Results for 'Data Science'

962 found
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  1. Data Science and Mass Media: Seeking a Hermeneutic Ethics of Information.Christine James - 2015 - Proceedings of the Society for Phenomenology and Media, Vol. 15, 2014, Pages 49-58 15 (2014):49-58.
    In recent years, the growing academic field called “Data Science” has made many promises. On closer inspection, relatively few of these promises have come to fruition. A critique of Data Science from the phenomenological tradition can take many forms. This paper addresses the promise of “participation” in Data Science, taking inspiration from Paul Majkut’s 2000 work in Glimpse, “Empathy’s Impostor: Interactivity and Intersubjectivity,” and some insights from Heidegger’s "The Question Concerning Technology." The description of (...)
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  2. OPTIMIZING DATA SCIENCE WORKFLOWS IN CLOUD COMPUTING.Tummalachervu Chaitanya Kanth - 2024 - Journal of Science Technology and Research (JSTAR) 4 (1):71-76.
    This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes (...)
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  3. Microethics for healthcare data science: attention to capabilities in sociotechnical systems.Mark Graves & Emanuele Ratti - 2021 - The Future of Science and Ethics 6:64-73.
    It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. (...)
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  4. (1 other version)Open data, open review and open dialogue in making social sciences plausible.Quan-Hoang Vuong - 2017 - Nature: Scientific Data Updates 2017.
    Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis’s (2012) statement that, “the scientific process demands the highest standards of quality, ethics and rigour,” they are worth implementing. More importantly, they are feasible to work (...)
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  5.  62
    Enabling the Nonhypothesis-Driven Approach: On Data Minimalization, Bias, and the Integration of Data Science in Medical Research and Practice.C. W. Safarlou, M. van Smeden, R. Vermeulen & K. R. Jongsma - 2023 - American Journal of Bioethics 23 (9):72-76.
    Cho and Martinez-Martin provide a wide-ranging analysis of what they label “digital simulacra”—which are in essence data-driven AI-based simulation models such as digital twins or models used for i...
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  6. How Data Governance Principles Influence Participation in Biodiversity Science.Beckett Sterner & Steve Elliott - 2023 - Science as Culture.
    Biodiversity science is in a pivotal period when diverse groups of actors—including researchers, businesses, national governments, and Indigenous Peoples—are negotiating wide-ranging norms for governing and managing biodiversity data in digital repositories. These repositories, often called biodiversity data portals, are a type of organization for which governance can address or perpetuate the colonial history of biodiversity science and current inequities. Researchers and Indigenous Peoples are developing and implementing new strategies to examine and change assumptions about which agents (...)
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  7. Transforming Data Analysis through AI-Powered Data Science.Mathan Kumar - 2023 - Proceedings of IEEE 2 (2):1-5.
    AI-powered records science is revolutionizing the way facts are analyzed and understood. It can significantly improve the exceptional of information evaluation and boost its speed. AI-powered facts technological know-how enables access to more extensive, extra complicated information sets, faster insights, faster trouble solving, and higher choice making. Using the use of AI-powered information technological know-how techniques and tools, organizations can provide more accurate outcomes with shorter times to choices. AI-powered facts technology also offers more correct predictions of activities and (...)
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  8. Open science, data sharing and solidarity: who benefits?Ciara Staunton, Carlos Andrés Barragán, Stefano Canali, Calvin Ho, Sabina Leonelli, Matthew Mayernik, Barbara Prainsack & Ambroise Wonkham - 2021 - History and Philosophy of the Life Sciences 43 (4):1-8.
    Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the “open science” movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel “Open science, data sharing and solidarity: who benefits?” held at the 2021 Biennial conference of the International Society for the (...)
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  9. Open Science, Open Data, and Open Scholarship: European Policies to Make Science Fit for the Twenty-First Century.Rene Von Schomberg, Jean-Claude Burgelman, Corina Pascu, Kataezyna Szkuta, Athanasios Karalopoulos, Konstantinos Repanas & Michel Schouppe - 2019 - Frontiers in Big Data 2:43.
