ECK: The Universal Scientific Knowledge Structure

The exponential expansion of scientific knowledge has generated an unprecedented organizational crisis. While the production of information accelerates at an exponential pace, humanity’s capacity to assimilate, integrate, and synthesize this knowledge remains constrained by fundamentally linear cognitive, institutional, and infrastructural limits. This growing asymmetry threatens the coherence, cumulative progress, and long-term viability of science itself. This dissertation introduces ECK (Estrutura Científica do Conhecimento / Universal Scientific Knowledge Structure), a formal meta-scientific framework conceived to address the foundational problem of organizing scientific knowledge in a way that prevents fragmentation, enables deep cross-domain synthesis, and remains scalable under conditions of exponential informational growth. In contrast to traditional disciplinary structures—which compartmentalize knowledge into increasingly isolated silos—ECK proposes a radical reconceptualization of scientific organization. Rather than relying on historically contingent academic disciplines, the framework organizes knowledge according to systems, scales, informational content, and domains of validity. This approach draws on Integration and Implementation Sciences (i2S), theories of transdisciplinary knowledge integration, and formal ontology frameworks such as the Unified Foundational Ontology (UFO), establishing both the philosophical grounding and the formal structure necessary for a universal organization of knowledge. The dissertation offers several theoretical and practical contributions. First, it demonstrates that conventional disciplinary architectures impose prohibitive integration burdens as the number of scientific domains grows, making comprehensive synthesis increasingly unfeasible. Second, it introduces the core architecture of ECK, composed of five interlocking components:(1) a Knowledge Block formalism that replaces traditional papers or isolated theories as the fundamental unit of scientific representation;(2) a multidimensional positioning system that situates all knowledge according to spatial scale, temporal scale, complexity, resource intensity, and informational uncertainty;(3) a hierarchical layering of knowledge ranging from empirical observations to patterns, models, principles, and metamodels;(4) a universal ontology consisting of eight foundational categories—system, state, interaction, information, energy, time, structure, and emergence—applicable across all scientific domains; and(5) a typed connection framework that explicitly represents relationships between knowledge blocks while preserving contradictions, uncertainty, and epistemic plurality inherent to scientific development. ECK is formally defined using concepts from graph theory, information theory, and category theory, allowing rigorous analysis of knowledge integration, validity propagation, and large-scale synthesis. Empirical validation through case studies in physics, biology, climate science, and transdisciplinary sustainability research demonstrates the framework’s ability to reveal hidden cross-domain relationships, identify redundant or overlapping research efforts, expose unresolved theoretical tensions, and enable automated or semi-automated synthesis at scales unattainable with current scientific infrastructures. The dissertation also examines the practical requirements for implementing ECK, including computational architecture, AI-assisted extraction and structuring of knowledge, and interoperability with existing ontology standards such as BFO, UFO, and DOLCE. Broader epistemological implications are addressed, including concerns related to reductionism, the preservation of epistemic diversity, the integration of indigenous and non-Western knowledge systems, and the safeguarding of domain-specific expertise within a universal organizational framework. This work argues that ECK constitutes essential infrastructure for twenty-first-century science. It is not intended to replace specialization, but rather to serve as the organizational substrate that allows specialists to contextualize their work within the broader landscape of human knowledge, discover relevant insights beyond disciplinary boundaries, and contribute to genuinely cumulative scientific progress. Without such an infrastructure, science risks approaching asymptotic stagnation, as its capacity to assimilate knowledge becomes increasingly saturated by the unchecked exponential growth of information.