Key Insights

• Shared scientific infrastructure enables breakthrough discoveries, accelerating research timelines and fostering global collaboration across multiple fields

• Open-core business models in scientific software balance open-source transparency with commercial sustainability through community-driven development and enterprise features

• Indigenous data sovereignty embraces CARE principles: Collective Benefit, Authority to Control, Responsibility, and Ethics in data governance

• Open science governance frameworks balance technical merit, community engagement, and institutional support through multi-tiered structures and transparent decision-making processes

• Guide offers best practices for scientific platforms, including tiered onboarding strategies, mentorship programs, and recognition systems beyond citations to build sustainable contributor communities

• W3C PROV-DM serves as the foundation for scientific knowledge graph provenance tracking, enabling attribution through ORCID integration and nanopublications

• Seven key success factors for open-source scientific infrastructure projects include community-centric development, sustainable governance, diversified funding, and technical excellence

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Open-Core, Open Science: Building a Collaborative Knowledge Ecosystem

The convergence of open science principles with sustainable business models has created unprecedented opportunities for collaborative discovery. As scientific challenges grow increasingly complex and interdisciplinary, the infrastructure supporting research must evolve from isolated tools to interconnected ecosystems that balance community needs with long-term sustainability. This exploration of open-core scientific platforms reveals how thoughtful architecture, governance, and community building can accelerate discovery while maintaining the trust and participation essential for scientific progress.

The open-core advantage for scientific infrastructure

The open-core model has emerged as a critical strategy for sustainable scientific software development, offering a sophisticated balance between collaborative innovation and financial viability ^[1] . Unlike purely commercial or purely open-source approaches, open-core creates a foundation where core scientific functionality remains freely accessible while enterprise features generate revenue for continued development ^[2] .

Recent implementations demonstrate remarkable success: Anaconda serves millions of data scientists with its free distribution while funding development through enterprise deployment tools, RStudio/Posit maintains open-source statistical computing environments while offering commercial scalability features, and the Jupyter ecosystem supports 20+ million users through a Linux Foundation partnership that ensures both openness and sustainability ^[3] . These projects share a critical insight: scientific reproducibility and transparency require open foundations, while institutional deployment often demands additional security, compliance, and management capabilities that justify commercial licensing.

The most successful implementations follow a “buyer-based open core” model, segmenting features by user type rather than technical complexity ^[4] . Individual researchers and academics retain full functionality under open-source licenses, while enterprises pay for advanced authentication, audit logging, and deployment management. This approach has generated hundreds of millions in annual revenue for leading projects while maintaining their commitment to open science ^[5] .

Collaborative knowledge systems and governance excellence

Building trust in distributed scientific collaboration requires more than technical infrastructure—it demands transparent governance that evolves with community needs. The Galaxy Project exemplifies this with its multi-tiered structure: an Executive Board for strategic direction, Technical Board representing working groups, and Community Board ensuring diverse stakeholder representation ^[6] . This model prevents single points of failure while maintaining coherent vision ^[7] .

The transition from benevolent dictator to distributed governance represents a critical maturation point. Projects like Jupyter successfully navigated this evolution by establishing clear succession planning, distributed authority across functional areas, and cross-institutional representation ^[8] . The Apache Foundation’s meritocracy model, where advancement comes through demonstrated contribution and consensus-based decision making, provides a proven template for scientific platforms ^[9] .

Trust mechanisms extend beyond governance to technical architecture. The W3C PROV standard enables comprehensive provenance tracking, recording not just what data was used but who contributed it, how it was processed, and what derived insights emerged ^[10] . Combined with persistent identifiers like ORCID for researchers and DOIs for datasets, these systems create an auditable trail of scientific contribution that satisfies both open science transparency and academic credit requirements ^[11] .

Technical foundations for interoperable knowledge graphs

Modern scientific discovery increasingly relies on connecting insights across disciplines, requiring technical infrastructure that enables seamless integration while preserving data sovereignty and attribution. The semantic web technology stack—RDF for flexible data representation, SPARQL for federated querying, and JSON-LD for web-friendly serialization—provides proven foundations for scientific knowledge graphs ^[12] .

OpenAIRE’s Research Graph demonstrates this at scale, connecting 240+ million publications with full provenance tracking across institutions globally ^[13] . The platform’s success stems from implementing FAIR principles (Findable, Accessible, Interoperable, Reusable) not as abstract goals but as concrete technical requirements: persistent identifiers for all entities, standardized metadata following Schema.org and Dublin Core, RESTful APIs with content negotiation, and clear licensing enabling reuse ^[14] .

Federation protocols enable collaboration without centralization. SPARQL federation allows researchers to query across distributed endpoints, combining data from Wikidata, domain-specific repositories, and institutional databases in single analyses ^[15] . The emerging SOLID protocol goes further, giving researchers personal data pods with fine-grained access control—enabling collaboration while maintaining sovereignty over sensitive research data ^[16] .

Recent advances in federated learning and privacy-preserving computation address the tension between open collaboration and data protection. Platforms can now enable joint analysis across institutions without sharing raw data, using differential privacy to prevent individual identification while preserving statistical utility ^[17] . This proves especially critical for biomedical research, where patient privacy must coexist with the need for large-scale analysis ^[18] .

