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  1. Data models and the acquisition and manipulation of data.Todd Harris - 2003 - Philosophy of Science 70 (5):1508-1517.
    This paper offers an account of data manipulation in scientific experiments. It will be shown that in many cases raw, unprocessed data is not produced, but rather a form of processed data that will be referred to as a data model. The language of data models will be used to provide a framework within which to understand a recent debate about the status of data and data manipulation. It will be seen that a description in terms of data models allows (...)
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  • Models of data.Patrick Suppes - 2009 - In Ernest Nagel, Patrick Suppes & Alfred Tarski (eds.), Provability, Computability and Reflection. Stanford, CA, USA: Elsevier.
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  • Constructing Quarks: A Sociological History of Particle Physics.Andrew Pickering - 1990 - Synthese 82 (1):163-174.
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  • (2 other versions)Error and the Growth of Experimental Knowledge.Deborah Mayo - 1997 - British Journal for the Philosophy of Science 48 (3):455-459.
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  • Reconstructing Reality: Models, Mathematics, and Simulations.Margaret Morrison - 2014 - New York, US: Oup Usa.
    The book examines issues related to the way modeling and simulation enable us to reconstruct aspects of the world we are investigating. It also investigates the processes by which we extract concrete knowledge from those reconstructions and how that knowledge is legitimated.
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  • Experimental practice and an error statistical account of evidence.Deborah G. Mayo - 2000 - Philosophy of Science 67 (3):207.
    In seeking general accounts of evidence, confirmation, or inference, philosophers have looked to logical relationships between evidence and hypotheses. Such logics of evidential relationship, whether hypothetico-deductive, Bayesian, or instantiationist fail to capture or be relevant to scientific practice. They require information that scientists do not generally have (e.g., an exhaustive set of hypotheses), while lacking slots within which to include considerations to which scientists regularly appeal (e.g., error probabilities). Building on my co-symposiasts contributions, I suggest some directions in which a (...)
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  • Error and the Growth of Experimental Knowledge.Deborah G. Mayo - 1996 - University of Chicago.
    This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.
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  • The Evidence for the Top Quark: Objectivity and Bias in Collaborative Experimentation.Kent W. Staley - 2004 - Cambridge University Press.
    The Evidence for the Top Quark offers both a historical and philosophical perspective on an important recent discovery in particle physics: evidence for the elementary particle known as the top quark. Drawing on published reports, oral histories, and internal documents from the large collaboration that performed the experiment, Kent Staley explores in detail the controversies and politics that surrounded this major scientific result. At the same time the book seeks to defend an objective theory of scientific evidence based on error (...)
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  • Constructing Quarks: A sociological history of particle physics.Andrew Pickering - 1984 - University of Chicago Press.
    Inviting a reappraisal of the status of scientific knowledge, Andrew Pickering suggests that scientists are not mere passive observers and reporters of nature.
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  • Representing Experimental Procedures through Diagrams at CERN’s Large Hadron Collider: The Communicatory Value of Diagrammatic Representations in Collaborative Research.Koray Karaca - 2017 - Perspectives on Science 25 (2):177-203.
    In relatively recent years, quite a number of diverse case studies concerning the use of visual displays—such as graphs, diagrams, tables, pictures, drawings, etc.—in both the physical and biological sciences have been offered in the literature of the history and philosophy of science —see, e.g., Miller 1984; Lynch and Woolgar 1990; Baigrie 1996; Pauwels 2006. These case studies have shown that visual representations fulfill important functions in both the theoretical and experimental practices of science, thereby emphasizing the non-verbal dimension of (...)
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  • A case study in experimental exploration: exploratory data selection at the Large Hadron Collider.Koray Karaca - 2017 - Synthese 194 (2):333-354.
    In this paper, I propose an account that accommodates the possibility of experimentation being exploratory in cases where the procedures necessary to plan and perform an experiment are dependent on the theoretical accounts of the phenomena under investigation. The present account suggests that experimental exploration requires the implementation of an exploratory procedure that serves to extend the range of possible outcomes of an experiment, thereby enabling it to pursue its objectives. Furthermore, I argue that the present account subsumes the notion (...)
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  • Model-Based Reasoning in Scientific Discovery.L. Magnani, Nancy Nersessian & Paul Thagard (eds.) - 1999 - Kluwer/Plenum.
    The book Model-Based Reasoning in Scientific Discovery, aims to explain how specific modeling practices employed by scientists are productive methods of ...
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  • Selectivity and the Production of Experimental Results: “Any fool can take data. Its taking good data that counts.” E. Commins.Allan Franklin - 1998 - Archive for History of Exact Sciences 53 (5):399-485.
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  • The hierarchy of models in simulation.Eric Winsberg - 1999 - In L. Magnani, Nancy Nersessian & Paul Thagard (eds.), Model-Based Reasoning in Scientific Discovery. Kluwer/Plenum. pp. 255--269.
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  • Model Landscapes in the Higgs Sector.Arianna Borrelli & Michael Stöltzner - 2013 - In Vassilios Karakostas & Dennis Dieks (eds.), EPSA11 Perspectives and Foundational Problems in Philosophy of Science. Cham: Springer. pp. 241--252.
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  • (2 other versions)Error and the growth of experimental knowledge.Deborah Mayo - 1996 - International Studies in the Philosophy of Science 15 (1):455-459.
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  • A hierarchy of models and electron microscopy.Todd Harris - 1999 - In L. Magnani, Nancy Nersessian & Paul Thagard (eds.), Model-Based Reasoning in Scientific Discovery. Kluwer/Plenum. pp. 139--148.
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