Everyday Adventures With Unruly Data

Paired informal and scholarly essays show how everyday events reveal fundamental concepts of data, including its creation, aggregation, management, and use. Whether questioning numbers on a scale, laughing at a misspelling of one’s name, or finding ourselves confused in a foreign supermarket, we are engaging with data. The only way to handle data responsibly, says Melanie Feinberg in this incisive work, is to take into account its human character. Though the data she discusses may seem familiar, close scrutiny shows it to be ambiguous, complicated, and uncertain: unruly. Drawing on the tools of information science, she uses everyday events such as deciding between Blender A and Blender B on Amazon to demonstrate a practical, critical, and generative mode of thinking about data: its creation, management, aggregation, and use. Each chapter pairs a self-contained main essay (an adventure) with a scholarly companion essay (the reflection). The adventure begins with an anecdote—visiting the library, running out of butter, cooking rice on a different stove. Feinberg argues that to understand the power and pitfalls of data science, we must attend to the data itself, not merely the algorithms that manipulate it. As she reflects on the implications of commonplace events, Feinberg explicates fundamental concepts of data that reveal the many tiny design decisions—which may not even seem like design at all—that shape how data comes to be. Through the themes of serendipity, objectivity, equivalence, interoperability, taxonomy, labels, and locality, she illuminates the surprisingly pervasive role of data in our daily thoughts and lives.

Why grassroots data activists in Latin America count feminicide—and how this vital social justice work challenges mainstream data science. What isn’t counted doesn’t count. And mainstream institutions systematically fail to account for feminicide, the gender-related killing of women and girls, including cisgender and transgender women. Against this failure, Counting Feminicide brings to the fore the work of data activists across the Americas who are documenting such murders—and challenging the reigning logic of data science by centering care, memory, and justice in their work. Drawing on Data Against Feminicide, a large-scale collaborative research project, Catherine D’Ignazio describes the creative, intellectual, and emotional labor of feminicide data activists who are at the forefront of a data ethics that rigorously and consistently takes power and people into account. Individuals, researchers, and journalists—these data activists scour news sources to assemble spreadsheets and databases of women killed by gender-related violence, then circulate those data in a variety of creative and political forms. Their work reveals the potential of restorative/transformative data science—the use of systematic information to, first, heal communities from the violence and trauma produced by structural inequality and, second, envision and work toward the world in which such violence has been eliminated. Specifically, D’Ignazio explores the possibilities and limitations of counting and quantification—reducing complex social phenomena to convenient, sortable, aggregable forms—when the goal is nothing short of the elimination of gender-related violence. Counting Feminicide showcases the incredible power of data feminism in practice, in which each murdered woman or girl counts, and, in being counted, joins a collective demand for the restoration of rights and a transformation of the gendered order of the world.

A guide to everything you need to understand to navigate a world increasingly governed by data. Data has become a defining issue of current times. Our everyday lives are shaped by the data that is produced about us (and by us) through digital technologies. In this book, Critical Data Literacies, Luci Pangrazio and Neil Selwyn introduce readers to the central concepts, ideas, and arguments required to make sense of life in the data age. The authors challenge the idea that datafication is an inevitable and inescapable condition. Drawing on emerging areas of scholarship such as data justice, data feminism, and other critical data studies approaches, they explore how individuals and communities can empower themselves to engage with data critically and creatively. Over the course of eight wide-ranging chapters, the book introduces readers to the main components of critical data literacies—from the fundamentals of identifying and understanding data to the complexities of engaging with more combative data tactics. Critical Data Literacies explores how the tradition of critical literacies can offer a powerful foundation to address the big concerns of the data age, such as issues of data justice and privacy, algorithmic bias, dataveillance, and disinformation. Bringing together cutting-edge thinking and discussion from across education, sociology, psychology, and media and communication studies, Critical Data Literacies develops a powerful argument for collectively rethinking the role that data plays in our everyday lives and re-establishing agency, free will, and the democratic public sphere.

Why we organize our personal digital data the way we do and how design of new PIM systems can help us manage our information more efficiently. Each of us has an ever-growing collection of personal digital data: documents, photographs, PowerPoint presentations, videos, music, emails and texts sent and received. To access any of this, we have to find it. The ease (or difficulty) of finding something depends on how we organize our digital stuff. In this book, personal information management (PIM) experts Ofer Bergman and Steve Whittaker explain why we organize our personal digital data the way we do and how the design of new PIM systems can help us manage our collections more efficiently. Bergman and Whittaker report that many of us use hierarchical folders for our personal digital organizing. Critics of this method point out that information is hidden from sight in folders that are often within other folders so that we have to remember the exact location of information to access it. Because of this, information scientists suggest other methods: search, more flexible than navigating folders; tags, which allow multiple categorizations; and group information management. Yet Bergman and Whittaker have found in their pioneering PIM research that these other methods that work best for public information management don't work as well for personal information management. Bergman and Whittaker describe personal information collection as curation: we preserve and organize this data to ensure our future access to it. Unlike other information management fields, in PIM the same user organizes and retrieves the information. After explaining the cognitive and psychological reasons that so many prefer folders, Bergman and Whittaker propose the user-subjective approach to PIM, which does not replace folder hierarchies but exploits these unique characteristics of PIM.

Digital library is commonly seen as a type of information retrieval system which stores and accesses digital content remotely via computer networks. However, the vision of digital libraries is not limited to technology or management, but user experience. This book is an attempt to share the practical experiences of solutions to the operation of digital libraries. To indicate interdisciplinary routes towards successful applications, the chapters in this book explore the implication of digital libraries from the perspectives of design, operation, and promotion. Without common agreement on a broadly accepted model of digital libraries, authors from diverse fields seek to develop theories and empirical investigations that to advance our understanding of digital libraries.

Influential writings make the case for open access to research, explore its implications, and document the early struggles and successes of the open access movement. Peter Suber has been a leading advocate for open access since 2001 and has worked full time on issues of open access since 2003. As a professor of philosophy during the early days of the internet, he realized its power and potential as a medium for scholarship. As he writes now, “it was like an asteroid crash, fundamentally changing the environment, challenging dinosaurs to adapt, and challenging all of us to figure out whether we were dinosaurs.” When Suber began putting his writings and course materials online for anyone to use for any purpose, he soon experienced the benefits of that wider exposure. In 2001, he started a newsletter—the Free Online Scholarship Newsletter, which later became the SPARC Open Access Newsletter—in which he explored the implications of open access for research and scholarship. This book offers a selection of some of Suber's most significant and influential writings on open access from 2002 to 2010. In these texts, Suber makes the case for open access to research; answers common questions, objections, and misunderstandings; analyzes policy issues; and documents the growth and evolution of open access during its most critical early decade.

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.