Big Data in Astronomy

Big Data in Astronomy
Author: Linghe Kong,Tian Huang,Yongxin Zhu,Shenghua Yu
Publsiher: Elsevier
Total Pages: 438
Release: 2020-06-13
ISBN 10: 012819085X
ISBN 13: 9780128190852
Language: EN, FR, DE, ES & NL

Big Data in Astronomy Book Review:

Big Data in Radio Astronomy: Scientific Data Processing for Advanced Radio Telescopes provides the latest research developments in big data methods and techniques for radio astronomy. Providing examples from such projects as the Square Kilometer Array (SKA), the world’s largest radio telescope that generates over an Exabyte of data every day, the book offers solutions for coping with the challenges and opportunities presented by the exponential growth of astronomical data. Presenting state-of-the-art results and research, this book is a timely reference for both practitioners and researchers working in radio astronomy, as well as students looking for a basic understanding of big data in astronomy. Bridges the gap between radio astronomy and computer science Includes coverage of the observation lifecycle as well as data collection, processing and analysis Presents state-of-the-art research and techniques in big data related to radio astronomy Utilizes real-world examples, such as Square Kilometer Array (SKA) and Five-hundred-meter Aperture Spherical radio Telescope (FAST)

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation
Author: Petr Skoda,Fathalrahman Adam
Publsiher: Elsevier
Total Pages: 472
Release: 2020-04-10
ISBN 10: 0128191554
ISBN 13: 9780128191552
Language: EN, FR, DE, ES & NL

Knowledge Discovery in Big Data from Astronomy and Earth Observation Book Review:

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. Addresses both astronomy and geosciences in parallel, from a big data perspective Includes introductory information, key principles, applications and the latest techniques Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation
Author: Petr Skoda,Fathalrahman Adam
Publsiher: Unknown
Total Pages: 400
Release: 2020-03
ISBN 10: 0128191546
ISBN 13: 9780128191545
Language: EN, FR, DE, ES & NL

Knowledge Discovery in Big Data from Astronomy and Earth Observation Book Review:

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants.

Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy
Author: Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publsiher: CRC Press
Total Pages: 744
Release: 2012-03-29
ISBN 10: 1439841748
ISBN 13: 9781439841747
Language: EN, FR, DE, ES & NL

Advances in Machine Learning and Data Mining for Astronomy Book Review:

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

Big Data Analytics in Astronomy Science and Engineering

Big Data Analytics in Astronomy  Science  and Engineering
Author: Shelly Sachdeva
Publsiher: Springer Nature
Total Pages: 135
Release: 2022
ISBN 10: 3030966003
ISBN 13: 9783030966003
Language: EN, FR, DE, ES & NL

Big Data Analytics in Astronomy Science and Engineering Book Review:

Astronomy and Big Data

Astronomy and Big Data
Author: Kieran Jay Edwards,Mohamed Medhat Gaber
Publsiher: Springer Science & Business Media
Total Pages: 105
Release: 2014-04-12
ISBN 10: 3319065998
ISBN 13: 9783319065991
Language: EN, FR, DE, ES & NL

Astronomy and Big Data Book Review:

With the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as “Uncertain”. This book reports on how to use data mining, more specifically clustering, to identify galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants.

Data Analysis in Astronomy

Data Analysis in Astronomy
Author: V. di Gesù,L. Scarsi,P. Crane,J.H. Friedman,S. Levialdi
Publsiher: Springer Science & Business Media
Total Pages: 541
Release: 2012-12-06
ISBN 10: 1461594332
ISBN 13: 9781461594338
Language: EN, FR, DE, ES & NL

Data Analysis in Astronomy Book Review:

The international Workshop on "Data Analysis in Astronomy" was in tended to give a presentation of experiences that have been acqui red in data analysis and image processing, developments and appli cations that are steadly growing up in Astronomy. The quality and the quantity of ground and satellite observations require more so phisticated data analysis methods and better computational tools. The Workshop has reviewed the present state of the art, explored new methods and discussed a wide range of applications. The topics which have been selected have covered the main fields of interest for data analysis in Astronomy. The Workshop has been focused on the methods used and their significant applications. Results which gave a major contribution to the physical interpre tation of the data have been stressed in the presentations. Atten tion has been devoted to the description of operational system for data analysis in astronomy. The success of the meeting has been the results of the coordinated effort of several people from the organizers to those who presen ted a contribution and/or took part in the discussion. We wish to thank the members of the Workshop scientific committee Prof. M. Ca paccioli, Prof. G. De Biase, Prof. G. Sedmak, Prof. A. Zichichi and of the local organizing committee Dr. R. Buccheri and Dr. M.C. Macca rone together with Miss P. Savalli and Dr. A. Gabriele of the E. Majo rana Center for their support and the unvaluable part in arranging the Workshop.

