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The present study had four objectives. First, we aimed at replicating the effects of provocation on reactive aggression in a monetary modified Taylor Aggression Paradigm (mTAP). Moreover, we examined the moderating role of gender expecting higher gender differences under conditions of low provocation and smaller gender differences under conditions of high provocation. In terms of convergent validity, we hypothesized a significant relationship between self-reported trait aggression and behavioral aggression outcomes in the laboratory paradigm. Finally, to explore the role of provocation sequence, the monetary stimuli (0 - 90 cents) were presented either randomly or in a fixed sequence. In contrast to the random sequence, the fixed sequence was generated as triplets of the same provocation category. Because of the more homogeneous provocation sequence in the fixed condition, we expected higher aggression levels after higher provocation and lower aggression levels after lower provocation in this experimental condition. In this experiment, 209 young healthy participants (104 males, 105 females) completed a mock competitive reaction time task with a fictional opponent with 40% preprogrammed win and 60% lose trials. In lose trials, participants were provoked by subtracting a low (0 - 20 euro cents), medium (30 - 60 cents) or high (70 - 90 cents) amount of money from their account.
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To address the issues in question, the empirical analysis explores annual panel data for 23 developed economies vis-à-vis 21 developing ones, over the period 2002-2017. The time span of the sample and country selection are constrained by data availability.
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Dataset_Effects of action type and performance type on young children's performance in a causal task
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This dataset include glutathione reactivity (RC50) and toxicity values to Tetrahymena pyriformis (IGC50) for 29 SN2 compounds activated by a carbonyl electron-withdrawing group. Calculated activation energy values (Eact) and predictions for both glutathione reactivity and toxicity to Tetrahymena pyriformis using these values are also included.
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This dataset is about a systematic review of unsupervised learning techniques for software defect prediction (our related paper: "A Systematic Review of Unsupervised Learning Techniques for Software Defect Prediction" in Information and Software Technology [accepted in Feb, 2020] ). We conducted this systematic literature review that identified 49 studies which satisfied our inclusion criteria containing 2456 individual experimental results. In order to compare prediction performance across these studies in a consistent way, we recomputed the confusion matrices and employed MCC as our main performance measure. From each paper we extracted: Title, Year, Journal/conference, 'Predatory' publisher? (Y | N), Count of results reported in paper, Count of inconsistent results reported in paper, Parameter tuning in SDP? (Yes | Default | ?) and SDP references(SDPRefs OrigResults | SDPRefs |SDPNoRefs | OnlyUnSDP). Then from within each paper, we extracted for each experimental result including: Prediction method name (e.g., DTJ48), Project name trained on (e.g., PC4), Project name tested on (e.g., PC4), Prediction type (within-project | cross-project), No. of input metrics (count | NA), Dataset family (e.g., NASA), Dateset fault rate (%), Was cross validation used? (Y | N | ?), Was error checking possible? (Y | N), Inconsistent results? (Y | N | ?), Error reason description (text), Learning type (Supervised | Unsupervised), Clustering method? (Y | N | NA), Machine learning family (e.g., Un-NN), Machine learning technique (e.g., KM), Prediction results (including TP, TN, FP, FN, etc.).
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Data for the paper, for the citation count regrssion
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Texture Profile Analysis, viscosity analysis, yield stress analysis and FTIR raw data.
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The banking prudential indicators time series. The use of the time series is free. However, we kindly ask the user to cite one of the following papers, in which the results were first published: Pfeifer, L., & M. Hodula (2018). A Profit-to-Provisioning Approach to Setting the Countercyclical Capital Buffer: The Czech Example. CNB Working Paper Series No 5/2018. Prague: Czech National Bank. or Pfeifer, L., & M. Hodula (2018). A Profit-to-Provisioning Approach to Setting the Countercyclical Capital Buffer: The Czech Example. ESRB Paper Series No 82/August 2018. Frankfurt am Main: European Systemic Risk Board. Disclaimer: We provide the time series in good faight. The views expressed are those of the authors and do not necessarily reflect the official views of the CNB or ESRB.
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Dual numbers are used to develop methods for computing velocities and accelerations
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This dataset is a combination of different biological tasks mentioned in this paper: "Green Model to Adapt Classical Conditioning Learning in Hippocampus"
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