Locating Data

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.

Locating Data



There are five ways to locate data within the Streaming Streaming Streaming Data Library (SDL) (SDL): 1) a listing of some of the datasets by category; 2) a complete list of all datasets according to their source; 3) a keyword search powered by Google; 4) exploring our maps directly through a Data Explorer; 5) exploring our maps by looking at examples made by others.

Let's look at each one of these individually.

Data By Category

Select the "Data by Category" link. CHECK
This data discovery option provides a sorted listing of a few of the datasets in the Streaming Data Library (SDL) based on the type of data they contain. A summary of each dataset, including a brief description, spatial and temporal resolutions, and spatial and temporal limits, is also offered. Feel free to browse the data categories. Note that the dataset names are also links to the datasets themselves.

Data By Source

Select the "Data by Source" link on the first page. CHECK
This is a complete list of the datasets in the Streaming Data Library (SDL) organized by their source. This method of finding datasets is typically utilized by users who are more familiar with climatic data and the providers that make it available. Below the "Datasets and variables" heading you see the list of sources and either a brief description of the source or a list of the subsections of data from that source. For example, let's take a closer look at one of the European contributors of data to the Streaming Data Library (SDL), Copernicus.

Scroll down to the "Copernicus" link.
You can see that there are three subsections of data from Copernicus. These include the Copernicus Land, Copernicus Marine, and Copernicus Atmosphere.

Select the "Copernicus" link.
You can now see these same three subsections as links to more data. We can now explore the underlying datasets.

Select the "Land" link.
Select the "Copernicus Type: Land Monitoring Products/Time-series BioPar Normalised Difference Vegetation Index (Global)" link.

Select the "Normalized Difference Vegetation Index 1KM" link. CHECK
We are now at the NDVI 1KM dataset main page. Note the source diagram, it has the same series of links that we saw before and represents the same steps we took from the Data by Source page to get here.

Data By Searching

Select the "Streaming Data Library (SDL)" link in the navigation banner. CHECK There is a search box at the top of the page.

This is a popular option for those who know exactly what dataset they want, but don't know where it is in the Streaming Data Library (SDL), as well as for those who are not sure what they are looking for at all. The hints listed on this page are valuable and worthy of a bit more discussion.

1. If you are looking for data from weather station reports, then include the word "station" as one of your keywords.

The Streaming Data Library (SDL) includes data from weather stations as well as gridded data. While searching with the word "station" often locates station data, including the word "grid" in a search will not effectively locate gridded datasets as it is commonly used on the pages of both types of datasets.

Entering the specific name of your desired station as a keyword will not help your search either. For example, suppose you want to find precipitation data for a station in Paris.

Enter the keywords "precipitation Paris".
There are no matches for that search. However, this does not mean that the Data Library does not contain any datasets with precipitation data for Paris.

Enter the keywords "precipitation station".
You have now found a handful of datasets that contain station-reported precipitation data. Data from specific stations (e.g., Paris) can be located after a dataset is selected and we will discuss how to do that in Part II.

2. If you are looking for a particular variable, then include the variable name as one of your keywords.
As in the example above, we included "precipitation" as a keyword in our search for precipitation data.

You may also find it valuable to use your desired temporal resolution as a keyword as well. For example, suppose you want to find daily data of maximum temperature.

Enter the keywords "maximum temperature monthly".
This search yields a list primarily consisting of datasets that contain monthly data of maximum temperature. Note the difference compared to the search results if you are looking for daily temperature data.

Enter the keywords "maximum temperature daily".
This technique will have different results for different variables, but it may be worth a try if you know your desired temporal resolution. Because this is not a fool-proof method, you should always confirm the temporal resolution of any dataset you find in this manner by noting its time grid. A more descriptive discussion of grids is in a subsequent section.


Making sense of the search results
Selecting a dataset from the search results is generally straightforward, but it could be made more clear with a brief discussion. All of the instruction in Part II will begin from a dataset main page. Therefore, the following discussion describes how to get to a dataset main page from the different kinds of pages that may appear in your search results. Let's use the search results for "leaf area index" as an example. One of options in the search results is Copernicus Land LAI LAI.


Select this link in the search results. CHECK

This is a dataset variable page. Specifically, the main dataset page of Leaf Area Index 1KM dataset. While you may want to use a specific variable of a dataset, it is often best to start your work from a dataset main page where you have the opportunity to select any of the variables available in that datatset.


There are other types of pages that may be included in search results, such as dataset help, outline, and documentation pages. You can always find your way back to the associated dataset main page by finding the data source in the Sources list.

Data By Exploring

Or explore our maps directly through a Web-based Map-viewer. Save unnecessary time searching for relevant maps by exploring our readily available map offering. Hey, you can even add new maps based on your own (Geo-spatial) content! https://ramani.ujuizi.com/cloud/docs/#upload We offer +300 of maps, incl. 2D to 3D time-series from Global monitoring missions such as Copernicus, the most ambitious Earth observation programme to date. There must be a map that is relevant to the use case your App is addressing!"

exploring our maps directly through a Data By Exploring.

Note! Some of our maps, such as Sentinel-2 imagery, can only be explored through Web Map-viewer, and only the getPoint and getMap methods are supported for these experimental maps.

Data By Example

Discover data by looking at examples of maps created by your peers.

exploring our example directly through a Data By Example.