Results for 'Data-Intensive Science'

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  1. 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 (...)
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  2. 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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  3. The Fate of Explanatory Reasoning in the Age of Big Data.Frank Cabrera - 2021 - Philosophy and Technology 34 (4):645-665.
    In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in Section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In Section 3, I argue that these methodological claims (...)
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  4.  38
    Ontology (Science).Barry Smith - 2008 - In Carola Eschenbach & Mike Grüninger (eds.), Formal Ontology in Information Systems. Proceedings of the Fifth International Conference (FOIS 2008). Amsterdam: IOS 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 (...)
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  5. On the Method: Quantitative Reasonsing and Social Science.Kiyoung Kim - 2015 - SSRN.
    The research on social science eventually comes through any meaning about the human and society. Its message is directed to the society and the principal object of research would be its components, generally research participants or samples in terms of research method. As for nature, it is per se obvious that humans or populace act on various factors to influence their decision. This complex nature of human strands generally prevail that the multivariate analysis is an usual challenge for the (...)
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  6. 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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  7. Open Data, Open Review and Open Dialogue in Making Social Sciences Plausible.Quan-Hoang Vuong - 2017 - Scientific Data 4.
    A growing awareness of the lack of reproducibility has undermined society’s trust and esteem in social sciences. In some cases, well-known results have been fabricated or the underlying data have turned out to have weak technical foundations.
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  8. The Epistemological Foundations of Data Science: A Critical Analysis.Jules Desai, David Watson, Vincent Wang, Mariarosaria Taddeo & Luciano Floridi - manuscript
    The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of (...)
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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. The Ethical Work That Regulations Will Not Do.Carusi Annamaria & De Grandis Giovanni - 2012 - Information, Communication and Society 15 (1):124-141.
    Ethical concerns in e-social science are often raised with respect to privacy, confidentiality, anonymity and the ethical and legal requirements that govern research. In this article, the authors focus on ethical aspects of e-research that are not directly related to ethical regulatory framework or requirements. These frameworks are often couched in terms of benefits or harms that can be incurred by participants in the research. The authors shift the focus to the sources of value in terms of which benefits (...)
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  11. 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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  12.  95
    Enhancing GO for the Sake of Clinical Bioinformatics.Anand Kumar & Barry Smith - 2004 - Proceedings of the Bio-Ontologies Workshop , Glasgow 133.
    Recent work on the quality assurance of the Gene Ontology (GO, Gene Ontology Consortium 2004) from the perspective of both linguistic and ontological organization has made it clear that GO lacks the kind of formalism needed to support logic-based reasoning. At the same time it is no less clear that GO has proven itself to be an excellent terminological resource that can serve to combine together a variety of biomedical database and information systems. Given the strengths of GO, it is (...)
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  13.  72
    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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  14. 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 Proceedings of DILS 2004 (Data Integration in the Life Sciences), (Lecture Notes in Bioinformatics 2994). Berlin: 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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  15. 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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  16.  38
    Intense Embodiment: Senses of Heat in Women’s Running and Boxing.Helen Owton & Jacquelyn Allen-Collinson - 2015 - Body and Society 21 (2):245-268.
    In recent years, calls have been made to address the relative dearth of qualitative sociological investigation into the sensory dimensions of embodiment, including within physical cultures. This article contributes to a small, innovative and developing literature utilizing sociological phenomenology to examine sensuous embodiment. Drawing upon data from three research projects, here we explore some of the ‘sensuousities’ of ‘intense embodiment’ experiences as a distance-running-woman and a boxing-woman, respectively. Our analysis addresses the relatively unexplored haptic senses, particularly the ‘touch’ of (...)
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  17. What is Data Ethics?Luciano Floridi & Mariarosaria Taddeo - 2016 - Philosophical Transactions of the Royal Society A 374 (2083).
    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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  18. 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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  19. 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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  20. 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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  21. 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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  22. 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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  23. 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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  24. 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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  25.  16
    Data.Luciano Floridi - 2008 - In William A. Darity (ed.), International Encyclopedia of the Social Sciences.