Ethics at the foundation of collaborative infrastructure

The most sophisticated technical infrastructure fails without ethical foundations that ensure equitable access and responsible use. The CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics) for Indigenous data sovereignty demonstrate how open science must evolve beyond simple accessibility to consider whose knowledge is shared and who benefits from its use ^[19] .

Implementation requires nuanced approaches. The Global Indigenous Data Alliance works with platforms to ensure Indigenous communities maintain authority over their data while participating in global research networks ^[20] . This includes not just technical access controls but governance structures that recognize Indigenous rights throughout the data lifecycle—from collection through analysis to publication and commercialization ^[21] .

AI integration introduces new ethical dimensions. UNESCO’s 2024 framework for AI ethics in research emphasizes transparency, bias mitigation, and explainability as non-negotiable requirements ^[22] . Successful platforms implement these through technical means (algorithmic auditing, diverse training sets) and governance processes (ethics review boards, community oversight) ^[23] . The EU AI Act’s research provisions create regulatory frameworks that platforms must navigate while maintaining innovation capacity.

Equitable access extends beyond ethics to practical implementation. With 2.6 billion people remaining digitally disconnected, platforms must design for resource-constrained environments ^[24] . This means optimizing for low-bandwidth access, providing offline capabilities, and ensuring core functionality works on basic hardware. Language barriers require similar attention—the most inclusive platforms provide interfaces and documentation in multiple languages while fostering local community development ^[25] .

Learning from success: Open-source scientific infrastructure

Analysis of successful projects reveals consistent patterns that new platforms should embrace. Community-centric development proves non-negotiable—projects like scikit-learn maintain 600+ contributors through rigorous onboarding processes, regular sprints, and mentorship programs ^[26] . NumPy and SciPy demonstrate that graduate student passion can defeat well-funded incumbents when combined with technical excellence and responsive community leadership ^[27] .

Sustainability requires diversified funding beyond initial grants. The NumFOCUS fiscal sponsorship model supports 30+ projects through combined corporate sponsorship, individual donations, and service revenue ^[28] . Protocol Labs explores innovative token-based incentives and quadratic funding mechanisms. The Chan Zuckerberg Initiative’s Essential Open Source Software program demonstrates how philanthropic funding can provide stability for critical infrastructure ^[29] .

Technical architecture decisions compound over decades. Consistent API design (scikit-learn’s universal fit/predict interface), modular extensibility (Jupyter’s kernel protocol), performance optimization (NumPy’s C integration), and comprehensive testing create foundations that attract both users and contributors ^[30] . Projects that prioritize documentation and examples see dramatically higher adoption and contribution rates.

Perhaps most critically, successful projects balance academic innovation with production reliability. Bioconductor’s six-month release cycle provides stability for research workflows while enabling regular improvements. ROOT’s 25+ years serving high-energy physics demonstrates how domain specialization and institutional support create lasting impact. These projects prove that serving specific communities deeply often creates more value than attempting universal solutions ^[31] .

Building incentive structures for sustainable collaboration

Traditional academic metrics poorly capture software contributions, creating barriers to participation in collaborative platforms. Progressive institutions now recognize software development in tenure decisions, following frameworks like the European Open Science Career Assessment Matrix (OS-CAM) ^[32] . Platforms can accelerate this shift by providing clear attribution mechanisms—from Git-style contribution tracking to formal software citations following emerging standards.

Financial incentives complement recognition. The Linux Foundation’s research into open source sustainability reveals that developers contribute for skill building (83%), professional networking (67%), and ideological commitment to open science (61%) ^[33] . Successful platforms address all three through conference speaking opportunities, training programs, and clear articulation of scientific impact.

Novel mechanisms show promise for scaling incentives. Retroactive public goods funding rewards successful projects based on demonstrated impact rather than predicted value. Quadratic funding democratically allocates resources by amplifying community preferences ^[34] . These mechanisms, pioneered in blockchain ecosystems, offer templates for scientific platforms seeking sustainable, community-driven funding.

The most effective incentive remains enabling impactful science. When researchers see their contributions accelerating discovery—whether through AlphaFold’s transformation of structural biology or LIGO’s gravitational wave detections—participation becomes intrinsically rewarding ^[35] . Platforms must therefore prioritize not just technical infrastructure but storytelling that connects individual contributions to collective achievements.

Community engagement as core competency

Building thriving scientific communities requires intentional design beyond technical excellence. The Carpentries model demonstrates effective onboarding through structured mentorship programs, combining technical skill development with community integration over 8-12 week cycles ^[36] . First-timer issues, carefully tagged and documented, provide clear entry points for new contributors. Documentation must serve multiple audiences, from API references for developers to conceptual guides for domain scientists.

Support infrastructure scales through thoughtful design. Multi-channel communication strategies balance real-time chat for quick questions with asynchronous forums for deep technical discussions ^[37] . Mozilla Science Lab’s study group model enables local community formation while maintaining global coherence. Regular events—from weekly community calls to annual conferences—create rhythms that sustain engagement through diverse participation modes.

