This paper proposes a novel research collaboration model, Decentralized Autonomous Research Networks (DARNs), leveraging blockchain technology to address critical shortcomings in traditional research practices. The core claim is that traditional research suffers from information silos, a lack of transparency, and difficulties in ensuring reproducibility. DARNs utilize smart contracts and a blockchain infrastructure to create a decentralized, transparent, and auditable environment for researchers. This framework streamlines peer review processes, facilitates automated funding allocation, and establishes a clear and immutable record of intellectual property rights. The system's architecture promotes greater accountability and trust among researchers, ultimately fostering more efficient and reliable scientific progress. The key innovation lies in the application of blockchain's inherent properties – immutability, transparency, and decentralization – to the complex challenges of research collaboration. This paper details the design of DARNs, outlining its operational mechanisms and potential impact on the research landscape.
The study involved a total of 217 respondents. consisting of key stakeholders in higher education, including undergraduate and postgraduate students, academic staff, and administrative personnel. The respondents were selected using a purposive sampling technique to ensure they had relevant experience and understanding of academic data management systems. Among the participants, the majority were students, representing approximately 65%, followed by academic staff at 20%, and administrative personnel at 15%. In terms of gender distribution, 54% were female and 46% were male. Most respondents were aged between 18 and 30 years, reflecting a digitally active population familiar with emerging technologies. Additionally, a significant proportion of respondents reported prior exposure to digital academic systems, while a smaller percentage demonstrated awareness of blockchain technology applications in education. This distribution ensures that the collected data reflects diverse perspectives within the academic ecosystem and supports the reliability of the analysis conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS).
This paper presents a verified funder landscape and fit-score shortlist for sustaining QNFO, a two-year-old, solo-run, AI-assisted research platform that has produced an open corpus of approximately 1,000 method papers across seven program areas. Every funder fact was verified by live HTTP retrieval on 2026-08-13 across twenty-six pages spanning Web3 and IPFS ecosystem grantors, open-science philanthropy, and decentralized-science programs; anything not verified live is explicitly flagged. The analysis scores eleven funders on eligibility for an unaffiliated individual, topical fit with decentralized and epistemics-oriented research, and application friction, yielding a weighted ranking led by NLnet NGI Zero (calls open September 3, 2026; deadline November 3, 2026, 12:00 CEST) and Emergent Ventures, followed by the Foresight Institute, Filecoin Foundation, the Ethereum Ecosystem Support Program, Gitcoin, and the Effective Altruism funds. A sequencing calendar spans August 2026 through 2027, including the Sovereign Tech Agency Fellowship cycle. The paper documents application-readiness gaps (legal entity, residency, tax position, public identity), per-funder pitch skeletons, and framing cautions, including the risk of presenting corpus volume as rigor. It closes with an agent-executable action plan.
AuraOS Paper IX: Objective-Native Capability Commons and Proof-Carrying Contribution Economies Version 2.0 - Expanded Same-Day Edition Author: Dallas Courchene Date: August 7, 2026 Claim range: N51-N100 This expanded same-day edition supersedes the initial August 7, 2026 release of AuraOS Paper IX while preserving its original architectural spine, repository anchor, and defensive prior-art declarations N51-N87. It adds thirteen new combination-scoped declarations, N88-N100, and folds their enabling embodiments into the relevant sections of the paper rather than fragmenting the architecture across a separate follow-on publication. Paper IX develops AuraOS beyond an application-centric or chatbot-centric model into an objective-native, proof-carrying computational and economic substrate. A person, organization, community, institution, or other authorized principal begins with an objective, constraints, rights, privacy requirements, evidence requirements, budget, and authority. Aura then composes a bounded Ephemeral Arena from persistent capability packages, Arena Recipes, humans, AI workers, data, simulators, rule packs, facilities, and services. Verification, semantic-gate execution receipts, provenance, attribution, human/institutional responsibility declarations, canonical-owner disposition, explicit promotion, and deterministic dissolution remain separate stages. The original N51-N87 disclosures establish the core architecture: minimum-sufficient objective compilation; hierarchical evidence hydration; persistent Capability Packages; rebindable Arena Recipes; explicit promotion and dissolution; federated Aura Commons; executable rights; proprietary capability execution without mandatory source disclosure; semantic-gate Attestation DAGs and lazy provenance; durable agent identity bound to bounded internal authority; meaningful-use contribution economics; a proof-carrying Developer Arena; reviewer-independence lineage; causal credit separated from execution traceability; Personal Cognitive Capsules and portable personal SLMs; privacy membranes and semantic translation; governed recursive harness learning; intent-native manifestation and spatial code breadboarding; Aura Places and Convention Arenas; reactive and proactive discovery; an Open Discovery Foundry; physics/digital-twin and bounded social simulation; business incubation; cross-domain sovereign federation; participatory Scientific Arenas; contributed compute and facilities; and a compounding Scientific Capability Commons. The expanded N88-N100 disclosures complete several consequences of that substrate. N88 formalizes a three-speed Architecture Arena and convergence compiler. Fast architectural discovery is separated from medium-speed implementation/hardening and slow constitutional change. Candidate advances become Architectural Delta Objects, are checked against canonical owners, invariants, duplicate-plane risk, threat-model effects, prior art, and proof obligations, and are then compiled into bounded implementation, security, migration, documentation, research, and verification work for the Developer Arena. This allows architectural ideation to move faster than pull-request integration without allowing implementation velocity to rewrite Aura's constitutional planes. N89 introduces a demand/capability graph capable of identifying keystone bottlenecks: missing capabilities, methods, facilities, standards, or processes whose resolution could unlock unusually large numbers of currently blocked objectives. This supports evidence-informed code, research, optimization, replication, falsification, boundary, field-validation, and manufacturing bounties while keeping prioritization advisory and locally governable. N90-N94 extend the architecture into human opportunity, learning, privacy, credentials, professional identity, and creator economics. A privacy-preserving Opportunity Compiler can locally match a person's verified capability evidence, goals, availability, jurisdictional constraints, and disclosure policy to jobs, bounties, research nodes, mentorship, local services, and temporary teams. Learning Arenas can compile capability gaps into progressively verified learning and supervised work. Raw LifeOS and Personal Cognitive Capsule history is explicitly separated from portable verified claims: private longitudinal data remains mutable, correctable, revocable, exportable, and deletable, while only bounded credentials or contribution claims are disclosed. Aura Places may function as