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170
datasets available to search
ShareScore release 0.9.0
Dataset results
170 results for “predictive mapping”
Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape
<p>Predicting forest recovery at landscape scales will aid forest restoration efforts. The first step in successful forest recovery is tree recruitment. Forecasts of tree recruit abundance, derived from the landscape-scale distribution of seed sources (i.e. adult trees), could assist efforts to identify sites with high potential for natural regeneration. However, previous work has revealed wide variation in the effect of seed sources on seedling abundance, from positive to no effect. We quantified the relationship between adult tree seed sources and tree recruits, and predicted where natural recruitment would occur in a fragmented tropical agricultural landscape. We integrated species-specific tree crown maps generated from hyperspectral imagery and property boundaries data on individual property ownership with field data on the spatial distribution of tree recruits from five species. We then developed hierarchical Bayesian models to predict landscape-scale recruit abundance. Our models revealed that species-specific maps of tree crowns improved recruit abundance predictions. Conspecific crown area had a much stronger impact on recruitment abundance (8.00% increase in recruit abundance when conspecific tree density increases from zero to one tree; 95% CI: 0.80 to 11.57%) than heterospecific crown area (0.03% increase with the addition of a single heterospecific tree, 95% CI: -0.60 to 0.68%).Individual property ownership was also an important predictor of recruit abundance: the best performing model had varying effects of conspecific and heterospecific crown area on recruit abundance, depending on individual property ownership. We demonstrate how novel remote sensing approaches and cadastral data can be used to generate high-resolution and landscape-level maps of tree recruit abundance. Spatial models parameterized with field, cadastral, and remote sensing data are poised to assist decision support for forest landscape restoration.</p>
Universal Rapid Weather Prediction Model (Sonagi Model) Korea Peninsula 5 Days Forecast Result (Pressure Isobar Map)
<p>Universal Rapid Weather Prediction Model (Sonagi Model) Korea Peninsula 5 Days Forecast Result (Pressure Isobar Map)</p> <p>Each file has its altitude in front of the name of the file, and by each isobar in map directs the air current heading higher or lower altitude. And rest of the name follows the target ed UTC time. Generally, iso-temperature lines are used to indicate where air flows, but it was simulatable in Sonagi model to where air is heading by pressure, so that pressure isobar is used to indicate where the air flows.</p> <p>Input data for the prediction in Universal Rapid Weather Prediction Model (Sonagi Model) is originated from GK2A satelite of Korea Meteorological Administration. (https://apihub.kma.go.kr/) For sharing the original prediction data, contact me at somehowme@gmail.com or flyingtext@nate.com (Prediction netCDF4 files are almost 16GB in sum total.)</p>
Universal Rapid Weather Prediction Model (Sonagi Model) Pacific Ocean 5 Days Forecast Result (Pressure Isobar Map)
<p>Universal Rapid Weather Prediction Model (Sonagi Model) Pacific Ocean 5 Days Forecast Result (Pressure Isobar Map)</p> <p>Each file has its altitude in front of the name of the file, and by each isobar in map directs the air current heading higher or lower altitude. And rest of the name follows the target ed UTC time. Generally, iso-temperature lines are used to indicate where air flows, but it was simulatable in Sonagi model to where air is heading by pressure, so that pressure isobar is used to indicate where the air flows.</p> <p>Input data for the prediction in Universal Rapid Weather Prediction Model (Sonagi Model) is originated from GK2A satelite of Korea Meteorological Administration. (https://apihub.kma.go.kr/) For sharing the original prediction data, contact me at somehowme@gmail.com or flyingtext@nate.com (Prediction netCDF4 files are almost 16GB in sum total.)</p>
Morphological heart age from CTA: Predictions, Performance and Saliency Maps
<p>This data distribution contains regression results for age prediction from computed tomography angiography images from the SCAPIS dataset, as well as proof-of-concept experiments concerning the prediction of known volumetric features estimated through segmentation of the images.</p> <p>Each sub-folder represents one experiment.</p> <p>For each experiment (sex-stratified), there is a csv file '0.csv'<br>with the following structure</p> <p>Row 1: subject id1, subject id2, subject id3, ...<br>Row 2: reference value1, reference value2, reference value3, ...<br>Row 3: predicted value1, predicted value2, predicted value3, ...</p> <p>There is also a file 'results_summary.txt' which provides a text output of the quality measures corresponding to each experiment:<br>Mean Absolute Error (MAE)<br>R^2 (R2)<br>Pearson correlation (r_p)<br>Spearman correlation (r_s)<br>Intraclass Correlation Coefficient (ICC)</p> <p>The following sub-folders/experiments contain saliency maps:<br>- main (the main experiment with PCA from the whole heart)<br>- main_linear (the main experiment without PCA from the whole heart)<br>- poc_lvv (proof-of-concept: left ventricle volume)<br>- poc_rvv (proof-of-concept: right ventricle volume)<br>- poc_lav (proof-of-concept: left atrium volume)<br>- poc_rav (proof-of-concept: right atrium volume)<br>- poc_myov (proof-of-concept: myocardium volume)<br>- poc_av (proof-of-concept: aorta volume)</p> <p>The feature subsets use the following encoding (for the feature subset experiments, the path contains the numbers representing the included features):<br>1: Median Density<br>2: Median Volume<br>3: Stddev Density<br>4: Stddev Volume</p> <p>Ethics:<br>The subjects/images are all anonymized, with a chronological age rounded to whole months.