⚗️ CultureMech

Comprehensive Microbial Culture Media Knowledge Graph

A production-ready knowledge base containing 10,657 culture media recipes from 10 major international repositories, with LinkML schema validation, ontology grounding, and browser-based exploration.

📊 Current Coverage

Total Recipes: 10,657 culture media formulations

Category	Recipes	Sources
Bacterial	10,134	MediaDive, TOGO, BacDive, ATCC, NBRC, KOMODO, MediaDB
Algae	242	UTEX, CCAP, SAG
Fungal	119	MediaDive, TOGO
Specialized	99	KOMODO
Archaea	63	MediaDive, TOGO

📈 Detailed Statistics

Recipes by Source

Source	Recipes	Type	Description
KOMODO	3,637	Bacterial	Korean microbial media database
MediaDive	3,327	Multi-kingdom	DSMZ comprehensive collection
TOGO Medium	2,917	Multi-kingdom	Japanese BRCs curated database
MediaDB	469	Defined	Chemically defined media
CCAP	113	Algae	UK algae & protozoa collection
UTEX	99	Algae	University of Texas algae
SAG	30	Algae	German algae culture collection
NBRC	2	Bacterial	Japanese biological resources
BacDive	1	Bacterial	DSMZ cultivation conditions

Medium Composition

Medium Type	Recipes	Percentage
Complex	8,399	79.3%
Defined	2,196	20.7%

Complex media contain undefined components (e.g., yeast extract, peptone), while defined media have all components chemically specified.

Physical State

State	Recipes	Percentage
Liquid	10,593	99.98%
Solid (Agar)	2	0.02%

Data Quality Metrics

Metric	Value	Percentage
Recipes with ingredients	6,815	64.3%
CHEBI-grounded ingredients	3,548	33.5%
Average ingredients/recipe	15.7	-
LinkML validated	10,595	100%

Ontology Grounding:

Chemicals: CHEBI (Chemical Entities of Biological Interest)
Biological materials: FOODON (Food Ontology), UBERON (Anatomy), ENVO (Environment)
Organisms: NCBITaxon (NCBI Taxonomy)
Media databases: DSMZ, TOGO, ATCC prefixes

Unmapped Ingredients Tracking System (2026-03-05):

🎯 136 unmapped ingredients identified across 522 media (4.9% of total)
📊 3,084 total instances requiring ontology term mapping
🔍 Automated detection of numeric placeholders ('1', '2', '3'), generic terms, and empty values
🧪 Chemical name extraction from notes fields for mapping assistance
📈 Priority-based mapping recommendations (critical: 51+ occurrences)
See UNMAPPED_INGREDIENTS_SUMMARY.md and docs/unmapped_ingredients_guide.md

Advanced Normalization & SSSOM Enrichment (2026-02):

✨ Integrated MicroMediaParam's production-grade 16-step chemical normalization pipeline
📚 100+ curated biological products (yeast extract, peptone, serum, DNA, agar, etc.)
🧪 100+ chemical formula mappings (Fe2(SO4)3 → iron(III) sulfate)
🔬 15+ buffer abbreviations (HEPES, MES, Tris)
💨 11 common laboratory gases (CO2, N2, O2, H2, CH4, etc.)
🔤 Unicode dot normalization (5 variants: ·, ・, •, ∙, ⋅)
📊 Coverage achieved: 45.6% (2,302 / 5,048 ingredients) - +935 new mappings from baseline
📈 68.4% increase in coverage (27.1% → 45.6%)
See PROJECT_STATUS_SUMMARY.md and GAS_MAPPING_SUMMARY.md for details

Enum Normalization (2026-02-20):

🔧 Normalized 10,657 YAML files for schema compliance
✅ Fixed capitalization: medium_type (COMPLEX, DEFINED), physical_state (LIQUID, SOLID_AGAR)
📁 Recategorized all "imported" files to proper organism types (bacterial, fungal, archaea, algae, specialized)
🎯 100% schema compliance across all enum fields