Inclusive design principles prove essential for global platforms. This means accessibility-first development, economic accommodation through free tiers and sponsored access, and cultural responsiveness in communication styles ^[38] . Burnout prevention requires explicit attention: rotating high-stress roles, distributing critical responsibilities, and normalizing sustainable contribution levels ^[39] . The goal shifts from extracting maximum volunteer effort to enabling long-term participation.

Recognition systems must expand beyond traditional citations. The All Contributors specification acknowledges diverse contributions from code to community management. Professional development opportunities—conference talks, skill certifications, leadership roles—create career pathways within open communities ^[40] . These “currencies” of participation often prove more valuable than financial compensation for establishing professional reputation.

Case studies in collaborative acceleration

Real-world impacts demonstrate the transformative potential of shared infrastructure. AlphaFold’s protein structure predictions accelerated research timelines from years to minutes while making 214 million structures freely accessible—compared to 170,000 experimentally determined structures accumulated over 60 years ^[41] . The platform’s success combined technical excellence (90+ GDT accuracy scores) with thoughtful infrastructure (APIs, bulk downloads, clustering algorithms) and commitment to open access under permissive licensing ^[42] .

The COVID-19 research response proved that pre-existing collaborative infrastructure enables rapid mobilization. Within months, platforms tracked 469,937 publications, shared 184,386 viral genome sequences, and coordinated $5.5 billion in research funding across 14,000+ projects ^[43] . International collaboration reached unprecedented levels, with 49.3% of publications involving cross-border partnerships ^[44] . Critically, this wasn’t built from scratch—it leveraged existing preprint servers, data repositories, and collaboration networks that activated instantly when crisis struck.

The Earth System Grid Federation demonstrates sustained collaboration at planetary scale. By federating 50+ climate research institutions to share 8+ petabytes of standardized model data, ESGF enables the comparative analyses underlying IPCC climate assessments ^[45] . The technical architecture balances institutional autonomy with coordinated standards, while governance structures ensure long-term sustainability despite changing political landscapes.

These successes share common patterns: technical infrastructure that scales globally, governance balancing coordination with autonomy, sustainable funding from diverse sources, and communities that prioritize collective benefit. They prove that shared infrastructure doesn’t just reduce costs—it enables discoveries impossible through isolated efforts.

Fylo’s commitment to next-generation research environments

These insights shape our approach to building Fylo as an open-core platform that advances collaborative science. We commit to:

Provenance-first architecture where every insight carries its full history—not as bureaucratic overhead but as the foundation for trust and attribution in collaborative discovery. Our knowledge graphs implement W3C PROV standards while innovating on usability, making provenance as intuitive as version control in modern software development ^[46] .

Governance transparency that evolves with our community. We’re establishing advisory structures incorporating researchers, institutions, and domain experts from day one. Decision-making processes, from technical architecture to licensing models, will be documented openly. As we grow, governance will distribute across our community while maintaining coherent vision.

Sustainable open core balancing broad accessibility with platform viability. Core knowledge graph functionality, provenance tracking, and collaboration features remain perpetually open source. Enterprise features focus on deployment, compliance, and integration—needs specific to institutional adoption that don’t compromise individual research access.

Inclusive by design ensuring global participation. This means performance optimization for resource-constrained environments, multilingual interfaces, and documentation that serves diverse communities ^[47] . We’re partnering with organizations worldwide to understand and address barriers to participation, from internet connectivity to cultural communication patterns.

Community investment that treats engagement as essential infrastructure. We’re building mentorship programs, comprehensive documentation, and contribution pathways that recognize diverse skills. Our metrics prioritize community health alongside technical performance, understanding that sustainable platforms require thriving ecosystems ^[48] .

Joining the collaborative future

The convergence of open science principles, sustainable business models, and collaborative technology creates unprecedented opportunity for accelerating discovery. But realizing this potential requires more than technical platforms—it demands communities committed to collective benefit while respecting individual contributions.

We invite researchers, developers, and institutions to join us in building this future. Whether contributing code, sharing use cases, participating in governance, or simply using Fylo for your research, every interaction strengthens our collective capacity for discovery.

The challenges facing humanity—from climate change to pandemic preparedness—demand collaborative science at unprecedented scale ^[49] . By building on lessons from successful open-source scientific infrastructure while innovating on knowledge representation and collaboration, we can create research environments that accelerate discovery while ensuring benefits reach all communities.

Together, we’re not just building software—we’re cultivating ecosystems where knowledge flows freely, contributions receive recognition, and collaborative intelligence emerges from distributed insight. This is the promise of open-core, open science: sustainable platforms that serve both individual advancement and collective discovery, creating value that compounds across generations of researchers ^[50] .

The future of science is collaborative, and the infrastructure supporting it must be both open and sustainable. Join us in building research environments worthy of humanity’s greatest challenges and brightest minds. Because when we share knowledge thoughtfully and govern platforms collectively, we don’t just accelerate individual discoveries—we transform the very nature of how science advances human understanding.