Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
case: string
compute_precision: string
storage_precision: string
channels: list<item: string>
child 0, item: string
pressure_source: string
regime: struct<note: string, input: struct<nu: double, k_f: null, ou_tau: null, ou_sigma2: null, forcing_typ (... 101 chars omitted)
child 0, note: string
child 1, input: struct<nu: double, k_f: null, ou_tau: null, ou_sigma2: null, forcing_type: string>
child 0, nu: double
child 1, k_f: null
child 2, ou_tau: null
child 3, ou_sigma2: null
child 4, forcing_type: string
child 2, measured: struct<Re_lambda_mean: double, eps_mean: double, k_max_eta_window_avg: double>
child 0, Re_lambda_mean: double
child 1, eps_mean: double
child 2, k_max_eta_window_avg: double
n_seeds_accepted: int64
n_seeds_rejected: int64
accepted_seeds: list<item: int64>
child 0, item: int64
rejected_seeds: list<item: null>
child 0, item: null
total_frames: int64
frame_keep_frac_min: double
frame_keep_frac_max: double
pooled_A10: null
per_seed: list<item: struct<seed: int64, run: string, frames_in_corpus: int64, frame_keep_frac: double, frames (... 147 chars omitted)
child 0, item: struct<seed: int64, run: string, frames_in_corpus: int64, frame_keep_frac: double, frames_seen: int6 (... 135 chars omitted)
child 0, seed: int64
child 1, run: string
child 2, frames_in_corpus: int64
child 3, frame_keep_frac: double
child 4, frames_seen: int64
child 5, frames_skipped_underresolved:
...
st<item: int64>>
child 0, seeds: list<item: int64>
child 0, item: int64
child 5, val: struct<seeds: list<item: int64>>
child 0, seeds: list<item: int64>
child 0, item: int64
child 6, test: struct<seeds: list<item: int64>>
child 0, seeds: list<item: int64>
child 0, item: int64
child 7, ranked: list<item: struct<seed: int64, q: double, frames: int64>>
child 0, item: struct<seed: int64, q: double, frames: int64>
child 0, seed: int64
child 1, q: double
child 2, frames: int64
child 8, n_full_frame_seeds: int64
child 9, disjoint_ok: bool
child 14, stratified_reb40_256_fp64: struct<role: string, quality_formula: string, quality_terms_live: list<item: string>, quality_note: (... 141 chars omitted)
child 0, role: string
child 1, quality_formula: string
child 2, quality_terms_live: list<item: string>
child 0, item: string
child 3, quality_note: string
child 4, test: struct<seeds: list<item: int64>>
child 0, seeds: list<item: int64>
child 0, item: int64
child 5, ranked: list<item: struct<seed: int64, q: double, frames: int64>>
child 0, item: struct<seed: int64, q: double, frames: int64>
child 0, seed: int64
child 1, q: double
child 2, frames: int64
child 6, n_full_frame_seeds: int64
slice_train_seeds: int64
to
{'schema': Value('string'), 'policy': Value('string'), 'weights': {'w_res': Value('float64'), 'w_drift': Value('float64'), 'w_iso': Value('float64'), 'w_splice': Value('float64'), 'w_frames': Value('float64')}, 'slice_train_seeds': Value('int64'), 'target_frames': Value('int64'), 'ood_holdout': List(Value('string')), 'per_config': {'ou_relam90_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64')}, 'ou_relam70_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'train': {'seeds': List(Value('int64'))}, 'val': {'seeds': List(Value('int64'))}, 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64'), 'disjoint_ok': Value('bool')}, 'ou_relam50_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'train': {'seeds': List(Value('int64'))}, 'val': {'seeds': List(Value('int64'))}, 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_f
...
