Reproducibility is a hot topic. A 2018 Nature special issue on challenges in irreproducible research noted that “there is growing alarm about results that cannot be reproduced.” In 2019, the National Academy of Science, Engineering, Medicine (NASEM) published a consensus report on “Reproducibility and Replicability in Science” to highlight reproducibility as “obtaining consistent results using the same input data, computational steps, methods, and conditions of analysis.” This IAP, Data Management Services is offering a series of workshops on topics that will help any researcher working with data build a reproducible workflow:
This series includes two introductory classes on managing data:
- “Quick & Dirty Data Management” – covering the basics for any project
- “Data Management for Postdocs and Research Scientists” – a longer data management overview
Plus, we’re offering these specialized workshops:
- “Introduction to Cleaning and Prepping Data with OpenRefine” – suitable for anyone who wants to get started with OpenRefine
- “Data Management: File Organization” – how to create efficient folder and file structures in big or small projects
- “Make Your Research Computationally Reproducible” – using R, Python, and other tools to make a reproducible research pipeline
- “Data Visualization: Introduction to Principles and Tools” – getting started with data visualization
- “Getting Credit for Your Code” – techniques for citing, sharing, and archiving research code
- “Data Visualization: Making Better Figures” – tips for making figures, building off the first data visualization workshop
To register for any of these IAP workshops, follow the links above. We hope to see you there!
