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1,773 results for “Predictive model”
Figure 2 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 2. Jackknifing test of variable importance in the development of the uncorrelated model at 250-m resolution. Blue bars indicate the gain achieved when including that predictor only. Green gray bars show how much the total gain is diminished without the given predictor. Red bar indicate the gain achieved when including all predictors.
Data for Spangenberg, Simpkins and Wiegand "Species distribution modeling using commonness optimization leads to poor predictions for rare species"
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Forecasting Cryptocurrency Markets: Predictive Modelling Using Statistical and Machine Learning Approaches
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GANDALF: Generative AttentioN based Data Augmentation and predictive modeLing Framework for personalized cancer treatment
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Microplastic deposit predictions on sandy beaches by geotech-nologies and machine learning models
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Raw data for PSL prediction model
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Predicting gut microbial behavior in human diseases via community metabolic modeling and machine learning
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Data from: A simple behavioral model predicts the emergence of complex animal hierarchies
Social dominance hierarchies are widespread, but little is known about the mechanisms that produce non-linear structures. In addition to despotic hierarchies, where a single individual dominates, shared hierarchies exist where multiple individuals occupy a single rank. In vertebrates, these complex dominance relationships are thought to develop from interactions that require higher cognition, but similar cases of shared dominance have been found in social insects. Combining empirical observations with a modeling approach, we show that all three hierarchy structures-linear, despotic, and shared-can emerge from different combinations of simple interactions present in social insects. Our model shows that a linear hierarchy emerges when a typical winner-loser interaction (dominance biting) is present. A despotic hierarchy emerges when a policing interaction is added that results in the complete loss of dominance status for an attacked individual (physical policing). Finally, a shared hierarchy emerges with the addition of a "winner-winner" interaction that results in a positive outcome for both interactors (antennal dueling). Antennal dueling is an enigmatic ant behavior that has previously lacked a functional explanation. These results show how complex social traits can emerge from simple behaviors without requiring advanced cognition.
Replication Data for: Interpretable machine learning prediction of fire emission and comparison with FireMIP process-based models
<p>The target and predictor variables used in the developed ML model.</p>
Data from: Comparison of seven simple loss models for runoff prediction at the plot, hillslope and catchment scale in the semiarid southwestern U.S.
<p>Infiltration excess overland flow is the dominant mechanism for runoff generation in many dryland watersheds. Event-based rainfall-runoff models therefore partition precipitation into two components: loss and excess precipitation. The latter is then transformed into a runoff hydrograph. Numerous loss models have been developed over the past century ranging from simple empirical to sophisticated physically based methods. Complex models can lead to equifinality and associated uncertainty at larger spatial scales with varying soil and cover conditions. Simple models are therefore widely used in hydrologic practice. In the absence of measured data in many arid and semiarid regions, model parameters are often estimated based on laboratory or field infiltrometer tests. Given the documented importance of spatial scale on the runoff response in dryland catchments, it is not clear how models parameterized at the point or soil column scale will perform at the hillslope or catchment scale under real-world conditions. In this study, we compared the performance of seven simple loss models with three or less parameters: the Philip, Smith-Parlange, Horton, Kostiakov, curve number (CN), initial and constant (IC) and the linear and constant (LC) models. The latter is a modification of the IC model introduced in this study. We estimated parameters at the plot scale (2.8 m<sup>2</sup><span><span><span><span><span><span><span><span><span>) using rainfall simulation and then tested model performance at the hillslope (1.5–3.7 ha) and catchment scale (2.4–2.8 km</span></span></span></span></span></span></span></span></span><sup>2</sup><span><span><span><span><span><span><span><span><span>) based on measured rainfall-runoff data at two sites in New Mexico and Arizona, U.S. Results show that rainfall simulation can be used successfully to parameterize loss models at the hillslope scale. At the catchment scale, most models showed positive bias, suggesting that other losses (such as channel or transmission losses) play an important role in determining the catchment runoff response. Rainfall intensity and temporal distribution were found to be crucial for accurate runoff prediction. Models that are sensitive to rainfall intensity during the entire simulation (Philip, Smith-Parlange, Horton, Kostiakov, LC) therefore performed better than those with an initial abstraction term (CN, IC). During intermittent rain, the best results were achieved by methods expressing infiltration capacity as a function of cumulative infiltration (LC, Smith-Parlange). </span></span></span></span></span></span></span></span></span></p>
A predictive group-contribution framework for the thermodynamic modelling of CO2 absorption in cyclic amines, alkyl polyamines, alkanolamines and phase-change amines: new data and SAFT- gamma Mie parameters.FPE 2022
<p>All data in the figures in the publication. </p>
Model codes and data for ``A control volume finite element model for predicting the morphology of cohesive-frictional debris flow deposits"
<p>The code and the dataset can be read/run by using Matlab. The description is as follows:</p> <p>1. Dataset (field_data) includes transect data of three field debris flow deposits from Coussot et al. (1996). The data can be read and calibrated with the analytical solution by the code field_calibration.m.</p> <p>2. Dataset (data_T01-T04, T11-T15_DT) includes experimental fan topography data, calibrated parameters, and simulation outputs. </p> <p>3. Two calibration codes are used for the model parameter calibrations of the two sets of experiments.</p> <p>4. Function aggradation_DT.m is the CVFEM model for simulating fan morphology. Use CVFEM_exp_simulation.m code to run simulations for the experiments.</p>
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.
Figure 89 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 89: Predicted and recorded distribution of Phanaeus difformis.
Figure 88 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 88: Predicted distribution of Phanaeus vindex species group.
Figure 87 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 87: Predicted and recorded distribution of Phanaeus victoriae.
Figure 86 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 86: Predicted and recorded distribution of Phanaeus tridens.
Figure 83 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 83: Predicted and recorded distribution of Phanaeus moroni.
Figure 74 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 74: Predicted distribution of Phanaeus tridens species group.
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.