With the large ring laser gyros for the geosciences, we enter the extreme high resolution regime of rotation sensing, observing rotation rates of less than 1 picoradian/s. This requires us to look closely at all potential noise sources in the ring laser itself, as well as in the data acquisition process. Our ring laser measurements for space geodesy are also based on ancillary sensors, such as high resolution tiltmeters, ambient pressure sensors, thermometers and an optical frequency comb for the stabilization of the laser frequency. This chapter discusses the required performance of the detection system together with the performance of the ancillary sensors. We also have to examine the reliability of our numerical algorithms, like frequency estimators, both in the time and frequency domains. With everything included and tested, observations of polar motion, solid Earth tides, ocean loading and the Chandler motion are now possible.

The purpose of this chapter is to set the stage for the book and for the upcoming chapters. We first overview classical information-theoretic problems and solutions. We then discuss emerging applications of information-theoretic methods in various data-science problems and, where applicable, refer the reader to related chapters in the book. Throughout this chapter, we highlight the perspectives, tools, and methods that play important roles in classic information-theoretic paradigms and in emerging areas of data science. Table 1.1 provides a summary of the different topics covered in this chapter and highlights the different chapters that can be read as a follow-up to these topics.

Six younger investigators of violent behavior comment on the autobiographies of their senior colleagues. They were chosen to represent investigators at different points in their careers, as well as different countries and different topics. Their comments highlight the family, economic, cultural, and professional contexts that influenced the lives and careers of their senior colleagues. They also highlight the general lessons to be learned from their research. They then describe how the last five decades of work has set the agenda for the next generations of scholars. Suggested future research topics include: 1) testing the extent to which reducing identified risk factors has preventive impacts; 2) studying genetics and epigenetics; 3) using machine learning; 4) using electronic tools to substantially increase data collection in longitudinal studies; 5) expanding the education and training of aggression researchers by integrating biological sciences, data sciences such as bioinformatics and machine learning, social sciences, moral sciences, and the art of policymaking; 6) creating international research teams to do cross-cultural studies and also address global violence research; and 7) scaling up evidence-based programs for wider dissemination and to achieve population-level impacts.

This chapter presents a detailed example that applies the compensation analytics concepts developed in Chapter 6. The reader is assumed to be a compensation consultant charged with evaluating whether gender-based discrimination in pay is present in a public university system in the sciences. Section 7.1 walks through the analysis step-by-step, from formulating the business question, to acquiring and cleaning data, to analyzing the data and interpreting the results from voluminous statistical output in light of the business question. Section 7.2 covers exploratory data mining, causality, and experiments. Exploratory data mining covers situations in which the manager does not know in advance which relationships in the data will be of interest, in contrast to the example in section 7.1 in which a statistical model and specific measures could be constructed that were directly tailored to address the business question at hand. Section 7.2 covers the challenges associated with establishing causality in compensation research and how experiments can sometimes be designed to address those challenges. Randomization and some pitfalls associated with compensation experiments are also covered

This chapter presents a detailed example that applies the compensation analytics concepts developed in Chapter 6. The reader is assumed to be a compensation consultant charged with evaluating whether gender-based discrimination in pay is present in a public university system in the sciences. Section 7.1 walks through the analysis step-by-step, from formulating the business question, to acquiring and cleaning data, to analyzing the data and interpreting the results from voluminous statistical output in light of the business question. Section 7.2 covers exploratory data mining, causality, and experiments. Exploratory data mining covers situations in which the manager does not know in advance which relationships in the data will be of interest, in contrast to the example in section 7.1 in which a statistical model and specific measures could be constructed that were directly tailored to address the business question at hand. Section 7.2 covers the challenges associated with establishing causality in compensation research and how experiments can sometimes be designed to address those challenges. Randomization and some pitfalls associated with compensation experiments are also covered

This chapter responds to the growing importance of business analytics on "big data" in managerial decision-making, by providing a comprehensive primer on analyzing compensation data. All aspects of compensation analytics are covered, starting with data acquisition, types of data, and formulation of a business question that can be informed by data analysis. A detailed, hands-on treatment of data cleaning is provided, equipping readers to prepare data for analysis by detecting and fixing data problems. Descriptive statistics are reviewed, and their utility in data cleaning explicated. Graphical methods are used in examples to detect and trim outliers. The basics of linear regression analysis are covered, with an emphasis on application and interpreting results in the context of the business question(s) posed. One section covers the question of whether or not the pay measure (as a dependent variable) should be transformed via a logarithm, and the implications of that choice for interpreting the results are explained. Precision of regression estimates is covered via an intuitive, non-technical treatment of standard errors. An appendix covers nonlinear relationships among variables.

This chapter responds to the growing importance of business analytics on "big data" in managerial decision-making, by providing a comprehensive primer on analyzing compensation data. All aspects of compensation analytics are covered, starting with data acquisition, types of data, and formulation of a business question that can be informed by data analysis. A detailed, hands-on treatment of data cleaning is provided, equipping readers to prepare data for analysis by detecting and fixing data problems. Descriptive statistics are reviewed, and their utility in data cleaning explicated. Graphical methods are used in examples to detect and trim outliers. The basics of linear regression analysis are covered, with an emphasis on application and interpreting results in the context of the business question(s) posed. One section covers the question of whether or not the pay measure (as a dependent variable) should be transformed via a logarithm, and the implications of that choice for interpreting the results are explained. Precision of regression estimates is covered via an intuitive, non-technical treatment of standard errors. An appendix covers nonlinear relationships among variables.

Neutrons, gamma rays, and X-rays are used to measure the subsurface elemental composition of Solar System bodies, providing insights into their formation and evolution. Neutrons and gamma rays are highly penetrating particles made by the steady bombardment of the regolith of airless bodies by galactic cosmic rays. Gamma rays are also made by the decay of natural radioelements. The escaping radiation can be detected in close-proximity orbits and analyzed to determine subsurface elemental composition to depths of a few decimeters. Because the radiation sensors have nearly omnidirectional response, spatial resolution depends on orbital altitude. X-ray fluorescence is induced by solar X-rays. Consequently, X-ray spectroscopy is most useful for studies of objects in the inner Solar System. Characteristic elemental X-rays are made within the uppermost ~100 micrometers of the surface. The suite of elements analyzed overlaps that of nuclear spectroscopy, providing complementary geochemical information. Because X-rays are easily collimated, relatively high spatial resolution measurements are possible. This chapter presents the fundamentals of neutron, gamma-ray, and X-ray production, transport, and detection along with an overview of the measurement principles, including modeling, analysis, and mapping methods.