Datasets:

dev-prescience
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elsie-workflow-cache

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Dataset Viewer

Split (1)

train

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
schema: string
artifact: string
assembly: string
version: string
packagedAt: string
default: bool
selection: string
records: int64
requested: int64
requestedFound: int64
mergedRsids: int64
source: struct<name: string, url: string, bytes: int64, md5: string>
  child 0, name: string
  child 1, url: string
  child 2, bytes: int64
  child 3, md5: string
columns: string
files: list<item: struct<path: string, bytes: int64, sha256: string>>
  child 0, item: struct<path: string, bytes: int64, sha256: string>
      child 0, path: string
      child 1, bytes: int64
      child 2, sha256: string
provenance: struct<workflowRepository: string, workflowRevision: string, workflowPackageVersion: string, pipelin (... 230 chars omitted)
  child 0, workflowRepository: string
  child 1, workflowRevision: string
  child 2, workflowPackageVersion: string
  child 3, pipeline: string
  child 4, pipelineRevision: string
  child 5, preparationHelperSha256: string
  child 6, sourceManifestSha256: string
  child 7, preparationMethod: string
  child 8, preparationTools: struct<bcftools: string, bgzip: string, tabix: string>
      child 0, bcftools: string
      child 1, bgzip: string
      child 2, tabix: string
  child 9, approvedForPublication: bool
verification: string
passingPopulationSites: int64
terms: string
archiveBytes: int64
archiveSha256: string
workflow: string
sourceBytes: int64
remainingResources: string
preparedAt: timestamp[s]
archive: string
completeBundle: bool
installedBytes: int64
to
{'schema': Value('string'), 'workflow': Value('string'), 'assembly': Value('string'), 'version': Value('timestamp[s]'), 'preparedAt': Value('timestamp[s]'), 'archive': Value('string'), 'archiveBytes': Value('int64'), 'archiveSha256': Value('string'), 'installedBytes': Value('int64'), 'passingPopulationSites': Value('int64'), 'sourceBytes': Value('int64'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}), 'completeBundle': Value('bool'), 'provenance': {'workflowRepository': Value('string'), 'workflowRevision': Value('string'), 'workflowPackageVersion': Value('string'), 'pipeline': Value('string'), 'pipelineRevision': Value('string'), 'preparationHelperSha256': Value('string'), 'sourceManifestSha256': Value('string'), 'preparationMethod': Value('string'), 'preparationTools': {'bcftools': Value('string'), 'bgzip': Value('string'), 'tabix': Value('string')}, 'approvedForPublication': Value('bool')}, 'remainingResources': Value('string'), 'terms': Value('string'), 'verification': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              schema: string
              artifact: string
              assembly: string
              version: string
              packagedAt: string
              default: bool
              selection: string
              records: int64
              requested: int64
              requestedFound: int64
              mergedRsids: int64
              source: struct<name: string, url: string, bytes: int64, md5: string>
                child 0, name: string
                child 1, url: string
                child 2, bytes: int64
                child 3, md5: string
              columns: string
              files: list<item: struct<path: string, bytes: int64, sha256: string>>
                child 0, item: struct<path: string, bytes: int64, sha256: string>
                    child 0, path: string
                    child 1, bytes: int64
                    child 2, sha256: string
              provenance: struct<workflowRepository: string, workflowRevision: string, workflowPackageVersion: string, pipelin (... 230 chars omitted)
                child 0, workflowRepository: string
                child 1, workflowRevision: string
                child 2, workflowPackageVersion: string
                child 3, pipeline: string
                child 4, pipelineRevision: string
                child 5, preparationHelperSha256: string
                child 6, sourceManifestSha256: string
                child 7, preparationMethod: string
                child 8, preparationTools: struct<bcftools: string, bgzip: string, tabix: string>
                    child 0, bcftools: string
                    child 1, bgzip: string
                    child 2, tabix: string
                child 9, approvedForPublication: bool
              verification: string
              passingPopulationSites: int64
              terms: string
              archiveBytes: int64
              archiveSha256: string
              workflow: string
              sourceBytes: int64
              remainingResources: string
              preparedAt: timestamp[s]
              archive: string
              completeBundle: bool
              installedBytes: int64
              to
              {'schema': Value('string'), 'workflow': Value('string'), 'assembly': Value('string'), 'version': Value('timestamp[s]'), 'preparedAt': Value('timestamp[s]'), 'archive': Value('string'), 'archiveBytes': Value('int64'), 'archiveSha256': Value('string'), 'installedBytes': Value('int64'), 'passingPopulationSites': Value('int64'), 'sourceBytes': Value('int64'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}), 'completeBundle': Value('bool'), 'provenance': {'workflowRepository': Value('string'), 'workflowRevision': Value('string'), 'workflowPackageVersion': Value('string'), 'pipeline': Value('string'), 'pipelineRevision': Value('string'), 'preparationHelperSha256': Value('string'), 'sourceManifestSha256': Value('string'), 'preparationMethod': Value('string'), 'preparationTools': {'bcftools': Value('string'), 'bgzip': Value('string'), 'tabix': Value('string')}, 'approvedForPublication': Value('bool')}, 'remainingResources': Value('string'), 'terms': Value('string'), 'verification': Value('string')}
              because column names don't match

