Join us over at our website to keep the conversation going!
On this page, you'll find resources that haven been shared during our Equity in Data Community of Practice (CoP) sessions. Some resources will be found in multiple categories so you can find what you need easily.
Use the Table of Contents to easily jump to the topic you want resources for.
Look for the following Tags that designate different resource types:
Report /Article Toolkit/Guide Podcast Resource Page Video/Webinar Book Tool
Demonstrating the range of poverty levels among racial groups when those groups are disaggregated by national origin categories.
Guidelines for ethically imputing race and ethnicity. Sometimes you need data disaggregated by race and ethnicity to inform decisions - but you don't have it. Data analysts use imputation to fill in gaps in data using scientific methods and this guide gives recommendations and standards about how to impute race and ethnicity ethically.
As data people, we often find ourselves having to work with data either in a way that wasn't intended, or to work with data that wasn't collected with intention. This piece offers options of what to do when we have to work with data that isn't ideal.
The questions we ask determine the answers we will get. Here are some articles to help in asking better questions:
"In an increasingly data-driven, automated world, the question of how to protect individuals’ civil liberties in the face of artificial intelligence looms larger by the day. Coded Bias follows M.I.T. Media Lab computer scientist Joy Buolamwini, along with data scientists, mathematicians, and watchdog groups from all over the world, as they fight to expose the discrimination within algorithms now prevalent across all spheres of daily life."
Podcast about ethics of artificial intelligence (ended in 2023, but still worth listening!).
Weapon of math descruction book Book
Talks at Google: In this talk, O'Neil sounds an alarm on the mathematical models that pervade modern life and threaten to rip apart our social fabric. Video/Webinar
Excellent page with information about how race and ethnicity (Hispanic Origin) are summarized in census data. Includes a helpful list of the tables that include race and Hispanic Origin, and also the letters the are added to the end of a table code for specific racial categories.
Description: The Census Bureau has ample demographic and socioeconomic data by race and ethnicity, and it's all available in our new data platform, data.census.gov. In this webinar we will define the concepts of race and ethnicity in accordance to the U.S. Office of Management and Budget Standards, and demonstrate how to navigate data.census.gov to access this data. We'll also show you key tips and tricks to zero-in on the data you need, and guide you to additional resources to help you in your search.
Using census race and ethnic origin data.
Using census race and ethnic origin data.
Recording from 2021 CoP Session Report /Article Video/Webinar
Includes sample questions for collecting data on gender identity and sexual orientation, race and ethnicity, and ability.
We all look for ways to increase the participation in our data collection, but have different perspectives on the appropriateness of paying people for data. Some see this as essential, others see it as manipulative. There is no easy answer, but we make decisions about it each time we collect data, perhaps without thinking through all aspects of the issue. This research brief will provide you with some information to help you make a more intentional decision.
Recording from 2021 CoP Session Video/Webinar
An article on how information and data has been visually encoded throughout history.
Onuoha is a data analyst and artist. In this episode she talks about her interesting Missing Data Project and gives some interesting ideas for us data people to think about.
Accessibility for Color Vision Deficiency (Color Blindness)
Color Blind Guide - Simulator Tool
DaltonLens Online Color Blindness Simulators - explains why there are differences between color vision simulators and which is most accurate Toolkit/Guide Tool
Accessibility for Low or No Vision
A New Vision for Data Viz Accessibility Report /Article
Blind news audiences are being left behind in the data visualization revolution: here's how we fix that by Johny Cassidy Report /Article
Data Viz Contrast - Check your line chart widths. Tool
Image Description Guidelines Toolkit/Guide
NV Access - Screen reader that is free and open source. Tool
WebAIM - "Web Accessibility in Mind" Contrast Checker with details about adherence to WCAG. Tool
What is High Contrast for Data Visualization? (with examples) by Report /Article
Writing Alternative Text for Data Visualizations by Amy Cesal Toolkit/Guide
Are your visualizations excluding people? from the Data Visualization Society Video/Webinar
Chartability Workbook Toolkit/Guide
Chartability is a set of principles and tests that are designed to help you find accessibility barriers in data visualizations.
Choosing Accessible Fonts: Enhancing Readability and Inclusivity by Monika Prasad Report /Article
Not all fonts are created with accessibility in mind! This guide will help you understand what makes a font accessible and introduces you to the best font for accessibility: Atkinson Hyperlegible.
Coolors - Color palette generator Tool
Designing for Neurodivergent Audiences by William Careri Report /Article
OpenDyslexia - font designed for people with dyslexia.
Readability checking
Hemingway Editor - Free online readilibity checker. Highlights sentences that are difficult to read. Tool
Microsoft Word has readability and level statistics, you just have to know where to find it. Click here for the steps to get to your document's score. Tool
State and Local Governments: First Steps Toward Complying with the Americans with Disabilities Act Title II Web and Mobile Application Accessibility Rule - Posted January 8th, 2025 Report /Article
Visually Accessible Data Visualization by Derek Torsani Report /Article
Designing data visualization to be more visually accessible using patterns, shapes, and high contrast colors.
