Increased productivity is the main contributor to growth in U.S. agriculture. This data set provides estimates of productivity growth in the U.S. farm sector for the 1948-2011 period, and estimates of the growth and relative levels of productivity across the States for the period 1960-2004.

The project provides an updated Energy Return on Investment (EROI) for Enhanced Geothermal Systems (EGS). Results incorporate Argonne National Laboratory's Life Cycle Assessment and base case assumptions consistent with other projects in the Analysis subprogram. EROI is a ratio of the energy delivered to the consumer to the energy consumed to build, operate, and decommission the facility. EROI is important in assessing the viability of energy alternatives. Currently EROI analyses of geothermal energy are either out-of-date, of uncertain methodology, or presented online with little supporting documentation. This data set is a collection of files documenting data used to calculate the Energy Return On Investment (EROI) of Engineered Geothermal Systems (EGS) and erratum to publications prior to the final report. Final report is available below, or from the OSTI web site (http://www.osti.gov/geothermal/). Data in this collections includes the well designs used, input parameters for GETEM, a discussion of the energy needed to haul materials to the drill site, the baseline mud program, and a summary of the energy needed to drill each of the well designs. EROI is the ratio of the energy delivered to the customer to the energy consumed to construct, operate, and decommission the facility. Whereas efficiency is the ratio of the energy delivered to the customer to the energy extracted from the reservoir.

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots. A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications. Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results. Units can be found in the readme data resource.

This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk), input rasters of feature sets, and positive/negative training sites. See readme .txt files and final report for additional metadata. A submission linking the full codebase for generating machine learning output models is available under "related resources" on this page.