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# System prepended metadata

title: Treekipedia Capability Maturity Model and Roadmap

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# **Treekipedia Capability Maturity Model and Roadmap**

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## **Capability Maturity Model**

### **Level 1: Initial (Ad Hoc)**
- **Data Management**: Unstructured collection of tree occurrence data from multiple sources.
- **Ontologies**: Basic ontology with foundational classes and properties.
- **APIs and Tools**: No APIs; data access is manual and limited.
- **Integration**: No integration with external applications like Silvi Protocol.
- **Analytics**: No analytics or biodiversity index calculations.
- **User Interfaces**: Limited backend tools for data exploration and validation.

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### **Level 2: Repeatable**
- **Data Management**: Basic cleaning, deduplication, and standardization of datasets.
- **Ontologies**: Expanded ontology to include ecosystem-level metadata.
- **APIs and Tools**: Basic APIs for retrieving species-level data and querying geospatial species density.
- **Integration**: Initial integration with external platforms (e.g., Silvi Protocol).
- **Analytics**: Species density calculations by geopolygons.
- **User Interfaces**: Public **portal to explore the data**, offering basic query and visualization features.

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### **Level 3: Defined**
- **Data Management**: Standardized data schema with real-time ingestion capabilities.
- **Ontologies**: Ontology harmonized with global biodiversity standards and expanded to include ecological relationships.
- **APIs and Tools**: Advanced APIs for querying by species, region, and biodiversity indices.
- **Integration**: Functional integration with external apps for live data sharing.
- **Analytics**: Implementation of biodiversity indices such as Shannon and Simpson diversity calculations.
- **User Interfaces**: Public **portal to contribute**, allowing data submission, validation, and collaborative editing.

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### **Level 4: Managed**
- **Data Management**: Automated validation processes for real-time data ingestion.
- **Ontologies**: Ontology incorporating traditional ecological knowledge.
- **APIs and Tools**: APIs for predictive analytics and custom partner integrations.
- **Integration**: Bi-directional data exchange with decentralized platforms.
- **Analytics**: Correlation analysis, growth rate estimation, and canopy-volume models.
- **User Interfaces**: Advanced dashboards for contributors and researchers.

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### **Level 5: Optimized**
- **Data Management**: Fully automated global data pipelines.
- **Ontologies**: Comprehensive ontology incorporating cultural, social, and ecological dimensions.
- **APIs and Tools**: Scalable APIs offering biodiversity credit calculations as a service.
- **Integration**: Seamless interoperability with blockchain ecosystems.
- **Analytics**: Real-time ecosystem monitoring and biodiversity credit scoring.
- **User Interfaces**: Fully customizable interfaces for different user roles and use cases.

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## **Roadmap**

### **Phase 1: Foundation**
- Develop the core ontology structure with basic classes and properties.
- Perform data cleaning and standardization for initial datasets.
- Build backend tools for limited data exploration and validation.
- Start integration with Silvi Protocol for data exchange.
- Prepare the infrastructure for public-facing API development.

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### **Phase 2: Expansion**
- Scale ontology to include ecosystem-level metadata and geospatial relationships.
- Develop and release the **portal to explore the data**, enabling basic queries and visualizations.
- Implement species density calculations by geopolygons in APIs.
- Expand API capabilities for geospatial and species-level data queries.
- Conduct pilot integrations with Silvi Protocol and other platforms.

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### **Phase 3: Analytics and Collaboration**
- Harmonize ontology with global biodiversity standards.
- Develop and release the **portal to contribute**, allowing data submission and validation.
- Implement biodiversity indices (e.g., Shannon, Simpson) in analytics APIs.
- Enable advanced data ingestion workflows with automated validation.
- Introduce community-driven collaboration features, including peer reviews and metadata contributions.

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### **Phase 4: Advanced Analytics**
- Incorporate traditional ecological knowledge into the ontology and data models.
- Expand APIs to include predictive analytics and custom partner integrations.
- Implement advanced analytics such as correlation analysis and growth rate models.
- Develop advanced dashboards for contributors and researchers with detailed analytics tools.

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### **Phase 5: Global Optimization**
- Fully automate global data pipelines and validation processes.
- Launch scalable APIs offering biodiversity credit scoring and analytics as services.
- Integrate seamlessly with decentralized platforms and blockchain ecosystems.
- Enable real-time ecosystem monitoring with predictive models and live data updates.

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## **Key Adjustments**
1. **Phased Development of Portals**:
   - Release a **portal to explore** in Phase 2 for initial public engagement.
   - Introduce the **portal to contribute** in Phase 3 to enable collaboration and data submission.

2. **Prioritization of Analytics**:
   - Implement basic species density calculations early (Level 2).
   - Add biodiversity indices (Shannon, Simpson) later during collaborative development (Level 3).

3. **Focus on Scalability**:
   - Ensure APIs and ontologies are designed to handle increased data complexity over time.
   - Prepare for decentralized integrations and biodiversity credit services in later stages.

This roadmap provides a clear, phased approach to developing Treekipedia, ensuring feasibility and scalability while aligning with the platform's collaborative and ecological goals.