Exploring and visualizing data
Introducing different types of datasets
The WorldCereal Reference Data Module (RDM) is the outcome of collaborative approach to build a reference data store on reference data which can be used for land cover and crop type model training and validation. The products generated support worldwide crop monitoring. In the RDM we have two types of data storage:
- Consortium data store
- Community data store
Datasets in both data stores can be set to be publicly available or private. Datasets that are inside the consortium data storage have been collected, harmonized, and maintained by expert moderators (project partners) and have been made available to the public according to their governing data licenses. Private datasets in consortium datastore will be available only for products generation.
The datasets that are in the Community data storage are harmonized and uploaded by Community users. The uploaded datasets can be made public with appropriate licenses and will be reviewed by moderators before being published. Private user datasets will be available only for the owner of such data sets and will not be shared for use by other users or consortium for product generation until the owner decides to make the data public. The owner decides who will be allowed to use data and under what restrictions.
User can choose from below license types.
| License types* | Remarks |
|---|---|
| CC0 | No Rights Reserved |
| CC BY | Attribution |
| CC BY-SA | Attribution-ShareAlike |
| CC BY-NC | Attribution-NonCommercial |
| CC BY-NC-SA | Attribution-NonCommercial-ShareAlike |
| Private | Only accessible for the owner |
| Other | To be defined by the owner |
Explore data through our User Interface
The interactive RDM user interface for data exploration can be accessed here.
Click the following image to watch our video tutorial on data exploration:
Retrieve data through API
The RDM provides REST APIs to access data.
The WorldCereal processing module makes use of these REST APIs to request reference data prior to training crop classification algorithms.
RDM demo notebook
In this Jupyter notebook we have implemented some user-friendly functionalities in Python allowing anyone to find, query, inspect and download entire datasets, metadata and individual observations. The notebook teaches you the practical skills needed to interact with reference data, representing the first step towards building your own crop type models.
The notebook includes instructions on how to run the notebook without the need for installing anything to your local system.
Other resources
In case you would like to learn more about the underlying API functionalities, here are some additional resources for you to consider:
- Jupyter notebook on how to access public datasets
More detailed insights into how API requests are constructed to access public datasets.
- Jupyter notebook on how to access private datasets
To access user uploaded private datasets through APIs, credentials are required. These credentials are the same as those that were used during upload of the datasets. The following Python notebook demonstrates how to get user private datasets:
- Swagger API Documentation
Full documentation on the different API requests can be found on our dedicated Swagger page: