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  1. (4 other versions)The Structure of Scientific Revolutions.Thomas Samuel Kuhn - 1962 - Chicago: University of Chicago Press. Edited by Otto Neurath.
    A scientific community cannot practice its trade without some set of received beliefs. These beliefs form the foundation of the "educational initiation that prepares and licenses the student for professional practice". The nature of the "rigorous and rigid" preparation helps ensure that the received beliefs are firmly fixed in the student's mind. Scientists take great pains to defend the assumption that scientists know what the world is like...To this end, "normal science" will often suppress novelties which undermine its foundations. Research (...)
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  • (4 other versions)The Structure of Scientific Revolutions.Thomas S. Kuhn - 1962 - Chicago, IL: University of Chicago Press. Edited by Ian Hacking.
    Thomas S. Kuhn's classic book is now available with a new index.
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  • A theory of data.C. H. Coombs - 1960 - Psychological Review 67 (3):143-159.
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  • (1 other version)Data-driven sciences: From wonder cabinets to electronic databases.Bruno J. Strasser - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (1):85-87.
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  • Data feminism.Catherine D'Ignazio - 2020 - Cambridge, Massachusetts: The MIT Press. Edited by Lauren F. Klein.
    We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data scientists seeking to learn (...)
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  • What difference does quantity make? On the epistemology of Big Data in biology.Sabina Leonelli - 2014 - Big Data and Society 1 (1):2053951714534395.
    Is Big Data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of Big Data science does not lie in the sheer quantity of data involved, but rather in the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community and the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments (...)
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  • A place for Big Data: Close and distant readings of accessions data from the Arnold Arboretum.Yanni Alexander Loukissas - 2016 - Big Data and Society 3 (2).
    Place is a key concept in environmental studies and criticism. However, it is often overlooked as a dimension of situatedness in social studies of information. Rather, situatedness has been defined primarily as embodiment or social context. This paper explores place attachments in Big Data by adapting close and distant approaches for reading texts to examine the accessions data of the Arnold Arboretum, a living collection of trees, vines and shrubs established by Harvard University in 1872. Although it is an early (...)
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  • New Knowledge from Old Data: The Role of Standards in the Sharing and Reuse of Ecological Data.Ann S. Zimmerman - 2008 - Science, Technology, and Human Values 33 (5):631-652.
    This article analyzes the experiences of ecologists who used data they did not collect themselves. Specifically, the author examines the processes by which ecologists understand and assess the quality of the data they reuse, and investigates the role that standard methods of data collection play in these processes. Standardization is one means by which scientific knowledge is transported from local to public spheres. While standards can be helpful, the results show that knowledge of the local context is critical to ecologists' (...)
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