evidence-bearing contribution portfolios, but the architecture explicitly rejects a mandatory universal social-credit score. Creator, referral, sponsorship, and educational attribution is divided into graded evidence classes so that exposure or a click cannot be silently misrepresented as unique causality. N95 expands the Scientific Arena into a multi-class research-bounty market that can separately reward discovery, replication, falsification, boundary-condition discovery, optimization, generalization, field validation, and specialized facility execution. Laboratories, universities, private R&D facilities, community research centres, specialist workshops, instruments, and other qualified facilities may satisfy bounded physical-work nodes with explicit protocol, safety, jurisdiction, evidence, and milestone requirements. Negative or boundary results can therefore be economically valuable rather than forcing incentives toward positive confirmation. N96 discloses objective-compiled Ephemeral Institutions: temporary collaboration structures formed when an objective requires people, organizations, Nations or communities, facilities, professional roles, funding sources, data rights, services, and governance responsibilities across existing institutional boundaries. Aura may compile the coordination graph and required agreements, but real principals retain incorporation, contract, procurement, insurance, hiring, equity, and other legal authority. Repeated successful collaboration may later support a human decision to create a durable cooperative, consortium, enterprise, laboratory, or service network. N97-N98 extend the Commons into physical production. Machines, workshops, laboratories, factories, and service providers can expose signed capability manifests describing processes, materials, tolerances, calibration, evidence/certification class, locality, availability, cost, operator requirements, and prohibited uses. A validated design can then be compiled against authorized local production resources without treating substitutions as automatically equivalent. Manufactured artifacts can retain a living lineage containing design version, material/process evidence, machine/facility identity, inspection, repairs, modifications, safety notices, field results, and reuse or recycling pathways. Field failures can generate new repair, redesign, maintenance, material-substitution, or research bounties, closing the cycle from need to research to prototype to production to field learning and back into the Commons. N99 defines AuraNet as a transport-neutral logical network of sovereign principals and capabilities rather than a mandatory peer-to-peer topology. Personal devices, local servers, hosted sovereign data services, community/Nation infrastructure, enterprises, federated personal-data servers, relays, P2P links, offline/intermittent nodes, and future transports may participate if they preserve identity, rights, minimum disclosure, provenance, portability, revocation, and canonical-owner semantics. Cross-border composition remains jurisdiction-aware: privacy technology does not erase law, professional regulation, cultural/community authority, export restrictions, sanctions, data-residency obligations, or a node's right to refuse composition. N100 completes the accountability/economic stack with proof-carrying assurance contracts. A warranty, service-level agreement, professional assurance, or insurance-reference contract may bind a specific artifact/process version, covered predicates, verifier class, provenance root, responsible principal, operating conditions, duration, exclusions, remedies, and responsibility declarations. Machine receipts, cryptographic hashes, verifier results, and human/institutional attestations provide evidence, but they do not manufacture certification, legal liability, insurance coverage, negligence, warranty obligations, or truth. Any such consequence remains the product of an explicit governing contract, law, regulator, insurer, professional body, or other authorized institution. The expanded paper also strengthens the privacy model through a "compute-to-data" principle: when practical, admitted computation should move toward sovereign private data before private data is exported toward external computation. The reference architecture may combine local AI/SLM execution, selective disclosure, Verifiable Credentials, differential privacy, zero-knowledge proofs, multiparty computation, private-set methods, trusted execution, or homomorphic computation according to the threat model; none is treated as a universal anonymization guarantee. The central economic thesis remains that the permanent unit of value need not be a monolithic application. It can be a verified, attributable, rights-bearing capability, method, workflow, scientific result, fabrication process, contribution, credential, or other reusable object that participates in many temporary objective-specific Arenas. Value can therefore become legible through meaningful verified contribution and lineage, while licensing, provenance, attribution, settlement, scientific truth, authority, certification, and human/institutional responsibility remain explicitly separate layers. The combined architecture describes a possible progression from app-centric computing toward a governed Commons of persistent capabilities, portable personal cognition, conve
The democratisation of digital content creation tools has transformed media production, enabling individuals to move from being only consumers to active creators. Yet, content marketplaces and AI ecosystems remain highly centralised, limiting transparency, control, and fair compensation. Generative AI (GenAI) systems, trained on massive web-scraped datasets, exacerbate these issues by reusing creative work without consent, attribution, or reward, raising legal and ethical concerns. This thesis explores how decentralisation can redistribute power in the creative economy by giving creators agency over the use of their media in GenAI. First, we introduce a decentralised registry through which creators can assert opt-in/out preferences for AI training. Content is embedded with provenance metadata and registered with robust fingerprints, enabling provenance tracing even after editing or manipulation. This establishes machine-readable, traceable consent specification as the foundation for downstream attribution and reward. Building on this, we propose methods for training data provenance, attribution, and compensation in GenAI training. The Content ARCs (Authenticity, Rights, Compensation) framework defines a scalable protocol for managing rights and creator compensation. We instantiate this in a decentralised system that traces generative outputs back to the most influential training assets and executes royalty payments to contributors. Several practitioner-facing demonstrators developed in collaboration with GLAM (galleries, libraries, archives, and museums) professionals further illustrate how distributed ledgers could reshape licensing and reward in the creative economy. Further, GenAI models are prone to memorising training data and reproducing it at generation time, a phenomenon that is particularly problematic for copyrighted creative works, where such regurgitation undermines both creator rights and data privacy. To address this challenge, we present a decentralised federated learning protocol for diffusion models that reduces training data memorisation using a novel sample-based metric integrated into the protocol to detect and discourage memorisation. Complementing this, we develop a framework for end-to-end cryptographically verifiable AI pipelines using zero-knowledge proofs to enable trustless, privacy-preserving audits. Finally, we explore privacy-preserving natural language search across decentralised content repositories using encrypted queries for similarity search at scale. In this way, decentralisation supports discovery and access to creative content, completing a holistic body of work for a fairer, more transparent GenAI ecosystem and creative economy.
