The Art of Data Science

To flourish in the new data-intensive environment of 21st century science, we need to evolve new skills. These can be expressed in terms of the systemized framework that formed the basis of mediaeval education - the trivium (logic, grammar, and rhetoric) and quadrivium (arithmetic, geometry, music, and astronomy). However, rather than focusing on number, data is the new keystone. We need to understand what rules it obeys, how it is symbolized and communicated and what its relationship to physical space and time is. In this paper, we will review this understanding in terms of the technologies and processes that it requires. We contend that, at least, an appreciation of all these aspects is crucial to enable us to extract scientific information and knowledge from the data sets which threaten to engulf and overwhelm us.Comment: 12 pages, invited talk at Astrostatistics and Data Mining in Large Astronomical Databases workshop, La Palma, Spain, 30 May - 3 June 2011, to appear in Springer Series on Astrostatistic

Trapping in irradiated p-on-n silicon sensors at fluences anticipated at the HL-LHC outer tracker

The degradation of signal in silicon sensors is studied under conditions expected at the CERN High-Luminosity LHC. 200 $\mu$m thick n-type silicon sensors are irradiated with protons of different energies to fluences of up to $3 \cdot 10^{15}$ neq/cm$^2$. Pulsed red laser light with a wavelength of 672 nm is used to generate electron-hole pairs in the sensors. The induced signals are used to determine the charge collection efficiencies separately for electrons and holes drifting through the sensor. The effective trapping rates are extracted by comparing the results to simulation. The electric field is simulated using Synopsys device simulation assuming two effective defects. The generation and drift of charge carriers are simulated in an independent simulation based on PixelAV. The effective trapping rates are determined from the measured charge collection efficiencies and the simulated and measured time-resolved current pulses are compared. The effective trapping rates determined for both electrons and holes are about 50% smaller than those obtained using standard extrapolations of studies at low fluences and suggests an improved tracker performance over initial expectations

Physically Abused Children’s Adjustment at the Transition to School: Child, Parent, and Family Factors

Childhood physical abuse predicts emotional/behavioral, self-regulatory, and social problems. Yet factors from multiple ecological levels contribute to children’s adjustment. The purpose of this study was to examine the degree to which the social-emotional adjustment of physically abused children in first grade would be predicted by a set of child-, parent-, and family-level predictors in kindergarten. Drawing on a short-term longitudinal study of 92 physically abused children and their primary caregivers, the current study used linear regression to examine early childhood child (i.e., gender, IQ, child perceptions of maternal acceptance), parent (i.e., parental mental health), and family relationship (i.e., sensitive parenting, hostile parenting, family conflict) factors as predictors of first grade internalizing and externalizing symptomatology, emotion dysregulation, and negative peer interactions. We used a multi-method, multi-informant approach to measuring predictors and children’s adjustment. Internalizing symptomatology was significantly predicted by child IQ, parental mental health, and family conflict. Externalizing symptomatology and emotion dysregulation were predicted by child IQ. Although a large proportion of variance in measures of adjustment was accounted for by the set of predictors, few individual variables were unique predictors of child adjustment. Variability in the predictors of adjustment for physically abused children underscores the need for individualized treatment approaches