Toolkit

   Data

Rather than impose a singular definition of data upon students, data in this toolkit is presented from a variety of perspectives to highlight its relations to bodies, contexts, ethics, rhetoric, and power.

Keywords: Data, Definition

  Resource: Term  //     Domain: Understanding Data  //     Subdomain: Defining Data

   Data Harms

  Date: August 2020   //     Author: Joanna Redden, Jessica Brand, and Vanesa Terzieva

According to Joanna Redden, Jessica Brand and Vanesa Terzieva, we can think about data harms as "the adverse effects caused by uses of data that may impair, injure, or set back a person, entity or society's interests."

  Resource: Term  //     Domain: Understanding Data  //     Subdomain: Defining Data

   Data Life Cycle

  Date: February 21, 2021   //     Author: Tim Stobierski

The data life cycle refers to the reiterative processes of working with data during a particular project. While the states are often identified and arranged differently by various organizations, generally the different processes include: generation, collection, processing, storage, management, analysis, visualization, and interpretation. As noted by Tim Stobierski, "The data life cycle is often described as a cycle because the lessons learned and insights gleaned from one data project typically inform the next. In this way, the final step of the process feeds back into the first."

Keywords: Data Life Cycle, Data

  Resource: Term  //     Domain: Understanding Data  //     Subdomain: Defining Data

   Formal Critical Reflection: Defining Data and Doing Data Advocacy

This two-part formal critical reflection asks students to write a 5-7 page essay in which they a.) reflect back on their learning about data feminism, rhetorical data studies, and an equity framework and forward their own understandings of data, the data life cycle, and best practices for doing data advocacy; b.) apply learning to describe and analyze a data advocacy website; and c.) reflect on how this learning prepares them to do data advocacy.

  Resource: Assignment  //     Domain: Understanding Data  //     Subdomain: Defining Data

   Interview with Catherine D'Ignazio: 'Data is Never a Raw, Truthful Input – and It is Never Neutral'

  Date: March 21, 2020   //     Author: Zoë Corbyn

In this interview, the co-author of Data Feminism identifies how how sexism, racism and other forms of discrimination manifest in data products and emphasizes, among other things, the importance of recognising discrimination in algorithms, understanding how it operates on a technical level, and designing measures to stamp it out.

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Defining Data

   Introduction to Data, Data Harms, and Data Advocacy

  Date: 2023   //     Author: Laurie Gries

During this two-day lesson plan, students will consider different conceptions of data and learn that while data can and often does do harm, data can also be leveraged for social good through data advocacy.

Keywords: Data Advocacy, Data Ethics, Data Harm

  Resource: Lesson Plan  //     Domain: Understanding Data  //     Subdomain: Defining Data

   Raw Data

  Date: January 25, 2013   //     Author: Geoffrey Bowker, Lisa Gitelman, and Virginia Jackson

Data never exists in a 'raw,' unfiltered form and is never a neutral representation of reality.

Keywords: Critical Data Studies, Data Collection

  Resource: Term  //     Domain: Understanding Data  //     Subdomain: Defining Data

   The History of Data-as-Rhetoric

  Date: September 2016   //     Author: Mark Carrigan

This blog post emphasizes how the definition of data is ever changing, as it shifts with argumentative strategy and context.

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Defining Data

   The What, Where and How of Data for Data Science

  Date: June 2018   //     Author: Iliya Valchanov

This blog post unpacks the notion of data from a data science perspective while also unpacking the complexity of data science and offering a useful infographic to explain the key processes of data science.

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Defining Data

   Thick Data

  Date: September 06, 2024   //     Author: Nathan Pieplow

This slide deck introduces the concept of "thick data" (Wang 2013) using Clifford Geertz's classic example of the difference between a wink and a blink (or squint). It references both C. Thi Nguyen's "The Limits of Data" (2024) and Tricia Wang's "Why Big Data Needs Thick Data" (2013) and can reinforce concepts introduced to students via those readings, or else substitute for them.

Keywords: Ethnography, Thick Data, Thick Description

  Resource: Slides  //     Domain: Understanding Data  //     Subdomain: Defining Data

   Thick Data vs. Big Data

  Date: January 20, 2016   //     Author: Tricia Wang

According to Tricia Wang, it is important to understand what thick data is and why it is valuable, especially in an age when Big Data gets all the hype. According to Wang, 'Thick Data is data brought to light using qualitative, ethnographic research methods that uncover people's emotions, stories, and models of their world. It's the sticky stuff that's difficult to quantify. It comes to us in the form of a small sample size and in return we get an incredible depth of meanings and stories. Thick Data is the opposite of Big Data, which is quantitative data at a large scale that involves new technologies around capturing, storing, and analyzing. For Big Data to be analyzable, it must use normalizing, standardizing, defining, clustering, all processes that strips the the data set of context, meaning, and stories. Thick Data can rescue Big Data from the context-loss that comes with the [Read More]

Keywords: Big Data, Ethnography, Qualitative Research, Thick Data

  Resource: Term  //     Domain: Understanding Data  //     Subdomain: Defining Data

   What is Data?

  Date: June 01, 2024   //     Author: Laurie Gries

This Google Slides presentation, in PDF form, presents conceptions of data from different perspectives, sources, and fields of inquiry.

  Resource: Slides  //     Domain: Understanding Data  //     Subdomain: Defining Data

   What is Data? Definition and Examples

  Author: Market Business News

This article offers a general understanding of data for the field of business and includes a glossary of related terms as well as two educational videos about what data is and what data storytelling is.

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Defining Data

   What is Data?: An Activity

  Date: June 01, 2024   //     Author: Laurie Gries

This activity challenges students to consider what data is from different perspectives, sources, and fields of inquiry as well as reflect on their own understandings of data.

Keywords: Data

  Resource: Activity  //     Domain: Understanding Data  //     Subdomain: Defining Data

   Critical Data Studies: An Introduction

  Author: Andrew Iliadis and Federica Russo

In this scholarly essay, the authors identify the relations between data and power and, among other things, briefly describe the framework of Critical Data Studies, its orientations, and principles.

Keywords: Big Data, Critical Data Studies, Data As Power

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Critiquing Data

   Data Feminism

  Author: Catherine D'Ignazio and Lauren F. Klein

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. [Read More]

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Critiquing Data

   Data Feminism

Data feminism can be understood as a framework for thinking about data and its relation to power through the lens of intersectional feminism as well as working toward just data practices.

Keywords: Data Feminism, Data, Feminism, Justice

  Resource: Term  //     Domain: Understanding Data  //     Subdomain: Critiquing Data

   Dataset Documentation Project (assignment sequence)

  Date: July 23, 2024   //     Author: Nathan Pieplow

This sequence of assignments asks students to find a dataset to document and then leads them through a multi-step process of critical engagement with that dataset. The ultimate product is a set of documentation that is not merely technical but also critical and deeply contextual.

Keywords: Data Biography, Data Documentation, Data Ethnography, Datasheets For Datasets, Thick Data, Finding Datasets

  Resource: Assignment  //     Domain: Understanding Data  //     Subdomain: Critiquing Data

   Feminist Data Manifest-no

  Date: 2019   //     Author: Marika Cifor, Patricia Garcia, TL Cowan, Jasmine Rault, Tonia Sutherland, Anita Say Chan, Jennifer Rode, Anna Lauren Hoffmann, Niloufar Salehi, and Lisa Nakamura.

This digital manifesto offers a set of refusals and committments in order to speak out against harmful data regimes and manifest new data futures.

Keywords: Data Ethics, Critical Data Studies, Data Feminism

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Critiquing Data

   Rhetorical Data Studies

As a critical and constructive framework, rhetorical data studies explores how data-driven stories, arguments, and visualizations communicate knowledge, garner public attention, and, among other actions, mediate socio-cultural change in order to help establish more ethically- minded and effective data-informed practices.

