Open Standards Research Articles

Open Science Standards at Journals that Inform Evidence-Based Policy.

Evidence-based policy uses intervention research to inform consequential decisions about resource allocation. Research findings are often published in peer-reviewed journals. Because detrimental research practices associated with closed science are common, journal articles report more false-positives and exaggerated effect sizes than would be desirable. Journal implementation of standards that promote open science-such as the transparency and openness promotion (TOP) guidelines-could reduce detrimental research practices and improve the trustworthiness of research evidence on intervention effectiveness. We evaluated TOP implementation at 339 peer-reviewed journals that have been used to identify evidence-based interventions for policymaking and programmatic decisions. Each of ten open science standards in TOP was not implemented in most journals' policies (instructions to authors), procedures (manuscript submission systems), or practices (published articles). Journals implementing at least one standard typically encouraged, but did not require, an open science practice. We discuss why and how journals could improve implementation of open science standards to safeguard evidence-based policy.

Aggregation of Italian Lichen Data in ITALIC 7.0.

The creation of a coordinated publishing and aggregation system of biodiversity data is a challenging task, which calls for the adoption of open data standards. ITALIC, the information system on Italian lichens, originated from the conversion of the first Italian checklist into a database. While the first version was "frozen", the present version is continuously updated and provides access to several other data sources and services, such as ecological indicator values, ecological notes and information, traits, images, digital identification keys, etc. The identification keys especially are an ongoing work that will lead to a complete national flora by 2026. Last year, new services were added, one for aligning lists of names with the national checklist, the other for aggregating occurrence data deriving from the digitization of 13 Italian herbaria, forming a total of ca. 88,000 records, which are distributed under a CC BY license and can be exported as CSV files in the Darwin Core format. An aggregator for lichen data will encourage the national community of lichenologists to produce and aggregate further data sets, and it will stimulate data reuse according to the paradigms of open science.

Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging.

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

Machine learning approaches for improving integration of advanced sensors on legacy aircraft

AbstractRapid integration of advanced sensors onto legacy military aircraft is critical for maintaining technological advantage in warfighting domains. Integration of these sensors is accomplished through upgrade programs that often fail during integration due to defect discovery and interoperability issues. Existing Department of Defense initiatives related to open architectures have improved sensor integration but have not eliminated the need for custom interface software to account for behavioral disparities across different sensors. The subject research proposes that reinforcement machine learning algorithms can be applied to aircraft sensor interfaces during integration and verifies effectiveness by training and testing Greedy, Q‐Learning, Deep Q‐Learning, Double Deep Q‐Learning, and Instance‐Based Learning algorithms against modeled Global Positioning System (GPS), Optical, Light Detection and Ranging (LIDAR), and Infrared sensor functions. The results are useful to open architecture standards management groups, sensor vendors, and systems and software engineers who are developing strategies and designs to accelerate subsystem integration timelines by reducing failures discovered during integration.

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simulating the whole rodent brain in the Swiss EPFL Blue Brain Project (BBP) required a solution to managing large-scale highly heterogeneous data, and tracking their provenance to ensure quality, reproducibility and attribution throughout these iterative cycles. Here, we describe Blue Brain Nexus (BBN), an ecosystem of open source, domain agnostic, scalable, extensible data and knowledge graph management systems built by BBP to address these challenges. BBN builds on open standards and interoperable semantic web technologies to enable the creation and management of secure RDF-based knowledge graphs validated by W3C SHACL. BBN supports a spectrum of (meta)data modeling and representation formats including JSON and JSON-LD as well as more formally specified SHACL-based schemas enabling domain model-driven runtime API. With its streaming event-based architecture, BBN supports asynchronous building and maintenance of multiple extensible indices to ensure high performance search capabilities and enable analytics. We present four use cases and applications of BBN to large-scale data integration and dissemination challenges in computational modeling, neuroscience, psychiatry and open linked data.

