From planning to post publication, data are at the center of any research, requiring proficient organization skills in order to save time and nerves. This course will introduce you to useful tools and best practices that will make your work with research data much easier, more efficient, and enjoyable.

Data management plans: We will demonstrate how starting with a solid data management plan will help you to develop an idea of how your data are to be handled during and after your research project.

Useful R packages and GitHub: When it comes to streamlined data handling and automating repeated operations, the statistics and computing software R offers some great packages such as dplyr, tidyr and purrr which we will demonstrate the utility of. We will also present a way of getting rid of files such as final_data_version_9.csv with version control using git and GitHub.

Data in modeling: In the context of modeling, we will share how it is important to organize your input, output and processed data and how sometimes it is crucial to be aware of the storage availability for your data set.

Data citation and Reuse: As it gets more and more important to publish your data in a citable way, we will introduce the FAIR concept which allows data to be Findable, Accessible, Interoperable and Reusable. We will also address the question of why metadata is important and what you need to do to get a citable DOI (Digital Object Identifier) for your data set or a version of it.

At the end of the course, we will make some time to talk about specific questions concerning your own data sets. The course is not limited to any field of geosciences and the presented efficiency tools can be widely applied through all kinds of data sets. There are no specific software requirements for the course.