Haoxian Chen, Gerald Whitters, Mohammad Javad Amiri, Yuepeng Wang · 5 authors
This paper presents DeCon, a declarative programming language for implementing smart contracts and specifying contract-level properties. Driven by the observation that smart contract operations and contract-level properties can be naturally expressed as relational constraints, DeCon models each smart contract as a set of relational tables that store transaction records. This relational representation of smart contracts enables convenient specification of contract properties, facilitates run-time monitoring of potential property violations, and brings clarity to contract debugging via data provenance. Specifically, a DeCon program consists of a set of declarative rules and violation query rules over the relational representation, describing the smart contract implementation and contract-level properties, respectively. We have developed a tool that can compile DeCon programs into executable Solidity programs, with instrumentation for run-time property monitoring. Our case studies demonstrate that DeCon can implement realistic smart contracts such as ERC20 and ERC721 digital tokens. Our evaluation results reveal the marginal overhead of DeCon compared to the open-source reference implementation, incurring 14% median gas overhead for execution, and another 16% median gas overhead for run-time verification.
The article describes the use and possible value creation of Non-Fungible Tokens (NFT) in the academic and open access publishing environment. It defines NFTs, describes disadvantages and possible solutions, especially in the intended scientific environment. An overview of existing NFT service providers from the publishing environment illustrates that there is not yet a suitable one for researchers. Accordingly, three possible scenarios are shown where NFT services could be located in a science-friendly way. One would be with library- or scholarly-led university presses, repositories, and other publication infrastructures (such as OJS or OMP). Another would be to use centralizing and channelling article submission platforms with which universities have contracts, such asChronosHub. The third and broadest approach would be through Digital ObjectIdentifier (DOI) registration agencies such as ChronosHub and DataCite, although complexities come into play here due to the triangular relationship with publishers registering DOIs (some of them having exclusive usage rights transferred to themselves). This complexity could be reduced by registeringNFTs only for open access publications with a Creative Commons Attribution license. A summary and outlook provide an overview of open questions and initial starting points to get started.
Massimo Bartoletti, J. Chiang, Tommi Junttila, Alberto Lluch Lafuente · 6 authors
Decentralised Finance (DeFi) applications constitute an entire financial ecosystem deployed on blockchains. Such applications are based on complex protocols and incentive mechanisms whose financial safety is hard to determine. Besides, their adoption is rapidly growing, hence imperilling an increasingly higher amount of assets. Therefore, accurate formalisation and verification of DeFi applications is essential to assess their safety. We have developed a tool for the formal analysis of one of the most widespread DeFi applications: Lending Pools (LP). This was achieved by leveraging an existing formal model for LPs, the Maude verification environment and the MultiVeStA statistical analyser. The tool supports several analyses including reachability analysis, LTL model checking and statistical model checking. In this paper we show how the tool can be used to analyse several parameters of LPs that are fundamental to assess and predict their behaviour. In particular, we use statistical analysis to search for threshold and reward parameters that minimize the risk of unrecoverable loans.
Fundamental science and applied research and technology development (RTD) are facing significant challenges that particularly compound to the notorious credibility, reproducibility, funding and sustainability crises. The underlying, serious shortcomings are substantially amplified by a metrics-obsessed publication culture, and a growing cohort of academics fishing for fairly stagnant (public) funding budgets. This work presents, for the first time, a groundbreaking strategy to successfully address these severe issues; the novel strategy proposed here leverages the distributed ledger technology (DLT) “blockchain” to capitalize on cryptoeconomic mechanisms, such as tokenization, consensus, crowdsourcing, smart contracts, reputation systems as well as staking, reward and slashing mechanisms. This powerful toolbox, which is so far widely unfamiliar to traditional scientific and RTD communities (“TradSci”), is synergistically combined with the exponentially growing computing capabilities for virtualizing experiments through digital twin methods in a future scientific “metaverse”. Project contributions, such as hypotheses, methods, experimental data, modelling, simulation, assessment, predictions and directions are crowdsourced using blockchain, and captured by so-called non-fungible tokens (“NFTs”). The so enabled, highly integrative approach, termed decentralized science (“DeSci”), is destined to move research out of its present silos, and to markedly enhance quality, credibility, efficiency, transparency, inclusiveness, sustainability, impact, and sustainability of a wide spectrum of academic and commercial research initiatives.