✨ Features

✅ 10,657 recipes - Production-ready dataset from 10 authoritative sources ✅ Four-tier architecture - Clean separation: raw → raw_yaml → normalized_yaml → merge_yaml ✅ Recipe deduplication - Merge recipes with same ingredient sets (~344 unique base formulations) ✅ LinkML schema validation - Comprehensive data quality enforcement ✅ Ontology grounding - CHEBI for chemicals, NCBITaxon for organisms ✅ Full provenance tracking - Complete source attribution and curation history ✅ Automated pipelines - Fetchers, converters, and importers for all sources ✅ Browser interface - Faceted search and filtering ✅ UMAP visualization - Interactive 2D embeddings visualization for exploring media similarity ✅ Knowledge graph export - Biolink-compliant KGX format ✅ Literature verification - 6-tier cascading PDF retrieval for cross-reference validation ✅ ATCC cross-references - Automated equivalency detection with DSMZ media ✅ Unmapped ingredients tracking - Automated detection and prioritization of ingredients needing ontology mapping ✅ Comprehensive documentation - 30+ guides in docs/

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/CultureBotAI/CultureMech.git
cd CultureMech

# Install dependencies (requires uv)
just install

# Optional: Install Koza for KG export
just install-koza

View the Browser

# Generate browser data from recipes
just gen-browser-data

# Serve locally
just serve-browser

# Open http://localhost:8000/app/

Generate UMAP Visualization

# Generate interactive UMAP visualization (requires KG-Microbe embeddings)
just gen-media-umap /path/to/embeddings.tsv.gz

# View locally
open docs/media_umap.html

# See docs/MEDIA_UMAP_GUIDE.md for detailed instructions

Count Recipes

just count-recipes
# Output:
#   algae:      242
#   bacterial:  10,072
#   fungal:     119
#   archaea:    63
#   specialized: 99
#   Total:      10,595

Validate Recipes

# Validate a single recipe
just validate data/normalized_yaml/bacterial/LB_Broth.yaml

# Validate all recipes
just validate-all

# Schema validation only
just validate-schema data/normalized_yaml/bacterial/LB_Broth.yaml

📚 Data Sources

CultureMech integrates culture media recipes from 10 major international repositories:

Integrated Sources ✅

Source	Recipes	Description	Status
KOMODO	3,637	Korean microbial media database	✅ Complete
MediaDive (DSMZ)	3,327	German Collection, comprehensive bacterial/fungal media	✅ Complete
TOGO Medium	2,917	Japanese BRCs, curated media database	✅ Complete
MediaDB	469	Chemically defined media database	✅ Complete
CCAP	113	UK Culture Collection of Algae and Protozoa	✅ Complete
UTEX	99	University of Texas algae collection	✅ Complete
SAG	30	German algae culture collection (Göttingen)	✅ Complete
NBRC	2	Japanese NITE Biological Resource Center	🔄 Initial
BacDive	1	DSMZ cultivation conditions database	🔄 Initial

Planned Expansions 🚀

Source	Potential	Description	Notes
BacDive	~2,500+	Additional organism-specific cultivation conditions	Requires API access
ATCC	~900	American Type Culture Collection media	Web scraping needed
NBRC	~420	Additional NITE media formulations	Incremental import

Algae Collections (New! 🎉)

Three major algae culture collections fully integrated:

UTEX (Austin, TX): 99 recipes - Full composition details
CCAP (Oban, Scotland): 113 recipes - Metadata + PDF references
SAG (Göttingen, Germany): 30 recipes - Metadata + PDF references

Total: 242 algae media recipes covering:

Freshwater algae (BG-11, Bold's Basal, TAP)
Marine phytoplankton (f/2, Erdschreiber's)
Cyanobacteria (Spirulina, BG-11 variants)
Specialized media (diatoms, euglenoids, volvocales)

See docs/ALGAE_PIPELINE_COMPLETE.md for details.

Fetching Data

# Fetch all available sources
just fetch-algae-collections    # UTEX, CCAP, SAG
just fetch-bacdive 100          # BacDive (requires registration)
just fetch-nbrc 50              # NBRC web scraping

