The Shape of Data in Digital Humanities by Julia Flanders (Editor); Fotis Jannidis (Editor)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.
Publication Date: 2018-11-02
Data Analytics in Digital Humanities by Shalin Hai-Jew (Editor)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.
Publication Date: 2017-05-12
Research Methods for Reading Digital Data in the Digital Humanities by Gabriele Griffin (Editor); Matt Hayler (Editor)Digital Humanities has become one of the new domains of academe at the interface of technological development, epistemological change, and methodological concerns. This volume explores how digital material might be read or utilized in research, whether that material is digitally born as fanfiction, for example, mostly is, or transposed from other sources. The volume asks questions such as what happens when text is transformed from printed into digital matter, and how that impacts on the methods we bring to bear on exploring that technologized matter, for example in the case of digital editions. Issues such as how to analyse visual material in digital archives or Twitter feeds, how to engage in data mining, what it means to undertake crowd-sourcing, big data, and what digital network analyses can tell us about online interactions are dealt with. This will give Humanities researchers ideas for doing digitally based research and also suggest ways of engaging with new digital research methods.
Publication Date: 2016-03-01
Humanities Data in R by Taylor Arnold; Lauren TiltonThis 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.
Frictionless Data shortens the path from data to insight with a collection of specifications and software for the publication, transport, and consumption of data.
A 'digital / data research space' to enable researchers to easily download often large amounts of data and digital collections in order for them to conduct their own experiments manually and / or computationally.
Evan Peter Williamson
From The Programming Historian: "OpenRefine is a powerful tool for exploring, cleaning, and transforming data. In this lesson you will learn how to use Refine to fetch URLs and parse web content."
Jon Crump
From The Programming Historian: "This tutorial illustrates strategies for taking raw OCR output from a scanned text, parsing it to isolate and correct essential elements of metadata, and generating an ordered data set (a python dictionary) from it."
Beatrice Alex
From The Programming Historian: "This tutorial teaches users how to use the Edinburgh Geoparser to process a piece of English-language text, extract and resolve the locations contained within it, and plot them as a web map."
Seth van Hooland, Ruben Verborgh, and Max De Wilde
From The Programming Historian: "This tutorial focuses on how scholars can diagnose and act upon the accuracy of data."
Nabeel Siddiqui
From The Programming Historian: "This tutorial explores how scholars can organize 'tidy' data, understand R packages to manipulate data, and conduct basic data analysis."
Taryn Dewar
From The Programming Historian: "This lesson teaches a way to quickly analyze large volumes of tabular data, making research faster and more effective."
Jeff Blackadar
From The Programming Historian: "This lesson will help you store large amounts of historical data in a structured manner, search and filter that data, and visualize some of the data as a graph."
Quinn Dombrowski, Tassie Gniady, and David Kloster
From The Programming Historian: "Jupyter notebooks provide an environment where you can freely combine human-readable narrative with computer-readable code. This lesson describes how to install the Jupyter Notebook software, how to run and create Jupyter notebook files, and contexts where Jupyter notebooks can be particularly helpful."