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license: cc-by-nc-4.0
pretty_name: SuppDB — Supplements & Nootropics Dataset (Free Sample)
tags:
- health
- chemistry
- supplements
- nootropics
- nih-dsld
- pubchem
- recommender-systems
size_categories:
- 1K<n<10K
---
# 💊 SuppDB — Supplements & Nootropics Dataset (Free Sample)
> **Full dataset:** [suppdb.dataengineered.io](https://suppdb.dataengineered.io/) · **$49 one-time** snapshot · **$199** Safety & Interaction Layer → [Buy snapshot](https://buy.stripe.com/9B69AU5t63QOfUfeqQ38406) · [Buy safety layer](https://buy.stripe.com/4gM9AU5t61IGdM7fuU3840l) · the same sample on [Kaggle](https://www.kaggle.com/datasets/dataengineered/suppdb-supplements-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)](https://dsld.od.nih.gov/)** — 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](https://suppdb.dataengineered.io) — full snapshot $99 one-time
* 🏆 **Kaggle:** [SuppDB Supplements Sample](https://www.kaggle.com/datasets/dataengineered/suppdb-supplements-sample)
* 💊 **Interactive Explorer:** [Dataset Sample Explorers Space — SuppDB tab](https://huggingface.co/spaces/Ichlibitiche/dataset-sample-explorers)
## 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
```python
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](https://suppdb.dataengineered.io); 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@dataengineered.io`.
## 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://suppdb.dataengineered.io
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