Roadmap
Review our development strategy and product road map.
Core Features
1. Comprehensive Database (currently data is in R Studio):
A centralized repository for tree species data, integrating information from multiple global sources to create a unified database.
2. Tree Intelligence Ontology:
Comprehensive schema for tree knowledge that defines the relationships between key datasets.
3. Open Platform:
Tools for users to contribute to Treekipedia Pages with their observations, document tree planting efforts, and participate in data validation.
4. Ecological Modeling with AI / ML:
Advanced tools for species identification, data validation, and predictive analytics to enhance the platform’s capabilities.
5. Community-Driven Validation:
A peer-review system to ensure data accuracy and foster collaboration among users.
6. Open Access and Collaboration:
A commitment to open access, allowing researchers, conservationists, and the public to freely use Treekipedia’s data and contribute to global biodiversity efforts.
Product Roadmap
2024 Q3: Data Collection, Cleaning, and Ontology Development
1. Finalize Data Cleaning:
Complete the process of data cleaning, ensuring that all data is accurate, consistent, and ready for integration into the comprehensive database.
2. Column Selection and Dataset Integration:
Finish selecting the most relevant columns from each dataset. Focus on essential information for all user types, ensuring these columns align with the ontology structure.
Develop the data integration pipeline for merging multiple global sources into the unified tree species database.
3 . Finalize Ontology in Protegé:
Complete the definition of the Tree Intelligence Ontology in Protégé to structure relationships between species, ecological functions, occurrences, and observations.
4. Prepare for GraphDB Integration:
Validate that the cleaned datasets match the fields and data types in the ontology, and prepare the CSV files for seamless import into GraphDB.
2024 Q4: Database Launch
1. SPARQL Query Design:
Design and test basic SPARQL queries for retrieving key tree data, ensuring the system is ready to handle beta-stage queries.
2. Community-Driven Validation:
Implement a system for validating user-contributed data in real-time, ensuring that contributions align with the ontology structure.
Set up a peer-review system for community-driven validation, where users can review, edit, and approve incoming data submissions.
3. GraphDB Integration Testing:
Test API integration between the GraphDB SPARQL endpoint and external apps (e.g., Silvi), ensuring smooth data retrieval and contribution processes.
4. Open-Source API Release:
Release Treekipedia's API infrastructure as an open-source project, providing full access to the knowledge graph through well-documented API endpoints.
Encourage external developers to build custom frontends or integrations with existing applications using the open-source API.
2025 Q1 - Q2: Community Collaboration and Working Groups
1. Advanced SPARQL Query Features:
Introduce advanced SPARQL query capabilities, including faceted search and filtered results based on species, regions, and ecological functions.
2. Initial AI Tools:
Implement AI tools for species identification and data validation to enhance user experience and improve data quality.
3. Open-Source API Release:
Release Treekipedia's API infrastructure as an open-source project, providing full access to the knowledge graph through well-documented API endpoints.
Encourage external developers to build custom frontends or integrations with existing applications using the open-source API.
Create and release public API documentation for external integrations, allowing other reforestation and biodiversity platforms to interact with Treekipedia’s knowledge graph.
4. Working Groups for Collaboration:
Invite external organizations, NGOs, ReFi projects, and academic institutions to participate in Working Groups. These groups will collaborate on the development of Treekipedia’s structure and content, contributing to data validation, ontology expansion, and benefiting from shared data. This initiative will foster a collaborative ecosystem, allowing partners to leverage Treekipedia’s resources for reforestation, biodiversity research, and ecological modeling.
2025 Q3 - Q4: Frontend “Treekipedia Pages” Launch
1. Official Launch:
Roll out the full version of Treekipedia with all core features, including comprehensive data access, community tools, and educational resources.
2. Community-Driven Validation:
Implement a system for validating user-contributed data in real-time, ensuring that contributions align with the ontology structure.
Set up a peer-review system for community-driven validation, where users can review, edit, and approve incoming data submissions.
3. Gamification and User Incentives:
Introduce gamification elements such as badges, leaderboards, and rewards to incentivize user engagement and contributions.
4. Predictive Ecological Modeling
Launch AI-driven predictive analytics for ecological modeling, enabling species verification and forecasting of species success in reforestation projects based on Treekipedia’s knowledge graph.\
2026 and Beyond: Growth and Innovation
1. Expand Data Sources:
Continue integrating new data sources, increasing Treekipedia’s global coverage and dataset diversity.
2. AI and ML Innovations:
Develop AI-powered ecological forecasting models that predict species success in various environments, allowing for better planning and reforestation project outcomes.
3. Global Partnerships:
Foster partnerships with global biodiversity platforms, research institutions, and conservation organizations to enhance Treekipedia’s impact.
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