The Level of Conceptual Interoperability Model (LCIM) is a widely used framework that represents inter-relationship among interoperability and composability of different information systems. Although this model has been successfully applied to various domains such as cybernetics and informatics, there are many challenges in directly adopting the model for blockchain-based systems. This paper identifies those challenges and proposes a new Level of Conceptual Interoperability Model for blockchain systems based on the original LCIM. We define five different levels of interoperability for blockchain-based systems and theoretically evaluate the level of interoperability (LOI) achieved by different blockchain networks. The evaluation outcomes show that there exists technical interoperability (Level 1) between Bitcoin and Ethereum networks, whereas Solana and Binance achieve pragmatic interoperability (Level 4) by conveying state changes with the Ethereum network and Polkadot achieve dynamic interoperability (level 5) by suitably conveying state changes within the ecosystem of its networks. We present case studies that demonstrate how the proposed LCIM for blockchain systems map various real-world applications to their respective levels.
Visara Urovi, Vikas Jaiman, Arno Angerer, Michel Dumontier
Easy access to data is one of the main avenues to accelerate scientific research. As a key element of scientific innovations, data sharing allows the reproduction of results and helps prevent data fabrication, falsification, and misuse. Although the research benefits from data reuse are widely acknowledged, the data collections existing today are still kept in silos. Indeed, monitoring what happens to data once they have been handed to a third party is currently not feasible within the current data sharing practices. We propose a blockchain-based system to trace data collections and potentially create a more trustworthy data sharing process. In this paper, we present the LUCE (License accoUntability and CompliancE) architecture as a decentralized blockchain-based platform supporting data sharing and reuse. LUCE is designed to provide full transparency on what happens to the data after they are shared with third parties. The contributions of this work consist of i) the design of a decentralized data sharing solution with accountability and compliance by design and ii) the inclusion of a dynamic consent model for personalized data sharing preferences and for enabling legal compliance mechanisms. We test the scalability of the platform in a real-time environment where a growing number of users access and reuse different datasets. Compared to existing data sharing solutions, LUCE provides transparency over data sharing practices, enables data reuse, and supports regulatory requirements. The experimentation shows that the platform can be scaled for a large number of users.
Timotej Knez, Domen GaĆĄperlin, Marko Bajec, Slavko Ćœitnik
Knowledge graphs are commonly represented by ontology-based databases. Tracking the provenance of ontological changes and ensuring ontology consistency is important. In this work, we propose a transaction manager for ontology-based database manipulation that combines blockchain and Semantic Web technologies. The latter is used for the efficient querying and modification of data, whereas the blockchain is used for the secure storage and tracking of changes. The blockchain enables a decentralized setup and data restoration. We evaluate our solution by measuring cost and time. Our solution introduces some overhead for updates whereas querying works at the same speed as the underlying ontology database.
