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| data science for humanities: Humanities Data Analysis Folgert Karsdorp, Mike Kestemont, Allen Riddell, 2021-01-12 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 science for humanities: Human-Centered Data Science Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, Gina Neff, 2022-03-01 Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns. |
| data science for humanities: Data Analytics in Digital Humanities Shalin Hai-Jew, 2017-05-03 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. |
| data science for humanities: Humanities Data in R Taylor Arnold, |
| data science for humanities: Big Data in Computational Social Science and Humanities Shu-Heng Chen, 2018-11-21 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. |
| data science for humanities: Big Data in the Arts and Humanities Giovanni Schiuma, Daniela Carlucci, 2018-04-27 As digital technologies occupy a more central role in working and everyday human life, individual and social realities are increasingly constructed and communicated through digital objects, which are progressively replacing and representing physical objects. They are even shaping new forms of virtual reality. This growing digital transformation coupled with technological evolution and the development of computer computation is shaping a cyber society whose working mechanisms are grounded upon the production, deployment, and exploitation of big data. In the arts and humanities, however, the notion of big data is still in its embryonic stage, and only in the last few years, have arts and cultural organizations and institutions, artists, and humanists started to investigate, explore, and experiment with the deployment and exploitation of big data as well as understand the possible forms of collaborations based on it. Big Data in the Arts and Humanities: Theory and Practice explores the meaning, properties, and applications of big data. This book examines therelevance of big data to the arts and humanities, digital humanities, and management of big data with and for the arts and humanities. It explores the reasons and opportunities for the arts and humanities to embrace the big data revolution. The book also delineates managerial implications to successfully shape a mutually beneficial partnership between the arts and humanities and the big data- and computational digital-based sciences. Big data and arts and humanities can be likened to the rational and emotional aspects of the human mind. This book attempts to integrate these two aspects of human thought to advance decision-making and to enhance the expression of the best of human life. |
| data science for humanities: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| data science for humanities: The Shape of Data in Digital Humanities Julia Flanders, Fotis Jannidis, 2018-11-02 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. |
| data science for humanities: Public Policy Analytics Ken Steif, 2021-08-18 Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government. |
| data science for humanities: Data Science and Social Research II Paolo Mariani, Mariangela Zenga, 2020-11-25 The peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences. The data revolution in social science research has not only produced new business models, but has also provided policymakers with better decision-making support tools. In this volume, statisticians, computer scientists and experts on social research discuss the opportunities and challenges of the social data revolution in order to pave the way for addressing new research problems. The respective contributions focus on complex social systems and current methodological advances in extracting social knowledge from large data sets, as well as modern social research on human behavior and society using large data sets. Moreover, they analyze integrated systems designed to take advantage of new social data sources, and discuss quality-related issues. The papers were originally presented at the 2nd International Conference on Data Science and Social Research, held in Milan, Italy, on February 4-5, 2019. |
| data science for humanities: Computational History and Data-Driven Humanities Bojan Bozic, Gavin Mendel-Gleason, Christophe Debruyne, Declan O'Sullivan, 2016-11-07 This book constitutes the refereed post-proceedings of the Second IFIP WG 12.7 International Workshop on Computational History and Data-Driven Humanities, held in Dublin, Ireland, in May 2016. The 7 full papers presented together with 2 invited talks and 4 lightning talks were carefully reviewed and selected from 14 submissions. The papers focus on the challenge and opportunities of data-driven humanities and cover topics at the interface between computer science, social science, humanities, and mathematics. |
