Search Data.gov
Found 2273 dataset(s) matching "learning".
-
This dataset contains a comparison of packet loss counts vs handovers using four different methods: baseline, heuristic, distance, and machine learning, as well as the data used to train a machine...
-
Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing...
-
The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep...
-
This webinar explained how states are using remote/distance training and learning for CCWIS rollout and implementation. This webinar session provided an overview of key strategies for effective...
-
PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION GUICHONG LI, NATHALIE JAPKOWICZ, IAN HOFFMAN, R. KURT UNGAR ABSTRACT....
-
Mathematics Teaching and Learning Strategies in PISA uses data from the PISA 2003 assessment to examine the relationships between teaching strategies, student learning strategies and mathematics...
-
This dataset contains surface-ocean partial pressure of carbon dioxide (pCO2) that the ensemble mean of six two-step clustering-regression machine learning methods. The ensemble is a combination...
-
This child item describes a public-supply delivery machine learning model that was developed to estimate public-supply deliveries. Publicly supplied water may be delivered to domestic users or to...
-
This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 Students Entering Kindergarten Ready To Learn - This indicator...
-
These data represent an annotated training data for machine learned life history classification of the daily activity of dabbling ducks (f. Anatidae sf. Anatinae) using hourly GPS data. Each row...
-
<p> Stream networks with reservoirs provide a particularly hard modeling challenge because reservoirs can decouple physical processes (e.g., water temperature dynamics in streams) from...
-
The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data...
-
Service-Learning and Community Service in K-12 Public Schools, 1999 (FRSS 71), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at...
-
This dataset consists of tabular data of observed streamflow, URL links to timelapse images, and deep learning model predictions for 11 sites in western Massachusetts. The dataset also includes a...
-
The data are a set of fluorescent images that were generated to support the development of a machine learning model. The approach combines fluorescence imaging, deep learning, a mobile...
-
The Teaching and Learning International Survey, 2013 (TALIS:13), is a study that is part of the Teaching and Learning International Survey (TALIS) program. TALIS:13...
-
Global demand for lithium, the primary component of lithium-ion batteries, greatly exceeds known supplies and this imbalance is expected to increase as the world transitions away from fossil fuel...
-
A multiple machine-learning model (Asquith and Killian, 2024) implementing Cubist and Random Forest regressions was used to predict monthly mean groundwater levels through time for the available...
-
ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models
This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using...
-
This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream temperature models to accurately...
