Modern scientists require a flexible and scalable compute environment to develop and run their workflows. Cloud computing, through Amazon Web Services, and containerized environments such as Docker provide one avenue for scalability that we use heavily at Earth Lab. It enables Earth Lab researchers to use familiar development environments such as Jupyter notebooks or RStudio on a variety of machines to meet the ever-changing computing requirements presented by Earth data science.


Cloud computing can be intimidating, but by deploying familiar development environments such as RStudio and Jupyter Notebooks in the cloud, we are lowering the barrier to entry for our scientists. By working in familiar environments, we can get our work done more quickly, with less time and effort invested in learning totally new computational workflows. As a side effect of this decision, we ensure that each user has access to their own resources, which means that they have total control over what is installed in their computational environment. 

To make it easy to spin up these environments, Earth Lab curates a set of Docker images that have commonly used packages pre-installed. These Docker images are hosted and built on Docker Hub, and are free to use: https://hub.docker.com/u/earthlab/


So What?

By using Docker containers, we can run identical development environments and workflows locally and in the cloud with minimal configuration pain.

Project Team

Project Lead

Natasha Stavros

Dr. E. Natasha Stavros is the Director of Earth Lab Analytics Hub. She specializes in complex systems science, data science, image processing, and information technologies. She developed these skills as a fire ecologist, but has applied them in other complex systems including NASA Flight Projects, biodiversity science, and urban ecology.