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Datasets:
THULab
/
phenology_normal_hawaii

Tasks:
Time Series Forecasting
Modalities:
Time-series
Image
Size:
10K<n<100K
Tags:
tsfile
timeseries
modality:timeseries
format:tsfile
biology
image
Dataset card Files
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Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "tsfile/tsfile_py_cpp.pyx", line 567, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
              tsfile.exceptions.FileOpenError: 28: 
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
                  with self._open_reader(file) as reader:
                       ~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
                  return TsFileReader(file)
                File "tsfile/tsfile_reader.pyx", line 323, in tsfile.tsfile_reader.TsFileReaderPy.__init__
              SystemError: <class '_weakrefset.WeakSet'> returned a result with an exception set
              
              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/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Phenology-Normal-Hawaii (TsFile)

This dataset is an Apache TsFile conversion of imageomics/phenology-normal-hawaii.

Modalities: Time-series.

Overview

  • Vegetation color-index time series (GCC / RCC) from the PUUM (Pu'u Maka'ala) site, Hawaii, for fine-grained phenological analysis.

  • Extracted from NEON PhenoCam images; daily curves with gcc_mean, gcc_50, rcc_*, midday_* etc.

  • Each monitoring site is a device identified by the site TAG.

  • Converted observations: 10,945 rows across 1 TsFile file(s)

  • Source format: csv

TsFile schema

  • Time — source date (datetime), converted to INT64 milliseconds.
Column Role Type Meaning
Time TIME INT64 (ms) sample timestamp
site TAG STRING site id (EB_xxxx)
year FIELD FLOAT year
doy FIELD FLOAT day of year
image_count FIELD FLOAT —
midday_r FIELD FLOAT —
midday_g FIELD FLOAT —
midday_b FIELD FLOAT —
midday_gcc FIELD FLOAT —
midday_rcc FIELD FLOAT —
r_mean FIELD FLOAT —
r_std FIELD FLOAT —
g_mean FIELD FLOAT —
g_std FIELD FLOAT —
b_mean FIELD FLOAT —
b_std FIELD FLOAT —
gcc_mean FIELD FLOAT —
gcc_std FIELD FLOAT —
gcc_50 FIELD FLOAT —
gcc_75 FIELD FLOAT —
gcc_90 FIELD FLOAT —
rcc_mean FIELD FLOAT —
rcc_std FIELD FLOAT —
rcc_50 FIELD FLOAT —
rcc_75 FIELD FLOAT —
rcc_90 FIELD FLOAT —
max_solar_elev FIELD FLOAT —

Conversion notes

  • site (from the source file name) is a TAG so each site is a separate device.
  • Flag columns (snow_flag, outlierflag_*) dropped as per-sample quality flags, not measurements.

Source & license

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("phenology_normal_hawaii.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())
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