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
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2019Temperature Considerations for Charging Li-Ion Batteries10citations
  • 2019Integrating laboratory and field studies to assess impacts of discharge from a uranium mine and validate a water quality guideline value for magnesium7citations
  • 2018Transmitting and decoding facial expressions of emotion during healthy aging: More similarities than differences15citations

Places of action

Chart of shared publication
Bhagat, Rohit
1 / 14 shared
Abbott, Mike
1 / 1 shared
Remy, Guillaume
1 / 1 shared
Maddar, Faduma M.
1 / 2 shared
Tan, Chaou C.
1 / 1 shared
Dixon, Shaun
1 / 1 shared
Barai, Anup
1 / 3 shared
Loveridge, Melanie J.
1 / 2 shared
Amor-Segan, Mark
1 / 1 shared
Lain, Mike
1 / 1 shared
Curnick, Oliver J.
1 / 2 shared
Mcmahon, Richard
1 / 2 shared
Greenwood, Dave
1 / 1 shared
Trenfield, Melanie
1 / 2 shared
Dam, Rick A. Van
1 / 1 shared
Humphrey, Chris
1 / 1 shared
Mooney, Thomas
1 / 1 shared
Harford, Andrew
1 / 1 shared
Scrimgeour, Susan
1 / 1 shared
Papasavva, Michael
1 / 1 shared
Ciripan, Oana
1 / 1 shared
Grühn, Daniel
1 / 1 shared
Bevitt, Ann
1 / 1 shared
Smith, Marie
1 / 1 shared
Ewing, Louise
1 / 1 shared
Chart of publication period
2019
2018

Co-Authors (by relevance)

  • Bhagat, Rohit
  • Abbott, Mike
  • Remy, Guillaume
  • Maddar, Faduma M.
  • Tan, Chaou C.
  • Dixon, Shaun
  • Barai, Anup
  • Loveridge, Melanie J.
  • Amor-Segan, Mark
  • Lain, Mike
  • Curnick, Oliver J.
  • Mcmahon, Richard
  • Greenwood, Dave
  • Trenfield, Melanie
  • Dam, Rick A. Van
  • Humphrey, Chris
  • Mooney, Thomas
  • Harford, Andrew
  • Scrimgeour, Susan
  • Papasavva, Michael
  • Ciripan, Oana
  • Grühn, Daniel
  • Bevitt, Ann
  • Smith, Marie
  • Ewing, Louise
OrganizationsLocationPeople

article

Transmitting and decoding facial expressions of emotion during healthy aging: More similarities than differences

  • Scrimgeour, Susan
  • Papasavva, Michael
  • Ciripan, Oana
  • Grühn, Daniel
  • Ellis, Mark
  • Bevitt, Ann
  • Smith, Marie
  • Ewing, Louise
Abstract

Older adults tend to perform more poorly than younger adults on emotional expression identification tasks. The goal of the present study was to test a processing mechanism that might explain these differences in emotion recognition – specifically, age-related variation in the utilization of specific visual cues. Seventeen younger and 17 older adults completed a reverse correlation emotion categorization task (Bubbles paradigm), consisting of a large number of trials in each of which only part of the visual information used to convey an emotional facial expression was revealed to participants. The task allowed us to pinpoint the visual features each group used systematically to correctly recognize the emotional expressions shown. To address the possibility that faces of different age groups are differently processed by younger and older adults, we included younger, middle-aged, and older adult face models displaying happy, fearful, angry, disgusted, and sad facial expressions. Our results reveal strong similarity in the utilization of visual information by younger and older adult participants in decoding the emotional expressions from faces across ages – particularly for happy and fear emotions. These findings suggest that age-related differences in strategic information use are unlikely to contribute to the decline of facial expression recognition skills observed in later life.

Topics
  • aging
  • aging