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5,805 results for “Data model”
Data for 'SPH modelling of wind-companion interactions in eccentric AGB binary systems'
<p>Additional material to "Malfait et al. 2021, SPH modelling of wind-companion interactions in eccentric AGB binary systems": <a href="https://ui.adsabs.harvard.edu/link_gateway/2021A&A...652A..51M/arxiv:2107.01074" target="_blank" rel="noreferrer noopener">arXiv:2107.01074</a></p> <p>This contains input files and final output dumps of the Phantom simulations of this paper, and additional movies of 4 of the simulations.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p>
Data from: Response repetition biases in human perceptual decisions are explained by activity decay in competitive attractor models
Animals and humans have a tendency to repeat recent choices, a phenomenon known as choice hysteresis. The mechanism for this choice bias remains unclear. Using an established, biophysically informed model of a competitive attractor network for decision making, we found that decaying tail activity from the previous trial caused choice hysteresis, especially during difficult trials, and accurately predicted human perceptual choices. In the model, choice variability could be directionally altered through amplification or dampening of post-trial activity decay through simulated depolarizing or hyperpolarizing network stimulation. An analogous intervention using transcranial direct current stimulation (tDCS) over left dorsolateral prefrontal cortex (dlPFC) yielded a close match between model predictions and experimental results: net soma depolarizing currents increased choice hysteresis, while hyperpolarizing currents suppressed it. Residual activity in competitive attractor networks within dlPFC may thus give rise to biases in perceptual choices, which can be directionally controlled through non-invasive brain stimulation.
Data for thesis: Online Discovery and Model-to-Model Comparison of DCR Models from Event Streams
<p>This is the collection of data referenced in the Thesis</p>
An integrated population model to project viability of a northern bobwhite population in Ohio [DATA]
<p>Increased variation in interannual weather due to climate change can exert a powerful influence on the population dynamics of a species. Understanding the influence of severe weather is important for managing weather-sensitive species. While best management practices target vital rates that are affected by weather, focusing on a single vital rate may not be sufficient if other vital rates are secondarily limiting. A comprehensive modeling framework to forecast future population dynamics while incorporating weather scenarios and vital rate variation within observed ranges that can be affected by management actions are necessary. A potential approach is to combine an integrated population model (IPM) with a population viability analysis (PVA) to generate novel insights about population dynamics. We used the northern bobwhite (<i>Colinus virginianus</i>), a rapidly declining gamebird sensitive to snowfall along the northern extents of the species' range, to demonstrate the utility of a coupled IPM-PVA framework for projecting the response of a population to weather, management, and changes in vital rates. We created an IPM using two sources of count data spanning seven years, five years of winter survival data, and two years of breeding season demographics for a declining bobwhite population in southwestern Ohio during 2007–2015. Quasi-extinction probability at the end of the decadal projection during 2019–2029 was 0.384–0.410 for mild, average, and severe winter weather scenarios. Quasi-extinction probability declined to 0.326 with 20% improvement in nest success and summer survival rates. A concurrent 20% increase in winter survival further reduced quasi-extinction probability to 0.263, which is a ~36% reduction in quasi-extinction probability compared to the baseline scenario with no changes in vital rates. These results suggest that long-term viability of this population may depend on extensive management of winter habitat to improve survival but will also require management actions to improve fecundity after severe winters. Our modeling approach demonstrated how IPMs can be used to project population responses to future weather conditions and overcome some of the pitfalls of traditional PVA. The coupled framework presented here can serve as a tool for managers to make climate informed management decisions.</p>
"A theoretical model of Surtseyan bomb fragmentation" code and data
<p>This is Matlab code and permeability and porosity data to accompany the manuscript "A theoretical model of Surtseyan bomb fragmentation", which is accepted for publication in the Proceedings of the Royal Society London, Series A, and for which a preprint will be deposited in arXiv.</p>
