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Author: Lisa Gitelman
Publisher: MIT Press
ISBN: 0262312336
Category : Technology & Engineering
Languages : en
Pages : 203
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Book Description
Episodes in the history of data, from early modern math problems to today's inescapable “dataveillance,” that demonstrate the dependence of data on culture. We live in the era of Big Data, with storage and transmission capacity measured not just in terabytes but in petabytes (where peta- denotes a quadrillion, or a thousand trillion). Data collection is constant and even insidious, with every click and every “like” stored somewhere for something. This book reminds us that data is anything but “raw,” that we shouldn't think of data as a natural resource but as a cultural one that needs to be generated, protected, and interpreted. The book's essays describe eight episodes in the history of data from the predigital to the digital. Together they address such issues as the ways that different kinds of data and different domains of inquiry are mutually defining; how data are variously “cooked” in the processes of their collection and use; and conflicts over what can—or can't—be “reduced” to data. Contributors discuss the intellectual history of data as a concept; describe early financial modeling and some unusual sources for astronomical data; discover the prehistory of the database in newspaper clippings and index cards; and consider contemporary “dataveillance” of our online habits as well as the complexity of scientific data curation. Essay Authors Geoffrey C. Bowker, Kevin R. Brine, Ellen Gruber Garvey, Lisa Gitelman, Steven J. Jackson, Virginia Jackson, Markus Krajewski, Mary Poovey, Rita Raley, David Ribes, Daniel Rosenberg, Matthew Stanley, Travis D. Williams
Author: Lisa Gitelman
Publisher: MIT Press
ISBN: 0262312336
Category : Technology & Engineering
Languages : en
Pages : 203
Get Book Here
Book Description
Episodes in the history of data, from early modern math problems to today's inescapable “dataveillance,” that demonstrate the dependence of data on culture. We live in the era of Big Data, with storage and transmission capacity measured not just in terabytes but in petabytes (where peta- denotes a quadrillion, or a thousand trillion). Data collection is constant and even insidious, with every click and every “like” stored somewhere for something. This book reminds us that data is anything but “raw,” that we shouldn't think of data as a natural resource but as a cultural one that needs to be generated, protected, and interpreted. The book's essays describe eight episodes in the history of data from the predigital to the digital. Together they address such issues as the ways that different kinds of data and different domains of inquiry are mutually defining; how data are variously “cooked” in the processes of their collection and use; and conflicts over what can—or can't—be “reduced” to data. Contributors discuss the intellectual history of data as a concept; describe early financial modeling and some unusual sources for astronomical data; discover the prehistory of the database in newspaper clippings and index cards; and consider contemporary “dataveillance” of our online habits as well as the complexity of scientific data curation. Essay Authors Geoffrey C. Bowker, Kevin R. Brine, Ellen Gruber Garvey, Lisa Gitelman, Steven J. Jackson, Virginia Jackson, Markus Krajewski, Mary Poovey, Rita Raley, David Ribes, Daniel Rosenberg, Matthew Stanley, Travis D. Williams
Author: Lisa Gitelman
Publisher: MIT Press
ISBN: 0262518287
Category : Computers
Languages : en
Pages : 203
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Book Description
We live in the era of Big Data, with storage and transmission capacity measured not just in terabytes but in petabytes (where peta- denotes a quadrillion, or a thousand trillion). Data collection is constant and even insidious, with every click and every "like" stored somewhere for something. This book reminds us that data is anything but "raw, " that we shouldn't think of data as a natural resource but as a cultural one that needs to be generated, protected, and interpreted. The book's essays describe eight episodes in the history of data from the predigital to the digital. Together they address such issues as the ways that different kinds of data and different domains of inquiry are mutually defining; how data are variously "cooked" in the processes of their collection and use; and conflicts over what can -- or can't -- be "reduced" to data. Contributors discuss the intellectual history of data as a concept; describe early financial modeling and some unusual sources for astronomical data; discover the prehistory of the database in newspaper clippings and index cards; and consider contemporary "dataveillance" of our online habits as well as the complexity of scientific data curation.
Author: Sandra Gonzalez-Bailon
Publisher: MIT Press
ISBN: 0262343460
Category : Social Science
Languages : en
Pages : 257
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Book Description
How data science and the analysis of networks help us solve the puzzle of unintended consequences. Social life is full of paradoxes. Our intentional actions often trigger outcomes that we did not intend or even envision. How do we explain those unintended effects and what can we do to regulate them? In Decoding the Social World, Sandra González-Bailón explains how data science and digital traces help us solve the puzzle of unintended consequences—offering the solution to a social paradox that has intrigued thinkers for centuries. Communication has always been the force that makes a collection of people more than the sum of individuals, but only now can we explain why: digital technologies have made it possible to parse the information we generate by being social in new, imaginative ways. And yet we must look at that data, González-Bailón argues, through the lens of theories that capture the nature of social life. The technologies we use, in the end, are also a manifestation of the social world we inhabit. González-Bailón discusses how the unpredictability of social life relates to communication networks, social influence, and the unintended effects that derive from individual decisions. She describes how communication generates social dynamics in aggregate (leading to episodes of “collective effervescence”) and discusses the mechanisms that underlie large-scale diffusion, when information and behavior spread “like wildfire.” She applies the theory of networks to illuminate why collective outcomes can differ drastically even when they arise from the same individual actions. By opening the black box of unintended effects, González-Bailón identifies strategies for social intervention and discusses the policy implications—and how data science and evidence-based research embolden critical thinking in a world that is constantly changing.
