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jpark01
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sv-qtl-coloc-data

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The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 17 new columns ({'p_value', 'n_cases', 'ci_upper', 'base_pair_location', 'chromosome', 'standard_error', 'beta', 'variant_id', 'effect_allele', 'other_allele', 'het_pvalue', 'n_controls', 'het_isq', 'effect_allele_frequency', 'variant_alternate_id', 'odds_ratio', 'ci_lower'}) and 5 missing columns ({'AD_Dx', 'Age', 'PD_Dx', 'ID', 'Sex'}).

This happened while the csv dataset builder was generating data using

gzip://GCST90027158.tsv::hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/GWAS/GCST90027158.tsv.gz, ['hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/CSF/sample_info.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/GWAS/GCST90027158.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/GWAS/GCST90275127.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/Methylation/sample_info.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/Plasma/sample_info.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/RNA/sample_info.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr1.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr10.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr11.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr12.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr13.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr14.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr15.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr16.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr17.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr18.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr19.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr2.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr20.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr21.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr22.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr3.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr4.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr5.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr6.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr7.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr8.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr9.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr1.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr10.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr11.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr12.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr13.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr14.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr15.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr16.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr17.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr18.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr19.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr2.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr20.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr21.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr22.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr3.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr4.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr5.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr6.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr7.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr8.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr9.tsv.gz']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 784, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 795, in _write_table
                  pa_table = table_cast(pa_table, self._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
              variant_id: string
              p_value: double
              chromosome: int64
              base_pair_location: int64
              effect_allele: string
              other_allele: string
              effect_allele_frequency: double
              odds_ratio: double
              ci_lower: double
              ci_upper: double
              beta: double
              standard_error: double
              n_cases: int64
              n_controls: int64
              het_isq: double
              het_pvalue: double
              variant_alternate_id: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2360
              to
              {'ID': Value('string'), 'Sex': Value('float64'), 'Age': Value('float64'), 'AD_Dx': Value('float64'), 'PD_Dx': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 17 new columns ({'p_value', 'n_cases', 'ci_upper', 'base_pair_location', 'chromosome', 'standard_error', 'beta', 'variant_id', 'effect_allele', 'other_allele', 'het_pvalue', 'n_controls', 'het_isq', 'effect_allele_frequency', 'variant_alternate_id', 'odds_ratio', 'ci_lower'}) and 5 missing columns ({'AD_Dx', 'Age', 'PD_Dx', 'ID', 'Sex'}).
              
              This happened while the csv dataset builder was generating data using
              
              gzip://GCST90027158.tsv::hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/GWAS/GCST90027158.tsv.gz, ['hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/CSF/sample_info.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/GWAS/GCST90027158.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/GWAS/GCST90275127.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/Methylation/sample_info.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/Plasma/sample_info.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/RNA/sample_info.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr1.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr10.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr11.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr12.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr13.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr14.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr15.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr16.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr17.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr18.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr19.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr2.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr20.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr21.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr22.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr3.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr4.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr5.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr6.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr7.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr8.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SNV/SNV.chr9.variants.tsv', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr1.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr10.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr11.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr12.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr13.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr14.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr15.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr16.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr17.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr18.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr19.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr2.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr20.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr21.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr22.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr3.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr4.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr5.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr6.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr7.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr8.tsv.gz', 'hf://datasets/jpark01/sv-qtl-coloc-data@02821371c26603cc9460396582eb4ebfbc3257df/sv-qtl-coloc/input/genotype/SV/SV.chr9.tsv.gz']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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.

