# Big Data in Astronomy

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## Big Data in Astronomy

Author | : Chris Broekema,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

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

## Big Data in Astronomy

Author | : Linghe Kong,Tian Huang,Yongxin Zhu,Shenghua Yu,Chris Broekema |

Publsiher | : Elsevier |

Total Pages | : 475 |

Release | : 2020-06 |

ISBN 10 | : 9780128190845 |

ISBN 13 | : 0128190841 |

Language | : EN, FR, DE, ES & NL |

**Big Data in Astronomy Book Review:**

Big Data in Radio Astronomy 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), which is the world's largest radio telescope and 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. As new technologies emerge, it is important to consider the challenges of how to process, record, calibrate, and clean astronomical big data, as well as how to optimize and accelerate the algorithms for processing, and how to extract knowledge from big data.Presenting state-of-the-art results and research, Big Data in Radio Astronomy 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.

## 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

## 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.

## The Astronomy Book

Author | : DK |

Publsiher | : Penguin |

Total Pages | : 352 |

Release | : 2017-09-05 |

ISBN 10 | : 1465470719 |

ISBN 13 | : 9781465470713 |

Language | : EN, FR, DE, ES & NL |

**The Astronomy Book Book Review:**

Since the dawn of humankind, people have looked upward to the heavens and tried to understand them. This encyclopedia takes you on an expedition through time and space to discover our place in the universe. We invite you to take a journey through the wonders of the universe. Explore the cosmos, from planets to black holes, the Big Bang, and everything in-between! Get ready to discover the story of the universe one page at a time! This educational book for young adults will launch you on a wild trip through the cosmos and the incredible discoveries throughout history. Filled to the brim with beautifully illustrated flowcharts, graphics, and jargon-free language, The Astronomy Book breaks down hard-to-grasp concepts to guide you in understanding almost 100 big astronomical ideas. Big Ideas How do we measure the universe? Where is the event horizon? What is dark matter? Now you can find out all the answers to these questions and so much more in this inquisitive book about our universe! Using incredibly clever visual learning devices like step-by-step diagrams, you'll learn more about captivating topics from the Copernican Revolution. Dive into the mind-boggling theories of recent science in a user-friendly format that makes the information easy to follow. Explore the biographies, theories, and discoveries of key astronomers through the ages such as Ptolemy, Galileo, Newton, Hubble, and Hawking. To infinity and beyond! Journey through space and time with us: - From Myth to Science 600 BCE - 1550 CE - The Telescope Revolution 1550 - 1750 - Uranus to Neptune 1750 - 1850 - The Rise of Astrophysics 1850 - 1915 - Atom, Stars, And Galaxies 1915 - 1950 - New Windows on The Universe 1950 - 1917 - The Triumph of Technology 1975 - Present The Series Simply Explained With over 7 million copies sold worldwide to date, The Astronomy Book is part of the award-winning Big Ideas Simply Explained series from DK Books. It uses innovative graphics along with engaging writing to make complex subjects easier to understand. Shortlisted: A Young Adult Library Services Association Outstanding Books for the College Bound and Lifelong Learners list selection A Mom's Choice Awards® Honoring Excellence Gold Seal of Approval for Young Adult Books A Parents' Choice Gold Award winner

## Big Data

Author | : Viktor Mayer-Schönberger,Kenneth Cukier |

Publsiher | : Houghton Mifflin Harcourt |

Total Pages | : 240 |

Release | : 2013-03-05 |

ISBN 10 | : 0544002938 |

ISBN 13 | : 9780544002937 |

Language | : EN, FR, DE, ES & NL |

**Big Data Book Review:**

A revelatory exploration of the hottest trend in technology and the dramatic impact it will have on the economy, science, and society at large. Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict the spread of the H1N1 flu outbreak? The key to answering these questions, and many more, is big data. “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior. In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing. www.big-data-book.com

## 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.

## 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

## Data Analysis in Cosmology

Author | : Vicent J. Martinez,Enn Saar,Enrique Martinez Gonzales,Maria Jesus Pons-Borderia |

Publsiher | : Springer |

Total Pages | : 636 |

Release | : 2009-07-09 |

ISBN 10 | : 3540447679 |

ISBN 13 | : 9783540447672 |

Language | : EN, FR, DE, ES & NL |

**Data Analysis in Cosmology Book Review:**

The amount of cosmological data has dramatically increased in the past decades due to an unprecedented development of telescopes, detectors and satellites. Efficiently handling and analysing new data of the order of terabytes per day requires not only computer power to be processed but also the development of sophisticated algorithms and pipelines. Aiming at students and researchers the lecture notes in this volume explain in pedagogical manner the best techniques used to extract information from cosmological data, as well as reliable methods that should help us improve our view of the universe.

