Summary and Schedule
The lesson encompasses best practices around Research Data Management.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Project-Level README.txt |
What is a README.txt file?What is the bare minimum to include in a Project-Level README.txt file? |
| Duration: 00h 11m | 2. Dataset README and Data Dictionary |
What is a dataset-level README file? How is this different from a project-level README file? What is a data dictionary and how is it different from a data-level README file? |
| Duration: 00h 23m | 3. File Orgainization and Naming |
What is the best file organization scheme for you? What is the best file naming convention for you? |
| Duration: 00h 35m | 4. Data Storage |
Where at UC would you want to store your research data? What is the basic information you should have about where your research data is stored? What is the importance of backing up your research data? |
| Duration: 00h 47m | 5. Data Management |
What are the benefits of writing a living Data Management Plan? Why is it a good idea to think of data stewardship? |
| Duration: 00h 59m | 6. Data Sharing |
What data repository would you publish your data in? What are the benefits of sharing you data? Do you need to include all your data in a repository? |
| Duration: 01h 11m | 7. Ending the Research Project | How do you write a lesson using R Markdown and sandpaper? |
| Duration: 01h 25m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
The lessons herein are based on the work done by Kristin Briney in “The Research Data Management Workbook1
What is Research Data Management (RDM)
RDM involves practices and choices that simplify the process of finding, comprehending, and utilizing research data for you, your collaborators, and your future self. These practices encompass the entire data lifecycle, from collection and analysis to sharing and reuse. Research is hard enough as it is without having to fight with your files, so the goal of data management is for you to maximize your time doing research instead of spending extra time with file handling.
Emphasize that proper Research Data Management is a way to make dealing with data a better experience. It allows for understanding your data even after comming back to it after a long period of time.
Why do RDM
Most researchers have spent time, at some point in their careers, digging through their computer to find a specific file that can’t be located. It’s incredibly frustrating and a waste of time and resources, especially if you end up recollecting missing data. The good news is that it is possible to avoid this situation entirely by strategically managing your data better.
Done well, RDM means:
- Understanding of data and its collection methods, even after a year
- Quick and easy retrieval of desired files
- Prevention of data loss, even if your hard drive crashes
- Clarity around the rights and responsibilities you have over your data
- Knowledge of how and where to share your data to comply with your funder’s data sharing policy
- Ability to pick up and easily reuse data from a past project
Key Points
- RDM is really about helping researchers understand their data
- RDM helps researchers keep their data safe and backed up
- RDM helps researchers use their data even after leaving it for a long period
- RDM allows you to more easily share your research data
Briney, K. (2023). The Research Data Management Workbook. Caltech Library. https://doi.org/10.7907/z6czh-7zx60↩︎