Datasets:

LiteFold
/

MegaScale-Tsuboyama2023

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
dataset_id: string
row: struct<WT_name: string, burial: string, ddG_A: string, ddG_D: string, ddG_E: string, ddG_F: string,  (... 869 chars omitted)
  child 0, WT_name: string
  child 1, burial: string
  child 2, ddG_A: string
  child 3, ddG_D: string
  child 4, ddG_E: string
  child 5, ddG_F: string
  child 6, ddG_G: string
  child 7, ddG_H: string
  child 8, ddG_I: string
  child 9, ddG_K: string
  child 10, ddG_L: string
  child 11, ddG_M: string
  child 12, ddG_N: string
  child 13, ddG_P: string
  child 14, ddG_Q: string
  child 15, ddG_R: string
  child 16, ddG_S: string
  child 17, ddG_T: string
  child 18, ddG_V: string
  child 19, ddG_W: string
  child 20, ddG_Y: string
  child 21, dg_hydrophobic_wt_average: string
  child 22, dg_wt_average: string
  child 23, dssp: string
  child 24, evo_2nd_best: string
  child 25, evo_hydrophobic_normalized: string
  child 26, evo_hydrophobic_wt_average: string
  child 27, evo_normalized: string
  child 28, evo_wt_average: string
  child 29, func: string
  child 30, func_2nd_best: string
  child 31, func_hydrophobic: string
  child 32, gemme_A: string
  child 33, gemme_D: string
  child 34, gemme_E: string
  child 35, gemme_F: string
  child 36, gemme_G: string
  child 37, gemme_H: string
  child 38, gemme_I: string
  child 39, gemme_K: string
  child 40, gemme_L: string
  child 41, gemme_M: string
  child 42, gemme_N: string
  child 43, gemme_P: string
  child 44, gemme_Q: string
  child 45, gemme_R: string
  child 46, gemme_S: string
  child 47, gemme_T: string
  child 48, gemme_V: string
  child 49, gemme_W: string
  child 50, gemme_Y: string
  child 51, pos: string
  child 52, sasa_sc: string
  child 53, wt_aa: string
row_index: int64
source_file: string
format: string
tables: list<item: struct<bytes: int64, category: string, dataset_id: string, output_file: string, rows: int (... 41 chars omitted)
  child 0, item: struct<bytes: int64, category: string, dataset_id: string, output_file: string, rows: int64, source_ (... 29 chars omitted)
      child 0, bytes: int64
      child 1, category: string
      child 2, dataset_id: string
      child 3, output_file: string
      child 4, rows: int64
      child 5, source_file: string
      child 6, status: string
category: string
total_rows: int64
to
{'category': Value('string'), 'dataset_id': Value('string'), 'format': Value('string'), 'tables': List({'bytes': Value('int64'), 'category': Value('string'), 'dataset_id': Value('string'), 'output_file': Value('string'), 'rows': Value('int64'), 'source_file': Value('string'), 'status': Value('string')}), 'total_rows': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              dataset_id: string
              row: struct<WT_name: string, burial: string, ddG_A: string, ddG_D: string, ddG_E: string, ddG_F: string,  (... 869 chars omitted)
                child 0, WT_name: string
                child 1, burial: string
                child 2, ddG_A: string
                child 3, ddG_D: string
                child 4, ddG_E: string
                child 5, ddG_F: string
                child 6, ddG_G: string
                child 7, ddG_H: string
                child 8, ddG_I: string
                child 9, ddG_K: string
                child 10, ddG_L: string
                child 11, ddG_M: string
                child 12, ddG_N: string
                child 13, ddG_P: string
                child 14, ddG_Q: string
                child 15, ddG_R: string
                child 16, ddG_S: string
                child 17, ddG_T: string
                child 18, ddG_V: string
                child 19, ddG_W: string
                child 20, ddG_Y: string
                child 21, dg_hydrophobic_wt_average: string
                child 22, dg_wt_average: string
                child 23, dssp: string
                child 24, evo_2nd_best: string
                child 25, evo_hydrophobic_normalized: string
                child 26, evo_hydrophobic_wt_average: string
                child 27, evo_normalized: string
                child 28, evo_wt_average: string
                child 29, func: string
                child 30, func_2nd_best: string
                child 31, func_hydrophobic: string
                child 32, gemme_A: string
                child 33, gemme_D: string
                child 34, gemme_E: string
                child 35, gemme_F: string
                child 36, gemme_G: string
                child 37, gemme_H: string
                child 38, gemme_I: string
                child 39, gemme_K: string
                child 40, gemme_L: string
                child 41, gemme_M: string
                child 42, gemme_N: string
                child 43, gemme_P: string
                child 44, gemme_Q: string
                child 45, gemme_R: string
                child 46, gemme_S: string
                child 47, gemme_T: string
                child 48, gemme_V: string
                child 49, gemme_W: string
                child 50, gemme_Y: string
                child 51, pos: string
                child 52, sasa_sc: string
                child 53, wt_aa: string
              row_index: int64
              source_file: string
              format: string
              tables: list<item: struct<bytes: int64, category: string, dataset_id: string, output_file: string, rows: int (... 41 chars omitted)
                child 0, item: struct<bytes: int64, category: string, dataset_id: string, output_file: string, rows: int64, source_ (... 29 chars omitted)
                    child 0, bytes: int64
                    child 1, category: string
                    child 2, dataset_id: string
                    child 3, output_file: string
                    child 4, rows: int64
                    child 5, source_file: string
                    child 6, status: string
              category: string
              total_rows: int64
              to
              {'category': Value('string'), 'dataset_id': Value('string'), 'format': Value('string'), 'tables': List({'bytes': Value('int64'), 'category': Value('string'), 'dataset_id': Value('string'), 'output_file': Value('string'), 'rows': Value('int64'), 'source_file': Value('string'), 'status': Value('string')}), 'total_rows': Value('int64')}
              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.

