Today, scientific research is increasingly becoming data-centric and compute-intensive, relying on data and models across distributed sources. However, challenges still exist in the traditional cooperation mode, given the high storage and computing costs, geolocation barriers, and local confidentiality regulations. The Jupyter environment has recently emerged and evolved into a vital virtual research environment for scientific computing, which researchers can use to scale computational analyses up to larger datasets and high-performance computing resources. Nevertheless, existing approaches lack robust support of a decentralized cooperation mode to unlock the full potential of decentralized collaborative scientific research, e.g., seamlessly secure data sharing. In this work, we change the basic structure and legacy norms of current research environments via the seamless integration of Jupyter with Ethereum blockchain capabilities. As such, it creates a Decentralized Virtual Research Environment (D-VRE) from private computational notebooks to a decentralized collaborative research ecosystem. We propose a novel architecture for the D-VRE and prototype some essential D-VRE elements for enabling secure data sharing with decentralized identity, user-centric agreement-making, membership, and research asset management. To validate our method, we conduct an experimental study to test all functionalities of D-VRE smart contracts and their gas consumption. In addition, we deploy the D-VRE prototype on a test net of the Ethereum blockchain for demonstration. The feedback from the studies showcases the current prototype's usability, ease of use, and potential, and suggests further improvements.
I Gede Agus Krisna Warmayana, Yuichiro Yamashita, Nobuto Oka "Decentralized Materials Data Management using Blockchain, Non-Fungible Tokens, and Interplanetary File System in Web3" Journal of Applied Data Sciences, 2025, Vol.6, No.1, p.742-752 https://doi.org/10.47738/jads.v6i1.380 æČèŒ
Akwan Maroso, Dwi Shinta Angreni, Rizka Ardiansyah, Kadek Agus Dwiwijaya
This study investigates the application of blockchain technology in enhancing the security and authenticity of digital certificates. Addressing key challenges such as fraud and the lack of a standardized verification process, the paper proposes a comprehensive framework aimed at fortifying the integrity of digital credentials. This framework is the utilization of blockchain as a distributed ledger, serving as a tamper-proof repository for recording certification transactions. Through this decentralized ledger, each certification issuance and verification action is securely recorded, enhancing trust and transparency in the certification process. The methodology includes the integration of a decentralized ledger for immutable record-keeping and implementation of smart contracts for automated authenticity checks, and the use of cryptographic measures to ensure data security. This approach promises significant implications for various sectors reliant on credential verification, advocating for a broader adoption of blockchain in digital certificates systems.
This article discusses the issues with traditional scientific publishing and the solutions offered by DeSci. It covers problems caused by oligopoly in scientific publishing, author-reviewer-editor triangulation, predatory journals, and funding and resource allocation. These issues result in gated publishing, peer review bias, publish or perish culture, centralized funding, poor publication quality, low accessibility, low transparency, and low reproducibility. This study discusses DeSci's solutions to the aforementioned problems through blockchain technologies, including DAO, DBDAO, NFT, Zero-Knowledge Proofs, IP-NFT, and IPFS. Sample applications and projects are provided to illustrate these solutions. DeSci's solutions represent a transition from oligopoly in scientific production to the true Open Science era.
Abstract Scientific workflows are essential for many applications, enabling the configuration and execution of complex tasks across distributed resources. In this paper, we contribute an Ethereum blockchain-based scientific workflow execution manager, which distributes workflows to run on cluster computing providers that utilize the Slurm workload manager to execute them. We extended our blockchain-based autonomous resource broker called eBlocBroker, which is a DAO-based decentralized coordinator, by providing distributed workflow execution via blockchain. Through various tests, we demonstrate how our eBlockBroker autonomous organization, which is programmed as a smart contract, can manage scientific workflow submission, scheduling, and execution on cluster computing providers. The utilization of blockchain for distributed workflow execution is a new concept. We are motivated because our system has been developed with e-Science in mind where scientific workflows are widely utilized.
Soil is an indispensable resource with critical implications in various fields such as agriculture, environmental science, climate change, hydrology, ecology, and geoscience. Accuracy and accessibility of soil data are crucial for informed decision making. However, the sharing and harmonization of soil data present significant challenges, particularly owing to the lack of a comprehensive identification system that ensures privacy and stewardship in a federated data sharing framework. Moreover, the inherent heterogeneity of soil properties across space and time complicates the establishment of connections between soil profiles and their corresponding properties. To address these challenges, a novel and persistent soil-data identifier, called SoilPrint, akin to a fingerprint, was proposed. SoilPrint utilizes a mathematical algorithm to effectively integrate the properties of soil profile layers (SPLP) with Geohashes, providing an efficient solution. The incorporation of SoilPrint streamlines the data federation process within a secure and distributed ledger, eliminating the need for complex data mapping or alignment. This approach ensures data privacy throughout the sharing process and addresses concerns associated with data management. To demonstrate the practical applications of SoilPrint, a case study using soil data from Ontario, Canada was presented. The results underscored the unique identification capabilities of SoilPrint for soil profiles and their associated properties, establishing it a promising tool for soil data management. SoilPrint facilitates data tracking, reuse, and analysis, thereby enhancing the efficiency and effectiveness of soil-related research and decision-making processes.