Doing Data Science

Doing Data Science
Author: Cathy O'Neil,Rachel Schutt
Publsiher: "O'Reilly Media, Inc."
Total Pages: 408
Release: 2013-10-09
ISBN 10: 144936389X
ISBN 13: 9781449363895
Language: EN, FR, DE, ES & NL

Doing Data Science Book Review:

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

The Decade of Discovery in Astronomy and Astrophysics

The Decade of Discovery in Astronomy and Astrophysics
Author: National Research Council,Division on Engineering and Physical Sciences,Commission on Physical Sciences, Mathematics, and Applications,Board on Physics and Astronomy,Astronomy and Astrophysics Survey Committee
Publsiher: National Academies Press
Total Pages: 200
Release: 1991-02-01
ISBN 10: 0309043816
ISBN 13: 9780309043816
Language: EN, FR, DE, ES & NL

The Decade of Discovery in Astronomy and Astrophysics Book Review:

Astronomers and astrophysicists are making revolutionary advances in our understanding of planets, stars, galaxies, and even the structure of the universe itself. The Decade of Discovery presents a survey of this exciting field of science and offers a prioritized agenda for space- and ground-based research into the twenty-first century. The book presents specific recommendations, programs, and expenditure levels to meet the needs of the astronomy and astrophysics communities. Accessible to the interested lay reader, the book explores: The technological investments needed for instruments that will be built in the next century. The importance of the computer revolution to all aspects of astronomical research. The potential usefulness of the moon as an observatory site. Policy issues relevant to the funding of astronomy and the execution of astronomical projects. The Decade of Discovery will prove valuable to science policymakers, research administrators, scientists, and students in the physical sciences, and interested lay readers. Alternate Selection, Astronomy Book Club

Statistics Data Mining and Machine Learning in Astronomy

Statistics  Data Mining  and Machine Learning in Astronomy
Author: Željko Ivezić,Andrew J. Connolly,Jacob T VanderPlas,Alexander Gray
Publsiher: Princeton University Press
Total Pages: 560
Release: 2014-01-12
ISBN 10: 0691151687
ISBN 13: 9780691151687
Language: EN, FR, DE, ES & NL

Statistics Data Mining and Machine Learning in Astronomy Book Review:

As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers

The Big R Book

The Big R Book
Author: Philippe J. S. De Brouwer
Publsiher: John Wiley & Sons
Total Pages: 928
Release: 2020-09-29
ISBN 10: 1119632765
ISBN 13: 9781119632764
Language: EN, FR, DE, ES & NL

The Big R Book Book Review:

Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices. Provides a practical guide for non-experts with a focus on business users Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting Uses a practical tone and integrates multiple topics in a coherent framework Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R Shows readers how to visualize results in static and interactive reports Supplementary materials includes PDF slides based on the book’s content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.

Big Data

Big Data
Author: Timandra Harkness
Publsiher: Bloomsbury Publishing
Total Pages: 304
Release: 2016-06-02
ISBN 10: 1472920066
ISBN 13: 9781472920065
Language: EN, FR, DE, ES & NL

Big Data Book Review:

What is Big Data, and why should you care? Big data knows where you've been and who your friends are. It knows what you like and what makes you angry. It can predict what you'll buy, where you'll be the victim of crime and when you'll have a heart attack. Big data knows you better than you know yourself, or so it claims. But how well do you know big data? You've probably seen the phrase in newspaper headlines, at work in a marketing meeting, or on a fitness-tracking gadget. But can you understand it without being a Silicon Valley nerd who writes computer programs for fun? Yes. Yes, you can. Timandra Harkness writes comedy, not computer code. The only programmes she makes are on the radio. If you can read a newspaper you can read this book. Starting with the basics – what IS data? And what makes it big? – Timandra takes you on a whirlwind tour of how people are using big data today: from science to smart cities, business to politics, self-quantification to the Internet of Things. Finally, she asks the big questions about where it's taking us; is it too big for its boots, or does it think too small? Are you a data point or a human being? Will this book be full of rhetorical questions? No. It also contains puns, asides, unlikely stories and engaging people, inspiring feats and thought-provoking dilemmas. Leaving you armed and ready to decide what you think about one of the decade's big ideas: big data.