Web Content Accessibility Guidelines (WCAG) 2.1 W3C Recommendation 06 May 2025 Report /Article Toolkit/Guide
Big picture frameworks, guiding principles, and rethinking evaluation or research to center equity.
Library of resources “to help change norms and practices of data use to advance equity and prevent harm to communities of color and people with low incomes.”
Outlines 3 basic principles we can apply to our data practice, along with bulleted examples and links with in-depth examples.
Great resource for identifying ways to integrate equity principles even if you’re not involved with data integration (which is in the title).
This report, a joint publication between the Equitable Evaluation Initiative and Grantmakers for Effective Organizations "encourages those engaged in evaluative work to consider long-standing beliefs and assumptions in their practice with a general overview of the EEF, which seeks to seed and grow a field of EEF Practice Partners expanding notions of validity, objectivity and rigor — while also embracing the complexity of this work and all in the service of equity." Here's more information about the Equitable Evaluation Framework.
2,700 resources on racial equity, from basic concepts to integrating equity into data work (under the “Evaluate” section).
Resources about shifting power, involving communities, and sharing decision-making in data work.
“Our Data Bodies (ODB) has conducted research and produced a workbook of popular education activities focused on data, surveillance, and community safety to co-create and share knowledge, analyses, and tools for data justice and data access for equity.”
"Lean Research is an approach and an initiative to improve the practice of data collection involving people and communities in development and humanitarian contexts."
We all look for ways to increase the participation in our data collection, but have different perspectives on the appropriateness of paying people for data. Some see this as essential, others see it as manipulative. There is no easy answer, but we make decisions about it each time we collect data, perhaps without thinking through all aspects of the issue. This research brief will provide you with some information to help you make a more intentional decision.
A tool for anyone who collects, shares, or uses data. It helps identify and manage ethical issues - at the start of a project that uses data, and throughout.
As data people, we often find ourselves having to work with data either in a way that wasn't intended, or to work with data that wasn't collected with intention. This piece offers options of what to do when we have to work with data that isn't ideal.
A Guidebook for Community Organizations, Researchers, and Funders to Help Us Get from Insufficient Understanding to More Authentic Truth. "If we do not address the power dynamic in the creation of research, at best, we are driving decision-making from partial truths. At worst, we are generating inaccurate information that ultimately does more harm than good in our communities. This is why we must care about how research is created." This guidebook is designed "to help shift the power dynamic and the way community organizations, researchers, and funders uncover knowledge together."
CTData Blog Post and Recording from our 2022 Conference Report /Article Video/Webinar
“Our Data Bodies (ODB) has conducted research and produced a workbook of popular education activities focused on data, surveillance, and community safety to co-create and share knowledge, analyses, and tools for data justice and data access for equity.”
"The United Nations has declared internet access a human right, and...I believe classifying internet access as a human right means a lot more than just enabling people to watch TikToks." This article highlights the potential cost to businesses for violating data privacy, but it also implicates an important idea relevant to anyone who collects data that we talk about a lot as a group: Trustworthiness.
This piece reframes the narrative from vaccine hesitancy, which blames communities of color for not receiving vaccine, to lack of vaccine access.
Lessons learned during our first ever community of practice, back in June of 2020.
This piece reframes the narrative from vaccine hesitancy, which blames communities of color for not receiving vaccine, to lack of vaccine access.
Demonstrates the range of poverty levels among racial groups when those groups are disaggregated by national origin categories.
An article on how information and data has been visually encoded throughout history.
CTData blog post and recording from June 2022 CoP Report /Article Video/Webinar
Trinity College students' analysis finds state far from promises make to desegrate Hartford Schools from Connecticut Public Radio Report /Article Podcast
Guidelines for ethically imputing race and ethnicity. Sometimes you need data disaggregated by race and ethnicity to inform decisions - but you don't have it. Data analysts use imputation to fill in gaps in data using scientific methods and this guide gives recommendations and standards about how to impute race and ethnicity ethically.
Boyd talks about the importance of naming racism when you include race as a variable in a data analysis. The variable isn't actually race but racism (since there's no biological reality of race), but the category of race doesn't convey that clearly.
Flexner Report Report /Article
The Equity in Data community of Practice has discussed shifting the onus of trust from individuals to our institutions. The Association of American Medical Colleges has put a great deal of thought into this idea and developed a list of 10 principles, toolkits, and videos on this idea.
"The United Nations has declared internet access a human right, and...I believe classifying internet access as a human right means a lot more than just enabling people to watch TikToks." This article highlights the potential cost to businesses for violating data privacy, but it also implicates an important idea relevant to anyone who collects data that we talk about a lot as a group: Trustworthiness.