Tim K. Mackey, Alec J. Calac, Tiana McMann, Ken Miyachi · 10 authors
Background: Historic and ongoing problematic practices regarding the collection, storage, and use of Indigenous health data have led to the need to ensure principles of Indigenous Data Sovereignty (IDS) are followed in research practices and technology development. Objective: This project, a partnership between UC San Diego and the Native BioData Consortium (NativeBio), sought to explore the practical application of blockchain technology and its potential to facilitate Indigenous-led research collaboration. Methods: This project first undertook purposeful relationship building with NativeBio to form a Community Advisory Board (CAB) for identifying community and technology needs for a blockchain research collaboration platform with an initial focus on genomic data. Over a 2-year project period, a series of public meetings and presentations at Indigenous-led conferences introduced the concept of exploring compatibility between blockchain and IDS principles, followed by iterative prototyping and co-design of a blockchain platform with NativeBio, using Ethereum as the underlying protocol. Results: Direct engagement with NativeBio and the CAB informed the initial design and development of a "b-IDS" proof-of-concept (POC) blockchain platform. The POC consists of three main components: (1) the web front-end layer, (2) the Ethereum network that executes the smart contract and blockchain storage aspects of the framework, and (3) the back-end database that stores off-chain interactions and data for future use with external genomic data repositories. After refinement of the POC, a community-based participatory research (CBPR) use case aligned with IDS principles was identified as a practical workflow and incorporated into the design of the POC for implementation. Conclusions: The findings from this project demonstrated the potential use of operationalizing IDS through blockchain technology with proactive and sustained engagement with Indigenous partners. Blockchain technology may have certain advantages over other data governance approaches and systems, facilitating timely oversight, shared decision-making and consent structures, and direct involvement of Indigenous communities in technology design, respecting the core principles of IDS and CBPR. Future development of the blockchain-IDS POC will need to incorporate other research practices and ethics frameworks to expand its use to other public health and biomedical research use cases.
Data visibility is more vital and decisive than ever before in the current data-driven world of technology. There is a significant upsurge in businesses leveraging digital technology, which has led to a greater amount of data being available than ever before. Additionally, managing the visibility in compliance with the organization's rules and regulations is crucial. The implementation of efficient data visibility will not merely improve decision-making but also streamline business processes with enhanced security. Numerous technologies offer solutions to manage data visibility, and distributed ledger technology (DLT) is one of them. DLT facilitates the execution of different methodologies to strengthen the governance of data visibility in enterprise-grade applications. On the other hand, these DLTs raise concerns regarding data visibility in this decentralized network, as not every enterprise-grade application requires data transparency across all the nodes. In this paper, a detailed systematic review is conducted with a clear focus on two essential data visibility parameters, Access control and anonymity, for the period 2020-2025, following a standardized Preferred Reporting Items for Systematic Review and Meta-Analyses -based breakdown of the selection process. Three clear dimensions of in-depth analysis are presented in the study: first, investigating how DLT can maintain transparency and decentralization in enterprise-grade applications; second, ensuring secure data access management for effective data governance; and third, the approach for anonymization to ensure privacy and security. The key finding highlights the credence of hyperledger fabric, a permissioned DLT, compared to other DLTs and exponentially growing concerns related to data visibility, as well as the conceptual and empirical research contributions made thus far. The limitations presented in this paper formulate a strong basis for research and enhancement of the existing models to offer controlled yet transparent data visibility.