  Resource: Term  //     Domain: Understanding Data  //     Subdomain: Critiquing Data

   Seven Principles of Data Feminism

  Author: Catherine d'Ignazio and Lauren Klein

This reading offers a brief explanation of data feminism, its goals, and its seven guiding principles.

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Critiquing Data

   The Limits of Data

  Date: 2024   //     Author: C. Thi Nguyen

In this opinion piece for Issues in Science and Technology, philosopher C. Thi Nguyen argues that data creation is a process of decontextualization. Nguyen also explains the politics of classification and metrics and ultimately calls for a critical approach to data. This article provides an excellent introduction to critical data studies, although it does not use that term.

Keywords: Classification, Critical Data Studies, Decontextualization, Metrics

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Critiquing Data

   What does Critical Data Studies look like and Why do we Care?

  Date: May 12, 2014   //     Author: Craig Dalton and Jim Thatcher

This online post presents seven key provocations as to why a critique of data, "big" or not, is important. In addition to discussing the potentials of counter data, the authors describe, in particular, how geographers and others interested in working with spatial data might benefit from a critical data studies approach.

Keywords: Big Data, Counter Data, Critical Data Studies, Geography, Spatial Data

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Critiquing Data

   An Introduction to Data Ethics

  Date: 2018-01-23   //     Author: Shannon Vallor and William Rewak

This reading, divided into five parts, introduces students to the ethics of data practice and includes discussions about data benefits and harms; challenges and obligations of ethical data practice; and ethical frameworks and best practices for data practitioners.

Keywords: Data Advocacy, Data Ethics, Public Good

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   Collect, Analyze, Imagine, Teach

  Date: March 16, 2020   //     Author: Catherine D'Ignazio and Lauren Klein

In this chapter of Data Feminism, Catherine D'Ignazio and Lauren Klein not only emphasize the ways data can both reinforce and challenge systems of oppression but also introduce notions and examples of data ethics so that practioners are better prepared to work toward equity and co-liberation in their data practices.

Keywords: Critical Data Studies, Data Ethics, Data Feminism, Data Justice, Power Structures

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   Data Ethics

  Date: 2024   //     Author: Data Advocacy for All

Data ethics refers to the moral obligations one has to responsibly collect, process, and share data as well as the undergirding principles one should consider when working with data throughout its entire lifecycle.

  Resource: Term  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   Data Ethics Unveiled: Principles & Frameworks Explored

  Date: 2023-11-28   //     Author: Atlan

This blog post, intended for a quick read, defines data ethics; identifies various reasons as to why data ethics is so important; outlines useful frameworks, guiding principles, and challenges of data ethics; and describes real world examples of data ethics, including the situations, concerns, and outcomes of each.

Keywords: Data Ethics, Frameworks, Guiding Principles

  Resource: Example Project  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   Data Justice

Data Justice refers to equity and fairness in the way people and pressing social issues are disclosed, represented, and treated as a result of the collection, analysis, production, and presentation of data.

  Resource: Term  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   Data, Ethics, and Society

  Date: 2022   //     Author: Julian Chambliss et al.

This section of the open access book, Making Sense of Digital Humanities, offers a variety of readings that discuss ethical concerns related to data, technology, and communities. Themes include but are not limited to: data and race, data and discrimination, algorithms and oppression, and coded biases.

Keywords: Algorithms, Black Studies, Data Ethics, Data Humanities, Racialized Technologies

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   Deon Ethics Checklist for Data Scientists

  Author: Driven Data

This online tutorial introduces you to Deon, which is a command line tool that generates an ethics checklist to help decide if a data project is ethical. This tutorial explains how one can create a custom checklist or use a default checklist split into different sections based upon the stage of ones project. This tutorial also includes a bibliography of readings about data ethics.

Keywords: Checklists, Coding, Data Science, Ethics

  Resource: Tutorial  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   Digital Defense Playbook

  Date: 2018   //     Author: Seeta Peña Gangadharan, Tawana Petty, Tamika Lewis, and Mariella Saba

This freely accessible, online curriculum helps practitioners develop technology and work with data in inclusive and consentful ways with the aims of generating processes and education to push back against the weaponization of data.

Keywords: Data Ethics, Consentful Technologies, Critical Data Studies, Data Justice

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   Of Oaths and Checklists

  Date: July 17, 2018   //     Author: DJ Patil, Hilary Mason and Mike Loukides

This reading discusses the value of using checklists for data projects and offers a checklist with 13 valuable questions to ask by people working on them.

Keywords: Checklist, Data Management, Ethics

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   Principles for Advancing Equitable Data Practice

  Date: June 2020   //     Author: Marcus Gaddy and Kassie Scott

In this blog post, Gaddy and Scott share highlights from the session around what data equity means, why 'data is objective' is a myth, and how you can begin to build a more equitable data practice.

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   Six Steps to Get Started Decolonizing your Data for Development

  Date: May 22, 2022   //     Author: Chisenga Muyoya, Andrea Jimenez Cisenors, and Ronda Železný-Green

This reading discusses how a decolonizing approach to data can help one better understand how data-based technologies often reproduce and reinforce colonial structures of inequality. As the title suggests, the reading also offers six steps that one can take for decolonizing data when working for public good.

Keywords: Data Colonialism, Data For Good, Decolonization, Ethics

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   The Consentful Tech Project

  Date: 2017   //     Author: Una Lee and Dann Toliver

This freely accessible, online curriculum helps practitioners develop technology and work with data in inclusive and consentful ways with the aims of generating processes and education to push back against the weaponization of data.

Keywords: Data Ethics, Consentful Technologies, Data Justice

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   The Ethics of Managing People's Data

  Date: May 22, 2022   //     Author: Michael Segalla and Dominique Rouziès

This reading discusses the ethics of working with data from a business perspective and describes 5 key principles of ethical data handling: provenance, purpose, protection, privacy, and preparation.

Keywords: Ai, Business, Data Management, Ethics

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   Write Your Own Data Advocacy Values Statement

  Date: July 23, 2024   //     Author: Nathan Pieplow

This activity asks students to consider their values before embarking on a data advocacy project (group or individual). It asks them to read the Appendix "Our Values and Our Metrics for Holding Ourselves Accountable" at the end of Data Feminism and then use it as inspiration to create the initial draft of a values statement that articulates their own primary concerns for how to avoid ethical transgressions during their project.

Keywords: Accountability, Ethics, Stakeholders, Values

  Resource: Assignment  //     Domain: Understanding Data  //     Subdomain: Acting Ethically with Data

   A Rhetorical Data Studies Approach to Data Advocacy

  Date: 2024   //     Author: Laurie Gries

In this whitepaper crafted for Data Advocacy for All, data advocacy is defined and explained as a deeply rhetorical and ethical action while rhetorical data studies is forwarded as a critical and constructive framework for helping students learn how to ethically collect, process, and deploy data alongside narratives and other rhetorical strategies.

Keywords: Data Advocacy, Data, Rhetoric, Storytelling

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   Advocacy Projects addressing Gun Violence

  Date: 2024

Links to three data advocacy projects addressing gun violence. Note: These projects are included in the activity titled "Rhetorical Analysis of Data Advocacy Projects," which can be accessed in the DA4All toolkit under the Thinking Rhetorically about Data subdomain.

  Resource: Example Project  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   Black Health in America: Exploring Racial Disparities in COVID-19 Vaccination Data

  Date: February 24, 2021   //     Author: Jamelle Watson-Daniels/Data for Black Lives

This Data Advocacy project assists public health efforts to make visible existing racial disparities in our healthcare system, specifically in relation to the Covid-19 pandemic. This project asks "Are Black people, who remain overrepresented among COVID-19 deaths, receiving sufficient access to the vaccines?" Data findings presented in a chart "help to visualize the gap between how many Black people are dying from COVID-19 and how many are receiving the vaccine."