From the Five Freedoms to a more holistic perspective on animal welfare in the Dutch Animals Act

One of the aims of the Dutch Animals Act is to protect animal welfare. The assumption that animal welfare risks are managed and mitigated by the Act, however, has not been studied before. The aim of this paper is to evaluate whether or not animal welfare risks can be managed adequately under the current Dutch Animals Act and what modifications to the legislation could improve animal welfare in the Netherlands. For that purpose, welfare consequences identified in various supply chain risk assessments from the Office of Risk Assessment & Research (BuRO) were assessed in conjunction with the Dutch Animals Act and related legislation. A distinction was made between means- and goal-oriented legislation. The current Dutch Animals Act uses the “Five Freedoms” to define animal welfare. However, this seems outdated, given that current scientific insight also indicates that positive experiences should be included as an integral part of animal welfare. Currently, most welfare consequences in supply chains are linked to factors that are regulated by Dutch legislation as qualitative, goal-oriented, open standards. Furthermore, there is no species-specific legislation for some of the most common farm animals in the Netherlands, e.g., dairy cattle. By applying the latest scientific insights, both the current Dutch Animals Act and associated legislation can be improved to more appropriately manage animal welfare risks. As suggestions for improvement, we propose that the definition of animal welfare in the Dutch Animals Act is updated, that species-specific legislation for farm animals is developed where not already applicable, and that animal-based measures (ABMs) are integrated into legislation. As amendments to animal welfare legislation at the European level are currently being developed, our proposals to include the most recent scientific insights in animal welfare legislation also hold for European legislators.

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

UD‐SV: Urban data services and visualization framework for sharing multidisciplinary research

AbstractWith the widespread availability of a large volume of urban data, stakeholders from different domains require advanced tools to manage, visualize and understand cities and their evolution. During the last few years, researchers have proposed numerous research works and applications to illustrate the cities of the past and possible scenarios of the future under different conditions. However, many of these approaches are one‐time solutions and not based on standards, making them obsolete and unusable for reproducible research. In this article, we present UD‐SV: an Urban data‐Services and Visualization open‐source framework for multidisciplinary research to handle complex processing, analysis, and visualization of urban data. However, our goal is not to present a one‐time monolithic software solution for urban data management and analysis, but we demonstrate the design and development of an open and interoperable software framework driven by use cases from diverse users to solve applied research challenges. The main contribution of UD‐SV is that it uses open standards and open data with documented and reproducible processes with a particular emphasis on the reuse of existing open‐source software components. We also show an enhanced use of standards to enable a shift toward components that are interchangeable or composable with other existing components in the GIS community.

Application of MES/MOM for Industry 4.0 supply chains: A cross-case analysis

Manufacturing is facing challenges in integrating information technology (IT) with operational technology (OT) and implementing Industrial Internet of Things (IIoT) concepts in the industry to increase manufacturing flexibility. This paper addresses the research gap in designing and using next-generation manufacturing execution systems (MES)/manufacturing operations management (MOM) in IIoT to improve manufacturing flexibility through reconfigurability. For this, we follow an abductive research design and build on the literature on Industry 4.0′s information architectures and models to propose a framework for building smart factory capabilities. Using the framework, we collect empirical data on MES/MOM implementation objectives for smart factories for six case studies conducted over a 4-year research project in Denmark (2018–2021), primarily through semi-structured interviews.Through cross-case analysis, we identify seven dominant themes that capture focus areas for MES/MOM implementation for IT/OT integration. We use these findings to present generalized design recommendations for IIoT-connected MES/MOM to support reconfigurability for Industry 4.0 supply chains. Our findings indicate that despite considerable investments from many companies in Industry 4.0 initiatives such as artificial intelligence-based analytics and digital twins, the industry is not yet in a state to extract the data from all its legacy production equipment. Therefore, we present design recommendations to enable Industry 4.0 supply chains with IIoT-connected MES/MOM by using the data from OT devices. Our analysis helps us conclude that open standards and open application programming interfaces (APIs) are key requirements for enhancing IIoT interconnectivity and interoperability to achieve end-to-end integration in supply chains.

Automated Knowledge Organization AI ML based Subject Indexing System for Libraries

The research study as reported here is an attempt to explore the possibilities of an AI/ML-based semi-automated indexing system in a library setup to handle large volumes of documents. It uses the Python virtual environment to install and configure an open source AI environment (named Annif) to feed the LOD (Linked Open Data) dataset of Library of Congress Subject Headings (LCSH) as a standard KOS (Knowledge Organization System). The framework deployed the Turtle format of LCSH after cleaning the file with Skosify, applied TF-IDF as a language model (backend algorithm), and selected Snowball as an analyzer. The training of Annif was conducted with a large set of bibliographic records populated with subject descriptors (MARC tag 650$a) and indexed by trained LIS professionals. The training dataset is first treated with MarcEdit to export it in a format suitable for OpenRefine, and then in OpenRefine it undergoes many steps to produce a bibliographic record set suitable to train Annif. The framework, after training, has been tested with a bibliographic dataset to measure indexing efficiencies, and finally, the automated indexing framework is integrated with a data wrangling software (OpenRefine) to produce suggested headings on a mass scale. The entire framework is based on open source software, open datasets, and open standards.

