We have covered a lot of content in this course, research data is a broad topic and a lot of it is changing very quickly. The most important thing to take away from this course is that your data has value to yourself and others, beyond just your institution or collaborators, and there are ways you can care for your data that will ensure its longevity and impact.
Data management isn’t a one-size-fits-all topic, so what ends up being the right way to manage it will depend on your data, your resources, and other factors. You can always adjust your data management practices if something isn’t working for you, just remember to document those changes for yourself or anyone you’re working with! And remember, good data management is all about building good habits. It can be hard to implement these practices consistently at first, but it will become second nature with time!
Some of the practices we’ve suggested here may also differ by your discipline. Some disciplines have specific metadata standards, commonly used repositories, or preferred software or programming languages. If you have questions about disciplinary best practices, subject librarian, Research Data Services, or check back for discipline specific micro courses that are in the works!
The best practices we’ve covered in this module can often be wrapped up together in a document called a ‘data management plan’ or a DMP. You can use a data management plan at the outset of a project to help you stick to good data management habits throughout the lifecycle.
Data management plans are also often a required part of the grant writing process. These often formally detail data file formats and size, where the data will be stored at the various stages of a project and who has access to it, how the data will be described, and where it will be archived and shared in the long-term. To get a look at an example DMP, complete the activity below. To learn more about them and how to write them, complete the next course Responsible Data Planning, Use, and Sharing.
Review the "Lake Sampling Data Management Plan" and identify one of the best practices we've covered in this course: