Navegación por la Biblioteca de Datos del IRI: Un Tutorial

La Biblioteca de Datos es una herramienta extremedamente poderosa que permite acceder, manipular, y visualizar una grande variedad de datos de las ciencias de la tierra por todo tipo de formato (por ejemplo, archivos de datos, tablas), de representación gráfica (por ejemplo, contornos, colores, vectores), y de formato gráfico (por ejemplo, PostScript, GIF). Este tutorial es una introducción a la estructura de la Biblioteca de Datos y a las diferentes manejas de navégarla.

Find and Understand
Locating Data
There are three ways to locate data within the Data Library: 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.
Dataset Structure
You should now be more comfortable with finding datasets in the Data Library. 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 Malawi daily temperature dataset is a commonly used dataset and is the example for this section.
Select
Selecting Stations and Regions
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.
Selecting Data Variables
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.
Selecting a Time Period
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
Manipulating Data
This section covers arithmetic operations, setting limits, averages, as well as some other analyses.
Visualizing Data
The data selection and manipulation capabilities of the Data Library are extremely valuable on their own, but its data visualization techniques make the Data Library the truly complete tool that it is.
Downloading Data
In addition to the tools that select, manipulate, and visualize data, the Data Library makes those results and all data available in a variety of formats.