Author: Cal (Crystal) Biruk
Publisher: Duke University Press
ISBN: 0822371820
Category : Social Science
Languages : en
Pages : 302
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Book Description
In Cooking Data Crystal Biruk offers an ethnographic account of research into the demographics of HIV and AIDS in Malawi to rethink the production of quantitative health data. While research practices are often understood within a clean/dirty binary, Biruk shows that data are never clean; rather, they are always “cooked” during their production and inevitably entangled with the lives of those who produce them. Examining how the relationships among fieldworkers, supervisors, respondents, and foreign demographers shape data, Biruk examines the ways in which units of information—such as survey questions and numbers written onto questionnaires by fieldworkers—acquire value as statistics that go on to shape national AIDS policy. Her approach illustrates how on-the-ground dynamics and research cultures mediate the production of global health statistics in ways that impact local economies and formulations of power and expertise.
Author: Cole Nussbaumer Knaflic
Publisher: John Wiley & Sons
ISBN: 1119621496
Category : Computers
Languages : en
Pages : 454
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Book Description
Influence action through data! This is not a book. It is a one-of-a-kind immersive learning experience through which you can become—or teach others to be—a powerful data storyteller. Let’s practice! helps you build confidence and credibility to create graphs and visualizations that make sense and weave them into action-inspiring stories. Expanding upon best seller storytelling with data’s foundational lessons, Let’s practice! delivers fresh content, a plethora of new examples, and over 100 hands-on exercises. Author and data storytelling maven Cole Nussbaumer Knaflic guides you along the path to hone core skills and become a well-practiced data communicator. Each chapter includes: ● Practice with Cole: exercises based on real-world examples first posed for you to consider and solve, followed by detailed step-by-step illustration and explanation ● Practice on your own: thought-provoking questions and even more exercises to be assigned or worked through individually, without prescribed solutions ● Practice at work: practical guidance and hands-on exercises for applying storytelling with data lessons on the job, including instruction on when and how to solicit useful feedback and refine for greater impact The lessons and exercises found within this comprehensive guide will empower you to master—or develop in others—data storytelling skills and transition your work from acceptable to exceptional. By investing in these skills for ourselves and our teams, we can all tell inspiring and influential data stories!
Author: Adrian Mackenzie
Publisher: MIT Press
ISBN: 0262036827
Category : Social Science
Languages : en
Pages : 269
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Book Description
If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking. Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures. Mackenzie's account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.
Author: Giorgia Lupi
Publisher: Chronicle Books
ISBN: 1616895462
Category : Design
Languages : en
Pages : 304
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Book Description
Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates "the infinitesimal, incomplete, imperfect, yet exquisitely human details of life," in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere.
Author: Nanna Bonde Thylstrup
Publisher: MIT Press
ISBN: 026203901X
Category : Social Science
Languages : en
Pages : 212
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Book Description
A new examination of mass digitization as an emerging sociopolitical and sociotechnical phenomenon that alters the politics of cultural memory. Today, all of us with internet connections can access millions of digitized cultural artifacts from the comfort of our desks. Institutions and individuals add thousands of new cultural works to the digital sphere every day, creating new central nexuses of knowledge. How does this affect us politically and culturally? In this book, Nanna Bonde Thylstrup approaches mass digitization as an emerging sociopolitical and sociotechnical phenomenon, offering a new understanding of a defining concept of our time. Arguing that digitization has become a global cultural political project, Thylstrup draws on case studies of different forms of mass digitization—including Google Books, Europeana, and the shadow libraries Monoskop, lib.ru, and Ubuweb—to suggest a different approach to the study of digital cultural memory archives. She constructs a new theoretical framework for understanding mass digitization that focuses on notions of assemblage, infrastructure, and infrapolitics. Mass digitization does not consist merely of neutral technical processes, Thylstrup argues, but of distinct subpolitical processes that give rise to new kinds of archives and new ways of interacting with the artifacts they contain. With this book, she offers important and timely guidance on how mass digitization alters the politics of cultural memory to impact our relationship with the past and with one another.
Author: Lisa Gitelman
Publisher: MIT Press
ISBN: 0262572478
Category : Social Science
Languages : en
Pages : 222
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Book Description
In Always Already New, Lisa Gitelman explores the newness of new media while she asks what it means to do media history. Using the examples of early recorded sound and digital networks, Gitelman challenges readers to think about the ways that media work as the simultaneous subjects and instruments of historical inquiry. Presenting original case studies of Edison's first phonographs and the Pentagon's first distributed digital network, the ARPANET, Gitelman points suggestively toward similarities that underlie the cultural definition of records (phonographic and not) at the end of the nineteenth century and the definition of documents (digital and not) at the end of the twentieth. As a result, Always Already New speaks to present concerns about the humanities as much as to the emergent field of new media studies. Records and documents are kernels of humanistic thought, after all—part of and party to the cultural impulse to preserve and interpret. Gitelman's argument suggests inventive contexts for "humanities computing" while also offering a new perspective on such traditional humanities disciplines as literary history. Making extensive use of archival sources, Gitelman describes the ways in which recorded sound and digitally networked text each emerged as local anomalies that were yet deeply embedded within the reigning logic of public life and public memory. In the end Gitelman turns to the World Wide Web and asks how the history of the Web is already being told, how the Web might also resist history, and how using the Web might be producing the conditions of its own historicity.
Author: Catherine D'Ignazio
Publisher: MIT Press
ISBN: 0262358530
Category : Social Science
Languages : en
Pages : 328
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Book Description
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.