AI collaboration increasingly spans untrusted, heterogeneous nodes from edge devices to multi-clouds raising acute concerns around privacy, integrity, and verifiability of shared models and updates. This paper proposes a secure distributed computing framework that unifies privacy-preserving learning, verifiable coordination, and incentive-aligned governance for decentralized AI model sharing. The architecture composes federated and peer-to-peer training with secure aggregation, differential privacy, and hardware-backed confidential computing to prevent data leakage while mitigating gradient inversion risks. Model provenance, access control, and policy enforcement are anchored via a lightweight, append-only ledger with decentralized identifiers, enabling auditability without central authorities. To counter poisoning, backdoors, and Sybil attacks, the framework integrates robust aggregation, reputation-weighted participation, and update attestation with zero-knowledge proofs for selective disclosure. A resource-aware scheduler adapts to edge variability using gossip-based dissemination, opportunistic bandwidth utilization, and erasure-coded checkpoints to preserve liveness under churn. Interoperability is ensured through portable model artifacts (e.g., ONNX), secure enclaves for cross-framework execution, and privacy budgets tracked as first-class governance assets. We outline threat models, compliance hooks for jurisdictional constraints, and a token-free contribution accounting mechanism that rewards data quality and validation work. Simulated and real-world deployments illustrate improved end-to-end trust, reduced coordination overhead, and resilient performance under adversarial conditions, positioning the framework as a practical substrate for open, secure, and accountable AI collaboration in decentralized environments
Non-fungible tokens (âNFTsâ) are an emerging digital asset that has captured global attention with multi-million-dollar price tags for seemingly basic pixelated JPEG files. In March 2021, British auction house Christieâs sold a digital artwork, âEverydays: The First 5,000 Daysâ, by artist Mike Winkelmann (âBeepleâ) for the Ether equivalent of $69.3 million, making it the third-most expensive artwork by a living artist.1 Beepleâs sale was by no means aloneâSotheby's sold an NFT collection of 101 âBored Apesâ for $24.4 million;2 CryptoPunk #7804, one of 10,000 unique âCryptoPunkâ NFTs sold for $7.56 million,3 and Twitter founder Jack Dorseyâs first-ever tweet sold for $2.9 million as an NFT.4 NFT sales in the first-half of 2021 have already exceeded $2.5 billion,5 and, as of October 2021, the total value of NFTs on the Ethereum blockchain is estimated to be at least $14.3 billion.6 On one hand, NFTs may be poised to revolutionize creative industries and drastically alter consumer interaction with digital media.7 On the other hand, the NFT market is simultaneously both ripe for speculative investment and vulnerable to criminal activity.8 To date, there appears to be no consensus on the regulation of NFTs, neither from the perspective of generally applicable laws, regulatory capture under existing financial market regulation, nor the implementation of new digital asset laws. This paper attempts to highlight several pertinent dangers of NFTs, from a profound misunderstanding of what an NFT transaction entails, their bubble-like pricing, to various criminal activity concerns. By illustrating how existing laws and regulations may not fully capture nor address these dangers, as well as the potential oversight of NFTs in newly proposed digital asset laws, this paper proposes a categorial approach to regulating NFTs, by reducing the current (and likely future) use-cases of NFTs to their constituent categories and in turn, suggesting the most appropriate regulatory approach to each. Ultimately, given the (potential) wide-ranging use-cases of NFTs, this paper proposes that the NFTâs intended use-case described in broad categorical terms, or more aptly, its underlying reference asset and simultaneous conveyance, or lack thereof, should dictate the regulatory approach.
Abstract We report our experience in the formal verification of the reference implementation of the Beacon Chain. The Beacon Chain is the backbone component of the new Proof-of-Stake Ethereum 2.0 network: it is in charge of tracking information about the validators , their stakes , their attestations (votes) and if some validators are found to be dishonest, to slash them (they lose some of their stakes). The Beacon Chain is mission-critical and any bug in it could compromise the whole network. The Beacon Chain reference implementation developed by the Ethereum Foundation is written in Python, and provides a detailed operational description of the state machine each Beacon Chainâs network participant (node) must implement. We have formally specified and verified the absence of runtime errors in (a large and critical part of) the Beacon Chain reference implementation using the verification-friendly language Dafny. During the course of this work, we have uncovered several issues, proposed verified fixes. We have also synthesised functional correctness specifications that enable us to provide guarantees beyond runtime errors. Our software artefact with the code and proofs in Dafny is available at https://github.com/ConsenSys/eth2.0-dafny .
