Learn, Grow, Connect With Arcus Education Team: Q&A With Joy Payton

Joy Payton, supervisor of data education for the Department of Biomedical and Health Informatics, leads the development and implementation of education programs that promote high-impact use of data made available through the Arcus platform.

Data science techniques are increasingly becoming research methods. The days of gathering up an Excel spreadsheet of data and then passing that to a statistician for analysis are coming to a close. Investigators who learn to wield the modern tools of machine learning, modeling, and computational data analysis are going to come out ahead of their peers.

In this Cornerstone Q&A post, meet Joy Payton, supervisor of data education for the Department of Biomedical and Health Informatics, who leads the development and implementation of education programs that promote high-impact use of data made available through the Arcus platform.

Arcus is an internal program that positions Children’s Hospital of Philadelphia’s research data more favorably, so that researchers can perform data discovery and innovative research more quickly. Read on to learn how Payton and the Arcus Education team also want to position CHOP’s research talent more favorably, so that investigators and research staff are equipped with the knowledge and skills they need to conduct research in today’s emerging research paradigm that emphasizes greater reproducibility and transparency. Check out many new online learning sessions!

Tell us about the role of education in the Arcus program and what your team does to support the goal.

Education is key to helping researchers develop the computational and quantitative skills necessary to conduct reproducible research, which is one of the Arcus program’s main goals. We want to be a lightweight, nimble resource where researchers can get just what they need, whether that’s how to create a compelling data visualization, how to do natural language processing, or how to do a quick power analysis. Because reproducible research depends on the use of scripted, open source languages like R and Python, we spend a lot of time supporting the R and Python communities at CHOP and meeting with researchers who need to learn more about these languages.

You have a varied background as an educator, both here at CHOP and in your previous experience. How has that informed your approach to Arcus education?

It’s true; I’ve taught children and adults and have been involved in education in several countries. I’m an educator because I love the multiplicative aspect of education — I can do more by teaching and enabling others, and then deputizing them to educate their peers in turn, than I could ever do on my own.

The most important lesson I’ve learned in my history as a teacher, data instructor, and professor has been the central role that empathy plays. It’s important for my team to empathize with our learners and understand what it’s like to be involved in biomedical research. For example, if you’re a principal investigator with research staff, you experience a lot of pressure. You feel not only the responsibility for conducting rigorous science and advancing your career, but also feel the duty to keep a funding stream open so that your staff will have secure employment.

How can we as educators best support someone who’s extremely busy, under many competing pressures, and needs to be able to learn some computational skills in a hurry? The first step is understanding our learners and appreciating their experience.

What can someone expect when they contact the Arcus Education team?

First of all, expect a personal response. We’re happy to have one-on-one conversations with anyone involved in research, whether or not you intend to contribute data to Arcus or create a project within Arcus. We don’t have a one-size-fits-all mentality; we want to know what your needs are, how you (or your team) prefers to learn, and what success looks like to you. We can then make some specific suggestions, like setting up group training to help your team use version control more effectively, or giving you some self-guided modules to help you learn how to do hypothesis testing in R. We also can explain what Arcus is and how it might prove helpful in your research.

How do you work with other members of the Arcus team to develop your educational programming content?

I like to say that as a data scientist and data educator, it’s my job to “know a little about a lot” and to “know who knows more.” Those of us who make up the Arcus Education group rely on our peers’ expertise in other areas of Arcus. For example, if a researcher asks me the best way to use Arcus Cohort Discovery (one of Arcus’ self-service tools for researchers) to discover differences in age distributions in recent visits to various primary care locations, I’d provide a tentative response while also checking with the experts who built the tool and know the underlying data better than I do.

We ask our Arcus peers to write posts for us on the Arcus Education website, help us write job aids for Arcus tools, and serve as consultants when a learner asks a question we’re not sure how to answer. I’m never afraid to tell a researcher, “I don’t know, but I can find out,” because I’m surrounded by experts in a variety of fields, from cloud computing to privacy law to file management best practices.

Why are you passionate about helping all scientists at the Research Institute become data scientists?

Just as scientists need to learn how to use a pipette or a microscope or how to administer an EEG or an IQ test, scientists also need to learn the basic computational tools of the trade. That’s easy to say, but if you’ve been using Excel or SPSS or Prism throughout your career, it can be hard to make the switch to writing your own computational scripts. It can seem overwhelming to learn how to program in Python, when you’re busy on service, mentoring postdocs, writing grants, and producing manuscripts.

I love helping people climb the data science learning curve and provide just-in-time, right-sized education because I am certain it makes their science more nimble, generalizable, reproducible, and innovative.

Fast-forward five years from now. What are some of the professional goals you anticipate accomplishing by being part of the Arcus project?

I have so many dreams for where CHOP can go with a research workforce that is data savvy, diverse, and dedicated to research reproducibility. I would like to position CHOP as a thought leader in professional data science in the biomedical research space.

CHOP’s strategic partnerships also will position us well to help our global health partners (for example in the Dominican Republic and Botswana) grow their data literacy and data analytics skills. This means better clinical and research outcomes in these programs, as well as the development of a highly computationally skilled workforce in geographic areas that need it.

The National Institutes of Health has put data science and reproducible research as major priorities, and CHOP is rising to the occasion. Our researchers have led the way in developing medical devices, vaccines, and novel research methods, and we will continue to do the same in the area of statistics, computation, and machine learning.

How can researchers learn more about Arcus?

Anyone interested in exploring what Arcus is all about can feel free to arcus-support [at] email.chop.edu (reach out by email)title="Email Arcus". The Arcus Education team offers training on a variety of fascinating data science topics on a frequent basis. Find a link to the Arcus Education team’s Event Calendar, and view upcoming sessions. Many are now available as online learning opportunities.