Datasets:

prakhya15
/

human-pathogen-literature

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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 2 new columns ({'title', 'abstract'})

This happened while the csv dataset builder was generating data using

hf://datasets/prakhya15/human-pathogen-literature/organism_disease_pmid_evidence.csv (at revision 3def1b7994425803d38309a05743815ad5127477), ['hf://datasets/prakhya15/human-pathogen-literature@3def1b7994425803d38309a05743815ad5127477/organism_disease_evidence.csv', 'hf://datasets/prakhya15/human-pathogen-literature@3def1b7994425803d38309a05743815ad5127477/organism_disease_pmid_evidence.csv', 'hf://datasets/prakhya15/human-pathogen-literature@3def1b7994425803d38309a05743815ad5127477/pathogen_master_candidates.csv']

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 765, 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 773, 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
              organism: string
              pmid: int64
              disease_or_infection: string
              title: string
              abstract: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 867
              to
              {'organism': Value('string'), 'pmid': Value('int64'), 'disease_or_infection': Value('string')}
              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 2 new columns ({'title', 'abstract'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/prakhya15/human-pathogen-literature/organism_disease_pmid_evidence.csv (at revision 3def1b7994425803d38309a05743815ad5127477), ['hf://datasets/prakhya15/human-pathogen-literature@3def1b7994425803d38309a05743815ad5127477/organism_disease_evidence.csv', 'hf://datasets/prakhya15/human-pathogen-literature@3def1b7994425803d38309a05743815ad5127477/organism_disease_pmid_evidence.csv', 'hf://datasets/prakhya15/human-pathogen-literature@3def1b7994425803d38309a05743815ad5127477/pathogen_master_candidates.csv']
              
              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.

