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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 26 new columns ({'amount_per_serving_mg', 'inchikey', 'product_id', 'source_url', 'product_max_pct_ul', 'upper_safety_limit_mg', 'molecular_formula', 'product_interaction_count', 'upc_barcode', 'pubchem_cid', 'ingredient_category', 'product_name', 'brand', 'dataset_version', 'ingredient_form', 'recommended_daily_mg', 'ingredient', 'form_type', 'product_over_ul_flag', 'dsld_label_id', 'canonical_smiles', 'serving_size_count', 'servings_per_container', 'is_proprietary_blend', 'serving_size_unit', 'molecular_weight'}) and 8 missing columns ({'supplement', 'source', 'partner_type', 'partner_name', 'severity', 'mechanism', 'effect', 'evidence_grade'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Ichlibitiche/suppdb-supplements-sample/suppdb_sample.csv (at revision 9d4d2009394bf341bb4e9999b54f00bb06b4681f), ['hf://datasets/Ichlibitiche/suppdb-supplements-sample@9d4d2009394bf341bb4e9999b54f00bb06b4681f/interactions_sample.csv', 'hf://datasets/Ichlibitiche/suppdb-supplements-sample@9d4d2009394bf341bb4e9999b54f00bb06b4681f/suppdb_sample.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
product_id: int64
brand: string
product_name: string
upc_barcode: string
form_type: string
serving_size_count: double
serving_size_unit: string
servings_per_container: double
ingredient: string
ingredient_form: string
ingredient_category: string
amount_per_serving_mg: double
is_proprietary_blend: int64
recommended_daily_mg: double
upper_safety_limit_mg: double
pubchem_cid: double
molecular_formula: string
molecular_weight: double
inchikey: string
canonical_smiles: string
product_max_pct_ul: double
product_over_ul_flag: int64
product_interaction_count: int64
dsld_label_id: int64
source_url: string
dataset_version: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 3672
to
{'supplement': Value('string'), 'partner_type': Value('string'), 'partner_name': Value('string'), 'severity': Value('string'), 'mechanism': Value('string'), 'effect': Value('string'), 'evidence_grade': 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 26 new columns ({'amount_per_serving_mg', 'inchikey', 'product_id', 'source_url', 'product_max_pct_ul', 'upper_safety_limit_mg', 'molecular_formula', 'product_interaction_count', 'upc_barcode', 'pubchem_cid', 'ingredient_category', 'product_name', 'brand', 'dataset_version', 'ingredient_form', 'recommended_daily_mg', 'ingredient', 'form_type', 'product_over_ul_flag', 'dsld_label_id', 'canonical_smiles', 'serving_size_count', 'servings_per_container', 'is_proprietary_blend', 'serving_size_unit', 'molecular_weight'}) and 8 missing columns ({'supplement', 'source', 'partner_type', 'partner_name', 'severity', 'mechanism', 'effect', 'evidence_grade'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Ichlibitiche/suppdb-supplements-sample/suppdb_sample.csv (at revision 9d4d2009394bf341bb4e9999b54f00bb06b4681f), ['hf://datasets/Ichlibitiche/suppdb-supplements-sample@9d4d2009394bf341bb4e9999b54f00bb06b4681f/interactions_sample.csv', 'hf://datasets/Ichlibitiche/suppdb-supplements-sample@9d4d2009394bf341bb4e9999b54f00bb06b4681f/suppdb_sample.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.