# Import to normalized format
just import-algae-collections
just import-bacdive
just import-nbrc

🏗️ Project Structure

CultureMech/
├── src/culturemech/              # Python package
│   ├── schema/                   # LinkML schema definitions
│   │   ├── culturemech.yaml     # Main schema (1800+ lines)
│   │   └── unmapped_ingredients_schema.yaml  # Unmapped ingredients schema
│   ├── fetch/                    # Data fetchers (10 sources)
│   │   ├── utex_fetcher.py      # UTEX algae media
│   │   ├── ccap_fetcher.py      # CCAP algae media
│   │   ├── sag_fetcher.py       # SAG algae media
│   │   └── ... (7 more fetchers)
│   ├── convert/                  # Raw YAML converters
│   ├── import/                   # Normalized importers (11 total)
│   │   ├── utex_importer.py     # Full UTEX pipeline
│   │   ├── ccap_importer.py     # CCAP metadata importer
│   │   ├── sag_importer.py      # SAG metadata importer
│   │   └── ... (8 more importers)
│   ├── export/                   # Export modules
│   │   ├── browser_export.py    # Browser data generator
│   │   └── kgx_export.py        # Knowledge graph export
│   └── render.py                 # HTML page generator
│
├── scripts/                      # Utility scripts
│   ├── aggregate_unmapped_ingredients.py  # Aggregate unmapped ingredients
│   └── unmapped_ingredients_stats.py      # Generate statistics reports
│
├── output/                       # Generated outputs
│   └── unmapped_ingredients.yaml # Aggregated unmapped ingredients (502KB)
│
├── data/                         # Three-tier data architecture
│   ├── raw/                      # Layer 1: Source files (git ignored)
│   │   ├── utex/                # UTEX raw data
│   │   ├── ccap/                # CCAP raw data
│   │   ├── sag/                 # SAG raw data
│   │   └── ... (10+ sources)
│   ├── raw_yaml/                 # Layer 2: Unnormalized YAML (git ignored)
│   └── normalized_yaml/          # Layer 3: Curated recipes (in git)
│       ├── algae/               # 242 algae recipes
│       ├── bacterial/           # 10,072 bacterial recipes
│       ├── fungal/              # 119 fungal recipes
│       ├── archaea/             # 63 archaeal recipes
│       └── specialized/         # 99 specialized recipes
│
├── docs/                         # Comprehensive documentation
│   ├── QUICK_START.md           # 5-minute getting started
│   ├── DATA_LAYERS.md           # Three-tier architecture
│   ├── ALGAE_PIPELINE_COMPLETE.md  # Algae integration guide
│   └── ... (27 more docs)
│
├── app/                          # Browser interface
│   ├── index.html               # Faceted search UI
│   └── schema.js                # Browser configuration
│
├── tests/                        # Test suite
├── conf/                         # Configuration files
├── project.justfile              # Build automation (80+ commands)
└── pyproject.toml               # Python project config

📖 Documentation

Comprehensive documentation is available in the docs/ directory:

Getting Started

Quick Start Guide - Get up and running in 5 minutes
Quick Reference - Command cheat sheet
Contributing Guide - How to contribute

Architecture

Data Layers - Three-tier architecture explained
Migration Guide - Directory structure reference
Implementation Status - Integration progress

Integration Guides

Algae Pipeline - UTEX/CCAP/SAG integration (242 recipes)
UTEX Deployment - Full UTEX pipeline details
CCAP/SAG Deployment - Metadata import details
Data Sources Summary - All source repositories

Data Quality & Enrichment

Enrichment Guide - Data quality improvement workflow
Implementation Summary - Literature verification & enum normalization
Unmapped Ingredients Guide - System for tracking ingredients needing ontology mapping
Unmapped Ingredients Summary - Executive summary with statistics and priorities

🧬 Recipe Format

Recipes are stored as YAML files following the LinkML schema:

name: BG-11 Medium
category: algae
medium_type: COMPLEX
physical_state: LIQUID

description: Standard cyanobacteria medium from UTEX Culture Collection

ingredients:
  - agent_term:
      preferred_term: NaNO3
    amount: 1.5 g/L

  - agent_term:
      preferred_term: K2HPO4
    amount: 0.04 g/L

preparation_steps:
  - step_number: 1
    instruction: Dissolve all ingredients in distilled water

  - step_number: 2
    instruction: Autoclave at 121°C for 20 minutes

# Algae-specific fields
light_intensity: 50-100 µmol photons m⁻² s⁻¹
light_cycle: 12:12 or 16:8 light:dark
temperature_range: 15-30°C depending on species

applications:
  - Algae cultivation
  - Cyanobacteria culture
  - Phytoplankton research

curation_history:
  - curator: utex-import
    date: '2026-01-28'
    action: Imported from UTEX Culture Collection

references:
  - reference_id: UTEX:bg-11-medium
  - reference_id: https://utex.org/products/bg-11-medium

See data/normalized_yaml/ for complete examples.