## Astronomical Data Analysis Software and Systems XXVI

Author | : Marco Molinaro,Keith Shortridge,Fabio Pasian |

Publsiher | : Unknown |

Total Pages | : 788 |

Release | : 2019 |

ISBN 10 | : 9781583819302 |

ISBN 13 | : 1583819304 |

Language | : EN, FR, DE, ES & NL |

**Astronomical Data Analysis Software and Systems XXVI Book Review:**

## 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.

## 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.

## Statistical Methods for Astronomical Data Analysis

Author | : Asis Kumar Chattopadhyay,Tanuka Chattopadhyay |

Publsiher | : Springer |

Total Pages | : 349 |

Release | : 2014-10-01 |

ISBN 10 | : 149391507X |

ISBN 13 | : 9781493915071 |

Language | : EN, FR, DE, ES & NL |

**Statistical Methods for Astronomical Data Analysis Book Review:**

This book introduces “Astrostatistics” as a subject in its own right with rewarding examples, including work by the authors with galaxy and Gamma Ray Burst data to engage the reader. This includes a comprehensive blending of Astrophysics and Statistics. The first chapter’s coverage of preliminary concepts and terminologies for astronomical phenomenon will appeal to both Statistics and Astrophysics readers as helpful context. Statistics concepts covered in the book provide a methodological framework. A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis. Readers can then use the appropriate statistical packages for their particular data analysis needs. The ideas of statistical inference discussed in the book help readers determine how to apply statistical tests. The authors cover different applications of statistical techniques already developed or specifically introduced for astronomical problems, including regression techniques, along with their usefulness for data set problems related to size and dimension. Analysis of missing data is an important part of the book because of its significance for work with astronomical data. Both existing and new techniques related to dimension reduction and clustering are illustrated through examples. There is detailed coverage of applications useful for classification, discrimination, data mining and time series analysis. Later chapters explain simulation techniques useful for the development of physical models where it is difficult or impossible to collect data. Finally, coverage of the many R programs for techniques discussed makes this book a fantastic practical reference. Readers may apply what they learn directly to their data sets in addition to the data sets included by the authors.

## Data Science and Big Data Analytics

Author | : EMC Education Services |

Publsiher | : John Wiley & Sons |

Total Pages | : 432 |

Release | : 2015-01-05 |

ISBN 10 | : 1118876059 |

ISBN 13 | : 9781118876053 |

Language | : EN, FR, DE, ES & NL |

**Data Science and Big Data Analytics Book Review:**

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Corresponding data sets are available from the book’s page at Wiley which you can find on the Wiley site by searching for the ISBN 9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

## Big Data Meets Survey Science

Author | : Craig A. Hill,Paul P. Biemer,Trent D. Buskirk,Lilli Japec,Antje Kirchner,Stas Kolenikov,Lars E. Lyberg |

Publsiher | : John Wiley & Sons |

Total Pages | : 800 |

Release | : 2020-09-29 |

ISBN 10 | : 1118976320 |

ISBN 13 | : 9781118976326 |

Language | : EN, FR, DE, ES & NL |

**Big Data Meets Survey Science Book Review:**

Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.

## Big Data Science in Finance

Author | : Irene Aldridge,Marco Avellaneda |

Publsiher | : John Wiley & Sons |

Total Pages | : 336 |

Release | : 2021-01-08 |

ISBN 10 | : 1119602971 |

ISBN 13 | : 9781119602972 |

Language | : EN, FR, DE, ES & NL |

**Big Data Science in Finance Book Review:**

Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.

## 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.

## Working Papers

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 | : 356 |

Release | : 1991-02-01 |

ISBN 10 | : 0309043832 |

ISBN 13 | : 9780309043830 |

Language | : EN, FR, DE, ES & NL |

**Working Papers Book Review:**

This volume contains working papers on astronomy and astrophysics prepared by 15 non-National Research Council panels in areas ranging from radio astronomy to the status of the profession.

## Data Science Landscape

Author | : Usha Mujoo Munshi,Neeta Verma |

Publsiher | : Springer |

Total Pages | : 339 |

Release | : 2018-03-01 |

ISBN 10 | : 9811075158 |

ISBN 13 | : 9789811075155 |

Language | : EN, FR, DE, ES & NL |

**Data Science Landscape Book Review:**

The edited volume deals with different contours of data science with special reference to data management for the research innovation landscape. The data is becoming pervasive in all spheres of human, economic and development activity. In this context, it is important to take stock of what is being done in the data management area and begin to prioritize, consider and formulate adoption of a formal data management system including citation protocols for use by research communities in different disciplines and also address various technical research issues. The volume, thus, focuses on some of these issues drawing typical examples from various domains. The idea of this work germinated from the two day workshop on “Big and Open Data – Evolving Data Science Standards and Citation Attribution Practices”, an international workshop, led by the ICSU-CODATA and attended by over 300 domain experts. The Workshop focused on two priority areas (i) Big and Open Data: Prioritizing, Addressing and Establishing Standards and Good Practices and (ii) Big and Open Data: Data Attribution and Citation Practices. This important international event was part of a worldwide initiative led by ICSU, and the CODATA-Data Citation Task Group. In all, there are 21 chapters (with 21st Chapter addressing four different core aspects) written by eminent researchers in the field which deal with key issues of S&T, institutional, financial, sustainability, legal, IPR, data protocols, community norms and others, that need attention related to data management practices and protocols, coordinate area activities, and promote common practices and standards of the research community globally. In addition to the aspects touched above, the national / international perspectives of data and its various contours have also been portrayed through case studies in this volume.