The Supplement Cost-per-Effective-Dose & Evidence Dataset
Free and open (CC BY 4.0). 72 supplement products across 16 categories, each with its elemental dose, cost per clinically-effective daily dose, third-party certification, an evidence grade, and the PubMed IDs behind that grade. Built for journalists, researchers, developers, and anyone who wants supplement data that's actually structured and sourced.
New · v1.0
Ingredient Master Dataset — every layer, joined
The dataset above is product-level. This one is ingredient-level: one row per ingredient (27 of them) that fuses five separate data layers into a single record — so you can see, for any ingredient at a glance, what it costs to dose it properly, what the studied dose is, how often it shows up in FDA adverse-event reports, how many people are actually deficient, and whether a third-party-tested option even exists.
- Cost per effective dose — min, median, and our top pick per ingredient (our proprietary normalization)
- Clinical dose target — the studied dose (PubMed-cited), present for 13/27
- FDA CAERS/FAERS safety signal — total adverse events, serious-event rate, top reaction, present for 19/27
- NHANES deficiency prevalence — who's actually deficient, present for the 5 nutrients NHANES measures
- Certification availability — whether any third-party-tested product exists in the category at all
Coverage is uneven by design — we only join a layer where the source actually has the ingredient (NHANES measures 5 nutrients; CAERS covers 19 categories). Blank cells mean "no public data," not zero.
New · v1.0
Condition × Ingredient Evidence Crosswalk
The map of what's linked to what: 29 ingredients × 66 conditions/uses, 137 links. Each link carries a relationship type — is this ingredient a fix for a deficiency that causes the symptom, a studied therapeutic dose for the use, or a pair we've reviewed in depth — plus an evidence grade where one exists, the dose for that use, and the PubMed IDs behind it. Every row traces to a source: a PMID-bearing dataset or a researched page on this site. No invented pairs, no made-up grades.
⬇ Crosswalk CSV ⬇ Crosswalk JSON
Both granular and canonical condition IDs are included, so you can roll up (e.g. always-tired + energy-fatigue → Fatigue) or keep the specific page-level link.
More crosswalk tables
The supplement graph, sliced by other axes — every row carries its source. All CC BY 4.0.
Form × Bioavailability
39 forms across 18 ingredients — which form of each ingredient actually absorbs (glycinate vs oxide, methylfolate vs folic acid, ubiquinol vs ubiquinone), what it's best for, what to avoid it for, dose, and side-effects. The buying-decision data — and the formulation spec for a product line.
⬇ CSV ⬇ JSONWhy this dataset exists
Supplement information is scattered: clinical evidence sits in PubMed, product labels sit in the NIH DSLD, certifications sit across USP and NSF, and prices sit on retailer pages. Nobody joins them. So a simple question — "which product gives me the clinically-studied dose for the lowest cost per day, and how good is the evidence?" — has no single answer.
This dataset is that join. The column most people come for is cost_per_day_usd: not cost per pill or per serving, but the cost to take the dose actually used in trials. A $10 bottle and a $30 bottle routinely flip rankings once you normalize to the effective dose — and this is the only public dataset we know of that computes it across categories.
What's inside
- 72 products across 16 categories (magnesium glycinate, omega-3, vitamin D3, CoQ10, creatine, iron bisglycinate, methylfolate, B12, and more)
- Evidence grades on every row — 53 graded strong (systematic reviews / large RCTs), 19 moderate
- PubMed IDs linking each category's grade to its underlying trials
- Cost-per-effective-dose normalized to the clinically-studied dose, plus elemental dose, certification, and ASIN
Download
⬇ CSV (spreadsheet-ready) ⬇ JSON (with field definitions & metadata)
Prices reflect the catalog review of March 2026 — see our data-freshness policy. The live retailer price always governs at checkout.
Column reference
| Column | Description |
|---|---|
name | Full product name as listed by the manufacturer |
brand | Brand / manufacturer |
category | Supplement category slug (e.g. magnesium-glycinate, omega-3) |
form | Specific form (e.g. bisglycinate, ubiquinol, rTG triglyceride) |
dose_per_serving | Elemental / active dose per serving — not compound weight |
dose_unit | mg, mcg, IU, g |
serving_size | Physical serving description |
servings_per_container | Servings per container |
price_usd | Retail price (Amazon US) |
cost_per_day_usd | Cost to take the clinically-studied daily dose — our core normalization metric |
third_party_certification | USP, NSF, IFOS, Informed Sport, etc. (or None) |
evidence_grade | strong / moderate / limited — by highest available evidence tier |
primary_use | Primary clinical use case(s) |
pubmed_ids | PubMed IDs supporting the category evidence grade |
editorial_pick | Our pick label (best-value, quality, budget) if any |
amazon_asin | Amazon product identifier |
How we built it
Doses are reported as elemental/active ingredient per serving (elemental magnesium, not magnesium glycinate compound weight; combined EPA+DHA, not "fish oil"). Cost-per-day is calculated as (price ÷ servings per container) × (clinical dose ÷ dose per serving). Evidence grades reflect the highest available tier — strong for systematic reviews and large RCTs, moderate for individual RCTs or strong observational data, limited for preliminary or mechanistic evidence. Full detail on our methodology page.
License & how to cite
This dataset is released under Creative Commons Attribution 4.0. You're free to use it commercially, remix it, and build on it — just credit the source. We'd genuinely love to see what you make.
Cite as:
Verified Supplement Data. (2026). Cost-per-Effective-Dose & Evidence Dataset (Version 1.0) [Data set]. https://verifiedsupplementdata.com/data/
Found an error or have a product to add? Email us — corrections are logged on our corrections page.
Related
- Methodology — how we collect, normalize, and grade
- llms.txt & JSON API — machine-readable access for AI agents
- Deficiency statistics & safety scores — our other open data