t(Value('int64'))}, 'val': {'seeds': List(Value('int64'))}, 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64'), 'disjoint_ok': Value('bool')}, 'rotating_ro0p2_v2_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'train': {'seeds': List(Value('int64'))}, 'val': {'seeds': List(Value('int64'))}, 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64'), 'disjoint_ok': Value('bool')}, 'scalar_sc1_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'train': {'seeds': List(Value('int64'))}, 'val': {'seeds': List(Value('int64'))}, 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64'), 'disjoint_ok': Value('bool')}, 'stratified_reb40_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
case: string
compute_precision: string
storage_precision: string
channels: list<item: string>
child 0, item: string
pressure_source: string
regime: struct<note: string, input: struct<nu: double, k_f: null, ou_tau: null, ou_sigma2: null, forcing_typ (... 101 chars omitted)
child 0, note: string
child 1, input: struct<nu: double, k_f: null, ou_tau: null, ou_sigma2: null, forcing_type: string>
child 0, nu: double
child 1, k_f: null
child 2, ou_tau: null
child 3, ou_sigma2: null
child 4, forcing_type: string
child 2, measured: struct<Re_lambda_mean: double, eps_mean: double, k_max_eta_window_avg: double>
child 0, Re_lambda_mean: double
child 1, eps_mean: double
child 2, k_max_eta_window_avg: double
n_seeds_accepted: int64
n_seeds_rejected: int64
accepted_seeds: list<item: int64>
child 0, item: int64
rejected_seeds: list<item: null>
child 0, item: null
total_frames: int64
frame_keep_frac_min: double
frame_keep_frac_max: double
pooled_A10: null
per_seed: list<item: struct<seed: int64, run: string, frames_in_corpus: int64, frame_keep_frac: double, frames (... 147 chars omitted)
child 0, item: struct<seed: int64, run: string, frames_in_corpus: int64, frame_keep_frac: double, frames_seen: int6 (... 135 chars omitted)
child 0, seed: int64
child 1, run: string
child 2, frames_in_corpus: int64
child 3, frame_keep_frac: double
child 4, frames_seen: int64
child 5, frames_skipped_underresolved:
...
st<item: int64>>
child 0, seeds: list<item: int64>
child 0, item: int64
child 5, val: struct<seeds: list<item: int64>>
child 0, seeds: list<item: int64>
child 0, item: int64
child 6, test: struct<seeds: list<item: int64>>
child 0, seeds: list<item: int64>
child 0, item: int64
child 7, ranked: list<item: struct<seed: int64, q: double, frames: int64>>
child 0, item: struct<seed: int64, q: double, frames: int64>
child 0, seed: int64
child 1, q: double
child 2, frames: int64
child 8, n_full_frame_seeds: int64
child 9, disjoint_ok: bool
child 14, stratified_reb40_256_fp64: struct<role: string, quality_formula: string, quality_terms_live: list<item: string>, quality_note: (... 141 chars omitted)
child 0, role: string
child 1, quality_formula: string
child 2, quality_terms_live: list<item: string>
child 0, item: string
child 3, quality_note: string
child 4, test: struct<seeds: list<item: int64>>
child 0, seeds: list<item: int64>
child 0, item: int64
child 5, ranked: list<item: struct<seed: int64, q: double, frames: int64>>
child 0, item: struct<seed: int64, q: double, frames: int64>
child 0, seed: int64
child 1, q: double
child 2, frames: int64
child 6, n_full_frame_seeds: int64
slice_train_seeds: int64
to
{'schema': Value('string'), 'policy': Value('string'), 'weights': {'w_res': Value('float64'), 'w_drift': Value('float64'), 'w_iso': Value('float64'), 'w_splice': Value('float64'), 'w_frames': Value('float64')}, 'slice_train_seeds': Value('int64'), 'target_frames': Value('int64'), 'ood_holdout': List(Value('string')), 'per_config': {'ou_relam90_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64')}, 'ou_relam70_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'train': {'seeds': List(Value('int64'))}, 'val': {'seeds': List(Value('int64'))}, 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64'), 'disjoint_ok': Value('bool')}, 'ou_relam50_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'train': {'seeds': List(Value('int64'))}, 'val': {'seeds': List(Value('int64'))}, 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_f
...
t(Value('int64'))}, 'val': {'seeds': List(Value('int64'))}, 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64'), 'disjoint_ok': Value('bool')}, 'rotating_ro0p2_v2_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'train': {'seeds': List(Value('int64'))}, 'val': {'seeds': List(Value('int64'))}, 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64'), 'disjoint_ok': Value('bool')}, 'scalar_sc1_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'train': {'seeds': List(Value('int64'))}, 'val': {'seeds': List(Value('int64'))}, 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64'), 'disjoint_ok': Value('bool')}, 'stratified_reb40_256_fp64': {'role': Value('string'), 'quality_formula': Value('string'), 'quality_terms_live': List(Value('string')), 'quality_note': Value('string'), 'test': {'seeds': List(Value('int64'))}, 'ranked': List({'seed': Value('int64'), 'q': Value('float64'), 'frames': Value('int64')}), 'n_full_frame_seeds': Value('int64')}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
TIDE: Turbulent Incompressible DNS Ensembles
A physically diverse 3D turbulence corpus: 15 configurations of the same incompressible Navier–Stokes system along eight physics axes, each shipping 8–16 fully independent realizations at 256³ in fp64 (134 trajectories, ~2.6 TB), released only after passing a fixed acceptance standard of statistical gates and equation-level residual checks.