| data science for humanities: Virtual Knowledge Paul Wouters, Anne Beaulieu, Andrea Scharnhorst, Sally Wyatt, 2012-10-19 An examination of emerging forms of knowledge creation using Web-based technologies, analyzed from an interdisciplinary perspective. Today we are witnessing dramatic changes in the way scientific and scholarly knowledge is created, codified, and communicated. This transformation is connected to the use of digital technologies and the virtualization of knowledge. In this book, scholars from a range of disciplines consider just what, if anything, is new when knowledge is produced in new ways. Does knowledge itself change when the tools of knowledge acquisition, representation, and distribution become digital? Issues of knowledge creation and dissemination go beyond the development and use of new computational tools. The book, which draws on work from the Virtual Knowledge Studio, brings together research on scientific practice, infrastructure, and technology. Focusing on issues of digital scholarship in the humanities and social sciences, the contributors discuss who can be considered legitimate knowledge creators, the value of “invisible” labor, the role of data visualization in policy making, the visualization of uncertainty, the conceptualization of openness in scholarly communication, data floods in the social sciences, and how expectations about future research shape research practices. The contributors combine an appreciation of the transformative power of the virtual with a commitment to the empirical study of practice and use. Contributors Anne Beaulieu, Sarah de Rijcke, Bas van Heur, Smiljana Antonijević, Stefan Dormans, Sally Wyatt, Matthijs Kouw, Charles van den Heuvel, Andrea Scharnhorst, Rebecca Moody, Victor Bekkers, Clement Levallois, Stephanie Steinmetz, Paul Wouters, Clifford Tatum, Nicholas W. Jankowski, Jan Kok |
| data science for humanities: Data Science John D. Kelleher, Brendan Tierney, 2018-04-13 A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects. |
| data science for humanities: Information-Theoretic Methods in Data Science Miguel R. D. Rodrigues, Yonina C. Eldar, 2021-04-08 The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning. |
| data science for humanities: Scientometrics for the Humanities and Social Sciences R. Sooryamoorthy, 2020-11-09 Scientometrics for the Humanities and Social Sciences is the first ever book on scientometrics that deals with the historical development of both quantitative and qualitative data analysis in scientometric studies. It focuses on its applicability in new and emerging areas of inquiry. This important book presents the inherent potential for data mining and analysis of qualitative data in scientometrics. The author provides select cases of scientometric studies in the humanities and social sciences, explaining their research objectives, sources of data and methodologies. It illustrates how data can be gathered not only from prominent online databases and repositories, but also from journals that are not stored in these databases. With the support of specific examples, the book shows how data on demographic variables can be collected to supplement scientometric data. The book deals with a research methodology which has an increasing applicability not only to the study of science, but also to the study of the disciplines in the humanities and social sciences. |
| data science for humanities: Quantitative Methods in the Humanities Claire Lemercier, Claire Zalc, 2019 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. |
| data science for humanities: Access and Control in Digital Humanities Shane Hawkins, 2021-05-13 Access and Control in Digital Humanities explores a range of important questions about who controls data, who is permitted to reproduce or manipulate data, and what sorts of challenges digital humanists face in making their work accessible and useful. Contributors to this volume present case studies and theoretical approaches from their experience with applications for digital technology in classrooms, museums, archives, in the field and with the general public. Offering potential answers to the issues of access and control from a variety of perspectives, the volume acknowledges that access is subject to competing interests of a variety of stakeholders. Museums, universities, archives, and some communities all place claims on how data can or cannot be shared through digital initiatives and, given the collaborative nature of most digital humanities projects, those in the field need to be cognizant of the various and often competing interests and rights that shape the nature of access and how it is controlled. Access and Control in Digital Humanities will be of interest to researchers, academics and graduate students working in a variety of fields, including digital humanities, library and information science, history, museum and heritage studies, conservation, English literature, geography and legal studies. |
| data science for humanities: Interdisciplining Digital Humanities Julie Thompson Klein, 2015-01-05 Interdisciplining Digital Humanities sorts through definitions and patterns of practice over roughly sixty-five years of work, providing an overview for specialists and a general audience alike. It is the only book that tests the widespread claim that Digital Humanities is interdisciplinary. By examining the boundary work of constructing, expanding, and sustaining a new field, it depicts both the ways this new field is being situated within individual domains and dynamic cross-fertilizations that are fostering new relationships across academic boundaries. It also accounts for digital reinvigorations of “public humanities” in cultural heritage institutions of museums, archives, libraries, and community forums. |
| data science for humanities: Text Analysis with R Matthew L. Jockers, Rosamond Thalken, 2020-03-30 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. |
