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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ArrowNotImplementedError
Message:      Cannot write struct type 'attributes' with no child field to Parquet. Consider adding a dummy child field.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1821, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 781, in finalize
                  self.write_rows_on_file()
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 771, in _write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 812, in _build_writer
                  self.pa_writer = pq.ParquetWriter(
                                   ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 1070, in __init__
                  self.writer = _parquet.ParquetWriter(
                                ^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/_parquet.pyx", line 2363, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'attributes' with no child field to Parquet. Consider adding a dummy child field.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1342, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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shape
list
data_type
string
chunk_grid
dict
chunk_key_encoding
dict
fill_value
bool
codecs
list
attributes
dict
zarr_format
int64
node_type
string
storage_transformers
list
[ 55 ]
bool
{ "name": "regular", "configuration": { "chunk_shape": [ 1 ] } }
{ "name": "default", "configuration": { "separator": "/" } }
false
[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": false } } ]
{}
3
array
[]
[ 55 ]
bool
{ "name": "regular", "configuration": { "chunk_shape": [ 1 ] } }
{ "name": "default", "configuration": { "separator": "/" } }
false
[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": false } } ]
{}
3
array
[]
[ 55 ]
bool
{ "name": "regular", "configuration": { "chunk_shape": [ 1 ] } }
{ "name": "default", "configuration": { "separator": "/" } }
false
[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": false } } ]
{}
3
array
[]
[ 55 ]
bool
{ "name": "regular", "configuration": { "chunk_shape": [ 1 ] } }
{ "name": "default", "configuration": { "separator": "/" } }
false
[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": false } } ]
{}
3
array
[]

calvingdb — Calving Front Benchmark

First benchmark dataset for data-driven annual calving front forecasting, covering 123 marine-terminating glaciers in Svalbard from 2013 to 2023.

Each benchmark sample asks: given five past calving front observations (spanning roughly four years), predict where the calving front will be 365 days in the future.


Dataset at a glance

Property Value
Glaciers 123 (RGI 6.0, region 07 — Svalbard)
Total observations 17,358 calving front scenes
Benchmark samples 1,234 (866 train / 109 val / 259 test)
Temporal coverage 2013–2023
Spatial resolution 30 m
CRS EPSG:3995 (Arctic Polar Stereographic)
Prediction horizon 365 days
Input sequence length 5 snapshots
Version 1.0.0
License CC BY 4.0

Repository layout

calvdb/
├── zarr_zipped/                 # per-glacier zip archives (for download)
│   └── RGI60-07.XXXXX.zarr.zip
├── splits/
│   ├── train.json               # 866 benchmark samples
│   ├── val.json                 # 109 benchmark samples
│   ├── test.json                # 259 benchmark samples
│   ├── normalisation_stats.json # channel statistics (train split only)
│   └── sampling_params.json     # reproducibility config
├── croissant.json               # MLCommons Croissant metadata
└── README.md

Normalisation

splits/normalisation_stats.json contains per-channel mean and standard deviation computed from the training split only. Apply z-score normalisation before training.

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

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