ID string	Sex float64	Age float64	AD_Dx float64	PD_Dx float64
T0162	-0.897325	62.757381	-0.40409	-0.767447
T0163	-0.640098	58.276604	-1.084066	-1.015667
T0164	1.102297	72.389977	-1.091725	-1.030457
T0165	1.733239	64.05172	-0.432334	-0.9685
T0166	1.179838	69.377625	-0.841335	-0.93112
T0167	0.009444	68.484627	-0.834046	-0.960105
T0168	1.377604	71.086357	-2.001204	-0.995545
T0169	-2.077943	66.258415	-1.04623	-0.961069
T0170	-1.543766	63.957005	-0.228539	-0.966086
T0171	-0.461401	72.748871	-0.845448	-0.947287
T0172	0.203647	75.975807	-2.035096	-1.01732
T0173	1.396329	74.997276	0.334791	-0.994777
T0174	-0.963546	56.826832	0.105944	-0.903072
T0175	-2.221293	62.487461	-1.043694	-0.966794
T0176	-0.042925	69.079704	-1.07233	-0.575873
T0177	0.082134	66.473007	-0.795598	-0.899079
T0178	0.251291	74.480423	-1.413485	-0.877111
T0179	0.063285	68.555367	-1.039346	-0.955893
T0180	0.171399	66.395294	-1.066562	-1.00054
T0181	-1.005788	69.320412	0.049717	-0.937137
T0182	-1.52443	74.472855	-0.824694	-0.929381
T0183	-0.858033	71.851189	-0.418282	-0.799691
T0184	-0.566556	76.100609	-0.803296	-0.907166
T0185	0.473144	65.409325	-0.806555	-0.904452
T0186	-0.126378	67.120056	-1.062083	-0.992707
T0187	-0.528814	76.24588	-0.076383	-1.004336
T0188	-1.058665	61.566551	-1.098439	-1.051156
T0189	-2.075798	67.999886	-1.086908	-1.020263
T0190	0.776177	72.262993	-1.06779	-0.992368
T0191	-0.731853	60.540009	-0.880768	-0.974152
T0192	1.719052	75.942276	-0.412192	-0.781978
T0193	0.860085	71.895172	-0.850216	-0.965565
T0194	-0.127313	71.258225	-0.870669	-1.002543
T0195	-0.462295	66.0532	-1.042149	-0.968177
T0196	-1.620796	71.20443	-0.822668	-0.921347
T0197	-0.024343	70.052483	-1.07715	-1.015942
T0198	0.428275	60.850002	-0.867697	-0.957639
T0199	0.273095	64.05114	0.340208	-0.880353
T0200	-0.204387	65.241516	-0.075432	-0.93683
T0201	-0.423268	77.339104	-0.661844	-0.886885
T0202	-0.688631	68.431976	-1.07099	-1.01027
T0203	0.406484	79.283569	-1.109354	-1.055321
T0204	-0.953455	61.265526	-1.752555	-0.964763
T0205	-1.969934	63.573418	-1.043851	-0.958351
T0206	-0.783156	70.157768	-0.416707	-0.796844
T0207	1.760383	61.104721	-0.203164	-0.872469
T0208	-0.444399	63.985786	-1.333499	-0.76041
T0209	-0.094222	57.006626	-0.871005	-0.973181
T0210	0.488111	67.385338	-0.408527	-0.943644
T0211	-0.002046	66.645363	-0.675968	-2.314664
T0212	0.021895	71.863464	-0.853479	-2.264658
T0213	-0.718177	73.962662	-1.057004	-0.975067
T0214	0.135389	64.478081	-0.392008	-1.700972
T0215	-0.842491	56.215134	-1.817226	-0.940229
T0216	0.073235	74.539002	-0.880714	-0.976577
T0217	-1.39181	64.42881	-1.040476	-0.955479
T0218	0.69081	68.158463	-1.103144	-1.05432
T0219	0.344956	57.773136	-1.692212	-0.934335
T0220	-0.402549	68.93541	-0.821258	-0.943474
T0221	-0.750272	66.110069	-1.145348	-0.762562
T0222	1.326776	68.20475	-0.86365	-0.947938
T0223	0.836373	68.871162	-0.665586	-0.897707
T0224	-0.529926	70.018448	-1.042367	-0.957767
T0225	0.777231	61.165554	-0.158921	-1.017529
T0226	0.110926	68.401985	-1.345082	-0.912173
T0227	1.886199	80.619713	-0.131511	-1.032536
T0228	-1.842393	73.319977	-0.775285	-0.863777
T0229	-0.190232	77.350334	-1.103404	1.326792
T0230	0.598303	75.880852	-0.396429	-0.763459
T0231	-0.797394	67.503548	-0.804166	-0.87024
T0232	-0.367664	69.049736	-0.797707	-0.853571
T0233	0.576297	66.350555	-0.656681	-0.851418
T0234	1.666184	73.18145	-0.636669	-0.86202
T0235	-0.366517	68.707283	-0.845109	-0.980147
T0236	-0.021195	63.999622	-1.07009	-1.865003
T0237	0.847171	64.008347	-1.067194	-0.9956
T0238	-0.600204	65.876396	-0.117067	-1.054768
T0239	-1.46288	73.362801	-0.436893	-0.820498
T0240	0.135218	67.41407	-1.844334	-1.0212
T0241	-1.455192	66.112122	-1.063544	-0.998738
T0242	0.762047	71.368279	-0.657784	-0.855311
T0243	-1.261325	66.128243	-0.852664	-0.93797
T0244	-1.201469	70.458351	-0.660784	-0.872556
T0245	-1.977115	63.546475	-0.824324	-0.383004
T0246	1.240096	83.055687	-0.519231	-1.057302
T0247	-0.51126	67.000954	-1.866591	-0.783668
T0248	-2.113564	63.94796	-0.77813	-0.878324
T0249	-0.241412	76.030067	-1.017587	-0.931224
T0250	-0.267683	67.992531	-1.083237	-1.018729
T0251	-0.689886	71.880562	-1.073356	-1.003479
T0252	-1.929439	64.612396	-0.848761	-0.938465
T0253	1.805761	65.85643	-2.434604	-0.935915
T0254	-0.651168	62.743507	-0.814009	-0.92643
T0255	-0.089763	75.377953	-0.884738	-1.00599
T0256	1.424374	65.902473	-0.85577	-0.952094
T0257	0.36032	79.513115	-2.099646	-0.878702
T0258	-1.362693	64.523079	-1.058775	-0.990889
T0259	-0.68804	69.386856	0.328689	-1.017953
T0260	-0.5298	67.833847	-1.096308	-1.035681
T0261	1.241165	67.514664	-0.818876	-0.273118

