Cultural Data Science
Description
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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Person
Dr. Harald Klinke, M.Sc., leads the Big Data Analysis business unit in a German federal authority and serves as a private lecturer at Ludwig-Maximilians-University Munich (LMU). As the founder and editor-in-chief of the International Journal for Digital Art History , he plays a key role in shaping discourse and research in the field, integrating data science with visual culture. His expertise spans art history, digital humanities, and artificial intelligence, with a focus on computational approaches to cultural heritage. Through courses, workshops, and summer schools, Dr. Klinke equips scholars and professionals with the tools to apply digital methods in research and practice. Beyond academia, he has advised institutions on leveraging technology for cultural analysis, fostering innovation at the intersection of AI, data, and the arts.
Content
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.