    Open science will make science more efficient, reliable, and responsive to societal challenges. The European Commission has sought to advance open science policy from its inception in a holistic and integrated way, covering all aspects of the research cycle from scientific discovery and review to sharing knowledge, publishing, and outreach. We present the steps taken with a forward-looking perspective on the challenges laying ahead, in particular the necessary change of the rewards and incentives system for researchers (for (...)
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  10. Research Data Preservation Practices of Library and Information Science Faculties.A. Subaveerapandiyan & Anuradha Maurya - 2022 - Defence Journal of Library and Information Science Technology 42 (4):259-264.
    Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It (...)
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  11. Classificatory Theory in Data-intensive Science: The Case of Open Biomedical Ontologies.Sabina Leonelli - 2012 - International Studies in the Philosophy of Science 26 (1):47 - 65.
    Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical sciences, (...)
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  12. What is data ethics?Luciano Floridi & Mariarosaria Taddeo - 2016 - Philosophical Transactions of the Royal Society A 374 (2083):20160360.
    This theme issue has the founding ambition of landscaping Data Ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing, and use), algorithms (including AI, artificial agents, machine learning, and robots), and corresponding practices (including responsible innovation, programming, hacking, and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data Ethics builds on the foundation provided by (...)
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  13. Autonoesis and the Galilean science of memory: Explanation, idealization, and the role of crucial data.Nikola Andonovski - 2023 - European Journal for Philosophy of Science 13 (3):1-42.
    The Galilean explanatory style is characterized by the search for the underlying structure of phenomena, the positing of "deep" explanatory principles, and a view of the relation between theory and data, on which the search for "crucial data" is of primary importance. In this paper, I trace the dynamics of adopting the Galilean style, focusing on the science of episodic memory. I argue that memory systems, such as episodic and semantic memory, were posited as underlying competences producing (...)
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  14. Reframing the environment in data-intensive health sciences.Stefano Canali & Sabina Leonelli - 2022 - Studies in History and Philosophy of Science Part A 93:203-214.
    In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. (...)
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  15. Ihde’s Missing Sciences: Postphenomenology, Big Data, and the Human Sciences.Daniel Susser - 2016 - Techné: Research in Philosophy and Technology 20 (2):137-152.
    In Husserl’s Missing Technologies, Don Ihde urges us to think deeply and critically about the ways in which the technologies utilized in contemporary science structure the way we perceive and understand the natural world. In this paper, I argue that we ought to extend Ihde’s analysis to consider how such technologies are changing the way we perceive and understand ourselves too. For it is not only the natural or “hard” sciences which are turning to advanced technologies for help in (...)
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  16. Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS.Stefano Canali - 2016 - Big Data and Society 3 (2).
    Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal (...)
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  17.  99
    Big Data and reality.Ryan Shaw - 2015 - Big Data and Society 2 (2).
    DNA sequencers, Twitter, MRIs, Facebook, particle accelerators, Google Books, radio telescopes, Tumblr: what do these things have in common? According to the evangelists of “data science,” all of these are instruments for observing reality at unprecedentedly large scales and fine granularities. This perspective ignores the social reality of these very different technological systems, ignoring how they are made, how they work, and what they mean in favor of an exclusive focus on what they generate: Big Data. But (...)
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  18.  37
    Who's Anthropocene?: a data driven look at the prospects for collaboration between natural science, social science, and the humanities.Carlos Santana, K. Petrozzo & Timothy Perkins - 2024 - Digital Scholarship in the Humanities 39 (2):723-735.
    Although the idea of the Anthropocene originated in the earth sciences, there have been increasing calls for questions about the Anthropocene to be addressed by pan-disciplinary groups of researchers from across the natural sciences, social sciences, and humanities. We use data analysis techniques from corpus linguistics to examine academic texts about the Anthropocene from these disciplinary families. We read the data to suggest that barriers to a broadly interdisciplinary study of the Anthropocene are high, but we are also (...)