Big Data in Complex Systems

Big Data in Complex Systems
Author: Aboul Ella Hassanien,Ahmad Taher Azar,Vaclav Snasael,Janusz Kacprzyk,Jemal H. Abawajy
Publsiher: Springer
Total Pages: 499
Release: 2015-01-02
ISBN 10: 331911056X
ISBN 13: 9783319110561
Language: EN, FR, DE, ES & NL

Big Data in Complex Systems Book Review:

This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.

Deep Learning in Solar Astronomy

Deep Learning in Solar Astronomy
Author: Long Xu
Publsiher: Springer Nature
Total Pages: 135
Release: 2022
ISBN 10: 9811927464
ISBN 13: 9789811927461
Language: EN, FR, DE, ES & NL

Deep Learning in Solar Astronomy Book Review:

The Last Stargazers

The Last Stargazers
Author: Emily Levesque
Publsiher: Sourcebooks, Inc.
Total Pages: 336
Release: 2020-08-04
ISBN 10: 1492681083
ISBN 13: 9781492681083
Language: EN, FR, DE, ES & NL

The Last Stargazers Book Review:

The story of the people who see beyond the stars—an astronomy book for adults still spellbound by the night sky. Humans from the earliest civilizations through today have craned their necks each night, using the stars to orient themselves in the large, strange world around them. Stargazing is a pursuit that continues to fascinate us: from Copernicus to Carl Sagan, astronomers throughout history have spent their lives trying to answer the biggest questions in the universe. Now, award-winning astronomer Emily Levesque shares the stories of modern-day stargazers in this new nonfiction release, the people willing to adventure across high mountaintops and to some of the most remote corners of the planet, all in the name of science. From the lonely quiet of midnight stargazing to tall tales of wild bears loose in the observatory, The Last Stargazers is a love letter to astronomy and an affirmation of the crucial role that humans can and must play in the future of scientific discovery. In this sweeping work of narrative science, Levesque shows how astronomers in this scrappy and evolving field are going beyond the machines to infuse creativity and passion into the stars and space and inspires us all to peer skyward in pursuit of the universe's secrets.

Big Data Little Data No Data

Big Data  Little Data  No Data
Author: Christine L. Borgman
Publsiher: MIT Press
Total Pages: 416
Release: 2017-02-03
ISBN 10: 0262529912
ISBN 13: 9780262529914
Language: EN, FR, DE, ES & NL

Big Data Little Data No Data Book Review:

An examination of the uses of data within a changing knowledge infrastructure, offering analysis and case studies from the sciences, social sciences, and humanities. “Big Data” is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data—because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure—an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation—six “provocations” meant to inspire discussion about the uses of data in scholarship—Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.

A Closer Look at Big Data Analytics

A Closer Look at Big Data Analytics
Author: R. Anandan
Publsiher: Nova Science Publishers
Total Pages: 366
Release: 2021
ISBN 10: 9781536194265
ISBN 13: 1536194263
Language: EN, FR, DE, ES & NL

A Closer Look at Big Data Analytics Book Review:

"Big Data Analytics is a field that dissects, efficiently extricates data from, or in any case manages informational indexes that are excessively huge or complex to be managed by customary information preparing application programming. Information with numerous cases (lines) offers more noteworthy factual force, while information with higher multifaceted nature may prompt a higher bogus disclosure rate. Enormous information challenges incorporate catching information, information stockpiling, information investigation, search, sharing, move, representation, and questioning, refreshing, data security and data source. Large information was initially connected with three key ideas: volume, variety and velocity. Consequently, huge information regularly incorporates information with sizes that surpass the limit of conventional programming to measure inside a satisfactory time and worth. Current utilization of the term enormous information will in general allude to the utilization of predictive analytics, user behavior analytics, or certain other progressed information investigation techniques that concentrate an incentive from information, and sometimes to a specific size of informational index. There is little uncertainty that the amounts of information now accessible are undoubtedly enormous, however that is not the most important quality of this new information biological system. Investigation of informational indexes can discover new relationships to spot business patterns or models. Researchers, business persons, clinical specialists, promoting and governments consistently meet challenges with huge informational collections in territories including Internet look, fintech, metropolitan informatics, and business informatics. Researchers experience constraints in e-Science work, including meteorology, genomics, connectomics, complex material science reproductions, science and ecological exploration. The main objective of this book is to write about issues, challenges, opportunities, and solutions in novel research projects about big data in various domains. The topics of interest include, but are not limited to: efficient storage, management and sharing large scale of data; novel approaches for analyzing data using big data technologies; implementation of high performance and/or scalable and/or real-time computation algorithms for analyzing big data; usage of various data sources like historical data, social networking media, machine data and crowd-sourcing data; using machine learning, visual analytics, data mining, spatio-temporal data analysis and statistical inference in different domains (with large scale datasets); Legal and ethical issues and solutions for using, sharing and publishing large datasets; and the results of data analytics, security and privacy issues"--