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.

Elsie prepared workflow data

Versioned public reference data for Elsie workflow installations. No participant reads, genotypes or analysis results are included.

Available artifacts

The Talos GRCh38 October 2026 public subset contains prepared AlphaMissense, MANE/gene regions and ClinVar/ClinvArbitration tables. See its manifest.json for the preparation date, exact file list, measured sizes and SHA-256 hashes. It excludes intermediate VCFs, APOGEE/nAPOGEE/MitoTIP archives and PanelApp data. Talos installation obtains and prepares those remaining small components locally from the original resources. Its separate large reference pack is still needed.

Rare Disease GRCh38 public population annotations are available under nf-core-raredisease/GRCh38/2026-10-27/. Its manifest records measured sizes, source versions, site counts and checksums. Reference sequences, indexes, VEP and provider support files remain separately acquired resources.

The SNP-array reference sites (snp-arrays/GRCh38/dbsnp-b157/) are dbSNP build 157 GRCh38 records (CHROM, POS, ID, REF, ALT only) for converting consumer SNP-array exports to VCF. They come as three packs, each with its own manifest.json:

consumer-chips (15 MB, installed by default): the rsIDs carried by 23andMe v2–v5, AncestryDNA, MyHeritage, FamilyTreeDNA, Dynamic DNA/Illumina GSA and Illumina HumanOmni2.5-8v1_A exports.
omni25-kgp (16 MB, optional): records at the GRCh38 positions of HumanOmni2.5-8v1_A markers named without an rsID.
imputed (64 MB, optional): the rsIDs of imputed exports such as Genes for Good.

Each optional pack is complete on top of consumer-chips. rsid-merges.tsv.gz maps exported rsIDs that dbSNP has since merged to their current rsID. Chip position maps are not included; installs download them from their source.

Source terms

AlphaMissense predictions: Google DeepMind, Cheng et al., Science (2023), doi:10.1126/science.adg7492. CC BY 4.0. Predictions are reformatted and encoded into Talos's echtvar archive; original prediction values are represented using the upstream encoding configuration. https://github.com/google-deepmind/alphamissense#alphamissense-predictions-license
ClinVar: NCBI/NLM/NIH and its submitters. Attribute ClinVar and its release; retain the original data-use and clinical-review notices. ClinVar classifications are transformed by Talos/ClinvArbitration into Hail tables; these are derived data, not an official NCBI release. NIH does not independently verify submitted information. The information is not intended for direct diagnostic use or medical decision-making without review by a genetics professional. https://www.ncbi.nlm.nih.gov/clinvar/docs/maintenance_use/
MANE and gene-region mappings: NCBI/Ensembl reference data, transformed by Talos. Preserve source attribution. Ensembl-generated data have no use restrictions; third-party data retain their own terms. https://useast.ensembl.org/info/about/legal/disclaimer.html
dbSNP: National Center for Biotechnology Information, National Library of Medicine. dbSNP build 157 (GRCh38), retrieved from https://ftp.ncbi.nih.gov/snp/archive/b157/ ; records are subset and reduced to CHROM, POS, ID, REF and ALT, and contigs renamed to chr style. NCBI molecular data are not subject to copyright restrictions: https://www.ncbi.nlm.nih.gov/home/about/policies/
Talos's preparation workflow: Population Genomics, MIT-licensed source at https://github.com/populationgenomics/talos/tree/2085846814d4e92df9e9fcc14dcc6af466f27a9b

The cache does not grant additional rights over any upstream data and does not imply endorsement by the providers. APOGEE and nAPOGEE scores are excluded from the published Talos subset; their current provider terms specify CC BY-NC 4.0 and require a separate licence for commercial use: https://mitimpact.mcb2lab.org/license

Reproducibility

Installers use a fixed Hugging Face Git revision, exact archive length and SHA-256. Fallback source-preparation recipes stay in the workflow packages.

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Total file size:

19.9 GB