Humanities Data Analysis


Book Description

A practical guide to data-intensive humanities research using the Python programming language The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment. The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter. An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions. Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python Applicable to many humanities disciplines, including history, literature, and sociology Offers real-world case studies using publicly available data sets Provides exercises at the end of each chapter for students to test acquired skills Emphasizes visual storytelling via data visualizations




Data Analytics in Digital Humanities


Book Description

This book covers computationally innovative methods and technologies including data collection and elicitation, data processing, data analysis, data visualizations, and data presentation. It explores how digital humanists have harnessed the hypersociality and social technologies, benefited from the open-source sharing not only of data but of code, and made technological capabilities a critical part of humanities work. Chapters are written by researchers from around the world, bringing perspectives from diverse fields and subject areas. The respective authors describe their work, their research, and their learning. Topics include semantic web for cultural heritage valorization, machine learning for parody detection by classification, psychological text analysis, crowdsourcing imagery coding in natural disasters, and creating inheritable digital codebooks.Designed for researchers and academics, this book is suitable for those interested in methodologies and analytics that can be applied in literature, history, philosophy, linguistics, and related disciplines. Professionals such as librarians, archivists, and historians will also find the content informative and instructive.




Humanities Data in R


Book Description




Humanities Data Analysis


Book Description

A practical guide to data-intensive humanities research using the Python programming language The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment. The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter. An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions. Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python Applicable to many humanities disciplines, including history, literature, and sociology Offers real-world case studies using publicly available data sets Provides exercises at the end of each chapter for students to test acquired skills Emphasizes visual storytelling via data visualizations




Quantitative Methods in the Humanities


Book Description

This timely and lucid guide is intended for students and scholars working on all historical periods and topics in the humanities and social sciences--especially for those who do not think of themselves as experts in quantification, "big data," or "digital humanities." The authors reveal quantification to be a powerful and versatile tool, applicable to a myriad of materials from the past. Their book, accessible to complete beginners, offers detailed advice and practical tips on how to build a dataset from historical sources and how to categorize it according to specific research questions. Drawing on examples from works in social, political, economic, and cultural history, the book guides readers through a wide range of methods, including sampling, cross-tabulations, statistical tests, regression, factor analysis, network analysis, sequence analysis, event history analysis, geographical information systems, text analysis, and visualization. The requirements, advantages, and pitfalls of these techniques are presented in layperson's terms, avoiding mathematical terminology. Conceived primarily for historians, the book will prove invaluable to other humanists, as well as to social scientists looking for a nontechnical introduction to quantitative methods. Covering the most recent techniques, in addition to others not often enough discussed, the book will also have much to offer to the most seasoned practitioners of quantification.




Visualization and Interpretation


Book Description

An analysis of visual epistemology in the digital humanities, with attention to the need for interpretive digital tools within humanities contexts. In the several decades since humanists have taken up computational tools, they have borrowed many techniques from other fields, including visualization methods to create charts, graphs, diagrams, maps, and other graphic displays of information. But are these visualizations actually adequate for the interpretive approach that distinguishes much of the work in the humanities? Information visualization, as practiced today, lacks the interpretive frameworks required for humanities-oriented methodologies. In this book, Johanna Drucker continues her interrogation of visual epistemology in the digital humanities, reorienting the creation of digital tools within humanities contexts. Drucker examines various theoretical understandings of visual images and their relation to knowledge and how the specifics of the graphical are to be engaged directly as a primary means of knowledge production for digital humanities. She draws on work from aesthetics, critical theory, and formal study of graphical systems, addressing them within the specific framework of computational and digital activity as they apply to digital humanities. Finally, she presents a series of standard problems in visualization for the humanities (including time/temporality, space/spatial relations, and data analysis), posing the investigation in terms of innovative graphical systems informed by probabilistic critical hermeneutics. She concludes with a final brief sketch of discovery tools as an additional interface into which modeling can be worked.




The Shape of Data in Digital Humanities


Book Description

Data and its technologies now play a large and growing role in humanities research and teaching. This book addresses the needs of humanities scholars who seek deeper expertise in the area of data modeling and representation. The authors, all experts in digital humanities, offer a clear explanation of key technical principles, a grounded discussion of case studies, and an exploration of important theoretical concerns. The book opens with an orientation, giving the reader a history of data modeling in the humanities and a grounding in the technical concepts necessary to understand and engage with the second part of the book. The second part of the book is a wide-ranging exploration of topics central for a deeper understanding of data modeling in digital humanities. Chapters cover data modeling standards and the role they play in shaping digital humanities practice, traditional forms of modeling in the humanities and how they have been transformed by digital approaches, ontologies which seek to anchor meaning in digital humanities resources, and how data models inhabit the other analytical tools used in digital humanities research. It concludes with a glossary chapter that explains specific terms and concepts for data modeling in the digital humanities context. This book is a unique and invaluable resource for teaching and practising data modeling in a digital humanities context.