| data science for humanities: Research Methods for the Digital Humanities lewis levenberg, Tai Neilson, David Rheams, 2018-11-04 This volume introduces the reader to the wide range of methods that digital humanities employ, and offers a practical guide to the study, interpretation, and presentation of cultural material and practices. In this instance, the editors consider digital humanities to include both the use of computing to understand cultural material in new ways, and the application of theories and methods from the humanities to interpret new technologies. Each chapter provides a step-by-step guide to cutting-edge methodologies so that students can make informed decisions about the methods they use, consider ethical practices, follow practical procedures, and present their work effectively. Readers will develop practical and reflexive understandings of the software and digital devices that they study and use for research, and the book will help new researchers collaborate and contribute to their scholarly communities, and to public discourse. As contemporary humanities work becomes increasingly interdisciplinary, and increasingly permeated by and with digital technologies, this volume helps new researchers navigate an evolving academic environment. Humanities and social sciences students will find this textbook an invaluable resource for assessing and creating digital projects. |
| data science for humanities: Uncharted Erez Aiden, Jean-Baptiste Michel, 2013-12-26 “One of the most exciting developments from the world of ideas in decades, presented with panache by two frighteningly brilliant, endearingly unpretentious, and endlessly creative young scientists.” – Steven Pinker, author of The Better Angels of Our Nature Our society has gone from writing snippets of information by hand to generating a vast flood of 1s and 0s that record almost every aspect of our lives: who we know, what we do, where we go, what we buy, and who we love. This year, the world will generate 5 zettabytes of data. (That’s a five with twenty-one zeros after it.) Big data is revolutionizing the sciences, transforming the humanities, and renegotiating the boundary between industry and the ivory tower. What is emerging is a new way of understanding our world, our past, and possibly, our future. In Uncharted, Erez Aiden and Jean-Baptiste Michel tell the story of how they tapped into this sea of information to create a new kind of telescope: a tool that, instead of uncovering the motions of distant stars, charts trends in human history across the centuries. By teaming up with Google, they were able to analyze the text of millions of books. The result was a new field of research and a scientific tool, the Google Ngram Viewer, so groundbreaking that its public release made the front page of The New York Times, The Wall Street Journal, and The Boston Globe, and so addictive that Mother Jones called it “the greatest timewaster in the history of the internet.” Using this scope, Aiden and Michel—and millions of users worldwide—are beginning to see answers to a dizzying array of once intractable questions. How quickly does technology spread? Do we talk less about God today? When did people start “having sex” instead of “making love”? At what age do the most famous people become famous? How fast does grammar change? Which writers had their works most effectively censored by the Nazis? When did the spelling “donut” start replacing the venerable “doughnut”? Can we predict the future of human history? Who is better known—Bill Clinton or the rutabaga? All over the world, new scopes are popping up, using big data to quantify the human experience at the grandest scales possible. Yet dangers lurk in this ocean of 1s and 0s—threats to privacy and the specter of ubiquitous government surveillance. Aiden and Michel take readers on a voyage through these uncharted waters. |
| data science for humanities: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science for humanities: Breaking the Social Media Prism Chris Bail, 2022-09-27 A revealing look at how user behavior is powering deep social divisions online—and how we might yet defeat political tribalism on social media In an era of increasing social isolation, platforms like Facebook and Twitter are among the most important tools we have to understand each other. We use social media as a mirror to decipher our place in society but, as Chris Bail explains, it functions more like a prism that distorts our identities, empowers status-seeking extremists, and renders moderates all but invisible. Breaking the Social Media Prism challenges common myths about echo chambers, foreign misinformation campaigns, and radicalizing algorithms, revealing that the solution to political tribalism lies deep inside ourselves. Drawing on innovative online experiments and in-depth interviews with social media users from across the political spectrum, this book explains why stepping outside of our echo chambers can make us more polarized, not less. Bail takes you inside the minds of online extremists through vivid narratives that trace their lives on the platforms and off—detailing how they dominate public discourse at the expense of the moderate majority. Wherever you stand on the spectrum of user behavior and political opinion, he offers fresh solutions to counter political tribalism from the bottom up and the top down. He introduces new apps and bots to help readers avoid misperceptions and engage in better conversations with the other side. Finally, he explores what the virtual public square might look like if we could hit reset and redesign social media from scratch through a first-of-its-kind experiment on a new social media platform built for scientific research. Providing data-driven recommendations for strengthening our social media connections, Breaking the Social Media Prism shows how to combat online polarization without deleting our accounts. |