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  19.  35
    New paper about data publishing in DSI.Admin Portal - 2024 - Sm3D Portal.
    This post is to inform you about the publishing of an online-first version of our article titled “Critical remarks on current practices of data article publishing: issues, challenges, and recommendations”. The manuscript has gone through approximately 18 months of preparation and peer review to appear in the journal Data Science and Informetrics (DSI), finally.
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  20. Reframing data ethics in research methods education: a pathway to critical data literacy.Javiera Atenas, Leo Havemann & Cristian Timmermann - 2023 - International Journal of Educational Technology in Higher Education 20:11.
    This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. (...)
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  21. Comparative views on research productivity differences between major social science fields in Vietnam: Structured data and Bayesian analysis, 2008-2018.Quan-Hoang Vuong, La Viet Phuong, Vuong Thu Trang, Ho Manh Tung, Nguyen Minh Hoang & Manh-Toan Ho - manuscript
    Since Circular 34 from the Ministry of Science and Technology of Vietnam required the head of the national project to have project results published in ISI/Scopus journals in 2014, the field of economics has been dominating the number of nationally-funded projects in social sciences and humanities. However, there has been no scientometric study that focuses on the difference in productivity among fields in Vietnam. Thus, harnessing the power of the SSHPA database, a comprehensive dataset of 1,564 Vietnamese authors (854 (...)
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  22. When data drive health: an archaeology of medical records technology.Colin Koopman, Paul D. G. Showler, Patrick Jones, Mary McLevey & Valerie Simon - 2022 - Biosocieties 17 (4):782-804.
    Medicine is often thought of as a science of the body, but it is also a science of data. In some contexts, it can even be asserted that data drive health. This article focuses on a key piece of data technology central to contemporary practices of medicine: the medical record. By situating the medical record in the perspective of its history, we inquire into how the kinds of data that are kept at sites of (...)
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  23. Data models, representation and adequacy-for-purpose.Alisa Bokulich & Wendy Parker - 2021 - European Journal for Philosophy of Science 11 (1):1-26.
    We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides (...)
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  24. Data quality, experimental artifacts, and the reactivity of the psychological subject matter.Uljana Feest - 2022 - European Journal for Philosophy of Science 12 (1):1-25.
    While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what (...)
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  25.  62
    Data Synthesis for Big Questions: From Animal Tracks to Ecological Models.Rose Trappes - 2024 - Philosophy, Theory, and Practice in Biology 16 (1):4.
    This paper addresses a relatively new mode of ecological research: data synthesis studies. Data synthesis studies involve reusing data to create a general model as well as a reusable, aggregated dataset. Using a case from movement ecology, I analyse the trade-offs and strategies involved in data synthesis. Like theoretical ecological modelling, I find that synthesis studies involve a modelling trade-off between generality, precision and realism; they deal with this trade-off by adopting a pragmatic kludging strategy. I (...)
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  26. On the application of formal principles to life science data: A case study in the Gene Ontology.Jacob Köhler, Anand Kumar & Barry Smith - 2004 - In Köhler Jacob, Kumar Anand & Smith Barry (eds.), Proceedings of DILS 2004 (Data Integration in the Life Sciences), (Lecture Notes in Bioinformatics 2994). Springer. pp. 79-94.
    Formal principles governing best practices in classification and definition have for too long been neglected in the construction of biomedical ontologies, in ways which have important negative consequences for data integration and ontology alignment. We argue that the use of such principles in ontology construction can serve as a valuable tool in error-detection and also in supporting reliable manual curation. We argue also that such principles are a prerequisite for the successful application of advanced data integration techniques such (...)
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  27.  43
    Efficient Data Center Management: Advanced SLA-Driven Load Balancing Solutions.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):368-376.
    In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm optimization, and (...)
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  28. Data, Representation, and Evidential Values in Biology.Jinyeong Gim - 2023 - Korean Journal for the Philosophy of Science 26 (2):31-58.
