This familiar guide to information resources in the humanities and the arts, organized by subjects and emphasizing electronic resources, enables librarians, teachers, and students to quickly find the best resources for their diverse needs. Authoritative, trusted, and timely, Information Resources in the Humanities and the Arts: Sixth Edition introduces new librarians to the breadth of humanities collections, experienced librarians to the nature of humanities scholarship, and the scholars themselves to a wealth of information they might otherwise have missed. This new version of a classic resource—the first update in over a decade—has been refreshed to account for the myriad of digital resources that have rewritten the rules of the reference and research world, and been expanded to include significantly increased coverage of world literature and languages. This book is invaluable for a wide variety of users: librarians in academic, public, school, and special library settings; researchers in religion, philosophy, literature, and the performing and visual arts; graduate students in library and information science; and teachers and students in humanities, the arts, and interdisciplinary degree programs.

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.

"A history of the role of information in the United States since 1870"--

Library and Information Studies for Arctic Social Sciences and Humanities serves as a key interdisciplinary title that links the social sciences and humanities with current issues, trends, and projects in library, archival, and information sciences within shared Arctic frameworks and geographies. Including contributions from professionals and academics working across and on the Arctic, the book presents recent research, theoretical inquiry, and applied professional endeavours at academic and public libraries, as well as archives, museums, government institutions, and other organisations. Focusing on efforts that further Arctic knowledge and research, papers present local, regional, and institutional case studies to conceptually and empirically describe real-life research in which the authors are engaged. Topics covered include the complexities of developing and managing multilingual resources; working in geographically isolated areas; curating combinations of local, regional, national, and international content collections; and understanding historical and contemporary colonial-industrial influences in indigenous knowledge. Library and Information Studies for Arctic Social Sciences and Humanities will be essential reading for academics, researchers, and students working the fields of library, archival, and information or data science, as well as those working in the humanities and social sciences more generally. It should also be of great interest to librarians, archivists, curators, and information or data professionals around the globe.

Pairing full-length scholarly essays with shorter pieces drawn from scholarly blogs and conference presentations, as well as commissioned interviews and position statements, Debates in the Digital Humanities 2016 reveals a dynamic view of a field in negotiation with its identity, methods, and reach. Pieces in the book explore how DH can and must change in response to social justice movements and events like #Ferguson; how DH alters and is altered by community college classrooms; and how scholars applying DH approaches to feminist studies, queer studies, and black studies might reframe the commitments of DH analysts. Numerous contributors examine the movement of interdisciplinary DH work into areas such as history, art history, and archaeology, and a special forum on large-scale text mining brings together position statements on a fast-growing area of DH research. In the multivalent aspects of its arguments, progressing across a range of platforms and environments, Debates in the Digital Humanities 2016 offers a vision of DH as an expanded field—new possibilities, differently structured. Published simultaneously in print, e-book, and interactive webtext formats, each DH annual will be a book-length publication highlighting the particular debates that have shaped the discipline in a given year. By identifying key issues as they unfold, and by providing a hybrid model of open-access publication, these volumes and the Debates in the Digital Humanities series will articulate the present contours of the field and help forge its future. Contributors: Moya Bailey, Northeastern U; Fiona Barnett; Matthew Battles, Harvard U; Jeffrey M. Binder; Zach Blas, U of London; Cameron Blevins, Rutgers U; Sheila A. Brennan, George Mason U; Timothy Burke, Swarthmore College; Rachel Sagner Buurma, Swarthmore College; Micha Cárdenas, U of Washington–Bothell; Wendy Hui Kyong Chun, Brown U; Tanya E. Clement, U of Texas–Austin; Anne Cong-Huyen, Whittier College; Ryan Cordell, Northeastern U; Tressie McMillan Cottom, Virginia Commonwealth U; Amy E. Earhart, Texas A&M U; Domenico Fiormonte, U of Roma Tre; Paul Fyfe, North Carolina State U; Jacob Gaboury, Stony Brook U; Kim Gallon, Purdue U; Alex Gil, Columbia U; Brian Greenspan, Carleton U; Richard Grusin, U of Wisconsin, Milwaukee; Michael Hancher, U of Minnesota; Molly O’Hagan Hardy; David L. Hoover, New York U; Wendy F. Hsu; Patrick Jagoda, U of Chicago; Jessica Marie Johnson, Michigan State U; Steven E. Jones, Loyola U; Margaret Linley, Simon Fraser U; Alan Liu, U of California, Santa Barbara; Elizabeth Losh, U of California, San Diego; Alexis Lothian, U of Maryland; Michael Maizels, Wellesley College; Mark C. Marino, U of Southern California; Anne B. McGrail, Lane Community College; Bethany Nowviskie, U of Virginia; Julianne Nyhan, U College London; Amanda Phillips, U of California, Davis; Miriam Posner, U of California, Los Angeles; Rita Raley, U of California, Santa Barbara; Stephen Ramsay, U of Nebraska–Lincoln; Margaret Rhee, U of Oregon; Lisa Marie Rhody, Graduate Center, CUNY; Roopika Risam, Salem State U; Stephen Robertson, George Mason U; Mark Sample, Davidson College; Jentery Sayers, U of Victoria; Benjamin M. Schmidt, Northeastern U; Scott Selisker, U of Arizona; Jonathan Senchyne, U of Wisconsin, Madison; Andrew Stauffer, U of Virginia; Joanna Swafford, SUNY New Paltz; Toniesha L. Taylor, Prairie View A&M U; Dennis Tenen; Melissa Terras, U College London; Anna Tione; Ted Underwood, U of Illinois, Urbana–Champaign; Ethan Watrall, Michigan State U; Jacqueline Wernimont, Arizona State U; Laura Wexler, Yale U; Hong-An Wu, U of Illinois, Urbana–Champaign.

Information and Knowledge Organisation explores the role of knowledge organisation in the digital humanities. By focusing on how information is described, represented and organised in both research and practice, this work furthers the transdisciplinary nature of digital humanities. Including contributions from Asia, Australia, Europe, North America and the Middle East, the volume explores the potential uses of, and challenges involved in, applying the organisation of information and knowledge in the various areas of Digital Humanities. With a particular focus on the digital worlds of cultural heritage collections, the book also includes chapters that focus on machine learning, knowledge graphs, text analysis, text annotations, and network analysis. Other topics covered include: semantic technologies, conceptual schemas, and data augmentation, digital scholarly editing, metadata creation, browsing, visualisation and relevance ranking. Most importantly, perhaps, the book provides a starting point for discussions about the impact of information and knowledge organisation and related tools on the methodologies used in the Digital Humanities field. Information and Knowledge Organisation is intended for use by researchers, students and professionals interested in the role information and knowledge organisation plays in the Digital Humanities. It will be essential reading for those working in library and information science, computer science and across the humanities.

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