Laboratory Science with Space Data

Laboratory Science with Space Data
Author: Daniel Beysens
Publisher: Springer Science & Business Media
Total Pages: 230
Release: 2011-09-01
Genre: Science
ISBN: 3642211445

For decades experiments conducted on space stations like MIR and the ISS have been gathering data in many fields of research in the natural sciences, medicine and engineering. The EU-sponsored Ulisse Internet Portal provides metadata from space experiments of all kinds and links to the data. Complementary to the portal, this book will serve as handbook listing space experiments by type of infrastructure, area of research in the life and physical sciences, data type, what their mission was, what kind of data they have collected and how one can access this data through Ulisse for further research. The book will provide an overview of the wealth of space experiment data that can be used for research, and will inspire academics (e.g. those looking for topics for their PhD thesis) and research departments in companies for their continued development.

S.O.S. from Outer Space

S.O.S. from Outer Space
Author: Ada Hopper
Publisher: Simon and Schuster
Total Pages: 128
Release: 2022-03-29
Genre: Juvenile Fiction
ISBN: 1665902981

In the ninth DATA Set adventure, the kids blast off into outer space! When Laura hears a strange but familiar buzzing coming from her radio, she instantly knows who’s trying to get in touch. It’s none other than Fave, the DATA Set’s alien friend who crashed down on Earth! The kids excitedly decode the message but learn that Fave is in trouble! Join the kids as they suit up and blast off into the galaxy on a mission to help an old friend. With easy-to-read language and illustrations on almost every page, the DATA Set chapter books are perfect for emerging readers.

Displaying Time Series, Spatial, and Space-Time Data with R

Displaying Time Series, Spatial, and Space-Time Data with R
Author: Oscar Perpinan Lamigueiro
Publisher: CRC Press
Total Pages: 210
Release: 2014-04-04
Genre: Mathematics
ISBN: 1466565209

Code and Methods for Creating High-Quality Data Graphics A data graphic is not only a static image, but it also tells a story about the data. It activates cognitive processes that are able to detect patterns and discover information not readily available with the raw data. This is particularly true for time series, spatial, and space-time datasets. Focusing on the exploration of data with visual methods, Displaying Time Series, Spatial, and Space-Time Data with R presents methods and R code for producing high-quality graphics of time series, spatial, and space-time data. Practical examples using real-world datasets help you understand how to apply the methods and code. The book illustrates how to display a dataset starting with an easy and direct approach and progressively adding improvements that involve more complexity. Each of the book’s three parts is devoted to different types of data. In each part, the chapters are grouped according to the various visualization methods or data characteristics. Web Resource Along with the main graphics from the text, the author’s website offers access to the datasets used in the examples as well as the full R code. This combination of freely available code and data enables you to practice with the methods and modify the code to suit your own needs.

Space-Efficient Data Structures, Streams, and Algorithms

Space-Efficient Data Structures, Streams, and Algorithms
Author: Andrej Brodnik
Publisher: Springer
Total Pages: 389
Release: 2013-08-13
Genre: Computers
ISBN: 3642402739

This Festschrift volume, published in honour of J. Ian Munro, contains contributions written by some of his colleagues, former students, and friends. In celebration of his 66th birthday the colloquium "Conference on Space Efficient Data Structures, Streams and Algorithms" was held in Waterloo, ON, Canada, during August 15-16, 2013. The articles presented herein cover some of the main topics of Ian's research interests. Together they give a good overall perspective of the last 40 years of research in algorithms and data structures.