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Implementing Reproducible Research
In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden. Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided i...
Analytics, Policy, and Governance
Cover -- Half-title -- Title -- Copyright -- Contents -- Introduction -- PART I: ENGAGING THE DATA -- 1 Measuring Political and Policy Preferences Using Item Response Scaling -- 2 Causal Inference with Observational Data -- 3 Causal Inference with Experimental Data -- PART II: EMERGING DATA SOURCES AND TECHNIQUES -- 4 Descriptive Network Analysis: Interest Group Lobbying Dynamics Around Immigration Policy -- 5 Learning from Place in the Era of Geolocation -- 6 Text Analysis: Estimating Policy Preferences from Written and Spoken Words -- 7 Machine Learning and Governance -- PART III: IMPLICATIONS FOR GOVERNANCE -- 8 Governing a Data-Driven Society -- 9 Big Data and Privacy -- 10 Reflections on Analytics: Knowledge and Power -- List of Contributors -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Y
Privacy, Big Data, and the Public Good
Data access is essential for serving the public good. This book provides new frameworks to address the resultant privacy issues.
Fostering Integrity in Research
The integrity of knowledge that emerges from research is based on individual and collective adherence to core values of objectivity, honesty, openness, fairness, accountability, and stewardship. Integrity in science means that the organizations in which research is conducted encourage those involved to exemplify these values in every step of the research process. Understanding the dynamics that support â€" or distort â€" practices that uphold the integrity of research by all participants ensures that the research enterprise advances knowledge. The 1992 report Responsible Science: Ensuring the Integrity of the Research Process evaluated issues related to scientific responsibility and th...
Religion and the New Technologies
This book is a printed edition of the Special Issue "Religion and the New Technologies" that was published in Religions
Learning from the Science of Cognition and Perception for Decision Making
Beginning in October 2017, the National Academies of Sciences, Engineering, and Medicine organized a set of workshops designed to gather information for the Decadal Survey of Social and Behavioral Sciences for Applications to National Security. The fourth workshop focused on the science of cognition and perception, and this publication summarizes the presentations and discussions from this workshop.
Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results
Questions about the reproducibility of scientific research have been raised in numerous settings and have gained visibility through several high-profile journal and popular press articles. Quantitative issues contributing to reproducibility challenges have been considered (including improper data measurement and analysis, inadequate statistical expertise, and incomplete data, among others), but there is no clear consensus on how best to approach or to minimize these problems. A lack of reproducibility of scientific results has created some distrust in scientific findings among the general public, scientists, funding agencies, and industries. While studies fail for a variety of reasons, many ...
Displaying Time Series, Spatial, and Space-Time Data with R
Code and Methods for Creating High-Quality Data Graphics A data graphic is not only a static image, but it also tells a story about the data. It activates cognitive processes that are able to detect patterns and discover information not readily available with the raw data. This is particularly true for time series, spatial, and space-time datasets. Focusing on the exploration of data with visual methods, Displaying Time Series, Spatial, and Space-Time Data with R presents methods and R code for producing high-quality graphics of time series, spatial, and space-time data. Practical examples using real-world datasets help you understand how to apply the methods and code. The book illustrates h...
Roundtable on Data Science Postsecondary Education
Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.
The Search for Physics. Infinity.
Well known that mathematics and physics have problems in their development. Only one mathematician, Morris Kline, discovered illogicality of development of mathematics. Despite this, he attempted to justify illogicality in math by fruitfulness of usage of mathematics in physics, instead to stay problem about illogical development of physics. Here is discussing inconsistencies of undefined notions which are reasons of paradoxes. Main initial notion of mathematics is notion of infinity, and it has inconsistence and this inconsistency is distributed to derived notions of infinitesimal and continuity. Those notions related to almost all branches of mathematics which used physics. Also in work is considering miss inconsistencies of Euclid's and non-Euclid's geometries. A lot approaches like "physics is geometry or geometry is physics" was and is ignoring those inconsistencies of geometries.
