1,612 research outputs found

    Mapping the Focal Points of WordPress: A Software and Critical Code Analysis

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    Programming languages or code can be examined through numerous analytical lenses. This project is a critical analysis of WordPress, a prevalent web content management system, applying four modes of inquiry. The project draws on theoretical perspectives and areas of study in media, software, platforms, code, language, and power structures. The applied research is based on Critical Code Studies, an interdisciplinary field of study that holds the potential as a theoretical lens and methodological toolkit to understand computational code beyond its function. The project begins with a critical code analysis of WordPress, examining its origins and source code and mapping selected vulnerabilities. An examination of the influence of digital and computational thinking follows this. The work also explores the intersection of code patching and vulnerability management and how code shapes our sense of control, trust, and empathy, ultimately arguing that a rhetorical-cultural lens can be used to better understand code\u27s controlling influence. Recurring themes throughout these analyses and observations are the connections to power and vulnerability in WordPress\u27 code and how cultural, processual, rhetorical, and ethical implications can be expressed through its code, creating a particular worldview. Code\u27s emergent properties help illustrate how human values and practices (e.g., empathy, aesthetics, language, and trust) become encoded in software design and how people perceive the software through its worldview. These connected analyses reveal cultural, processual, and vulnerability focal points and the influence these entanglements have concerning WordPress as code, software, and platform. WordPress is a complex sociotechnical platform worthy of further study, as is the interdisciplinary merging of theoretical perspectives and disciplines to critically examine code. Ultimately, this project helps further enrich the field by introducing focal points in code, examining sociocultural phenomena within the code, and offering techniques to apply critical code methods

    Smart city governance from an innovation management perspective: Theoretical framing, review of current practices, and future research agenda

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    Smart city transitions are a fast-proliferating example of urban innovation processes, and generating the insight required to support their unfolding should be a key priority for innovation scholars. However, after decades of research, governance mechanisms remain among the most undertheorized and relatively overlooked dimensions of smart city transitions. To address this problem, we conduct a systematic literature review that connects the fragmented knowledge accumulated through the observation of smart city transition dynamics in 6 continents, 43 countries, and 146 cities and regions. Our empirical work is instrumental in achieving a threefold objective. First, we assemble an overarching governance framework that expands the theoretical foundations of smart city transitions from an innovation management perspective. Second, we elaborate on this framework by providing a thorough overview of documented governance practices. This overview highlights the strengths and weaknesses in the current approaches to the governance of smart city transitions, leading to evidence-based strategic recommendations. Third, we identify and address critical knowledge gaps in a future research agenda. In linking innovation theory and urban scholarship, this agenda suggests leveraging promising cross-disciplinary connections to support more intense research efforts probing the interaction patterns between institutional contexts, urban digital innovation, and urban innovation ecosystems

    Incremental schema integration for data wrangling via knowledge graphs

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    Virtual data integration is the current approach to go for data wrangling in data-driven decision-making. In this paper, we focus on automating schema integration, which extracts a homogenised representation of the data source schemata and integrates them into a global schema to enable virtual data integration. Schema integration requires a set of well-known constructs: the data source schemata and wrappers, a global integrated schema and the mappings between them. Based on them, virtual data integration systems enable fast and on-demand data exploration via query rewriting. Unfortunately, the generation of such constructs is currently performed in a largely manual manner, hindering its feasibility in real scenarios. This becomes aggravated when dealing with heterogeneous and evolving data sources. To overcome these issues, we propose a fully-fledged semi-automatic and incremental approach grounded on knowledge graphs to generate the required schema integration constructs in four main steps: bootstrapping, schema matching, schema integration, and generation of system-specific constructs. We also present NextiaDI, a tool implementing our approach. Finally, a comprehensive evaluation is presented to scrutinize our approach.This work was partly supported by the DOGO4ML project, funded by the Spanish Ministerio de Ciencia e Innovación under project PID2020-117191RB-I00, and D3M project, funded by the Spanish Agencia Estatal de Investigación (AEI) under project PDC2021-121195-I00. Javier Flores is supported by contract 2020-DI-027 of the Industrial Doctorate Program of the Government of Catalonia and Consejo Nacional de Ciencia y Tecnología (CONACYT, Mexico). Sergi Nadal is partly supported by the Spanish Ministerio de Ciencia e Innovación, as well as the European Union – NextGenerationEU, under project FJC2020-045809-I.Peer ReviewedPostprint (published version

    Improving the Quality and Utility of Electronic Health Record Data through Ontologies

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    The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs

    Management of socio-economic transformations of business processes: current realities, global challenges, forecast scenarios and development prospects

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    The authors of the scientific monograph have come to the conclusion that мanagement of socio-economic transformations of business processes requires the use of mechanisms to support of entrepreneurship, sectors of the national economy, the financial system, and critical infrastructure. Basic research focuses on assessment the state of social service provision, analysing economic security, implementing innovation and introducing digital technologies. The research results have been implemented in the different models of costing, credit risk and capital management, tax control, use of artificial intelligence and blockchain. The results of the study can be used in the developing of policies, programmes and strategies for economic security, development of the agricultural sector, transformation of industrial policy, implementation of employment policy in decision-making at the level of ministries and agencies that regulate the management of socio-economic and European integration processes. The results can also be used by students and young scientists in the educational process and conducting scientific research on global challenges and creation scenarios for the development of socio-economic processes

    Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the AgBioData Consortium

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    Over the last several decades, there has been rapid growth in the number and scope of agricultural genetics, genomics and breeding (GGB) databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as 'databases' throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, conducted a survey to assess the status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data sharing practices by AgBioData databases are in a healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that ontology use has not substantially changed since a similar survey was conducted in 2017. We recommend 1) providing training for database personnel in specific data sharing techniques, as well as in ontology use; 2) further study on what metadata is shared, and how well it is shared among databases; 3) promoting an understanding of data sharing and ontologies in the stakeholder community; 4) improving data sharing and ontologies for specific phenotypic data types and formats; and 5) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means.Comment: 17 pages, 8 figure

    Politiques des architectures numériques. Cheminements ethnographiques dans la conception d'alternatives à la centralisation des données

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    La thèse porte sur les architectures des systèmes et plateformes numériques dont elle s’attache à montrer la portée politique. Elle s'appuie sur un travail de terrain d'un an et demi dans le secteur agricole suisse, où deux projets de plateformes numériques sectorielles pour la gestion des données, l'une centralisée et l'autre distribuée, se sont opposés. Elle comprend cinq articles. Le 1er présente la démarche d’enquête, celle d'un discours non pas sur, mais avec les acteurs de terrain, dont l'ambition est de contribuer à la critique du discours des sciences sociales. Les articles 2 à 4 décrivent l'argument principal de la thèse. Les architectures des systèmes numériques sont politiques parce qu'elles incarnent des structures de gouvernance (article 2). Les architectures sont également politiques dans le sens où elles peuvent être utilisées pour favoriser ou prévenir des asymétries dans les relations de dépendance entre les acteurs appelés à utiliser ces systèmes (article 3). Enfin, elles sont politiques dans le sens où elles peuvent perturber les pratiques associées aux données des acteurs, jusqu'à menacer leur autonomie (article 4). Le 5ème et dernier article revient sur la notion d'architecture numérique et montre la portée d'une perspective processuelle sur celle-ci. L'originalité des contributions de la thèse réside dans cette perspective processuelle et sectorielle des architectures numériques. Elle ouvre sur un questionnement d'actualité sur le rôle des plateformes, et en particulier des plateformes alternatives, c'est-à-dire ne reposant ni sur la centralisation ni sur la standardisation des données, pour la constitution d'infrastructures informationnelles. -- The thesis focuses on the architectures of digital systems and platforms and explores their political significance. It is based on a year and a half of fieldwork in the Swiss agricultural sector, where two sectoral digital platform projects for data management, one centralised and the other distributed, were opposed. It consists of five articles. The first presents the research approach: a discourse not on, but with the actors in the field, whose ambition is to contribute to the critique of the social science discourse. Articles 2 to 4 describe the main argument of the thesis. The architectures of digital systems are political because they embody structures of governance (article 2). Architectures are also political in the sense that they can be used to promote or prevent asymmetries in the dependency relations between the actors called upon to use these systems (article 3). Finally, they are political in the sense that they can disrupt the practices associated with data of actors, to the point of threatening the latter’s autonomy (article 4). The fifth and last article returns to the notion of digital architecture and discusses the scope of its processual perspective. The originality of the contributions of the thesis lies in this processual and sectoral perspective of digital architectures. It opens up a questioning of the role of platforms for the constitution of information infrastructures, and in particular of alternative platforms, i.e., those that do not rely on the centralisation or the standardisation of data

    Interoperability framework of virtual factory and business innovation

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    Interoperability framework of virtual factory and business innovationTask T51 Design a common schema and schema evolution framework for supporting interoperabilityTask T52 Design interoperability framework for supporting datainformation transformation service composition and business process cooperation among partnersA draft version is envisioned for month 44 which will be updated to reflect incremental changes driven by the other working packages for month 72 deliverable 7.

    The role of SEW and TMT behaviours in family business innovation: Evidence from China

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    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the Degree of Doctor of Philosophy.Innovation is the lifeblood of a family business and plays an important role in developing the firm's competitiveness and achieving sustainable growth. As the majority of Chinese private firms and the main foundation of China's private sector, Chinese family businesses are an emerging force for innovation. This thesis attempts to unravel the innovation “black box” of family firms by exploring the mechanisms of how and why family firms are more efficient during the innovation process. Drawing upon stewardship theory and upper echelon theory, this study investigates how socioemotional wealth (SEW) influences the innovation inputs, the relationship between innovation inputs and outputs, and the role of top management team (TMT) behaviours during the conversion from innovation inputs to outputs. Based on a mixed-method study, this thesis investigates the mechanism of the innovation process using quantitative survey data from 473 Chinese family-controlled small and medium-sized enterprises (SMEs) and qualitative interview data from 12 Chinese family-controlled SMEs. The key findings of this thesis revealed that different dimensions of SEW shape decision-making on innovation inputs for family firms in China. Specifically, the results indicate that family influence and control have negative implications for innovation inputs, while binding social ties, emotional attachment, and renew family bonds positively affect the innovation inputs. Moreover, this thesis finds that innovation inputs have indirect effects on innovation outputs through TMT behaviours. The use of knowledge and skills, trust, and cognitive conflicts by TMTs partially mediate the relationship between innovation inputs and outputs. This research extends the understanding of the innovation process in the family businesses by exploring SEW-related innovation decision-making processes and administrative behaviour at the TMT level, which tackles the conundrum of how family firms can win innovations with limited innovation inputs. Moreover, it also enriches the literature on Chinese family business innovation, which provides new insights about family business innovation in emerging economies, thus contributing towards a more holistic picture of family business innovation globally. Practically, this research provides a comprehensive understanding of the innovation process in Chinese family businesses. It juxtaposes the viewpoints of family owners, policymakers, and managers on how family businesses in China can innovate and thrive in an emerging market

    KG-Hub-building and exchanging biological knowledge graphs.

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    MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. AVAILABILITY AND IMPLEMENTATION: https://kghub.org
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