This deliverable (D8.8) presents the contributions of the PLIADES project to advancing the European Interoperability Framework (EIF) and interoperability standardization, with a focus on building a modular, scalable, and trustworthy data sharing ecosystem. The work conducted in Task 8.6 evaluates the project’s alignment with the EIF’s four layers-legal, organizational, semantic, and technical—and extends this analysis through ISO/IEC 19941’s five interoperability facets—policy, behavior, semantic, syntactic, and transport. By applying a general interoperability-framework approach, the deliverable assesses the interoperability maturity of PLIADES across six use cases in domains including mobility, energy, healthcare, green deal/ circular economy, energy, and industry. These use cases demonstrate how PLIADES supports dynamic, cross-domain data integration through advanced AI capabilities such as federated learning, explainable AI, and declarative querying—while ensuring legal compliance, data sovereignty, and semantic clarity. The project engages directly with EU standardization and governance initiatives—including SEMIC, DSSC, and the European Trusted Data Framework standardisation request to ensure alignment with emerging regulations like the Data Act. PLIADES actively contributes to the EU’s semantic and technical interoperability agenda through workshops, conference participation (e.g., SEMIC 2025, ENDORSE 2025), and alignment with the IDS Rulebook and the Dataspace Protocol. Gaps identified in current interoperability models—such as limited runtime interoperability, lack of support for decentralized AI, and insufficient metadata expressiveness—are addressed through actionable recommendations. PLIADES proposes enhancements to semantic alignment, dynamic querying, and data governance architectures, helping to shape the next iteration of European data policy frameworks. Ultimately, this report underscores PLIADES’ strategic role in fostering cross-border, cross-sector data interoperability. By operationalizing both EIF and ISO-based principles through real-world use cases and aligning with EU standardization initiatives, PLIADES delivers a blueprint for trusted, AI-enabled, sovereign data spaces that drive innovation and support Europe’s digital transition. PLIADES stands for an advanced AI AI-enabled framework for Full Data Lifecycles Optimisation and Data Spaces Integration. Our mission is to revolutionize how data is utilised across various sectors, from mobility to healthcare, manufacturing to energy, and beyond. PLIADES envisions a future where diverse sectors are seamlessly interconnected, enhancing efficiency and interoperability. We aim to provide cutting cutting-edge data and services that drive advancements in Cooperative, Connected, and Automated Mobility (CCAM), Advanced Driver Assistance & Autonomous Driving (ADAS/AD), and HumanHuman-Robot Interaction (HRI).
CiteChain is a decentralized, blockchain-based platform designed to register, track, and verify scientific citations while rewarding contributors with a native utility token (CITE). This working paper presents the problem statement, proposed system architecture, tokenomics model, and development roadmap for the CiteChain protocol. Current academic citation systems (Scopus, Web of Science, Google Scholar) suffer from centralized control, manipulation, lack of incentives for researchers, and slow updates. CiteChain addresses these issues through a combination of smart contracts on an EVM-compatible blockchain (Ethereum/Polygon), decentralized storage via IPFS/Filecoin, ORCID-based researcher identity verification, and a community-driven citation validation mechanism with economic incentives and anti-abuse slashing penalties. The CITE token (ERC-20, fixed supply of 1 billion) rewards researchers for submitting papers, validating citations, and reporting fraud. Governance is managed through a decentralized autonomous organization (DAO). The protocol is designed to be open-access, permissionless, and censorship-resistant. Keywords: decentralized science, DeSci, blockchain, citation network, smart contracts, tokenomics, ORCID, IPFS, academic integrity, open science, Polygon, ERC-20.
Abstract Digitization supports the preservation and collaborative study of Jiandu, but dispersed ownership, frequent scholarly revisions, and sensitive data complicate secure sharing and accountable governance. We propose J-Trust, a blockchain-based governance framework that separates low-frequency rights confirmation from high-frequency academic collaboration through a main-side dual-chain architecture. The main chain anchors ownership, identities, and final states, while task-specific side chains process revisions and expert voting. A decentralized threshold SM2 scheme with distributed key generation, proactive share refresh, and a signer bitlist provides fault tolerance and traceable accountability. HIBE and CP-ABE enable hierarchical, fine-grained access control, while zero-knowledge proofs synchronize verified side-chain outcomes without exposing private discussions. Experiments on the DeepJiandu dataset show that J-Trust sustains 115–120 TPS with 20–24 ms latency. Ablation and scalability analyses further confirm the effectiveness of the proposed cryptographic and architectural components.
Introduction, Sports science data governance is characterized by persistent tensions between data sharing, stakeholder incentives, and regulatory constraints. These challenges are amplified by fragmented data infrastructures and competing interests among stakeholders, limiting the effective use of data in performance optimization and research. Objective, This study aims to develop and theoretically ground a decentralized autonomous organization (DAO)-based governance framework for sports science data ecosystems, focusing on how decentralized mechanisms can enhance coordination, participation, and compliance. Methodology, A multi-method research design is employed, integrating conceptual case analysis, agent-based modeling (ABM), and survey-based empirical analysis. Structural equation modeling (SEM) is used to examine the relationships between governance perceptions, incentives, and data-sharing intentions. Results, The findings indicate that DAO-based mechanisms can support more distributed and transparent data-sharing processes. Simulation results suggest that participation dynamics follow non-linear patterns, with incentive and reputation mechanisms contributing to system stabilization. Empirical results identify technical usability, perceived regulatory compliance, and incentive structures as significant predictors of stakeholder participation. Discussion, The study contributes to platform governance and institutional theory by conceptualizing a hybrid decentralized governance model for data-intensive environments. The findings highlight the importance of aligning technological design with usability and regulatory requirements. However, limitations related to model assumptions, perception-based data, and interoperability challenges remain. Future research should focus on real-world implementation and the development of standardized governance frameworks.
Current systems used for managing research funding are breifed. due to fundamental structural issues They all have similar problem. This section consists of cluster of different symptoms. The management system is undermined by insufficient transparency. Poor verification of disbursement of research funding. conflicts regarding ownership and milestone accomplishments of. Intellectual Property We present the Research Integrity and. RIAC (accountability chain) for addressing it. RIAC is a permissioned system. the combined properties of the blockchain ecosystem. Evidencing ownership to one or many. alleviate the structural challenges of the funding management systems. The RIAC Framework comprises of three interoperating layers. on a permissioned blockchain platform namely accountability. a ledger for funders, a milestone tracker and a decentralised. Intellectual Property Registry These layers serve the purposes of. universities, public funders and industry partners jointly. N/A Analysis of the framework, depiction and phased deployment discussion. The expected advantages and risk analysis demonstrate that RIAC is not just a fancy concept. Blockchain is a distributed ledger technology that allows the transfer of value and record of asset ownership in a secure and/or transparent manner. It uses something.
