We are a pioneering DeSci network uniting Earth Observation scientists from NASA JPL, to global Ocean Science Institutions on 7 coasts, Marine Biologists, Soil Scientists, and Satellite integration experts.
ÆRTH is developing an open source, distributed environmental data set, unifying on the ground research streams with satellite observation streams. Our science network is building multi modal data integration and shared computational resources to better model solutions for climate adaptation, biodiversity and regeneration strategies.
Let’s Defeat Moloch! Today we face the global coordination failure of protecting our life support systems. The planet is one interconnected environmental system, yet our data models are not. This systemic issue has led to a waste of investment into ineffective solutions and a mystified "sustainable" asset class that poorly represents the realities of our Earth (Carbon, Biodiversity, SDG;s etc).
We are creating unified, agreed upon tools and standards to facilitate coordination. This infrastructure empowers the development of advanced predictive environmental models that can compute the complexity of our natural systems.
Blockchain enables secure data sharing with clear ownership tracking and usage metrics. This unified standard allows global researchers and ML scientists to compute over previously siloed data, building advanced models through competitions. Governments, NGOs, scientific institutions, and citizens can offer bounties for scenario modeling and solution development, fostering a collaborative ecosystem that accelerates environmental research and innovation.
ÆRTH is developing a pioneering platform that serves as a science-based standards organisation, offering comprehensive data integration and shared computational resources to address climate change, biodiversity loss, and fragmented environmental research.
This detailed summary outlines ÆRTH's core technical features, strategic advantages, and its role in revolutionising environmental science collaboration.
Establishes unified data standards for diverse environmental research datasets globally
Implements advanced data harmonisation techniques to ensure interoperability
Develops and maintains comprehensive ontologies for environmental science domains
Facilitates real-time data sharing and analysis across scales (local, regional, global)
Addresses critical data fragmentation issues in current environmental practices
Provides data validation and quality assurance protocols
Offers APIs and SDKs for seamless integration with existing research systems
Deploys a distributed high-performance computing network
Utilises cloud-based technologies for scalable processing power
Implements secure data storage and access protocols
Offers containerised environments for reproducible research
Develops a user-friendly interface for resource allocation and job scheduling
Ensures data sovereignty and compliance with international regulations
Utilises state-of-the-art causal inference models for ecosystem-specific insights
Implements ensemble learning techniques to combine diverse model outputs
Develops transfer learning capabilities to apply models across different ecosystems
Continuously adapts to changing environmental conditions through online learning
Integrates ground-truth data for precise, actionable recommendations
Provides uncertainty quantification for model predictions
Offers interpretable AI techniques to enhance transparency and trust
Implements a decentralised, blockchain-based system for data and compute credit allocation
Develops smart contracts for automated research collaboration agreements
Creates a reputation system to incentivize high-quality contributions
Facilitates peer review processes within the platform
Supports version control and provenance tracking for datasets and models
Enables the creation of virtual research environments for cross-institutional projects
Integrates with existing academic publishing platforms for seamless dissemination
Implements a community-driven process for standard development and revision
Utilises machine learning to identify emerging patterns in data collection and usage
Develops automated metadata generation tools to enhance data discoverability
Creates feedback loops between model outcomes and data collection strategies
Offers a sandbox environment for testing and validating new standards
Provides tools for impact assessment of standard changes on existing research
Implements a federated identity management system for secure, streamlined access
Develops a sophisticated permission system to manage data access granularly
Creates a marketplace for trading computational r- resources and datasets
Offers workflow optimization tools to enhance research efficiency
Provides automated data cleaning and preprocessing pipelines
Develops intelligent search and recommendation systems for relevant datasets and collaborators
Implements a token-based economy for quantifying and exchanging research contributions
Develops algorithms for fair allocation of resources based on contribution and need
Creates mechanisms for institutions to offset infrastructure costs through network participation
Offers analytics tools for measuring the impact and ROI of research contributions
Provides a platform for crowdfunding specific research initiatives
Develops smart contracts for automating royalty distributions from commercialised research
Potentially implement sharding techniques for horizontal scaling of the data infrastructure
Utilises edge computing to reduce latency and enhance real-time capabilities
Develops AI-driven load balancing for optimal resource utilisation
Creates incentive structures to encourage network growth and participation
Implements automated onboarding processes for new institutions and researchers
Provides tools for measuring and visualising network growth and impact
Overcomes data silos through a unified, standards-based approach to environmental data management
Enables complex, multi-scale modeling by providing standardized, high-quality datasets and computational resources
Accelerates research through automated data preprocessing, model training, and result sharing
ÆRTH has already secured pivotal agreements with leading ocean conservation organisations, significantly expanding its data ecosystem and collaborative network. These partnerships demonstrate our growing influence and credibility in the environmental science community.