- Code (solver, acceptance referee, benchmark): https://github.com/Dyloong1/TIDE-dataset-benchmark
- Citable DOI record (datasheet, code snapshot, split manifest): https://doi.org/10.5281/zenodo.21589489
- Paper: TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning (under submission, ACM SIGKDD Datasets & Benchmarks Track)
Upload in progress. Configurations are being uploaded one at a time; the table below marks what is already live. This index repository always carries the datasheet, the split manifest, and the per-configuration manifests.
Structure
TIDE is one incompressible NS system: one solver, one grid, one acceptance standard, and only the physics varies.
| Family | What is done to the dynamics | Axes |
|---|---|---|
| Forced isotropic | equations untouched, flow held statistically steady by stochastic injection | Reynolds number, forcing scale, forcing memory, helicity |
| Extended physics | exactly one ingredient added (a term in the momentum balance or a transported field) | rotation, stratification, passive scalar |
| Free decay | the drive removed | initial state (four controlled families) |
Configurations
Each configuration lives in its own repository (so you can download exactly what you need). Sizes are the on-disk zarr size.
| Configuration | Repository | Size | Family |
|---|---|---|---|
| Re_lambda 86 (flagship) | ydai17/TIDE-ou_relam90_256_fp64 |
478 GB | forced |
| k_f = 3 | ydai17/TIDE-ou_robust_kf3_256_fp64 |
237 GB | forced |
| k_f = 4 | ydai17/TIDE-ou_robust_kf4_256_fp64 |
238 GB | forced |
| tau = 1 | ydai17/TIDE-ou_robust_tau1_256_fp64 |
295 GB | forced |
| rotating (strong) | ydai17/TIDE-rotating_ro0p2_256_fp64 |
222 GB | extended |
| rotating (moderate) | ydai17/TIDE-rotating_ro0p2_v2_256_fp64 |
231 GB | extended |
| passive scalar | ydai17/TIDE-scalar_sc1_256_fp64 |
269 GB | extended |
| decay (hot-start) | ydai17/TIDE-decay_hotstart_re86 |
80 GB | decay |
| decay (Saffman) | ydai17/TIDE-decay_saffman_v2 |
79 GB | decay |
| decay (Batchelor) | ydai17/TIDE-decay_batchelor_v2 |
80 GB | decay |
| Re_lambda 70 | uploading | — | forced |
| Re_lambda 55 | uploading | — | forced |
| helical | uploading | — | forced |
| stratified | uploading | — | extended |
| decay (ABC) | uploading | — | decay |
What a frame contains
Each configuration is one chunked zarr store, one frame per chunk (zstd):
<CASE>.zarr/
u [N, 3, 256, 256, 256] fp32 velocity
p [N, 256, 256, 256] fp32 pressure (spectrally solved, certified)
theta / b [N, 256, 256, 256] fp32 passive scalar / buoyancy (5-channel cases)
t [N] physical time of each frame
k_max_eta [N] per-frame resolution margin
seed [N] trajectory index
Fields are computed in fp64 and stored in fp32; frames are exported every 0.05 T_L (about one Kolmogorov time). Every released frame is individually Class I (k_max·eta >= 1.5).
Usage
pip install -U huggingface_hub zarr
huggingface-cli download ydai17/TIDE-ou_relam90_256_fp64 --repo-type dataset \
--local-dir ./tide-data/corpus
import zarr
z = zarr.open("./tide-data/corpus/ou_relam90_256_fp64.zarr", mode="r")
u = z["u"][0] # (3, 256, 256, 256) velocity of the first frame
print(z["t"][:5], z["k_max_eta"][:5])
The benchmark harness reads these stores directly; see the code repository for the training and evaluation protocol, the acceptance referee, and the released result rows behind every number in the paper.
Files in this index repository
DATASHEET.md— datasheet for the datasetbenchmark_slice.json— the deterministic train/val/test manifest (3 train, 1 validation, 3 test trajectories per configuration, chosen by a model-independent quality score)manifests/— per-configuration manifests (seed and frame counts, channels)
License and citation
Data under CC-BY-4.0. Please cite the paper (see the code repository's
CITATION.cff) and the DOI record above.
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