OmniSky v1
Collection
Crossmatched, normalized multimodal astronomy: 20.4M DESI DR1 objects x 24 catalogs, 822 GB. Start with mmu-norm-index. • 20 items • Updated
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: CastError
Message: Couldn't cast
bands: struct<cfa_cfa3:I: int64, cfa_cfa3:B: int64, cfa_cfa3:R: int64, cfa_cfa3:U: int64, cfa_cfa3:i': int6 (... 1398 chars omitted)
child 0, cfa_cfa3:I: int64
child 1, cfa_cfa3:B: int64
child 2, cfa_cfa3:R: int64
child 3, cfa_cfa3:U: int64
child 4, cfa_cfa3:i': int64
child 5, cfa_cfa3:V: int64
child 6, cfa_cfa3:r': int64
child 7, cfa_cfa4:B: int64
child 8, cfa_cfa4:U: int64
child 9, cfa_cfa4:i': int64
child 10, cfa_cfa4:V: int64
child 11, cfa_cfa4:u': int64
child 12, cfa_cfa4:r': int64
child 13, cfa_seccsn:U: int64
child 14, cfa_seccsn:R: int64
child 15, cfa_seccsn:Ks: int64
child 16, cfa_seccsn:V: int64
child 17, cfa_seccsn:i': int64
child 18, cfa_seccsn:B: int64
child 19, cfa_seccsn:u': int64
child 20, cfa_seccsn:r': int64
child 21, cfa_seccsn:I: int64
child 22, cfa_seccsn:H: int64
child 23, cfa_seccsn:J: int64
child 24, cfa_snii:U: int64
child 25, cfa_snii:R: int64
child 26, cfa_snii:Ks: int64
child 27, cfa_snii:V: int64
child 28, cfa_snii:i': int64
child 29, cfa_snii:B: int64
child 30, cfa_snii:u': int64
child 31, cfa_snii:r': int64
child 32, cfa_snii:I: int64
child 33, cfa_snii:H: int64
child 34, cfa_snii:J: int64
child 35, csp_csp:B: int64
child 36, csp_csp:H: int64
child 37, csp_csp:Jrc2: int64
child 38, csp_csp:J: int64
child 39, csp_csp:Ydw: int64
child 40, csp_csp:V0: int64
child 41, csp_csp:V: int64
child 42, csp_csp:Y: int64
child 43, csp_csp:u: int64
child 44, csp_csp:r: in
...
child 45, csp_csp:g: int64
child 46, csp_csp:i: int64
child 47, des_y3_sne_ia:g: int64
child 48, des_y3_sne_ia:i: int64
child 49, des_y3_sne_ia:z: int64
child 50, des_y3_sne_ia:r: int64
child 51, foundation:g: int64
child 52, foundation:i: int64
child 53, foundation:r: int64
child 54, foundation:z: int64
child 55, ps1_sne_ia:g: int64
child 56, ps1_sne_ia:i: int64
child 57, ps1_sne_ia:r: int64
child 58, ps1_sne_ia:z: int64
child 59, snls:g: int64
child 60, snls:z: int64
child 61, snls:r: int64
child 62, snls:i: int64
child 63, swift_sne_ia:N: int64
child 64, swift_sne_ia:W: int64
child 65, swift_sne_ia:X: int64
child 66, swift_sne_ia:V: int64
child 67, swift_sne_ia:B: int64
child 68, swift_sne_ia:U: int64
child 69, yse_dr1:X: int64
child 70, yse_dr1:i: int64
child 71, yse_dr1:g: int64
child 72, yse_dr1:r: int64
child 73, yse_dr1:z: int64
child 74, yse_dr1:Y: int64
surveys: struct<cfa_cfa3: int64, cfa_cfa4: int64, cfa_seccsn: int64, cfa_snii: int64, csp_csp: int64, des_y3_ (... 102 chars omitted)
child 0, cfa_cfa3: int64
child 1, cfa_cfa4: int64
child 2, cfa_seccsn: int64
child 3, cfa_snii: int64
child 4, csp_csp: int64
child 5, des_y3_sne_ia: int64
child 6, foundation: int64
child 7, ps1_sne_ia: int64
child 8, snls: int64
child 9, swift_sne_ia: int64
child 10, yse_dr1: int64
band_vocabulary: null
survey_vocabulary: null
split: string
warning: string
reversal: string
view: string
source_repo: string
to
{'source_repo': Value('string'), 'view': Value('string'), 'reversal': Value('string'), 'band_vocabulary': Value('null'), 'survey_vocabulary': Value('null'), 'warning': Value('string'), 'split': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
bands: struct<cfa_cfa3:I: int64, cfa_cfa3:B: int64, cfa_cfa3:R: int64, cfa_cfa3:U: int64, cfa_cfa3:i': int6 (... 1398 chars omitted)
child 0, cfa_cfa3:I: int64
child 1, cfa_cfa3:B: int64
child 2, cfa_cfa3:R: int64
child 3, cfa_cfa3:U: int64
child 4, cfa_cfa3:i': int64
child 5, cfa_cfa3:V: int64
child 6, cfa_cfa3:r': int64
child 7, cfa_cfa4:B: int64
child 8, cfa_cfa4:U: int64
child 9, cfa_cfa4:i': int64
child 10, cfa_cfa4:V: int64
child 11, cfa_cfa4:u': int64
child 12, cfa_cfa4:r': int64
child 13, cfa_seccsn:U: int64
child 14, cfa_seccsn:R: int64
child 15, cfa_seccsn:Ks: int64
child 16, cfa_seccsn:V: int64
child 17, cfa_seccsn:i': int64
child 18, cfa_seccsn:B: int64
child 19, cfa_seccsn:u': int64
child 20, cfa_seccsn:r': int64
child 21, cfa_seccsn:I: int64
child 22, cfa_seccsn:H: int64
child 23, cfa_seccsn:J: int64
child 24, cfa_snii:U: int64
child 25, cfa_snii:R: int64
child 26, cfa_snii:Ks: int64
child 27, cfa_snii:V: int64
child 28, cfa_snii:i': int64
child 29, cfa_snii:B: int64
child 30, cfa_snii:u': int64
child 31, cfa_snii:r': int64
child 32, cfa_snii:I: int64
child 33, cfa_snii:H: int64
child 34, cfa_snii:J: int64
child 35, csp_csp:B: int64
child 36, csp_csp:H: int64
child 37, csp_csp:Jrc2: int64
child 38, csp_csp:J: int64
child 39, csp_csp:Ydw: int64
child 40, csp_csp:V0: int64
child 41, csp_csp:V: int64
child 42, csp_csp:Y: int64
child 43, csp_csp:u: int64
child 44, csp_csp:r: in
...