    Leonelli (2016) suggested a relational view of data against a representational view by emphasizing data-centric biology rather than the theory-centric tradition in the philosophy of science. This is because the first view allows for data journeys across laboratories using public database resources, whereas the second does not. This paper examines Leonelli’s strategies to defend the relational view of data. Contrary to Leonelli’s intention, it indicates that her strategies led to unnecessary misunderstandings of the relationships among (...)
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  29. (1 other version)Towards a Taxonomy of the Model-Ladenness of Data.Alisa Bokulich - forthcoming - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association.
    Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include (...) conversion, data correction, data interpolation, data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data. (shrink)
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  30.  92
    Critical remarks on current practices of data article publishing: Issues, challenges, and recommendations.Quan-Hoang Vuong, Viet-Phuong La & Minh-Hoang Nguyen - 2024 - Data Science and Informetrics 4 (2):1-14.
    The contribution of the data paper publishing paradigm to the knowledge generation and validation processes is becoming substantial and pivotal. In this paper, through the information-processing perspective of Mindsponge Theory, we discuss how the data article publishing system serves as a filtering mechanism for quality control of the increasingly chaotic datasphere. The overemphasis on machine-actionality and technical standards presents some shortcomings and limitations of the data article publishing system, such as the lack of consideration of humanistic values, (...)
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  31.  80
    ADVANCE DATA SECURITY IN CLOUD NETWORK SYSTEMS.Tummalachervu Chaitanya Kanth - 2023 - Journal of Science Technology and Research (JSTAR) 4 (1):29-36.
    This research presents a novel and efficient public key cryptosystem known as the Enhanced Schmidt Samoa (ESS) cryptosystem, proposed to safeguard the data of a single owner in cloud computing environments. Data storage is a one-time process in the cloud, while data retrieval is a frequent operation. Experimental results demonstrate that the ESS cryptosystem offers robust data confidentiality in the cloud, surpassing the security provided by traditional cryptosystems. The research also introduces a secure cloud framework designed (...)
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  32. Big Data and Changing Concepts of the Human.Carrie Figdor - 2019 - European Review 27 (3):328-340.
    Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human.
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  33. Is an archaeological contribution to the theory of social science possible? Archaeological data and concepts in the dispute between Jean-Claude Gardin and Jean-Claude Passeron.Sébastien Plutniak - 2017 - Palethnologie 9:7-21.
    The issue of the definition and position of archaeology as a discipline is examined in relation to the dispute which took place from 1980 to 2009 between the archaeologist Jean-Claude Gardin and the sociologist Jean-Claude Passeron. This case study enables us to explore the actual conceptual relationships between archaeology and the other sciences (as opposed to those wished for or prescribed). The contrasts between the positions declared by the two researchers and the rooting of their arguments in their disciplines are (...)
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  34. Zemblanity and Big Data: the ugly truths the algorithms remind us of.Ricardo Cavassane - 2022 - Acta Scientiarum. Human and Social Sciences 44 (1):1-7.
    In this paper, we will argue that, while Big Data enthusiasts imply that the analysis of massive data sets can produce serendipitous (that is, unexpected and fortunate) discoveries, the way those models are currently designed not only does not create serendipity so easily but also frequently generates zemblanitous (that is, expected and unfortunate) findings.
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  35. Open science (key points).Lo Abraham - 2022 - Understanding Modern Sciences.
    Open science is the movement to make scientific research (including publications, data, physical samples, and software) and its dissemination accessible to all levels of society, amateur or professional.
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  36. From Galton’s Pride to Du Bois’s Pursuit: The Formats of Data-Driven Inequality.Colin Koopman - 2024 - Theory, Culture and Society 41 (1):59-78.
    Data increasingly drive our lives. Often presented as a new trajectory, the deep immersion of our lives in data has a history that is well over a century old. By revisiting the work of early pioneers of what would today be called data science, we can bring into view both assumptions that fund our data-driven moment as well as alternative relations to data. I here excavate insights by contrasting a seemingly unlikely pair of early (...)
