Modular design and automated testing with Python

This 4-hour course will be split over 2 days (11/18/24 and 11/20/24) and offered via Zoom. Attendance is required as the course will not be recorded.

Research is relying on increasingly complex code. However, most academics have not received formal training in software development. This course will introduce participants to some of the best practices of professional software development. Participants will learn to structure their Python code into reusable functions and store those functions as separate scripts and/or packages. Participants will learn to write automated tests for their functions, to help confirm that results are as expected and to ensure that future changes to code do not cause unexpected changes to results.

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This 4-hour course will be held online only. Attendance is required as it will not be recorded.

Cognitive interviewing in survey research is a tool that allows researchers to improve survey questions and questionnaires by better understanding how respondents understand and respond to the questions they are being asked. Data gathered through cognitive interviews help researchers identify and remedy issues with their questionnaires. This short course provides participants with fundamentals on where cognitive interviews fit in the survey process and on how to design, conduct, and analyze cognitive interviews. Participants will have the opportunity to practice specific cognitive interviewing techniques, including think-alouds, probing, and observation. Participants will also learn about revising survey questions based on interpreting cognitive interview results and about the research on which cognitive interview practices are based.

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How to improve reproducibility of team science: A CTSA BERD*s eye view

With larger teams and ever more data, practicing reproducible research is becoming increasingly complex. Those analyzing the data are often different from those who collect and generate the data, and different from those interpreting the data. This can lead to gaps in the team’s knowledge around key data processing or analysis decisions. Transparent, robust documentation is also a key step to uncovering errors. This talk will describe implications of these challenges as well as some generalizable, practical solutions developed within two CTSA Biostatistics, Epidemiology, and Research Design (BERD) units.

Speakers:
Leah J. Welty, PhD
Professor and Director, Biostatistics Collaboration Center
Northwestern University Feinberg School of Medicine

Manisha Desai, PhD
Professor of Medicine and of Biomedical Data Science
Stanford University

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UNC NRP November Education Session: Developing a Culture of Psychological Safety in Research Teams

This NRP Education session will be presented by Josh Hinson from the Relational Leadership Institute (RLI). Kelsey Haywood, an RLI alum, will be providing insight into practical use in everyday team dynamics.

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NC TraCS Data Science Seminar Series: A Cozy, Accessible Introduction to AI and Cloud Technologies

UNC-Chapel Hill recently announced initiatives for enhancing engagement with AI and cloud technologies across our campus community. Want to get a better handle on what some of these terms mean? In this seminar, JP Powers, PhD, a research data scientist in the TraCS Data Science Lab, will give a beginner-friendly introduction to concepts like AI, generative AI (genAI), cloud computing, and big data.

Seminars in the NC TraCS Data Science Seminar Series will cover a range of topics related to health care data science, clinical data, data engineering, and working in these areas at UNC-Chapel Hill. These hybrid seminars will be held on the third Tuesday of each month from 12:30-1:30 p.m. in the NC TraCS suite on the 2nd floor of Brinkhous-Bullitt or via Zoom.

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Modular design and automated testing with Python

This 4-hour course will be split over 2 days (11/18/24 and 11/20/24) and offered via Zoom. Attendance is required as the course will not be recorded.

Research is relying on increasingly complex code. However, most academics have not received formal training in software development. This course will introduce participants to some of the best practices of professional software development. Participants will learn to structure their Python code into reusable functions and store those functions as separate scripts and/or packages. Participants will learn to write automated tests for their functions, to help confirm that results are as expected and to ensure that future changes to code do not cause unexpected changes to results.

Register

Tranexamic Acid in Patients Undergoing Liver Resection: The HeLiX Randomized Clinical Trial

This NIH Collaboratory Rethinking Clinical Trials Grand Rounds features:

Paul J. Karanicolas, MD, PhD, FRCSC
Sherif and Mary-Lou Hanna Chair, Surgical Oncology Research
The Edmond Odette Cancer Centre @ Sunnybrook Health Sciences Centre
Professor of Surgery
University of Toronto

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