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5,805 results for “Data model”
Data for: Habitat functionality: integrating environmental and geographic space in niche modelling for conservation planning
<p>Niche modelling is typically used to assess the effects of anthropogenic land use and climate change on species distributions and to inform spatial conservation planning. These models focus on the suitability of local biotic and abiotic conditions for a species in environmental space (E-space). Although movements also affect species occurrence, efforts to formally integrate geographic space (G-space) into niche modelling have been hindered by the lack of comprehensive theoretical frameworks. </p> <p>We propose the 'functional habitat' framework to define areas that are simultaneously of high-quality in E-space and functionally connected to other suitable habitat in G-space. Originating in metapopulation ecology, approaches have been developed to assess the amount of suitable connected habitat, based on the proximity between pairs of locations. Using network theory, which operates in topological space (T-space, defined by a network), we extended these metapopulation approaches to integrate movement constraints in G-space with niche modelling in E-space. </p> <p>We demonstrate the functional habitat framework using empirical data (GPS-tracking and population monitoring) throughout the European wild mountain reindeer (<em>Rangifer t. tarandus</em>) distribution range. We show that functional habitat outperforms traditional suitability in explaining the species' distribution. This approach integrates effects from habitat loss and fragmentation for spatial conservation planning and avoids overemphasizing small, inaccessible areas with locally suitable habitat. The functional habitat framework formally integrates biotic, abiotic, and movement constraints in niche modeling using network theory, thus opening a wide range of applications in spatial conservation planning.</p>
Data for: Combining stable isotopes, trace elements, and distribution models to assess the geographic origins of migratory bats
<p>The expansion of industrial-scale wind-energy facilities has increased the production of low-carbon emission energy but has also resulted in mortality of wildlife, including migratory bats. Management decisions can be limited by a lack of understanding of the geographic impact of bats killed at wind-energy facilities. Several studies have leveraged stable hydrogen isotope ratios (δ<sup>2</sup>H) of bat fur to illuminate this issue but are limited in the precision of conclusion because δ<sup>2</sup>H values vary primarily across latitudinal and elevational bands. One approach to increase the precision of geographic assignment is to combine independent inferences about spatial location from additional biomarkers and other related information. To test this possibility, we assigned known-origin individuals of three bat species commonly killed at on-shore wind-energy facilities in North America (<em>Lasiurus</em> <em>borealis</em>, <em>L</em>. <em>cinereus</em>, and <em>Lasionycteris</em> <em>noctivagans</em>) to probable origin using δ<sup>2</sup>H values, trace element concentrations, and species distribution models. We used cross-validation calibrated combined model tuning to determine the degree to which assignment probabilities improved when combining datasets. We found that combining markers typically performed better than single approaches. For <em>L. borealis</em> and <em>L. cinereus</em>, combining all three data sources outperformed any single or other combined approach. With an accuracy set at 80%, an average of 39.7% and 36.0% of each species' total geographic range was considered a potential origin, respectively; stable hydrogen alone included 51.8% and 50.6% of the total geographic area. In contrast, for <em>L. noctivagans</em>, including trace elements did not increase precision, and adding distribution data to δ<sup>2</sup>H values only improved precision by 0.6%. Thus, we found that a combination of multiple biomarkers typically, but not always, outperforms single marker approaches, and optimized combinations of different markers outperform equal weighting of each marker. From a practical perspective, δ<sup>2</sup>H values performed better than trace elements alone; in cases where cost is a limiting factor, the stable hydrogen should be the single biomarker used in conjunction with species distribution models. Overall, these results highlight the importance of validating methods for each species they are applied to and show that combining information from intrinsic biomarker approaches is a useful tool to document bat movements.</p>
All data used for thermochemical modeling of wet-dry cycles in Gale crater
<p>All data used for thermochemical modeling of wet-dry cycles in Gale crater including starting parameters, modeled and extrapolated results, and result comparisons.</p>
Data for: Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model
<p>Epidemics, such as COVID-19, have caused significant harm to human society worldwide. A better understanding of epidemic transmission dynamics can contribute to more efficient prevention and control measures. Compartmental models, which assume homogeneous mixing of the population, have been widely used in the study of epidemic transmission dynamics, while agent-based models rely on a network definition for individuals. In this study, we developed a real-scale contact-dependent dynamic (CDD) model and combined it with the traditional susceptible-exposed-infectious-recovered (SEIR) compartment model. </p>