Keywords: Critical Data Studies, Data Advocacy, Data For Black Lives, Public Health Data

  Resource: Example Project  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   Data Advocacy

Data advocacy is a deeply ethical and rhetorical practice of integrated analysis, design, and communication in which insights from a dataset are effectively gleaned and conveyed to raise public awareness and drive social change. (Laurie Gries, "A Rhetorical Data Studies Approach to Data Storytelling and Advocacy")

Keywords: Data Advocacy, Data, Ethics, Social Change

  Resource: Term  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   Data Harm Record

  Date: August 2020   //     Author: Joanna Redden, Jessica Brand and Vanesa Terzieva

This online document provides a running record of "data harms"--specifically harms that have been caused by uses of algorithmic systems. The social advocacy goal is to promote learning and inspire collaborative work to redress harms and prevent further harm.

  Resource: Example Project  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   Introduction to Rhetorical Data Studies

  Date: 2023   //     Author: Laurie Gries

This lesson plan introduces students to rhetorical data studies to help deepen their understanding of how this critical and productive framework can be useful for analyzing and generating data advocacy projects. During this lesson plan, students have opportunities to review key concepts related to rhetorical data studies, explore the rhetorical dimensions of data and data advocacy, and consider best practices and possible roadmaps for doing data advocacy.

Keywords: Rhetoric, Rhetorical Data Studies

  Resource: Lesson Plan  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   Mapping Police Violence

  Author: We the Protestors-Campaign Zero

This interactive website features a collection of live tracking tools, maps, and visualizations that document police violence in the United States and demonstrate how race and ethnicity, location, and crime are connected to police violence. To assist various stakeholders, visitors can download open-access data and figures, learn about the project's methodology, and access a resource to directly contact representatives. NOTE: This content may activate emotional and distressing experiences for some students. Please provide a content warning as needed.

Keywords: Accountability, Advocacy, Black Data Studies, Data Practice, Data Visualization

  Resource: Example Project  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   Rhetorical Analysis of Data Advocacy Projects

  Date: December 15, 2024   //     Author: Laurie Gries

This two-day lesson plan teaches students to analyze a data advocacy project in order to deepen their abilities to identify the rhetorical situations of data advocacy projects and understand how they are rhetorically designed to meet a community's or organizations' advocacy goals.

Keywords: Data Advocacy Projects, Rhetoric, Rhetorical Data Studies

  Resource: Lesson Plan  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   Rhetorical Data Studies Bingo

  Date: 2023   //     Author: Gries, Laurie

This Rhetorical Data Studies Bingo game has been created to help students recall and better understand the concepts and ideas related to Rhetorical Data Studies. This activity is particularly useful for helping to establish a shared vocabulary to discuss data advocacy from a rhetorical perspective.

Keywords: Rhetoric, Rhetorical Data Studies

  Resource: Activity  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   SPLC Hate Map

  Author: Southern Poverty Law Center

This data advocacy project tracks hate and anti-government groups across the United States.

Keywords: Data Visualization, Hate Tracking, Mapping, Public Good, Social Advocacy

  Resource: Example Project  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   Swastika Counter Project

  Date: 2023   //     Author: Laurie Gries and Kelly Wheeler

The Swastika Counter Project is a public humanities project that aims to educate the general public about the targets, circulation, and intensity of antisemitic signs on the streets of the United States. To support community stakeholders, this data advocacy website aims to provide reliable data, relevant research findings, and useful educational resources.

Keywords: Accountability, Advocacy, Data Practice, Data Visualization, Rhetorical Data Studies

  Resource: Example Project  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   Virulent Hate Project

  Date: 2023   //     Author: Melissa Borja

This data advocacy project investigates anti-Asian racism by identifying, analyzing, and mapping incidents of anti-Asian harassment, violence, discrimination, and stigmatization. In addition, this project studies how Asian American activism takes place on local, state, and national levels in order to better understand how individuals and communities are responding to the recent surge in anti-Asian racism and violence.

Keywords: Data Visualization, Mapping, Public Good, Social Advocacy

  Resource: Example Project  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   World Happiness Report (2023)

  Date: 2023   //     Author: Helliwell, J. F., Layard, R., Sachs, J. D., Aknin, L. B., De Neve, J.-E., & Wang, S. , eds.

The World Happiness Report 2023 website presents the 11th edition of this annual publication, which measures and ranks countries based on their citizens' happiness and well-being. The report includes several chapters covering topics such as trust and social connections during crises, state effectiveness, altruism's impact on well-being, and using social media to measure well-being across cultures and time. The site provides access to the full report, its executive summary, individual chapters, and downloadable data sets used in the analysis. It emphasizes the growing importance of happiness as a metric for national success and governmental objectives. The report is a collaborative effort involving various organizations and is edited by prominent researchers in the field. The website also offers information about the report's background, methodology, and ways to explore the data further. We present this resource as an Example Project.

Keywords: Data Advocacy, Data Analysis, Global Data, Well-Being Data, World Happiness

  Resource: Example Project  //     Domain: Understanding Data  //     Subdomain: Thinking Rhetorically about Data

   Gapminder World Health Chart Activity

  Date: 2022   //     Author: Gapminder

The World Health Chart on Gapminder's website is an interactive visualization tool that displays the relationship between income and life expectancy for countries worldwide. It presents this data as a graph where the x-axis represents income (GDP per capita) and the y-axis represents life expectancy. Each country is represented by a bubble, with the size of the bubble indicating population size. The chart allows users to view data from 1800 to 2021, showing how countries have progressed over time in terms of health and wealth. Users can play an animation to see the changes occur dynamically or search for specific countries. The page includes a video explanation by Hans Rosling, demonstrating the correlation between income and health across nations. It also offers downloadable resources such as printable PDFs, presentation files, and a fullscreen version of the chart.

Keywords: Data Analysis, Data Visualization, Gapminder, Global Income, Health Data

  Resource: Activity  //     Domain: Understanding Data  //     Subdomain: Mapping Data

   Mapping, Society, and Technology

  Author: Manson, S.M. (ed.)

This online, open-source textbook provides an excellent introduction to digital mapping that includes topics such as reading, using, and creating maps. A central focus of the text is the relationship between cartography and broader societal and technological developments. It also includes topics related to geospatial data ethics, the history of maps and mapping, and maps as rhetorical devices. Instructors can select chapters in this book to provide students with essential background information and context before moving into more hands-on cartography instruction; the background material provided in this text will allow students to approach the task of making their own data-advocacy related maps with greater insight, sophistication, and integrity.

Keywords: Geospatial Data, History Of Maps And Mapping, Maps And Rhetoric, Maps And Society

  Resource: Reading  //     Domain: Understanding Data  //     Subdomain: Mapping Data

   Practical Tips for Ethical Data Sharing

  Date: 2023-02-2018   //     Author: Michelle N. Meyer

This scholarly article spells out practical dos and don'ts for sharing newly collected research data in ways that are effective and ethical.

Keywords: Data Management, Data Repository, Data Sharing, Data Stewardship

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data,  Acting Ethically with Data

   Collecting Thick Data

  Date: July 08, 2024   //     Author: Cameron Blevins

In this activity, students apply the concept of "thick data," working in small groups to collect information about the customs, culture, and social practices of the student body at their school.

Keywords: Ethnography, Thick Data, Thick Description

  Resource: Activity  //     Domain: Processing Data  //     Subdomain: Collecting Data

   Data Biography

  Date: 2023-07-2023   //     Author: Cameron Blevins

In this assignment, students apply the concept of a "data biography" to analyze the history behind a particular dataset: the who, what, when, how, and why of the dataset and its creation. In doing so, they learn about the different interpretative filters that shape the historical trajectory of a dataset, from its initial collection to its availability and usability today.