End of preview.

sv-qtl-coloc task inputs

Agent-visible input data for the sv-qtl-coloc task in Terminal-Bench Science. This is a development copy used while the task PR is under review; the task's Dockerfile pulls sv-qtl-coloc/input/* at a pinned commit and verifies every file against a sha256 checksum list.

Layout

sv-qtl-coloc/input/
├── genotype/SV/       structural-variant genotypes, chr1-22
├── genotype/SNV/      SNV genotypes, chr1-22
├── RNA/               phenotype.bed + sample_info.tsv
├── Plasma/            phenotype.bed + sample_info.tsv
├── CSF/               phenotype.bed + sample_info.tsv
├── Methylation/       phenotype.bed + sample_info.tsv
└── GWAS/              GCST90027158 and GCST90275127: .tsv.gz + .tsv-meta.yaml

78 files, about 6.1 GB.

Provenance

Genotype and molecular-phenotype files are a synthetic twin of the Stanford Alzheimer's Disease Research Center (ADRC) cohort, generated with SPHERE (He et al., bioRxiv 2026). SPHERE is a model-free method that releases a synthetic twin while the original records stay in the local environment. No original participant records are included. The twin keeps the original means, variances and correlations exactly, so linear analyses give the same effect sizes and P values; nonlinear analyses largely keep their utility.
Every value in the twin is a continuous real number, including variables that are binary or integer in the original data (e.g. Sex, AD_Dx, PD_Dx, genotype dosages). Use them as numeric values.
GWAS summary statistics and their -meta.yaml metadata are the NHGRI-EBI GWAS Catalog files for GCST90027158 and GCST90275127.

License and acknowledgment

Released under CC BY 4.0. Please acknowledge: "Stanford Alzheimer's Disease Research Center, NIH/NIA grant P30 AG066515."

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