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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 ({'value', 'metric'}) and 6 missing columns ({'intermediate_range', 'antibiotic', 'resistant_threshold', 'susceptible_threshold', 'organism_group', 'method'}).
This happened while the csv dataset builder was generating data using
hf://datasets/mabera/nigeria-amr-classifier-v2-dataset/amr_clinical_context.csv (at revision f6b5e02d4ab573156f44d2052900ab3b90e68633), ['hf://datasets/mabera/nigeria-amr-classifier-v2-dataset@f6b5e02d4ab573156f44d2052900ab3b90e68633/amr_breakpoint_registry.csv', 'hf://datasets/mabera/nigeria-amr-classifier-v2-dataset@f6b5e02d4ab573156f44d2052900ab3b90e68633/amr_clinical_context.csv', 'hf://datasets/mabera/nigeria-amr-classifier-v2-dataset@f6b5e02d4ab573156f44d2052900ab3b90e68633/amr_combined_dataset.csv', 'hf://datasets/mabera/nigeria-amr-classifier-v2-dataset@f6b5e02d4ab573156f44d2052900ab3b90e68633/amr_national_surveillance.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 1837, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
metric: string
value: string
source: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 612
to
{'organism_group': Value('string'), 'antibiotic': Value('string'), 'method': Value('string'), 'susceptible_threshold': Value('string'), 'intermediate_range': Value('string'), 'resistant_threshold': Value('string'), 'source': 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 1683, 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 1839, 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 ({'value', 'metric'}) and 6 missing columns ({'intermediate_range', 'antibiotic', 'resistant_threshold', 'susceptible_threshold', 'organism_group', 'method'}).
This happened while the csv dataset builder was generating data using
hf://datasets/mabera/nigeria-amr-classifier-v2-dataset/amr_clinical_context.csv (at revision f6b5e02d4ab573156f44d2052900ab3b90e68633), ['hf://datasets/mabera/nigeria-amr-classifier-v2-dataset@f6b5e02d4ab573156f44d2052900ab3b90e68633/amr_breakpoint_registry.csv', 'hf://datasets/mabera/nigeria-amr-classifier-v2-dataset@f6b5e02d4ab573156f44d2052900ab3b90e68633/amr_clinical_context.csv', 'hf://datasets/mabera/nigeria-amr-classifier-v2-dataset@f6b5e02d4ab573156f44d2052900ab3b90e68633/amr_combined_dataset.csv', 'hf://datasets/mabera/nigeria-amr-classifier-v2-dataset@f6b5e02d4ab573156f44d2052900ab3b90e68633/amr_national_surveillance.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_group string | antibiotic string | method string | susceptible_threshold string | intermediate_range string | resistant_threshold string | source string |
|---|---|---|---|---|---|---|
Enterobacterales | Ampicillin | MIC (ug/mL) | <=8 | 16 | >=32 | CLSI M100, 35th Edition (2025) |
Enterobacterales | Ceftriaxone | MIC (ug/mL) | <=1 | 2 | >=4 | CLSI M100, 35th Edition (2025) |
Enterobacterales | Ciprofloxacin | MIC (ug/mL) | <=0.25 | 0.5 | >=1 | CLSI M100, 35th Edition (2025) |
Enterobacterales | Meropenem | MIC (ug/mL) | <=1 | 2 | >=4 | CLSI M100, 35th Edition (2025) |
Enterobacterales | Gentamicin | MIC (ug/mL) | <=4 | 8 | >=16 | CLSI M100, 35th Edition (2025) |
Enterobacterales | Trimethoprim-sulfamethoxazole | MIC (ug/mL) | <=2/38 | none (no I category) | >=4/76 | CLSI M100, 35th Edition (2025) |
Staphylococcus aureus | Oxacillin (MRSA screen) | MIC (ug/mL) | <=2 | none (no I category) | >=4 | CLSI M100, 35th Edition (2025) |
Enterobacterales | Colistin | MIC (ug/mL) | no S category (intrinsic) | <=2 | >=4 | CLSI M100, 35th Edition (2025); note: colistin has no Susceptible category per current CLSI |
null | null | null | null | null | null | GRAM/IHME, University of Washington, 2023, cited via WHO AFRO, Dec 2025 |
null | null | null | null | null | null | WHO Regional Office for Africa, Nigeria AMR Survey launch, Dec 2025 |
null | null | null | null | null | null | WHO Regional Office for Africa, Nigeria AMR Survey launch, Dec 2025 |
null | null | null | null | null | null | Poudel AN et al., PLoS One, 2023, cited via WHO AFRO Dec 2025 |
null | null | null | null | null | null | Poudel AN et al., PLoS One, 2023, cited via WHO AFRO Dec 2025 |
null | null | null | null | null | null | Poudel AN et al., PLoS One, 2023, cited via WHO AFRO Dec 2025 |
null | null | null | null | null | null | PMC12659808, prospective cohort study, Libya, 2022-2024 |
null | null | null | null | null | null | PMC12659808, prospective cohort study, Libya, 2022-2024 |
