Based on their genomic data types and goals, this course will help learners find educational resources and tools to help them process and interpret data.
This course introduces more advanced tools to increase the reproducibility of data analyses; building upon the Intro to Reproducibility course. GitHub, Docker, Code Review, and GitHub actions are discussed.
This course on AI for software development explores the use of AI large language models (ChatGPT, Bard, etc) and their potential benefits and challenges. Hands-on activities show the ways in which AI can speed up software development tasks and free up time for more creative and strategic work, maximizing benefits/efficiency while limiting harm.
This course covers the basics of creating documentation and tutorials to maximize the usability of informatics tools. It is meant for individuals developing tools for informatics.
This course is designed to help investigators understand more about computing basics, as well as familiarize researchers with various computing platform options.
This course covers the pitfalls of informatics research and discusses best practices and tools to overcome the challenges of working with and managing multidisciplinary teams. It also covers guidelines to promote diversity and inclusion in your lab and research.
This course covers how to use containers for scientific software development. Scientific software benefits from the concepts of continuous integration (CI) and continuous deployment (CD). Containers play a critical role in CI/CD by providing a consistent, portable, and isolated environment for building, testing, and deploying software.
This set of four mini courses helps leaders make strategic decisions, drive innovation, enhance efficiency, and foster a culture that embraces the transformative power of these technologies.
This course introduces the concepts of reproducibility and replicability in the context of cancer informatics. It is the first course in a two part course on reproducibility. It uses hands-on exercises to demonstrate in practical terms how to increase the reproducibility of data analyses.
This course is designed to help researchers and investigators understand the key principles of data management from an ethics, privacy, security, usability and discoverability perspective.
This course is designed to help researchers and trainees write scientific articles using LaTeX and Overleaf.
This course surveys common plot types as well as considerations and best practices for effective, accessible, and ethical data visualization, especially within the scientific field.
This course is designed to help researchers understand clinical data, covering questions such as: Where does clinical data come from? What makes it unique? What are special considerations for its handling and management?
This course walks through why's and the how's for using automation to boost scientific software development process.
Data management and sharing best practices for biomedical data handling folks
Learn about the new NIH data sharing policy, places where you might want to share your particular kind of data, and how to deal with possible challenges associated with the policy.
A capstone course for the ITN Reproducibility Series with hands-on activities
To associate your repository with the itn-course topic, visit your repo's landing page and select "manage topics."