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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                  ~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
                  ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  yield from 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 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Note: This description was drafted with AI assistance and is subject to revision.

MOSES Initiative — Matai'an Open Science and Engineering Sharing Platform

The MOSES Initiative (Mataian Open Science and Engineering Sharing Initiative) publishes scientific and engineering datasets for the Matai'an Creek (馬太鞍溪) watershed in eastern Taiwan (23.56°N–24.15°N, 121.16°E–121.68°E). All datasets are discoverable through a STAC catalog.


Datasets

1. Radar Rainfall — Typhoon Events (stac_catalog/assets/radar-rain-monthly)

Machine-learning-predicted gridded rainfall for the Matai'an area, organized by typhoon event (2012–2026).

  • Format: Monthly NetCDF (.nc) and Zarr (.zarr)
  • Variables: 1-hr accumulated rainfall, 10-min intensity, AOI mask, elevation
  • Spatial: 48 × 42 grid points, ~1.2 km resolution
  • Temporal: 10-minute intervals per month
  • Model: XGBoost, trained on CWA radar data
  • License: Proprietary (contact lab for access)

See rain/zarr_tutorial.md for a Python example using xarray + STAC to access Zarr data without downloading entire files.

2. Downstream Sediment (stac_catalog/assets/sediment)

Bed material particle-size distribution and physical properties sampled along Matai'an Creek.

  • Samples: 22 sites (Dec 2025 – Jan 2026)
  • Methods: Sieve analysis + hydrometer sedimentation test
  • Measurements: Grain size distribution (Gravel → Clay), D10/D30/D50/D60, Cu, Cc, specific gravity
  • Reference: MOSES Report No. 001Dataset of Bed Material Particle-Size Distribution and Physical Properties in Matai'an Creek
  • License: CC BY 4.0

See sediment_README.md for the full data dictionary.


STAC Catalog

All datasets are indexed in a SpatioTemporal Asset Catalog (STAC). Browse with STAC Browser or load programmatically:

import pystac
catalog = pystac.Catalog.from_file(
    "https://huggingface.co/datasets/NTU-CompHydroMet-Lab/mataian-moses-stac"
    "/resolve/main/stac_catalog/catalog.json"
)

Citation

If you use these datasets, please cite:

MOSES Initiative. (2026). Mataian Open Science and Engineering Sharing Platform [Dataset]. National Taiwan University. https://huggingface.co/datasets/NTU-CompHydroMet-Lab/mataian-moses-stac


Contact

CompHydroMet Lab & MHRG Lab
Department of Civil Engineering, National Taiwan University

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