In this piece, we overview Isaac Asimov's most iconic work, the Foundation series, with two primary goals: to provide quantitative insights about the novels and bridge data science with digital art. First, we rely on data science and text processing tools to describe certain properties of Asimov's career and the novels, focusing on the different worlds in Asimov's universe. Then we transform the books' texts into a network centered around Asimov's planets and their semantic context. Finally, we introduce the world of crypto art and non-fungible tokens (NFTs) by transforming the visualized network into a high-end digital piece of art minted as an NFT. Additionally, to pay tribute to Asimov's devotion to robotics and artificial intelligence, we use OpenAI's Generative Pre-trained Transformer 3 (GPT-3) to draft several paragraphs of this paper.
BACKGROUND: An image sharing framework is important to support downstream data analysis especially for pandemics like Coronavirus Disease 2019 (COVID-19). Current centralized image sharing frameworks become dysfunctional if any part of the framework fails. Existing decentralized image sharing frameworks do not store the images on the blockchain, thus the data themselves are not highly available, immutable, and provable. Meanwhile, storing images on the blockchain provides availability/immutability/provenance to the images, yet produces challenges such as large-image handling, high viewing latency while viewing images, and software inconsistency while storing/loading images. OBJECTIVE: This study aims to store chest x-ray images using a blockchain-based framework to handle large images, improve viewing latency, and enhance software consistency. BASIC PROCEDURES: We developed a splitting and merging function to handle large images, a feature that allows previewing an image earlier to improve viewing latency, and a smart contract to enhance software consistency. We used 920 publicly available images to evaluate the storing and loading methods through time measurements. MAIN FINDINGS: The blockchain network successfully shares large images up to 18 MB and supports smart contracts to provide code immutability, availability, and provenance. Applying the preview feature successfully shared images 93% faster than sharing images without the preview feature. PRINCIPAL CONCLUSIONS: The findings of this study can guide future studies to generalize our framework to other forms of data to improve sharing and interoperability.
Scientific publication and its Peer Review system strongly rely on a few major industry players controlling most journals (e.g. Elsevier), databases (e.g. Scopus) and metrics (e.g. JCR Impact Factor), while keeping most articles behind paywalls. Critics to such system include concerns about fairness, quality, performance, cost, unpaid labor, transparency, and accuracy of the evaluation process. The Open Access movement has tried to provide free access to the published research articles, but most of the aforementioned issues remain. In such context, decentralized technologies such as blockchain offer an opportunity to experiment with new models for scientific production and dissemination relying on a decentralized infrastructure, aiming to tackle multiple of the current system shortcomings. This paper makes a proposal for an interoperable decentralized system for an open peer review ecosystem, relying on emerging distributed technologies such as blockchain and IPFS. Such system, named âDecentralized Scienceâ (DecSci), aims to enable a decentralized reviewer reputation system, which relies on an Open Access by-design infrastructure, together with transparent governance processes. Two prototypes have been implemented: a proof-of-concept prototype to validate DecSciâs technological feasibility, and a Minimum Viable Product (MVP) prototype co-designed with journal editors. In addition, three evaluations have been carried out: an exploratory survey to assess interest on the issues tackled; two sets of interviews to confirm both the main problems for editors and to validate the MVP prototype; and a cost analysis of the main operations, both execution cost and actual price. Additionally, the paper discusses the multiple interoperability challenges such proposal faces, including an architecture to tackle them. This work finishes with a review of some of the open challenges that this ambitious proposal may face.
An Internet of Things (IoT) scenario is a heterogeneous and complex environment, where large volumes of data are constantly generated, manipulated, and transferred between different devices. In this context, some difficulties may arise, such as the correct identification of the devices generating the data, the trustworthiness of these devices and their generated data, detecting abnormal behavior, and controlling access to the data. Data provenance allows maintaining information about the origin of the data, the operations through which this data has undergone, and its processing history, from its creation to its current state. Aiming to provide means to mitigate the mentioned problems, we propose an architecture for data provenance management in IoT environments, enabling different levels of granularity, using a distributed ledger architecture.