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  37.  33
    Optimizing Data Center Operations with Enhanced SLA-Driven Load Balancing".S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):368-376.
    The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. Extensive simulations are conducted using synthetic and real-world datasets to evaluate the performance of the proposed algorithm. The results demonstrate that the optimized load balancing approach outperforms traditional algorithms in terms of SLA compliance, resource utilization, and energy efficiency. The findings suggest that the integration of optimization techniques into load balancing algorithms can significantly enhance (...)
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  38. Practical and Philosophical Considerations for Defining Information as Well-formed, Meaningful Data in the Information Sciences.Jesse David Dinneen & Christian Brauner - 2015 - Library Trends 63 (3):378-400.
    This paper demonstrates the practical and philosophical strengths of adopting Luciano Floridi’s “general definition of information” (GDI) for use in the information sciences (IS). Many definitions of information have been proposed, but little work has been done to determine which definitions are most coherent or useful. Consequently, doubts have been cast on the necessity and possibility of finding a definition. In response to these doubts, the paper shows how items and events central to IS are adequately described by Floridi’s conception (...)
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  39. The Case Study Method in Philosophy of Science: An Empirical Study.Moti Mizrahi - 2020 - Perspectives on Science 28 (1):63-88.
    There is an ongoing methodological debate in philosophy of science concerning the use of case studies as evidence for and/or against theories about science. In this paper, I aim to make a contribution to this debate by taking an empirical approach. I present the results of a systematic survey of the PhilSci-Archive, which suggest that a sizeable proportion of papers in philosophy of science contain appeals to case studies, as indicated by the occurrence of the indicator words (...)
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  40. Data Mining in the Context of Legality, Privacy, and Ethics.Amos Okomayin, Tosin Ige & Abosede Kolade - 2023 - International Journal of Research and Innovation in Applied Science 10 (Vll):10-15.
    Data mining possess a significant threat to ethics, privacy, and legality, especially when we consider the fact that data mining makes it difficult for an individual or consumer (in the case of a company) to control accessibility and usage of his data. Individuals should be able to control how his/ her data in the data warehouse is being access and utilize while at the same time providing enabling environment which enforces legality, privacy and ethicality on (...)
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  41. Retractions Data Mining #1.Quan-Hoang Vuong & Viet-Phuong La - 2019 - Open Science Framework 2019 (2):1-3.
    Motivation: • Breaking barriers in publishing demands a proactive attitude • Open data, open review and open dialogue in making social sciences plausible .
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  42. Data’ in the Royal Society's Philosophical Transactions, 1665–1886.Chris Meyns - 2019 - Notes and Records: The Royal Society Journal of the History of Science.
    Was there a concept of data before the so-called ‘data revolution’? This paper contributes to the history of the concept of data by investigating uses of the term ‘data’ in texts of the Royal Society's Philosophical Transactions for the period 1665–1886. It surveys how the notion enters the journal as a technical term in mathematics, and charts how over time it expands into various other scientific fields, including Earth sciences, physics and chemistry. The paper argues that (...)
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  43. Towards a Contextual Approach to Data Quality.Stefano Canali - 2020 - Data 4 (5):90.
    In this commentary, I propose a framework for thinking about data quality in the context of scientific research. I start by analyzing conceptualizations of quality as a property of information, evidence and data and reviewing research in the philosophy of information, the philosophy of science and the philosophy of biomedicine. I identify a push for purpose dependency as one of the main results of this review. On this basis, I present a contextual approach to data quality (...)
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  44. Ontology (science).Barry Smith - 2001 - In Barry Smith & Christopher Welty (eds.), Formal Ontology in Information Systems (FOIS). ACM Press. pp. 21-35.