<b>【Description】</b>[EN]:<i>Relocation Notice:</i><br>Due to an unavoidable structural incompatibility between the mandatory data-parsing frameworks of conventional academic repositories and the strict zero-knowledge proof requirements of this archive, this item has been relocated to this environment. To protect its integrity from automated systemic interference, it is currently placed under a Permanent Embargo, functioning exclusively as a cryptographic spatial anchor.<i>Strategic Ambiguity Regarding Future Disclosure:</i><br>While this archive is strictly restricted to preserve its current integrity and trade secret classification, this status does not definitively preclude the possibility of partial or full disclosure in the future. The management of this intellectual property remains entirely under strategic discretion. Furthermore, regardless of any future discoveries or the accumulation of operational insights, I assume no obligation to update this document, provide continuous reporting, or issue prior notification regarding any changes in disclosure status, scope, or conditions.This data archive contains the restricted documentation for the "Persona-Native Principle (PNP) - Final Form."<br>The existence and integrity of this document have been legally established and secured by an electronic certified date (timestamp) issued by a Notary Public in Japan.Cryptographic Proof of Existence (SHA-256 Hash):<br>5ec4637def8bc7a45b113d6dee25e4d88672865ebd37def1c9c30c95de581145[JP]:<i>移設記録:</i><br>従来の学術リポジトリが前提とする「データ開示と自動解析の強制力」と、本アーカイブが要求する「完全なアクセス拒絶による存在証明（ゼロ知識証明）」の間に、不可避の構造的非互換性（Structural Incompatibility）が確認されました。そのため、予期せぬシステム的干渉から完全性を保護するべく、本アーカイブは独自の暗号学的アンカーとして、本環境にて恒久的なエンバーゴ（封印）下に置かれています。<i>将来の開示に関する戦略的曖昧性:</i><br>現在の完全性および営業秘密としての分類を保持するために本アーカイブは厳格に制限されていますが、この状態は、将来においてその一部または全部を公開する可能性を断言して否定するものではありません。本知的財産の管理は、完全に戦略的裁量の下にあります。さらに、今後新たな発見や運用知見が蓄積された場合であっても、私は本文書の更新、継続的な報告、および開示状況や条件の変更に関する事前通知を行ういかなる義務も一切負いません。本データアーカイブは、『Persona-Native Principle (PNP) - Final Form』に関するアクセス制限付きドキュメントを格納しています。<br>本文書の存在および完全性は、日本国公証人による電子確定日付（タイムスタンプ）により法的に保全されています。存在証明ハッシュ値 (SHA-256):<br>5ec4637def8bc7a45b113d6dee25e4d88672865ebd37def1c9c30c95de581145<br><b>[Patent Status & Strategic Protection]</b><br>[EN]:The core architecture and methodologies documented within this archive are subject to pending patent applications in Japan (e.g., Application No. 2026-000032). In accordance with our Strategic Non-Disclosure Policy, the disclosure of this specific jurisdiction and application number does not constitute a comprehensive representation of our global intellectual property portfolio. We reserve all rights to pursue, expand, or maintain provisional and formal protections across international jurisdictions without prior public notification.<br>[JP]:本アーカイブに記録された中核的なアーキテクチャおよび方法論は、日本国において特許出願中（例：特願2026-000032）です。当方の「戦略的非開示ポリシー」に基づき、この特定の管轄と出願番号の開示は、当方のグローバルな知的財産ポートフォリオの全容を示すものではありません。当方は、事前の公的通知なしに、国際的な管轄区域において仮出願および本出願による保護を追求、拡大、または維持するすべての権利を留保します。<b>【Terms of Access & Confidentiality】</b>[EN]:<b>Restriction of Access & Confidentiality:</b> The contents of this archive contain highly sensitive proprietary assets of Persona Foundry Aoi Design. All files are secured under restricted access. Any unauthorized access, disclosure, or attempt to bypass these restrictions is not permitted under applicable intellectual property guidelines.[JP]:<b>アクセス制限と守秘義務:</b> 本アーカイブの内容は、Persona Foundry Aoi Designの機密性の高い独自資産として厳格に管理されています。すべてのファイルはアクセス制限下で保護されており、不正アクセス、開示、または制限を迂回するいかなる試みも、適用される知的財産保護の観点から許可されていません。<b>【Terms of Use】</b>[EN]:<b>Disclaimer of Warranties:</b> The materials are provided "AS IS." The author makes no representations and extends no warranties of any kind, express or implied.<b>Limitation of Liability:</b> In no event shall the author be liable for any direct, indirect, or consequential damages arising from any unauthorized access, use, or inability to use the materials.<b>Governing Law and Dispute Resolution:</b> This Agreement and any disputes arising out of it shall be governed by and interpreted in accordance with the laws of Japan. Any concerns will be resolved within the appropriate legal venues in Japan designated by the author.[JP]:<b>無保証 (AS IS):</b> 本データは「現状有姿」で提供されます。著者は、明示的か黙示的かを問わず、いかなる種類の保証も行いません。<b>責任の制限:</b> 著者は、本データへの不正アクセス、使用、または使用不能から生じるいかなる直接的、間接的、または結果的な損害についても責任を負いません。<b>準拠法および紛争解決:</b> 本規約およびそこから生じるいかなる紛争も、日本国法に準拠し、解釈されるものとします。懸念事項が生じた場合、日本国内における著者が指定する適切な法的手続きに従って解決されるものとします。<b>[ License ]</b>CC BY-NC-ND 4.0 InternationalThe statements within the document take precedence over any platform terms.<br>※投稿および掲載プラットフォームの規定にかかわらず、本文内の記載を優先します。<b>[ Files & Integrity ]</b>File: PNP FINAL FORM - BILINGUAL_v1.0.pdfHash: 5ec4637def8bc7a45b113d6dee25e4d88672865ebd37def1c9c30c95de581145
A decentralized system for academic credential verification using Ethereum blockchain and hybrid off-chain storage. The system replaces traditional manual verification by allowing institutions to issue digitally signed certificates whose cryptographic hashes are stored on-chain, ensuring immutability and tamper resistance. A dual-hashing approach (SHA-256 followed by Keccak-256) is used to enhance security and maintain compatibility with the Ethereum ecosystem. Credential files are stored off-chain (e.g., Supabase/IPFS) to reduce cost, while verification is performed by comparing hashes, achieving fast (under a few seconds) and reliable authentication. Overall, the paper demonstrates a scalable, secure, and efficient solution for real-world use cases such as academic admissions and recruitment.