Data associated with Cell Reports publication: Dura-Bernal et al. 2023, "Multiscale model of primary motor cortex circuits predicts in vivo cell type-specific, behavioral state-dependent dynamics"
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data. The source code for the associated M1 model and data analysis can be found here: https://github.com/suny-downstate-medical-center/M1_NetPyNE_CellReports_2023</p> <p>Please download the data_v2.zip file, which contains the most updated and complete version of the data.</p> <p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Automated patent extraction powers generative modeling in focused chemical spaces: Training data and model checkpoints release
<p>Training data and model checkpoints accompanying paper on "Automated patent extraction powers generative modeling in focused chemical spaces". If you use this data, please cite the following manuscript:</p> <pre>@article{subramanian2023automated, title={Automated patent extraction powers generative modeling in focused chemical spaces}, author={Subramanian, Akshay and Greenman, Kevin P and Gervaix, Alexis and Yang, Tzuhsiung and G{\'o}mez-Bombarelli, Rafael}, journal={Digital Discovery}, year={2023}, publisher={Royal Society of Chemistry} }</pre>
Enhancer design manuscript data and models
<p>This repository holds the trained DNA accessibility and enhancer activity models, and the data used to train and evaluate the model.</p> <p> </p> <p><strong>Files for DNA accessibility models:</strong></p> <ul> <li><strong>Accessibility_models_training_data.tar.gz</strong> <ul> <li><fold>_sequences_<set>.fa <ul> <li>FASTA files with DNA sequences of genomic regions from train/val/test sets.</li> </ul> </li> <li><fold>_sequences_activity_<set>.txt <ul> <li>Files with accessibility scores for sequences from train/val/test sets.</li> </ul> </li> </ul> </li> <li><strong>Accessibility_model_files.tar.gz</strong> <ul> <li>model.h5 and model.json files for each Results_<fold>_<tissue>_DeepSTARR2_<rep></li> </ul> </li> <li><strong>Accessibility_models_test_set_predictions.rds</strong> <ul> <li>RDS object with predictions for accessibility</li> </ul> </li> </ul> <p> </p> <p><strong>Files for enhancer activity models:</strong></p> <ul> <li><strong>EnhancerActivity_model_files.tar.gz</strong> <ul> <li><fold>_sequences_<set>.fa <ul> <li>FASTA files with DNA sequences of genomic regions from train/val/test sets.</li> </ul> </li> <li><fold>_sequences_activity_<set>.txt <ul> <li>Files with enhancer activity labels for sequences from train/val/test sets.</li> </ul> </li> </ul> </li> <li><strong>EnhancerActivity_model_files.tar.gz</strong> <ul> <li>model.h5, model.json and Model_evaluation.pdf files for each Results_<fold>_<tissue>_<rep></li> </ul> </li> <li><strong>EnhancerActivity_models_clean_evaluation_data.rds</strong> <ul> <li>RDS object with sequences used for final evaluation of the models</li> </ul> </li> <li><strong>EnhancerActivity_models_results_per_tissue_test_set.rds</strong> <ul> <li>RDS object with predictions and results of the enhancer activity models</li> </ul> </li> </ul>
Data supporting 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'
<p>This data set contains the data produced for the paper 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'</p> <p>The data set contains output from LADDIE simulations, including basal melt rates.</p> <p>The main simulations used in the man text are:</p> <p>- Crosson-Dotson: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc">CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc </a><br> - Filchner-Ronne: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/FRIS_1.0_linear_S134.8_T1-2.3_720.nc">FRIS_1.0_linear_S134.8_T1-2.3_720.nc </a><br> </p> <p>The additional simulations included in the Appendix are:</p> <p>- 3D forcing of Crosson-Dotson: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/CrossDots_0.5_mitgcm_2003_2008_100.nc">CrossDots_0.5_mitgcm_2003_2008_100.nc </a><br> - Tuning of Crosson-Dotson: XX_YY_ZZ.nc, where XX is the resolution in km, YY is the value for Cd,top, and ZZ is the value for Dmin<br> - Pine Island Ice Shelf: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/PIG.nc">PIG.nc </a><br> </p>
Updated Rainfall data with 2022 data for few more NMME models
<p>The dataset used in various plots of the manuscript submitted to GMD, It now has values for 2022</p> <p>for few more NMME models</p>
Data for dissertation titled 'Topic modelling for the stratification of neurological patients'
