Datasets:

LiteFold
/

SKEMPI2

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
dataset_id: string
row: struct<#Pdb;Mutation(s)_PDB;Mutation(s)_cleaned;iMutation_Location(s);Hold_out_type;Hold_out_protein (... 2000 chars omitted)
  child 0, #Pdb;Mutation(s)_PDB;Mutation(s)_cleaned;iMutation_Location(s);Hold_out_type;Hold_out_proteins;Affin (... 378 chars omitted): string
  child 1, column_1: string
  child 2, column_2: string
  child 3, column_3: string
  child 4, column_4: string
  child 5, column_5: string
  child 6, column_6: string
  child 7, column_7: string
  child 8, column_8: string
  child 9, column_10: string
  child 10, column_9: string
  child 11, column_11: string
  child 12, column_12: string
  child 13, column_13: string
  child 14, column_14: string
  child 15, column_15: string
  child 16, column_16: string
  child 17, column_17: string
  child 18, column_18: string
  child 19, column_19: string
  child 20, column_20: string
  child 21, column_21: string
  child 22, column_22: string
  child 23, column_23: string
  child 24, column_24: string
  child 25, column_25: string
  child 26, column_26: string
  child 27, column_27: string
  child 28, column_28: string
  child 29, column_29: string
  child 30, column_30: string
  child 31, column_31: string
  child 32, column_32: string
  child 33, column_33: string
  child 34, column_34: string
  child 35, column_35: string
  child 36, column_36: string
  child 37, column_37: string
  child 38, column_38: string
  child 39, column_39: string
  child 40, column_40: string
  child 41, column_41: s
...
, column_55: string
  child 56, column_56: string
  child 57, column_57: string
  child 58, column_58: string
  child 59, column_59: string
  child 60, column_60: string
  child 61, column_61: string
  child 62, column_62: string
  child 63, column_63: string
  child 64, column_64: string
  child 65, column_65: string
  child 66, column_66: string
  child 67, column_67: string
  child 68, column_68: string
  child 69, column_69: string
  child 70, column_70: string
  child 71, column_71: string
  child 72, column_72: string
  child 73, column_73: string
  child 74, column_74: string
  child 75, column_75: string
  child 76, column_76: string
  child 77, column_77: string
  child 78, column_78: string
  child 79, column_79: string
  child 80, column_80: string
  child 81, column_81: string
  child 82, column_82: string
  child 83, column_83: string
  child 84, column_84: string
  child 85, column_85: string
row_index: int64
source_file: 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
format: 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<#Pdb;Mutation(s)_PDB;Mutation(s)_cleaned;iMutation_Location(s);Hold_out_type;Hold_out_protein (... 2000 chars omitted)
                child 0, #Pdb;Mutation(s)_PDB;Mutation(s)_cleaned;iMutation_Location(s);Hold_out_type;Hold_out_proteins;Affin (... 378 chars omitted): string
                child 1, column_1: string
                child 2, column_2: string
                child 3, column_3: string
                child 4, column_4: string
                child 5, column_5: string
                child 6, column_6: string
                child 7, column_7: string
                child 8, column_8: string
                child 9, column_10: string
                child 10, column_9: string
                child 11, column_11: string
                child 12, column_12: string
                child 13, column_13: string
                child 14, column_14: string
                child 15, column_15: string
                child 16, column_16: string
                child 17, column_17: string
                child 18, column_18: string
                child 19, column_19: string
                child 20, column_20: string
                child 21, column_21: string
                child 22, column_22: string
                child 23, column_23: string
                child 24, column_24: string
                child 25, column_25: string
                child 26, column_26: string
                child 27, column_27: string
                child 28, column_28: string
                child 29, column_29: string
                child 30, column_30: string
                child 31, column_31: string
                child 32, column_32: string
                child 33, column_33: string
                child 34, column_34: string
                child 35, column_35: string
                child 36, column_36: string
                child 37, column_37: string
                child 38, column_38: string
                child 39, column_39: string
                child 40, column_40: string
                child 41, column_41: s
              ...
              , column_55: string
                child 56, column_56: string
                child 57, column_57: string
                child 58, column_58: string
                child 59, column_59: string
                child 60, column_60: string
                child 61, column_61: string
                child 62, column_62: string
                child 63, column_63: string
                child 64, column_64: string
                child 65, column_65: string
                child 66, column_66: string
                child 67, column_67: string
                child 68, column_68: string
                child 69, column_69: string
                child 70, column_70: string
                child 71, column_71: string
                child 72, column_72: string
                child 73, column_73: string
                child 74, column_74: string
                child 75, column_75: string
                child 76, column_76: string
                child 77, column_77: string
                child 78, column_78: string
                child 79, column_79: string
                child 80, column_80: string
                child 81, column_81: string
                child 82, column_82: string
                child 83, column_83: string
                child 84, column_84: string
                child 85, column_85: string
              row_index: int64
              source_file: 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
              format: 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

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SKEMPI v2

SKEMPI v2 protein-protein binding affinity mutation dataset, normalized to newline-delimited JSON with row-level provenance.

Processed and uploaded by the MegaData post-download pipeline (internal repo). Original source: https://life.bsc.es/pid/skempi2.

Statistics


Table files	1
Total rows	7,085
Total bytes	5.86 MiB (6,148,969)

Tables

Table	Rows	Bytes
`labeled_skempi2_skempi_v2.csv.jsonl`	7,085	5.86 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/SKEMPI2 --repo-type dataset --local-dir ./skempi2

Programmatic streaming:

import json
from pathlib import Path
from huggingface_hub import snapshot_download

local = snapshot_download(repo_id="LiteFold/SKEMPI2", 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

See upstream SKEMPI v2 license.

Citation

Jankauskaite J, et al. SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics, 35(3):462-469, 2019.

Provenance

Built from the local manifest entry skempi2 of manifests/atlas_download_plan.json. Pipeline source: megadata-post normalize --dataset skempi2 --tables-only.

Downloads last month: -

Total file size:

6.15 MB