Schedule for English 146 (W 2021):
Class 1 (Jan. 5, 2021)
- “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
Class 2 (Jan. 7, 2021) — Taking a First Look at Data Stories
1. For this “taking a first look” reading assignment, quickly explore the following examples of data stories to get an overall sense of what they are and how they work. The goal is to familiarize ourselves with some recent data stories.
- Jin Wu, et al., “How the Virus Got Out,” New York Times, March 22, 2020. (If you do not subscribe to The New York Times, you will be prompted to create a free online account to access this article.)
- Nate Cohn, et al., “Some U.S. Cities Could Have Coronavirus Outbreaks Worse Than Wuhan’s,” New York Times, March 27, 2020
- Laure Leatherby, “Why Are Coronavirus Cases Decreasing? Experts Say Restrictions Are Working,” New York Times, Aug. 24, 2020. (If you do not subscribe to The New York Times, you will be prompted to create a free online account to access this article.)
- Derek Watkins, et al., “How the Virus Won,” New York Times, June 24, 2020.
- Thomas Curwen, “How Will We Grieve Once the Coronavirus Pandemic Is Over?” Los Angeles Times, April 22, 2020; updated April 28, 2020) (original headline: “How do we craft the narratives that will define a pandemic? Look to the numbers”)
- Matthew C. Klein, “How Americans Die,” Bloomberg.com, April 17, 2014.
- “A Nation Divided,” Zeit Online, Oct. 29-Nov. 19, 2014. (Click on “Einverstanden,” which means “I agree,” to read the article for free but with the newspaper’s normal advertising and tracking. [Translation of what you are agreeing to.])
- “The Humanities Matter!”, 4Humanities.org, 2014. (Download the PDF of the infographic.)
- Denise Lu, “There Are 2,373 Squirrels in Central Park. I Know Because I Helped Count Them,” New York Times, Jan. 8, 2020.
2. Please also go to this table of data stories classified by the kinds of techniques they use and click around to explore. The table is from Charles D. Stolper, et al., “Emerging and Recurring Data-Driven Storytelling Techniques: Analysis of a Curated Collection of Recent Stories” (2016). (Reading the article is not required.)
1. What’s a (Good) Story?
Class 3 (Jan. 12, 2021) — The Idea of Narrative
- H. Porter Abbott, The Cambridge Introduction to Narrative (2002). Read the following:
- Chapter 1: “Narrative and Life” (Alternative online source for this chapter with better quality illustrations)
- Chapter 2: “Defining Narrative”
- Example of a contemporary children’s story: Michael Perry (with illustrations by Lee Ballard), Daniel’s Ride (2001)
Class 4 (Jan. 14, 2021) — Narrative Discourse & Structure
- H. Porter Abbott, The Cambridge Introduction to Narrative (2002). Read the following:
- Chapter 4: “The Rhetoric of Narrative”
- Chapter 5: “Closure”
- Summary of Aristotle’s Poetics (c. 335 BC) on the nature of narrative (specifically, Classical Greek tragedies)
- After reading the summary, you may wish to read these selections from Aristotle’s Poetics in English translation.
- Allan Parsons, Summary: “Story (Fabula) and Plot (Sjuzet or Sjuzhet)” (2016)
- Rebecca Ray, “Narrative Structure” (2020)
- Ella Saltmarshe, “Using Story to Change Systems” (2018)
- Example of a narrative episode in the film A Christmas Story (dir. Bob Clark, 1983): video clip
Related Materials (not required)
Students who wish to learn more about the theory and analysis of narrative may be interested in the field of “narratology.” See the following online resource for a guide:
Due on This Date: Solo Assignment 1 — Narrative Analysis
For this class, reread the children’s narrative by Michael Perry (with illustrations by Lee Ballard) titled Daniel’s Ride (2001). Then, using this Narrative Analysis Form, conduct an analysis of the work in which you identify or describe the narrative’s background; characters; beginning, middle, and end (or narrative arc of rising action, agon, and falling action); difference between “story (fabula)” and “plot (sjuzet)” (if any); and method of telling. Also say if you think this is a good story or not, and briefly why. Submit the form as a PDF through this course’s Gauchospace site here.