ÆRTH has built a large, informal decentralised network with top scientists at earth observation groups such as Nasa JPL and Arose. As ÆRTH continues to grow its partner network, it strengthens its position as a central hub for global environmental data integration, standardisation, and collaborative research. Key partners include:
SOA (Sustainable Ocean Alliance)
Voz da Natureza (Brazil)
Pana Sea (Panama)
Amigos da Jubarte (Brazil)
BLOOM (global, soil)
Earth Guardians (global, soil)
Citizens of the Reef (MOU pending)
Bazaruto Center for Scientific Studies (BCSS)
The Great Institute, Gambia
Panama Ghost Net Initiative (Panama)
Beta Diversidad (México, Baja - pending)
Txai (Amazonian soil regeneration networks)
These partnerships represent a diverse range of marine conservation efforts, from regional initiatives to global movements. By integrating data and expertise from these organisations, ÆRTH is positioned to:
Access vast amounts of ocean-specific, coastal and potential soil health data
Enhance the granularity and coverage of marine ecosystem models
Facilitate cross-organizational collaboration on pressing oceanic issues
Accelerate the development and implementation of marine conservation strategies
Provide a unified platform for these organizations to share insights and resources
The long term phased approach outlined below will drive the development of a transformative platform for science-backed metrics to protect and restore critical ecosystems at scale.
Phase 1: $2M (Digital Twin)
Develop the first science data-based digital twin of a Marine Protected Area (MPA)
Create foundational ontologies and knowledge graphs
Establish core data integration and standardisation protocols
Outcome: Proof of concept for a single ecosystem digital twin
Phase 2: $4.9M
Expand the digital twin platform
Implement real-time data integration, predictive modeling, and live visualisation
Enhance the standardized framework and automated processes
Outcome: Scalable model for multiple ecosystem digital twins
Phase 3: $12M
Launch regional biodiversity assessment & validation tool
Develop potential blue carbon assessment capabilities
Further refine and scale the platform's core technologies
Expand partnerships and data sources
Outcome: Comprehensive regional ecosystem management platform
Phase 4: TBD $M+ (Scaling)
Scale the platform to cover other critical regions autonomously
Leverage the standardized framework for rapid deployment across diverse marine ecosystems
Implement advanced machine learning for cross-ecosystem insights
Expand to terrestrial and freshwater ecosystems
Outcome: Global ecosystem monitoring and management platform
ÆRTH’s normalised data mechanisms will allow sustainability assets to be globally understood, quantified, and exchanged, fostering a transparent registry with an interoperable standards system for sustainable assets.
Your support will enable our efforts to:
Provide real-time, actionable insights for conservation efforts
Create a collaborative platform for scientists, policymakers, and conservationists
Scale solutions rapidly to address urgent global environmental challenges
Link to on registry sustainable asset class and drive real world impact.
Our community is at the forefront of a paradigm shift in how we understand, monitor, and protect the world's most critical ecosystems and enable the creation of a sustainable asset class that is globally exchangeable and backed by real world data.