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nasa-ibm-ai4science
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core-sdo

Size:
100K<n<1M
Tags:
Helio
Pretraining
faoundation-model
solar
SDO
EUV
DOI:
License:
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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:    ReadError
Message:      unexpected end of data
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2431, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1975, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 155, in _convert_to_arrow
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 249, in __iter__
                  for key_example in islice(self.generate_examples_fn(**gen_kwags), shard_example_idx_start, None):
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 118, in _generate_examples
                  for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
                                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 44, in _get_pipeline_from_tar
                  current_example[field_name] = f.read()
                                                ^^^^^^^^
                File "/usr/local/lib/python3.12/tarfile.py", line 693, in read
                  raise ReadError("unexpected end of data")
              tarfile.ReadError: unexpected end of data

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image/png

ML-Ready Multi-Modal Image Dataset from SDO

Overview

This dataset provides machine learning (ML)-ready solar data curated from NASA’s Solar Dynamics Observatory (SDO), covering observations from May 13, 2010, to Dec 31, 2024. It includes Level-1.5 processed data from: Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI).

The dataset is designed to facilitate large-scale learning applications in heliophysics, such as space weather forecasting, unsupervised representation learning, and scientific foundation model development.

Dataset Download Instructions

To download the data please visit https://registry.opendata.aws/surya-bench/

  • Resource type: S3 Bucket
  • Amazon Resource Name (ARN): arn:aws:s3:::nasa-surya-bench
  • AWS CLI Access (No AWS account required): aws s3 ls --no-sign-request s3://nasa-surya-bench/

Dataset Structure

Data Variables:

- aia94    (y, x) float32   : AIA 94 Å
- aia131   (y, x) float32   : AIA 131 Å
- aia171   (y, x) float32   : AIA 171 Å
- aia193   (y, x) float32   : AIA 193 Å
- aia211   (y, x) float32   : AIA 211 Å
- aia304   (y, x) float32   : AIA 304 Å
- aia335   (y, x) float32   : AIA 335 Å
- aia1600  (y, x) float32   : AIA 1600 Å (UV continuum)
- hmi_m    (y, x) float32   : HMI LOS Magnetogram
- hmi_bx   (y, x) float32   : HMI Magnetic Field - x component
- hmi_by   (y, x) float32   : HMI Magnetic Field - y component
- hmi_bz   (y, x) float32   : HMI Magnetic Field - z component
- hmi_v    (y, x) float32   : HMI Doppler Velocity

Dataset Details

Field Description
Temporal Coverage May 13, 2010 – Dec 31, 2024
Data Format netCDF (.nc), float32
Temporal Granularity 12 minutes
Data Shape [13, 4096, 4096] per file
Channels 13 total (AIA EUV ×8 + HMI magnetograms ×5)
Size per File ~570 MB
Total Size ~360TB

📦 Downstream Data Repositories

All the downstream tasks that uses core-sdo dataset can be found in the SuryaBench Hugging Face Collections

Each sub-dataset targets a specific task within the heliophysics domain:

Repository Task Description
Surya-bench-solarwind Solar wind speed prediction with a 4-day forecast horizon.
surya-bench-flare-forecasting Binary classification for solar flare occurrence within 24 hours.
surya-bench-ar-segmentation Pixel-wise segmentation of active regions from solar disk images.
euv_spectra Time-aligned Extreme Ultraviolet (EUV) irradiance spectra from NASA’s SDO/EVE instrument.
surya-bench-coronal-extrapolation Magnetic field extrapolation from photosphere to corona.
ar_emergence Forecasting active region emergence based on historical features.

License

This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Authors

Sujit Roy, Dinesha V Hegde, Johannes Schmude, Rohit Lal, Vishal Gaur
corr: [email protected]

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