Eranga Bandara, Sachin Shetty, Deepak K. Tosh, Xueping Liang
Enterprise-level energy delivery systems (EDSs) depend on different software or hardware vendors to achieve operational efficiency. Critical components of these systems are typically manufactured and integrated by overseas suppliers, which expands the attack surface to adversaries with additional opportunities to infiltrate into EDSs. Due to this reason, the risk management of the EDS supply chain is crucial to ensure that we are knowledgeable about the vulnerabilities in software and hardware components that comprise any critical part, quantifiable risk metrics to assess the severity and exploitability of the attack, and provide remediation solutions that can influence a prioritized mitigation plan. There is a need to realize cyber supply chain risk management for industrial control systemsâ hardware, software, and computing and networking services associated with bulk electric system (BES) operations. This article proposes a blockchain-based cyber supply chain provenance platform (âVindâ) for EDSs to realize data provenance in a cyber supply chain ecosystem.
Hyeong Joon Kim, Hye Hyeon Kim, Hosuk Ku, Kyung Don Yoo · 11 authors
BACKGROUND: ) for data privacy and participatory medicine; however, its fully decentralized architecture has come at the expense of decentralized data management and data provenance. OBJECTIVE: The introduction of blockchain and smart contract technologies to the legacy Health Avatar Platform with a clinical metadata registry remarkably strengthens decentralized health data integrity and immutable transaction traceability at the corresponding data-element level in a privacy-preserving fashion. A crypto-economy ecosystem was built to facilitate secure and traceable exchanges of sensitive health data. METHODS: The Health Avatar Platform decentralizes patient data in appropriate locations (ie, on patients' smartphones and on physicians' smart devices). We implemented an Ethereum-based hash chain for all transactions and smart contract-based processes to guarantee decentralized data integrity and to generate block data containing transaction metadata on-chain. Parameters of all types of data communications were enumerated and incorporated into 3 smart contracts, in this case, a health data transaction manager, a transaction status manager, and an application programming interface transaction manager. The actual decentralized health data are managed in an off-chain manner on appropriate smart devices and authenticated by hashed metadata on-chain. RESULTS: Metadata of each data transaction are captured in a Health Avatar Platform blockchain node by the smart contracts. We provide workflow diagrams each of the 3 use cases of data push (from a physician app or an intelligent agents to a patient Avatar), data pull (request to a patient Avatar by other entities), and data backup transactions. Each transaction can be finely managed at the corresponding data-element level rather than at the resource or document levels. Hash-chained metadata support data element-level verification of data integrity in subsequent transactions. Smart contracts can incentivize transactions for data sharing and intelligent digital health care services. CONCLUSIONS: Health Avatar Platform and interconnected patient Avatars, physician apps, and intelligent agents provide a decentralized blockchain ecosystem for health data that enables trusted and finely tuned data sharing and facilitates health value-creating transactions with smart contracts.
In order to create a transparent and sound academic communication ecosystem centered on researchers, we developed a system that applied blockchain technology to an open peer review system. In this study, an open peer review system was developed based on Hyperledger Fabric, which is a private blockchain. The system can be operated in connection with the reviewer recommendation module of the existing submission management system. In the reviewer recommendation module, reviewers are recommended by excluding co-authors and colleagues after an expertise test. The blockchain system performs an open peer review process based on smart contracts, while the submission management system selects reviewers for peer review. A service broker intervenes between these two systems for data interchange. The system developed herein is expected to be used as a researcher-centered scholarly communication model in the open science era, in which the intervention of publishers is minimized, and authors and reviewers (as researchers) are centered.
