Leadership for Cancer Informatics Research (The Course)
This course includes 1 attempt.
This course is part of a series of courses for the Informatics Technology for Cancer Research (ITCR) called the Informatics Technology for Cancer Research Education Resource. This material was created by the (ITCR Training Network (ITN) which is a collaborative effort of researchers around the United States to support cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: National Cancer Institute (NCI) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators, scientists, and analysts to integrate cancer informatics into their workflows. Please see our website at www.itcrtraining.org for more information.
This course aims to provide research leaders with guidance about:
1) How to effectively lead and support team members on informatics projects
2) How to perform informatics projects well
3) How to support informatics collaborators, mentees, and employees
4) How to better support diversity within your team
5) Tools that can help you perform informatics projects well
The course is intended for researchers who lead research teams or collaborate with others to perform multidisciplinary work. We have especially aimed the material for those with moderate to no computational experience who may lead or collaborate with informatics experts. However this material is also applicable to informatics experts working with others who have less computational experience.
Students will be graded based on their performance on short multiple choice quizzes.
- About this course
- Target audience
- Informatics teams challenges
- Meet the team!
- Guidelines for multidisciplinary informatics teams
- Finding and creating informatics teams
- Communication for informatics teams
- Record keeping practices
- Leadership best practices
- Informatics project guidelines
- Identifying good informatics questions
- Informatics project pitfalls
- Informatics project pitfall mitigation methods
- Informatics relationships
- Cultivating good multidisciplinary lab relationships
- Collaborating with informatics experts
- Employing informatics experts
- Mentoring informatics students
- Promoting diversity, equity, and inclusion
- Diversity is beneficial
- Underrepresentation in cancer informatics
- Examples of contributions by individuals of underrepresented groups
- Underrepresentation in clinical trials
- Strategies to promote more equitable inclusion in clinical trials
- What does it take for clinical research to be ethical?
- Research practices to reduce cancer health disparities
- Ways to better support a more diverse research team
- Informatics lab management tools
- Git and GitHub
- RStudio and R Markdown
- Note-taking apps
- About the authors
Carrie Wright is an Assistant Scientist in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. She is a faculty member of the Johns Hopkins Data Science Lab, where her work focuses on making data science and informatics more approachable and accessible. She is also a faculty member of the open case studies project, the Informatics Technology for Cancer Research (ITCR) Training Network (ITN), and a co-founder of the LIBD rstats club, a community designed to encourage others to learn more about R programming and statistics. Learn more about Carrie at https://carriewright11.github.io/.
Sarah Wheelan is an associate professor of Oncology and Molecular Biology and Genetics at the Johns Hopkins University School of Medicine, and has an appointment in the department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. She is a founder and co-director of the Center for Computational Genomics and a co-director of the Experimental and Computational Genomics Core.
Jeff is a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. His group develops statistical methods, software, data resources, and data analyses that help people make sense of massive-scale genomic and biomedical data. As the co-director of the Johns Hopkins Data Science Lab he has helped to develop massive online open programs that have enrolled more than 8 million individuals and partnered with community-based non-profits to use data science education for economic and public health development. He is a Fellow of the American Statistical Association and Mortimer Spiegelman Award recipient.
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