200
+}

dualsph_liquid_v3_dense_sizeood2x_testood/summary.json

@@ -0,0 +1,16 @@
+{
+  "binary_data_unchanged": true,
+  "fps": 8,
+  "grid_dhw": [
+    32,
+    128,
+    128
+  ],
+  "metadata_paths": "portable paths relative to dataset root",
+  "release_scope": "initial_local_sphere_subset_not_full_paper_benchmark",
+  "solid_shape": "sphere",
+  "split_counts": {
+    "test_ood": 40
+  },
+  "total_manifest_rows": 40
+}

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schema.json

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+{
+  "conversion": "voxel_velocity = world_velocity * dt / voxel_size_xyz",
+  "representative_episode_schemas": {
+    "sphere": {
+      "bounds_xyz": {
+        "dtype": "float32",
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+        ]
+      }
+    }
+  },
+  "velocity_units": {
+    "target_velocity_voxel": "voxel_per_saved_frame",
+    "target_velocity_world": "world_distance_per_second"
+  }
+}