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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 1 new columns ({'Trial'})
This happened while the csv dataset builder was generating data using
hf://datasets/hugging-science/gut-microbiome-allergy-data/metadata/goldberg/T1.csv (at revision 9f73e3c30b3ea1ef2b9d8a2d5b55f570258bae24)
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/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Trial: string
sid: string
label: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 584
to
{'sid': Value('string'), 'label': Value('int64')}
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 1339, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
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 1 new columns ({'Trial'})
This happened while the csv dataset builder was generating data using
hf://datasets/hugging-science/gut-microbiome-allergy-data/metadata/goldberg/T1.csv (at revision 9f73e3c30b3ea1ef2b9d8a2d5b55f570258bae24)
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.
sid
string | label
int64 |
|---|---|
SRS1719091
| 0
|
SRS1719092
| 1
|
SRS1719094
| 0
|
SRS1719095
| 1
|
SRS1719096
| 0
|
SRS1719097
| 0
|
SRS1719101
| 1
|
SRS1719102
| 1
|
SRS1719103
| 1
|
SRS1719108
| 0
|
SRS1719109
| 0
|
SRS1719110
| 0
|
SRS1719118
| 1
|
SRS1719140
| 0
|
SRS1719141
| 1
|
SRS1719142
| 0
|
SRS1719157
| 0
|
SRS1719158
| 0
|
SRS1719159
| 0
|
SRS1719161
| 0
|
SRS1719162
| 0
|
SRS1719163
| 0
|
SRS1719165
| 0
|
SRS1719173
| 1
|
SRS1719175
| 1
|
SRS1719176
| 0
|
SRS1719178
| 0
|
SRS1719180
| 0
|
SRS1719186
| 1
|
SRS1719195
| 0
|
SRS1719196
| 0
|
SRS1719208
| 0
|
SRS1719209
| 0
|
SRS1719222
| 0
|
SRS1719240
| 0
|
SRS1719248
| 0
|
SRS1719252
| 0
|
SRS1719253
| 0
|
SRS1719262
| 1
|
SRS1719267
| 1
|
SRS1719271
| 1
|
SRS1719285
| 0
|
SRS1719287
| 1
|
SRS1719289
| 0
|
SRS1719291
| 1
|
SRS1719298
| 0
|
SRS1719306
| 0
|
SRS1719328
| 1
|
SRS1719329
| 1
|
SRS1719330
| 1
|
SRS1719331
| 1
|
SRS1719335
| 1
|
SRS1719344
| 0
|
SRS1719355
| 1
|
SRS1719361
| 1
|
SRS1719363
| 0
|
SRS1719381
| 0
|
SRS1719382
| 0
|
SRS1719383
| 1
|
SRS1719386
| 0
|
SRS1719388
| 1
|
SRS1719399
| 1
|
SRS1719406
| 0
|
SRS1719408
| 0
|
SRS1719413
| 0
|
SRS1719426
| 1
|
SRS1719431
| 1
|
SRS1719442
| 1
|
SRS1719469
| 1
|
SRS1719479
| 1
|
SRS1719485
| 0
|
SRS1719486
| 0
|
SRS1719496
| 0
|
SRS1719502
| 1
|
SRS1719503
| 0
|
SRS1719508
| 1
|
SRS1719513
| 0
|
SRS1719522
| 1
|
SRS1719525
| 0
|
SRS1719527
| 1
|
SRS1719537
| 1
|
SRS1719550
| 0
|
SRS1719553
| 1
|
SRS1719558
| 0
|
SRS1719560
| 0
|
SRS1719570
| 0
|
SRS1719571
| 1
|
SRS1719584
| 0
|
SRS1735437
| 0
|
SRS1735443
| 1
|
SRS1735445
| 0
|
SRS1735452
| 1
|
SRS1735462
| 0
|
SRS1735474
| 0
|
SRS1735475
| 1
|
SRS1735476
| 1
|
SRS1735478
| 0
|
SRS1735488
| 0
|
SRS1735491
| 0
|
SRS1735493
| 1
|
End of preview.
Dataset Card for Gut Microbiome–Food Allergy Prediction Datasets
Dataset Summary
This repository contains multiple human gut microbiome datasets curated for predicting food allergy development. Each dataset corresponds to a distinct cohort with longitudinal microbiome sampling, providing both metadata and derived embeddings suitable for machine learning.
The datasets are designed to support binary classification of subjects into healthy vs allergic categories, enabling research into early microbial predictors of food allergy.
Project Reference
This dataset is derived from the ML-Based Prediction of Food Allergy Development From Gut Microbiome Data project. The original repository, including code, preprocessing pipelines, and documentation, is available here:
https://github.com/AI-For-Food-Allergies/gut_microbiome_project
Users are encouraged to consult the original repository for additional details on data processing, embedding generation, and experimental setup.
Dataset Structure
Data Instances
A single data instance corresponds to a biological sample at a specific timepoint, uniquely identified by a sample identifier (e.g., SRS ID). Instances from the same subject may appear at multiple timepoints.
All modalities are aligned via the same sample identifier.
Directory Layout
.
├── metadata/
│ ├── diabimmune/
│ │ └── Month_<N>.csv
│ ├── gadir/
│ │ └── gadir_<time_range>.csv
│ ├── goldberg/
│ │ └── T<N>.csv
│ ├── tanaka/
│ │ └── month_<N>.csv
│ └── sample_data.csv
│
├── processed/
│ ├── dna_sequences/
│ ├── dna_embeddings/
│ └── microbiome_embeddings/
│
└── README.md
Metadata (metadata/)
The metadata/ directory contains the canonical ground-truth tables for each cohort.
General Properties
- Metadata files are timepoint-specific
- Each CSV represents a snapshot cohort (not pooled unless explicitly stated)
- All downstream representations must map back to these files
CSV Schema (Minimum)
| Field | Type | Description |
|---|---|---|
sample_id |
string | Unique sample identifier (e.g., SRS ID) |
label |
int | Binary target label (0 or 1) |
* |
varies | Optional cohort- or timepoint-specific covariates |
Cohort-Specific Notes
- DiabImmune: 38 monthly snapshots for longitudinal infant cohort
- Gadir: Time-range–stratified and aggregated CSVs for adult cohort
- Goldberg: Discrete experimental phases (
T1–T3) - Tanaka: Selected longitudinal months (1, 2, 6, 12, 24, 36)
Processed Data (processed/)
The processed/ directory contains derived, reproducible artifacts generated from upstream biological inputs.
DNA Sequences (processed/dna_sequences/)
- Preprocessed DNA sequences per sample
- Organized by cohort
- One-to-one mapping with
sample_id
DNA Embeddings (processed/dna_embeddings/)
- Fixed-length numeric vectors
- Derived from DNA sequences using pretrained or custom models
- Intended for direct consumption by ML models
Microbiome Embeddings (processed/microbiome_embeddings/)
- Fixed-length numeric vectors representing microbiome composition
- Derived from taxonomic or functional profiles
- Aligned with metadata sample identifiers
Contact
Open issues in this repository (or on GitHub) for questions or clarifications.
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