In the past few decades, there has been a sharp rise of research irreproducibility and retraction, to a point that now is deemed as a crisis. Addressing this crisis, we present a peer-to-peer (P2P) publication model that utilizes blockchain and smart contract technologies. Focusing primarily on researchers and reviewers, the conceptual P2P publication model addresses the sociocultural and incentivization aspects of the irreproducibility crisis. In the P2P publication model, instead of a complete publication, a preapproved experimental design will be published on an incremental basis (unit-by-unit) and authorship will be shared with reviewers. The concept of the P2P publication model was inspired by the transformational journey the music publishing industry has undertaken as it traverses through vinyl age (complete albums) to the Spotify age (single-by-single), where there is a growing inclination among artists toward building an incremental album, taking account of feedback from fans and utilizing automated revenue collection and sharing systems. The ability to publish incrementally through the P2P publication model will relieve researchers from the burden of publishing complete and âgood resultsâ while simultaneously incentivizing reviewers to undertake rigorous review work to gain authorship credit in the research. The proposed P2P publication model aims to transform the century-old publication model and incentivization structure in alignment with open access publication ethos of the 21st century.
This personal reaction is written from multiple perspectives. First and foremost, as the corresponding author of the original FAIR article. Second as the chair of the first High Level Expert Group (HLEG) of European Open Science Cloud (EOSC) (which is how I met Jean-Claude) and third from my current GO FAIR and CODATA perspective. None of what I write below is to be seen as a formal position of any of the organisations I am associated with.Let me start by stating that, after some periods silent of hope and of deep despair, I now strongly feel that, with the governance of the EOSC Association in place, EOSC will become a success after all. It will still be critical that the Association involves the member states (MSs) and actual researchers in an agile and non-bureaucratic manner, for which we need bottom-up mechanisms such as operated by the Research Data Alliance (RDA) and GO FAIR. But a balancing formal entity operating along the formalised Strategic Research and Innovation Agenda [1] and the Partnership proposal as well as the various âdeclarationsâ including the recent one under the German presidency [2] are an excellent guiding roadmap to a successful EOSC, obviously in global context.That said, at the risk of sounding like broken record, this reaction should also look at the points where it went âalmostâ wrong, as we should try and learn from our mistakes. I may make some enemiesâor strengthen the opinion of existing onesâin the process, but then, a wise old friend, who also wrote one of the reactions once told me: âBarend, unless you made some enemies you probably lived in vain.â So I will speak my mind (âwhat's new'?). I also like to say that âEOSCâ brought me some real new friends for life!First of all, the fact that quickly after its inception FAIR became a hype termâ , which was probably partly even accelerated by the prominent role it played in early EOSC discussions with EC's Director General, also has its downsides. Like for the term âAIâ, everyone co-opts the term and some start watering the concept down to a bloodless caricature from what it originally meant. In the case of FAIR this includes removing the central notion of machine actionability, mis-characterising it as a standard, conflating it with âopenâ, only linking it to data sensu stricto, ignoring software, algorithms and more. In general terms, people that sometimes seem to have never read the original article [3], the most flagrant abuse of the term I have heard (obviously not from an active researcher) is this: âIf data are Findable, Accessible and Interoperable it is âautomatically' Reusable.â This is of course âswearing in FAIR churchâ as the R (principles R1â3) [3] clearly state that rich provenance and reuse conditions are critical and in particular the provenance. The decision whether (even high quality) data are fit for purpose (reuse in a particular study) is a critical step and is imho (in my humble opinion) at the basis of the reproducibility problem we currently face. Therefore, I would like to re-emphaisize here my current one liner to summarise the aim of the FAIR guiding principles: âThe Machine Knows what I meanâ. Those who feel that FAIR is too ambitious and for instance promote that âachieving F and A is enough for nowâ in my humble opinion fail to see the disruptive character of the solutions we need to make EOSC and its sister around the globe a