    The word data (sing. datum) is originally Latin for “things given or granted”. Because of such a humble and generic meaning, the term enjoys considerable latitude both in its technical and in its common usage, for almost anything can be referred to as a “thing given or granted” (Cherry [1978]). With some reasonable approximation, four principal interpretations may be identified in the literature. The first three captures part of the nature of the concept and are discussed in the next (...)
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  26.  40
    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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  27.  42
    Enabling Posthumous Medical Data Donation: An Appeal for the Ethical Utilisation of Personal Health Data.Jenny Krutzinna, Mariarosaria Taddeo & Luciano Floridi - 2019 - Science and Engineering Ethics 25 (5):1357-1387.
    This article argues that personal medical data should be made available for scientific research, by enabling and encouraging individuals to donate their medical records once deceased, similar to the way in which they can already donate organs or bodies. This research is part of a project on posthumous medical data donation developed by the Digital Ethics Lab at the Oxford Internet Institute at the University of Oxford. Ten arguments are provided to support the need to foster posthumous medical (...)
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  28. Annotating Affective Neuroscience Data with the Emotion Ontology.Janna Hastings, Werner Ceusters, Kevin Mulligan & Barry Smith - 2012 - In Third International Conference on Biomedical Ontology. ICBO. pp. 1-5.
    The Emotion Ontology is an ontology covering all aspects of emotional and affective mental functioning. It is being developed following the principles of the OBO Foundry and Ontological Realism. This means that in compiling the ontology, we emphasize the importance of the nature of the entities in reality that the ontology is describing. One of the ways in which realism-based ontologies are being successfully used within biomedical science is in the annotation of scientific research results in publicly available databases. (...)
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  29. Promoting Coherent Minimum Reporting Guidelines for Biological and Biomedical Investigations: The MIBBI Project.Chris F. Taylor, Dawn Field, Susanna-Assunta Sansone, Jan Aerts, Rolf Apweiler, Michael Ashburner, Catherine A. Ball, Pierre-Alain Binz, Molly Bogue, Tim Booth, Alvis Brazma, Ryan R. Brinkman, Adam Michael Clark, Eric W. Deutsch, Oliver Fiehn, Jennifer Fostel, Peter Ghazal, Frank Gibson, Tanya Gray, Graeme Grimes, John M. Hancock, Nigel W. Hardy, Henning Hermjakob, Randall K. Julian, Matthew Kane, Carsten Kettner, Christopher Kinsinger, Eugene Kolker, Martin Kuiper, Nicolas Le Novere, Jim Leebens-Mack, Suzanna E. Lewis, Phillip Lord, Ann-Marie Mallon, Nishanth Marthandan, Hiroshi Masuya, Ruth McNally, Alexander Mehrle, Norman Morrison, Sandra Orchard, John Quackenbush, James M. Reecy, Donald G. Robertson, Philippe Rocca-Serra, Henry Rodriguez, Heiko Rosenfelder, Javier Santoyo-Lopez, Richard H. Scheuermann, Daniel Schober, Barry Smith & Jason Snape - 2008 - Nature Biotechnology 26 (8):889-896.
    Throughout the biological and biomedical sciences there is a growing need for, prescriptive ‘minimum information’ (MI) checklists specifying the key information to include when reporting experimental results are beginning to find favor with experimentalists, analysts, publishers and funders alike. Such checklists aim to ensure that methods, data, analyses and results are described to a level sufficient to support the unambiguous interpretation, sophisticated search, reanalysis and experimental corroboration and reuse of data sets, facilitating the extraction of maximum value from (...)
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  30. 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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  31.  81
    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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  32.  35
    Precision Medicine, Data, and the Anthropology of Social Status.Hugh Desmond - 2021 - American Journal of Bioethics 21 (4):80-83.
    The success of precision medicine depends on obtaining large amounts of information about at-risk populations. However, getting consent is often difficult. Why? In this commentary I point to the differentials in social status involved. These differentials are inevitable once personal information is surrendered, but are particularly intense when the studied populations are socioeconomically or socioculturally disadvantaged and/or ethnically stigmatized groups. I suggest how the deep distrust of the latter groups can be partially justified as a lack of confidence that their (...)
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  33. The Science of Belief: A Progress Report.Nicolas Porot & Eric Mandelbaum - forthcoming - WIREs Cognitive Science 1.
