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Pursuing Data Equity

How to Incorporate Equity and Inclusion In Research Data Management

Jodi Coalter, Life Sciences and Outreach Librarian

Dave Durden, Data Services Librarian

Leigh Dunewood, Diversity Intern

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What Are We Covering?

Brief (very brief) introduction into RDM

If you would like to learn more details about this, we have another workshop that you can find here

Join the Discussion!

Going beyond the IRB.

Pre- and during research

Giving people the tools necessary to understand their data.

Post research product

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Introduction

What is Research Data Management (RDM)?

What does RDM have to do with equity?

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What is Data Management?

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What Counts as Research Data?

Data that are used as:

primary sources to support technical or scientific enquiry, research, scholarship, or artistic activity
used as evidence in the research process and/or are commonly accepted in the research community as necessary to validate research findings and results

All other digital and non‑digital content have the potential of becoming research data.

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Data Lifecycle

https://www.dataone.org/data‑life‑cycle10

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So Why Equity and Inclusion?

“...Data are not neutral or objective.”

-Data Feminism, Chapter 6

This might seem counterintuitive? How can data be anything but neutral or objective? Isn’t that the point of science/research?

How you collect data, who collects the data, how the data is stored, who can access the data

All of these are human driven projects! That means that data themselves cannot be neutral.

Key points where equity overlaps with RDM:

Using diversity/inclusion principles in designing a research study has little to do with RDM, however, once you begin collecting those data, you are obligated to protect those individuals and their data.
When you collect data from marginalized/under-represented groups, you may not have a second chance. Losing data is always bad, but you may not get a second chance with certain groups due to breach of trust/availability/etc.
Are there issues of translation/context in your data where a non-expert could come away with the wrong conclusion? If so, were these issues addressed in any supplementary documentation (e.g. code book, data dictionary, readme file) that provide adequate cross-walks, translations, or context?
Sharing and publishing data, especially when those data represent people who may take issue with how they are presented or how their data are reused, requires a clear understanding of the terms under which that data were collected. Did you make it clear that you are obligated to share the data with the larger research community? Did you present an option to flag certain variables that the studied populations may feel comfortable giving to you, but might not want made freely available? Are the data unnecessarily precise in terms of geographic location or indirect identifiers?

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When things go bad...

Examples of neglecting equity in data.

AI that doesn’t recognize Black faces

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When things go bad...

Medical research that doesn’t recognize health disparity: who’s dying from COVID and who is getting a vaccine?

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When things go bad...

Examples of neglecting equity in data.

The Library of Missing Datasets

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When things go bad...

Examples of neglecting equity in data.

Policing Statistics

Statistical “predictive” modeling are built off the statistics from these departments

Statistical modeling

Racist departments choose to police black people more.

Racially profiled policing

These models suggest that black people commit more crimes and therefore should be policed more

More policing indicated

Based on The Condemnation of Blackness: Race, Crime, and the Making of Modern Urban America by Khalil Gibran Muhammad

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Moral of the Story

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Going Beyond Informed Consent

Let’s discuss data equity from research planning to research product.

https://go.umd.edu/Data1

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Example: Indigenous rights to data

Some of the best examples of data equity beyond IRB come from Indigenous communities. Indigenous tribes are sovereign nations (much like the US).

More Info: https://guides.lib.umich.edu/c.php?g=283427&p=1884014

If you collect data from/about their members, who owns that data?

Who has access to that data? Where? How?

How did John and Paige incorporate Indigenous “ways of knowing”?

Are you incorporating their way of knowing? - what does this mean?

You might decide that x, y, and z deserve studying - do they want you to study that?

EXAMPLE: John Aini (Ailan Awareness) and Paige West (Barnard College and Columbia University) - Papua New Guinea

“For example, our people thought it was their right to poison a whole reef ecosystem to get enough fish for their family’s dinner. They thought God created everything on earth and that he also created all the fishing methods, so even though they were using what we now know as destructive fishing methods that did not matter to them. They did not understand that this would not be okay in the long run.”

In this example, they ended up asking communities what they were seeing and what they wanted to know about.

They were seeing some fishing populations declining, so the fisheries scientists studied those populations and gave that information back to the community.

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Example: Gemstone Team METR

“Performance-related musculoskeletal disorders (PRMDs) are a set of disorders that affect musicians... Previous research has investigated the prevalence of PRMDs and possible risk factors, but little discusses the biomechanical correlates, especially those of the hand and fingers, of PRMDs, particularly in guitarists.”

What does an IRB cover in this example?

What is NOT covered? (Maybe as it relates to disability stigma in musicians?)

What parts of a RDM Plan would you want to supplement to account for these issues?

Highlight that the issues do not reflect an issue with their project, but rather our forecasting of potential issues that could be identified/questions asked about data equity in similar projects.
Cultural/professional context of a disability, e.g. injury and musical performance, may have unintended consequences to an individual’s career, i.e. professional musical networks are dense and pedagogical lineage is important; personal details about institution, expertise, etc. may give the individual’s identity away.

Finite number of professors - tenure track
Onus and responsibility on the person with the disability instead of the system

Studies aren’t always directly beneficial to participants
Studies are sometimes arduous/stressful on the participant

What is the “so what” for your research participants

You are getting a citation/degree, but what are they getting?

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Data literacy for the studied population

Data equity post research product

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City Data Portals

Miami-Dade government data portal

Baltimore City Data Portal

https://data.baltimorecity.gov/

Washington, DC Data Portal

https://opendata.dc.gov/

Describe one aspect of the DC Data Portal that is beneficial to DC residents.

2. What aspect of this data portal could be improved?

3. After looking at how these data portals work, what changes would you make to your own data?

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Help us do better!

https://go.umd.edu/Data1Assess