  Resource: Assignment  //     Domain: Processing Data  //     Subdomain: Collecting Data

   Dear Data

  Date: July 12, 2024   //     Author: Cameron Blevins

This assignment, which is inspired by Giorgia Lupi and Stefanie Posavec's project Dear Data, challenges students to collect data from their daily lives and to reflect critically on the data collection practice.

  Resource: Assignment  //     Domain: Processing Data  //     Subdomain: Collecting Data

   The Point of Collection

  Date: February 10, 2016   //     Author: Mimi Onuoha

In this article, Mimi Onuoha presents five theses about data collection that are important to keep in mind while working with data.

Keywords: Critical Data Studies, Data Collection, Data Ethics, Data Transparency

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Collecting Data

   What Gets Counted Counts

  Date: March 2020   //     Author: Catherine D'Ignazio and Lauren Klein

In Chapter 4 of their book Data Feminism, Catherine D'Ignazio and Lauren Klein urge us 'to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression.'

Keywords: Data Classification, Gender Binary, Intersectional Feminism, Quantification

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Collecting Data

   Why Big Data Needs Thick Data

  Date: January 20, 2016   //     Author: Tricia Wang

Tricia Wang calls for the importance of integrating 'thick data' alongside 'big data,' or qualitative insights gathered from human interactions, emotions, and stories. These can fill the gaps left by big data, offering a more comprehensive understanding of human behavior and aiding in better decision-making.

Keywords: Big Data, Ethnography, Thick Data

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Collecting Data

   Classifying Household Goods

  Date: July 12, 2024   //     Author: Cameron Blevins

This activity, which focuses on the practices of categorization and classification of data, is designed to help students think critically about how and why we describe, organize, and categorize information in certain ways.

Keywords: Categorization, Classification

  Resource: Activity  //     Domain: Processing Data  //     Subdomain: Preparing Data

   Data Cleaning

  Author: Alice Macfarlan

In this brief article, Alice Macfarlan describes the motivation behind careful data preparation and outlines a set of steps and questions to ask oneself when preparing data. Macfarlan also provides links to more information about the process of and motivation for cleaning data.

Keywords: Bad Data, Data Analysis, Data Cleaning, Data Preparation

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Preparing Data

   Strangers in the Dataset

  Date: March 2020   //     Author: Catherine D'Ignazio and Lauren Klein

This section from chapter five of D'Ignazio and Klein's Data Feminism takes a critical look at the metaphor of "cleaning" data, assesses the implications of thinking about data in this way, and challenges readers to think more deeply about the assumptions that guide data the data gathering and preparation process.

Keywords: Data Analysis, Data Cleaning, Justice

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Preparing Data

   The Importance of Data Cleaning: Three Visualization Examples

  Date: February 2020   //     Author: Christine P. Chai

This article, published in the online journal *CHANCE* by the American Statistical Association, provides examples of how inadequate data preparation can dramatically impact analysis and presents case studies that demonstrate why data inspection and preparation is such an important part of the data science life cycle.

Keywords: Bad Data, Data Analysis, Data Cleaning, Data Preparation

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Preparing Data

   Attending to the Cultures of Data Science Work

  Date: 2023   //     Author: Lindsay Poirier

This essay argues that data science communities have a responsibility to attend not only to the cultures that orient the work of domain communities, but also to the cultures that orient their own work. The author also describes how ethnographic frameworks such as thick description can be enlisted to encourage more reflexive data science work.

Keywords: Data Culture, Data Documentation, Data Infrastructure, Ethnography

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Analyzing Data

   Basic Descriptive Statistics: Measures of Central Tendency

  Date: 2024

This lesson plan introduces students to some of the foundational tools and concepts for basic descriptive statistics, emphasizing measures of central tendency. Students will spend some time defining key terms, and then will see those concepts in action through analyzing a dataset they create.

Keywords: Descriptive Statistics, Measures Of Central Tendency, Statistical Analysis

  Resource: Lesson Plan  //     Domain: Processing Data  //     Subdomain: Analyzing Data

   Basic Descriptive Statistics: Measures of Variation

  Date: 2024

Phenomena in the world vary, and statistical analysis provides powerful tools for describing this variability and for understanding what such variation means. Understanding how phenomena vary–or remain stable–can be a first step towards identifying how social pressures or power differentials might be impacting observed realities. This lesson plan provides a set of activities to introduce students to basic tools for measuring and understanding variation in data.

Keywords: Descriptive Statistics, Measures Of Variation, Statistical Analysis, Global Data

  Resource: Lesson Plan  //     Domain: Processing Data  //     Subdomain: Analyzing Data

   Covariation, Correlation, and Causation

  Date: September 01, 2018   //     Author: Matthew J.C. Crump

This chapter from Crump's textbook, _Answering Questions with Data_, provides an accessible and illuminating discussion of the phenomena of covariation, correlation, and the difference between correlation and causation. The reading can provide the basis of a classroom discussion about these key concepts in statistical analysis. Paired with other datasets in the Data Advocacy Toolkit, the reading can help students develop the tools they need to calculate correlation and begin to think critically about the concept of causation..

Keywords: Causation, Correlation, Statistical Analysis

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Analyzing Data

   Critically Analyzing the World Happiness Report

  Date: 2023   //     Author: J. F. Helliwell, Layard, R., Sachs, J. D., Aknin, L. B., De Neve, J.-E., & Wang, S. , eds.

This activity invites students to think about the variables included in the World Happiness Report dataset, about the relations between variables, and about the advantages and disadvantages of the authors' approach to measuring happiness. This exercise is designed to help cultivate habits of critical reflection and to provide practice in data analysis, including reflection on correlation.

Keywords: Critical Data Analysis, Data Analysis, Global Data, Correlation

  Resource: Activity  //     Domain: Processing Data  //     Subdomain: Analyzing Data

   Exploring Data

This assignment challenges students to examine, explore, and think critically about a dataset. In crtically analyzing a subset of the 2019 American Community Survey performed by the United States Census Bureau, students come to learn how counting the US population is inherently messy, and implicitly (and sometimes explicitly) caught up in questions of power.

Keywords: Descriptive Statistics, Measures Of Variation, Measures Of Central Tendency, Critical Data Analysis

  Resource: Assignment  //     Domain: Processing Data  //     Subdomain: Analyzing Data

   FBI Hate Crimes Dataset (2021)

  Date: 2022   //     Author: United States Department of Justice

The FBI tracks hate crimes in many different ways; this summary dataset from 2021 focuses on specific types of crimes and numbers of incidents, victims and known offenders. The dataset provides an excellent resource for inviting students to explore and think critically about data. This activity prompts students to think about how data represents phenomena and to think critically about the kinds of choices that go into creating a dataset.

Keywords: Data Analysis, Data Biography

  Resource: Activity  //     Domain: Processing Data  //     Subdomain: Analyzing Data

   Philadelphia African-American Census 1847

  Author: Friends Historical Library

In 1847, Philadelphia Quakers conducted a census of the city's African American population. Their intention was to document the existence of an "industrious and thriving" portion of that population, and also to discover what sectors of the community may have been in need of attention and assistance. Over 150 years later, the original data - held in manuscript at the Friends Historical Library of Swarthmore College - proves a rich resource for studying African American history, genealogy, Philadelphia history, and more.

  Resource: Example Project  //     Domain: Processing Data  //     Subdomain: Analyzing Data

   Understanding and Applying Key Statistical Concepts

  Date: April 23, 2022   //     Author: Douglas Shafer and Zhiyi Zhang

Douglas Shafer and Zhiyi Zhang's open source textbook, Introductory Statistics, provides in its opening chapter a basic introduction to some of the key concepts and terms that form the basis of statistical analysis. The resource provides an opportunity for students to begin developing a basic understanding of how statisticians view the world and produce knowledge about the world.

