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"Bringing together neuroscientists, social scientists, and humanities scholars in cross-disciplinary exploration of the topic of cultural memory, this collection moves from seminal discussions of the latest findings in neuroscience to variegated, specific case studies of social practices and artistic expressions. This volume highlights what can be gained from drawing on broad interdisciplinary contexts in pursuing scholarly projects involving cultural memory and associated topics"--

This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham's tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader's R skills are gradually honed, with the help of "your turn" exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrapis introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods - generalized additive models and random forests (an important and versatile machine learning method - are introduced intuitively with applications. The book will be of great interest to economists - students, teachers, and researchers alik - who want to learn R. It will help economics students gain an intuitive appreciation of appliedeconomics and enjoy engaging with the material actively, while also equipping them with key data science skills.

Intro; Table of Contents; The Science and Religion Forum; Acknowledgements; Contributors; Introduction; Section I: Invited Contributions; Section II: Offered Contributions; Section III: Coda; Index; Chapter One; Chapter Two ; Chapter Three; Chapter Four; Chapter Five; Chapter Six; Chapter Seven; Chapter Eight; Chapter Nine; Chapter Ten; Chapter Eleven; Chapter Twelve; Chapter Thirteen; Chapter Fourteen

The concept of science process skills encompasses the conceptual understanding of the basic scientific inquiries that underlie the achievement of the trending science, technology, engineering, and mathematics (STEM) education being encouraged by governments and educators globally and which Nigeria is not exempted. Nevertheless, the importance of science process skills is well documented in the literature. However, research investigating the variation in science process skills among undergraduates is scarce. Thus, the current study aimed to compare differences in science process skills between undergraduates in public and private tertiary institutions in Nigeria. Two hundred and six undergraduates pooled from public and private tertiary institutions in Kogi State, Nigeria, participated in the study. The independent t-test found no statistically significant difference between the private and public tertiary institution students in SPS, t (204) = 1.147, p = 234. The study attributes this outcome to the poor knowledge of SPS among undergraduates in public and private tertiary educations. Thus, it is recommended that effort should be geared towards imparting SPS knowledge to the learners.

AbstractDevelopments in genomic science and biotechnology are creating new governance challenges concerning funding, oversight, and regulation of the underlying science and its applications. Among forms of genomics and biotechnology, human stem cell research has been one of the most controversial. It holds great promise for the development of medical therapies, but the link between human reproduction and research on embryonic stem cells has fuelled serious opposition. We contend that good policy design can reduce tensions around the advancement of controversial science and technologies flowing from it. This article examines issues in the governance of human stem cell research using evidence from California. Four lessons are drawn for the effective governance of other areas of human genomics and biotechnology. They are (1) isolate structural design from controversy; (2) make room for laypeople in the governance structures; (3) promote transparency, minimize secrecy; and (4) create opportunities for learning and innovation.