
This course has been designed to help you understand NetCDF files, get data out of them and create your own NetCDF files. The course is suitable for beginners who know nothing about NetCDF files and those who are new to Python. However, more experienced users should also be able to learn a lot from this course. You may be here because you are interested in publishing FAIR data. Did you know that a NetCDF file is not necessarily FAIR unless it adheres to certain conventions? You will learn about the Climate and Forecast (CF) conventions (https://cfconventions.org/) and the Attribute Convention for Data Discovery (ACDD - https://wiki.esipfed.org/Attribute_Convention_for_Data_Discovery_1-3), and how to make sure your files are compliant with them.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
