Data organization refers to the method of classifying and organizing data sets to make them more useful. Once you create, gather, or start manipulating data and files, they can quickly become disorganized. To save time and prevent errors later on, you and your team should decide how you will structure folders and organize your files.
This seminar will review best practices for when it comes to organizing you research project, data, and files effectively.
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.
Data is a collection of facts, such as numbers, words, measurements, observations or just descriptions of things. But data comes in many types, formats and sizes. Understanding what data you will be collecting or working with in a project is important for planning for the management of that data throughout a project.
This seminar will explore data types and help you think about what you need to manage your unique data.
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!
Research Data Management (RDM) is essential for responsible research and planning should begin early. Your well-organized and documented data will meet funding agency requirements, be preserved, discoverable and reproducible.
This seminar will review what RDM is, how it applies to your research, and who to contact for assistance.
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
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.
What's in a name? File naming, when done in a well-organized fashion, can contribute to project documentation, workflow organization, and sharing. Moreover, certain choices in file naming are essential to accessing and sharing files across a computing systems. Establishing naming conventions for your files and using them consistently will ensure maximum access to your data and records.
This virtual session will review how file naming conventions will save you time by keeping your work organized and understandable.
To ensure that you understand your own data and to enable others to find, use and properly cite your data, it helps to add ‘documentation’ or ‘metadata’ (data about data) to the documents and datasets you create.
This virtual session will explore the critical role documentation plays in data management and how you can ensure good documentation throughout your research.