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Datasets:
hanzi-bulk-collab
/
bulk-datasets-private

Tags:
biology
genomics
rna-seq
gene-expression
benchmark
License:
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4645, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2866, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3073, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2581, in _iter_arrow
                  yield from 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/csv/csv.py", line 196, in _generate_tables
                  csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/streaming.py", line 73, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1275, in xpandas_read_csv
                  out = pd.read_csv(f, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
                  return _read(filepath_or_buffer, kwds)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 620, in _read
                  parser = TextFileReader(filepath_or_buffer, **kwds)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
                  self._engine = self._make_engine(f, self.engine)
                                 ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1898, in _make_engine
                  return mapping[engine](f, **self.options)
                         ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 93, in __init__
                  self._reader = parsers.TextReader(src, **kwds)
                                 ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "pandas/_libs/parsers.pyx", line 574, in pandas._libs.parsers.TextReader.__cinit__
                File "pandas/_libs/parsers.pyx", line 663, in pandas._libs.parsers.TextReader._get_header
                File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "pandas/_libs/parsers.pyx", line 2053, in pandas._libs.parsers.raise_parser_error
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte

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PRTS data: PreBULK, RealBulk and the benchmark

Public data resources accompanying In-context learning predicts clinical endpoints from bulk transcriptomes across diseases and platforms. Code, figures and source tables · Model checkpoint.

This repository is public and has no access gate. Its name retains private from its original use as a private collaboration repository.

File Contents
prebulk-dataset.zip Quality-controlled PreBULK: 536,287 public RNA-seq profiles and 20,010 genes, with sample and gene metadata. Details
realbulk-dataset.zip 1,160 curated bulk RNA-seq and microarray cohorts, 194,265 people, collected and harmonized expression and labels, and provenance. Apply the correction overlay below. Current documentation
realbulk-corrections-20261112.zip Required overlay correcting GSE14333 disease-free survival labels, endpoint metadata and checksums, and updating the collection documentation.
prts-release-data.zip Benchmark expression matrices, labels, frozen task definitions, per-task scores, per-sample predictions, the clean RealBulk subset and TCGA site-grouped folds. Details

Version notes

The public PreBULK archive is a later quality-controlled edition of the original 581,503-profile corpus described in the pretraining Methods. It retains 536,287 profiles; source_prebulk_row links each retained profile to its original row. The original training-corpus count and the deposited release count should not be used interchangeably.

The original RealBulk archive predates the corrected interpretation of GSE14333 disease-free survival. The correction uses DFS_Cens = 0 for an event and 1 for censoring, and reads DFS_Time in months. It yields 50 events among 226 labelled people. The overlay updates collected and harmonized labels, endpoint metadata, the affected checks and manifest entries. Expression matrices, sample membership and Dukes-stage labels are unchanged. No released benchmark task uses GSE14333. The correction script implements the correction recorded in the cohort's curation code.

Download and unpack

Download only the resources needed for the task, using the Files tab or the Hugging Face CLI:

hf download hanzi-bulk-collab/bulk-datasets-private prts-release-data.zip --repo-type dataset --local-dir .
unzip prts-release-data.zip

The benchmark archive creates prts-release/data/. Point the benchmark CLI to it with --data prts-release/data, or set PRTS_BENCH_DATA to that directory. If working inside the original full release package, the archive places data/ in the expected location automatically.

For the complete RealBulk collection, extract the base archive and then the overlay into the same directory, allowing the overlay's files to replace the corresponding originals:

unzip realbulk-dataset.zip
unzip -o realbulk-corrections-20261112.zip

The resulting realbulk/MANIFEST.tsv describes the corrected files. The overlay is not a replacement for the full expression archive.

Licensing

These collections derive from public GEO, ARCHS4, ArrayExpress, GDC and other source repositories. The source datasets retain their original licences and access terms; cite the originating studies and respect their conditions. Apache-2.0 applies to the author-owned PRTS code and model weights, not as a blanket relicensing of the source data. Accession and provenance records are included in the archives.
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