Keywords: Descriptive Statistics, Inferential Statistics, Kinds Of Data, Population, Sample

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Analyzing Data

   A Guide to Choosing a Data Repository for NIH-Funded Research

  Author: FASEB

This resource provides a useful primer on data repositories. It focuses specifically on NIH-funded research, but also contains a lot of useful general information (such as a discussion of different repository types, repository features, and considerations for sensitive data).

Keywords: Data Management, Data Stewardship

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Creating Digital Research Notebooks

  Date: 2024-07-19   //     Author: Brown University Library

[This guide]([url](https://libguides.brown.edu/DataManagement/notebook)) from Brown University Library discusses ideas for creating a digital notebook to document your data research and management process. It also provides links to and instructions for using Open Science Framework (OSF) and (ELN) to design and store digital notebooks for free.

Keywords: Data Management

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Data Governance

  Date: February 2011   //     Author: National Forum on Education Statistics

Data governance is best understood as both an organizational process and a structure that attempts to identify, address, and prevent problems with data in order to improve data quality through the creation of social-technical systems and enforcement of policies, roles, responsibilities, and procedures. Data governance is a continuous and iterative process of handling data throughout the information life cycle.

Keywords: Data Collection, Data Governance, Data Sovereignty, Data Stewardship

  Resource: Term  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Data Management

  Date: 2024-08-27   //     Author: University of Chicago Library

Data management is a general term for describing the actions taken to plan, acquire, store, process, analyze, preserve, share, find, and reuse data.

Keywords: Data Management, Data Processing

  Resource: Term  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Data Management Tutorials

  Date: 2024-07-19   //     Author: Brown University Library

This guide from Brown University Library offers a broad set of tutorials to assist naming and organizing files; storing, backing up, and versioning data; and documenting methods and describing data. On this website, you will also find links to a useful video series for data management from the University of Minnesota.

Keywords: Data Management

  Resource: Tutorial  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Data Registry

  Author: ITU

A Data Registry is a centralized system designed to collect, manage, and store information on specific datasets or data repositories, making it easier for various fields or organizations to store and access data and for users to find and use the data they need. Data registries help to ensure data quality and consistency as well as facilitate data sharing and interoperability among different systems and stakeholders.

Keywords: Data Management, Data Sharing, Data Stewardship

  Resource: Term  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Data Repository

  Author: FASEB

A data repository can generally be understood as a tool to share, preserve, and make accessible data or datasets. A data repository can be embargoed, but they are often a website that provides either public access or controlled access.

Keywords: Data Management, Data Stewardship

  Resource: Term  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Data Sharing and Management Snafu in 3 Short Acts

  Author: New York University Health Sciences Library

An amusing video, from librarians at NYU, that shows you what you should NOT do, and why data management is important. Topics include storage, documentation, and file formats.

Keywords: Data Management, Data Sharing

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Data Sovereignty

  Date: 2020   //     Author: Stephanie Russo Carroll et al

Data Sovereignty can be understood as an assertion of the rights and interests of Indigenous Peoples in relation to data that is collected, stored, processed, and published about them, their territories, and their ways of life.

Keywords: Data Collection, Data Governance, Data Sovereignty, Data Stewardship

  Resource: Term  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Data Stewardship

  Date: March 15, 2016   //     Author: Mark Wilkinson et al

Data stewardship can be understood as the " 'long-term care' of valuable digital assets, with the goal that they should be discovered and re-used for downstream investigations, either alone, or in combination with newly generated data" (Wilkinson et al).

Keywords: Data Reproducibility, Data Reusability, Digital Assets

  Resource: Term  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   FAIR Principles

  Author: Go FAIR

A resource that provides a condensed summary of the FAIR principles for data management and stewardship, based on Wilkenson et al's original article. Click on the blue links for a fuller description of each principle or sub-principle. A free PDF download is also available.

Keywords: Data Management, Data Reproducibility, Data Reusability, Data Stewardship, Digital Assets

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   FAIR: Foundational Principles for Good Data Management and Stewardship

  Date: March 15, 2016   //     Author: Mark Wilkinson et al

The FAIR principles function as a guide for data publishers and stewards to help render data and digital assets so that they are Findable, Accessible, Interoperable, and Reusable.

Keywords: Data Management, Data Reproducibility, Data Reusability, Data Stewardship, Digital Assets

  Resource: Term  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Game-Based Research Data Management Training

  Date: 2024-05-03   //     Author: The Hong Kong University of Science and Technology

This website offers links to six different games that can provide an engaging and fun learning approach to data management.

Keywords: Data Management

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Guide to Social Science Data Preparation and Archiving

  Date: January 2020   //     Author: Inter-university Consortium for Political and Social Research (ICPSR)

The guide is a comprehensive compilation of best practices for archiving and preserving data. The guide offers a thorough definition of the data lifecycle and offers guidance on data management plans, data collection and file creation, data analysis, and deposits.

Keywords: Data Lifecycle, Data Management

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Introduction to Data Management Best Practices for Research

  Date: 2023   //     Author: UIC Library

This webinar by UIC Library outlines best practices for the various steps of data management to help ease the research process as well as ensure sharing compliance.

Keywords: Data Management

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Managing and Sharing Data

  Date: March 16, 2011   //     Author: Veerle Van den Eynden, Louise Corti, Matthew Woollard, Libby Bishop and Laurence Horton.

This guide is designed to help researchers and data managers produce high quality research data with potential for long-term use. Content covers best practices for documenting, formatting, and storing data as well as legal and ethical issues for consideration.

Keywords: Data Lifecyle, Data Management

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Practical Tips for Ethical Data Sharing

  Date: 2023-02-2018   //     Author: Michelle N. Meyer

This scholarly article spells out practical dos and don'ts for sharing newly collected research data in ways that are effective and ethical.

Keywords: Data Management, Data Repository, Data Sharing, Data Stewardship

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data,  Acting Ethically with Data

   Primer for Researchers on How to Manage Data

  Date: 2023   //     Author: Maria Arteaga Cuevas,Shawna Taylor, and Mikala Narlock

This primer overviews research data management and sharing practices for the planning, implementation and closing phases of typical research projects. This primer covers key aspects of data management, particularly data curation, and offers tangible suggestions for all stages of the research data lifecycle.

Keywords: Data Lifecycle, Data Management

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Research Data Management and Sharing

This guide published by the University of Chicago Library defines and discusses the benefits of research data management and sharing. This guide also includes information about data management plans, data formats, data descriptions, and data storage.

Keywords: Data Management, Data Sharing

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   The CARE Principles for Indigenous Data Governance

  Date: 2020   //     Author: Stephanie Russo Carroll et al

This scholarly article describes the CARE principles for Indigenous data management and stewardship that have been built around the concept of data sovereignty and designed to complement the existing FAIR principles. Readers are challenged to think about what researchers owe to communities (particularly indigenous communities) who helped to create the data that researchers collect and publish.

Keywords: Data Collection, Data Governance, Data Sovereignty, Data Stewardship

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   The FAIR Guiding Principles For Scientific Data Management And Stewardship

  Date: March 15, 2016   //     Author: Mark Wilkinson et al

This article identifies practical guidelines for data management and data stewardship so that data can be published and preserved in ways that ensure transparency, reproducibility, and reusability.

Keywords: Data Management, Data Reproducibility, Data Reusability, Data Stewardship, Data Transparency

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   The Research Data Management Workbook

  Date: 2023   //     Author: Kristin, Briney

The Research Data Management Workbook offers a collection of exercises across the data lifecycle intended to help researchers improve their data management practices. The workbook is comprised of seven chapters, loosely organized by phases of the data lifecycle, with one or more exercises in each chapter.

Keywords: Data Lifecycle, Data Management

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   What is Data Governance and why does it Matter?

  Date: February 2011   //     Author: National Forum on Education Statistics

This book chapter defines what data governance is and identifies several of its affordances–from improving data quality to increasing data usefulness to providing timelier access and enhanced security.