| data science for humanities: The Digital Humanities Coursebook Johanna Drucker, 2021-03-25 The Digital Humanities Coursebook provides critical frameworks for the application of digital humanities tools and platforms, which have become an integral part of work across a wide range of disciplines. Written by an expert with twenty years of experience in this field, the book is focused on the principles and fundamental concepts for application, rather than on specific tools or platforms. Each chapter contains examples of projects, tools, or platforms that demonstrate these principles in action. The book is structured to complement courses on digital humanities and provides a series of modules, each of which is organized around a set of concerns and topics, thought experiments and questions, as well as specific discussions of the ways in which tools and platforms work. The book covers a wide range of topics and clearly details how to integrate the acquisition of expertise in data, metadata, classification, interface, visualization, network analysis, topic modeling, data mining, mapping, and web presentation with issues in intellectual property, sustainability, privacy, and the ethical use of information. Written in an accessible and engaging manner, The Digital Humanities Coursebook will be a useful guide for anyone teaching or studying a course in the areas of digital humanities, library and information science, English, or computer science. The book will provide a framework for direct engagement with digital humanities and, as such, should be of interest to others working across the humanities as well. |
| data science for humanities: Academic Crowdsourcing in the Humanities Mark Hedges, Stuart Dunn, 2017-11-15 Academic Crowdsourcing in the Humanities lays the foundations for a theoretical framework to understand the value of crowdsourcing, an avenue that is increasingly becoming important to academia as the web transforms collaboration and communication and blurs institutional and professional boundaries. Crowdsourcing projects in the humanities have, for the most part, focused on the generation or enhancement of content in a variety of ways, leveraging the rich resources of knowledge, creativity, effort and interest among the public to contribute to academic discourse. This book explores methodologies, tactics and the citizen science involved. - Addresses crowdsourcing for the humanities and cultural material - Provides a systematic, academic analysis of crowdsourcing concepts and methodologies - Situates crowdsourcing conceptually within the context of related concepts, such as 'citizen science', 'wisdom of crowds', and 'public engagement' |
| data science for humanities: The Evaluation of Research in Social Sciences and Humanities Andrea Bonaccorsi, 2018-01-04 This book examines very important issues in research evaluation in the Social Sciences and Humanities. It is based on recent experiences carried out in Italy (2011-2015) in the fields of research assessment, peer review, journal classification, and construction of indicators, and presents a systematic review of theoretical issues influencing the evaluation of Social Sciences and Humanities. Several chapters analyse original data made available through research assessment exercises. Other chapters are the result of dedicated and independent research carried out in 2014-2015 aimed at addressing some of the debated and open issues, for example in the evaluation of books, the use of Library Catalog Analysis or Google Scholar, the definition of research quality criteria on internationalization, as well as opening the way to innovative indicators. The book is therefore a timely and important contribution to the international debate. |
| data science for humanities: Debates in the Digital Humanities 2016 Matthew K. Gold, Lauren F. Klein, 2016-05-18 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. |
| data science for humanities: Intermediate Horizons Mark Vareschi, Heather Wacha, 2022-09-27 Foreword: Intermediate horizons / Matthew Kirschenbaum -- Section I. Approach -- Benjamin Franklin's postal work / Christy L. Pottroff -- Linking book history and the digital humanities via museum studies / Jayme Yahr -- Section II. Access -- Material and digital traces in patterns of nature: early modern botany books and seventeenth-century needlework / Mary Learner -- Opening the book: the utopian dreams and uncertain future of open access textbook publishing / Joseph L. Locke and Ben Wright -- Books of ours: what libraries can learn about social media from books of hours / Alexandra Alvis -- Section III. Assessment -- Whose books are online? Diversity, equity, and inclusion in online text collections / Catherine A. Winters and Clayton P. Michaud -- Electronic versioning and digital editions / Paul A. Broyles -- Materialisms and the cultural turn in digital humanities / Mattie Burkert. |
