Institution
University of Vienna
Education•Vienna, Austria•
About: University of Vienna is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Population & Stars. The organization has 44686 authors who have published 95840 publications receiving 2907492 citations.
Topics: Population, Stars, Galaxy, Transplantation, Crystal structure
Papers published on a yearly basis
Papers
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TL;DR: A global optimization algorithm based on multilevel coordinate search that is guaranteed to converge if the function is continuous in the neighborhood of a global minimizer is presented.
Abstract: Inspired by a method by Jones et al. (1993), we present a global optimization algorithm based on multilevel coordinate search. It is guaranteed to converge if the function is continuous in the neighborhood of a global minimizer. By starting a local search from certain good points, an improved convergence result is obtained. We discuss implementation details and give some numerical results.
554 citations
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TL;DR: A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regressors of Tehran, Iran to create a probability surface of spatiotemporal states of built-up land use for the years 2006, 2016, and 2026.
553 citations
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University of Vienna1, University of Edinburgh2, Massey University3, Newcastle University4, University of Copenhagen5, University of Glasgow6, Massachusetts Institute of Technology7, Boston College8, Fred Hutchinson Cancer Research Center9, University of Aberdeen10, San Diego State University11, Institut national de la recherche agronomique12, University of Birmingham13, Agricultural Research Organization, Volcani Center14, University of Jena15, University of Lausanne16, University of Warwick17, University of Amsterdam18, Delft University of Technology19, Temple University20, Technical University of Denmark21, Columbia University22
TL;DR: In this paper, the authors argue that the ability to predict and manage the function of these highly complex, dynamically changing communities is limited, and that close coordination of experimental data collection and method development with mathematical model building is needed to achieve significant progress in understanding of microbial dynamics and function.
Abstract: The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.
552 citations
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Leibniz Association1, Deutscher Wetterdienst2, Swiss Federal Laboratories for Materials Science and Technology3, Stockholm University4, University of Birmingham5, University of Vienna6, Hungarian Academy of Sciences7, Ghent University8, National Institutes of Health9, National Research Council10, University of Augsburg11, University of Huelva12, Eötvös Loránd University13, Energy Research Centre of the Netherlands14, Environment Agency15
TL;DR: In this article, the authors synthesize data on aerosol (particulate matter, PM) physical and chemical characteristics, which were obtained over the past decade in aerosol research and monitoring activities.
551 citations
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Rotem Botvinik-Nezer1, Rotem Botvinik-Nezer2, Felix Holzmeister3, Colin F. Camerer4 +217 more•Institutions (78)
TL;DR: The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.
Abstract: Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.
551 citations
Authors
Showing all 45262 results
Name | H-index | Papers | Citations |
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Tomas Hökfelt | 158 | 1033 | 95979 |
Wolfgang Wagner | 156 | 2342 | 123391 |
Hans Lassmann | 155 | 724 | 79933 |
Stanley J. Korsmeyer | 151 | 316 | 113691 |
Charles B. Nemeroff | 149 | 979 | 90426 |
Martin A. Nowak | 148 | 591 | 94394 |
Barton F. Haynes | 144 | 911 | 79014 |
Yi Yang | 143 | 2456 | 92268 |
Peter Palese | 132 | 526 | 57882 |
Gérald Simonneau | 130 | 587 | 90006 |
Peter M. Elias | 127 | 581 | 49825 |
Erwin F. Wagner | 125 | 375 | 59688 |
Anton Zeilinger | 125 | 631 | 71013 |
Wolfgang Waltenberger | 125 | 854 | 75841 |
Michael Wagner | 124 | 351 | 54251 |