null | null | null | null | null | null | PMC12659808, prospective cohort study, Libya, 2022-2024 |
null | null | null | null | null | null | BMC Infectious Diseases, Bacterial-associated bloodstream infections in Lagos, Nigeria, 2025 |
null | null | null | null | null | null | Blomberg et al., BMC Infectious Diseases, 2007 (foundational African pediatric BSI outcome study) |
null | null | null | null | null | null | CLSI M100, 35th Edition (2025); PMC12659808, Libya prospective cohort, 2022-2024 |
null | null | null | null | null | null | CLSI M100, 35th Edition (2025); Poudel AN et al., PLoS One, 2023 |
null | null | null | null | null | null | CLSI M100, 35th Edition (2025); PMC12659808, Libya prospective cohort, 2022-2024 |
null | null | null | null | null | null | CLSI M100, 35th Edition (2025); Discover Public Health, Antimicrobial resistance in Nigeria's healthcare system, 2025 |
null | null | null | null | null | null | CLSI M100, 35th Edition (2025) |
null | null | null | null | null | null | CLSI M100, 35th Edition (2025); BMC Infectious Diseases, Bacterial-associated bloodstream infections in Lagos, Nigeria, 2025 |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
null | null | null | null | null | null | MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022 (data collected 2016-2018) |
Nigeria AMR Classifier v2 Dataset
Author: Hussein Adeiza (mabera) Role: Licensed Environmental Health Officer, Abuja Nigeria Built for: AutoScientist Challenge 2026, Part 2 — Science Category
Dataset Description
A closed-label antimicrobial resistance classification dataset combining two independently verifiable task types: individual MIC-based susceptibility classification (CLSI M100 breakpoints) and population-level resistance rate classification from real Nigerian national surveillance data (MAAP/Fleming Fund Country Report, 2022).
Unlike open-ended interpretation datasets, every classification here is deterministically checkable, not subjectively judged.
Files
amr_combined_dataset.csv
The complete 34-row training file (prompt/completion/source), combining both task types.
amr_breakpoint_registry.csv
The CLSI M100 (2025) breakpoint ground truth used for individual MIC classification.
amr_national_surveillance.csv
28 real rows from Nigeria's national AMR surveillance report, covering 25 sentinel laboratories and 23,963 positive cultures (2016-2018). Every resistance rate independently recomputed from raw N/n counts and matched exactly against the report's own published percentages.
amr_clinical_context.csv
11 real cited clinical outcome statistics used for external validation.
Verification
All 34 rows in this dataset were programmatically verified before training, demonstrated live in the accompanying Kaggle notebook: https://www.kaggle.com/code/yunusahusseinadeiza/notebookb703acf2e7
Data Quality Diagnostic
Run automatically via verify_full_dataset.py, included in this repo:
- Total rows: 34
- Duplicate prompts: 0
- Null/missing values: 0
- Label distribution: balanced across Susceptible/Intermediate/Resistant (MIC layer), and spread across the full 14.5%-81.6% resistance rate range (surveillance layer), avoiding a degenerate single-class dataset
Reproducibility
All build and verification scripts are included in this repository.
Run python verify_full_dataset.py to independently re-derive every
classification label from raw ground truth. See REPRODUCIBILITY.md
for full pipeline documentation.
Key Cited Findings
- 3rd-generation cephalosporin resistance in Enterobacterales: 67-73% (2016-2018)
- MRSA rates reached 81.6% by 2018
- Carbapenem resistance in Enterobacterales fell from 19.0% to 14.5% (2016-2018), one of the few improving trends
- Resistant infections carry an 84% higher mortality risk globally; 32.1% vs 18.8% 30-day mortality, MDR vs non-MDR bloodstream infections
Sources
- CLSI M100, 35th Edition (2025)
- MAAP/Fleming Fund Regional Grant (Round 1), Nigeria Country Report, 2022
- Poudel AN et al., PLoS One, 2023
- BMC Infectious Diseases, Bacterial-associated bloodstream infections in Lagos, Nigeria, 2025
- PMC12659808, Libya prospective cohort, 2022-2024
Related Links
- 🤗 Model: https://huggingface.co/mabera/nigeria-amr-classifier-v2
- 📊 Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/amr-classifier-v2-national-surveillance
Credits
Powered by Adaptive Data — Adaption Labs AutoScientist Challenge 2026, Part 2 — Science Category
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