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The goal of this Pursuit is to bring together a diverse team of collaborators to examine how soil health can act as a social-ecological feedback to encourage soil enhancing practices through a transdisciplinary integration of biophysical, climatic and social science data at multiple scales.
Our team is made up of scientists and policymakers from University institutions in Oregon, Iowa, Nebraska, Iowa, Washington – as well as staff from the USDA and the NRCS.
Our workshops are run out of the Socio-Environmental Synthesis Center (SESYNC) – a NSF funded effort thru the University of Maryland.
We use R, python, and Solr to organize, structure, and model data, and then use ESRI’s StoryMaps technology to describe and visualize outcomes.
The SOIL SES Search Engine is an Apache Solr server that runs on Ubuntu using Jetty. We use Solr to meta tag and dynamically present web-accessible data for use in model construction.
SOIL SES uses the Dublin Core metadata standard, which consists of fifteen core elements to describe a dataset. Dublin Core is one of the most basic metadata standards, and is used extensively by libraries and other multi-disciplinary groups that have a wide diversity of data types.
Our modeling efforts use Structural Equation Modeling (SEM) approaches to understand latent constructs in relationship to soil health and climate resilience.
With Esri Story Maps, you can combine your maps with narrative text, images, and multimedia content to create compelling, user-friendly web apps.