Readings

Please read the following before the first class of the course:
• Otto Willmann, "The Seven Liberal Arts" (Catholic Encylopedia, 1907)
• Sister Miriam Joseph, "What Are the Liberal Arts?" (2002)
• Rens Bod, New History of the Humanities (2013), chapters 1-2 and chapter 6 [course password required]
  • Read chap. 1
  • Scan quickly through chap. 2 to get a sense of what Bod is doing. Slow down for any discipline that interests you. Read more carefully the conclusion of the chapter (pp. 71-73)
  • Read chap. 6
• U. S. Congress, National Foundation on the Arts and the Humanities Act of 1965 (P.L. 89-209) (as amended) [read through the definition of "the humanities" in the "Definitions" section]
• Judith Hallinen, “STEM: Education Curriculum” (Encyclopedia Britannica, 2019)

Readings

• Browse the following sites (and inductively try to answer the question: "what is data science?"):
  • UC Berkeley Division of Data Science and Information
    • UC Berkeley Data Science Major
  • Carnegie Mellon U. data science graduate programs
  • NYU Center for Data Science
  • Northwestern U. Master's in Data Science
  • UCSB Data Science Initiative
• David Donoho, “50 Years of Data Science” (2017)
• Os Keyes, “Counting the Countless” (2019)

Readings

• Lisa Gitelman, ed., “Raw Data” Is an Oxymoron (2013) [PDF]. Read the following:
  • Gitelman, and Virginia Jackson, "Introduction"
  • Daniel Rosenberg, "Data before the Fact"
• Soraya de Chadarevian and Theodore M. Porter, “Introduction: Scrutinizing the Data World” (2018) (paywalled; requires UCSB institutional access; click on "View Full Page PDF" in right sidebar to download as PDF)
• Matthew L. Jones, “How We Became Instrumentalists (Again): Data Positivism since World War II" (2018) (paywalled; requires UCSB institutional access; click on "View Full Page PDF" in right sidebar to download as PDF)
• Johanna Drucker, "Humanities Approaches to Graphical Display" (2011) (paragraphs 1-30 on "capta" vs "data")
• Geoffrey C. Bowker and Susan Leigh Star "Introduction" from Sorting Things out: Classification and Its Consequences (1999)

Readings

• Yin Liu, "Ways of Reading, Models for Text, and the Usefulness of Dead People" (2013)
• Wikipedia
  • "Data Structure"
  • "Data Model"
• Oracle, Inc. “What Is an Object?” (2017)
• Relational Databases
  • Alan Liu, "Escaping History: New Historicism, Databases, and Contingency", pp. 239-41 and 249-54 (chap. 9 of Liu, Local Transcendence, 2008)
  • E. F. Codd, “A Relational Model of Data for Large Shared Data Banks” (1970) – read this canonical article through page 383 for basic concepts
• Wikipedia, "NoSQL"
• Ontotext, Inc., “What Is RDF Triplestore?” (2019)
• Luc Moreau and Paul Groth, Provenance: An Introduction to PROV (2013) – Read chapters 1-2

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Assignment due by this class: Prospectus for research topic and project.

Readings

• Jonathan Stuart Ward and Adam Barker, “Undefined By Data: A Survey of Big Data Definitions” (2013)
• Matthew L. Jones, “Querying the Archive: Data Mining from Apriori to PageRank” (2017) [PDF]
• Maryam M. Najafabadi, et al., “Deep Learning Applications and Challenges in Big Data Analytics” (2015)
• Shannon Mattern, “The Big Data of Ice, Rocks, Soils, and Sediments” (2017)
• Anne Pasek, “Managing Carbon and Data Flows: Fungible Forms of Mediation in the Cloud” (2019)
• Wikipedia,
  • “Apache Hadoop”
  • "Lambda Architecture"

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Assignment due by this class: Research blog post 1

Readings

• Archives
  • Jefferson Bailey, “Disrespect Des Fonds: Rethinking Arrangement and Description in Born-Digital Archives” (2013)
• Corpora
  • Tim Sherratt, “‘A Map and Some Pins’: Open Data and Unlimited Horizons” (2013)
  • Alan Liu, "N + 1: A Plea for Cross-Domain Data in the Digital Humanities" (2016)
  • Katherine Bode, “The Equivalence of ‘Close’ and ‘Distant’ Reading; or, Toward a New Object for Data-Rich Literary History" (2017) -- read pp. 91-102
  • Some major corpora used by the digital humanities (take a quick look at these):
    • HathiTrust Digital Library
      • See also HathiTrust Research Center
    • Digital Public Library of America
    • Europeana Collections
    • Trove
    • Living with Machines (project underway) -- British Library press release: “Living with Machines”
  • Corpus Linguistics
    • Browse and explore some of the corpus linguistics corpora available from Mark Davies' corpora.byu.edu
• Digital Humanities (two introductory essays; for fuller resources, see the web site for the instructor's graduate course on "Digital Humanities: Introduction to the Field")
  • Alan Liu, “The Meaning of the Digital Humanities” (2013)
  • Andrew Piper, from Enumerations: Data and Literary Study (2018), "Introduction (Reading's Refrain)" [course password required]

