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Revitalizing a Core Scientific Python Package

K. Jarrod Millman
2020 Zenodo  
The NetworkX grant presentation from the December 2020 EOSS meeting.
doi:10.5281/zenodo.4316019 fatcat:2cqcpyrrvjhuhjagbeptqwar5a

Python for Scientists and Engineers

K. Jarrod Millman, Michael Aivazis
2011 Computing in science & engineering (Print)  
K. Jarrod Millman  ... 
doi:10.1109/mcse.2011.36 fatcat:b23hrx4o3zho7oazoumontv6tu

Data Sharing for Computational Neuroscience

Jeffrey L. Teeters, Kenneth D. Harris, K. Jarrod Millman, Bruno A. Olshausen, Friedrich T. Sommer
2008 Neuroinformatics  
Computational neuroscience is a subfield of neuroscience that develops models to integrate complex experimental data in order to understand brain function. To constrain and test computational models, researchers need access to a wide variety of experimental data. Much of those data are not readily accessible because neuroscientists fall into separate communities that study the brain at different levels and have not been motivated to provide data to researchers outside their community. To foster
more » ... sharing of neuroscience data, a workshop was held in 2007, bringing together experimental and theoretical neuroscientists, computer scientists, legal experts and governmental observers. Computational neuroscience was recommended as an ideal field for focusing data sharing, and specific methods, strategies and policies were suggested for achieving it. A new funding area in the NSF/NIH Collaborative Research in Computational Neuroscience (CRCNS) program has been established to support data sharing, guided in part by the workshop recommendations. The new funding area is dedicated to the dissemination of high quality data sets with maximum scientific value for computational neuroscience. The first round of the CRCNS data sharing program supports the preparation of data sets which will be publicly available in 2008. These include electrophysiology and behavioral (eye movement) data described towards the end of this article.
doi:10.1007/s12021-008-9009-y pmid:18259695 fatcat:w3ht7cwt5zfwxmx3uaad6342ia

Learning from open source software projects to improve scientific review

Satrajit S. Ghosh, Arno Klein, Brian Avants, K. Jarrod Millman
2012 Frontiers in Computational Neuroscience  
Peer-reviewed publications are the primary mechanism for sharing scientific results. The current peer-review process is, however, fraught with many problems that undermine the pace, validity, and credibility of science. We highlight five salient problems: (1) reviewers are expected to have comprehensive expertise; (2) reviewers do not have sufficient access to methods and materials to evaluate a study; (3) reviewers are neither identified nor acknowledged; (4) there is no measure of the quality
more » ... of a review; and (5) reviews take a lot of time, and once submitted cannot evolve. We propose that these problems can be resolved by making the following changes to the review process. Distributing reviews to many reviewers would allow each reviewer to focus on portions of the article that reflect the reviewer's specialty or area of interest and place less of a burden on any one reviewer. Providing reviewers materials and methods to perform comprehensive evaluation would facilitate transparency, greater scrutiny, and replication of results. Acknowledging reviewers makes it possible to quantitatively assess reviewer contributions, which could be used to establish the impact of the reviewer in the scientific community. Quantifying review quality could help establish the importance of individual reviews and reviewers as well as the submitted article. Finally, we recommend expediting post-publication reviews and allowing for the dialog to continue and flourish in a dynamic and interactive manner. We argue that these solutions can be implemented by adapting existing features from open-source software management and social networking technologies. We propose a model of an open, interactive review system that quantifies the significance of articles, the quality of reviews, and the reputation of reviewers.
doi:10.3389/fncom.2012.00018 pmid:22529798 pmcid:PMC3328792 fatcat:efwyhl4cg5e35h5i5vsmotw4bm

Array Programming with NumPy [article]

Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus (+14 others)
2020 arXiv   pre-print
Array programming provides a powerful, compact, expressive syntax for accessing, manipulating, and operating on data in vectors, matrices, and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It plays an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, material science, engineering, finance, and economics. For example, in astronomy, NumPy was an important part
more » ... the software stack used in the discovery of gravitational waves and the first imaging of a black hole. Here we show how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring, and analyzing scientific data. NumPy is the foundation upon which the entire scientific Python universe is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Because of its central position in the ecosystem, NumPy increasingly plays the role of an interoperability layer between these new array computation libraries.
arXiv:2006.10256v1 fatcat:cveua56uardwrkqggzn3ym242i

Array programming with NumPy

Charles R Harris, K Jarrod Millman, Stéfan J van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J Smith, Robert Kern, Matti Picus (+14 others)
2020 Nature  
Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of
more » ... the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
doi:10.1038/s41586-020-2649-2 pmid:32939066 fatcat:lbjamcrccfcrhbmytt6u4pqlym

Teaching computational reproducibility for neuroimaging [article]

