DMINE: Reproducible Data Science for Spatiotemporal Data Mining

DMINE: Reproducible Data Science for Spatiotemporal Data Mining

We are a grouping of researchers and students that use data science techniques to explore spatiotemporal relationships (agriculture, climate, human health). As part of our collaboration, have developed an R and python-based framework, which we are using to create data mining and machine learning processes.

Data Dashboards

Our Agriculture dashboards are analytic web applications that use data mining and machine learning techniques to explore ag commodity systems

Data Portal

Our DMINE Data Portal uses Apache SOLR to organize agricultural data used for our modeling and dashboard analyses

API and Libraries

We expose our agricultural commodity models as JSON-accessible web service APIs, as well as via R packages

DMINE Topical Research Areas

Human Health

Agricultural Systems

Scientific Reproducibility

Climate

Institutions supporting this research

DMINE is developed as part of collaboration with scientists @ the University of Idaho and Oregon State University. DMINE is funded thru the National Oceanic and Atmospheric Association (NOAA), under NOAA grant # NA15OAR4310145
University of Idaho
University of Idaho
National Oceanic and Atmospheric Administration
National Oceanic and Atmospheric Administration
Oregon State University
Oregon State University
University of Washington
University of Washington
National Integrated Drought Information Systems
National Integrated Drought Information Systems
Climate Impacts Research Consortium
Climate Impacts Research Consortium