Talk, Conference: Building Research Integrity Through Reproducibility, Salt Lake City, UT
Panel Discussion: What Universities Do (and Don’t Do) to Influence (or not) Research Reproducibility. Conference: Building Research Integrity Through Reproducibility. June 2018. Moderated by Victoria Stodden.
Talk, Grand Rounds Research Reproducibility, University of Utah, Eccles Health Sciences Library, Salt Lake City, UT
Teaching Undergrads Research Reproducibility, Grand Rounds: Research Reproducibility. University of Utah, Eccles Health Sciences Library. October 2017.
Talk, 2017 Ecological Society of America Annual Meeting, Portland, OR
As the data that we collect dramatically increases in both quantity and complexity, all college graduates will need more quantitative and computational skills to be productive and successful members of society. Ecology students are no exception. Instructors are often looking for efficient and exciting (or at least non-off-putting) ways to introduce undergraduates early in their careers to the powerful data analysis tools that they will learn and use later, such as the statistical programming language, R, without overwhelming introductory students. Our question was: what is the most accessible and effective framework for teaching quantitative and computational skills in introductory ecology classes that will meet our learning goals and prepare students for continued development in statistics and advanced courses?
Co-presented with Christy Clay, Ph.D., also of Westminster College
Talk, CSAIL, Hood River, OR
As the data that we collect dramatically increases in both quantity and complexity, all college graduates will need more quantitative and computational skills to be productive and successful members of society. Neuroscience students are no exception. Instructors are often looking for efficient and exciting (or at least non-off-putting) ways to introduce undergraduates early in their careers to the powerful data analysis tools that they will learn and use later, such as the programming languages, R and Python, without overwhelming introductory students. My question was: what is the most accessible and effective framework for teaching quantitative and computational skills in introductory neuroscience and genetics classes that will meet our learning goals and prepare students for continued development in data analysis and advanced courses? I have developed inquiry based, active learning materials in the Jupyter Notebook system to teach coding and quantitative skills to undergraduate students at several different levels. I will present our materials and discuss our experience using this open source system as an effective and accessible tool to teach quantitative and computational skills in neuroscience classes. My materials address basic calculation and graphing skills, reproducible data analysis, and basic statistics using R or Python. I will discuss the advantages of Jupyter Notebooks for teaching and learning over using more basic tools such as Excel or systems with a relatively steep learning curve such as Rstudio. The Jupyter Notebook system is relatively easy to install or use in the cloud, and combines text, code, and output all in one place that is easily exportable to HTML and PDF. It is also easy to guide students through solving scaffolded problems with code that they can adapt and modify, and to record and evaluate students’ thought processes as they work through everything from simple exercises to complex data analysis projects. I see Jupyter Notebooks as an easily accessible tool to get students at various levels engaged in doing data science related to neuroscience.
More information on CSAIL
Talk, csv,conf, Portland, OR
As the data that we collect dramatically increases in both quantity and complexity, all college graduates will need more quantitative and computational skills to be productive and successful members of society. I will present my experience using the open source Jupyter Notebook system as an undergraduate instructor and mentor. My colleagues and I have developed inquiry based, active learning materials in Jupyter Notebooks to teach coding and quantitative skills to undergraduate students at several different levels. Our materials address basic calculation and graphing skills, reproducible research, and include a semester long scientific computing course using Python. Jupyter Notebooks have several advantages for teaching and learning over traditional coding in the shell or an IDE. The system is relatively easy to install, combines text, code, and output all in one place that is easily exportable. It also makes it easy to guide students through solving problems with code and to see students’ thought process as they work through everything from simple exercises to complex data analysis projects. We see Jupyter Notebooks as an easily accessible tool to get students at various levels engaged in doing data science.
More information on csv,conf