Readings

• Wikipedia, “Allegorical Interpretation of the Bible"
• Topic Modeling
  • David M. Blei, “Probabilistic Topic Models” (2013) — (concentrate on beginning to p. 79; read the rest of the article for concepts without needing to follow the math or technical notation)
  • Ted Underwood, “Topic Modeling Made Just Simple Enough” (2012)
  • Andrew Goldstone’s Dfr-browser interface for exploring topic models (browse the models in Dfr-browser; for a user manual, see “Interpreting the topic model of Signs“)
    • topic models of 100 years of literary criticism journals
    • topic models of 40 years of the Signs journal of “Women in Culture and Society”
• Neural Networks (read for concepts without needing to follow technical details or notations)
  • Ujjwal Karn
    • "A Quick Introduction to Neural Networks" (2016)
    • "An Intuitive Explanation of Convolutional Neural Networks" (2016)
  • Chris Nicholson, "A Beginner's Guide to Generative Adversarial Networks (GANs)" (n. d.)
• Machine-learning "Interpretability" field. Explore the following materials for basic concepts (following the math or code is not necessary):
  • Michael Hind, et al. “TED: Teaching AI to Explain Its Decisions” (2018)
  • David Alvarez-Melis, et al., “Towards Robust Interpretability with Self-Explaining Neural Networks” (2018) (read at the conceptual level; following the math and technical notation is not necessary)
  • IBM Research Lab, AI Explainability 360 open-source toolkit – browse this site, including the sections on "Guidance" and "Glossary." Also see some of the "Tutorials." (Reading the cited articles among the toolkit materials is not necessary.)
• Jo Guldi, “Critical Search: A Procedure for Guided Reading in Large-Scale Textual Corpora" (2018)

Readings

• “Hans Rosling’s 200 Countries, 200 Years, 4 Minutes - The Joy of Stats - BBC Four” (video, 2010)
  • Also see analysis of the video by Anjali Sharma
• Michelle Scalise Sugiyama, “The Forager Oral Tradition and the Evolution of Prolonged Juvenility” (2011)
  • (Optional: see also Polly W. Wiessner, “Embers of Society: Firelight Talk among the Ju/’hoansi Bushmen” [2014])
• From Nathalie Henry Riche et al., ed., Data-driven Storytelling (2018)
  • 2: Barbara Tversky, "Storytelling in the Wild: Implications for Data Storytelling" – read pp. 19-43
  • 3: Alice Thudt et al., "Exploration and Explanation in Data-Driven Storytelling"
  • See also: “NAPA Cards: Narrative Patterns for Data Stories” (2016)
• Martha Kang, “Exploring the 7 Different Types of Data Stories” (2015)
• European Journalism Centre, “Data Stories” (2012)
• Automated Data Narrative
  • Kristin Veel, “Make Data Sing: The Automation of Storytelling” (2018)
  • Yolanda Gil and Daniel Garijo, “Towards Automating Data Narratives” (2017). See also:
    • WINGS (data workflow system for science)
    • David Garijo and Yolanda Gil, DAta NArratives (DANA) (2017)

Readings

• Johanna Drucker, "Graphesis: Visual Knowledge Production and Representation" (2011) (online paper preceding the publication of her 2014 book of this title) – read pp. 1-11
• Alan Liu, excerpt from chapter 3 of Friending the Past: The Sense of History in the Digital Age (2019)
• Lauren Manning, “Visualizing Information" (2011)
• Stephen Few
  • “Visual Pattern Recognition: Meaningful Patterns in Quantitative Business Information” (2006)
  • "Eenie, Meenie, Minie, Moe: Selecting the Right Graph for Your Message" (2004)
• E. B. Dubois, et al., Visualizations for the "Exhibit of American Negroes" at the 1900 Paris Exposition:
  • Allison Meier, “W. E. B. Du Bois’s Modernist Data Visualizations of Black Life” (2016)
  • Whitney Battle-Baptiste and Britt Rusert, eds., E.B Du Bois’s Data Portraits: Visualizing Black America--The Color Line at the Turn of the Twentieth Century (2018) [book will be brought into class by the instructor]
• The Life and Death of Data (Harvard MetaLab visualization project, 2015)
  • Go directly to the visualization (takes a minute to load; explore data in left panel; scroll the right panel to read about the project)
• Florian Kräutli, "Visualising Cultural Data: Exploring Digital Collections Through Timeline Visualisations" (PhD thesis, 2017) – read pp. 52-61, 100-122
  • Also see “Timelines” (playlist of YouTube videos created by Kräutli to show timelines discussed in his dissertation)
• Giorgia Lupi and Stefanie Posavec, “Dear Data" (web site for their project and 2016 book)
• Data Art (examples from UCSB digital new media artists)
  • Lisa Jevbratt, 1:1 (2) (1999-2002)
  • George Legrady
    • Pockets Full of Memories (2003-2006)
    • Making Visible the Invisible (2005-2014)

Student Presentations

Class presentations of research proposals.

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• Assignment due by this class: Research proposal (in form of mock grant proposal)
• Assignment due by Tuesday of week following last class: Research blog post 2

A Note About Access to Reading Materials For This Course

Except were noted, all materials are online. Paywalled articles can be accessed over the UCSB network (or from off-campus by using the campus Pulse VPN service or the campus Library Proxy Server. You can also try to find open-access versions of paywalled materials using the Unpaywall extension for the Chrome or Firefox browsers. Because so many readings are online (an increasingly prevalent trend in college courses), students will need to develop a method or workflow for themselves that optimizes their ability to study the materials. While everyone has their own personal preferences and technical preferences, the following guide includes suggested options for handling online materials: Guide to Downloading and Managing Online Readings