wirthal1990-tech/USDA-Phytochemical-Database-JSON

⚡ Early Access — First 10 buyers: 57 % off. Single €299 · Team €549 · Enterprise €899. → ethno-api.com

USDA Phytochemical & Ethnobotanical Database — Enriched v2.2

The only phytochemical dataset combining USDA botanical records, PubMed citation counts, ClinicalTrials.gov study counts, ChEMBL bioactivity scores, and USPTO patent density — in production-ready JSON + Parquet.

Free 400-Row Sample ↓ · ⚡ Single €299 Early Bird → · ⚡ Team €549 Early Bird → · ⚡ Enterprise €899 Early Bird →

Enrichment status (March 2026): All four enrichment layers (PubMed, ClinicalTrials.gov, ChEMBL, PatentsView) are complete and final. The free 400-row sample contains real enrichment values.

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Records	Compounds	Species	Enrichment Layers
76,907	24,746	2,313	4

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Data Quality: Dataset was audit-validated on 2026-03-16. Original 104,388 records cleaned to 76,907 by removing macronutrients (WATER, GLUCOSE etc.) and exact duplicates. [Audit report available on request.]

Schema (v2.2)

Column	Type	Nulls	Description
chemical	string	0%	Standardised compound name (USDA Duke’s nomenclature)
plant_species	string	0%	Binomial Latin species name
application	string	~50%	Traditional medicinal application (e.g. “Antiinflammatory”)
dosage	string	~87%	Reported dosage, concentration, or IC50 value
pubmed_mentions_2026	int32	0%	Total PubMed publications mentioning this compound (March 2026 snapshot)
clinical_trials_count_2026	int32	0%	ClinicalTrials.gov study count per compound (March 2026)
chembl_bioactivity_count	int32	0%	ChEMBL documented bioactivity measurement count
patent_count_since_2020	int32	0%	US patents since 2020-01-01 mentioning compound (USPTO PatentsView)

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Pricing & Licensing

Tier	Price	Includes	Purchase
Single Entity	⚡ €299 netto (Early Bird, reg. €699)	JSON + Parquet + SHA-256 Manifest. 1 juristische Person, interne Nutzung. Perpetual license.	⚡ Buy Now →
Team	⚡ €549 netto (Early Bird, reg. €1.349)	Alles aus Single + duckdb_queries.sql (20 Queries, 5 Kategorien) + compound_priority_score.py + 4 Pre-computed Views (Top-500 nach PubMed, Trials, Patent-Dichte, Anti-Inflammatory Panel). Unbegrenzte interne Nutzer einer juristischen Person.	⚡ Buy Now →
Enterprise	⚡ €899 netto (Early Bird, reg. €1.699)	Alles aus Team + snowflake_load.sql + chromadb_ingest.py + pinecone_ingest.py + embedding_guide.md (ClinicalBERT, RAG-Pipelines) + Compound Opportunity Matrix + Clinical Pipeline Gaps CSV + Pre-chunked RAG JSONL. Multi-Entity / Konzernnutzung, interne Produktintegration erlaubt.	⚡ Buy Now →

Gemäß § 19 UStG wird keine Umsatzsteuer berechnet. Alle Preise netto. One-time purchase — keine Subscription, keine wiederkehrenden Kosten.

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Why Not Build This Yourself?

Normalising and cross-referencing 24,746 phytochemicals across multiple authoritative sources is not a weekend project.

Scope	Effort	Cost @ $85/hr
USDA cleaning + normalization + enrichment + exports + QA	48–60h	~$4,080–$5,100

This dataset: €299 Early Bird (regular €699). No subscription. No API calls. Download link sent instantly after payment. Valid for 72 hours. See ethno-api.com.

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Why This Dataset Exists

Large language models hallucinate botanical taxonomy. A biotech team’s RAG pipeline confidently outputting “Quercetin found in 450 species at 2.3 mg/g” sounds plausible — but the real number of species in our data is 215, and dosage varies by three orders of magnitude depending on the plant part.

The raw USDA Dr. Duke’s database is spread across 16 relational tables. Joining them correctly requires understanding non-obvious foreign keys, handling >40% null values in application fields, and normalising species names against accepted binomial nomenclature. Most teams give up after a week.

Quickstart

Python — Load 400-row sample

import pandas as pd

url = "https://raw.githubusercontent.com/wirthal1990-tech/USDA-Phytochemical-Database-JSON/main/ethno_sample_400.json"
df = pd.read_json(url)
print(f"{df.shape[0]} records, {df['chemical'].nunique()} unique compounds")
df.head()

PyArrow — Parquet (full dataset, after purchase)

Download link delivered instantly after payment (valid 72h). See ethno-api.com.

import pyarrow.parquet as pq

table = pq.read_table("ethno_dataset_2026_v2.parquet")
print(f"Schema: {table.schema}")
print(f"Rows: {table.num_rows}  Memory: {table.nbytes / 1e6:.1f} MB")

DuckDB (analytical queries — sample included)

import duckdb

result = duckdb.sql("""
    SELECT
        chemical,
        MAX(pubmed_mentions_2026)       AS pubmed_score,
        MAX(clinical_trials_count_2026) AS trial_count,
        MAX(chembl_bioactivity_count)   AS bioassays,
        COUNT(DISTINCT plant_species)   AS species_count
    FROM read_json_auto('ethno_sample_400.json')
    GROUP BY chemical
    ORDER BY trial_count DESC
    LIMIT 20
""")
result.show()

HuggingFace Datasets

from datasets import load_dataset

ds = load_dataset(
    "wirthal1990-tech/USDA-Phytochemical-Database-JSON",
    split="sample",
    trust_remote_code=False
)
df = ds.to_pandas()
print(f"Records: {len(df)} | Columns: {list(df.columns)}")
df.head()

Note: The split="sample" loads ethno_sample_400.json (400 rows, 8 columns). The full 76,907-row dataset is available at ethno-api.com.