Digital Banking Transformation Through Cooperation with Fintech Startups in Ukraine

The purpose of the article/hypothesis: Is to identify the peculiarities of Fintech startups activities, to track trends and perspectives of interaction between classic banks and Fintech companies, as well as to research the activities of neobanks and formulate proposals for improving the Fintech ecosystem in Ukraine with the aim of providing quality digital financial services to various categories of consumers.
 Methodology: Methods of statistical analysis, analogy, synthesis and theoretical generalization were used when researching the materials of specialized analytical agencies, official websites of foreign and domestic banks, state financial strategies and scientific literature.
 Results of the research: The active growth of a number of Fintech startups in the world and in Ukraine forms a positive dynamic of the digital transformation of the banking sector. The cooperation between banks and Fintechs allows the use new innovative products in banking, such as artificial intelligence, robo-consultation, blockchain, cryptocurrencies, the Internet of Things, virtual and augmented reality. For the further digital transformation of banking in Ukraine the financial market participants must implement international open banking standards and tools for remote identification and verification of clients, actively introduce artificial intelligence and machine learning tools into finance, and develop a Fintech ecosystem.

The digital commons, cosmolocalism, and open cooperativism: The cases of P2P Lab and Tzoumakers

The digital commons support novel organizational models such as cosmolocalism and open cooperativism that seek to challenge the capitalist mode of production. They set out to establish a counter-hegemony vis-à-vis the current hegemony of neoliberalism. The paper engages in the debate between Marinus Ossewaarde, Wessel Reijers and Vasilis Kostakis over the emancipatory potential of the digital commons by reviewing the P2P Lab and Tzoumakers as illustrative cases of cosmolocalism and open cooperativism. The paper shows that the P2P Lab and Tzoumakers exhibit core features of cosmolocalism and prefigure a sketch of open cooperativism. For the digital commons in general and P2P Lab/Tzoumakers in particular to contribute to the counter-hegemony of open cooperativism, it is necessary to link to a chain of equivalence criss-crossing the commons, ethical market entities and a partner state via cross-sectoral value propositions, inclusive governance, and economic models, innovative law policies and open sustainability standards, all aiming to force capitalism adjust to a commons-oriented post-capitalist transition.

Metaverse governance: An empirical analysis of voting within Decentralized Autonomous Organizations

In this paper we explore the importance of platform governance. We discuss various problems of centralized architecture in the context of the metaverse or sharing economy applications which may lead to monopoly market structures. We argue that open standards and blockchain-based governance can potentially mitigate some of these issues. We then collect governance data from the first blockchain-based virtual world and conduct an empirical analysis to study voter behavior within Decentralized Autonomous Organizations (DAOs). We provide empirical evidence that open standards and blockchain-based governance are a necessary but not a sufficient condition for a decentralized and neutral platform. Centralization and concentrated voting power may lead to dependencies, rent extraction behavior and create hold-up problems. Consequently, producers, prosumers and service providers must evaluate the governance structure of the platform before establishing a presence.

Automation of a National Noise Model

The United Kingdom Department for Food and Rural Affairs (Defra) commissioned the design and build of an environmental noise modelling system (NMS). The NMS has been developed to support Defra in developing its environmental noise evidence base by preparing national road and railway noise models. This paper discusses the automation aspects of the NMS with respect to the various elements required for a noise model. This includes the development of sophisticated data processing engines and the techniques utilised to automate the delivery of a 3-dimensional noise propagation environment, and road and railway noise emissions. It highlights several innovative methods which are now possible due to the data landscape in England and how through open data standards the NMS has the potential to improve the fidelity of national noise models.

OpTIFlow – An optimized end-to-end dataflow for accelerating deep learning workloads on heterogeneous SoCs

A typical edge compute SoC capable of handling deep learning workloads at low power is usually heterogeneous by design. It typically comprises multiple initiators such as real-time IPs for capture and display, hardware accelerators for ISP, computer vision, deep learning engines, codecs, DSP or ARM cores for general compute, GPU for 2D/3D visualization. Every participating initiator transacts with common resources such as L3/L4/DDR memory systems to seamlessly exchange data between them. A careful orchestration of this dataflow is important to keep every producer/consumer at full utilization without causing any drop in real-time performance which is critical for automotive applications. The software stack for such complex workflows can be quite intimidating for customers to bring-up and more often act as an entry barrier for many to even evaluate the device for performance. In this paper we propose techniques developed on TI’s latest TDA4V-Mid SoC, targeted for ADAS and autonomous applications, which is designed around ease-of-use but ensuring device entitlement class of performance using open standards such as DL runtimes, OpenVx and GStreamer.