Background: Rapid advancements in Distributed Ledger Technology (DLT), including blockchain, are foundational to a new era of digital innovation. This innovation has catalyzed the emergence of ‘Decentralized Science (DeSci),’ a new concept and movement that aims to address the challenges of modern science. Objective: Given the novelty of the field of DeSci, this study aims to provide a comprehensive definition of the term as well as explore and conceptualize shared values and guiding principles inherent to DeSci. Methods: In line with the objectives of this study, an exploratory literature review was conducted to identify and synthesize the scholarly and secondary literature. The search and selection process included six databases (PubMed, Google Scholar, Web of Science, IEEE Xplore, arXiv, and Social Science Research Network), and the search period was limited to the last 15 years, from 2008 to 2023. To identify relevant secondary literature, such as articles, reports, blog posts, and website content, a keyword search was conducted in three search engines (Google.com, Bing.com, and Yahoo.com). Owing to the novelty of the concept and movement of DeSci, the exploratory literature review was supplemented by an anonymous online-based expert survey using a combination of single-choice and open-ended questions. The experts were selected based on predefined inclusion criteria, in association with their activities in the field of DeSci. The responses to the single-choice questions were subject to statistical analysis, whereas the open-ended questions were analyzed using qualitative content analysis. Results: Seven studies were selected for evaluation as part of the search and selection process to identify relevant scholarly literature. Following the review of secondary literature, additional 24 publications were included in the analysis. In the expert survey, 39 valid datasets were collected and analyzed. Following the synthesis of the results of the exploratory literature review and expert survey, a comprehensive definition of the term ‘Decentralized Science’ (DeSci) was formulated to reflect recurring themes. As no publications that explicitly discussed or addressed the values or principles of DeSci in the exploratory literature review could be identified, a set of shared values and guiding principles for DeSci were defined based on the results of the expert survey. Conclusion: The results of this study underscore the emerging nature of DeSci, as evidenced by the limited availability of relevant information and scarcity of academic publications. While this study proposes a comprehensive definition of DeSci as well as a set of shared values and guiding principles, the results of this study highlight the importance of ongoing evaluation and validation. Furthermore, the results of this study indicate a clear need for future research in the field of DeSci, emphasizing its dynamic and developing nature.
Blockchain is a decentralized distributed ledger technology. The application of blockchain technology in the management and sharing of scientific research data in universities is conducive to improving the storage and sharing efficiency of scientific research data, and promoting the effective sharing and application of scientific research data across departments, institutions and regions. This paper analyzes the application status and problems of blockchain technology in the development of scientific research data sharing platform in colleges and universities, expounds the development ideas of scientific research data sharing mode based on blockchain technology, and proposes a digital educational resource sharing model with data standard model structure and process, data standard sharing and data storage as the main characteristics based an alliance chain. Experiments show that the proposed model has a high probability to ensure that the colleges and universities can reach a consensus and share scientific research data in alliance.
Rob J. Lewis, KjellâErik Marstein, JohnâArvid Grytnes
Research centred on understanding scientistsâ attitudes towards open data in ecology and evolution point to an increased acceptance of and willingness to engage in open data practices 1 , 2 , but also identifies common threads of concern which present barriers to data sharing . Mindsets concerning data as proprietary are common 3 , especially where data production is resource intensive 4 . Fears of competing research in concert with loss of exclusivity to hard earned data are pervasive 1 , 5 , 6 , 7 . This is for good reason given that current reward structures in academia focus overwhelmingly on journal prestige and high publication counts 8 , and not accredited publication of open datasets. And, then there exists reluctance of researchers to cede control to centralised repositories, citing concern over the lack of trust and transparency over the way complex data are used and interpreted 6 , 9 , 10 .
The successful operation of a blockchain system crucially relies on its management of software development activities. Not only does the technological functionality of a blockchain system depend on the quality of its source code, but also the âneutralityâ of a blockchain system depends on access to and control over its source code. In this paper, we delve into the dynamic evolution of collaboration within the open-source developer community of Ethereum, a leading blockchain platform today. Utilizing network analysis techniques, we demonstrate the tendency of initially decentralized open-source communities to gravitate toward local modular structures. Additionally, our analysis reveals that inputs to the blockchainâs software code are concentrated among a small group of contributors. Our findings have implications for the managers of decentralized open-source technology and policymakers who aim to preserve impartial access to technology.
Reagan Hoopes, Hamilton Hardy, Min Long, Gaby G. Dagher
Researchers collaborating from different locations need a method to capture and store scientific workflow provenance that guarantees provenance integrity and reproducibility. As modern science is moving towards greater data accessibility, researchers also need a platform for open access data sharing. We propose SciLedger, a blockchain-based platform that provides secure, trustworthy storage for scientific workflow provenance to reduce research fabrication and falsification. SciLedger utilizes a novel invalidation mechanism that only invalidates necessary provenance records. SciLedger also allows for workflows with complex structures to be stored on a single blockchain so that researchers can utilize existing data in their scientific workflows by branching from and merging existing workflows. Our experimental results show that SciLedger provides an able solution for maintaining academic integrity and research flexibility within scientific workflows.