2. What’s (Good) Data?
Class 5 (Jan. 19, 2021) — The Idea of Data
- Lisa Gitelman, ed., “Raw Data” Is an Oxymoron (2013). Read the following two chapters in the book:
- Lisa Gitelman and Virginia Jackson, “Introduction”
- Daniel Rosenberg, “Data before the Fact”
Class 6 (Jan. 21, 2021) — From Data to Big Data
- 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)
- “Querying the Archive: Data Mining from Apriori to PageRank” (2017)
Form project teams of 3-4 members each. (Students will be added as members to a Google “shared drive” assigned for each team that will serve as a common workspace for team activities.)
Class 7 (Jan. 26, 2021) — Data Structures & Models
- Yin Liu, “Ways of Reading, Models for Text, and the Usefulness of Dead People” (2013)
- Joshua M. Epstein, “Why Model?” (2008)
Class 8 (Jan. 28, 2021) — Data Formats & Datasets
- Clara Llebot Lorente and Diana Castillo, “Data Types & File Formats” (2020)
- Vijay Kotu and Bala Deshpande, “Data Science Process” (2019)
Due on this Date: Solo Assignment 2 — Conceptual Spreadsheets
Using a spreadsheet program (Excel or Google Spreadsheets), prepare two spreadsheets of data according to the following instructions. When you are done, save your spreadsheets as PDFs and also export their data as CSV (comma separated values) files in .csv format. Submit both the PDFs and your CSV files on this course’s Gauchospace site here. (We will go over all this in the previous class so that everyone is familiar with this assignment. See example of “easy” and “hard” spreadsheets.)
- Conceptually easy data spreadsheet: Using any books, music tracks, videos, films, or similar items with familiar data values (e.g., author, genre, date, etc.) that are easily available to you in your residence or on your computer or internet, make a very small spreadsheet of data about just 5 to 10 of those items. Each row in your spreadsheet will be the data record of one item. Columns (with labels you create at the top) will be for the kinds of data values you are recording about your items (e.g., author name, genre, length, publisher, date, gender of author, etc.). Make a decision about the purpose of the spreadsheet—i.e., what kind of pattern or meaning you might want it to allow you to discover. On the basis of that purpose, choose what kinds of data values you want your columns to record (create 4 to 10 columns with labels for such values). For example, if your items are films, do you want to record the gender of the director, or language of the film, and why?
Finally, write in an empty cell at the bottom of your spreadsheet about the purpose of your spreadsheet, and include any thoughts you have about your choice of data values or examples. This writing should be the equivalent of about 1-2 paragraphs, or about 200-300 words. (Use word-wrap and/or merge-cells to make all the text visible in the exported PDF.)
- Conceptually difficult data spreadsheet: Using anything ready to hand in your residence or area, or that you can find on the internet, follow exactly the same instructions as above to create a data spreadsheet for a set of items that do not have obvious, pre-established, or familiar data values (though they may have values assigned by scholarly specialists). For example, consider traditional quilting patterns or traditional African masks, which do not have the typical kind of data values that libraries or playlists use (“author,” “publisher,” etc.). What are the important data values you can think of to record about these items, and why?
The “why” is the purpose of the spreadsheet, which in an empty cell at the bottom of your spreadsheet you should write about along with any thoughts you have on your choice of data values or examples (about 1-2 paragraphs, or 200-300 words).
Class 9 (Feb. 2, 2021) — Exploring, Assessing, & Critiquing Datasets
In advance of this class, explore the sources for public datasets listed below. In this class and the next, teams will draw on these sources to choose a dataset as the basis for their data-narrative project.