child 45, csp_csp:g: int64
child 46, csp_csp:i: int64
child 47, des_y3_sne_ia:g: int64
child 48, des_y3_sne_ia:i: int64
child 49, des_y3_sne_ia:z: int64
child 50, des_y3_sne_ia:r: int64
child 51, foundation:g: int64
child 52, foundation:i: int64
child 53, foundation:r: int64
child 54, foundation:z: int64
child 55, ps1_sne_ia:g: int64
child 56, ps1_sne_ia:i: int64
child 57, ps1_sne_ia:r: int64
child 58, ps1_sne_ia:z: int64
child 59, snls:g: int64
child 60, snls:z: int64
child 61, snls:r: int64
child 62, snls:i: int64
child 63, swift_sne_ia:N: int64
child 64, swift_sne_ia:W: int64
child 65, swift_sne_ia:X: int64
child 66, swift_sne_ia:V: int64
child 67, swift_sne_ia:B: int64
child 68, swift_sne_ia:U: int64
child 69, yse_dr1:X: int64
child 70, yse_dr1:i: int64
child 71, yse_dr1:g: int64
child 72, yse_dr1:r: int64
child 73, yse_dr1:z: int64
child 74, yse_dr1:Y: int64
surveys: struct<cfa_cfa3: int64, cfa_cfa4: int64, cfa_seccsn: int64, cfa_snii: int64, csp_csp: int64, des_y3_ (... 102 chars omitted)
child 0, cfa_cfa3: int64
child 1, cfa_cfa4: int64
child 2, cfa_seccsn: int64
child 3, cfa_snii: int64
child 4, csp_csp: int64
child 5, des_y3_sne_ia: int64
child 6, foundation: int64
child 7, ps1_sne_ia: int64
child 8, snls: int64
child 9, swift_sne_ia: int64
child 10, yse_dr1: int64
band_vocabulary: null
survey_vocabulary: null
split: string
warning: string
reversal: string
view: string
source_repo: string
to
{'source_repo': Value('string'), 'view': Value('string'), 'reversal': Value('string'), 'band_vocabulary': Value('null'), 'survey_vocabulary': Value('null'), 'warning': Value('string'), 'split': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
L2 model-ready views (release v1)
The mmu-l2-* repositories provide processed, model-ready versions of the matching mmu-norm-* L1 data, while L1 keeps the normalized source measurements. L2 applies documented processing steps for training and evaluation, with the details for reversing each transformation stored in the row or in provenance.json.
| Repo | View | Reversal |
|---|---|---|
mmu-l2-tess |
per-sector relative flux f/median−1, time from first valid cadence |
flux = (rel+1)×flux_median_e_per_s; t = t_rel + t0 |
mmu-l2-sdss |
spectra zero-padded to 4,800 samples with valid mask |
drop valid=false samples |
mmu-l2-chandra |
bins zero-padded to 512 with valid mask |
drop padding |
mmu-l2-sne |
time-sorted sequences, band/survey tokens, ONE scale per object (flux: ÷ median |f|; mag: − median m), representation column |
flux rows: v×scale; mag rows: v+scale |
mmu-l2-provabgs |
16/50/84 posterior percentiles per parameter | samples remain in L1 |
Each row keeps its assignment from splits/v1 (train, val, test, or unassigned). Images, DESI and VIPERS spectra, and Gaia already have a fixed shape, so their L2 preparation uses the L1 tensors with the training-only robust scaling statistics in mmu-norm-index under l2_stats/v1/ (z = (x − median) / iqr).
In every L2 view, padding is marked valid=false, one amplitude scale is used per object to preserve colors, statistics are fit on the training split only, and gaps are left as gaps.
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