🔬 Data Model

LinkML Schema

The schema (src/culturemech/schema/culturemech.yaml) defines:

Key Classes:

MediaRecipe - Root entity (one per YAML file)
IngredientDescriptor - Chemicals with CHEBI terms
OrganismDescriptor - Target organisms with NCBITaxon IDs
SolutionDescriptor - Stock solutions
PreparationStep - Ordered protocol steps
MediaVariant - Related formulations

Ontology Bindings:

CHEBI - Chemical ingredients
NCBITaxon - Target organisms
UO - Units of measurement
Source databases - DSMZ, TOGO, ATCC, UTEX, CCAP, SAG

Enums:

MediumTypeEnum: DEFINED, COMPLEX, MINIMAL, SELECTIVE, DIFFERENTIAL, ENRICHMENT
PhysicalStateEnum: LIQUID, SOLID_AGAR, SEMISOLID, BIPHASIC
PreparationActionEnum: DISSOLVE, MIX, HEAT, AUTOCLAVE, FILTER_STERILIZE
SterilizationMethodEnum: AUTOCLAVE, FILTER, DRY_HEAT, TYNDALLIZATION

Algae-Specific Extensions

Added fields for algae culture conditions:

light_intensity - µmol photons m⁻² s⁻¹
light_cycle - Photoperiod (e.g., "16:8 light:dark")
light_quality - Light source type
temperature_range - Cultivation temperature
salinity - Marine vs freshwater
aeration - CO₂ supplementation
culture_vessel - Flask, tube, bioreactor

✅ Data Quality

Three-Tier Architecture

Layer 1: raw/          → Raw source files (JSON, TSV, SQL)
         ↓
Layer 2: raw_yaml/     → Unnormalized YAML (preserves original structure)
         ↓
Layer 3: normalized_yaml/ → LinkML-validated, ontology-grounded recipes

Benefits:

Reproducible pipeline from source to curated data
Easy to re-import with schema changes
Clear separation of concerns
Version control on curated layer only

Validation

# Full validation (schema + ontologies)
just validate data/normalized_yaml/algae/BG-11_Medium.yaml

# Schema validation only
just validate-schema data/normalized_yaml/algae/BG-11_Medium.yaml

# Validate all recipes
just validate-all

Provenance

Every recipe includes:

Source database attribution
Fetch date and version
Import date and curator
Cross-references to original sources
PDF URLs for detailed protocols (CCAP/SAG)

🔬 Literature Verification

NEW (2026-02-20): CultureMech now includes a comprehensive literature verification system for validating cross-references through scientific papers.

6-Tier Cascading PDF Retrieval

The system attempts to retrieve PDFs from multiple sources in order:

Direct Publisher Access - ASM, PLOS, Frontiers, MDPI, Nature, Science, Elsevier
PubMed Central (PMC) - NCBI idconv API
Unpaywall API - Open access aggregator
Semantic Scholar - Open PDF endpoint
Sci-Hub Fallback - Optional, disabled by default (requires explicit opt-in)
Web Search - arXiv, bioRxiv, Europe PMC

Key Features

✅ Legal sources first - Always tries publisher, PMC, Unpaywall, and Semantic Scholar before fallback
✅ Sci-Hub opt-in only - Disabled by default, requires --enable-scihub-fallback flag
✅ Full provenance - Tracks which tier successfully retrieved each PDF
✅ Evidence extraction - 8 regex patterns for detecting media equivalencies
✅ Batch processing - Verify multiple candidates efficiently
✅ Caching layer - Metadata and PDFs cached locally to avoid repeated requests

Usage Examples

# Generate ATCC-DSMZ cross-reference candidates (name-based matching only)
python -m culturemech.enrich.atcc_crossref_builder generate

# Verify candidates using legal sources only (no Sci-Hub)
python -m culturemech.enrich.atcc_crossref_builder generate \
    --verify-literature

# Verify with Sci-Hub fallback enabled (explicit opt-in)
python -m culturemech.enrich.atcc_crossref_builder generate \
    --verify-literature \
    --enable-scihub-fallback

# Configure via environment variables
export ENABLE_SCIHUB_FALLBACK=true
export LITERATURE_EMAIL="your@email.com"
export FALLBACK_PDF_MIRRORS="https://sci-hub.se,https://sci-hub.st"
python -m culturemech.enrich.atcc_crossref_builder generate --verify-literature

Institutional Compliance

⚠️ Important: The Sci-Hub fallback tier is disabled by default and requires explicit opt-in. Use may violate publisher agreements or local laws. Users are responsible for compliance with institutional policies.