K. Jarrod Millman, Matthew Brett, Ross Barnowski, Jean-Baptiste Poline
2018 pre-print
then showed the students how to run standard statistical procedures on imaging data using these tools (Millman and Brett, 2007) .  ...  Second, we intended to teach the students efficient reproducible practice with the standard tools that experts use for this purpose (Millman and Pérez, 2014) .  ...  Copyright © 2018 Millman, Brett, Barnowski and Poline. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).  ... 
doi:10.3389/fnins.2018.00727 pmid:30405329 pmcid:PMC6204391 arXiv:1806.06145v1 fatcat:z7s2vwt5i5a5bnua4sqbtr4x34

SciPy 1.0: fundamental algorithms for scientific computing in Python

Pauli Virtanen, SciPy 1.0 Contributors, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt (+23 others)
2020 Nature Methods  
In 2013, the time complexity of the k-nearest-neighbor search from cKDTree.query was approximately loglinear 68 , consistent with its formal description 69 .  ...  The scipy.spatial.ckdtree module, which implements a space-partitioning data structure that organizes points in k-dimensional space, was rewritten in C++ with templated classes.  ... 
doi:10.1038/s41592-019-0686-2 pmid:32015543 pmcid:PMC7056644 fatcat:f3btsgkmwfcybix7nyi66baqti

Array programming with NumPy

Charles R. Harris, K. Jarrod Millman, Stéfan J. Van Der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus (+15 others)
2020
Abstract: Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an
more » ... t part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
doi:10.17863/cam.62701 fatcat:2t7vcej3crapreiwdcwc74dqgi

Evaluating Machine Common Sense via Cloze Testing [article]

Ehsan Qasemi, Lee Kezar, Jay Pujara, Pedro Szekely
2022 arXiv   pre-print
Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W.  ...  We use this test k i=0 for two reasons.  ... 
arXiv:2201.07902v1 fatcat:hesoiavzkbcorpd2noqnrv6d3e

PyCIL: A Python Toolbox for Class-Incremental Learning [article]

Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, De-Chuan Zhan
2021 arXiv   pre-print
Charles R Harris, K Jarrod Millman, Stéfan J van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J Smith, et al.  ...  distributed systems. arXiv preprint arXiv:1512.01274, 2015. 5 Technical report Casper da Costa-Luis, SK Larroque, K  ... 
arXiv:2112.12533v1 fatcat:ru4kahb3dngpjdgbrsc6ikubga

Localizing flares to understand stellar magnetic fields and space weather in exo-systems [article]

Ekaterina Ilin, Katja Poppenhäger, Julián D. Alvarado-Gómez
2021 arXiv   pre-print
Stéfan van der Walt & Jarrod Millman (Eds.), Proceed- Mazeh, T., Perets, H. B., McQuillan, A., & Goldstein, E.  ...  R., Millman, K. J., van der Walt, S. J. et al. 2020, Sep 01, Reiners, A., & Basri, G. 2008, September, ApJ, 684(2), 1390-1403. Nature, 585(7825), 357-362.  ... 
arXiv:2112.09676v1 fatcat:mkm4v7mupbhvjktwv5sib4nma4

Paperfetcher: A tool to automate handsearch for systematic reviews [article]

Akash Pallath, Qiyang Zhang
2022 arXiv   pre-print
Dozier, and Conference, edited by Stéfan van der Walt and Jarrod H. McIntosh, Handsearching did not yield additional Millman (2010) pp. 56 – 61.  ...  K. focuses on literature related to chemicals, drugs, and Jorgensen, K. Hammerstrom, and N. Sathe, Searching substances [33].  ... 
arXiv:2110.12490v3 fatcat:3mxeajrxhrcx5aaa44lu7vquiq

Impact of the [C II]_158 μm luminosity scatter on the line-intensity mapping power spectrum from the EoR [article]

Chandra Shekhar Murmu, Karen P. Olsen, Thomas R. Greve, Suman Majumdar, Kanan K. Datta, Bryan R. Scott, T. K. Daisy Leung, Romeel Dave, Gergo Popping, Raul Ortega Ochoa, David Vizgan, Desika Narayanan
2021 arXiv   pre-print
In the latter case, the power spectrum is enhanced by a factor of ∼ 2.7-2.9 for 0.1 ≤ k ≤ 1 Mpc^-1 (at z=6); we present this alternative approach to interpret this enhancement in the clustering power in  ...  Wes McKinney 2010, in Stéfan van der Walt Jarrod Millman eds, Pro- org/10.5281/zenodo.5495979 ceedings of the 9th Python in Science Conference. pp  ...  Murmu and K. P.  ... 
arXiv:2110.10687v2 fatcat:h4g2ne7vqbfwxog2lcwmlrivm4

High performance Wannier interpolation of Berry curvature and related quantities: WannierBerri code [article]

Stepan S. Tsirkin
2020 arXiv   pre-print
Wannier interpolation is a powerful tool for performing Brillouin zone integrals over dense grids of 𝐤 points, which are essential to evaluate such quantities as the intrinsic anomalous Hall conductivity  ...  Wilson, K. Jarrod Millman, N. Mayorov, [39] This is always possible unless Nki is a prime number. A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C.  ...  l n where δOi (k) ≡ O(k, i+1 ) − O(k, i ).  ... 
arXiv:2008.07992v2 fatcat:epflq6b37relnp24hujdxo2q24
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