organism string	pmid int64	disease_or_infection string
Bundibugyo ebolavirus	34,467,242	Hemorrhagic Fever, Ebola
Bundibugyo ebolavirus	28,356,221	Hemorrhagic Fever, Ebola
Bundibugyo ebolavirus	26,861,827	Hemorrhagic Fever, Ebola
Marburg virus	40,788,011	Coinfection
Marburg virus	38,526,940	Hemorrhagic Fever, Ebola
Marburg virus	37,866,271	Hemorrhagic Fever Virus, Crimean-Congo
Marburg virus	37,805,032	Coronavirus Infections
Marburg virus	37,805,032	Zika Virus Infection
Marburg virus	37,301,278	Zika Virus Infection
Marburg virus	37,208,958	Hemorrhagic Fever, Ebola
Marburg virus	36,599,498	Hemorrhagic Fever, Ebola
Marburg virus	35,908,851	Hemorrhagic Fever, Ebola
Marburg virus	32,740,063	Coronavirus Infections
Marburg virus	32,740,063	Hemorrhagic Fever, Ebola
Marburg virus	32,740,063	Pneumonia, Viral
Marburg virus	32,404,328	Laboratory Infection
Marburg virus	32,295,912	Filoviridae Infections
Marburg virus	32,093,889	Filoviridae Infections
Marburg virus	29,500,195	Hemorrhagic Fever, Ebola
Marburg virus	28,396,467	Hemorrhagic Fever, Ebola
Marburg virus	28,356,221	Hemorrhagic Fever, Ebola
Marburg virus	25,282,746	Hemorrhagic Fever, Ebola
Marburg virus	24,590,073	Filoviridae Infections
Marburg virus	24,590,073	Hemorrhagic Fever, Ebola
Marburg virus	20,217,155	Coronavirus Infections
Marburg virus	20,019,654	Diarrhea
Marburg virus	20,019,654	Hepatitis
Marburg virus	19,718,950	Rhabdoviridae Infections
Marburg virus	18,977,691	Hemorrhagic Fever, Ebola
Marburg virus	18,977,691	West Nile Fever
Marburg virus	4,966,280	Diarrhea
Marburg virus	4,966,280	Fever
Marburg virus	4,966,280	Laboratory Infection
MERS Coronavirus	31,968,702	Coronavirus Infections
MERS Coronavirus	31,843,650	Coronavirus Infections
MERS Coronavirus	31,355,779	Coronavirus Infections
MERS Coronavirus	30,869,000	Coronavirus Infections
MERS Coronavirus	30,060,038	Coronavirus Infections
MERS Coronavirus	30,012,113	Coronavirus Infections
MERS Coronavirus	30,012,113	Streptococcus pneumoniae
MERS Coronavirus	29,976,185	Coronavirus Infections
MERS Coronavirus	29,129,042	Coronavirus Infections
MERS Coronavirus	28,846,484	Coronavirus Infections
MERS Coronavirus	28,846,484	Zika Virus Infection
MERS Coronavirus	28,840,828	Coronavirus Infections
MERS Coronavirus	28,840,828	Cross Infection
MERS Coronavirus	28,840,828	Infection Control
MERS Coronavirus	28,774,161	Coronavirus Infections
MERS Coronavirus	30,182,701	Coronavirus Infections
MERS Coronavirus	27,997,933	Coronavirus Infections
MERS Coronavirus	27,918,958	Bacterial Infections
MERS Coronavirus	27,840,203	Coronavirus Infections
MERS Coronavirus	27,775,012	Coronavirus Infections
MERS Coronavirus	27,528,677	Coronavirus Infections
MERS Coronavirus	27,449,387	Coronavirus Infections
MERS Coronavirus	26,930,074	Coronavirus Infections
MERS Coronavirus	26,695,637	Coronavirus Infections
MERS Coronavirus	26,695,637	Cross Infection
MERS Coronavirus	26,678,874	Coinfection
MERS Coronavirus	26,678,874	Coronavirus Infections
MERS Coronavirus	26,216,974	Coronavirus Infections
MERS Coronavirus	25,874,632	Coronavirus Infections
MERS Coronavirus	25,640,653	Coronavirus Infections
MERS Coronavirus	25,248,734	Coronavirus Infections
MERS Coronavirus	25,192,975	Coronavirus Infections
MERS Coronavirus	25,075,637	Coronavirus Infections
MERS Coronavirus	24,896,817	Coronavirus Infections
MERS Coronavirus	24,857,749	Coronavirus Infections
MERS Coronavirus	24,781,747	Coronavirus Infections
MERS Coronavirus	24,766,432	Coronavirus Infections
Nipah virus	42,387,669	Henipavirus Infections
Nipah virus	42,075,770	Henipavirus Infections
Nipah virus	42,075,770	Encephalitis, Viral
Nipah virus	41,894,354	Henipavirus Infections
Nipah virus	41,830,685	Henipavirus Infections
Nipah virus	41,802,635	Henipavirus Infections
Nipah virus	41,729,872	Henipavirus Infections
Nipah virus	41,729,872	Cross Infection
Nipah virus	41,729,872	Infection Control
Nipah virus	41,481,750	Rift Valley fever virus
Nipah virus	40,944,996	Henipavirus Infections
Nipah virus	40,857,938	Henipavirus Infections
Nipah virus	40,622,505	Henipavirus Infections
Nipah virus	40,456,231	Hemorrhagic Fever, Ebola
Nipah virus	40,244,690	Hemorrhagic Fever, Ebola
Nipah virus	39,549,708	Henipavirus Infections
Nipah virus	39,480,207	Eye Infections
Nipah virus	38,399,954	Henipavirus Infections
Nipah virus	38,086,402	Hemorrhagic Fever, Ebola
Nipah virus	37,925,626	Henipavirus Infections
Nipah virus	37,805,032	Coronavirus Infections
Nipah virus	37,805,032	Zika Virus Infection
Nipah virus	37,756,301	Henipavirus Infections
Nipah virus	37,260,063	Encephalitis, Japanese
Nipah virus	37,260,063	Encephalitis
Nipah virus	37,153,606	Hemorrhagic Fever, Ebola
Nipah virus	37,153,606	Henipavirus Infections
Nipah virus	36,208,644	African Swine Fever
Nipah virus	35,657,574	Henipavirus Infections
Nipah virus	33,529,237	Hemorrhagic Fever, Ebola

End of preview.

Human Infection-Associated Microorganisms

A literature-derived dataset of microorganisms associated with human infections, with NCBI Taxonomy identifiers and PubMed/MeSH evidence.

Dataset files

`pathogen_master_candidates.csv`

NCBI Taxonomy-normalized candidate microorganisms with literature-based human-association evidence.

`organism_disease_evidence.csv`

Organism-disease/infection relationships derived from PubMed MeSH annotations.

`organism_disease_pmid_evidence.csv`

PMID-level evidence linking organisms with disease/infection terms and associated publication metadata.

Data sources

NCBI Taxonomy
PubMed
Medical Subject Headings (MeSH)
Human Epidemic Database (HED)

Important limitation

This is a research dataset. A microorganism appearing in human-related literature does not automatically establish that it is a confirmed human pathogen.

Similarly, co-occurrence of an organism and a MeSH disease term in a paper does not necessarily establish a causal relationship.

Taxonomy identifiers and PubMed identifiers are retained for provenance and future validation.

Intended uses

Biomedical NLP
Infectious disease research
Pathogen-disease knowledge graphs
Literature mining
Information retrieval
Drug repurposing research

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Total file size:

3.75 MB