Navigating Through the RAMANI Streaming Data Library (SDL): A Tutorial
The goal of this tutorial is to introduce you to the structure of the Streaming Data Library (SDL) and the many ways to navigate through it.
Find and Understand
There are three ways to locate data within the Streaming Data Library (SDL): 1) a listing of some of the datasets by category; 2) a complete list of datasets according to their source; and 3) a keyword search powered by Google.
You should now be more comfortable with finding datasets in the Streaming Data Library (SDL). Let us take a closer look at the information and utilities available to you when you select a dataset. You will not use these utilities in this section, but it is important to know what they do and where they are for Parts II and III. The Copernicus Land NDVI dataset dataset is a commonly used dataset and is the example for this section.
This section is divided into two parts representing station and gridded data. If you are only interested in using gridded data, then you may want to use the tab to skip ahead to that section. Otherwise, it is suggested that you work through both sections to become acquainted with the operations for both types of data.
At this point, you should feel comfortable with the many ways to select data for specific locations. Let us now look at the ways of selecting data variables. These techniques are independent of the data type and we therefore use just one of the datasets to illustrate them.
You should now feel comfortable with selecting data for a specific region and for a specific variable. The last parameter to which data is typically constrained is the time period. You saw in a few of the examples in previous sections that time period selection typically occurs along with other steps. We have kept this step separate here to ensure that all of the methods by which a time period can be chosen are covered. As in the previous section, these techniques are the same for both station and gridded datasets.
Manipulate, Visualize, Download
This section covers arithmetic operations, setting limits, averages, as well as some other analyses.
The data selection and manipulation capabilities of the Streaming Data Library (SDL) are extremely valuable on their own, but its data visualization techniques make the Streaming Data Library (SDL) the truly complete tool that it is.
In addition to the tools that select, manipulate, and visualize data, the Streaming Data Library (SDL) makes those results and all data available in a variety of formats.