Humanities Data Analysis
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Humanities Data Analysis
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Author : Folgert Karsdorp
language : en
Publisher: Princeton University Press
Release Date : 2021-01-12
Humanities Data Analysis written by Folgert Karsdorp and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-12 with Computers categories.
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
Humanities Data Analysis
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Author : Folgert Karsdorp
language : en
Publisher: Princeton University Press
Release Date : 2021-01-12
Humanities Data Analysis written by Folgert Karsdorp and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-12 with Computers categories.
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
Humanities Data In R
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Author : Taylor Arnold
language : en
Publisher: Springer
Release Date : 2015-09-23
Humanities Data In R written by Taylor Arnold and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-23 with Computers categories.
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.
Multivariate Humanities
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Author : Pieter M. Kroonenberg
language : en
Publisher: Springer Nature
Release Date : 2021-06-29
Multivariate Humanities written by Pieter M. Kroonenberg and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-29 with Social Science categories.
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.
The Shape Of Data In Digital Humanities
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Author : Julia Flanders
language : en
Publisher: Routledge
Release Date : 2018-11-02
The Shape Of Data In Digital Humanities written by Julia Flanders and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-02 with Language Arts & Disciplines categories.
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.
Guide To Programming For The Digital Humanities
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Author : Brian Kokensparger
language : en
Publisher: Springer
Release Date : 2018-08-23
Guide To Programming For The Digital Humanities written by Brian Kokensparger and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-23 with Computers categories.
As an introduction to programming for the Digital Humanities (DH), this book presents six key assignments oriented on DH topics. The topics include Computing Change Over Time (calculating burials at a historic cemetery), Visualizing Change Over Time (visualizing the burials at the historic cemetery), Textual Analysis (finding word frequencies and “stop words” in public domain texts), XML Transformation (transforming a simplified version of XML into HTML styled with CSS), Stylometry (comparing the measured features of graphic images), and Social Network Analysis (analyzing extended relationships in historic circles). The book focuses on the practical application of these assignments in the classroom, providing a range of variations for each assignment, which can be selected on the basis of students’ specific programming background and skills; “atomic” assignments, which can be used to give students the experience they need to successfully complete the main assignments; and some common pitfalls and gotchas to manage in the classroom. The book’s chief goals are to introduce novice computer science (CS) students to programming for DH, and to offer them valuable hands-on experience with core programming concepts.
Text Mining For Qualitative Data Analysis In The Social Sciences
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Author : Gregor Wiedemann
language : en
Publisher: Springer
Release Date : 2016-08-23
Text Mining For Qualitative Data Analysis In The Social Sciences written by Gregor Wiedemann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-23 with Social Science categories.
Gregor Wiedemann evaluates text mining applications for social science studies with respect to conceptual integration of consciously selected methods, systematic optimization of algorithms and workflows, and methodological reflections relating to empirical research. In an exemplary study, he introduces workflows to analyze a corpus of around 600,000 newspaper articles on the subject of “democratic demarcation” in Germany. He provides a valuable resource for innovative measures to social scientists and computer scientists in the field of applied natural language processing.
Text Analysis With R
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Author : Matthew L. Jockers
language : en
Publisher: Springer Nature
Release Date : 2020-03-30
Text Analysis With R written by Matthew L. Jockers and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-30 with Computers categories.
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.
Humanities Data In R
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Author : Taylor Arnold
language : en
Publisher: Springer Nature
Release Date : 2024-07-22
Humanities Data In R written by Taylor Arnold and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-22 with Computers categories.
This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language. Methods for general-purpose visualization and analysis are introduced first, followed by domain-specific techniques for working with networks, text, geospatial data, temporal data, and images. The book is designed to be a bridge between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. The second edition of the text is a significant revision, with almost every aspect of the text rewritten in some way. The most notable difference is the incorporation of new R packages such as ggplot2 and dplyr that center broad data-science concepts. This 2nd edition of Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis, with one chapter dedicated to each stage of the data-science pipeline (data collection, visualization, manipulation, and relational joins). Following this, text analysis, networks, temporal data, geospatial data, and image analysis each have a dedicated chapter. These are grounded in examples to move readers beyond the intimidation of adding new tools to their research. The final section of the book extends the core material with additional computer science techniques for processing large datasets. Everything is hands-on: image analysis is explained using digitized photographs from the 1930s, and networks are applied to page links on Wikipedia. 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. 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.