Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python

SciPy 1.0 Contributors, Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew Nelson, Eric D. Jones, Robert Kern, Eric R. Larson, CJ Carey, İlhan Polat, Yu Feng, Eric Moore, Jake Vanderplas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, C. R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt

Nature Methods, 2020

University of Jyväskylä, Quansight (United States), Brigham Young University, Enthought (United States), Mayo Clinic in Florida, California Polytechnic State University, University of California, Los Angeles, Anaconda (United States), Los Alamos National Laboratory, National Research University Higher School of Economics, Tallinn University of Technology, University of California, Berkeley, University of Birmingham, Film Independent, WayRay (Russia), Skolkovo Foundation, Australian Nuclear Science and Technology Organisation, University of Washington, Amherst College, University of Massachusetts Amherst, Bruker (United States), University of West Bohemia in Pilsen, The University of Texas at Austin, Utah State University Space Dynamics Laboratory, Universidade Federal de Minas Gerais, Google (Canada), Google (United States)

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Abstract

This research paper explore the methodology and findings associated with Nature Methods. The study delves into the core aspects of the research field, providing significant data and citation impact. (Full abstract processing is available via the OpenAlex API).

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Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python

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