real paradigm shift towards Open Science (OS). Or they are just trying to preserve the status quo and move incrementally at a pace they can follow.This nicely bridges to the first observation on EOSC as such. I indeed think that the first âCommunicationâ that needed 126 iterations mentioned by Jean-Claude, which happened in the same time frame as our âHLEG-1â period, was symptomatic for a basic flaw in the discussions, which haunts us still today. Conflating the âICTâ/HPC (or basic e-infrastructure) with the data and end user applications for analytics, has caused an enormous hurdle. In the entire journey of the HLEG we had to carefully navigate around this cliff and it is still a highly controversial issue today. This part was the âDunning Kruger effectâ [4] pur sang: The âother side is easyâ (because I am not hindered by any knowledge about it) and is âmore or less already doneâ (because I do not understand the complexity). This is not only true for the active researchers who cannot use the current e-infrastructure efficiently (and naturally that is âentirely the fault of the nerds who build things I do not understand or cannot operateâ), but also for e-infrastructure engineers who know everything about ICT and âthusâ (?) also about data (because âthat is just ones and zerosâ) as Jean- Claude also noted. I also believe however, that it is a mistake to completely separate e-infrastructure for the data and services layer, as the e-infrastructure should route (and understand at least at middleware level) what processes are needed on the data and how the FAIR services ârunâ. Nowadays (after many iterations) I use the diagram below (Figure 1) to explain that all three basic elements of the âInternet of FAIR Data and Servicesâ are needed. Each of them should be adorned with FAIR (machine actionable) metadata to seamlessly form a Web of FAIR Data and Services on top of the current, proven Internet backbones, thus forming the âInternet of FAIR Data and Servicesâ, eventually creating an âInternet for Social Machinesâ [5] where people and machines can both efficiently use all services, independently and in collaboration.This does absolutely not mean that the foundation (e-infrastructure) of the triangle is âtrivialâ or âcan be reused as isâ. Not only middleware, but also the crucial and fundamental concept of FDOs needs to be developed in close collaboration between data and computer experts and is largely domain-agnostic.The seamless combination will become the principle âpackageâ of information that machines (and also people) can understand and act upon. Major infrastructure builders should actually co-lead this, while domain scientists need to decide on which data formats and metadata schemes (i.e. FAIR Implementation Profiles [9]) should be built on this basic schema.Together with the Dunning Kruger effect, too many overlapping and redundant projects supporting the talking/meeting/landscaping, re-landscaping and re-re landscaping' has resulted in what I became to call the âEOSC is a bigger Me syndromeâ. On the one hand, countless people voluntarily invested (and still invest) their time in the development of the EOSC, but others seem to only see EOSC as âyet another way to collect EC funding for their current solutions that are in my opinion not future- and OS proof. This misbalance between people investing their own time and effort based on intrinsic motivation and vision and on the other hand the âreliance on EC subsidyâ caused a dichotomy during the scoping years of EOSC between disruptive and âpreservativeâ approaches. The heavy reliance on EC subsidy also largely ignored the subsidiarity principle [10] and the fact that 90% of the eventual infrastructures and services that we need for EOSC will be paid by the MSs. Also data and research intensive industry was largely kept out of the loop, which was another mistake I have frequently pointed out. This helped to create and sustain the âBrussels Bubbleâ that Jean-Claude described. The Association will hopefully reverse that trend.Finally, the influence on the HLEG report of the then-commissioner was rather profound. The report was not only delayed almost 6 months after its proposed publication version, but there is also a nice additional âuntold storyâ here: The originally proposed title of the report was: âA Cloud on the 2020 Horizonâ. In my original foreword I explained the slightly âgloomingâ connotation of that title. When the report was finally approved, it appeared that the title had been unilaterally changed into âRealising A European Open Science Cloud [11]â. Not only did I have to hastily change my foreword (because it made no sense anymore) but also, my notorious statement that the âresultâ should neither be âEuropeanâ (only), nor Open (only) nor (only) for Science and certainly not (just) a âCloudâ was entirely ignored in changing that title. But it again emphasises the âThis is an