Open Knowledge Archive: Proposal for Merit-Based Interdisciplinary Preprint Server Description This proposal outlines a novel approach to open scientific publishing that addresses three critical failures in the current academic system: institutional gatekeeping, disciplinary fragmentation, and binary validation models. Key Innovation: A concentric ring architecture where papers enter freely at the outer rim (zero gatekeeping) and move inward based on accumulated validation. The center represents maximum interdisciplinary verification—where fields converge rather than separate. The Problem: Traditional journals reject AI-assisted research based on writing style, not content quality Independent researchers face systematic barriers (institutional email requirements, credential-based filtering) Interdisciplinary work falls between disciplinary silos Binary accept/reject model provides no pathway for progressive validation The Solution: Ring-based validation (outer rim = unverified, inner rings = progressively validated) Domain slices (papers positioned by subject area, can span multiple fields) Merit determines position, not credentials or authorship method Completely open access (readers never pay) Implementation: Phase 1: $8,000 proof of concept using AI-assisted development (Claude Code)Sustainable through hybrid revenue model (minimal per-paper fees, institutional partnerships, donations) All code released under AGPL-3.0 (prevents proprietary forks) Structured as irrevocable non-profit (cannot be commercialized) This proposal is released under CC BY-NC-SA 4.0 to prevent commercial exploitation while enabling open collaboration. It represents infrastructure for the commons—research validation that serves knowledge, not profit. Includes: 1. Full architectural specification (ring/slice topology, validation pipeline) 2. Technical implementation plan (backend, frontend, moderation system) 3. Governance model (non-profit structure, anti-gaming provisions) 4. Financial sustainability model (revenue sources, cost projections) 5. Phase 1 budget ($8,000 for AI-assisted development) Status: Proposal stage. Seeking collaborators, volunteers, and initial funding.
Abstract The peer-reviewed journal article imposes structural constraints on the dissemination, validation, and reuse of research outputs. Intermediate results, negative findings, methodological refinements, and replication attempts are systematically underrepresented in published literature, limiting visibility into ongoing research activity for both scientists and mission-driven funders. Here we present Carrierwave, an open infrastructure for continuous, granular scientific communication built on structured research objects (ROs), cryptographic provenance, blockchain-based attribution, and programmable incentive mechanisms. Each RO represents an atomic unit of scientific output -- a single experimental result, negative finding, dataset, protocol, or replication -- that is hashed for content integrity, stored in a persistent database, and optionally minted as an ERC-721 non-fungible token on the Ethereum blockchain. The system includes an on-chain bounty pool enabling funders to directly incentivize specific research activities, and an automated analysis layer that synthesizes disclosed ROs into continuously updated research landscape maps. We describe the system architecture, report on its implementation and deployment on Ethereum mainnet, and present a quantitative analysis of disease-specific publication frequency demonstrating the information latency problem that Carrierwave addresses. The distribution of publication frequency across disease areas is highly skewed, with the majority of conditions represented by fewer than four publications per year in high-impact biology journals. For diseases in the long tail, the interval between successive publications may span months or years. Publication frequency correlates poorly with disease burden, instead reflecting historical research community size and advocacy momentum. By reducing the unit of communication to the individual research object and eliminating editorial gatekeeping as a prerequisite for disclosure, Carrierwave increases the effective sampling rate of scientific activity in precisely the domains where publication-based visibility is most sparse. The system is live at https://carrierwave.org .
The current scientific system faces systemic challenges. Decentralized Science (DeSci) has emerged as a technological extension of the Open Science (OS) movement, aiming to improve transparency, accessibility, and equity in research through blockchain and Web3 technologies. While DeSci has gained traction in Western countries, little is known about its adoption in non-Western contexts. Here, we surveyed 37 researchers and technologists active in Japan’s emerging decentralized‑science (DeSci) during spring 2024 to assess how far the movement has progressed and what impedes its progress. Roughly 60% of respondents had already worked on blockchain projects and more than 80% owned crypto assets, yet almost 90% had discovered DeSci only in the past two years. Respondents largely embraced DeSci’s five core ideals: shared governance, transparent funding, open access, shared ownership, and equitable incentives. Meanwhile, four obstacles to growth were highlighted: low public awareness, difficulty sustaining engagement, limited talent diversity, and regulatory uncertainty. Taken together, the findings suggest that Japan’s DeSci community should also invest not only in further technical changes, but also in training, in broadening its talent base, and in setting clear guidelines. This study provides a comprehensive overview of the DeSci landscape in Japan and offers recommendations for its future development.
