Continuing our Practicing Data Equity series, Part 2 of our Tools for Data Equity post explores additional frameworks, approaches, and tools for building data equity in your research practices, especially those that center community-based approaches. This post is meant to serve as an introduction to these resources and we encourage you to click through to explore them more in depth on your own. This blog has been divided into two parts. You can read Part 1, which focuses on how our worldviews impact the research process here.
As we work to incorporate data equity into our practices, it is our responsibility as researchers and research support staff to evaluate our practices so that we can disrupt the encoding of implicit biases in our research designs, data, and analyses. It can feel challenging to even know where to begin, but little by little, over time, small shifts in our practices can make a big impact. Shifting our frameworks and getting away from the idea that data and technology are neutral can help us think more critically about how we engage with both throughout our research process. Building on the previous post in our Practicing Data Equity series, The CARE Priniciples for Indigenous Data Governance – #BeFAIRandCARE, we put together a list of additional frameworks, approaches, and tools to help you make sure that you are building data equity into your research practices. This post is meant to serve as an introduction to these resources and we encourage you to click through to explore them more in depth on your own. This blog has been divided into two parts. You can read Part 2, which focuses on community-based approaches here. (more…)
In our introductory post to our Practicing Data Equity series, we mentioned that research institutions, especially research intensive or predominantly white institutions, often wield inequitable power in research partnerships with communities. Historically research projects across disciplines have also caused harm to communities by sharing data or findings inappropriately, fundamentally misrepresenting communities, or ignoring community agency and input. This applies as well to data or digital collections that may make data from such projects available to researchers and other data users. The CARE Principles provide a great framework for thinking through key considerations of data management and sharing with, specifically in the case of these principles, indigenous communities. We’ve included information below introducing the principles, but encourage everyone to read them fully on their website.
The documentary “Coded Bias” will be available on Netflix starting on April 5! We’ve recommended it in past blog posts and we hope you’ll give it a watch!
Virginia Tech will be hosting a panel called “Bring Your Own Brain! Celebrating Neurodiversity in STEM Careers” on April 28th.
Throughout February, UW-Madison’s Data Science Hub will host the second annual Data Science Research Bazaar. This year’s theme is Data Science for the Social Good and will feature lightning talks, posters, interactive discussions, and workshops that address how data science can augment equity along racial lines, in health and environmentally, and in cities.
In honor of Black History Month, we’d like to highlight projects that honor and celebrate the accomplishments of Black data and computer scientists, past and present, including the Black [Data] History timeline at Washington University in St. Louis and #BlackInData, an organization that aims to provide community and a support system for Black people in data across the Black diaspora.
As we celebrate Black History Month, it’s a critical time to engage in actively anti-racist and social justice-focused work. However, it’s not enough to celebrate and focus on this work during one month every year. Working anti-racism, ethics, equity, and care into our work is necessary to ensure a better future for everyone.
As the year winds down, we’re putting together a list of our favorite data-related resources and books from 2020 that help readers reflect and think critically about how they work with and present data. Take a look and let us know some of your favorites!