Business Plan

Open Infrastructure for Verifiable Science

Prepared by: Olivier V. Raven  ·  olivier.raven@scienceecosystem.org
This document describes a public-interest infrastructure project and is intended for institutional, academic, and funding stakeholders.

Executive Summary

ScienceEcosystem is an independent, public-interest digital infrastructure designed to improve how science is conducted, shared, verified, and preserved across generations. Its core purpose is to link scientific results directly to the data, code, and workflows that produced them, creating a transparent and verifiable research record.

The platform is not a publisher, journal, repository, or start-up. It is infrastructure. ScienceEcosystem operates as a connective layer that integrates existing systems — such as repositories, identifier services, and development environments — into a coherent, navigable network of research objects.

ScienceEcosystem is designed to remain free for all end users, without paywalls or commercial extraction. It is governed with a long-term stewardship mindset, recognising that meaningful improvements to scientific practice require decades rather than funding cycles.

The platform already exists in an early functional state, with core components operational and further development underway. Over time, ScienceEcosystem aims to become a trusted backbone for open, reproducible, and transparent science.

The Problem

Modern science generates knowledge at unprecedented scale, yet the scientific record remains fragmented and difficult to verify. Publications, datasets, software, and methods are typically distributed across disconnected platforms, often behind paywalls or with inconsistent metadata.

Key structural issues include:

These problems are systemic rather than individual. They cannot be solved through policy alone, but require shared digital infrastructure.

Mission and Vision

Mission

To develop and steward a free, open, and durable digital infrastructure that links scientific papers, data, code, and research context into a single, verifiable, and reusable ecosystem.

Vision

ScienceEcosystem envisions a future in which the global scientific record functions as a living, navigable system rather than a static archive of documents. Transparency, reproducibility, and collaboration emerge as properties of the system itself.

Progress will be incremental and conservative, prioritising trust, legitimacy, and continuity over speed or disruption.

Core Concept: The Research Object Layer

ScienceEcosystem introduces a research object layer that sits above existing repositories and tools. Each research object aggregates and links:

This structure allows results to be traced, cited, verified, and reused without duplicating storage or replacing existing platforms.

Functional Scope

Near-Term Capabilities

  1. Unified search across publications, authors, institutions, and topics
  2. Persistent linkage between papers, datasets, and code repositories
  3. Project pages aggregating all research components
  4. Integration with GitHub (source control), Zenodo or equivalent (archival DOI), and ORCID (identity)
  5. Citation management and export
  6. Lightweight collaborative working groups

Longer-Term Development

Future phases may introduce open annotation, transparent peer review, and versioned web-native publications ("living papers"), contingent on community readiness and institutional support.

Guiding Principles

Governance and Independence

ScienceEcosystem is governed to prevent capture and ensure long-term legitimacy. Governance structures will evolve gradually and include:

The founder serves as initial steward; authority and decision-making will transition institutionally over time.

Technology and AI Philosophy

ScienceEcosystem adopts an infrastructure-first and conservative approach to technology. Artificial intelligence is used only to:

AI will not be used to generate research content, assign opaque scores, or replace scientific judgement.

Funding and Sustainability

ScienceEcosystem will never monetise access, data, metrics, or user attention. Permitted funding sources include:

Long-term sustainability will be pursued through diversified funding and, eventually, an endowment model.

Partnerships

ScienceEcosystem collaborates with:

All partnerships must preserve platform neutrality and openness.

Risks and Mitigation

Long-Term Outlook

ScienceEcosystem is conceived as a century-scale investment in scientific infrastructure. Success will be measured not by growth metrics or revenue, but by whether future scientists inherit a research system that is more transparent, reproducible, and humane than the one that preceded it.

ScienceEcosystem exists to make that outcome possible — patiently and irreversibly.

Appendix A

Governance Charter

This Charter defines how ScienceEcosystem is stewarded, how decisions are made, and how independence, transparency, and long-term integrity are safeguarded. It is designed to function from the earliest founder-led stage through future institutional maturity. This document establishes constraints on authority, not merely roles.

Nature of ScienceEcosystem

ScienceEcosystem is a public-interest digital infrastructure created to support open, verifiable, and reproducible science across generations. It is not a commercial product, publisher, or analytics platform. It exists to serve the scientific community as a whole and is governed accordingly.

Core Governance Principles

Stewardship Model

Founder Stewardship (Initial Phase). ScienceEcosystem is currently stewarded by its founder, who acts as initial maintainer, custodian of its mission and principles, and interim decision-maker for technical and organisational matters. Founder stewardship is explicitly temporary. Its purpose is to enable early development while preparing for institutional governance.