This paper provides an update on the Canadian Humanities and Social Sciences (HSS) Commons, an in-development online hub for open social scholarship in Canada and beyond, and considers the next steps for the platform in an ever-evolving digital landscape. It outlines various recent outreach and engagement events intended to introduce the Canadian HSS Commons to the larger communities to which it belongs. Because the Canadian HSS Commons is committed to supporting the growth and evolving needs of these communities, this paper also considers how increasingly popular internet technologies such as Web3 and blockchain might play a part in the future of digital research infrastructure and the Canadian HSS Commons specifically. It concludes that while Web3 and blockchain currently raise important questions and concerns about governance, accountability, and commercialization, in the near future, these same technologies could also help engender new forms of functionality and participation on the Commons.
This article explores non-fungible tokens, better known as NFTs, or blockchain-based certificates of ownership for visual or physical assets, a cultural phenomenon which has come to the media attention following the sale of a non-fungible token photo collage, Everyday: The First 5,000 Days, for more than 69 million USD at a Christie's auction in March 2021. The sale made Mike Winkelman (aka Beeple) the third most valuable living artist, behind Jeff Koons and David Hockney starting off a conversation about NFTs which has covered a wide range of issues, including: the contested relationship between art and the market, the long debated question of collective and/or individual authorship within digital aesthetics, the most recent developments in Artificial Intelligence and their creative potential with regard to NFTs â i.e. the appearance of intelligent non-fungible tokens (iNFTs) â and, lastly, the role of cryptocurrencies as a tool for artistsâ empowerment or, conversely, as a selling out, under new technological guises, to the capitalist logic of the market. The article reviews and evaluates such debates with the aim to offer critical pointers to help the reader navigate the emerging world of Crypto art.
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.
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.
Provenance in computing systems is the key to establishing data integrity. It provides a historical ledger of data's life cycle through creation, ownership, consumption, and manipulation. With provenance in hand, it is possible to reverse engineer the state of the data that can lead to understanding how it was derived and verify its accuracy. This need for data integrity is extremely critical in scientific workflows to ensure verifiability and repeatability of the derived results. Due to the vast computational power required by scientific workflows, many operate within high performance computing (HPC) environments, where data is consumed and manipulated by a multitude of processes running on highly distributed infrastructure. The current landscape of HPC environments range from on-premise systems to cloud and grid based solutions. While the majority of research in digital provenance has been focused on standalone HPC environments, provenance in a heterogeneous HPC environment remains a challenge. In this paper we propose HyperProvenance, a high level system architecture especially for next generation heterogeneous HPC environments, which aims to increase confidence in workflow result accuracy through secure provenance collection.
Kevin Wittek, Neslihan Wittek, James H. Lawton, Iryna Dohndorf · 6 authors
The traditional Proof of Existence blockchain service on the Bitcoin network can be used to verify the existence of any research data at a specific point of time, and to validate the data integrity, without revealing its content. Several variants of the blockchain service exist to certify the existence of data relying on cryptographic fingerprinting, thus enabling an efficient verification of the authenticity of such certifications. However, nowadays research data is continuously changing and being modified through different processing steps in most scientific research workflows such that certifications of individual data objects seem to be constantly outdated in this setting. This paper describes how the blockchain and distributed ledger technology can be used to form a new certification model, that captures the research process as a whole in a more meaningful way, including the description of the used data through its different stages and the associated computational pipeline, code for analysis and the experimental design. The scientific blockchain infrastructure bloxberg, together with a deep learning based analysis from the behavioral science field are used to show the applicability of the approach.
The state-of-the-art for auditing and reproducing scientific applications on high-performance computing (HPC) systems is through a data provenance subsystem. While recent advances in data provenance lie in reducing the performance overhead and improving the user's query flexibility, the fidelity of data provenance is often overlooked: there is no such way to ensure that the provenance data itself has not been fabricated or falsified. This paper advocates leveraging blockchains to deliver immutable and autonomous data provenance services such that scientific discoveries are trustworthy. The challenges for adopting blockchains to HPC include designing a new blockchain architecture compatible with the HPC platforms and, more importantly, a set of new consensus protocols for scientific applications atop blockchains. To this end, we have designed the proof-of-scalable-traceability (POST) protocol and implemented it in a blockchain prototype, namely SciChain, the very first practical blockchain system for provenance services on HPC. We evaluated SciChain by comparing it with multiple state-of-the-art systems; experimental results showed that SciChain guaranteed trustworthy data provenance while incurring orders of magnitude lower overhead than existing solutions.
Scientific Computing and Data Management
Innovative Microfluidic and Catalytic Techniques Innovation
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
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).
Key points The integration of crypto assets into the traditional journal model offers potential for both user incentives and financial security, especially for new or small journals. The case study reveals low uptake by users, which may be attributed to a resistance to new technology and underestimation of the incentives required. Implementation of cryptocurrencies is likely to be more successful if it is implemented by many journals and in an open environment for publishers.