Keywords: Data Governance, Data Management, Data Stewardship

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   What is a Data Registry?

  Author: ITU

This webpage offers an easy to understand definition of data registry and describes key features, functions, and benefits of effective data registries.

Keywords: Data Management, Data Sharing, Data Stewardship

  Resource: Reading  //     Domain: Processing Data  //     Subdomain: Storing and Preserving Data

   Data and Indigenous Knowledge

  Date: July 23, 2024   //     Author: Nathan Pieplow

This activity includes a set of discussion questions based on the article "The Leading Edge: What Inuit Can Teach Us About Climate Monitoring And Adaptation" from Forbes Magazine. These questions could form the basis for an in-class discussion (15-20 minutes long) or they could be used as prompts for a reading reflection and response assignment.

Keywords: Climate Change, Global Warming, Indigenous Knowledge, Indigenous Rights, Ways Of Knowing

  Resource: Activity  //     Domain: Persuading with Data  //     Subdomain: Making Claims with Data

   Demonstrating Causation

  Date: July 27, 2024   //     Author: Nathan Pieplow

This slide deck reviews the problem of correlation and causation. It provides a quick mathematical review of what correlation is, followed by a look at the difference between correlation and causation and a high-level overview of three requirements in proving causation. The slide deck with instructor notes could be used as a reading or an activity. It can take 30-60 minutes to present in class, depending on the amount of discussion.

Keywords: Causation vs Correlation, Statistical Claims, Quantitative Reasoning, Data-driven Argument, Numerical Argumentation

  Resource: Slides  //     Domain: Persuading with Data  //     Subdomain: Making Claims with Data

   Evaluating Statistical Claims

  Date: July 27, 2024   //     Author: Nathan Pieplow

This slide deck, which can be used as a reading or an activity, briefly introduces nine questions to ask when evaluating the validity of a statistical claim. It then provides a series of statistical claims for students to evaluate and ends by asking students to apply this same type of evaluation to a statistical claim from their own project. It can take 40-60 minutes to present in class, depending on the amount of discussion.

Keywords: Data-Driven Argument, Quantitative Reasoning, Quantitative Rhetoric, Statistical Claims, Numerical Argumentation

  Resource: Slides  //     Domain: Persuading with Data  //     Subdomain: Making Claims with Data

   Framing Statistics

  Date: July 27, 2024   //     Author: Nathan Pieplow

This slide deck, which can be used as a reading or an activity, introduces students to the rhetorical framework of framing theory and explores how this framework can be applied to study and generate statistics and statistical claims. The first 17 slides take about 25 minutes to present in class. The activity on the last slide can take 20 or more minutes.

Keywords: Data-Driven Argument, Numerical Argumentation, Quantitative Rhetoric, Statistical Claims

  Resource: Slides  //     Domain: Persuading with Data  //     Subdomain: Making Claims with Data

   Getting Started with Data for Advocacy

  Date: July 27, 2024   //     Author: Nathan Pieplow

This slide deck introduces basic principles behind numerical argumentation. It demonstrates that single numbers are good for showing sizes, and two or more numbers are good for comparisons, but entire datasets are necessary to show patterns. It prepares students to recognize what is and isn't a dataset when they are searching for data online, briefly discusses ways to find data for advocacy, and briefly mentions U.S. privacy laws that govern data availability (HIPAA and FERPA). The slide deck with instructor notes could be used as a reading or an activity. It can take up to 45 minutes to present in class, depending on the amount of discussion.

Keywords: Data Privacy Laws, Finding Datasets, Numerical Argumentation, Data-driven Argument, Quantitative Rhetoric

  Resource: Slides  //     Domain: Persuading with Data  //     Subdomain: Making Claims with Data

   Group Data Advocacy Project Part 1: White Paper (assignment sequence)

  Date: July 04, 2025   //     Author: Nathan Pieplow

This assignment sequence is designed as the first half of a semester-long project in an upper-division writing course. Across several weeks, students propose data advocacy projects, seek interviews with stakeholders, engage in background research, and compose a collaborative White Paper that analyzes the rhetorical situation in which they hope to pursue advocacy.

Keywords: Quantitative Rhetoric, Statistical Claims, Data-driven Argument, Numerical Argumentation

  Resource: Assignment  //     Domain: Persuading with Data  //     Subdomain: Making Claims with Data

   Group Data Advocacy Project Part 2: Deliverables (assignment sequence)

  Date: July 04, 2025   //     Author: Nathan Pieplow

This assignment sequence is designed as the second half of a semester-long project in an upper-division writing course. Across several weeks, students propose deliverables, carry out an analysis of deliverable genres, and draft, workshop, and revise the final product.

Keywords: Quantitative Rhetoric, Statistical Claims, Data-driven Argument, Numerical Argumentation

  Resource: Assignment  //     Domain: Persuading with Data  //     Subdomain: Making Claims with Data

   Rhetorical Numbers: A Case for Quantitative Writing in the Composition Classroom

  Date: February 2010   //     Author: Joanna Wolfe

This scholarly article, which has been written for writing instructors, argues that textbooks, assignments, and professonial development training needs to focus more on quantitative information and reasoning.

Keywords: Quantitative Reasoning, Quantitative Rhetoric, Rhetorical Data Studies, Data-driven Argument, Numerical Argumentation

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Making Claims with Data

   Rhetorical Numbers: Quantitative Argument Across the Curriculum

  Date: 2016   //     Author: Joanna Wolfe

In this 1-hour recorded lecture with slide presentation, Joanna Wolfe calls for a rhetorical education that combines verbal and mathematical literacies to help students better understand how numbers are used in the service of argument at public, professional, and personal levels.

Keywords: Quantitative Reasoning, Quantitative Rhetoric, Rhetorical Data Studies, Data-driven Argument, Numerical Argumentation

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Making Claims with Data

   Snopes.com Fact-checking Article (assignment sequence)

  Date: July 25, 2024   //     Author: Nathan Pieplow

Snopes.com is a well-known fact-checking website. This assignment sequence, designed for an upper-division writing class, asks students to propose, research, and write their own Snopes.com article using quantitative argumentation to fact-check a claim they have found on the internet. In its current form the sequence is designed to take 1 to 1.5 weeks of class.

Keywords: Quantitative Rhetoric, Data-driven Argument, Numerical Argumentation, Statistical Claims

  Resource: Assignment  //     Domain: Persuading with Data  //     Subdomain: Making Claims with Data

   The Many Ways to Write a Statistic

  Date: July 17, 2024   //     Author: Nathan Pieplow

This 30-45 minute in-class activity invites students to explore big rhetorical differences that can result from small changes in phrasing when statistical claims are relayed in words.

Keywords: Data-Driven Argument, Quantitative Rhetoric, Statistical Claims, Numerical Argumentation, Rhetorical Data Studies

  Resource: Activity  //     Domain: Persuading with Data  //     Subdomain: Making Claims with Data

   Analysis of National Geographic's 'Fish Pharm' Visualization

  Date: July 25, 2024   //     Author: Nathan Pieplow

Currently structured as a homework assignment tied to Data Feminism Chapter 3, this lesson could also be used as a 15-20 minute in-class activity without the reading. It asks students to rhetorically analyze a provocative visualization published by National Geographic in 2010.

Keywords: Rhetoric Of Data Visualization, Visual Rhetoric, Infographics, Statistical Claims

  Resource: Assignment  //     Domain: Persuading with Data  //     Subdomain: Visualizing Data

   Analysis of Theodore Roosevelt's Reelection Flyer (lesson plan)

  Date: July 27, 2024   //     Author: Nathan Pieplow

In this 20-minute in-class activity, students rhetorically analyze the flyer "Stand Pat Under Present Prosperity," published by Theodore Roosevelt's reelection campaign in 1904. The flyer is a masterful piece of visual rhetoric that affords excellent opportunities for students to analyze the careful choices by the designers that make the visualization more convincing.