Cultural Science


Book Description

Cultural Science introduces a new way of thinking about culture. Adopting an evolutionary and systems approach, the authors argue that culture is the population-wide source of newness and innovation; it faces the future, not the past. Its chief characteristic is the formation of groups or 'demes' (organised and productive subpopulation; 'demos'). Demes are the means for creating, distributing and growing knowledge. However, such groups are competitive and knowledge-systems are adversarial. Starting from a rereading of Darwinian evolutionary theory, the book utilises multidisciplinary resources: Raymond Williams's 'culture is ordinary' approach; evolutionary science (e.g. Mark Pagel and Herbert Gintis); semiotics (Yuri Lotman); and economic theory (from Schumpeter to McCloskey). Successive chapters argue that: -Culture and knowledge need to be understood from an externalist ('linked brains') perspective, rather than through the lens of individual behaviour; -Demes are created by culture, especially storytelling, which in turn constitutes both politics and economics; -The clash of systems - including demes - is productive of newness, meaningfulness and successful reproduction of culture; -Contemporary urban culture and citizenship can best be explained by investigating how culture is used, and how newness and innovation emerge from unstable and contested boundaries between different meaning systems; -The evolution of culture is a process of technologically enabled 'demic concentration' of knowledge, across overlapping meaning-systems or semiospheres; a process where the number of demes accessible to any individual has increased at an accelerating rate, resulting in new problems of scale and coordination for cultural science to address. The book argues for interdisciplinary 'consilience', linking evolutionary and complexity theory in the natural sciences, economics and anthropology in the social sciences, and cultural, communication and media studies in the humanities and creative arts. It describes what is needed for a new 'modern synthesis' for the cultural sciences. It combines analytical and historical methods, to provide a framework for a general reconceptualisation of the theory of culture – one that is focused not on its political or customary aspects but rather its evolutionary significance as a generator of newness and innovation.




Big Data in Computational Social Science and Humanities


Book Description

This edited volume focuses on big data implications for computational social science and humanities from management to usage. The first part of the book covers geographic data, text corpus data, and social media data, and exemplifies their concrete applications in a wide range of fields including anthropology, economics, finance, geography, history, linguistics, political science, psychology, public health, and mass communications. The second part of the book provides a panoramic view of the development of big data in the fields of computational social sciences and humanities. The following questions are addressed: why is there a need for novel data governance for this new type of data?, why is big data important for social scientists?, and how will it revolutionize the way social scientists conduct research? With the advent of the information age and technologies such as Web 2.0, ubiquitous computing, wearable devices, and the Internet of Things, digital society has fundamentally changed what we now know as "data", the very use of this data, and what we now call "knowledge". Big data has become the standard in social sciences, and has made these sciences more computational. Big Data in Computational Social Science and Humanities will appeal to graduate students and researchers working in the many subfields of the social sciences and humanities.




Digital Humanities and Film Studies


Book Description

This book highlights the quantitative methods of data mining and information visualization and explores their use in relation to the films and writings of the Russian director, Dziga Vertov. The theoretical basis of the work harkens back to the time when a group of Russian artists and scholars, known as the “formalists,” developed new concepts of how art could be studied and measured. This book brings those ideas to the digital age. One of the central questions the book intends to address is, “How can hypothetical notions in film studies be supported or falsified using empirical data and statistical tools?” The first stage involves manual and computer-assisted annotation of the films, leading to the production of empirical data which is then used for statistical analysis but more importantly for the development of visualizations. Studies of this type furthermore shed light on the field of visual presentation of time-based processes; an area which has its origin in the Russian formalist sphere of the 1920s and which has recently gained new relevance due to technological advances and new possibilities for computer-assisted analysis of large and complex data sets. In order to reach a profound understanding of Vertov and his films, the manual or computer-assisted data analysis must be combined with film-historical knowledge and a study of primary sources. In addition, the status of the surviving film materials and the precise analysis of these materials combined with knowledge of historical film technology provide insight into archival policy and political culture in the Soviet Union in the 1920s and 30s.