Safety features:

Default: use_fallback_pdf=False
Legal sources exhausted first
Clear warnings when Sci-Hub is enabled
Full provenance tracking
No auto-distribution of PDFs

See IMPLEMENTATION_SUMMARY.md for complete documentation.

🌐 Browser Interface

The faceted search browser (app/index.html) provides:

Full-text search - Name, organism, ingredient, application
Faceted filtering - Category, type, state, organisms, sterilization
Real-time filtering - Instant results from 10,595 recipes
External links - CHEBI, NCBITaxon, source databases
Mobile responsive - Works on all devices

Generate browser data:

just gen-browser-data
just serve-browser
# Open http://localhost:8000/app/

🔧 Development

Common Commands

just --list              # Show all 80+ commands
just count-recipes       # Count recipes by category
just fetch-utex          # Fetch UTEX algae media
just import-utex         # Import UTEX to normalized format
just validate-all        # Validate all recipes
just gen-browser-data    # Generate browser search data
just test                # Run test suite

Cross-Reference & Enrichment

# Generate ATCC-DSMZ cross-reference candidates
python -m culturemech.enrich.atcc_crossref_builder generate \
    --output data/curation/atcc_candidates.json

# Verify candidates via literature search (legal sources only)
python -m culturemech.enrich.atcc_crossref_builder generate \
    --verify-literature

# Verify with Sci-Hub fallback (opt-in, requires explicit flag)
python -m culturemech.enrich.atcc_crossref_builder generate \
    --verify-literature \
    --enable-scihub-fallback

# Normalize enum values (medium_type, physical_state, category)
python -m culturemech.enrich.normalize_enums --dry-run  # Preview changes
python -m culturemech.enrich.normalize_enums            # Apply changes

# Aggregate unmapped ingredients for mapping prioritization
python scripts/aggregate_unmapped_ingredients.py --verbose --min-occurrences 2

# View unmapped ingredients statistics
python scripts/unmapped_ingredients_stats.py --top 20

# View full aggregated data
less output/unmapped_ingredients.yaml

# Read the comprehensive guide
cat docs/unmapped_ingredients_guide.md

# Read the executive summary
cat UNMAPPED_INGREDIENTS_SUMMARY.md

Adding New Recipes

Create YAML file in appropriate category:

cp data/normalized_yaml/bacterial/LB_Broth.yaml \
   data/normalized_yaml/bacterial/Your_Medium.yaml

Edit following schema structure

Validate:

just validate data/normalized_yaml/bacterial/Your_Medium.yaml

Regenerate browser:
```
just gen-browser-data
```

Running Tests

# All tests
just test

# With coverage
just test-cov

# Specific test
pytest tests/test_kgx_export.py

🎯 Use Cases

For Researchers

Find media recipes for specific organisms
Compare formulations across culture collections
Access detailed protocols with preparation steps
Discover alternatives through variant relationships

For Culture Collections

Standardize media recipe formats
Cross-reference with other collections
Track provenance and curation history
Export to knowledge graphs for integration

For Bioinformaticians

Query via KG using Biolink model
Link organisms to cultivation conditions
Analyze ingredients with CHEBI ontology
Build applications on structured data

📊 Statistics

$ just count-recipes
Recipe count by category:

  algae:        242
  archaea:       63
  bacterial:  10,134
  fungal:       119
  specialized:   99

Total recipes:  10,657

Data Quality:

✅ 100% schema-validated
✅ 100% enum compliance (10,657 files normalized)
✅ Full source attribution
✅ Comprehensive provenance tracking
✅ LinkML compliance

Pipeline Coverage:

✅ 10 data sources integrated
✅ 11 import pipelines operational
✅ 3 algae collections (UTEX, CCAP, SAG)
✅ Automated fetch → convert → import workflow

Enrichment Features:

✅ Literature verification with 6-tier PDF retrieval
✅ ATCC-DSMZ cross-reference detection
✅ Automated enum normalization
✅ Evidence extraction from scientific papers
✅ Unmapped ingredients aggregation and tracking (136 ingredients, 3,084 instances)

🤝 Contributing

We welcome contributions! Ways to contribute:

Add recipes - Create YAML files following the schema
Enhance existing recipes - Add ontology terms, preparation details
Report issues - Found errors or have suggestions?
Improve documentation - Help make guides clearer
Add data sources - Know of other culture media databases?

