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Datasets:
nasa-ibm-ai4science
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core-sdo

Size:
100K<n<1M
Tags:
Helio
Pretraining
faoundation-model
solar
SDO
EUV
License:
Dataset card Files
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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:    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 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

Notes

Training Data for 1 month is available on Huggingface parallel to the main branch as Parquet files.

The full dataset is located in AWS S3 buckets. Note that full dataset is over 360 TB. Users can see the full list of files using the below command. 

aws s3 ls s3://nasa-surya-bench --no-sign-request

To download the individual files from aws S3 buckets, they can utilize the tutorial in the link [https://docs.aws.amazon.com/AmazonS3/latest/userguide/download-objects.html]. There are different options available for downloading and syncing data from S3.

Authors Sujit Roy, Dinesha Vasanta Hegde, Johannes Schmude, Amy Lin, Vishal Gaur, Talwinder Singh, Rohit Lal corr: [email protected]

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