Good sources of public datasets that might be the basis for a student team’s data-narrative project for this course:
- MEAD (Magazine of Early American Datasets)
- Philadelphia Migrant Landing Reports 1798-1801 Dataset
- York County Probate Records 1700-1800 (what people owned in Early America)
- U.S. Census Data (Census Bureau)
Suggested Data “Profiles” (In each section of a “profile,” click on a table, labeled in a format like “Table: DP05,” to see the underlying data and download it in CSV format):
- National Archives Datasets (U.S. National Archives, Open Government Initiative)
- Amending America: Proposed Amendments to the United States Constitution, 1787 to 2014
- National Historical Publications and Records Commission (NHPRC) Grants, 1965-Present
- Social Media at the National Archives (what people view or engage with among the National Archives’ social media posts and blogs; Excel data download)
- Pew Research Center Datasets (datasets from the Pew Research Center) (Data downloads are in SPSS .sav format and require SPSS to work directly with the data or to export to Excel or other formats. See UCSB student access to SPSS. However, Tableau Public will open .sav files for visualization.)
- World Bank Open Data
- World Health Organization Datasets (Only some datasests are downloadable)
- Maternal, Newborn, Child and Adolescent Helath and Ageing (download by clicking on “Export” to Excel icon)
- United Nations Statistics Division, “Other UNSD Databases” (CSV download of all data; data for particular countries and issues will need to be extracted manually into a separate spreadsheet for analysis and visualization)
- HUD Exchange (US Department of Housing and Urban Development)
Late-breaking discoveries of good dataset sources (suggested dataset sources will be added here as the instructors or students discover them):
- Datasets listed by Melanie Walsh for book and course at Cornell on “Introduction to Cultural Analytics & Python” (2020)
- Our World in Data — “Research and data to make progress against the world’s largest problems.” (This resource is organized as a collection of “articles” on topics related to important world problems. When viewing an article, scroll to the section at the bottom on “Data Sources” for the dataset sources. In the case of broken or obsolete links, try using a Web search engine to find the current location of a dataset source, or try finding an archived copy of the original site in the Internet Archive.)
- Kaggle (public datasets)
Other sources and search portals for datasets
- Google Dataset Search (search results include a variety of open and for-pay dataset sources)
- Humanities Data (“Humanitiesdata.com seeks to help collect and disseminate information about publicly available data of particular interest to digital humanities and humanities computing”) (tagged collection of links to datasets compiled by Matthew J. Lavin)
- Data Is Plural — Structured Archive (list of datasets compiled by Jeremy Singer-Vine)
- Wikidata (“Wikidata is a free and open knowledge base that can be read and edited by both humans and machines. Wikidata acts as central storage for the structured data of its Wikimedia sister projects including Wikipedia, Wikivoyage, Wiktionary, Wikisource, and others.”)
Teams discuss the above public dataset sources and choose a short list of two best sources, and three specific datasets from those sources, that they might want to base their data-narrative project on. The best datasets for the purpose will have four properties:
Best practice is for each team to start in its Google shared drive a collaboratively created document titled, for example, “Scouting Datasets” to take notes and record outcomes.
Class 10 (Feb. 4, 2021) — Exploring, Assessing, & Critiquing Datasets (continued)
Read the following works to gain an understanding of why it is important to reflect critically on datasets in regard to their source, what they include or exclude, the way they organize or form their data, and the validity of their data:
- Kim Gallon, “Making a Case for the Black Digital Humanities” (2016) — Read from the paragraph that begins “What does this mean for digital humanities?” to the end of the essay.
- Jessica Marie Johnson, “Markup Bodies: Black [Life] Studies and Slavery [Death] Studies at the Digital Crossroads” (2018) — Read pp. 57-65, and pp. 70-71
- Also explore this site discussed in the essay by Johnson above: Slave Voyages (including especially the Trans-Atlantic Slave Trade Database)
Continuing from the previous class, teams settle on a single public dataset (or combination of a small number of datasets) that they will use for their data-narrative project. (Teams are free to excerpt only parts of datasets and to adapt, restructure, or add to them, so long as appropriate credit is given to the original datasets.)
Best practice is for each team to create in its Google shared drive a new folder titled “Datasets,” and in that folder to create a document for notes and planning titled “Dataset Work Log.” Also, that folder can be the place to save any downloads from datasets (e.g., downloaded CSV files or spreadsheets, downloaded visualizations, etc.).
