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 "/src/services/worker/src/worker/job_runners/config/split_names.py", line 159, in compute
                  compute_split_names_from_info_response(
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 131, in compute_split_names_from_info_response
                  config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config)
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 567, in get_previous_step_or_raise
                  raise CachedArtifactError(
              libcommon.simple_cache.CachedArtifactError: The previous step failed.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 499, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 88, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              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 75, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 572, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 504, 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.

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.

Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies

Dataset Schema

This schema describes the columns in the GZ DECaLS catalogues; gz_decals_auto_posteriors, gz_decals_volunteers_1_and_2, and gz_decals_volunteers_5.

In all catalogues, galaxies are identified by their iauname. Galaxies are unique within a catalogue. gz_decals_auto_posteriors contains all galaxies with appropriate imaging and photometry in DECaLS DR5, while gz_decals_volunteers_1_and_2, and gz_decals_volunteers_5 contain subsets classified by volunteers in the respective campaigns.

The columns reporting morphology measurements are named like {some-question}_{an-answer}. For example, for the first question, both volunteer catalogues include the following:

Column Description
smooth-or-featured_total Total number of volunteers who answered the "Smooth of Featured" question
smooth-or-featured_smooth Count of volunteers who responded "Smooth" to the "Smooth or Featured" question
smooth-or-featured_featured-or-disk Count of volunteers who responded "Featured or Disk", similarly
smooth-or-featured_artifact Count of volunteers who responded "Artifact", similarly
smooth-or-featured_smooth_fraction Fraction of volunteers who responded "Smooth" to the "Smooth or Featured" question, out of all respondes (i.e. smooth count / total)
smooth-or-featured_featured-or-disk_fraction Fraction of volunteers who responded "Featured or Disk", similarly
smooth-or-featured_artifact_fraction Fraction of volunteers who responded "Artifact", similarly

The questions and answers are slightly different for gz_decals_volunteers_1_and_2 than gz_decals_volunteers_5. See the paper for more.

The volunteer catalogues include {question}_{answer}_debiased columns which attempt to estimate what the vote fractions would be if the same galaxy were imaged at lower redshift. See the paper for more. Note that the debiased measurements are highly uncertain on an individual galaxy basis and therefore should be used with caution. Debiased estimates are only available for galaxies with 0.02<z<0.15, -21.5>M_r>-23, and at least 30 votes for the first question (`Smooth or Featured') after volunteer weighting.

The automated catalogue, gz_decals_auto_posteriors, includes predictions for all galaxies and all questions even when that question may not be appropriate (e.g. number of spiral arms for a smooth elliptical). To assess relevance, we include {question}_proportion_volunteers_asked columns showing the estimated fraction of volunteers that would have been asked each question (i.e. the product of the vote fractions for the preceding answers). We suggest a cut of {question}_proportion_volunteers_asked > 0.5 as a starting point.

The automated catalogue does not include volunteer counts or totals (naturally).

Each catalogue includes a pair of columns to warn where galaxies may have been classified using an inappropriately large field-of-view (due to incorrect radii measurements in the NSA, on which the field-of-view is calculated). We suggest excluding galaxies (<1%) with such warnings.

Column Description
wrong_size_statistic Mean distance from center of all pixels above double the 20th percentile (i.e. probable source pixels)
wrong_size_warning True if wrong_size_statistic > 161.0, our suggested starting cut. Approximately the mean distance of all pixels from center

For convenience, each catalogue includes the same set of basic astrophysical measurements copied from the NASA Sloan Atlas (NSA). Additional measurements can be added my crossmatching on iauname with the NSA. See here for the NSA schema. If you use these columns, you should cite the NSA.

Column Description
ra Right ascension (degrees)
dec Declination (degrees)
iauname Unique identifier listed in NSA v1.0.1
petro_theta "Azimuthally-averaged SDSS-style Petrosian radius (derived from r band"
petro_th50 "Azimuthally-averaged SDSS-style 50% light radius (r-band)"
petro_th90 "Azimuthally-averaged SDSS-style 50% light radius (r-band)"
elpetro_absmag_r "Absolute magnitude from elliptical Petrosian fluxes in rest-frame" in SDSS r
sersic_nmgy_r "Galactic-extinction corrected AB flux" in SDSS r
redshift "Heliocentric redshift" ("z" column in NSA)
mag_r 22.5 - 2.5 log10(sersic_nmgy_r). Not the same as the NSA mag column!
@dataset{walmsley_mike_2020_4573248,
  author       = {Walmsley, Mike and
                  Lintott, Chris and
                  Tobias, Geron and
                  Kruk, Sandor J and
                  Krawczyk, Coleman and
                  Willett, Kyle and
                  Bamford, Steven and
                  Kelvin, Lee S and
                  Fortson, Lucy and
                  Gal, Yarin and
                  Keel, William and
                  Masters, Karen and
                  Mehta, Vihang and
                  Simmons, Brooke and
                  Smethurst, Rebecca J and
                  Smith, Lewis and
                  Baeten, Elisabeth M L and
                  Macmillan, Christine},
  title        = {{Galaxy Zoo DECaLS: Detailed Visual Morphology 
                   Measurements from Volunteers and Deep Learning for
                   314,000 Galaxies}},
  month        = dec,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {0.0.2},
  doi          = {10.5281/zenodo.4573248},
  url          = {https://doi.org/10.5281/zenodo.4573248}
}
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