supplement string | partner_type string | partner_name string | severity string | mechanism string | effect string | evidence_grade string | source string |
|---|---|---|---|---|---|---|---|
St. John's Wort | drug | cyclosporine | contraindicated | strong CYP3A4 / P-gp induction | sub-therapeutic immunosuppression; transplant-rejection risk | clinical | MedlinePlus |
5-HTP | drug_class | SSRIs / SNRIs / MAOIs | major | additive serotonin | serotonin syndrome risk | clinical | MedlinePlus |
Licorice | drug | digoxin | major | glycyrrhizin-induced hypokalemia potentiates digoxin | increased digoxin toxicity risk | clinical | MedlinePlus |
Licorice | drug_class | diuretics / antihypertensives | major | causes sodium retention and potassium loss | hypokalemia and reduced BP control | clinical | MedlinePlus |
Potassium | drug_class | ACE inhibitors / ARBs | major | both raise serum potassium | additive hyperkalemia (dangerous arrhythmia risk) | clinical | DailyMed ACE-inhibitor labels |
Potassium | drug_class | potassium-sparing diuretics | major | reduced potassium excretion plus supplementation | additive hyperkalemia | clinical | DailyMed diuretic labels |
St. John's Wort | drug_class | SSRIs / SNRIs (antidepressants) | major | additive serotonergic activity plus CYP induction | serotonin syndrome risk and reduced antidepressant levels | clinical | NIH ODS / MedlinePlus |
St. John's Wort | drug | digoxin | major | P-glycoprotein induction lowers digoxin absorption | reduced digoxin levels / loss of control | clinical | MedlinePlus |
St. John's Wort | drug_class | oral contraceptives | major | CYP3A4 induction accelerates hormone metabolism | reduced contraceptive efficacy / breakthrough bleeding | clinical | MedlinePlus |
St. John's Wort | drug | warfarin | major | CYP3A4 / P-glycoprotein induction | reduces warfarin plasma levels and anticoagulant effect | clinical | MedlinePlus |
Vitamin K | drug | warfarin | major | vitamin K restores clotting-factor synthesis that warfarin blocks | antagonizes anticoagulation; lowers INR and raises clot risk if intake swings | clinical | DailyMed warfarin label / NIH ODS |
Berberine | drug_class | CYP3A4 substrates (e.g. cyclosporine) | moderate | inhibits CYP3A4 and P-gp | raised plasma levels of co-administered CYP3A4 drugs | clinical | MedlinePlus |
Calcium | compound | iron | moderate | calcium competes with non-heme iron for absorption | reduced iron absorption; take apart | clinical | NIH ODS Iron |
Calcium | drug | levothyroxine | moderate | forms an insoluble complex in the gut | reduced thyroid-hormone absorption; separate doses by 4 h | clinical | DailyMed levothyroxine label |
Calcium | drug_class | tetracycline / quinolone antibiotics | moderate | cation chelation of the antibiotic | reduced antibiotic absorption and efficacy | clinical | DailyMed antibiotic labels |
Garlic | drug_class | HIV protease inhibitors (saquinavir) | moderate | reduces protease-inhibitor plasma levels | sub-therapeutic antiviral levels | clinical | MedlinePlus |
Garlic | drug | warfarin | moderate | antiplatelet activity | increased bleeding risk / raised INR | clinical | MedlinePlus |
Ginkgo | drug_class | NSAIDs / aspirin | moderate | additive antiplatelet effect | increased bleeding risk | theoretical | MedlinePlus |
Ginkgo | drug_class | anticoagulants / antiplatelets | moderate | inhibits platelet-activating factor | increased bleeding risk | clinical | MedlinePlus |
Green Tea | drug | nadolol | moderate | reduces nadolol absorption (OATP inhibition) | reduced beta-blocker effect | clinical | MedlinePlus |
Iron | drug | levothyroxine | moderate | forms a complex reducing absorption | reduced thyroid-hormone absorption; separate doses | clinical | DailyMed levothyroxine label |
Iron | drug_class | tetracycline / quinolone antibiotics | moderate | cation chelation | reduced absorption of both the antibiotic and iron | clinical | DailyMed antibiotic labels |
Magnesium | drug_class | bisphosphonates | moderate | reduced GI absorption | lower bisphosphonate efficacy; separate doses | clinical | DailyMed bisphosphonate labels |
Magnesium | drug_class | tetracycline / quinolone antibiotics | moderate | cation chelation | reduced antibiotic absorption; separate doses by 2-4 h | clinical | DailyMed antibiotic labels |
Melatonin | drug_class | anticoagulants | moderate | may reduce clotting | possible increased bleeding risk | theoretical | MedlinePlus |
Melatonin | drug_class | sedatives / benzodiazepines | moderate | additive CNS depression | excess sedation / drowsiness | clinical | MedlinePlus |
Niacin | drug_class | statins | moderate | additive myotoxicity at high niacin doses | increased myopathy / rhabdomyolysis risk | clinical | DailyMed statin labels |
Panax Ginseng | drug | warfarin | moderate | reduces warfarin effect | lowered INR / reduced anticoagulation | clinical | MedlinePlus |
Vitamin B6 | drug | levodopa (without carbidopa) | moderate | accelerates peripheral levodopa decarboxylation | reduced levodopa efficacy | clinical | NIH ODS |
Vitamin E | drug_class | anticoagulants / antiplatelets | moderate | high-dose vitamin E has antiplatelet activity | additive bleeding risk | clinical | NIH ODS |
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End of preview.
