Earth Science Information Partners (ESIP)

Making Earth science data matter.

Technologies
python, javascript, kubernetes, dask, xarray
Topics
data visualization, earth data, semantics, discovery, earth science
Making Earth science data matter.

Earth Science Information Partners (ESIP) is a community of Earth science data and information technology practitioners who steward Earth science data, ensuring discovery, access and use of the data to decision makers, researchers and the public. ESIP’s strength comes from the depth of its partner organizations, which now number 110. Among these are all NOAA, NASA and USGS Earth observing data facilities, as well as government research laboratories, research universities, modelers, education resource providers, technology developers, nonprofits and commercial enterprises.

During the last twenty years ESIP has developed significant collaboration methods and infrastructure that provide a scalable, neutral platform to support Earth science research, data and technical communities. Our partner organizations and community participants lead the advancement of Earth science information best practices in an open and transparent fashion.

The Earth Science Information Partners was founded in 1998 by NASA in response to a National Research Council (NRC) review of the Earth Observation System Data and Information System (EOSDIS). The NRC called on NASA to develop a new, distributed structure that would be operated and managed by the Earth science community that would include those responsible for all elements of Earth observation, including observation and research, application and education. In 2003, ESIP incorporated as a 501(c)3.

2018 Program

Successful Projects

Contributor
Aarif Shaikh
Mentor
Ryan Berkheimer
Organization
Earth Science Information Partners (ESIP)
Create Environmental Science Libraries in Python using the Workflow Paradigm for HPC
The objective of the proposal is twofold:- Build a Python library based on the workflow paradigm which would facilitate stream watershed delineation...
Contributor
Evandro C Taquary
Mentor
Lewis McGibbney
Organization
Earth Science Information Partners (ESIP)
Recurrent Neural Networks applied in the time-series classification over a high resolution data
The increasing number of sensors orbiting the earth is systematically producing larger volumes of data, with better spatiotemporal resolutions. To...