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  1. Validation and variability: Dual challenges on the path from systems biology to systems medicine.Annamaria Carusi - 2014 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 48:28-37.
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  • Systems biology, synthetic biology and data-driven research: A commentary on Krohs, Callebaut, and O'Malley and Soyer.Jane Calvert - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (1):81-84.
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  • Systems biology, synthetic biology and data-driven research: A commentary on Krohs, Callebaut, and O’Malley and Soyer.Jane Calvert - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (1):81-84.
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  • Repertoires: A post-Kuhnian perspective on scientific change and collaborative research.Rachel A. Ankeny & Sabina Leonelli - 2016 - Studies in History and Philosophy of Science Part A 60:18-28.
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  • How tracking technology is transforming animal ecology: epistemic values, interdisciplinarity, and technology-driven scientific change.Rose Trappes - 2023 - Synthese 201 (4):1-24.
    Tracking technology has been heralded as transformative for animal ecology. In this paper I examine what changes are taking place, showing how current animal movement research is a field ripe for philosophical investigation. I focus first on how the devices alter the limitations and biases of traditional field observation, making observation of animal movement and behaviour possible in more detail, for more varied species, and under a broader variety of conditions, as well as restricting the influence of human presence and (...)
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  • Computational Construction of the Reality: Abstraction and Exploration-Driven Strategies in Constructing Protein–Protein Interfaces.Sim-Hui Tee - 2019 - Axiomathes 29 (3):311-328.
    Computational modeling is one of the primary approaches to constructing protein–protein interfaces in the laboratory. The algorithm-driven computational protein design has been successfully applied to the construction of functional proteins with improved binding affinity and increased thermostability. It is intriguing how a computational protein modeling approach can construct and shape the reality of new functional proteins from scratch. I articulate an account of abstraction and exploration-driven strategies in this computational endeavor. I aim to show that how a computational modelling approach, (...)
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  • “Exploratory experimentation” as a probe into the relation between historiography and philosophy of science.Jutta Schickore - 2016 - Studies in History and Philosophy of Science Part A 55:20-26.
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  • Aspects of Theory-Ladenness in Data-Intensive Science.Wolfgang Pietsch - 2015 - Philosophy of Science 82 (5):905-916.
    Recent claims, mainly from computer scientists, concerning a largely automated and model-free data-intensive science have been countered by critical reactions from a number of philosophers of science. The debate suffers from a lack of detail in two respects, regarding the actual methods used in data-intensive science and the specific ways in which these methods presuppose theoretical assumptions. I examine two widely-used algorithms, classificatory trees and non-parametric regression, and argue that these are theory-laden in an external sense, regarding the framing of (...)
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  • Approaches in Post‐Experimental Science. The Case of Precision Medicine.Robert Meunier - 2022 - Berichte Zur Wissenschaftsgeschichte 45 (3):373-383.
    Berichte zur Wissenschaftsgeschichte, Volume 45, Issue 3, Page 373-383, September 2022.
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  • Approaches in Post‐Experimental Science. The Case of Precision Medicine.Robert Meunier - 2022 - Berichte Zur Wissenschaftsgeschichte 45 (3):373-383.
    Berichte zur Wissenschaftsgeschichte, Volume 45, Issue 3, Page 373-383, September 2022.
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  • What Counts as Scientific Data? A Relational Framework.Sabina Leonelli - 2015 - Philosophy of Science 82 (5):810-821.
    This paper proposes an account of scientific data that makes sense of recent debates on data-driven and ‘big data’ research, while also building on the history of data production and use particularly within biology. In this view, ‘data’ is a relational category applied to research outputs that are taken, at specific moments of inquiry, to provide evidence for knowledge claims of interest to the researchers involved. They do not have truth-value in and of themselves, nor can they be seen as (...)
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  • Ancient genetics to ancient genomics: celebrity and credibility in data-driven practice.Elizabeth D. Jones - 2019 - Biology and Philosophy 34 (2):27.
    “Ancient DNA Research” is the practice of extracting, sequencing, and analyzing degraded DNA from dead organisms that are hundreds to thousands of years old. Today, many researchers are interested in adapting state-of-the-art molecular biological techniques and high-throughput sequencing technologies to optimize the recovery of DNA from fossils, then use it for studying evolutionary history. However, the recovery of DNA from fossils has also fueled the idea of resurrecting extinct species, especially as its emergence corresponded with the book and movie Jurassic (...)
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  • Can biological complexity be reverse engineered?Sara Green - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 53:73-83.
    Concerns with the use of engineering approaches in biology have recently been raised. I examine two related challenges to biological research that I call the synchronic and diachronic underdetermination problem. The former refers to challenges associated with the inference of design principles underlying system capacities when the synchronic relations between lower-level processes and higher-level systems capacities are degenerate. The diachronic underdetermination problem regards the problem of reverse engineering a system where the non-linear relations between system capacities and lower-level mechanisms are (...)
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  • Scientific method.Brian Hepburn & Hanne Andersen - 2015 - Stanford Encyclopedia of Philosophy.
    1. Overview and organizing themes 2. Historical Review: Aristotle to Mill 3. Logic of method and critical responses 3.1 Logical constructionism and Operationalism 3.2. H-D as a logic of confirmation 3.3. Popper and falsificationism 3.4 Meta-methodology and the end of method 4. Statistical methods for hypothesis testing 5. Method in Practice 5.1 Creative and exploratory practices 5.2 Computer methods and the ‘third way’ of doing science 6. Discourse on scientific method 6.1 “The scientific method” in science education and as seen (...)
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