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This article examines what people conceal, who conceals from whom, and whether there are demographic differences in how much and what people conceal. We map concealment using a two-wave probability survey and behavioral experiment of U.S. adults (N = 1,281). Our survey measures self-reports of 37 different concealable attitudes, behaviors, and characteristics over a 12-month period, whereas the experiment provides a concrete behavioral measure of concealment. These data yield four principal findings. First, misinformation is commonplace in the United States, but it varies depending on what is being concealed. Second, certain demographic characteristics, such as age, predict rates of concealment, the proportion of things concealed, and lying in a behavioral experiment. Third, most demographic groups are similar in how much they conceal, but all demographic groups differ in what they conceal. Fourth, although some types of strong ties are more likely to be targets of concealment than weak ties, there is greater heterogeneity in concealment across different kinds of strong ties than between strong ties and weak ties, with spouses and partners being concealed from the least, on average. We conclude by discussing implications for theory and research on concealment. [ABSTRACT FROM AUTHOR]

Operational analytics is the direction of research related to the analysis of the current state of computing processes and the prediction of future states in order to anticipate imbalances and take timely measures to stabilize a complex system. There are two relevant areas in ATLAS Distributed Computing that are currently the focus of studies: user physics analysis including the forecast of popularity of data samples among users, and evaluating WLCG centers for their readiness to process user analysis payloads. Studying these areas is challenging due to the complexity involved, as it requires a comprehensive understanding of numerous boundary conditions typically found in large-scale distributed computing infrastructures. Forecasts of data popularity are problematic without the categorization of user tasks by their types (data transformation or physics analysis), which do not always appear on the surface but may induce noise, which introduces significant distortions for predictive analysis. Evaluating the WLCG resources by their analysis workloads is also a challenging task as it is necessary to find a balance between the workload of the resource, its performance, the waiting time for jobs on it, as well as the volume of jobs that it processes. This is especially difficult in a heterogeneous computing environment, where legacy resources are used along with modern high-performance machines. We will look at these areas of research in detail and discuss what tools and methods are used in our work, demonstrating results already obtained.