Towards a Taxonomy of the Model-Ladenness of Data

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are data-laden and data are model-laden. In this article I elaborate and defend the second, more controversial, component of the symbiosis view and construct a taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. 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 so-called raw data and, hence, beneficially serve the epistemic aims of science.

[1]  Lucien Wald,et al.  Some terms of reference in data fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[2]  Stephen John Norton,et al.  Why Atmospheric Modeling is Good Science , 2000 .

[3]  P. N. Edwards.  A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming , 2010 .

[4]  Keith Beven,et al.  On virtual observatories and modelled realities (or why discharge must be treated as a virtual variable) , 2012 .

[5]  P. N. Edwards.  GLOBAL CLIMATE SCIENCE, UNCERTAINTY AND POLITICS: DATA-LADEN MODELS, MODEL-FILTERED DATA , 1999 .

[6]  P. M. Sadler.  NAILS, JACKS AND CLAMPS: MECHANICAL ANALOGS AS A WAY TO THINK ABOUT ORGANIZING VAST AND VARIED STRATIGRAPHIC INFORMATION SETS FOR COMPUTER ALGORITHMS THAT OPTIMIZE TIME LINES AND CALIBRATE TIME SCALES , 2012 .

[7]  Eran Tal,et al.  The Epistemology of Measurement: A Model-based Account , 2013 .

[8]  R. Reichle.  Data assimilation methods in the Earth sciences , 2008 .

[9]  Wendy S. Parker,et al.  Computer Simulation, Measurement, and Data Assimilation , 2017, The British Journal for the Philosophy of Science.

[10]  R. Batterman.  The Tyranny of Scales , 2013 .

[11]  P. Sadler,et al.  QUANTITATIVE BIOSTRATIGRAPHY—ACHIEVING FINER RESOLUTION IN GLOBAL CORRELATION , 2004 .

[12]  Marc F. P. Bierkens,et al.  Upscaling and downscaling methods for environmental research , 2000 .

[13]  Antonio Pepe,et al.  Volcano Geodesy: Recent developments and future challenges , 2017 .

[14]  E. Holley,et al.  Abstracts with Programs , 2016 .

[15]  L. Edwards.  Insights on Why Graphic Correlation (Shaw's Method) Works , 1984, The Journal of Geology.

[16]  Hasok Chang,et al.  Inventing Temperature: Measurement and Scientific Progress , 2004 .

[17]  H. Rue,et al.  An explicit link between Gaussian fields and Gaussian Markov random fields; The SPDE approach , 2010 .

[18]  W. Parker.  II—Wendy S. Parker: Confirmation and adequacy-for-Purpose in Climate Modelling , 2009 .

[19]  J. Crampton,et al.  High-resolution geobiologic time-lines: progress and potential, fifty years after the advent of graphic correlation , 2014 .

[20]  W. Bean.  Patterns of Discovery : An Inquiry into the Conceptual Foundations of Science. , 1960 .

[21]  Jan de Leeuw,et al.  MODELS OF DATA , 2000 .

[22]  Elisabeth A. Lloyd,et al.  The role of ‘complex’ empiricism in the debates about satellite data and climate models , 2012 .

[23]  Xiangping Hu,et al.  Geophysical Field Data Interpolation Using Stochastic Partial Differential Equations for Gold Exploration in Dayaoshan, Guangxi, China , 2018, Minerals.

[24]  Wendy S. Parker,et al.  Reanalyses and observations : what's the difference? , 2016 .

[25]  H. Rue,et al.  An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach , 2011 .

[26]  J. L. Hatfield.  Upscaling and Downscaling Methods for Environmental Research , 2001 .

[27]  Sabina Leonelli,et al.  Data-Centric Biology: A Philosophical Study , 2016 .

[28]  Alisa Bokulich,et al.  Using models to correct data: paleodiversity and the fossil record , 2018, Synthese.

[29]  S. Strauss.  The Oxford Handbook Of Philosophy Of Physics , 2016 .

[30]  J. Jaime Gómez-Hernández,et al.  Upscaling transport with mass transfer models: Mean behavior and propagation of uncertainty , 2009 .