Many key granting organizations, like NSF, NIH, NEH and more, now require submitters to include a Data Management Plan (DMP) as part of their application. In short, these plans outline the best practices in data management that you will apply throughout the course of your grant. By creating a data management plan for your data at the beginning of the project, you save time and effort later on.

This session will provide an overview of the components of typical data management plans. 

Learning Objectives: 

To create reliable and more accurate data, a good understanding of data management terms is important. Get started with data management by understanding the resources, concepts, services, and tools involved throughout the research lifecycle.

This seminar will review key data management terms and jargon used in the field. Learn new terms and ace the RDM Spelling Bee!

Learning Objectives: 

• Learn data management terminology and jargon
• Explore concepts and tools for...

Turn messy data into tidy data! Much of your time will be spent in this ‘data wrangling’ stage. It’s not the most fun, but it’s necessary and food data organization is the foundation of data analysis. Learn the rules for a 'tidy dataset' in order to clean and prepare your data with examples and tools.

This virtual session will teach you how think about data organization and some practices for more effective data wrangling. As a note, we will not discuss analysis or visualisation

Learning Objectives: 

Final_v2_rev3_final_FINAL.docx got you down? Version tracking, or version control, is the management of changes to any file or document. Version control is like a savings program for your project. Implementing a file versioning strategy at the beginning of your research project will help to avoid confusion amongst collaborators and avoid lost time and effort trying to recover the "right" version of a file.

This virtual session will review a few ways for tracking different versions of a file, focusing on using git for version control.

...