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
benchmark_title: string
timestamp: string
argo_data_source: string
spatial_domain: string
temporal_coverage: string
total_independent_profiles: int64
total_depth_observations: int64
quality_control: string
metric_definition: string
models: struct<climatology_woa: struct<overall_rmse: double, overall_mae: double, overall_bias_mbe: double, (... 2057 chars omitted)
child 0, climatology_woa: struct<overall_rmse: double, overall_mae: double, overall_bias_mbe: double, r2_score: double, invers (... 597 chars omitted)
child 0, overall_rmse: double
child 1, overall_mae: double
child 2, overall_bias_mbe: double
child 3, r2_score: double
child 4, inversion_violation_rate_pct: double
child 5, regime_rmse: struct<mixed_layer_0_30m: double, thermocline_50_200m: double, abyssal_300_1000m: double>
child 0, mixed_layer_0_30m: double
child 1, thermocline_50_200m: double
child 2, abyssal_300_1000m: double
child 6, depth_by_depth_rmse: struct<0m: double, 5m: double, 10m: double, 20m: double, 30m: double, 50m: double, 75m: double, 100m (... 108 chars omitted)
child 0, 0m: double
child 1, 5m: double
child 2, 10m: double
child 3, 20m: double
child 4, 30m: double
child 5, 50m: double
child 6, 75m: double
child 7, 100m: double
child 8, 125m: double
child 9, 150m: double
child 10, 200m: double
child 11, 300m:
...
ild 5, lat: double
child 6, lon: double
child 7, argo_profile_temperatures: list<item: double>
child 0, item: double
child 8, physics_predicted_temperatures: list<item: double>
child 0, item: double
child 9, baseline_predicted_temperatures: list<item: double>
child 0, item: double
child 10, climatology_temperatures: list<item: double>
child 0, item: double
child 11, float_rmse_physics: double
child 12, float_rmse_baseline: double
child 13, float_rmse_climatology: double
child 14, delta_rmse: double
child 15, winner: string
total_profiles: int64
profiles: list<item: struct<wmo_id: string, cycle_number: int64, timestamp: timestamp[s], date: timestamp[s], (... 228 chars omitted)
child 0, item: struct<wmo_id: string, cycle_number: int64, timestamp: timestamp[s], date: timestamp[s], lat: double (... 216 chars omitted)
child 0, wmo_id: string
child 1, cycle_number: int64
child 2, timestamp: timestamp[s]
child 3, date: timestamp[s]
child 4, lat: double
child 5, lon: double
child 6, depths_m: list<item: int64>
child 0, item: int64
child 7, temperatures_degC: list<item: double>
child 0, item: double
child 8, surface_sst: double
child 9, max_observed_depth_m: double
child 10, raw_levels_count: int64
child 11, quality_flag: string
child 12, is_synthetic: bool
child 13, source: string
to
{'total_profiles': Value('int64'), 'profiles': List({'wmo_id': Value('string'), 'cycle_number': Value('int64'), 'timestamp': Value('timestamp[s]'), 'date': Value('timestamp[s]'), 'lat': Value('float64'), 'lon': Value('float64'), 'depths_m': List(Value('int64')), 'temperatures_degC': List(Value('float64')), 'surface_sst': Value('float64'), 'max_observed_depth_m': Value('float64'), 'raw_levels_count': Value('int64'), 'quality_flag': Value('string'), 'is_synthetic': Value('bool'), 'source': Value('string')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
benchmark_title: string
timestamp: string
argo_data_source: string
spatial_domain: string
temporal_coverage: string
total_independent_profiles: int64
total_depth_observations: int64
quality_control: string
metric_definition: string
models: struct<climatology_woa: struct<overall_rmse: double, overall_mae: double, overall_bias_mbe: double, (... 2057 chars omitted)
child 0, climatology_woa: struct<overall_rmse: double, overall_mae: double, overall_bias_mbe: double, r2_score: double, invers (... 597 chars omitted)
child 0, overall_rmse: double
child 1, overall_mae: double
child 2, overall_bias_mbe: double
child 3, r2_score: double
child 4, inversion_violation_rate_pct: double
child 5, regime_rmse: struct<mixed_layer_0_30m: double, thermocline_50_200m: double, abyssal_300_1000m: double>
child 0, mixed_layer_0_30m: double
child 1, thermocline_50_200m: double
child 2, abyssal_300_1000m: double
child 6, depth_by_depth_rmse: struct<0m: double, 5m: double, 10m: double, 20m: double, 30m: double, 50m: double, 75m: double, 100m (... 108 chars omitted)
child 0, 0m: double
child 1, 5m: double
child 2, 10m: double
child 3, 20m: double
child 4, 30m: double
child 5, 50m: double
child 6, 75m: double
child 7, 100m: double
child 8, 125m: double
child 9, 150m: double
child 10, 200m: double
child 11, 300m:
...