Filling the Gap between Research and Market: Portable Architecture for an Intelligent Autonomous Wheelchair.

Under the umbrella of assistive technologies research, a lot of different platforms have appeared since the 1980s, trying to improve the independence of people with severe mobility problems. Those works followed the same path coming from the field of robotics trying to reach users' needs. Nevertheless, those approaches rarely arrived on the market, due to their specificity and price. This paper presents a new prototype of an intelligent wheelchair (IW) that tries to fill the gap between research labs and market. In order to achieve such a goal, the proposed solution balances the criteria of performance and cost by using low-cost hardware and open software standards in mobile robots combined together within a modular architecture, which can be easily adapted to different profiles of a wide range of potential users. The basic building block consists of a mechanical chassis with two electric motors and a low-level electronic control system; driven by a joystick, this platform behaves similar to a standard electrical wheelchair. However, the underlying structure of the system includes several independent but connected nodes that form a distributed and scalable architecture that allows its adaptability, by adding new modules, to tackle autonomous navigation. The communication among the system nodes is based on the controller area network (CAN) specification, an extended standard in industrial fields that have a wide range of low-cost devices and tools. The system was tested and evaluated in indoor environments and by final users in order to ensure its usability, robustness, and reliability; it also demonstrated its functionality when navigating through buildings, corridors, and offices. The portability of the solution proposed is also shown by presenting the results on two different platforms: one for kids and another one for adults, based on different commercial mechanical platforms.

A full-STAC remedy for global digital health transformation: open standards, technologies, architectures and content

Abstract The global digital health ecosystem is project-centric: point solutions are developed for vertical health programs and financed through vertical funding allocations. This results in data fragmentation and technology lock-in, compromising health care delivery. A convergence of trends enabled by interoperability and digital governance makes possible a shift towards person-focused health. Together, open Standards, open Technologies, open Architectures and open Content represent a next-generation ‘full-STAC’ remedy for digital health transformation. Local developers and implementers can avoid reinventing the wheel, and instead build digital tools suited to local needs—where data travels with an individual over time, evidence-based practice is easily integrated, and insights are gleaned from harmonized data. This is the culmination of the vision endorsed by 194 WHO Member States in the Global Strategy on Digital Health 2020 to 2025.

Non-Deterministic Bitwise Programming to Solve the Eurobalise Telegram Encoding Problem

Eurobalise is a transmitter placed on railroad tracks to transmit information on a railway state to a passing train. The transmitted messages are here called Eurobalise telegrams. To transmit telegrams over the air, it is necessary to provide error-resistant encoding and corresponding decoding, which is carried out in accordance with an open standard. In this article, we continue our study of encoding such telegrams using languages ​​with formal semantics intended for verification purposes. We present ways to apply non-deterministic programming for this task and discuss issues of implementing an encoder in the Promela language.

METHODOLOGICAL RECOMMENDATIONS REGARDING THE USE OF SERVICES OF OPEN SCIENCE CLOUD-ORIENTED SYSTEMS IN THE PROCESS OF TRAINING TEACHERS OF NATURAL AND MATHEMATICAL SUBJECTS TO WORK IN SCIENTIFIC LYCEUMS

The statement of basic materials. The open science cloud-oriented systems allow solving a wide range of tasks in the course of scientific research, supporting joint work, collaboration, cooperation, high standards of transparency and openness, search for new information and presentation of scientific results. The article deals with the recommendations for the use of a separate group of services of OSCOS – scientometric and abstract databases. The training for improving the qualifications of teachers is described and practical tasks are offered.

An Orchestrated Game Streaming System for Transport and Mobility Research

A game streaming engine called laGGer based on open source technologies, standards and protocols that we have implemented is presented and analyzed. We provide the system's architecture based on multi agent systems and insights from the design and development process of the complex system. We argue that game streaming systems provide us with new and unseen possibilities in transport and mobility research and provide a number of use-cases on how such systems can facilitate emerging applications.