A Python Package to Search and Access NASA Earth Science Data.

The NASA Earth Observing System Data and Information System (EOSDIS) contains almost 9,000 datasets spanning a wide range of science domains and data processing levels. Across this data archive, many data search and access options are available suiting a variety of needs and use cases. We can use NASA Earthdata Search, we can use tools supported by individual NASA Distributed Active Archive Centers (DAACs), and we could even use data.gov! All of these portals are great, but they are not designed for programmatic access and reproducible workflows. Reproducible workflows are extremely important in the age of cloud data access, cloud computing, and open science. In this context, we are developing earthaccess, a python library that aims to simplify data discovery and access for those using the PyData ecosystem (xarray, dask, numpy). Using this library eliminates the need to know the intricacies of NASA's Application Programming Interfaces (APIs) and cloud data storage systems.

earthaccess is a result of a cross-DAAC collaboration through the NASA Openscapes program, which aims to build a cross-DAAC community to help scientists working with NASA Earth science data migrate their workflows to the cloud. earthaccess aims to simplify data discovery and access to address some of the pain points of programmatic access. It provides a higher abstraction for NASA's Common Metadata Repository (CMR) search API so that searching for the data can be done using a simpler notation instead of low level HTTP queries. With earthaccess, scientists do not need to concern themselves with where data is being distributed from (i.e. cloud-hosted or on-premises at a data center) because the library handles both workflows transparently and consistently. earthaccess provides authenticated sessions that can be used with xarray and other PyData libraries to access NASA EOSDIS datasets directly. earthaccess allows scientists to get to their science in an easier, simpler, and faster way, reducing barriers to cloud-based data analysis.