    Increasingly, in data-intensive areas of the life sciences, experimental results are being described in algorithmically useful ways with the help of ontologies. Such ontologies are authored and maintained by scientists to support the retrieval, integration and analysis of their data. The proposition to be defended here is that ontologies of this type – the Gene Ontology (GO) being the most conspicuous example – are a part of science. Initial evidence for the truth of this proposition (which some (...)
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  45. Using models to correct data: paleodiversity and the fossil record.Alisa Bokulich - 2018 - Synthese 198 (Suppl 24):5919-5940.
    Despite an enormous philosophical literature on models in science, surprisingly little has been written about data models and how they are constructed. In this paper, I examine the case of how paleodiversity data models are constructed from the fossil data. In particular, I show how paleontologists are using various model-based techniques to correct the data. Drawing on this research, I argue for the following related theses: first, the ‘purity’ of a data model is not (...)
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  46. Cognitive science-the frontier scientific field in new millennium.Haotian Zhou & Xiaolan Fu - 2019 - Journal of Human Cognition 3 (2):5-20.
    Cognitive science is a highly multidisciplinary and comprehensive subject in unraveling the mysteries of human mind. It investigates the most frontier scientific issues which able to influence the human destiny in future development, and has received widespread attention from the community. Based on a myriad of up-to-date information and data, this review introduces the status quo of cognitive science research around the globe, analyzes the future trends of its development, discusses the prospective application of cognitive science, (...)
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  47. Data and Safety Monitoring Board and the Ratio Decidendi of the Trial.Roger Stanev - 2015 - Journal of Philosophy, Science and Law 15:1-26.
    Decision-making by a Data and Safety Monitoring Board (DSMB) regarding clinical trial conduct and termination is intricate and largely limited by cases and rules. Decision-making by legal jury is also intricate and largely constrained by cases and rules. In this paper, I argue by analogy that legal decision-making, which strives for a balance between competing demands of conservatism and innovation, supplies a good basis to the logic behind DSMB decision-making. Using the doctrine of precedents in legal reasoning as my (...)
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  48. ICTs, data and vulnerable people: a guide for citizens.Alexandra Castańeda, Andreas Matheus, Andrzej Klimczuk, Anna BertiSuman, Annelies Duerinckx, Christoforos Pavlakis, Corelia Baibarac-Duignan, Elisabetta Broglio, Federico Caruso, Gefion Thuermer, Helen Feord, Janice Asine, Jaume Piera, Karen Soacha, Katerina Zourou, Katherin Wagenknecht, Katrin Vohland, Linda Freyburg, Marcel Leppée, Marta CamaraOliveira, Mieke Sterken & Tim Woods - 2021 - Bilbao: Upv-Ehu.
    ICTs, personal data, digital rights, the GDPR, data privacy, online security… these terms, and the concepts behind them, are increasingly common in our lives. Some of us may be familiar with them, but others are less aware of the growing role of ICTs and data in our lives - and the potential risks this creates. These risks are even more pronounced for vulnerable groups in society. People can be vulnerable in different, often overlapping, ways, which place them (...)
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  49. Data frauds, health risks, and the growing question of ethics during the COVID-19 pandemic.Vuong Quan-Hoang, Le Tam-Tri & Nguyen Minh-Hoang - manuscript
    In this essay, we advocate that the issue of health data ethics should no longer be considered on the level of individual scientists or research labs, but rather as a problem involving all stakeholders, from publishers, funders, ethical committees to governments, for the sake of research integrity.
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  50. Open Science and Intellectual Property Rights. How can they better interact? State of the art and reflections. Report of Study. European Commission.Javier de la Cueva & Eva Méndez - 2022 - Brussels: European Commission.
    Open science (OS) is considered the new paradigm for science and knowledge dissemination. OS fosters cooperative work and new ways of distributing knowledge by promoting effective data sharing (as early and broadly as possible) and a dynamic exchange of research outcomes, not only publications. On the other hand, intellectual property (IP) legislation seeks to balance the moral and economic rights of creators and inventors with the wider interests and needs of society. Managing knowledge outcomes in a new (...)
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