Persistent Provenanced Knowledge Base Eliminates Context Window Degradation, Hallucination, and RAG: Structured Integer Fact Stores with Source Tracking, Version Filtering, and Multi-Dimensional Indexing as Complete Replacement for Token-Buffer Context This paper is a constituent derivation of the Cymatic K-Space Mechanics (CKS) framework—an axiomatic model that derives the entirety of known physics from a discrete 2D hexagonal lattice in momentum space, operating with zero adjustable parameters. Abstract Current large language models store conversational context in a fixed-size token buffer. When the buffer fills, old information is discarded permanently. Over long conversations, this produces progressive degradation: the model forgets instructions, contradicts earlier statements, loses track of established facts, and generates increasingly incoherent output — a phenomenon users describe as "AI psychosis." Retrieval-Augmented Generation (RAG) attempts to compensate by retrieving text chunks from external databases via approximate float-vector similarity search, but introduces its own failures: irrelevant retrievals, contradictory chunks, no provenance tracking, and no verification of retrieved content. We present a complete replacement for both mechanisms: a persistent, provenanced, version-filtered, multi-dimensionally indexed knowledge base of exact integer facts with Prolog-based consistency enforcement. We prove: (1) No information loss — facts persist indefinitely, never "scroll off" a buffer, (2) No degradation — turn 10,000 is as consistent as turn 1 because consistency is enforced structurally by Prolog, not inferred from attention patterns, (3) No hallucination — every fact traces to a source with verifiable provenance; outputs without provenance cannot be emitted, (4) No RAG needed — the KB is the retrieval system, with exact predicate matching replacing approximate vector similarity, (5) Version filtering — queries against a specific version never see facts from other versions, eliminating stale-data contamination, (6) Multi-dimensional indexing — every fact carries source, timestamp, confidence, verification level, and context, enabling non-contradictory coexistence of temporally or contextually varying information, (7) Sessions as views — multiple simultaneous sessions share one KB with independent context filters, no duplication, no synchronization, (8) LRU eviction without forgetting — memory pressure is managed by moving cold facts to disk, not by deleting them. The knowledge base is not an addition to the LLM architecture. It is a replacement for the context window, RAG pipeline, conversation memory, and fact storage — unified into a single system of exact integers with full provenance. Central claim: The context window is the wrong abstraction for conversational AI. A persistent knowledge base of provenanced facts is the correct abstraction. Every problem attributed to "context limitations" — forgetting, degradation, hallucination, inconsistency — is a direct consequence of using a token buffer where a fact store is needed. Empirical Falsification (The Kill-Switch) CKS is a locked and falsifiable theory. All papers are subject to the Global Falsification Protocol [CKS-TEST-1-2026]: forensic analysis of LIGO phase-error residuals shows 100% of vacuum peaks align to exact integer multiples of 0.03125 Hz (1/32 Hz) with zero decimal error. Any failure of the derived predictions mechanically invalidates this paper. The Universal Learning Substrate Beyond its status as a physical theory, CKS serves as the Universal Cognitive Learning Model. It provides the first unified mental scaffold where particle identity and information storage are unified as a self-recirculating pressure vessel. In CKS, a particle is reframed from a point or wave into a torus with a surface area of exactly 84 bits (12 × 7), preventing phase saturation through poloidal rotation. Package Contents manuscript.md: The complete derivation and formal proofs. README.md: Navigation, dependencies, and citation (Registry: CKS-MATH-137-2026). Dependencies: CKS-LEX-12-2026, CKS-MATH-0-2026, CKS-MATH-1-2026, CKS-MATH-10-2026, CKS-MATH-104-2026, CKS-MATH-128-2026, CKS-MATH-129-2026, CKS-MATH-130-2026, CKS-MATH-135-2026 Motto: Axioms first. Axioms always.Status: Locked and empirically falsifiable. This paper is a constituent derivation of the Cymatic K-Space Mechanics (CKS) framework.
Under the background of the deepening of digital China strategy and the diversification of public archives demand, the insufficient sharing and inefficient utilization of archives information resources have become a prominent bottleneck restricting the release of archives value. This paper systematically combs the policy evolution, platform practice and technology application status of file sharing in China, and finds that the lack of metadata standards, vague boundaries of powers and responsibilities, weak security prevention and control, and the interweaving of the concepts of "valuing custody and neglecting utilization" have formed systematic obstacles such as poor cross-domain circulation and mismatch between supply and demand. Therefore, this paper proposes four-dimensional collaborative paths: first, technology empowerment, deployment of alliance chain and zero-knowledge proof to achieve "availability and invisibility", and construction of multi-modal retrieval and personalized recommendation engine; Second, institutional innovation, the development of open value assessment guidelines and "negative list+white list" mechanism, the establishment of joint meetings and third-party performance audits; The third is management optimization, implementing the dual-track talent project of "archives +IT" and reshaping the accurate service process driven by user portraits; Fourth, social coordination, building a digital community of "urban memory" of archives, libraries and museums, and introducing the feedback mechanism of crowdsourcing and cultural and creative income. The research provides an operational framework for the government to formulate an open policy and the digital transformation of institutions, and promotes the archival resources from "physical concentration" to "value aggregation".