<p>The uploaded zip-file entails the data needed for and obtained through the dissertation titled 'Topic modelling for the stratification of neurological patients' as part of the programme 'MSc. in Statistical Data Analysis' at Ghent University. The study aimed at exploring the applicability of hierarchical stochastic block models on resting-state functional magnetic resonance imaging (RS-fMRI) to cluster participants with known neurological disorders.</p> <p>The data is structured in different folders and aligns with the folder structure of the GitHub-repository that contains the analysis scripts (https://github.com/wvechelp/hsbm_on_fmri). The GitHub-repository already covers some example data, while additional data and results can be found in this zip-file. Additional comments on the analyses are also provided in the analysis scripts.</p> <p>The original raw data is obtained through the OpenFMRI project (<a href="http://openfmri.org/">http://openfmri.org/</a>, with label <em>ds000030</em>) and as a Stanford Digital Repository (<a href="https://purl.stanford.edu/mg599hw5271">https://purl.stanford.edu/mg599hw5271</a>) (Bilder<em> et al.</em>, 2016). It is obtained from the NIH Roadmap Initiative, as a result of the Consortium for Neuropsychiatric Phenomics (CNP) study (Poldrack<em> et al.</em>, 2016). Throughout the study, data was collected through interviews and rating scales, self-report measures, neurocognitive exams (using both paper-pencil and computerised tests), and a variety of neuroimaging data. More specific information on the selection procedure of the participants can be found in the description of the data (Bilder<em> et al.</em>, 2016) and the associated article (Poldrack<em> et al.</em>, 2016). Among the available neuroimaging data, the RS-fMRI data have been collected by asking participants to remain relaxed, while keeping their eyes open (with scans lasting 304 s and an image being collected every 2 seconds (Poldrack<em> et al.</em>, 2016)). This raw data was pre-processed by Rasero<em> et al.</em> (2019) to correct for motion and temporal alignment. Smoothing (6-mm full width at half-maximum Gaussian kernel), intensity normalisation, and a band-pass filter (between 0.01 and 0.08 Hz) were applied prior to the removal of linear and quadratic trends. Motion time courses, average CSF signal, and the average white matter signal were regressed out prior to data transformation into voxels with a volume of 3 mm x 3 mm x 3 mm. Ultimately, the functional atlas of Shen<em> et al.</em> (2013) was used to average the voxel signals per anatomical region of interest (ROI), resulting in a parcellation of 278 ROIs (and associated time series consisting of 152 measurements). From these ROI-specific time series, Rasero<em> et al.</em> (2019) generated 278 x 278 matrices with Pearson coefficients.</p> <p>References:<br> Bilder, R. M., Poldrack, R. A., Cannon, T., London, E., Freimer, N., Congdon, E., Karlsgodt, K., & Sabb, F. W. (2016). <em>UCLA Consortium for Neuropsychiatric Phenomics LA5c Study</em> Stanford Digital Repository. <a href="http://purl.stanford.edu/mg599hw5271">http://purl.stanford.edu/mg599hw5271</a> and <a href="https://openfmri.org/dataset/ds000030/">https://openfmri.org/dataset/ds000030/</a><br> Poldrack, R. A., Congdon, E., Triplett, W., Gorgolewski, K. J., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., Freimer, N. B., London, E. D., Cannon, T. D., & Bilder, R. M. (2016). A phenome-wide examination of neural and cognitive function. <em>Scientific Data</em>,<em> 3</em>(1), 160110. <a href="https://doi.org/10.1038/sdata.2016.110">https://doi.org/10.1038/sdata.2016.110</a><br> Rasero, J., Diez, I., Cortes, J. M., Marinazzo, D., & Stramaglia, S. A.-O. (2019). Connectome sorting by consensus clustering increases separability in group neuroimaging studies. <em>Network Neuroscience</em>,<em> 3</em>(2), 325-343. <a href="https://doi.org/https:/doi.org/10.1162/netn_a_00074">https://doi.org/https://doi.org/10.1162/netn_a_00074</a><br> Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. <em>NeuroImage</em>,<em> 82</em>, 403-415. <a href="https://doi.org/https:/doi.org/10.1016/j.neuroimage.2013.05.081">https://doi.org/https://doi.org/10.1016/j.neuroimage.2013.05.081</a></p> <p> </p>
Supporting data to reproduce figures and anaylisis presented in Bruciaferri et al. 2023 - submitted to Journal of Advances in Modeling Earth Systems (JAMES)
<p>Data for reproducing figures and the analysis of</p> <p>Diego Bruciaferri, Catherine Guiavarc’h, Helene T. Hewitt, James Harle, Mattia Almansi and Pierre Mathiot. Localised general vertical coordinates for quasi-Eulerian ocean models: the Nordic overflows test-case, submitted to JAMES.</p> <p>Data includes (© Crown copyright Met Office):</p> <p>1) models_geometry: bathymetry, horizontal grid and domain files needed to run the models and analyse their results.</p> <p>2) hpge: output data from HPG error idealised test.</p> <p>3) ideal_ovf: output data from the idealised overflow experiment</p> <p>4) realistic: output data from the realistic simulations</p>
Evolutionary adaptation of trees and modelled future larch forest extent in Siberia. Code and simulation data
<p>Code and datset used for the publication: "Evolutionary adaptation of trees and modelled future larch forest extent in Siberia" 2023 Gloy et al.</p>
Numerical experimental data about mixing time step of tracer-aided model
<p>This is the numerical experimental data about mixing time step of tracer-aided model related to the response letter of manuscript HYDROL49160 in Journal of Hydrology</p>
cGAN model data
<p>cGAN MODEL DATA.</p>
Data for: The biomechanics of tooth strength: testing the utility of simple models for predicting fracture in geometrically complex teeth