3. Making Data Stories
Class 11 (Feb. 9, 2021) — Telling (and Showing) Data Stories
- Michelle Scalise Sugiyama, “The Forager Oral Tradition and the Evolution of Prolonged Juvenility” (2011) — Read only the following:
- pp. 1-2
- pp. 8-14 (beginning with “To illustrate this point, consider three knowledge sets critical to success in the foraging niche….” and ending before the “Testing the Hypothesis” section)
- Martha Kang, “Exploring the 7 Different Types of Data Stories” (2015)
- FrameWorks Institute, “The Storytelling Power of Numbers” (2015)
- Alberto Cairo, Knight Center Courses, Lesson 5 on “Annotation and Narration”
For examples of many kinds of data stories, see the readings for Class 2 and also the links in this table of data stories classified by the kinds of techniques they use. The table is from Charles D. Stolper, et al., “Emerging and Recurring Data-Driven Storytelling Techniques: Analysis of a Curated Collection of Recent Stories” (2016).
Due on This Date: Solo Assignment 3 — Dataset Report
Each member of a team individually writes a two-page report (approximately 600 words) describing and critiquing their team’s chosen dataset. (Choose only one dataset to report on if the team is working with a combination of more than one.)
Description: The report should begin by describing the dataset objectively in regard to the following factors (add others as needed):
- What is the source(s) of the data?
- Who collected the data and made the dataset?
- What is the apparent authority of the data (e.g., on a scale that runs from government or university repositories at one end to the collection data of individual hobbyists on the other end)?
- What is the original purpose and audience of the dataset?
- How complete and representative is the data?
- How can you use, and how must you accredit, the dataset according to the terms of service of its source? (Only a summary of essentials is needed.)
Critique: The report should end by reflecting on what is good or bad about the dataset, whether in regard to the practical, sociocultural, or ethical.
Include notes that cite any sources, borrowings, or quotations used in the report.
This is a solo writing assignment. Of course, teams will have already discussed their dataset together. But each team member must write a report individually without borrowing directly from anyone else’s writing. It is fine, however, to draw on collective team discussion that has already occurred so long as there is a clear footnote or endnote crediting the team (e.g., “This idea comes from our team discussion,” or, “I borrow with variation an idea that came up in our team discussion”).
Submit this report as a PDF file through the course Gauchospace site here.
Class 12 (Feb. 11, 2021) — Showing (and Telling) Data Stories
- Edward Segel and Jeffrey Heer, “Narrative Visualization: Telling Stories with Data” (2010)
- Bongshin Lee and Nathalie Henry Riche, et al., “More Than Telling a Story: Transforming Data into Visually Shared Stories” (2015) (read only pp. 86-87)
- Stephen Few
- Cole Nussbaumer Knaflic, Storytelling with Data (2015). Read chapter 2, “Choosing an Effective Visual” (pp. 35-69)
Frameworking: Teams begin filling out a Framework Planning Document that prepares for making a data-narrative project. (Download this Framework Planning Document template with detailed instructions and copy it to your team workspace.) The document asks teams to imagine their audience; the purpose of their intended data narrative (and what is at stake); their key data; their primary media and form or genre; and their main distribution channel. It then asks for a “long sentence” about the dataset that is like a free-writing exercise from which a compelling data narrative can eventually be structured.
If the worksheet cannot be finished during class, teams are expected to collaborate on finishing it outside class.
Class 13 (Feb. 16, 2021) — Showing & Telling Data Stories: Story Maps
- Allen Carroll and Rupert Essinger, “Tell Your Story Using a Map” (2019/2020) (see some of the examples)
- Knight Lab, StoryMap.js (see some of the examples)
Storyboarding: Using their Framework Planning Document as a starting point, teams begin storyboarding their data narrative. (See Wikipedia article on the idea and applications of storyboarding.)
Class 14 (Feb. 18, 2021) –Showing & Telling Data Stories: Timelines
- Knight Lab, Timeline.js (see some of the examples)
- Florian Kräutli, “Visualising Cultural Data: Exploring Digital Collections Through Timeline Visualisations” (dissertation, 2017) – read pp. 100-122
- Also see Kräutli’s “Timelines” (playlist of YouTube videos showing timelines discussed in his dissertation)
Class 15 (Feb. 23, 2021) — Showing & Telling Data Stories: Data Art
- Giorgia Lupi and Stefanie Posavec, “Dear Data” (website for their project and 2016 book)
- Lisa Jevbratt, 1:1 (2) (1999-2002)
- George Legrady
Continued teamwork, now pivoting from storyboarding to creating the final data narrative project.
