Innovations in Incorporating Collaborative Skills throughout the Statistical Training Pathway

Music City Center 

Room: CC-Davidson Ballroom A3 

Effectively preparing students for careers in collaborative statistics and data science requires incorporation of specialized collaborative skills throughout their training pathway. Encompassing domains such as leadership, communication, and organization, it is crucial to pair these collaborative skills with methodological skills when training students to be effective team scientists. Panelists in this session are leaders of several initiatives that target collaborative skill building at the undergraduate, graduate, and workforce levels. Alex Hanlon (Virginia Tech) directs the Collaborative Undergraduate Biostatistics Experience (CUBE) program, which gives undergraduate students from underrepresented backgrounds the opportunity to engage in a collaborative data science experience. Supported by the American Statistical Association Member Initiative program, Julia Sharp (National Institute of Standards and Technology) has produced popular Statistical Collaboration Training videos and has also co-developed an ASA short course on navigating tough conversation with collaborators. Ryan Peterson (University of Colorado – Anschutz) created the Statistical Consulting Training Repository Initiative (statCTR), which collects and indexes simulated statistical consulting scenarios and other helpful resources related to collaborative skills. Gina-Maria Pomann (Duke University) and Emily Slade (University of Kentucky) created the Quantitative Team Science (QuanTS) Program, with the mission of creating online didactic training materials and an experiential mentorship program focused on team science skills for early-career quantitative scientists. Panelists will describe the use and outcomes of these initiatives as well as provide guidance on incorporating these materials into existing training programs at the undergraduate, graduate, and workforce levels. This session will also address overall challenges and offer recommendations for teaching collaborative skills, aiming to assist the audience in developing their own innovative experiential training tools and programs.

Panelists have collectively contributed to more than 20 related publications on topics such as scoping statistical projects, writing analysis plans, defining specific collaboration learning objectives, finding productive collaborative research partners, and more. The combined experience and dedication of these panelists to the training of collaborative skills will ensure a successful and engaging invited JSM panel. This session will be of interest to a diverse audience, providing participants with a valuable opportunity to learn about: (1) using these innovative resources to teach collaborative skills, (2) navigating novel resources to improve their own collaborative skills, and (3) understanding key features necessary to develop novel experiential training tools and programs.

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Section on Teaching of Statistics in the Health Sciences

Section on Statistical Consulting

Section on Statistics and Data Science Education