<p>Teeth must fracture foods while avoiding being fractured themselves. This study evaluated dome biomechanical models used to describe tooth strength. Finite element analysis (FEA) tested whether the predictions of the dome models applied to the complex geometry of an actual tooth. A finite element model (FEM) was built from microCT scans of a human M3. The FEA included three loading regimes simulating contact between 1) a hard object and a single cusp tip, 2) a hard object and all major cusp tips, and 3) a soft object and the entire occlusal basin. Our results corroborate the dome models with respect to the distribution and orientation of tensile stresses, but document heterogeneity of stress orientation across the lateral enamel. This implies that high stresses might not cause fractures to fully propagate between cusp tip and cervix under certain loading conditions. The crown is most at risk of failing during hard object biting on a single cusp. Geometrically simple biomechanical models are valuable tools for understanding tooth function but do not fully capture aspects of biomechanical performance in actual teeth whose complex geometries may reflect adaptations for strength.</p>
Data and codes for Landslide hazard spatiotemporal modelling: a unified and data-driven framework
<p>Data and codes for Landslide hazard spatiotemporal modelling: a unified and data-driven framework</p>
A sea state dependent gas transfer velocity for CO$_2$ unifying theory, model and field data
<p>Dataset for "A sea state dependent gas transfer velocity for CO2 unifying theory, model and field data"</p> <p>WaveWatch III simulated significant wave height (Hs, unit:m), volume of entrained air ('wva', unit m/s), 10-meter wind vector ( 'uwnd','vwnd', unit, m/s) for 9 datasets from 11 cruises.</p> <p>The information of dataset is shown in name of each file.</p>
IMPROVING THE AVAILABILITY AND USABILITY OF PLANETARY SPATIAL DATA RESEARCH BY SPATIAL DATA INFRASTRUCTURE OF CELESTIAL BODIES MODELING
<p>The rapid development of space technology and the increased interest in space exploration have resulted in the intensifying of observation of celestial bodies, mostly in the solar system, over the past decade with the prospect of an upward trend in the future. Data collected by space missions are stored and provided to users for use through the archives of individual space agencies and specialized portals of space missions. Users often encounter many problems when searching and retrieving data of interest, despite the fact that access to data is open for all groups of users. Current ways of storing and shearing this valuable data set are focused on their long-term archiving and are largely adapted for space scientists with inadequate access and search functionalities that do not meet the needs of a wider group of users. To search for data, users must have some prior knowledge and invest a lot of time and effort, and the available functionalities gives too many of the same or similar data filtering results that, in most cases, cannot be visualized before downloading. For this reason, the data remains unused, and in order to solve this problem, large amounts of collected data on space bodies, of which most are spatially defined, impose the need to develop the spatial data infrastructure of celestial bodies (SDICB) at the general level in order to enable standardized organization and storage of these data, and their efficient use and exchange. In order to approach to the development of such an infrastructure, it is necessary to investigate what data, as well as how and to what extent, are collected through the space observation, either from Earth, Earth orbit or from space probes. It is also necessary to investigate how this data can be obtained and to explore concepts of spatial data infrastructure, the possibilities of its establishment and operationalization. This doctoral dissertation provides a detailed overview of current ways of storing and distributing space research data and their shortcomings and explores the possibility of modeling the establishment of SDICB. In order to adequately approach the development of the model, user needs assessment and analysis of the current data archiving situation was conducted. These results served as input parameters for modeling SDICB and the adoption of guidelines (recommendations) for the establishment. The proposed model is focused on user needs and improving the functionality of data access by applying international standards of spatial data and open-source technologies to make space data available to the general public and enable their easy search, download and interpretation. For the proposed model, an implementation project with a five-year implementation period was created, for which a feasibility study was conducted, and the benefits of SDICB implementation for all involved stakeholders were investigated.</p>
Data and code for "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model"
<p>Original data and code associated with the paper "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model".<br> <br> Further details on the data are available in the readme.txt files.</p>
Data Archive for James and Ross, 2023 (submitted): The Timing of the ENSO Spring Barrier in the Copernicus Dynamical Models
<p>This archive contains data used in creating figures for James and Ross, 2023 (submitted): The Timing of the ENSO Spring Barrier in the Copernicus Dynamical Models.</p> <p>The data are provided in csv files, and the file "README" explains the contents of each file.</p> <p> </p>
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.