💊 SuppDB — Supplements & Nootropics Dataset (Free Sample)
A free sample of SuppDB: a structured dataset of real supplement & nootropic products built exclusively from the public NIH Dietary Supplement Label Database (DSLD) — every active ingredient normalized to milligrams, proprietary blends flagged where the dose is undisclosed, and compounds enriched with NIH PubChem chemical identity. Think INCIDecoder for supplements: one row per active ingredient, with dose, form, safety reference, and molecular identity.
This sample contains 2,249 ingredient records across 300 real products from 218 brands.
The full dataset covers 17,000+ products, 2,000+ brands, 115,000+ active-ingredient records, and 40,000+ proprietary-blend flags (SQLite · CSV · JSON).
Get the Full Dataset
- 🔗 Official Portal: suppdb.net — full snapshot $99 one-time
- 🏆 Kaggle: SuppDB Supplements Sample
- 💊 Interactive Explorer: Dataset Sample Explorers Space — SuppDB tab
Key Columns
| Column | Description |
|---|---|
brand, product_name, upc_barcode, form_type |
Product identity as printed on the label |
ingredient, ingredient_form, ingredient_category |
Active ingredient per row |
amount_per_serving_mg |
Dose normalized to mg (mcg, g, substance-specific IU handled correctly) |
is_proprietary_blend |
1 where the dose is hidden in a proprietary blend (amount = 0) — transparency, not omission |
recommended_daily_mg, upper_safety_limit_mg |
NIH DRI reference intakes; NULL where no official value exists |
pubchem_cid, molecular_formula, molecular_weight, canonical_smiles, inchikey |
PubChem chemistry — InChIKey canonicalizes the same molecule across label names |
dsld_label_id, source_url |
Exact NIH DSLD label page — every record re-verifiable |
Quick Start
import pandas as pd
df = pd.read_csv("hf://datasets/Ichlibitiche/suppdb-supplements-sample/suppdb_sample.csv")
print(len(df), "ingredient records,", df["product_id"].nunique(), "products,", df["brand"].nunique(), "brands")
hidden = df[df["is_proprietary_blend"] == 1]
print(len(hidden), "ingredients with doses hidden in proprietary blends")
Use Cases
- AI health co-pilots & supplement recommendation apps (structured dose + chemistry data)
- Ingredient/dose comparison and proprietary-blend transparency tools
- ML / RAG corpora over supplement labels
- Formulation, market, and assortment research across brands and ingredient categories
License
Sample data: CC BY-NC 4.0 — attribution, non-commercial. Full dataset commercially licensed at suppdb.net; underlying facts are public-domain U.S. Government data (NIH DSLD + PubChem) — the license covers SuppDB's curated, normalized compilation. Not medical advice — always verify against the current physical label. Contact: suppdb.doorframe589@simplelogin.com.
Safety layer
Beyond this catalog sample, a paid Safety layer is available, built on the same normalized mg doses: 8,800+ supplement x drug interactions (each cited to its public-domain source and evidence-graded), per-product % of the NIH tolerable upper limit with over-limit flags, condition contraindications, and WADA doping flags. This sample includes a 30-row interactions_sample.csv teaser plus per-product safety columns (product_max_pct_ul, product_over_ul_flag, product_interaction_count). Full layer -> https://supplements-nootropics-suppdb.pages.dev
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