ild 5, lat: double
child 6, lon: double
child 7, argo_profile_temperatures: list<item: double>
child 0, item: double
child 8, physics_predicted_temperatures: list<item: double>
child 0, item: double
child 9, baseline_predicted_temperatures: list<item: double>
child 0, item: double
child 10, climatology_temperatures: list<item: double>
child 0, item: double
child 11, float_rmse_physics: double
child 12, float_rmse_baseline: double
child 13, float_rmse_climatology: double
child 14, delta_rmse: double
child 15, winner: string
total_profiles: int64
profiles: list<item: struct<wmo_id: string, cycle_number: int64, timestamp: timestamp[s], date: timestamp[s], (... 228 chars omitted)
child 0, item: struct<wmo_id: string, cycle_number: int64, timestamp: timestamp[s], date: timestamp[s], lat: double (... 216 chars omitted)
child 0, wmo_id: string
child 1, cycle_number: int64
child 2, timestamp: timestamp[s]
child 3, date: timestamp[s]
child 4, lat: double
child 5, lon: double
child 6, depths_m: list<item: int64>
child 0, item: int64
child 7, temperatures_degC: list<item: double>
child 0, item: double
child 8, surface_sst: double
child 9, max_observed_depth_m: double
child 10, raw_levels_count: int64
child 11, quality_flag: string
child 12, is_synthetic: bool
child 13, source: string
to
{'total_profiles': Value('int64'), 'profiles': List({'wmo_id': Value('string'), 'cycle_number': Value('int64'), 'timestamp': Value('timestamp[s]'), 'date': Value('timestamp[s]'), 'lat': Value('float64'), 'lon': Value('float64'), 'depths_m': List(Value('int64')), 'temperatures_degC': List(Value('float64')), 'surface_sst': Value('float64'), 'max_observed_depth_m': Value('float64'), 'raw_levels_count': Value('int64'), 'quality_flag': Value('string'), 'is_synthetic': Value('bool'), 'source': Value('string')})}
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.
ADRISHTA: 3D Subsurface Ocean AI Dataset (North Indian Ocean 2024–2026)
This repository contains the complete open scientific assets for the ADRISHTA Subsurface Ocean AI system:
- Master Zarr Store (
zarr/oceanembed_multiyear_2024_2026.zarr): Harmonized 7-channel satellite surface inputs + 15 standard INCOIS target depths at 0.25° resolution across 2024-01-07 to 2026-10-03 (148 daily/weekly snapshots). - In-Situ ARGO Ground Truth (
argo/): 134 real physical CTD float soundings from the Coriolis GDAC / INCOIS array. - Model Checkpoints (
checkpoints/): Frozen Physics-Constrained and Baseline OceanEmbedNet weights. - Raw Multi-Parameter Copernicus Data (
raw/): 3D potential temperature, salinity, currents, and sea surface height NetCDFs.
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