    The empirical study of belief is emerging at a rapid clip, uniting work from all corners of cognitive science. Reliance on belief in understanding and predicting behavior is widespread. Examples can be found, inter alia, in the placebo, attribution theory, theory of mind, and comparative psychological literatures. Research on belief also provides evidence for robust generalizations, including about how we fix, store, and change our beliefs. Evidence supports the existence of a Spinozan system of belief fixation: one that is (...)
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  34.  85
    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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  35. The History of Science as a Graveyard of Theories: A Philosophers’ Myth?Moti Mizrahi - 2016 - International Studies in the Philosophy of Science 30 (3):263-278.
    According to the antirealist argument known as the pessimistic induction, the history of science is a graveyard of dead scientific theories and abandoned theoretical posits. Support for this pessimistic picture of the history of science usually comes from a few case histories, such as the demise of the phlogiston theory and the abandonment of caloric as the substance of heat. In this article, I wish to take a new approach to examining the ‘history of science as a (...)
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  36. 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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  37.  17
    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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  38.  59
    Crowdsourced Science: Sociotechnical Epistemology in the E-Research Paradigm.David Watson & Luciano Floridi - 2018 - Synthese 195 (2):741-764.
    Recent years have seen a surge in online collaboration between experts and amateurs on scientific research. In this article, we analyse the epistemological implications of these crowdsourced projects, with a focus on Zooniverse, the world’s largest citizen science web portal. We use quantitative methods to evaluate the platform’s success in producing large volumes of observation statements and high impact scientific discoveries relative to more conventional means of data processing. Through empirical evidence, Bayesian reasoning, and conceptual analysis, we show (...)
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  39. Public Misunderstanding of Science? Reframing the Problem of Vaccine Hesitancy.Maya J. Goldenberg - 2016 - Perspectives on Science 24 (5):552-581.
    The public rejection of scientific claims is widely recognized by scientific and governmental institutions to be threatening to modern democratic societies. Intense conflict between science and the public over diverse health and environmental issues have invited speculation by concerned officials regarding both the source of and the solution to the problem of public resistance towards scientific and policy positions on such hot-button issues as global warming, genetically modified crops, environmental toxins, and nuclear waste disposal. The London Royal Society’s influential (...)
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  40.  33
    Science Meets Philosophy: Metaphysical Gap & Bilateral Brain.Hermann G. W. Burchard - 2020 - Philosophy Study 10 (10):599-614.
    The essay brings a summation of human efforts seeking to understand our existence. Plato and Kant & cognitive science complete reduction of philosophy to a neural mechanism, evolved along elementary Darwinian principles. Plato in his famous Cave Allegory explains that between reality and our experience of it there exists a great chasm, a metaphysical gap, fully confirmed through particle-wave duality of quantum physics. Kant found that we have two kinds of perception, two senses: By the spatial outer sense we (...)
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  41.  48
    Information Quality, Data and Philosophy.Luciano Floridi & Phyllis Illari - 2014 - In Phyllis Illari & Luciano Floridi (eds.), The Philosophy of Information Quality. Switzerland: Springer International Publishing. pp. 5–23.
    In this opening chapter, we review the literature on information quality. Our major aim is to introduce the issues, and trace some of the history of the debates, with a view to situating the chapters in this volume – whose authors come from different disciplines – to help make them accessible to readers with different backgrounds and expertise. We begin in this section by tracing some influential analyses of IQ in computer science. This is a useful basis for examining (...)
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  42. Computer Simulation and the Features of Novel Empirical Data.Greg Lusk - 2016 - Studies in History and Philosophy of Science Part A 56:145-152.
    In an attempt to determine the epistemic status of computer simulation results, philosophers of science have recently explored the similarities and differences between computer simulations and experiments. One question that arises is whether and, if so, when, simulation results constitute novel empirical data. It is often supposed that computer simulation results could never be empirical or novel because simulations never interact with their targets, and cannot go beyond their programming. This paper argues against this position by examining whether, (...)
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  43. No Wisdom in the Crowd: Genome Annotation at the Time of Big Data - Current Status and Future Prospects.Antoine Danchin - 2018 - Microbial Biotechnology 11 (4):588-605.
    Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential (...)
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  44. An Ethics Framework for Big Data in Health and Research.Vicki Xafis, G. Owen Schaefer, Markus K. Labude, Iain Brassington, Angela Ballantyne, Hannah Yeefen Lim, Wendy Lipworth, Tamra Lysaght, Cameron Stewart, Shirley Sun, Graeme T. Laurie & E. Shyong Tai - 2019 - Asian Bioethics Review 11 (3):227-254.
    Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the (...), Health and Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)
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  45. Cloud Computing and Big Data for Oil and Gas Industry Application in China.Yang Zhifeng, Feng Xuehui, Han Fei, Yuan Qi, Cao Zhen & Zhang Yidan - 2019 - Journal of Computers 1.
    The oil and gas industry is a complex data-driven industry with compute-intensive, data-intensive and business-intensive features. Cloud computing and big data have a broad application prospect in the oil and gas industry. This research aims to highlight the cloud computing and big data issues and challenges from the informatization in oil and gas industry. In this paper, the distributed cloud storage architecture and its applications for seismic data of oil and gas industry (...)
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  46.  83
    Science de l’entrelacement des formes, science suprême, science des hommes libres : la dialectique dans le Sophiste 253b-254b.Nicolas Zaks - 2017 - Elenchos 38 (1-2):61-81.
    Despite intensive exegetical work, Plato’s description of dialectic in the Sophist still raises many questions. Through a close reading of this passage that contextualizes it in the general organisation of the Sophist, this paper provides answers to these questions. After presenting the difficult text, I contend that the “vowel-kinds” are necessary conditions for the blending of kinds. Then, I interpret the “cause of divisions” mentioned by the Stranger as the kinds responsible of the dichotomous division in the first half (...)
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  47.  41
    Professionalism in Science: Competence, Autonomy, and Service.Hugh Desmond - 2020 - Science and Engineering Ethics 26 (3):1287-1313.
    Some of the most significant policy responses to cases of fraudulent and questionable conduct by scientists have been to strengthen professionalism among scientists, whether by codes of conduct, integrity boards, or mandatory research integrity training programs. Yet there has been little systematic discussion about what professionalism in scientific research should mean. In this paper I draw on the sociology of the professions and on data comparing codes of conduct in science to those in the professions, in order to (...)
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  48. How Archaeological Evidence Bites Back: Strategies for Putting Old Data to Work in New Ways.Alison Wylie - 2017 - Science, Technology, and Human Values 42 (2):203-225.
    Archaeological data are shadowy in a number of senses. Not only are they notoriously fragmentary but the conceptual and technical scaffolding on which archaeologists rely to constitute these data as evidence can be as constraining as it is enabling. A recurrent theme in internal archaeological debate is that reliance on sedimented layers of interpretative scaffolding carries the risk that “preunderstandings” configure what archaeologists recognize and record as primary data, and how they interpret it as evidence. The selective (...)
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  49. LinkSuite™: Software Tools for Formally Robust Ontology-Based Data and Information Integration.Werner Ceusters, Barry Smith & James Matthew Fielding - 2004 - In Proceedings of DILS 2004 (Data Integration in the Life Sciences), (Lecture Notes in Bioinformatics, 2994). Springer. pp. 1-16.
    The integration of information resources in the life sciences is one of the most challenging problems facing bioinformatics today. We describe how Language and Computing nv, originally a developer of ontology-based natural language understanding systems for the healthcare domain, is developing a framework for the integration of structured data with unstructured information contained in natural language texts. L&C’s LinkSuite™ combines the flexibility of a modular software architecture with an ontology based on rigorous philosophical and logical principles that is designed (...)
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  50. Logical Theory Revision Through Data Underdetermination: An Anti-Exceptionalist Exercise.Sanderson Molick - 2021 - Principia: An International Journal of Epistemology 25 (1).
    The anti-exceptionalist debate brought into play the problem of what are the relevant data for logical theories and how such data affects the validities accepted by a logical theory. In the present paper, I depart from Laudan's reticulated model of science to analyze one aspect of this problem, namely of the role of logical data within the process of revision of logical theories. For this, I argue that the ubiquitous nature of logical data is responsible (...)
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