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We discuss our outreach efforts to introduce school students to network science and explain why networks researchers should be involved in such outreach activities. We provide overviews of modules that we have designed for these efforts, comment on our successes and failures, and illustrate the potentially enormous impact of such outreach efforts.
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Recently, Harrington et al. (2013) presented an outreach effort to introduce school students to network science and explain why researchers who study networks should be involved in such outreach activities. Based on the modules they designed and their comments on the success and failures of the activity, we have carried out a sequel with students from a high school in Madrid, Spain. We report on how we developed it and the changes we made to the original material.
Science students must deal with the errors inherent to all physical measurements and be conscious of the need to expressvthem as a best estimate and a range of uncertainty. Errors are routinely classified as statistical or systematic. Although statistical errors are usually dealt with in the first years of science studies, the typical approaches are based on manually performing repetitive observations. Our work proposes a set of laboratory experiments to teach error and uncertainties based on data recorded with the sensors available in many mobile devices. The main aspects addressed are the physical meaning of the mean value and standard deviation, and the interpretation of histograms and distributions. The normality of the fluctuations is analyzed qualitatively comparing histograms with normal curves and quantitatively comparing the number of observations in intervals to the number expected according to a normal distribution and also performing a Chi-squared test. We show that the distribution usually follows a normal distribution, however, when the sensor is placed on top of a loudspeaker playing a pure tone significant differences with a normal distribution are observed. As applications to every day situations we discuss the intensity of the fluctuations in different situations, such as placing the device on a table or holding it with the hands in different ways. Other activities are focused on the smoothness of a road quantified in terms of the fluctuations registered by the accelerometer. The present proposal contributes to gaining a deep insight into modern technologies and statistical errors and, finally, motivating and encouraging engineering and science students.
This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cutting-edge methods using relevant examples and illustrations in health services research are provided.
Analysis of stochastic processes can be used to engender critical thinking. Quantum dots have a reversible, stochastic transition between luminescent and non-luminescent states. The luminescence intermittency is known as blinking, and is not evident from ensemble measurements. In order to stimulate critical thinking, students design, perform, and analyze a semiconductor quantum dot blinking laboratory experiment. The design of the experiment and stochastic nature of the data collected require students to make judgements throughout the course of the single-particle measurement and analysis. Some of the decisions do not have uniquely correct answers, challenging the students to engage in critical thinking. We propose that students self-examined decision making develops a constructivist view of science. The experiment is visually striking, interdisciplinary, and develops higher order thinking.
The Multimessenger Diversity Network (MDN), formed in 2018, extends the basic principle of multimessenger astronomy -- that working collaboratively with different approaches enhances understanding and enables previously impossible discoveries -- to equity, diversity, and inclusion (EDI) in science research collaborations. With support from the National Science Foundation INCLUDES program, the MDN focuses on increasing EDI by sharing knowledge, experiences, training, and resources among representatives from multimessenger science collaborations. Representatives to the MDN become engagement leads in their collaboration, extending the reach of the community of practice. An overview of the MDN structure, lessons learned, and how to join are presented.
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