When research directly informs clinical care, transparency carries practical consequences. Rigorous documentation and open practices help findings stand up to scrutiny and support their translation into practice—from clinical acceptance to insurance coverage to patient access.
Alisha Bruton is a research scientist and biostatistician in the Center for Mental Health Innovation at Oregon Health & Science University, where she studies the role of nutrition, stress, inflammation, and body awareness in the health and well-being of people with ADHD. With training spanning biostatistics, integrative research methods, clinical research, and natural medicine, she brings a multifaceted perspective to questions of transparency, data stewardship, and research quality.
In this interview, Bruton shares how she regularly integrates open science practices into her workflow, navigates data sharing decisions in collaborative research environments, and applies FAIR principles to make research findable and reusable over time.
Q. Could you describe your roles and the kind of research you work on? What methods or approaches are most central to your work day to day?
A: I manage a lab that studies the role of nutrition in attention and emotion regulation in children with ADHD. I’m both a scientist and a biostatistician, so I do much of the data analysis for the lab and oversee all of our publications.
We run clinical trials and generate large, complex datasets, and we write papers with all of that data. I help write statistical analysis plans and study protocols, and we post many of those materials on OSF.
I’m also a mentor at the Comprehensive Research Evidence Synthesis Training Center (CREST), where we train graduate students and researchers in high-quality research methods for evidence synthesis, including systematic reviews. In that role, I serve as the data steward—I teach methods, design workflows for data collection and storage, and make decisions about where data and code live and when (or whether) they become public. We also develop protocols and registrations there, so I get to oversee all of that as well.
Q: What questions or problems motivate your current research?
A: The broad question underlying the psychology research we do is focused on nutrition. The brain obviously needs vitamins and minerals to function, but even for kids who aren’t necessarily malnourished, the question is whether they’re still getting everything they need.
There are also broader issues—like declining soil quality and highly processed foods. We’re interested in nutrition, but also in what else is playing a role. The microbiome is a big piece of this, as well as genetics and metabolism— how people process vitamins and minerals from different foods. So we’re interested not just in what you put in your mouth, but what happens after nutrients are consumed.
Q: How does the OSF fit into your research workflow? At what point in a project do you usually start using it?
A: I often use OSF for registering statistical analysis plans, especially when I’m doing secondary data analyses. For systematic reviews, I’ll also write a protocol and register it on OSF before the project begins.
Q: In your work, transparency often involves navigating real constraints (ethics, PI decisions, data access). How do you think about what can be shared, and how do you document or communicate those decisions?
A: Sharing data is usually the PI’s decision, and since I’m not the PI, I defer to them. Many journals now require a data availability statement where you state that either the data are publicly available and provide a link, or they’re not available. What the PIs I work with often want to state is that the data are available upon reasonable request—send an email, explain who you are, and we’ll talk about it.
I think there is sometimes a misunderstanding that transparency means giving away all of your data for free—especially data that took millions of dollars and many years to collect. But that’s not the case. You can share part of your data, or share it in stages—for example, in small chunks as papers are published.
I’ve had some pushback from PIs who are hesitant to share because it feels like giving away something they worked very hard for. I understand that concern, but I don’t think transparency has to work that way.
Q: As someone involved in data stewardship, what practices do you think make the biggest difference for making research understandable and trustworthy over time?
Deciding everything prospectively—deciding it all before the study begins, writing it down, putting it online, and making it findable.
There are principles I learned about in a math class that really stuck with me, called the FAIR principles. They focus mostly on data, but I think they apply to everything in research: making things findable, accessible, interoperable, and reusable. I try to follow those principles with data, code, protocols—everything.
Q: Looking ahead, how are you thinking about planning for future projects (like meta-analyses) where data may eventually be shared more openly?
A: I love the idea of making it standard practice to share data and code when you publish a meta-analysis. I also love the code part—that nerdy side really excites me—but it’s not standard right now for people to make their meta-analysis code public.
At the center where I work, I’d like to make it standard practice that every time we publish a meta-analysis, we put all of our data and code online. There aren’t really ethical concerns with meta-analyses since they’re based on publicly available data, so there aren’t major barriers. I hope that becomes something more people adopt.
Q: What originally got you interested or involved in open science practices, and what advice would you offer to researchers who are just starting out?
A: Well, I got originally interested when I learned about the FAIR principles in a math class. I was like, “This is so cool. This applies to everything.” So that's what initially turned me on to it.
For researchers starting out, especially folks who are working in integrative medicine fields like I do, the more transparent and rigorous you are, the more your research can stand up to scrutiny. And the more we are conducting this really high-quality, rigorous research, the more likely these interventions that we believe in and care about so much are going to become widely accepted, paid for by insurance, and accessible to people who need them.
So, in every way possible, if we are as above reproach as we can be, I think the better the outcomes will be—and the quicker we can get these things to the people who need them.