Keywords: Rhetoric Of Data Visualization, Visual Rhetoric, Infographics, Statistical Claims

  Resource: Activity  //     Domain: Persuading with Data  //     Subdomain: Visualizing Data

   Create an Original Data Visualization (assignment sequence)

  Date: July 31, 2024   //     Author: Nathan Pieplow

This assignment sequence asks students to sketch, draft, workshop, and revise an original data visualization using data they find themselves online.

Keywords: Rhetoric Of Data Visualization, Visual Rhetoric, Data-driven Argument, Numerical Argumentation, Quantitative Rhetoric

  Resource: Assignment  //     Domain: Persuading with Data  //     Subdomain: Visualizing Data

   Critique of the Longline Fishing Infographic

  Date: July 27, 2024   //     Author: Nathan Pieplow

This lesson plan offers instructions for a 20-30 minute activity in which students are challenged to rhetorically analyze a Greenpeace infographic about longline fishing. The ultimate goal of the activity is to give students practice aligning visuals with rhetorical purposes.

Keywords: Infographics, Rhetoric Of Data Visualization, Visual Rhetoric

  Resource: Lesson Plan  //     Domain: Persuading with Data  //     Subdomain: Visualizing Data

   How to Create a Data Visualization

  Date: July 31, 2024   //     Author: Nathan Pieplow

These slides explain two different step-by-step methods for creating high-quality, professional-looking data visualizations without needing to pay for software or learn to code. They are intended to follow the Introduction to Data Visualization videos and provide a lead-in to the Create an Original Data Visualization assignment sequence. The slide deck with instructor notes could be used as a reading or an activity. They take about 30-40 minutes to present in class.

Keywords: Rhetoric Of Data Visualization, Visual Rhetoric, Data-driven Argument, Numerical Argumentation, Quantitative Rhetoric

  Resource: Slides  //     Domain: Persuading with Data  //     Subdomain: Visualizing Data

   Introduction to Data Visualization (videos)

  Date: August 22, 2021   //     Author: Nathan Pieplow

In 25 minutes of total runtime, these two videos provide a concise in-depth introduction to the rhetorical principles of data visualization. The first video summarizes the history of persuasive visualizations with three key examples from the 19th century by John Snow, Florence Nightingale, and W.E.B. DuBois. The second video lays out six key principles of data visualization and explains what can go wrong when these principles are violated.

Keywords: History Of Data Visualization, Rhetoric Of Data Visualization, Visual Rhetoric, Quantitative Reasoning, Quantitative Rhetoric, Statistical Claims, Data-driven Argument, Numerical Argumentation

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Visualizing Data

   To Visualize or Not to Visualize?

  Date: July 27, 2024   //     Author: Nathan Pieplow

This slide deck asks students to consider the ways in which data visualizations, and visual rhetoric more broadly, can either help or fail to help an argument. It encourages students to evaluate the ethos, pathos, and logos of a visual, as well as how it responds to the exigence, audience, and purpose. It provides criteria and practice in distinguishing between actual data visualizations and the mere decorations that are so common in infographics. It also gives examples of situations in which words alone may remain more powerful than visuals. The slide deck with instructor notes could be used as a reading or an activity. It can take 30 minutes or more to present in class, depending on the amount of discussion.

Keywords: Infographics, Rhetoric Of Data Visualization, Visual Rhetoric, Data-driven Argument, Quantitative Rhetoric, Numerical Argumentation

  Resource: Slides  //     Domain: Persuading with Data  //     Subdomain: Visualizing Data

   Data Visualisation and Advocacy for Sexual and Gender Minority Health

  Date: November 16, 2022   //     Author: Janina Mueller

This paper provides a pedagogical primer on how to incorporate data and GIS-based advocacy into the classroom curriculum by offering a case study of LGBTQIA+ health challenges and barriers. In the paper's Appendix, the paper's author provides a sample lesson plan that invites students to critically evaluate maps and data visualizations.

Keywords: Geospatial Data, Healthcare, Maps, Social Justice

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Mapping Data

   Deconstructing a Published Map

This assignment invites students to find a map that represents information about a social issue that they are interested in, deconstruct how that map "works" from a rhetorical and data-advocacy perspective, and explore how it might be used as part of a broader data-based advocacy campaign.

  Resource: Assignment  //     Domain: Persuading with Data  //     Subdomain: Mapping Data

   Exploring the Geography of Systemic Racism With Spatial Data Science

  Date: July 16, 2024   //     Author: Ranganath, Aditya

This tutorial provides a step-by-step introduction to making maps in R, an open-source programming language commonly used by data scientists. Instead of making maps using a point-and-click interface (as in the QGIS tutorial), students will learn to write basic code in the R programming language. Substantively, the tutorial leverages data from the Open Policing Project at Stanford University to make a map of county-level variation in racially biased traffic policing practices in the state of Colorado. Given the diversity and richness of the Stanford Open Policing Project dataset, instructors could easily adapt the lesson by using data on states or geographic locales relevant to their students. At the end of the tutorial, students are invited to explore various questions that invite them to think broadly about the relationship between maps and effective data advocacy.

Keywords: Geographic Information Systems, Policing, R Programming Language, Spatial Data Science, Systemic Racism

  Resource: Tutorial  //     Domain: Persuading with Data  //     Subdomain: Mapping Data

   Map Design and Critical Cartography

  Date: July 16, 2024   //     Author: Aditya Ranganath

This lesson plan introduces students to map design as a practice of visual rhetoric and critical cartography. Students comes to learn that while maps function as social and political technologies that can be used as agents of control or domination, maps can also be reconfigured as tools of emancipation or social change.

Keywords: Cartography, Counter-Cartographies, Map Design And Symbology, Map Projections

  Resource: Lesson Plan  //     Domain: Persuading with Data  //     Subdomain: Mapping Data

   Mapping Broadband Health in America

  Date: October 24, 2023   //     Author: Connect2HealthFCC Task Force

Mapping Broadband Health in America 2023, is a useful example of how data advocacy is often used in the field of public health. This project enables users to visualize, intersect, and analyze broadband and health data at the national, state and county levels – informing policy and program prescriptions, future innovations, and investment decisions.

Keywords: Public Health

  Resource: Example Project  //     Domain: Persuading with Data  //     Subdomain: Mapping Data

   Mapping for Data Advocacy with Quantum Geographic Information Systems (QGIS)

  Date: October 10, 2023   //     Author: Ranganath, Aditya

This tutorial provides a step-by-step introduction to the process of making a basic choropleth map in Quantum Geographic Information Systems (QGIS), a widely used and supported open-source geospatial application. The tutorial uses data on student debt, and invites students to replicate a map of state-level variation in average student debt levels (in the United States) that was produced by the Institute for College Access and Success. The process of making this map that represents information about a salient contemporary issue invites students to reflect on ways in which map-making can play a role in their data advocacy efforts.

Keywords: Cartography, Geographic Information Systems, Qgis, Spatial Data, Student Debt

  Resource: Tutorial  //     Domain: Persuading with Data  //     Subdomain: Mapping Data

   Maps as Advocacy

  Date: July 27, 2024   //     Author: Nathan Pieplow

This slide deck provides an introduction to the ways in which maps can advocate for policies and positions. It uses concepts from critical cartography to challenge the notion that any map can be truly objective. The slide deck with instructor notes could be used as a reading or an activity.

Keywords: Critical Cartography, Rhetoric Of Data Visualization, Visual Rhetoric

  Resource: Slides  //     Domain: Persuading with Data  //     Subdomain: Mapping Data

   Maps, Mapmaking, and Critical Pedagogy: Exploring GIS and Maps as a Teaching Tool for Social Change

  Date: November 01, 2009   //     Author: Denise Pacheco and Veronica Nelly Fernandez

This article approaches Geographic Information Systems and cartography from the perspective of critical pedagogy, and explores how maps can used as instruments of social change and tools of data-based advocacy.

