Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
14
datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “Yield prediction modeling”
Figure 6 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 6. Empirical cumulative distribution function (ECDF) of the Predicted error |PE| (cft) in testing period for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
Figure 4 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 4. Box-plots of the Predicted error | PE| (cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
Figure 7 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 7. Taylor diagram showing the correlation coefficient between the predicted and observed yields (Blue pine and Silver fir) (cft) and standard deviation for the RF and KRR models.
Figure 5 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 5. Polar plots show the Predicted error |PE|(cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
Bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting as predicted by the Madingley General Ecosystem Model
<p>The datasets contain data generated using the Madingley General Ecosystem Model for experiments decribed in the paper: "T. Barychka, G.M.Mace and D.W.Purves (2021) The Madingley General Ecosystem Model predicts bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting. Oikos."</p> <p>The Madingley General Ecosystem Model was used to generate predictions of bushmeat yields, extinction rates and broader ecosystem impacts for a range of harvesting intensities of duiker-sized endothermic herbivores. Duiker antelope (such as <i>Cephalophus callipygus</i> and <i>Cephalophus dorsalis</i>) are the most heavily hunted species in sub-Saharan Africa, contributing 34%-95% of all bushmeat in the Congo Basin. In the Madingley, the harvested group was described as "Heterotroph – Herbivore – Terrestrial – Mobile – Iteroparous– Endotherm", with adult bodymasses of 13-21 kg and juvenile bodymasses of >100 g. Harvesting period was set at 30 years (<em>n </em>=30).</p> <p>In the first experiment, we used the Madingley model to predict bushmeat yields ("Harvested Biomasses") and extinction rates ("Density") from harvesting duiker-sized herbivores using proportional harvesting strategy, with harvest rates ranging from 0 to 0.90. These were compared to the estimates of bushmeat yields and survival probabilities for two duiker antelope species (<i>Cephalophus callipygus</i> and <i>Cephalophus dorsalis) </i>from conventional single-species Beverton-Holt model. </p> <p>In the second experiment, we used the Madingley model to generate data on the state ("State") of the harvested ecosystem. The datasets contain estimates of biomasses, abundances, adult and juvenile bodymasses, etc. of the harvested duiker-sized herbivores as well as unharvested herbivores, omnivores and carnivores present in the simulated ecosystem. In the paper we focused on abundances; however, other estimates e.g., adult bodymasses can be used to conduct further studies on the effects of harvesting in tropical ecosystems.</p> <p>Main results of the experiments are that: 1) the Madingley model gave estimates for optimal harvesting rate, and extinction rate, that were qualitatively and quantitatively similar to the estimates from conventional single-species Beverton-Holt model; 2) the Madingley model predicted a background local extinction probability for the target species of at least 10%; 3) at medium and high levels of harvesting of duiker-sized herbivores, the Madingley model predicted statistically significant, but moderate, reductions in the densities of the targeted functional group; increases in small-bodied herbivores; decreases in large-bodied carnivores; and minimal ecosystem-level impacts overall.</p>
Figure 3 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 3. Location map of the study area.
Figure 2 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 2. Schematic view of Kernel Ridge Regression (KRR) model.
Figure 1 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 1. High-resolution flow chart of the Random Forest (RF) model.
Ensemble BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone.
<p>Data and scripts exploring ensembling strategies using the models developed in <a href="https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkad006/6982634">Kick et al., 2023</a> (see also <a href="https://zenodo.org/record/7401113">1</a>, <a href="https://zenodo.org/record/6916775">2</a>). Download all files to a single directory then run setup.sh or manually unzip using tar.</p> <p> </p> <table> <tbody> <tr> <td><strong>Filename</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>setup.sh</td> <td>Simple script that unzips zipped directories</td> </tr> <tr> <td>ext_data</td> <td>Reduced data from Kick et al. 2023</td> </tr> <tr> <td>ext_data_notebooks</td> <td>Contains python notebooks containing analysis and R markdown file containing visualization of results. Python and R data objects are written to allow results to be read in instead of re-generated.</td> </tr> <tr> <td>output</td> <td>Folder containing a placeholder file.</td> </tr> </tbody> </table> <p> </p> <p>This research used resources provided by the United States Department of Agriculture’s Agricultural Research Service (project number 5070-21000-041-000-D). The SCINet project of the USDA Agricultural Research Service (project number 0500-00093-001-00-D) was instrumental in the training of the models used in this work. In addition, we would like to acknowledge those presently and historically involved in generating data for the Genomes to Fields Initiative.</p> <p> </p> <p> </p> <p> </p>
Bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting as predicted by the Madingley General Ecosystem Model
Open the record for dataset details and reuse information.
A Model Predicting The Relationships Between Seed Size, Yield, and Actual Yield in Dry Field Peas
<p>This is a model that predicts yield and actual yield in dry pea based on seed size. Seeding rate, seed cost, pod length, and expected grain yield can be varied in the model. The ideal seed size is displayed at the vertex of the curve in three different graphs. </p>
Dataset and codes for "BaHSYM: parsimonious Bayesian Hierarchical Model to predict river Sediment Yield"
<p>This folder contains:</p> <ul> <li>R project file</li> <li>R code for Best Fit model</li> <li>R code for temporal cross-validation</li> <li>R code for spatial cross-validation</li> <li>R code for cluster analysis</li> <li>dataset containing all input variables for the river gauges (and catchments) used for the development and testing of the BaHSYM model in Austria</li> </ul> <p>It also contains the same codes and datasets adapted to reproduce the model by de Vente et al. (2011), i.e. with the same structure but with the variables used in such model.</p>
Data from: Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions
Crop growth models and remote sensing are useful tools for predicting crop growth and yield, but each tool has inherent drawbacks when predicting crop growth and yield at a regional scale. To improve the accuracy and precision of regional corn yield predictions, a simple approach for assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) products into a crop growth model was developed, and regional yield prediction performance was evaluated in a major corn-producing state, Illinois, USA. Corn growth and yield were simulated for each grid using the Crop Environment Resource Synthesis (CERES)-Maize model with minimum inputs comprising planting date, fertilizer amount, genetic coefficients, soil, and weather data. Planting date was estimated using a phenology model with a leaf area duration (LAD)-logistic function that describes the seasonal evolution of MODIS-derived leaf area index (LAI). Genetic coefficients of the corn cultivar were determined to be the genetic coefficients of the maturity group [included in Decision Support System for Agrotechnology Transfer (DSSAT) 4.6], which shows the minimum difference between the maximum LAI derived from the LAD-logistic function and that simulated by the CERES-Maize model. In addition, the daily water stress factors were estimated from the ratio between daily leaf area/weight growth rates estimated from the LAD-logistic function and that simulated by the CERES-Maize model under the rain-fed and auto-irrigation conditions. The additional assimilation of MODIS data-derived water stress factors and LAI under the auto-irrigation condition showed the highest prediction accuracy and precision for the yearly corn yield prediction (R2 is 0.78 and the root mean square error is 0.75 t ha-1). The present strategy for assimilating MODIS data into a crop growth model using minimum inputs was successful for predicting regional yields, and it should be examined for spatial portability to diverse agro-climatic and agro-technology regions.
Data from: Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.