</p> <p>Ethics approval was obtained from the Swedish Ethical Review Authority (Dnr 2022-07308-01) to conduct this research study related to human subjects, with associated sex and age information. All subjects provided informed written consent for their collected data to be used for research and for that research to be published. The study adheres to the Declaration of Helsinki. SCAPIS has been approved as a multicentre trial by the ethics committee at Umea University and adheres to the Declaration of Helsinki.</p>
DMGAT: Predicting ncRNA-Drug resistance associations based on diffusion map and heterogeneous graph attention network
<p>Dataset for the paper: DMGAT: Predicting ncRNA-Drug resistance associations based on diffusion map and heterogeneous graph attention network</p>
Samples and 4-class predictions of agroforest-forest frontiers mapping in Peru
<p>This is a database for the study of agroforest-forest frontiers mapping conducted by Wanting Yang, etc.</p> <p>The data contained in the repository includes two parts.</p> <p>1. the training samples(polygons and rectagle_roi) annotated with Google Earth as reference. We used Google Earth Pro to make the date of Google Earth imagery match with PlanetScope imagery.</p> <p>2. the prediction from the 4-class model.</p> <p>Users can visualize the data with QGIS or other GIS software.</p>
Deep Learning-Based Intraoperative Dual-tracer Video Analysis of Sentinel Lymph Node Mapping for Metastasis Prediction in cN0 Papillary Thyroid Carcinoma
ClinicalTrials.gov study NCT07391514. IPD Sharing: UNDECIDED. Countries: 1. Publications: 46.
Default Mode Network fMRI Maps as a Predictive Index of Hepatic Encephalopathy Outcome
ClinicalTrials.gov study NCT02083367. IPD Sharing: Not stated. Countries: 1. Publications: 27.
Predicting Response to CRT Using Body Surface ECG Mapping
ClinicalTrials.gov study NCT01831518. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Exploiting Poisson additivity to predict fire frequency from maps of fire weather and land cover in boreal forests of Québec, Canada
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Data from: Is evolution predictable? quantitative genetics under complex genotype-phenotype maps
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Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape
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Detailed Mapping: Standardizing Heat-Related Diagnoses for Predictive Modeling in Healthcare
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Accelerating wheat breeding for end-use quality through association mapping and multivariate genomic prediction
<p>In hard winter wheat breeding, the evaluation of end-use quality is expensive and time-consuming, being relegated to the final stages of the breeding program after selection for many traits including disease resistance, agronomic performance and grain yield. In this study, our objectives were to identify genetic variants underlying baking quality traits through genome-wide association mapping (GWAS) and develop improved genomic selection (GS) models for the quality traits in hard winter wheat. Advanced breeding lines (n=462) from 2015-2017 were genotyped using genotyping-by-sequencing (GBS) and evaluated for baking quality. Significant associations were detected for mixograph mixing time and bake mixing time; most of which were within or in tight linkage to glutenin and gliadin loci, and could be suitable for marker-assisted breeding. Candidate genes for newly associated loci are phosphate-dependent decarboxylase and lipid transfer protein genes, which are believed to affect nitrogen metabolism and dough development, respectively. The use of GS can both shorten the breeding cycle time and significantly increase the number of lines that could be selected for quality traits; thus we evaluated various GS models for end-use quality traits. As a baseline, univariate GS models had 0.25 to 0.55 prediction accuracy in cross-validation and from 0 to 0.41 in forward-prediction. By including secondary traits as additional predictor variables (univariate GS with covariates) or correlated response variables (multivariate GS), the prediction accuracies were increased relative to the univariate model using only genomic information. The improved genomic prediction models have great potential to further accelerate wheat breeding for end-use quality.</p>
Selected studies of the Artificial Intelligence Algorithms to Predict College Students academic performance: a Systematic Mapping Study
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M3Net predicted saliency maps
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U2Net predicted saliency maps
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The BASNet predicted saliency maps
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Figure 91 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 91: Predicted and recorded distribution of Phanaeus vindex.
Figure 90 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 90: Predicted and recorded distribution of Phanaeus igneus.
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.