Keywords: Critical Geography, Critical Pedagogy, Geographic Information Systems

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Mapping Data

   Air Pollution and Public Health in the South Bronx

  Date: 2023   //     Author: South Bronx Unite

South Bronx Unite is a local environmental justice organization that leads data advocacy projects to champion better air quality, public health, and green space access for residents in the Bronx neighborhoods of Mott Haven and Port Morris. This resource provides an overview of their efforts to document the longstanding industrial air pollution in the area that continues to afflict local residents' health. The organization contextualizes their comprehensive data visualizations with commentary about the political history and socioeconomic factors that have led to the current situation. In addition to having students explore the South Bronx Unite data advocacy website, it may also be helpful to have them read a couple of the news stories (collected on the "Press & Media" page) that reporters have written about the organization's efforts and their contribution to the borough's future plans and zoning policies.

Keywords: Air Pollution, Data Storytelling, Environmental Justice, Persuading With Data

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   Caregiving is Real Work

  Date: October 2023   //     Author: Sharmi Surianarain

This presentation aims to capture the economic value of unpaid care work, which amounts to about 16 billion hours daily on a global basis. Surianarain draws on data to convey the immense scale and importance of caregiving, arguing that unpaid care work is what makes many forms of paid work possible. She then advocates for new workplace norms that might better accommodate this unpaid caregiving that paid workers often perform alongside their day jobs. This presentation's 5-minute length makes it especially valuable to view in-class and discuss as a model for student assignments.

Keywords: Data Storytelling, Persuading With Data, Social Advocacy

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   Data Advocacy Op-Ed

This assignment is designed to help hone students' abilities to persuade with data by challenging them to craft a multi-modal argument in the genre of an op-ed.

  Resource: Assignment  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   Data Storytelling Presentation Assignment

  Date: July 16, 2024   //     Author: John Tinnell

This assignment prompts students to make a short video presentation in which they blend data points and vivid examples to tell a story about a statistical trend that sheds critical light on a social issue of their choosing. The project will likely involve 3-5 weeks of work, as it asks students to do research outside of class, craft a rhetorically salient message, and deliver a polished presentation with compellings visuals.

Keywords: Data Storytelling, Persuading With Data

  Resource: Assignment  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   Data Storytelling: How to Effectively Tell a Story with Data

  Date: November 2021   //     Author: Catherine Cote

In this brief article, Catherine Cote invokes recent research psychology to show that, in most cases, human brains are wired to prefer making meaning out of stories rather than raw data. Emphasizing organizational communication and business contexts, the article should also resonate with humanities students as it offers a useful, quasi-literary framework for doing data storytelling that adopts concepts such as setting, character, and conflict.

Keywords: Data Stories, Data Storytelling, Persuading With Data

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   Elite Institution Cognitive Disorder

  Date: September 16, 2013   //     Author: Malcom Gladwell

In this 2013 talk at Google's Zeitgeist conference, Malcolm Gladwell sets out to explain why, at almost every university in the United States, college students drop out of STEM majors at an alarmingly consistent rate. While Gladwell references data on STEM education throughout the talk, he frames his engagement with the statistics in the manner of a detective story to lend compelling narrative tension to his presentation. He examines student STEM degree attainment statistics at several universities, from Harvard to Hartwick, to advance his insight that earning a STEM degree appears to be a function of a student's class rank and not primarily a function of a student's cognitive abilities (e.g., standardized tests scores). To make this counterintuitive finding more palatable, Gladwell contextualizes his analysis of the data with a concept psychologists call "relative deprivation," which suggests that individuals generally assess their own abilities and aptitudes in comparison to those [Read More]

Keywords: Data Storytelling, Persuading With Data, Stem Education

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   From Data Visualizations to Data Stories

  Date: July 16, 2024   //     Author: John Tinnell

This activity guides students through the process of pairing data visualizations with other research sources in order to outline a story about a neighborhood-level data collection effort and the value of its findings for local political advocacy.

Keywords: Air Pollution, Data Storytelling, Persuading With Data

  Resource: Activity  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   Individual Data Advocacy Project (assignment sequence)

  Date: July 27, 2024   //     Author: Nathan Pieplow

These two assignments are designed to form the final semester project in an upper-division writing course. Students begin with an informal project proposal, then create an original data visualization for use in the project, then draft and workshop the text to accompany the visualization. The final deliverable may assume numerous forms, including op/ed, white paper, or multimedia project.

Keywords: Data-Driven Argument, Quantitative Rhetoric, Statistical Claims, Numerical Argumentation

  Resource: Assignment  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   Road Map: Data Storytelling for Advocacy

  Date: 2024   //     Author: Laurie Gries

This visualization offers a road map for taking a rhetorical approach to data storytelling for advocacy. While road map appears linear, the roap map offers an iterative process.

Keywords: Rhetoric, Rhetorical Data Studies, Data Storytelling

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   Strategies for Analyzing and Composing Data Stories

  Date: 2023-9   //     Author: Angela M. Laflen

This book chapter by Angela Laflen situates data stories as a genre of multimodal writing, while emphasizing the specific rhetorical skills that data storytelling requires. Data stories, Laflen explains, often take the form of social media infographs or online feature articles, and they involve combining data with words and images to tell a story or make an argument. The defining goal of the genre is to make data comprehensible and compelling, so that the insights to be gleaned from a dataset might be readily grasped by readers in a manner that will inform their decision making. Laflen's piece also outlines a rhetorical framework for analyzing and creating data stories with students in university classroom contexts.

Keywords: Data Stories, Data Storytelling, Persuading With Data

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   Telling Counter-Stories with Data

This activity prompts students to search the internet for data points about STEM education in the US, and to then develop a counter-story in response to Malcom Gladwell's narrative that low success rates among prospective STEM majors is a psychologically inevitable outcome. This activity can be completed over the time of one or two class periods.

Keywords: Data Storytelling, Persuading With Data, Stem Education

  Resource: Activity  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   The Truth about Human Population Decline

  Date: April 2023   //     Author: Jennifer D. Sciubba

In this presentation, political demographer Jennifer D. Sciubba tells a data-driven story about global population trends and some ways governments might plan for them. While the world's total population continues to grow, the global fertility rate has been plummeting for decades, and this will make for a significant population decline by the next century. In many countries, the share of citizens over the age of 60 will increase from around 15% to over 30% within the next 100 years. Considering these demographic projections, Sciubba asks, "what possible worlds might we create if we thoughtfully planned for an older, smaller population?" She then moves to outline three possible scenarios in an attempt to dramatize a spectrum of possible outcomes we might move toward. As a pedagogical model to inspire student work, this presentation is interesting for the way it uses both historical data and future projections to underscore the long-term consequences [Read More]

Keywords: Data Storytelling, Demography, Immigration, Persuading With Data

  Resource: Example Project  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data

   Three Creative Ways to Fix Fashion's Waste Problem

  Date: November 2017   //     Author: Amit Kalra

In this presentation, Kalra blends personal accounts of his life in the fashion industry with data about consumer habits, the afterlives of our clothes, and the natural resources used to create new garments. The fashion industry's environmental footprint is second only to oil and gas, and Kalra excels at framing the industry's waste statistics through analogies and images that put these numbers into sharp perspective for a lay audience. On the basis of this data, he then proposes a series of solutions (composable fabrics, recyclable clothing, dying clothes with spices instead of chemicals) that each promise to bring measurable reductions in waste. As a model for student presentations, Kalra's talk is particularly notable the rhetorical strategies he uses to make giant, global statistics resonate clearly and vividly on a personal scale.

Keywords: Data Storytelling, Persuading With Data, Waste

  Resource: Reading  //     Domain: Persuading with Data  //     Subdomain: Telling Stories with Data