See CONTRIBUTING.md for detailed guidelines.

Pull Request Process

Fork the repository
Create a feature branch
Make your changes
Validate: just validate-all
Test: just test
Submit pull request

🔗 Related Resources

📄 License

This work is dedicated to the public domain under CC0 1.0 Universal.

You are free to:

Use for any purpose
Modify and distribute
Use commercially
No attribution required (but appreciated!)

📝 Citation

If you use CultureMech in your research, please cite:

@software{culturemech2026,
  title = {CultureMech: A Comprehensive Microbial Culture Media Knowledge Graph},
  author = {CultureBotAI},
  year = {2026},
  url = {https://github.com/CultureBotAI/CultureMech},
  note = {10,595 culture media recipes from 10 international repositories}
}

🙏 Acknowledgments

Data Sources: DSMZ, TOGO, ATCC, NBRC, BacDive, KOMODO, UTEX, CCAP, SAG, MediaDB

Architecture: Inspired by the dismech project

Ontologies: CHEBI, NCBITaxon, UO

Community: KG-Hub, LinkML, Biolink Model

📧 Contact

Issues: GitHub Issues
Discussions: GitHub Discussions

Built with ❤️ for microbiology research

10,657 recipes • 10 sources • Production ready • Public domain

Name		Name	Last commit message	Last commit date
Latest commit History 40 Commits
.claude/skills		.claude/skills
.github/workflows		.github/workflows
app		app
backups/pre-migration-20260127-095345		backups/pre-migration-20260127-095345
conf		conf
data		data
docs		docs
kb		kb
notes		notes
pages		pages
scripts		scripts
src/culturemech		src/culturemech
tests		tests
.gitignore		.gitignore
.nojekyll		.nojekyll
=3.0.0		=3.0.0
BROWSER_UPDATE_SUMMARY.md		BROWSER_UPDATE_SUMMARY.md
DATA_QUALITY_FIXES_SUMMARY.md		DATA_QUALITY_FIXES_SUMMARY.md
IMPLEMENTATION_SUMMARY.md		IMPLEMENTATION_SUMMARY.md
LICENSE		LICENSE
README.md		README.md
UMAP_IMPLEMENTATION_SUMMARY.md		UMAP_IMPLEMENTATION_SUMMARY.md
UNMAPPED_INGREDIENTS_SUMMARY.md		UNMAPPED_INGREDIENTS_SUMMARY.md
index.html		index.html
justfile		justfile
project.justfile		project.justfile
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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⚗️ CultureMech

📊 Current Coverage

📈 Detailed Statistics

Recipes by Source

Medium Composition

Physical State

Data Quality Metrics

✨ Features

🚀 Quick Start

Installation

View the Browser

Generate UMAP Visualization

Count Recipes

Validate Recipes

📚 Data Sources

Integrated Sources ✅

Planned Expansions 🚀

Algae Collections (New! 🎉)

Fetching Data

🏗️ Project Structure

📖 Documentation

Getting Started

Architecture

Integration Guides

Data Quality & Enrichment

🧬 Recipe Format

🔬 Data Model

LinkML Schema

Algae-Specific Extensions

✅ Data Quality

Three-Tier Architecture

Validation

Provenance

🔬 Literature Verification

6-Tier Cascading PDF Retrieval

Key Features

Usage Examples

Institutional Compliance

🌐 Browser Interface

🔧 Development

Common Commands

Cross-Reference & Enrichment

Adding New Recipes

Running Tests

🎯 Use Cases

For Researchers

For Culture Collections

For Bioinformaticians

📊 Statistics

🤝 Contributing

Pull Request Process

🔗 Related Resources

Culture Collections

Ontologies

Related Projects

📄 License

📝 Citation

🙏 Acknowledgments

📧 Contact

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