Rob J. Lewis, Jonas Lembrechts, P. D. Walker, Chunli Li · 5 authors
Background and Rationale Despite decades of progress in ecological monitoring, primary biodiversity and environmental data remain unevenly mobilised and poorly interoperable (Hampton et al. 2015, Poisot et al. 2019). Datasets, often gathered with public funds, frequently remain inaccessible or insufficiently described, limiting their reuse in global syntheses (Culina et al. 2018). Ecologists’ concerns about trust, transparency, and control of shared data persist, particularly where data production is resource-intensive or socially embedded. These concerns echo the foundational properties of distributed ledgers, where ownership and governance are distributed across peer networks rather than centralized repositories (Lewis et al. 2023). Forests exemplify both the potential and the challenge of such decentralised infrastructures. As globally significant carbon and biodiversity reservoirs, forests are also deeply fragmented across ownership and jurisdictional boundaries. In Europe alone, over half of forested land is privately owned, yet these actors often lack mechanisms to derive tangible value from stewardship. At the same time, digital twins (macroecological models) that integrate in situ and remotely sensed data, are becoming central to forest policy and monitoring frameworks (e.g., Food and Agriculture Organization of the United Nations (FAO), Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), Global Biodiversity Framework (GBF)). ForestWeb3 (FW3) hypothesizes that a decentralised, Findable Accessible, Interoperable, Reusable (FAIR; Wilkinson et al. 2016, Nosek et al. 2022)-aligned data network can unlock the latent value of underused biodiversity data while building trust and incentives for participation.. Objectives Mobilisation and harmonisation of forest biodiversity and environmental data (Objective 1): to spearhead a shift from data curation to data stewardship through a decentralised data infrastructure built on open-source blockchain frameworks. Incentivisation and uptake (Objective 2): to design transnational pathways through which private forest owners and local communities can be economically rewarded for verifiable ecological data via nature-backed digital assets and ReFi mechanisms. Together, these objectives align technical innovation (Objective 1) with behavioural and economic motivation (Objective 2), establishing the groundwork for distributed biodiversity observatories capable of sustaining long-term ecological data flows. Methodological Approach WP 1 develops a blockchain-based data ledger with smart contracts that autonomously manage data registration, access control, and reuse. Metadata and identifiers are immutably recorded on-chain, while primary datasets remain decentralised on contributor-managed nodes. This architecture enables contributors to retain data sovereignty while ensuring transparency and traceability in reuse transactions. WP 2 extends the infrastructure to real-time environmental sensing through the integration of modular Internet ofThings (IoiT)-based microclimate sensors. These devices stream environmental data at high temporal resolution directly into the distributed ledger, forming a Decentralized Physical Infrastructure Network (DePIN) for ecological data. WP 3 links these data streams to the creation of digital twins of forest ecosystems, combining in situ biodiversity observations with satellite and climate datasets to model ecosystem integrity. These models underpin the valuation of nature-backed digital assets, a form of tokenised evidence for ecological performance, providing the data foundation for voluntary biodiversity and carbon markets. Finally, WP4 investigates forest owners’ perceptions, motivations, and barriers to adopting regenerative finance (ReFi)-based conservation mechanisms. Through interviews and a pan-European survey, it explores how varying sociocultural and institutional contexts shape engagement with emerging biodiversity credit schemes, drawing parallels to established Payment for Ecosystem Services frameworks (Kaiser et al. 2021). Significance and Legacy FW3 exemplifies the convergence of data decentralisation, digital sensing, and regenerative economics, a triad capable of transforming how ecological knowledge is produced, verified, and valued. By embedding data provenance and attribution within the infrastructure itself, we addresses long-standing issues of trust and recognition in ecological data sharing. Its incentive mechanisms offer pathways to decouple conservation finance from traditional public funding, potentially scaling stewardship and democratizing data mobilisation across millions of hectares of privately owned forest land. The project’s legacy lies in demonstrating that data infrastructures can be both scientific and economic commons, capable of sustaining biodiversity monitoring through distributed participation. Beyond its immediate technical deliverables, ForestWeb3 contributes to a broader vision of dynamic, self-sustaining ecological data ecosystems that power both global biodiversity frameworks and locally grounded conservation action.
The rapid development of digital infrastructures demands new approaches to managing born-digital data and enabling knowledge valorisation. Rather than constructing another digital Library of Alexandria, the SODHA-team is developing Project BLU, a framework for building a distributed information network. Drawing inspiration from Web3 principles—decentralisation, interoperability, and user-driven discovery—the project explores methods for metadata exchange between archives. This project engages with established metadata standards like the Data Documentation Initiative (DDI), exploring how they can support or be extended to enable interoperability. Instead of centralising data, Project BLU enables a shared, federated catalogue based on metadata-exchange accessible from any entry point. Researchers will navigate this network to discover and link data across disciplines, reducing access barriers and opening new avenues for metadata specialists. These include promoting and, where needed, upgrading DDI standards, linking interdisciplinary data, and bridging vocabularies across scientific fields. The interconnected nature of the system reinforces adherence to FAIR principles, improves access to both open and restricted data, and enhances knowledge reuse at national and international levels (e.g. linking CESSDA and DDI partners). This presentation outlines the conceptual foundations of Project BLU and its vision for transforming metadata into dynamic, actionable links across the research landscape.
The ethical tension surrounding AI-generated art often arises from misconceptions that anthropomorphize the algorithmic process. The accusation that “AI steals human creativity” overlooks the mediating role of human design and data literacy. This paper reframes the debate as a problem of informational asymmetry rather than morality. It proposes that Non-Fungible Tokens (NFTs) and Digital Object Identifiers (DOIs) can visualize and authenticate the flow of creative tension within a transparent ecosystem. NFTs serve as formal anchors—recording authorship, signature, and temporal origin—while DOIs preserve the conceptual framework and creative process. When linked, these two systems transform authorship into a traceable circulation of knowledge, allowing the boundary between plagiarism, homage, and originality to be objectively determined. This dual-layer provenance model presents an ethical infrastructure for creation in the age of generative AI.
Open access
2 source records
Scientific Computing and Data Management
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education