Manhattan Project (U.S.) -- History.

Aurora Australis (Ship) -- History.

Walking -- Social aspects.

Neurosciences.

Sleep disorders.

Menopause.

Microorganisms -- History.

Magic.

United Kingdom, c.1935.

United Kingdom, c.1935.

England, 1935.

195?

Citizen science is a method of involving students and community members in scientific research and exploration using the same parameters as professional scientists. During the Huron River Watershed Council’s annual “Insect Identification Day” in Ann Arbor, Mich., students and volunteers identified insects to add to the group’s ecological survey. Such projects are part of a new wave of […]

Critics said the bill would have allowed teachers to bring in alternative theories about climate change and evolution.

The Association for Science Education's online website. The Wellcome Library subscribes to selected titles only.

Open access titles available on the Science Direct platform.

London : Longmans, Green, 1865-1869.

Londres : J.-B. Baillière, 1855.

London : J. Churchill, 1849.

Paris : Pihan Delaforest (Morinval), 1831.

Edinburgh : Daniel Lizars, 1829-

Edinburgh : Bell & Bradfute, 1807.

Paris : V. Masson, 1867.

London : John J. Griffin, 1851.

Detroit, Mich. : G.S. Davis, 1891.

Whitehaven : T. Brakenridge, 1895.

Edinburgh : A. and C. Black, 1873.

Madras : printed by C. Foster, 1876.

Bruxelles : Etablissement Encyclographique, 1833-46.

A small group of science teachers in Missouri is using the coronavirus as a teachable moment that's aligned to the Next Generation Science Standards.

Glasgow : Beatson Institute for Cancer Research, 2008-

Rockville, Maryland : National Institute on Drug Abuse, 1981.

Rockville, Maryland : National Institute of Drug Abuse, 1985.

Rockville, Maryland : National Institute on Drug Abuse, 1979.

John J. Dziak, Donna L. Coffman, Matthew Reimherr, Justin Petrovich, Runze Li, Saul Shiffman, Mariya P. Shiyko.

Source: Statistics Surveys, Volume 13, 150--180.

Abstract:
Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a semiparametric way via scalar-on-function regression. However, the resulting fitted coefficient regression function requires special care for correct interpretation, as it represents the joint relationship of time points to the outcome, rather than a marginal or cross-sectional relationship. We provide practical guidelines, based on experience with scientific applications, for helping practitioners interpret their results and illustrate these ideas using data from a smoking cessation study.

Sean L. Simpson, F. DuBois Bowman, Paul J. Laurienti

Source: Statist. Surv., Volume 7, 1--36.

Abstract:
Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function.

Yazhen Wang, Xinyu Song.

Source: Statistical Science, Volume 35, Number 1, 51--74.

Abstract:
Quantum science and quantum technology are of great current interest in multiple frontiers of many scientific fields ranging from computer science to physics and chemistry, and from engineering to mathematics and statistics. Their developments will likely lead to a new wave of scientific revolutions and technological innovations in a wide range of scientific studies and applications. This paper provides a brief review on quantum communication, quantum information, quantum computation, quantum simulation, and quantum metrology. We present essential quantum properties, illustrate relevant concepts of quantum science and quantum technology, and discuss their scientific developments. We point out the need for statistical analysis in their developments, as well as their potential applications to and impacts on statistics and data science.

Michael L. Stein.

Source: Statistical Science, Volume 35, Number 1, 31--41.

Abstract:
Climate science is a field that is arguably both data-rich and data-poor. Data rich in that huge and quickly increasing amounts of data about the state of the climate are collected every day. Data poor in that important aspects of the climate are still undersampled, such as the deep oceans and some characteristics of the upper atmosphere. Data rich in that modern climate models can produce climatological quantities over long time periods with global coverage, including quantities that are difficult to measure and under conditions for which there is no data presently. Data poor in that the correspondence between climate model output to the actual climate, especially for future climate change due to human activities, is difficult to assess. The scope for fruitful interactions between climate scientists and statisticians is great, but requires serious commitments from researchers in both disciplines to understand the scientific and statistical nuances arising from the complex relationships between the data and the real-world problems. This paper describes a small fraction of some of the intellectual challenges that occur at the interface between climate science and statistics, including inferences for extremes for processes with seasonality and long-term trends, the use of climate model ensembles for studying extremes, the scope for using new data sources for studying space-time characteristics of environmental processes and a discussion of non-Gaussian space-time process models for climate variables. The paper concludes with a call to the statistical community to become more engaged in one of the great scientific and policy issues of our time, anthropogenic climate change and its impacts.

Junhui Cai, Avishai Mandelbaum, Chaitra H. Nagaraja, Haipeng Shen, Linda Zhao.

Source: Statistical Science, Volume 34, Number 4, 669--691.

Abstract:
Statisticians are finding their place in the emerging field of data science. However, many issues considered “new” in data science have long histories in statistics. Examples of using statistical thinking are illustrated, which range from exploratory data analysis to measuring uncertainty to accommodating nonrandom samples. These examples are then applied to service networks, baseball predictions and official statistics.

Barry E. Stein
Jan 2, 2020; 40:3-11
Viewpoints

Emeran A. Mayer
Nov 12, 2014; 34:15490-15496
Symposium