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Author: Taylor Arnold Publisher: Springer ISBN: 3319207024 Category : Computers Languages : en Pages : 211
Book Description
This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries.
Author: Taylor Arnold Publisher: Springer ISBN: 3319207024 Category : Computers Languages : en Pages : 211
Book Description
This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries.
Author: Folgert Karsdorp Publisher: Princeton University Press ISBN: 0691172366 Category : Computers Languages : en Pages : 352
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
Author: Julia Flanders Publisher: Routledge ISBN: 1317016149 Category : Language Arts & Disciplines Languages : en Pages : 382
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.
Author: Matthew L. Jockers Publisher: Springer Nature ISBN: 3030396436 Category : Computers Languages : en Pages : 277
Book Description
Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source programming language R. R is an extremely popular programming language, used throughout the sciences; due to its accessibility, R is now used increasingly in other research areas. In this volume, readers immediately begin working with text, and each chapter examines a new technique or process, allowing readers to obtain a broad exposure to core R procedures and a fundamental understanding of the possibilities of computational text analysis at both the micro and the macro scale. Each chapter builds on its predecessor as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying. Text Analysis with R is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological toolkit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that readers simply cannot gather using traditional qualitative methods of close reading and human synthesis. This new edition features two new chapters: one that introduces dplyr and tidyr in the context of parsing and analyzing dramatic texts to extract speaker and receiver data, and one on sentiment analysis using the syuzhet package. It is also filled with updated material in every chapter to integrate new developments in the field, current practices in R style, and the use of more efficient algorithms.
Author: Folgert Karsdorp Publisher: Princeton University Press ISBN: 0691200335 Category : Computers Languages : en Pages : 360
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
Author: Christopher D. Cantwell Publisher: Walter de Gruyter GmbH & Co KG ISBN: 3110573024 Category : Religion Languages : en Pages : 360
Book Description
This volume provides practical, but provocative, case studies of exemplary projects that apply digital technology or methods to the study of religion. An introduction and 16 essays are organized by the kinds of sources digital humanities scholars use – texts, images, and places – with a final section on the professional and pedagogical issues digital scholarship raises for the study of religion.
Author: Pieter M. Kroonenberg Publisher: Springer Nature ISBN: 3030691500 Category : Social Science Languages : en Pages : 436
Book Description
This case study-based textbook in multivariate analysis for advanced students in the humanities emphasizes descriptive, exploratory analyses of various types of datasets from a wide range of sub-disciplines, promoting the use of multivariate analysis and illustrating its wide applicability. Fields featured include, but are not limited to, historical agriculture, arts (music and painting), theology, and stylometrics (authorship issues). Most analyses are based on existing data, earlier analysed in published peer-reviewed papers. Four preliminary methodological and statistical chapters provide general technical background to the case studies. The multivariate statistical methods presented and illustrated include data inspection, several varieties of principal component analysis, correspondence analysis, multidimensional scaling, cluster analysis, regression analysis, discriminant analysis, and three-mode analysis. The bulk of the text is taken up by 14 case studies that lean heavily on graphical representations of statistical information such as biplots, using descriptive statistical techniques to support substantive conclusions. Each study features a description of the substantive background to the data, followed by discussion of appropriate multivariate techniques, and detailed results interpreted through graphical illustrations. Each study is concluded with a conceptual summary. Datasets in SPSS are included online.
Author: Matthew K. Gold Publisher: U of Minnesota Press ISBN: 1452961670 Category : Social Science Languages : en Pages : 722
Book Description
The latest installment of a digital humanities bellwether Contending with recent developments like the shocking 2016 U.S. Presidential election, the radical transformation of the social web, and passionate debates about the future of data in higher education, Debates in the Digital Humanities 2019 brings together a broad array of important, thought-provoking perspectives on the field’s many sides. With a wide range of subjects including gender-based assumptions made by algorithms, the place of the digital humanities within art history, data-based methods for exhuming forgotten histories, video games, three-dimensional printing, and decolonial work, this book assembles a who’s who of the field in more than thirty impactful essays. Contributors: Rafael Alvarado, U of Virginia; Taylor Arnold, U of Richmond; James Baker, U of Sussex; Kathi Inman Berens, Portland State U; David M. Berry, U of Sussex; Claire Bishop, The Graduate Center, CUNY; James Coltrain, U of Nebraska–Lincoln; Crunk Feminist Collective; Johanna Drucker, U of California–Los Angeles; Jennifer Edmond, Trinity College; Marta Effinger-Crichlow, New York City College of Technology–CUNY; M. Beatrice Fazi, U of Sussex; Kevin L. Ferguson, Queens College–CUNY; Curtis Fletcher, U of Southern California; Neil Fraistat, U of Maryland; Radhika Gajjala, Bowling Green State U; Michael Gavin, U of South Carolina; Andrew Goldstone, Rutgers U; Andrew Gomez, U of Puget Sound; Elyse Graham, Stony Brook U; Brian Greenspan, Carleton U; John Hunter, Bucknell U; Steven J. Jackson, Cornell U; Collin Jennings, Miami U; Lauren Kersey, Saint Louis U; Kari Kraus, U of Maryland; Seth Long, U of Nebraska, Kearney; Laura Mandell, Texas A&M U; Rachel Mann, U of South Carolina; Jason Mittell, Middlebury College; Lincoln A. Mullen, George Mason U; Trevor Muñoz, U of Maryland; Safiya Umoja Noble, U of Southern California; Jack Norton, Normandale Community College; Bethany Nowviskie, U of Virginia; Élika Ortega, Northeastern U; Marisa Parham, Amherst College; Jussi Parikka, U of Southampton; Kyle Parry, U of California, Santa Cruz; Brad Pasanek, U of Virginia; Stephen Ramsay, U of Nebraska–Lincoln; Matt Ratto, U of Toronto; Katie Rawson, U of Pennsylvania; Ben Roberts, U of Sussex; David S. Roh, U of Utah; Mark Sample, Davidson College; Moacir P. de Sá Pereira, New York U; Tim Sherratt, U of Canberra; Bobby L. Smiley, Vanderbilt U; Lauren Tilton, U of Richmond; Ted Underwood, U of Illinois, Urbana-Champaign; Megan Ward, Oregon State U; Claire Warwick, Durham U; Alban Webb, U of Sussex; Adrian S. Wisnicki, U of Nebraska–Lincoln.
Author: Shu-Heng Chen Publisher: Springer ISBN: 3319954652 Category : Computers Languages : en Pages : 388
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.
Author: Taylor Arnold
Author: Taylor Arnold
Author: Folgert Karsdorp
Author: Julia Flanders
Author: Matthew L. Jockers
Author: Folgert Karsdorp
Author: Christopher D. Cantwell
Author: Pieter M. Kroonenberg
Author: Matthew K. Gold
Author: Shu-Heng Chen
Author: Rajesh Ekka