| data science for humanities: Spatial Synthesis Xinyue Ye, Hui Lin, 2020-11-30 This book describes how powerful computing technology, emerging big and open data sources, and theoretical perspectives on spatial synthesis have revolutionized the way in which we investigate social sciences and humanities. It summarizes the principles and applications of human-centered computing and spatial social science and humanities research, thereby providing fundamental information that will help shape future research. The book illustrates how big spatiotemporal socioeconomic data facilitate the modelling of individuals’ economic behavior in space and time and how the outcomes of such models can reveal information about economic trends across spatial scales. It describes how spatial social science and humanities research has shifted from a data-scarce to a data-rich environment. The chapters also describe how a powerful analytical framework for identifying space-time research gaps and frontiers is fundamental to comparative study of spatiotemporal phenomena, and how research topics have evolved from structure and function to dynamic and predictive. As such this book provides an interesting read for researchers, students and all those interested in computational and spatial social sciences and humanities. |
| data science for humanities: Data Science Vijay Kotu, Bala Deshpande, 2018-11-27 Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You'll be able to: - Gain the necessary knowledge of different data science techniques to extract value from data. - Master the concepts and inner workings of 30 commonly used powerful data science algorithms. - Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... - Contains fully updated content on data science, including tactics on how to mine business data for information - Presents simple explanations for over twenty powerful data science techniques - Enables the practical use of data science algorithms without the need for programming - Demonstrates processes with practical use cases - Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language - Describes the commonly used setup options for the open source tool RapidMiner |
| data science for humanities: Data Science in the Library Joel Herndon, 2022 This book considers the current environment for data driven research, instruction, and consultation from a variety of faculty and library perspectives and suggests strategies for engaging with the tools and methods of data driven research. |
| data science for humanities: Debates in the Digital Humanities 2019 Matthew K. Gold, Lauren F. Klein, 2019-04-30 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. |
| data science for humanities: Mindset Mathematics Jo Boaler, Jen Munson, Cathy Williams, 2017-08-28 Engage students in mathematics using growth mindset techniques The most challenging parts of teaching mathematics are engaging students and helping them understand the connections between mathematics concepts. In this volume, you'll find a collection of low floor, high ceiling tasks that will help you do just that, by looking at the big ideas at the first-grade level through visualization, play, and investigation. During their work with tens of thousands of teachers, authors Jo Boaler, Jen Munson, and Cathy Williams heard the same message—that they want to incorporate more brain science into their math instruction, but they need guidance in the techniques that work best to get across the concepts they needed to teach. So the authors designed Mindset Mathematics around the principle of active student engagement, with tasks that reflect the latest brain science on learning. Open, creative, and visual math tasks have been shown to improve student test scores, and more importantly change their relationship with mathematics and start believing in their own potential. The tasks in Mindset Mathematics reflect the lessons from brain science that: There is no such thing as a math person - anyone can learn mathematics to high levels. Mistakes, struggle and challenge are the most important times for brain growth. Speed is unimportant in mathematics. Mathematics is a visual and beautiful subject, and our brains want to think visually about mathematics. With engaging questions, open-ended tasks, and four-color visuals that will help kids get excited about mathematics, Mindset Mathematics is organized around nine big ideas which emphasize the connections within the Common Core State Standards (CCSS) and can be used with any current curriculum. |
| data science for humanities: Doing Digital Humanities Constance Crompton, Richard J Lane, Ray Siemens, 2016-09-13 Digital Humanities is rapidly evolving as a significant approach to/method of teaching, learning and research across the humanities. This is a first-stop book for people interested in getting to grips with digital humanities whether as a student or a professor. The book offers a practical guide to the area as well as offering reflection on the main objectives and processes, including: Accessible introductions of the basics of Digital Humanities through to more complex ideas A wide range of topics from feminist Digital Humanities, digital journal publishing, gaming, text encoding, project management and pedagogy Contextualised case studies Resources for starting Digital Humanities such as links, training materials and exercises Doing Digital Humanities looks at the practicalities of how digital research and creation can enhance both learning and research and offers an approachable way into this complex, yet essential topic. |
| data science for humanities: The Logic of the Sciences and the Humanities F. S. C. Northrop, 1953 |
| data science for humanities: Visualization and Interpretation Johanna Drucker, 2020-11-10 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. |