EC thingâ context, with the associated risk for confiscation of the concept by the âusual suspectsâ in EC subsidy land. However, I feel after three years of intensive deliberations, which may be considered lightning fast on the geological time scale, see George's reaction, we can conclude that most of the original HLEG recommendations are well-represented in the basic guiding documents of the EOSC Association, which makes me a happy man at the end of this crazy year.That leads me to the final observation: As a result of the (quote from Jean-Claude): ânon-paper seen as the political turning point in support of EOSCâ [12], GO FAIR (Global Open FAIR) [13] was started, originally by Germany and The Netherlands and soon joined by France as a temporary âkick-startâ, bottom-up approach to accelerate EOSC (see also recommendation I-2.1. in the HLEG report, annex 1).Soon, GO FAIR became really global and the agile modus operandi of practical Implementation Networks yielded a number of crucial approaches to speed up the adoption of the FAIR guiding principles and the hourglass approach [14]. Now, late 2020, when the EOSC Association is a fact, GO FAIR (1.0) has achieved its goals (early implementation steps) and we need to reflect on its future. Next to the intrinsic value of the active GO FAIR IN community [15] as such, several particular assets that I need to mention here are the development of the FAIR Implementation Profile and Metadata4Machines approach, the development of easy to install FAIR data points for open, FAIR metadata publication and indexing, and last but not least the international effort (involving many players, also outside the direct GO FAIR initiative) to develop the minimal specs of the FDO framework [7] in a more specified form than when coined in the FAIR expert group report [5]. These assets (all open source and open access) can be carried over, not only to EOSC, but will have much wider, international, impact most likely leading to a continuation of GO FAIR (2.0) beyond its original time scope, namely three years, the predicted time it would take to complete the international policy and bureaucracy process to reach the status of a formal association as we have today. I hope the leaders of the Association will optimally learn from the successes and failures and near-road-accidents of the last three years and see EOSC as the European contribution to a âGlobal Open Science Commonsâ, also known as the Internet of FAIR Data and Services, in full, open collaboration with the international organisations that are now joining forces in the Data Together initiative [16]. After all, the major challenges we face are global, so is the research needed to face them and so are the solutions we hope to fiend. I fully trust the current leadership of the association to make that vision reality.Policy recommendationsGovernance recommendationsImplementation recommendations
Research and development of novel molecular compounds in the pharmaceutical industry can be highly costly. Lack of confidentiality can prevent a product from being patented or commercialized. As an effect, cross-organizational collaboration is virtually non-existent. In this paper, we introduce a blockchain-based solution to the collaborative drug discovery problem so that participants can maintain full ownership of the asset and upload partial information about molecules without revealing the molecule itself. A prototype is also implemented using the blockchain technology Hyperledger Fabric and analyzed from security and performance perspectives. The prototype provides a set of functionalities that makes sure that ownership is maintained, integrity is protected, and critical information remains confidential. From a performance perspective, it provides a good throughput and latency in the order of milliseconds. However, further improvements could be done to the scalability of the syst em.
Open access
Scientific Computing and Data Management
Innovative Microfluidic and Catalytic Techniques Innovation
Jan 1, 2021·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Nowadays, scientific experiments are conducted collaboratively. In collaborative scientific experiments, we must consider aspects such as interoperability, privacy, and trust in shared data to allow the reproducibility of the results. A critical aspect associated with a scientific process is its provenance information, which can be defined as the origin or lineage of the data that helps understand the scientific experiment results. Another concern when conducting collaborative experiments is confidentiality, considering that only authorized personnel can share or view results. In this paper, we propose BlockFlow, a blockchain-based architecture, to bring reliability to the collaborative research, considering the capture, storage, and analysis of provenance data related to a scientific ecosystem platform (E-SECO).