Social Science for What?


Book Description

How the NSF became an important yet controversial patron for the social sciences, influencing debates over their scientific status and social relevance. In the early Cold War years, the U.S. government established the National Science Foundation (NSF), a civilian agency that soon became widely known for its dedication to supporting first-rate science. The agency's 1950 enabling legislation made no mention of the social sciences, although it included a vague reference to "other sciences." Nevertheless, as Mark Solovey shows in this book, the NSF also soon became a major--albeit controversial--source of public funding for them.




Next Generation Earth Systems Science at the National Science Foundation


Book Description

The National Science Foundation (NSF) has played a key role over the past several decades in advancing understanding of Earth's systems by funding research on atmospheric, ocean, hydrologic, geologic, polar, ecosystem, social, and engineering-related processes. Today, however, those systems are being driven like never before by human technologies and activities. Our understanding has struggled to keep pace with the rapidity and magnitude of human-driven changes, their impacts on human and ecosystem sustainability and resilience, and the effectiveness of different pathways to address those challenges. Given the urgency of understanding human-driven changes, NSF will need to sustain and expand its efforts to achieve greater impact. The time is ripe to create a next-generation Earth systems science initiative that emphasizes research on complex interconnections and feedbacks between natural and social processes. This will require NSF to place an increased emphasis on research inspired by real-world problems while maintaining their strong legacy of curiosity driven research across many disciplines ? as well as enhance the participation of social, engineering, and data scientists, and strengthen efforts to include diverse perspectives in research.




National Science Foundation


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A Patron for Pure Science


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Building Foundations of Scientific Understanding


Book Description

This is The most comprehensive science curriculum for beginning learners that you will find anywhere * Here are 41 lesson plans that cover all major areas of science. * Lessons are laid out as stepping stones that build knowledge and understanding logically and systematically. * Child-centered, hands-on activities at the core of all lessons bring children to observe, think, and reason. * Interest is maintained and learning is solidified by constantly connecting lessons with children's real-world experience * Skills of inquiry become habits of mind as they are used throughout. * Lessons integrate reading, writing, geography, and other subjects. * Standards, including developing a broader, supportive community of science learners come about as natural by-products of learning science in an organized way. Particular background or experience is not required. Instructions include guiding students to question, observe, think, interpret, and draw rational conclusions in addition to performing the activity. Teachers can learn along with their students and be exceptional role models in doing so. Need for special materials is minimized. Personal, on line, support is available free of charge (see front matter).







Foundations of Data Science


Book Description

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.










National Science Foundation Legislation, 1975


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