| data science for humanities: Digital Curation in the Digital Humanities Arjun Sabharwal, 2015-04-11 Archives and special collections departments have a long history of preserving and providing long-term access to organizational records, rare books, and other unique primary sources including manuscripts, photographs, recordings, and artifacts in various formats. The careful curatorial attention to such records has also ensured that such records remain available to researchers and the public as sources of knowledge, memory, and identity. Digital curation presents an important framework for the continued preservation of digitized and born-digital collections, given the ephemeral and device-dependent nature of digital content. With the emergence of analog and digital media formats in close succession (compared to earlier paper- and film-based formats) came new standards, technologies, methods, documentation, and workflows to ensure safe storage and access to content and associated metadata. Researchers in the digital humanities have extensively applied computing to research; for them, continued access to primary data and cultural heritage means both the continuation of humanities scholarship and new methodologies not possible without digital technology. Digital Curation in the Digital Humanities, therefore, comprises a joint framework for preserving, promoting, and accessing digital collections. This book explores at great length the conceptualization of digital curation projects with interdisciplinary approaches that combine the digital humanities and history, information architecture, social networking, and other themes for such a framework. The individual chapters focus on the specifics of each area, but the relationships holding the knowledge architecture and the digital curation lifecycle model together remain an overarching theme throughout the book; thus, each chapter connects to others on a conceptual, theoretical, or practical level. - Theoretical and practical perspectives on digital curation in the digital humanities and history - In-depth study of the role of social media and a social curation ecosystem - The role of hypertextuality and information architecture in digital curation - Study of collaboration and organizational dimensions in digital curation - Reviews of important web tools in digital humanities |
| data science for humanities: Routledge International Handbook of Research Methods in Digital Humanities Kristen Schuster, Stuart Dunn, 2020-08-23 This book draws on both traditional and emerging fields of study to consider consider what a grounded definition of quantitative and qualitative research in the Digital Humanities (DH) might mean; which areas DH can fruitfully draw on in order to foster and develop that understanding; where we can see those methods applied; and what the future directions of research methods in Digital Humanities might look like. Schuster and Dunn map a wide-ranging DH research methodology by drawing on both ‘traditional’ fields of DH study such as text, historical sources, museums and manuscripts, and innovative areas in research production, such as knowledge and technology, digital culture and society and history of network technologies. Featuring global contributions from scholars in the United Kingdom, the United States, Europe and Australia, this book draws together a range of disciplinary perspectives to explore the exciting developments offered by this fast-evolving field. Routledge International Handbook of Research Methods in Digital Humanities is essential reading for anyone who teaches, researches or studies Digital Humanities or related subjects. |
| data science for humanities: Transformative Digital Humanities Mary McAleer Balkun, Marta Mestrovic Deyrup, 2020-04-23 Transformative Digital Humanities takes a two-pronged approach to the digital humanities: it examines the distinct kinds of work currently being undertaken in the field, while also addressing current issues in the digital humanities, including sustainability, accessibility, interdisciplinarity, and funding. With contributions from humanities and LIS scholars based in China, Canada, England, Germany, Spain, and the United States, this collection of case studies provides a framework for readers to develop new projects as well as to see how existing projects might continue to develop over time. This volume also participates in the current digital humanities conversation by bringing forward emerging voices that offer new options for cooperation, by demonstrating how the digital humanities can become a tool for activism, and by illustrating the potential of the digital humanities to reexamine and reconstitute existing canons. Transformative Digital Humanities considers what sorts of challenges still exist in the field and suggests how they might be addressed. As such, the book will be essential reading for academics and students engaged in the study of information science and digital humanities. It should also be of great interest to practitioners around the globe. |
| data science for humanities: Data Science and Social Research N. Carlo Lauro, Enrica Amaturo, Maria Gabriella Grassia, Biagio Aragona, Marina Marino, 2017-11-17 This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a T…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, …
Belmont Forum Adopts Open Data Principles for Environment…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management …
Belmont Forum Data Accessibility Statement and …
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to …