Sample Record

Below is a real record from the dataset — QUERCETIN, one of the most-studied plant compounds:

{
  "chemical": "QUERCETIN",
  "plant_species": "Drimys winteri",
  "application": "5-Lipoxygenase-Inhibitor",
  "dosage": "IC50 (uM)=4",
  "pubmed_mentions_2026": 31310,
  "clinical_trials_count_2026": 81,
  "chembl_bioactivity_count": 2871,
  "patent_count_since_2020": 73
}

All 76,907 records contain all 8 schema fields. The 4 enrichment columns are always non-null; application (50% null) and dosage (87% null) reflect USDA source gaps. The free 400-row sample contains real, final enrichment values across all four layers.

File Manifest

File	Size	Format	Access
ethno_sample_400.json	108 KB	JSON	Free (this repo)
ethno_sample_400.parquet	20 KB	Parquet	Free (this repo)
quickstart.ipynb	9 KB	Notebook	Free (this repo)
ethno_dataset_2026_v2.json	~16 MB	JSON	Included in all tiers
ethno_dataset_2026_v2.parquet	~800 KB	Parquet	Included in all tiers
MANIFEST_v2.json (SHA-256)	~1 KB	JSON	Included in all tiers
duckdb_queries.sql (20 Queries)	~13 KB	SQL	Team + Enterprise
compound_priority_score.py	~5 KB	Python	Team + Enterprise
snowflake_load.sql	~6 KB	SQL	Enterprise
chromadb_ingest.py	~6 KB	Python	Enterprise
pinecone_ingest.py	~6 KB	Python	Enterprise
embedding_guide.md	~7 KB	Markdown	Enterprise

Data Sources & Provenance

All enrichment layers are derived from authoritative, publicly accessible scientific databases and represent a March 2026 snapshot.

Source	Snapshot	What it contributes
USDA Dr. Duke’s Phytochemical and Ethnobotanical Databases	2026	Canonical plant–compound–application baseline across 2,313 species
NCBI PubMed	March 2026	Compound-level publication evidence score
ClinicalTrials.gov	March 2026	Compound-level clinical research activity score
ChEMBL	March 2026	Compound-level bioactivity measurement depth
USPTO PatentsView	March 2026	Compound-level commercial IP activity score

Enrichment methodology is documented in METHODOLOGY.md. Source code is available to Enterprise license holders upon request under NDA.

Use Cases

• RAG Pipelines — Ground LLM responses with verified phytochemical data. Each record has a PubMed evidence score — use it to weight retrieval results and filter hallucinations.
• Drug Discovery — Prioritise natural product leads by combining PubMed citations, clinical trial presence, ChEMBL bioactivity depth, and patent landscape. One query replaces weeks of manual lit review.
• Market Intelligence — Patent density score reveals which compounds are attracting commercial investment. Cross-reference with clinical trials to identify underexplored compounds with IP whitespace.
• Academic Research — Pre-computed evidence scores save months of PubMed searching. The BibTeX citation block below makes this dataset citable in peer-reviewed publications.

Dataset Versions

Version	Records	Schema	Status
v1.0	104,388	5 columns (USDA baseline)	Deprecated
v2.2	76,907	8 columns (+ PubMed, ClinicalTrials, ChEMBL, Patents)	Current

The free sample (ethno_sample_400.json) uses the v2.2 schema with final enrichment values across all four layers.

Data Attribution

This dataset includes bioactivity count data (chembl_bioactivity_count field) derived from ChEMBL v35, licensed under CC BY-SA 3.0. Buyers who redistribute this field downstream must comply with ChEMBL attribution requirements.

License & Commercial Access

• Free 400-row sample: CC BY 4.0 — use for evaluation, academic research, and prototyping.
• Single Entity License — €299 Early Bird (reg. €699) one-time: ⚡ Buy → — 1 legal entity, internal use, perpetual. No redistribution.
• Team License — €549 Early Bird (reg. €1.349) one-time: ⚡ Buy → — all employees of 1 legal entity, unlimited internal users, includes analytics toolkit.
• Enterprise License — €899 Early Bird (reg. €1.699) one-time: ⚡ Buy → — multi-entity / group use, internal product integration rights, full RAG integration toolkit.

Gemäß § 19 UStG wird keine Umsatzsteuer berechnet.

Citation

If you use this dataset in your research, please cite:

Wirth, A. (2026). USDA Phytochemical Database — Enriched v2.2 (Sample). Zenodo. https://doi.org/10.5281/zenodo.19053087

@misc{ethno_api_v2_2026,
  title     = {USDA Phytochemical \& Ethnobotanical Database --- Enriched v2.2},
  author    = {Wirth, Alexander},
  year      = {2026},
  publisher = {Ethno-API},
  url       = {https://ethno-api.com},
  doi       = {10.5281/zenodo.19053087},
  note      = {76,907 records, 24,746 unique chemicals, 2,313 plant species, 8-column schema with PubMed, ClinicalTrials, ChEMBL, and PatentsView enrichment}
}

Contact

• Website: ethno-api.com
• Email: founder@ethno-api.com
• GitHub: @wirthal1990-tech

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_{Built by Alexander Wirth · PostgreSQL 15 · Python 3.12 · Hetzner CCX33}

PubChem coverage: 42.4% of unique compounds have canonical_smiles and pubchem_cid. The remaining entries are phytochemical trivial names not indexed in PubChem by name (documented as null).