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1,773 results for “Predictive model”
Figure 80 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 80: Predicted and recorded distribution of Phanaeus eximius.
Figure 46 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 46: Predicted and recorded distribution of Phanaeus amithaon.
Figure 72 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 72: Predicted and recorded distribution of Phanaeus texensis.
Figure 68 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 68: Predicted and recorded distribution of Phanaeus melibaeus.
Figure 21 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 21: Predicted distribution of Phanaeus endymion species group.
Figure 24 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 24: Predicted and recorded distribution of Phanaeus arletteae.
Figure 7 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 7: Predicted distribution of Phanaeus beltianus species group.
Figure 6 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 6: Predicted and recorded distribution of Phanaeus melampus.
Figure 12 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 12: Predicted and recorded distribution of Phanaeus bispinus.
Data Set: In-session dropout prediction model
<p>In-session dropout prediction model</p> <p>This project describes an in-session prediction model that predicts student early dropout from online learning exercises.<br> Dropout prediction models for Massive Open Online Courses (MOOCs) have shown high accuracy rates in<br> the past and make personalized interventions possible. While MOOCs have traditionally high dropout rates,<br> school homework and assignments are supposed to be completed by all learners. In the pandemic, online<br> learning platforms were used to support school teaching. In this setting, dropout predictions have to be designed differently as a simple dropout from the (mandatory) class is not possible. The aim of our work is to<br> transfer traditional temporal dropout prediction models to in-session dropout prediction for school-supporting<br> learning platforms. For this purpose, we used data from more than 164,000 sessions by 52,000 users of the<br> online language learning platform orthografietrainer.net. We calculated time-progressive machine learning<br> models that predict dropout after each step (completed sentence) in the assignment using learning process<br> data. The multilayer perceptron is outperforming the baseline algorithms with up to 87% accuracy. By extending the binary prediction with dropout probabilities, we were able to design a personalized intervention<br> strategy that distinguishes between motivational and subject-specific interventions. <br> A random state is not set, thus, results might differ marginally.</p> <p>Whole project described in: <br> N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart<br> Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios<br> Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,<br> SciTePress, 2022, ISBN 978-989-758-562-3 </p> <p> </p> <p> </p>
Early dendritic cell molecular signature modelling predicts late vaccine-induced adaptive T-cell responses (Codelink)
GEO Series GSE66930. Mus musculus. 113 samples. Type: Expression profiling by array.
Early dendritic cell molecular signature modelling predicts late vaccine-induced adaptive T-cell responses (Illumina)
GEO Series GSE66797. Mus musculus. 117 samples. Type: Expression profiling by array.
Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer
GEO Series GSE271689. Homo sapiens. 586 samples. Type: Expression profiling by high throughput sequencing.
Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer [GeoMx DSP]
GEO Series GSE292098. Homo sapiens. 315 samples. Type: Other.
Aggregated gene co-expression networks for predicting transcription factor regulatory landscapes in a non-model plant species
GEO Series GSE230186. Vitis vinifera. 4 samples. Type: Other.
Cell type-specific predictive model
<p>The analyzed single-nucleus gene expression data of ASD and controls in the paper of "Prioritization of Genes and Gene Sets Associated with Autism Based on Cell Type-specific Predictive Models".</p>
Models for Predicting the Architecture of Different Shoot Types in Apple
<p>In apple, the first-order branch of a tree has a characteristic architecture constituting three shoot types: bourses (rosettes), bourse shoots, and vegetative shoots. Its overall architecture as well as that of each shoot thus determines the distribution of sources (leaves) and sinks (fruits) and could have an influence on the amount of sugar allocated to fruits. Knowledge of architecture, in particular the position and area of leaves helps to quantify source strength. In order to reconstruct this initial architecture, rules equipped with allometric relations could be used: these allow predicting model parameters that are difficult to measure from simple traits that can be determined easily, non-destructively and directly in the orchard. Once such allometric relations are established they can be used routinely to recreate initial structures.</p> <p>Models based on allometric relations have been established in this study in order to predict the leaf areas of the three different shoot types of three apple cultivars with different branch architectures: ‘Fuji’, ‘Ariane’, and ‘Rome Beauty’. The allometric relations derived from experimental data allowed us to model the total shoot leaf area as well as the individual leaf area for each leaf rank, for each shoot type and each genotype. This was achieved using two easily measurable input variables: total leaf number per shoot and the length of the biggest leaf on the shoot. The models were tested using a different data set, and they were able to accurately predict leaf area of all shoot types and genotypes. Additional focus on internode lengths on spurs contributed to refine the models.</p>
Dataset and model of "Bridging Micromechanics and Effective Medium Theory to Predict Crustal Seismic Velocities"
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Data for "Battery lifetime prediction and performance assessment of different modeling approaches"
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Data for "Twin- model framework development for a comprehensive battery lifetime prediction validated with a realistic driving profile"
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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.