Institution
Los Alamos National Laboratory
Facility•Los Alamos, New Mexico, United States•
About: Los Alamos National Laboratory is a facility organization based out in Los Alamos, New Mexico, United States. It is known for research contribution in the topics: Neutron & Laser. The organization has 31079 authors who have published 74688 publications receiving 2999590 citations. The organization is also known as: LANL & Project Y.
Topics: Neutron, Laser, Scattering, Magnetic field, Electron
Papers published on a yearly basis
Papers
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TL;DR: The PHENIX software for macromolecular structure determination is described and its uses and benefits are described.
Abstract: Macromolecular X-ray crystallography is routinely applied to understand biological processes at a molecular level. However, significant time and effort are still required to solve and complete many of these structures because of the need for manual interpretation of complex numerical data using many software packages and the repeated use of interactive three-dimensional graphics. PHENIX has been developed to provide a comprehensive system for macromolecular crystallographic structure solution with an emphasis on the automation of all procedures. This has relied on the development of algorithms that minimize or eliminate subjective input, the development of algorithms that automate procedures that are traditionally performed by hand and, finally, the development of a framework that allows a tight integration between the algorithms.
18,531 citations
University of Jyväskylä1, California Polytechnic State University2, University of California, Los Angeles3, Los Alamos National Laboratory4, National Research University – Higher School of Economics5, University of California, Berkeley6, University of Birmingham7, Australian Nuclear Science and Technology Organisation8, University of Washington9, University of Massachusetts Amherst10, University of West Bohemia11, University of Texas at Austin12, Brigham Young University13, Universidade Federal de Minas Gerais14, Google15
TL;DR: SciPy as discussed by the authors is an open source scientific computing library for the Python programming language, which includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics.
Abstract: SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.
12,774 citations
TL;DR: In this paper, the concept of a fractional volume of fluid (VOF) has been used to approximate free boundaries in finite-difference numerical simulations, which is shown to be more flexible and efficient than other methods for treating complicated free boundary configurations.
11,567 citations
TL;DR: This letter extends the heuristic homology algorithm of Needleman & Wunsch (1970) to find a pair of segments, one from each of two long sequences, such that there is no other Pair of segments with greater similarity (homology).
10,262 citations
Donald G. York1, Jennifer Adelman2, John E. Anderson2, Scott F. Anderson3 +148 more•Institutions (29)
TL;DR: The Sloan Digital Sky Survey (SDSS) as discussed by the authors provides the data to support detailed investigations of the distribution of luminous and non-luminous matter in the universe: a photometrically and astrometrically calibrated digital imaging survey of π sr above about Galactic latitude 30° in five broad optical bands to a depth of g' ~ 23 mag.
Abstract: The Sloan Digital Sky Survey (SDSS) will provide the data to support detailed investigations of the distribution of luminous and nonluminous matter in the universe: a photometrically and astrometrically calibrated digital imaging survey of π sr above about Galactic latitude 30° in five broad optical bands to a depth of g' ~ 23 mag, and a spectroscopic survey of the approximately 106 brightest galaxies and 105 brightest quasars found in the photometric object catalog produced by the imaging survey. This paper summarizes the observational parameters and data products of the SDSS and serves as an introduction to extensive technical on-line documentation.
9,835 citations
Authors
Showing all 31540 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| George Davey Smith | 224 | 2540 | 248373 |
| David A. Weitz | 178 | 1038 | 114182 |
| Hongfang Liu | 166 | 2356 | 156290 |
| Moungi G. Bawendi | 165 | 626 | 118108 |
| Yang Yang | 164 | 2704 | 144071 |
| Hannes Jung | 159 | 2069 | 125069 |
| David Eisenberg | 156 | 697 | 112460 |
| Richard E. Smalley | 153 | 494 | 111117 |
| Albert-László Barabási | 152 | 438 | 200119 |
| James M. Tiedje | 150 | 688 | 102287 |
| Andrew White | 149 | 1494 | 113874 |
| Barton F. Haynes | 144 | 911 | 79014 |
| Liming Dai | 141 | 781 | 82937 |
| Josh Moss | 139 | 1019 | 89255 |
| Christopher T. Walsh | 139 | 819 | 74314 |