Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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1.080 Topics available

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977 Locations available

693.932 PEOPLE
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Goyal, Amit

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University of Lausanne

in Cooperation with on an Cooperation-Score of 37%

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Publications (3/3 displayed)

  • 2020Anomalies and False Rejections126citations
  • 2007Enhancement of dielectric strength in nanocomposites96citations
  • 2006Electrical properties of epoxy resin based nano-composites152citations

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Ellis, A. R.
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Sauers, Isidor
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James, D. Randy
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Co-Authors (by relevance)

  • Ellis, A. R.
  • Sauers, Isidor
  • James, D. Randy
  • Tuncer, Enis
  • Aytug, Tolga
  • Sathyamurthy, Srivatsan
  • Li, Jing
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article

Anomalies and False Rejections

  • Goyal, Amit
Abstract

<jats:title>Abstract</jats:title><jats:p>We use information from over 2 million trading strategies randomly generated using real data and from strategies that survive the publication process to infer the statistical properties of the set of strategies that could have been studied by researchers. Using this set, we compute $t$-statistic thresholds that control for multiple hypothesis testing, when searching for anomalies, at 3.8 and 3.4 for time-series and cross-sectional regressions, respectively. We estimate the expected proportion of false rejections that researchers would produce if they failed to account for multiple hypothesis testing to be about 45%.</jats:p>

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