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For researchers
The research surface of ecodish365. Run a nutrient composition deep-dive on any meal or 24-hour record, score it across every published lens at once, or run any single lens on its own. Every result is auditable, citable, and traceable to a versioned factor pack.
The substrate every lens sits on. Nutrient composition, food-group attribution, processing tier, and catalogue exploration across CNF and WAFCT.
Composition assessment for a meal or a 24-hour record. Full nutrient panel against IOM DRIs by life-stage, FPED food groups, NOVA processing tier, IOM AMDR macronutrient bands, and per-nutrient top-contributor ranking. Export JSON or long-format CSV.
Upload N recalls (NHANES What-We-Eat-in-America .xpt or any CSV with food_id, mass_g) and score the whole cohort across every lens in one pass. Distribution stats, per-respondent table, CSV export, and side-by-side comparison.
Side-by-side nutrient comparison for up to six foods. Per-100g or per-100kcal basis, delta from baseline, source badges for CNF and WAFCT, CSV and JSON export.
Multi-criteria food discovery. Find every food with fibre above 5 g and sodium below 200 mg per 100 g, ranked by ratio or by energy density.
Browse the CNF (groups 1–19) and WAFCT (groups 50–63) sidebars. Filter by name, thermal state, preservation state, and preparation coverage.
Database statistics, group distribution, and coverage heatmaps across CNF and WAFCT. Useful for scoping a study before committing to a sample.
Full-text and AI-enhanced semantic search across the combined catalogue. Source-scope filter for CNF, WAFCT, or both, with regional-signal warnings on the decomposer.
Each lens runs independently on the same substrate.
Run the full multi-lens scorecard, or run any single lens on its own. New lenses plug into the same substrate as they are published; the list is open, not fixed.
Run every published lens on the same food list in one view. Reflip the audience toggle for researcher-mode methodology breakdowns, data-quality flags, and citations.
Multi-lens composite view
Healthy Eating Food Index, 0 to 80, against Canada's Food Guide. Ten-component breakdown in researcher mode.
Brassard 2022, APNM
Health Nutritional Index: healthy-life minutes gained or lost per serving, dose-response from long-term disease research.
Stylianou 2021, Nature Food
Health Star Rating from 0.5 to 5 stars for packaged products, scored against the on-shelf category.
HSRAC Implementation Guide v9
FCS nutrient-profile score from 1 to 100, combining nutrition and NOVA processing.
Mozaffarian 2021, Nature Food
Climate, land, and water footprint with uncertainty bands from ReCiPe 2016 and AGRIBALYSE 3.2.
Huijbregts 2017; ADEME 2024
Classify a day against eight published eating-pattern prototypes (Mediterranean, DASH, EAT-Lancet, and more).
Trichopoulou 2003; Willett 2019
EAT-Lancet 2.0 Table 2 food-system share against planetary boundaries.
EAT-Lancet 2.0
What is in flight for the next research-platform cycle.
These are the gaps between a decision-support tool and a publication-ready research platform. None of them require new science; all of them require frontend surfaces on top of capabilities the backend already provides.
One-click methods.md plus BibTeX or RIS for every analysis. Drop the methods block straight into a manuscript.
Run ID, factor-pack SHA-256, git commit, API version, and a permalink that re-renders the exact result years later.
Name a meal or a cohort run, link it to a respondent ID, retrieve it later, export the manifest.
Baseline versus intervention across every lens. Delta tables, Pareto frontier when trade-offs exist.
Surface the Monte Carlo intervals the backend already computes. Render 95% CI in the results, parallel columns in the CSV.
Versioned REST endpoints with Python and R recipes. Recreate a manuscript figure in twelve lines.
Also see: Policy · Individuals · Methods & data