Team Project Presentations & Final Solo Work
Class 20 (Mar. 11, 2021) — Team Presentations of Data Narratives
Due on This Date: Team Data Narrative Projects
The data narrative that is the team project for this course can be relatively short (because of the limited time to work in UCSB’s quarter system). It should tell/show its data in a way that answers a question, makes a recommendation, or in some other way comes to a point (or concluding, further question)–where the telling/showing of that point is compelling because it follows some of the principles of good narrative.
The main goal is to demonstrate in compact form an understanding of the basic paradigm of an effective data story: using good data to make a good story that gets people to care about information.
Content: The data narrative project should include both text (or voice) and data visualizations (and other visual elements as appropriate). It should move through at least 5 logical or narrative “scenes” (where a “scene” is loosely defined to mean an identifiably separate unit of telling/showing).
Staging Location: During design and development, data narratives can be created in a team’s Google shared drive. Because some data narratives may include dynamic, interactive, or other content that requires hosting on a server, it is also possible to stage a project under development in a content-management system such as a WordPress site or elsewhere online. (Mechanics can be discussed with the instructors as needed.)
Final Location: By default, a project’s final location will be online (e.g., on the team’s Google shared drive) with permissions set to publicly viewable. Narratives may also be hosted on such online data visualization, mapping, and similar services. Blogging platforms or a student’s own hosting service (e.g., a WordPress.com or Reclaim Hosting site) are also possible. (Mechanics can be discussed with the instructors as needed.)
Intellectual Property: Projects must be careful to respect the copyright constraints and conditions of any materials they use and make publicly viewable. In regard to the copyright status of the projects themselves: a team’s data narratives should by default be put online with a declaration that it is published under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. However, teams are free to decide on a different option. For example, they can choose alternative Creative Commons options or declare traditional, restrictive copyright in the name of an individual or individuals. (By default, the “Student Work” page on the course site will include links to team projects, though students may request otherwise.)
Submit this assignment on the course Gauchospace site here in the form of the URL for the project.
(Due by Mar. 15, 2021) — Final Assignment
Due March 15: Solo Assignment 4 — Essay About Project
Each member of a team individually writes a three-page essay (approximately 900 words) that reflects critically on their team’s data narrative project. “Critically” means that the essay should identify both the strengths and problems of the specific data narrative, and possibly also those of data narratives in general.
The essay can begin with, or include, a description of the student’s team project and its essential message. But it must go beyond that to think critically about what works well and what doesn’t in the data narrative or in data narratives generally.
Conclude the essay with a paragraph offering a utopian vision of what the ideal version of the team data narrative would add if you had all the time and resources you needed.
Include notes that cite any sources, borrowings, or quotations used in the report.
This is a solo writing assignment. Of course, teams will have already discussed their data narrative project together. But each team member must write an essay individually without borrowing directly from anyone else’s writing. It is fine, however, to draw on collective team discussion that has already occurred so long as there is a clear footnote or endnote crediting the team (e.g., “This idea comes from our team discussion,” or, “I borrow with variation an idea that came up in our team discussion”).
Submit the essay as a PDF file through the course Gauchospace site here.
Additional Solo Grade for Participation in Team Project and in Class Discussion
The instructors will assign an additional 10% of the final grade based on their assessment of a student’s participation throughout the course in their team project (as witnessed in visible contributions to the final project or background contributions in a team’s shared drive) as well as in class discussion. Any student who participates equally in the team project and also speaks up during class discussion should be able to earn the full 10% of this grade.
A Note About Access to Reading Materials For This Course
All readings 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. (Advice: It is a good idea to download materials as early as possible in case, for example, PDFs that are currently available open-access, on the open net, or through a UCSB Library digital database subscription later become inaccessible.)
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 constraints, the following guide includes suggested options for handling online materials: