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5,805 results for “Data model”
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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Data for "Electro-aging model development of nickel-manganese-cobalt lithium-ion technology validated with light and heavy-duty real-life profiles"
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Data from: experiment using Large Language Models for unit testing generation
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Improve Artificial Intelligence Models for Insecticide Recognition by Incorporating Insect Toxicity Data
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Earthquake data for the Japan region from 2000 to 2023 and the CNN_LSTM magnitude prediction model.
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Vacuum Preloading Performance Assesment Using Monitoring Data Interpretation and Finite Element Modelling
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[OUTDATED] Data set [ref. paper "Predictive modeling of drivers' brake reaction time through machine learning methods"]
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A Flexible Framework for N-mixture Occupancy Models of Count Data Applications to Breeding Bird Surveys
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data: Exploring neural field theory: Modeling consciousness states and criticality in the brain
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Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers
<p>The 12 VS scenarios considered in this study employing six training-test data partitions<strong> </strong>(A-F). All training sets employ the same set of 371 actives (WO2015160641A2), but differ on the considered set of inactives and hence are uniquely identified by the latter (either TrueInactives, DeepCoys, RandomDecoys or ActivesOnly). Likewise, all test sets employ the same 297 actives (WO201503820A1), none of them also included in the training set, but different sets of inactives (TrueInactives or DeepCoys). </p> <p> </p> <table align="center"> <caption>Table 1. Six virtual screening scenarios corresponding to six pairs of training-test data for each type of SFs (classification or regression)</caption> <thead> <tr> <th scope="col">Partition ID</th> <th scope="col">Training set</th> <th scope="col">Test set</th> <th scope="col">Type</th> </tr> </thead> <tbody> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Regression</td> </tr> </tbody> </table> <p> </p>
scvi Model & Data Zoo | Tabula Sapiens | Datasets
<p>Tabula Sapiens <a href="https://www.science.org/doi/10.1126/science.abl4896">paper</a>. The Tabula Sapiens Consortium, Science 376, eabl4896 (2022).</p> <p>Tabula Sapiens <a href="https://tabula-sapiens-portal.ds.czbiohub.org/whereisthedata">datasets</a>. Before you use this data please see Tabula Sapiens' Data Release Policy available <a href="http://tabula-sapiens-portal.ds.czbiohub.org/whereisthedata">here</a>.</p> <p>Tabula Sapiens <a href="https://figshare.com/articles/dataset/Tabula_Sapiens_release_1_0/14267219">figshare</a>. Pisco, Angela; Consortium, Tabula Sapiens (2021): Tabula Sapiens Single-Cell Dataset. figshare. Dataset. https://doi.org/10.6084/m9.figshare.14267219.v4 </p>
Model data: outdated
<p>This is an outdated DOI link. The current model data version can be found here: https://doi.org/10.5281/zenodo.7229674</p>
Permafrost model for the Argentinian Andes - Calibration data set
<p><strong>Supplementary information to the following publication: </strong></p> <p>Tapia Baldis C, Trombotto Liaudat D. 2020. Permafrost debris-model in Central Andes of Argentina (28°-33° S). Cuadernos de Investigación Geográfica 46, http://doi.org/10.18172/cig.3802</p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>To predict regional-scale spatial patterns of permafrost occurrence, especially over remote environments with limited data, empiric-statistical models are widely used. This kind of approach correlates permafrost occurrence with topo-climatic factors (altitude, geographic position, slope, aspect, air temperature, ground temperature, solar radiation, etc.) easily available, in some cases. Different combinations of empiric-statistical models were tested to evaluate the permafrost spatial distribution in the study area.</p> <p>The study area (28° to 33°S and 70°30’ to 69°W) comprises the middle portion of the South American (Argentinian side) Central Andes (17°30’ to 35°S), named Dry Andes. The landscape is expressed as mountain ranges and valleys with 50% of the terrain surface above 3000 m a.s.l. The highest elevations are represented by mountain peaks such us Mercedario (6850 m a.s.l.) or La Ramada (6400 m a.s.l.). The Dry Andes could be further separated into Desert Andes (17°30’ to 31°S) and Central Andes (31° to 35°S), according to precipitation rates and landscape geomorphological characteristics. </p> <p>Models were trained in a calibration area to evaluate the correlation between geomorphological permafrost indicators (named explanatory variable) and the topoclimatic parameters (predictive variable). A logistic regression model with a logit link function was chosen as a mathematical approach.</p> <p>Data for model calibration was obtained from the Bramadero river basin, located at 31°50’ S and 70°00’ W in the Central Andes. From a geomorphological point of view, the landscape of the Dry Andes is characterized by the interdigitation of glacial, periglacial, alluvial, fluvial, and gravitational processes. The Bramadero river basin was largely glaciated during the LGM, even today it is possible to recognize erosive forms and glacial deposits all over the main valley and subordinated creeks. Even though Quaternary glacial stages modeled the landscape; periglacial features prevail today. Currently, periglacial processes are active in elevations exceeding 2700 m a.s.l. (lowest limit of seasonal freezing), however, a wide variety of periglacial deposits and permafrost indicating cryoforms occur between 3400 and >4500 m a.s.l. (permafrost periglacial belt).</p> <p>The complete geomorphological characterization of the Bramadero river basin and the geomorphometric data extracted from every kind of landform were used to set up the permafrost predictive categories. The first predictive category (presence) includes geoforms that indicate current permafrost, such as; active rock glaciers, inactive rock glaciers, protalus lobes, cryoplanation surfaces, and perennial snow patches. The second category (absence) includes geoforms without current permafrost (relict or fossil rock glaciers, bedrock outcrops, glacial abrasion surfaces, debris/mud flows, and Andean wetlands/peatlands types). It also includes geoforms where the presence of permafrost could not be certainly assessed such us: frozen and unfrozen talus slopes, glaciers and covered glaciers, moraines and morainic complexes, debris/snow avalanches, rock avalanches, and rock slides.</p> <p>The following link can accede model results:</p> <p>Tapia Baldis, Carla. (2018). Permafrost model for the Argentinian Andes - Results and climatic scenarios [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7229820</p>
The East Asia Moho depth model and the input gravity data
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Integrated N-mixture models generally perform well against other models for false positive prone data in ecological applications
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Data supporting the findings of "Projecting trends of arabica coffee yield under climate change: A process-based modelling study at continental scale"
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Raw data used to build models in SIMON Automated Machine Learning
<p>Here you can find all data and all information regarding each generated dataset.<br> For each dataset there are 4 files:</p> <p>json_info : This file contains, number of features with their names and number of subjects that are available for the same dataset<br> data_testing: data frame with data used to test trained model<br> data_training: data frame with data used to train models<br> results: direct unfiltered data from database</p> <p><br> Files are written in feather format.</p> <p><a href="https://gist.github.com/LogIN-/00d7628e0850f843ba84a678fac0a103">Here is an example</a> of data structure for each file in repository</p> <p> </p>
Data and generating files for the manuscript "A Multi-Model Analysis of Solute Plume Behavior in a Synthetic Braided-River Deposit", submitted to Water Resources Research, August 2018.
<p>Data and generating files for the manuscript "A Multi-Model Analysis of Solute Plume Behavior in a Synthetic Braided-River Deposit", submitted to Water Resources Research, August 2018.</p> <p>This zipped folder contains the codes and data generated for the manuscript. The main routine for generating the ensembles is fidelity/run_fidelity.py. The folders 'dtgeostats', 'flowtrans', and 'hyvr' contain utilities for generating parameter fields and running flow-and-transport simulations. Note that the codes and data are provided as-is and relative pathways, etc. in the code may not function correctly. The data can be found in the fidelity/runfiles/braid005 directory and includes the outputs for the synthetic virtual reality (fidelity/runfiles/braid005/braid_vr) and the following model ensembles: object-based with no conditioning (fidelity/runfiles/braid005/braid_1a), object-based with soft conditioning (fidelity/runfiles/braid005/braid_1b), MPS (fidelity/runfiles/braid005/braid_2a/), isotropic multi-Gaussian (fidelity/runfiles/braid005/braid_3/isn/), and anisotropic multi-Gaussian (fidelity/runfiles/braid005/braid_3/ann/) .</p>
Data and models of PINO-PC
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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.