Modelldatensatz für den Reflektorgraphit der Kugelhaufenreaktoren

A model data set covering the temperature range 300 - 800 °C and irradiation dose range 0 - 4$\cdot$10$^{2}$ n/cm$^{2}$ EDN has been compiled for the reflector graphite of the OTTO-type pebble-bed high temperature reactor. The stipulated irradiation-induced changes in the properties of the model graphite are based mainly, but not exclusively, on experimental data from an extruded pitchcoke graphite of british manufacture, which had been irradiated to a dose of 2.8$\cdot$10$^{22}$ n/cm$^{2}$EDN. Since the initial properties of the unirradiated graphite were not known in detail, the properties of the German extruded pitch coke graphite ATR-2E from the SIGRI company were substantially used in the definition of the pre-irradiation properties of the model graphite. The main philosophy behind the compilation of the model data set was to predict as well as possible at this point in time the irradiation behaviourof the reference graphite for the reflector of the OTTO-type pebble-bed reactor and thus provide material data for stress analysis calculations. In order to allow a comparison of these stress calculations, it is recommended to use this data set which has been agreed upon among the partners in the HTB programme

"Because it is the Right Thing to Do'': Taking Stock of the Peer Reviewers’ Openness Initiative

The Peer Reviewers’ Openness (PRO) Initiative promotes the sharing of data and code. PRO signatories pledge to provide a full review only for manuscripts that publicly share data and code, or include a justification why sharing is not possible. Since the punitive element of this approach attracted criticism, we conducted a survey to assess signatories’ experiences with PRO. Contrary to the criticism, the reported experiences were predominantly positive, and 92% (117/127) of the signatories indicated that they would sign the initiative again today. Only 19 out of 127 respondents (15%) experienced negative reactions. Almost 50 respondents suggested ways in which PRO could be improved. We conclude that, from the signatories’ perspective, the benefits of the PRO initiative outweigh its drawbacks

Testing, explaining, and exploring models of facial expressions of emotions

Models are the hallmark of mature scientific inquiry. In psychology, this maturity has been reached in a pervasive question-what models best represent facial expressions of emotion? Several hypotheses propose different combinations of facial movements [action units (AUs)] as best representing the six basic emotions and four conversational signals across cultures. We developed a new framework to formalize such hypotheses as predictive models, compare their ability to predict human emotion categorizations in Western and East Asian cultures, explain the causal role of individual AUs, and explore updated, culture-accented models that improve performance by reducing a prevalent Western bias. Our predictive models also provide a noise ceiling to inform the explanatory power and limitations of different factors (e.g., AUs and individual differences). Thus, our framework provides a new approach to test models of social signals, explain their predictive power, and explore their optimization, with direct implications for theory development

Testing, explaining, and exploring models of facial expressions of emotions

Models are the hallmark of mature scientific inquiry. In psychology, this maturity has been reached in a pervasive question-what models best represent facial expressions of emotion? Several hypotheses propose different combinations of facial movements [action units (AUs)] as best representing the six basic emotions and four conversational signals across cultures. We developed a new framework to formalize such hypotheses as predictive models, compare their ability to predict human emotion categorizations in Western and East Asian cultures, explain the causal role of individual AUs, and explore updated, culture-accented models that improve performance by reducing a prevalent Western bias. Our predictive models also provide a noise ceiling to inform the explanatory power and limitations of different factors (e.g., AUs and individual differences). Thus, our framework provides a new approach to test models of social signals, explain their predictive power, and explore their optimization, with direct implications for theory development