Megascale Stability (Tsuboyama et al. 2023)

Megascale protein-stability dataset from Tsuboyama et al. (Nature 2023), normalized to newline-delimited JSON with row-level provenance.

Processed and uploaded by the MegaData post-download pipeline (internal repo). Original source: https://doi.org/10.1038/s41586-023-06328-6.

Statistics


Table files	5
Total rows	299,271
Total bytes	171.19 MiB (179,507,879)

Tables

Table	Rows	Bytes
`data_unpacked_labeled_megascale_stability_tsuboyama_2023_megascale_tsuboyama_2023_Data_tables_for_figs_Data_tables_for_figs_dG_GEMME_non_redundant_natural_Fig6.csv.jsonl`	5,396	9.36 MiB
`data_unpacked_labeled_megascale_stability_tsuboyama_2023_megascale_tsuboyama_2023_Data_tables_for_figs_Data_tables_for_figs_dG_extdG_data_Fig1.csv.jsonl`	1,292	542.94 KiB
`data_unpacked_labeled_megascale_stability_tsuboyama_2023_megascale_tsuboyama_2023_Data_tables_for_figs_Data_tables_for_figs_dG_for_double_mutants_Fig4.csv.jsonl`	269,516	103.01 MiB
`data_unpacked_labeled_megascale_stability_tsuboyama_2023_megascale_tsuboyama_2023_Data_tables_for_figs_Data_tables_for_figs_dG_non_redundant_natural_Fig5.csv.jsonl`	5,974	4.27 MiB
`data_unpacked_labeled_megascale_stability_tsuboyama_2023_megascale_tsuboyama_2023_Data_tables_for_figs_Data_tables_for_figs_dG_site_feature_Fig3.csv.jsonl`	17,093	54.03 MiB

Layout

.
├── _MANIFEST.json                 # aggregate manifest (per-table counts)
└── tables/<source_slug>.jsonl    # normalized rows (one JSON object per line)

Each line in a tables/*.jsonl file is a JSON object with at least dataset_id, row (the raw upstream row), row_index, and source_file fields, so every row carries its upstream provenance.

Loading

hf download LiteFold/MegaScale-Tsuboyama2023 --repo-type dataset --local-dir ./megascale_stability_tsuboyama_2023

Programmatic streaming:

import json
from pathlib import Path
from huggingface_hub import snapshot_download

local = snapshot_download(repo_id="LiteFold/MegaScale-Tsuboyama2023", repo_type="dataset")
for jsonl in sorted(Path(local, "tables").glob("*.jsonl")):
    with jsonl.open() as f:
        for line in f:
            row = json.loads(line)
            ...  # row["row"] is the upstream record

License

CC BY 4.0 (Tsuboyama et al. 2023).

Citation

Tsuboyama K, et al. Mega-scale experimental analysis of protein folding stability in biology and design. Nature, 620(7973):434-444, 2023.

Provenance

Built from the local manifest entry megascale_stability_tsuboyama_2023 of manifests/atlas_download_plan.json. Pipeline source: megadata-post normalize --dataset megascale_stability_tsuboyama_2023 --tables-only.

Downloads last month: 72

Total file size:

180 MB