Institution
University of Colorado Boulder
Education•Boulder, Colorado, United States•
About: University of Colorado Boulder is a education organization based out in Boulder, Colorado, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 48794 authors who have published 115151 publications receiving 5387328 citations. The organization is also known as: CU Boulder & UCB.
Topics: Population, Galaxy, Poison control, Solar wind, Stars
Papers published on a yearly basis
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Massey University1, Miami University2, Washington University in St. Louis3, University of British Columbia4, Florida State University5, Temple University6, University of Tennessee7, University of California, Davis8, University of California, Santa Barbara9, University of Colorado Boulder10, University of North Carolina at Chapel Hill11, Michigan State University12
TL;DR: A roadmap of the most widely used and ecologically relevant approaches for analysis through a series of mission statements is provided, distinguishing two types of β diversity: directional turnover along a gradient vs. non-directional variation.
Abstract: A recent increase in studies of β diversity has yielded a confusing array of concepts, measures and methods. Here, we provide a roadmap of the most widely used and ecologically relevant approaches for analysis through a series of mission statements. We distinguish two types of β diversity: directional turnover along a gradient vs. non-directional variation. Different measures emphasize different properties of ecological data. Such properties include the degree of emphasis on presence/absence vs. relative abundance information and the inclusion vs. exclusion of joint absences. Judicious use of multiple measures in concert can uncover the underlying nature of patterns in β diversity for a given dataset. A case study of Indonesian coral assemblages shows the utility of a multi-faceted approach. We advocate careful consideration of relevant questions, matched by appropriate analyses. The rigorous application of null models will also help to reveal potential processes driving observed patterns in β diversity.
1,995 citations
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TL;DR: This paper shows that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned.
Abstract: Valiant's learnability model is extended to learning classes of concepts defined by regions in Euclidean space En. The methods in this paper lead to a unified treatment of some of Valiant's results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufficient conditions are provided for feasible learnability.
1,967 citations
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Max Planck Society1, Yale University2, Space Telescope Science Institute3, Harvard University4, University of Colorado Boulder5, Columbia University6, University of Toronto7, Argonne National Laboratory8, Ohio State University9, European Southern Observatory10, Aix-Marseille University11, ETH Zurich12, California Institute of Technology13, New York University14, Louisiana State University15, Australian National University16, Cornell University17, University College London18, Goddard Space Flight Center19, Leibniz Institute for Astrophysics Potsdam20
TL;DR: Astropy as mentioned in this paper provides core astronomy-related functionality to the community, including support for domain-specific file formats such as Flexible Image Transport System (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions.
Abstract: We present the first public version (v0.2) of the open-source and community-developed Python package, Astropy. This package provides core astronomy-related functionality to the community, including support for domain-specific file formats such as Flexible Image Transport System (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions. Significant functionality is under active development, such as a model fitting framework, VO client and server tools, and aperture and point spread function (PSF) photometry tools. The core development team is actively making additions and enhancements to the current code base, and we encourage anyone interested to participate in the development of future Astropy versions.
1,944 citations
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University of Chicago1, Pierre-and-Marie-Curie University2, Lawrence Berkeley National Laboratory3, University of Pennsylvania4, Argonne National Laboratory5, Fermilab6, University of Cape Town7, African Institute for Mathematical Sciences8, Texas A&M University9, University of Cambridge10, University of Portsmouth11, University of Toronto12, Wayne State University13, University of Colorado Boulder14, University of Tokyo15, California Institute of Technology16, University of Victoria17, University of California, Berkeley18, University of Illinois at Urbana–Champaign19, University of Chile20, Autonomous University of Barcelona21, Stockholm University22, University of Texas at Austin23, Princeton University24, University of Oxford25, University of California, Santa Barbara26, Las Cumbres Observatory Global Telescope Network27, Rutgers University28, University of Copenhagen29, Australian Astronomical Observatory30, Instituto Superior Técnico31, University of Utah32, Rochester Institute of Technology33, Space Telescope Science Institute34, Johns Hopkins University35, Pennsylvania State University36, University of the Western Cape37, University of Southampton38
TL;DR: In this article, the authors presented cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations.
Abstract: Aims. We present cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations. The dataset includes several low-redshift samples (z< 0.1), all three seasons from the SDSS-II (0.05
1,939 citations
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TL;DR: It is shown that applying qualitative and quantitative measures to the same data set can lead to dramatically different conclusions about the main factors that structure microbial diversity and can provide insight into the nature of community differences.
Abstract: The assessment of microbial diversity and distribution is a major concern in environmental microbiology. There are two general approaches for measuring community diversity: quantitative measures, which use the abundance of each taxon, and qualitative measures, which use only the presence/absence of data. Quantitative measures are ideally suited to revealing community differences that are due to changes in relative taxon abundance (e.g., when a particular set of taxa flourish because a limiting nutrient source becomes abundant). Qualitative measures are most informative when communities differ primarily by what can live in them (e.g., at high temperatures), in part because abundance information can obscure significant patterns of variation in which taxa are present. We illustrate these principles using two 16S rRNA-based surveys of microbial populations and two phylogenetic measures of community β diversity: unweighted UniFrac, a qualitative measure, and weighted UniFrac, a new quantitative measure, which we have added to the UniFrac website (http://bmf.colorado.edu/unifrac). These studies considered the relative influences of mineral chemistry, temperature, and geography on microbial community composition in acidic thermal springs in Yellowstone National Park and the influences of obesity and kinship on microbial community composition in the mouse gut. We show that applying qualitative and quantitative measures to the same data set can lead to dramatically different conclusions about the main factors that structure microbial diversity and can provide insight into the nature of community differences. We also demonstrate that both weighted and unweighted UniFrac measurements are robust to the methods used to build the underlying phylogeny.
1,927 citations
Authors
Showing all 49233 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yi Chen | 217 | 4342 | 293080 |
Robert J. Lefkowitz | 214 | 860 | 147995 |
Rob Knight | 201 | 1061 | 253207 |
Charles A. Dinarello | 190 | 1058 | 139668 |
Jie Zhang | 178 | 4857 | 221720 |
David Haussler | 172 | 488 | 224960 |
Bradley Cox | 169 | 2150 | 156200 |
Gang Chen | 167 | 3372 | 149819 |
Rodney S. Ruoff | 164 | 666 | 194902 |
Menachem Elimelech | 157 | 547 | 95285 |
Jay Hauser | 155 | 2145 | 132683 |
Robert E. W. Hancock | 152 | 775 | 88481 |
Robert Plomin | 151 | 1104 | 88588 |
Thomas E. Starzl | 150 | 1625 | 91704 |
Rajesh Kumar | 149 | 4439 | 140830 |