The Experts below are selected from a list of 99 Experts worldwide ranked by ideXlab platform
Oslund Patricia - One of the best experts on this subject based on the ideXlab platform.
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EAGER: Data Infrastructure to Enhance Research on the Scientific Workforce
2019Co-Authors: Ginther, Donna K., Zambrana Carlos, Oslund PatriciaAbstract:The purpose of this project is to provide users of SESTAT data with Stata DO files that convert the data in SAS transport Format to Stata data datasets having all variables (other than refid) in Numeric Format and with their values labeled according to each year’s label definitions.The National Center for Science and Engineering Statistics (NCSES) at the National Science Foundation collects high-quality data on the scientific workforce, but these data are not as widely utilized by the larger Science of Science and Innovation Policy (SciSIP) community because of changes in survey instruments over time and the data Format restrictions. The NSF only supports SAS for NCSES data whereas the majority of researchers use STATA. This project breaks down the barriers to using SESTAT and SDR data for the SciSIP community that in the long-run, could yield new insights about science policy.National Science Foundation (NSF SMA 1647187
Monica Baciu - One of the best experts on this subject based on the ideXlab platform.
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Neural correlates of the healthiness evaluation processes of food labels
Nutritional Neuroscience, 2017Co-Authors: M. Prevost, P. Hot, Laurent Muller, Bernard Ruffieux, E. Cousin, C. Pichat, Monica BaciuAbstract:Objectives: This fMRI study evaluated the cognitive mechanisms and the cerebral substrates when evaluating the healthiness of food products from nutritional inFormation displayed either with a traffic light (TL) system, a colored nutritional label, or with a guideline daily amount (GDA) system, a Numeric label. We postulated that TL label would recruit emotional processes and activation of subjacent cerebral regions (e.g. insula and amygdala). On the contrary, the nutritional inFormation presented in a GDA label, would recruit, due to its Numeric Format and higher complexity, supplementary cognitive processes and activation of related brain regions (e.g. middle and superior frontal as well as parietal cortices). Methods: We examined 50 healthy participants during an evaluation task on the healthiness of real food products from nutritional inFormation only. Per total, 60 food products nutritional labels have been presented, with either colored (TL) or Numeric (GDA) nutritional inFormation and three levels of complexity of nutritional inFormation. Results: In line with our predictions, evaluations based on GDA recruited prefrontal and parietal regions reported for analytic processes. Contrary to our predictions, the same network has been recruited when evaluations were based on TL. Finally, we found significant correlation between response time and the superior parietal lobule in the GDA condition. Discussion: Our results suggested that TL did not have an effect on the used strategy compared to GDA, based on calculation and arithmetic processes. Correlations between response time and brain activations suggested a significant involvement of the arithmetic mechanisms in the evaluation of food healthiness.
Patricia Oslund - One of the best experts on this subject based on the ideXlab platform.
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EAGER: Data Infrastructure to Enhance Research on the Scientific Workforce
2019Co-Authors: Donna K. Ginther, Carlos Zambrana, Patricia OslundAbstract:The purpose of this project is to provide users of SESTAT data with Stata DO files that convert the data in SAS transport Format to Stata data datasets having all variables (other than refid) in Numeric Format and with their values labeled according to each year’s label definitions.
Ginther, Donna K. - One of the best experts on this subject based on the ideXlab platform.
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EAGER: Data Infrastructure to Enhance Research on the Scientific Workforce
2019Co-Authors: Ginther, Donna K., Zambrana Carlos, Oslund PatriciaAbstract:The purpose of this project is to provide users of SESTAT data with Stata DO files that convert the data in SAS transport Format to Stata data datasets having all variables (other than refid) in Numeric Format and with their values labeled according to each year’s label definitions.The National Center for Science and Engineering Statistics (NCSES) at the National Science Foundation collects high-quality data on the scientific workforce, but these data are not as widely utilized by the larger Science of Science and Innovation Policy (SciSIP) community because of changes in survey instruments over time and the data Format restrictions. The NSF only supports SAS for NCSES data whereas the majority of researchers use STATA. This project breaks down the barriers to using SESTAT and SDR data for the SciSIP community that in the long-run, could yield new insights about science policy.National Science Foundation (NSF SMA 1647187
M. Prevost - One of the best experts on this subject based on the ideXlab platform.
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Neural correlates of the healthiness evaluation processes of food labels
Nutritional Neuroscience, 2017Co-Authors: M. Prevost, P. Hot, Laurent Muller, Bernard Ruffieux, E. Cousin, C. Pichat, Monica BaciuAbstract:Objectives: This fMRI study evaluated the cognitive mechanisms and the cerebral substrates when evaluating the healthiness of food products from nutritional inFormation displayed either with a traffic light (TL) system, a colored nutritional label, or with a guideline daily amount (GDA) system, a Numeric label. We postulated that TL label would recruit emotional processes and activation of subjacent cerebral regions (e.g. insula and amygdala). On the contrary, the nutritional inFormation presented in a GDA label, would recruit, due to its Numeric Format and higher complexity, supplementary cognitive processes and activation of related brain regions (e.g. middle and superior frontal as well as parietal cortices). Methods: We examined 50 healthy participants during an evaluation task on the healthiness of real food products from nutritional inFormation only. Per total, 60 food products nutritional labels have been presented, with either colored (TL) or Numeric (GDA) nutritional inFormation and three levels of complexity of nutritional inFormation. Results: In line with our predictions, evaluations based on GDA recruited prefrontal and parietal regions reported for analytic processes. Contrary to our predictions, the same network has been recruited when evaluations were based on TL. Finally, we found significant correlation between response time and the superior parietal lobule in the GDA condition. Discussion: Our results suggested that TL did not have an effect on the used strategy compared to GDA, based on calculation and arithmetic processes. Correlations between response time and brain activations suggested a significant involvement of the arithmetic mechanisms in the evaluation of food healthiness.