The Ascent of Information

The Ascent of Information
Author: Caleb Scharf
Publsiher: Penguin
Total Pages: 352
Release: 2021-06-15
ISBN 10: 0593087267
ISBN 13: 9780593087268
Language: EN, FR, DE, ES & NL

The Ascent of Information Book Review:

“Full of fascinating insights drawn from an impressive range of disciplines, The Ascent of Information casts the familiar and the foreign in a dramatic new light.” —Brian Greene, author of The Elegant Universe Your information has a life of its own, and it’s using you to get what it wants. One of the most peculiar and possibly unique features of humans is the vast amount of information we carry outside our biological selves. But in our rush to build the infrastructure for the 20 quintillion bits we create every day, we’ve failed to ask exactly why we’re expending ever-increasing amounts of energy, resources, and human effort to maintain all this data. Drawing on deep ideas and frontier thinking in evolutionary biology, computer science, information theory, and astrobiology, Caleb Scharf argues that information is, in a very real sense, alive. All the data we create—all of our emails, tweets, selfies, A.I.-generated text and funny cat videos—amounts to an aggregate lifeform. It has goals and needs. It can control our behavior and influence our well-being. And it’s an organism that has evolved right alongside us. This symbiotic relationship with information offers a startling new lens for looking at the world. Data isn’t just something we produce; it’s the reason we exist. This powerful idea has the potential to upend the way we think about our technology, our role as humans, and the fundamental nature of life. The Ascent of Information offers a humbling vision of a universe built of and for information. Scharf explores how our relationship with data will affect our ongoing evolution as a species. Understanding this relationship will be crucial to preventing our data from becoming more of a burden than an asset, and to preserving the possibility of a human future.

Big Data Concepts Theories and Applications

Big Data Concepts  Theories  and Applications
Author: Shui Yu,Song Guo
Publsiher: Springer
Total Pages: 437
Release: 2016-03-03
ISBN 10: 3319277634
ISBN 13: 9783319277639
Language: EN, FR, DE, ES & NL

Big Data Concepts Theories and Applications Book Review:

This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. It also focuses on high level concepts such as definitions of Big Data from different angles; surveys in research and applications; and existing tools, mechanisms, and systems in practice. Each chapter is independent from the other chapters, allowing users to read any chapter directly. After examining the practical side of Big Data, this book presents theoretical perspectives. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Big Data Concepts, Theories and Applications is designed as a reference for researchers and advanced level students in computer science, electrical engineering and mathematics. Practitioners who focus on information systems, big data, data mining, business analysis and other related fields will also find this material valuable.

Society and the Internet

Society and the Internet
Author: Mark Graham,William H. Dutton
Publsiher: Oxford University Press
Total Pages: 480
Release: 2019-07-18
ISBN 10: 0192590650
ISBN 13: 9780192590657
Language: EN, FR, DE, ES & NL

Society and the Internet Book Review:

How is society being reshaped by the continued diffusion and increasing centrality of the Internet in everyday life and work? Society and the Internet provides key readings for students, scholars, and those interested in understanding the interactions of the Internet and society. This multidisciplinary collection of theoretically and empirically anchored chapters addresses the big questions about one of the most significant technological transformations of this century, through a diversity of data, methods, theories, and approaches. Drawing from a range of disciplinary perspectives, Internet research can address core questions about equality, voice, knowledge, participation, and power. By learning from the past and continuing to look toward the future, it can provide a better understanding of what the ever-changing configurations of technology and society mean, both for the everyday life of individuals and for the continued development of society at large. This second edition presents new and original contributions examining the escalating concerns around social media, disinformation, big data, and privacy. Following a foreword by Manual Castells, the editors introduce some of the key issues in Internet Studies. The chapters then offer the latest research in five focused sections: The Internet in Everyday Life; Digital Rights and Human Rights; Networked Ideas, Politics, and Governance; Networked Businesses, Industries, and Economics; and Technological and Regulatory Histories and Futures. This book will be a valuable resource not only for students and researchers, but for anyone seeking a critical examination of the economic, social, and political factors shaping the Internet and its impact on society.