Limits of Founder Authority. The founder may not unilaterally introduce paywalls or monetise access to content; sell user data or behavioural analytics; enter exclusive partnerships that restrict openness; or alter core governance principles without a public process.

Governance Bodies

As ScienceEcosystem matures, governance authority will be distributed across:

Decision-Making and Accountability

All significant decisions must be documented publicly, accompanied by a rationale, and subject to community feedback. Changes to governance principles, data policies, or funding models require a public consultation period and formal approval by the Board of Trustees. ScienceEcosystem shall publish annual activity and governance reports, financial summaries, and roadmap updates.

Open Infrastructure and Forkability

ScienceEcosystem commits to open standards and open-source infrastructure wherever feasible. Forkability is treated as a governance safeguard, not a failure mode — it ensures that no single entity can capture or enclose the platform, and that the community retains a credible exit option.

Appendix B

Funding and Sustainability Framework

Financial sustainability is defined as the ability to operate reliably across decades without compromising independence, openness, or trust.

Funding Philosophy

ScienceEcosystem is funded as public-interest infrastructure. Its financial model is designed to support maintenance, stewardship, and gradual improvement rather than growth, profit, or market dominance.

Prohibited Revenue Sources

The following are explicitly prohibited and may not be overturned without formal governance reform:

Permitted Funding Sources

Financial Transparency

ScienceEcosystem commits to public annual financial summaries, independent audits once legally incorporated, and clear separation of operational and reserve funds.

Long-Term Stability

The long-term objective is to establish an endowment covering baseline operational costs, reducing dependency on short-term grants, and supporting multi-decade continuity. Key risks — grant dependency and funding volatility — are mitigated through funding diversification, conservative cost control, and multi-year planning horizons.

Appendix C

Technical Architecture Framework

This document outlines the technical principles, architecture, and integration strategy of ScienceEcosystem. It is intended to guide implementation, audits, and long-term maintenance while remaining adaptable to future standards.

Architectural Philosophy

ScienceEcosystem is designed as a connective infrastructure layer, not a monolithic platform. Its role is to link, contextualise, and preserve relationships between existing research components. Key design goals: modularity, interoperability, durability, and auditability.

Core System Components

Integration Strategy

ScienceEcosystem integrates with: source control systems for code; archival repositories for datasets; identifier services for authors and institutions; development environments for reproducible workflows. It acts as the research object and provenance layer binding these components.

Reproducibility and Verification

The platform supports reproducibility by linking code and data at version level, recording computational environments where possible, and allowing re-execution and verification workflows. Verification is optional but structurally enabled.

Security and Privacy

Compliance with international data protection regulations. Minimal collection of personal data. Regular security audits. No tracking or behavioural profiling is performed.

Open Source and Forkability

Core components are open-source where feasible. Documentation and deployment scripts are public to enable independent audits, prevent platform capture, and ensure continuity beyond any single operator.

Technical Governance

Major architectural changes require public technical proposals, review by qualified contributors, and alignment with governance principles. Stability and trust outweigh novelty.

Appendix D

Future Directions

Linked Data and Knowledge Graph

A publication system whose native format is a knowledge graph — where data entities, method entities, model entities, result entities, claims, provenance, uncertainty, and versions are all first-class objects that can be linked, queried, and traversed.

Living Papers

From the researcher's point of view, writing a living paper feels like writing a traditional paper — prose, figures, tables, code, data. What is actually happening behind the scenes is different: the knowledge graph is the truth, and the paper is one rendering of it. The same research object can be rendered as a PDF, a website, a structured summary, or an AI-readable output. The structure is: knowledge graph → rendered paper, not the reverse.

Memory Alpha

Scientific knowledge is often lost not because data disappears, but because context, methods, and meaning become disconnected over time. When datasets, code, and publications are stored separately, research becomes difficult to interpret, reuse, or reconstruct as technologies and institutions change.

ScienceEcosystem addresses this by acting as a connective knowledge layer — linking data, code, analyses, and publications into coherent, self-describing research objects that preserve provenance and assumptions alongside results. By prioritising meaning over storage, ScienceEcosystem creates a foundation that allows scientific knowledge to remain understandable and resilient in the long term. As this connective layer matures, it can evolve into a broader scientific memory infrastructure, supporting future preservation and continuity initiatives without being tied to any single technology or institution.