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Cultural data science = an introduction to R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cultural data science/ by Harald Klinke.
Reminder of title:	an introduction to R /
Author:	Klinke, Harald.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiii, 161 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1 Introduction -- Chapter 2 Setting up R -- Chapter 3 First Steps -- Chapter 4 First Data -- Chapter 5 Exploring Data -- Chapter 6 Compare data -- Chapter 7 Case Study 1: Picasso from creation to acquisition -- Chapter 8 Subdividing and combining data sets -- Chapter 9 Case Study 2: Photography Aspect Ratios -- Chapter 10 Case Study 3: Overview of the MNBAQ Dataset -- Chapter 11 Stacked bar plot -- Chapter 12 Working with Images -- Chapter 13 Case Study: Image plot -- Chapter 14 Dimensions -- Chapter 15 Network Visualizations -- Chapter 16 Image Analysis -- Chapter 17 Cloud Computing -- Chapter 18 Epilogue: The Future of Cultural Data Science.
Contained By:	Springer Nature eBook
Subject:	Arts - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-88130-5
ISBN:	9783031881305

Cultural data science = an introduction to R /
Klinke, Harald.

Cultural data sciencean introduction to R /[electronic resource] :by Harald Klinke. - Cham :Springer Nature Switzerland :2025. - xiii, 161 p. :ill., digital ;24 cm. - Quantitative methods in the humanities and social sciences,2199-0964. - Quantitative methods in the humanities and social sciences..

Chapter 1 Introduction -- Chapter 2 Setting up R -- Chapter 3 First Steps -- Chapter 4 First Data -- Chapter 5 Exploring Data -- Chapter 6 Compare data -- Chapter 7 Case Study 1: Picasso from creation to acquisition -- Chapter 8 Subdividing and combining data sets -- Chapter 9 Case Study 2: Photography Aspect Ratios -- Chapter 10 Case Study 3: Overview of the MNBAQ Dataset -- Chapter 11 Stacked bar plot -- Chapter 12 Working with Images -- Chapter 13 Case Study: Image plot -- Chapter 14 Dimensions -- Chapter 15 Network Visualizations -- Chapter 16 Image Analysis -- Chapter 17 Cloud Computing -- Chapter 18 Epilogue: The Future of Cultural Data Science.

This book addresses growing demand in the visual humanities for foundational knowledge in data analysis, image processing, and artificial intelligence. Intended for readers without prior technical knowledge, its eighteen chapters introduce key concepts in data science through examples from art history, visual computing, and cloud technologies. For advanced readers, it explores how digital methods enhance our understanding of our cultural past. Focusing on image-based humanities research, the book demonstrates practical applications using open source software such as R, RStudio, and ImageMagick, along with open data sets from institutions like the MoMA, the Met, and the Rijksmuseum. Ideal for students, researchers, and professionals in art history, digital humanities, and museum studies, this book bridges the gap between technology and the humanities, making complex digital methods accessible to a broad audience.

ISBN: 9783031881305

Standard No.: 10.1007/978-3-031-88130-5doiSubjects--Topical Terms:

540452
Arts
--Data processing.

LC Class. No.: NX260

Dewey Class. No.: 700.285

Cultural data science = an introduction to R /
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505 0 $a Chapter 1 Introduction -- Chapter 2 Setting up R -- Chapter 3 First Steps -- Chapter 4 First Data -- Chapter 5 Exploring Data -- Chapter 6 Compare data -- Chapter 7 Case Study 1: Picasso from creation to acquisition -- Chapter 8 Subdividing and combining data sets -- Chapter 9 Case Study 2: Photography Aspect Ratios -- Chapter 10 Case Study 3: Overview of the MNBAQ Dataset -- Chapter 11 Stacked bar plot -- Chapter 12 Working with Images -- Chapter 13 Case Study: Image plot -- Chapter 14 Dimensions -- Chapter 15 Network Visualizations -- Chapter 16 Image Analysis -- Chapter 17 Cloud Computing -- Chapter 18 Epilogue: The Future of Cultural Data Science.
520 $a This book addresses growing demand in the visual humanities for foundational knowledge in data analysis, image processing, and artificial intelligence. Intended for readers without prior technical knowledge, its eighteen chapters introduce key concepts in data science through examples from art history, visual computing, and cloud technologies. For advanced readers, it explores how digital methods enhance our understanding of our cultural past. Focusing on image-based humanities research, the book demonstrates practical applications using open source software such as R, RStudio, and ImageMagick, along with open data sets from institutions like the MoMA, the Met, and the Rijksmuseum. Ideal for students, researchers, and professionals in art history, digital humanities, and museum studies, this book bridges the gap between technology and the humanities, making complex digital methods accessible to a broad audience.
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