Data and Codes for Evaluating and Improving Scale-Awareness of a Convective Parameterization Closure Using Cloud-Resolving Model Simulations of Convection
<p>Provide necessary fields averaged over different subdomain sizes from 64 km to 4 km (see 64 to 4 .7z files) processed from the output of CRM simulation of MC3E case (for TWP-ICE case, please get the processed data and associated codes from http://doi.org/10.5281/zenodo.4542461). Also, associated codes for calculation of important fields (like dCAPEls, dCAPEe, Msa and so on) are also provided in code.7z. Please see all "note.txt" files in code.7z to know how to use these codes.</p>
Model outputs of ENETWILD density model for wild boar based on hunting yield data. August 2021
<p>These maps are models obtained from density hunting yield data of the ENETWILD project based on available raw data. These are frequently updated in order to improve results.</p> <p>Objectives:<br> - To evaluate whether an approach based on density data is capable to correct overpredictions of previous reports for high-resolution predicted patterns when raw data are collected at different spatial resolution.<br> - Downscaling to 10x10 km grid >>> file " sp_DensityModel_10km_20210621_MESS_crs3035.tif"<br> - Downscaling to 2x2 km grid >>> file "sp_DensityModel_2km_20210621_MESS_MaxPred50_crs3035.tif”<br> <br> Model settings and predictors: <br> - Assuming cells as municipality in 10x10 km grid downscaling.<br> - Assuming cells as hunting grounds in 2x2 km grid downscaling.</p> <p>Conclusions guiding future methodological steps:<br> - To explore approaches to manage spatial autocorrelation at European scale to improve the predictive performance of the results<br> - To compile complete data for each country for modeling temporal dimension of wild boar patterns.</p> <p>For further details and methodological approach see the report:</p> <p>ENETWILD-consortium, S. Illanas, S. Croft, G. C. Smith, J. Fernández-López, J. Vicente, J. A. Blanco-Aguiar, R. Pascual-Rico, M. Scandura, M. Apollonio, E. Ferroglio, O. Keuling, S. Zanet, F. Brivio, T. Podgorski, K. Plis, R. C. Soriguer, P. Acevedo. Update of model for wild boar abundance based on hunting yield and first models based on occurrence for wild ruminants at European scale. EFSA supporting publication 2021: EN-6825. 30 pp. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.2903%2Fsp.efsa.2021.EN-6825&data=04%7C01%7C%7C371a629e88d2444ab27f08d966f4158f%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637654020801719420%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=4XGKExGdoJYsJ%2B8xvdTrVxVzQHPRN8Aem%2BMX7zSd8f4%3D&reserved=0">https://doi.org/10.2903/sp.efsa.2021.EN-6825</a></p>
EURAKNOS data model v1
<p>EURAKNOS data model v1</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 Cost-Effective Semi-Ab Initio Approach to Model Relaxation in Rare-Earth Single-Molecule Magnets. Open data set
<p>Data supporting the original figures 2 and 3 of the related publication.</p>
EURAKNOS data model V2
<p>Version 2 of the EURAKNOS data model.</p>
Supplementary material 1 from: Eitzel M.V (2021) A modeler's manifesto: Synthesizing modeling best practices with social science frameworks to support critical approaches to data science. Research Ideas and Outcomes 7: e71553. https://doi.org/10.3897/rio.7.e71553
Modeler's Manifesto: Self-Situating Appendix
Figure 1 from: Eitzel M.V (2021) A modeler's manifesto: Synthesizing modeling best practices with social science frameworks to support critical approaches to data science. Research Ideas and Outcomes 7: e71553. https://doi.org/10.3897/rio.7.e71553
Figure 1 Workflow diagram for manifesto practices, showing which project stages may benefit from which practices. Interdisciplinary fluency, engaging with community-based modeling, and paying attention to power dynamics as well as impacts and implications are all important at all stages of modeling work. Epistemic consistency is important throughout model development (the three middle steps of model choice, construction, and description) and communication, while triangulation and mixed methods contribute largely to model development. The data biography is most important in the model description stage, though one may need to keep a journal and track details of the model development process in order to create the data biography. Treating uncertainty as openness is most important in model communication and application; however, this could feed back into iterative model development steps as well, or one could design models to aid in treating uncertainty as openness.
Data from: A general model for seed and seedling respiratory metabolism
The ontogeny of seed plants usually involves a dormant dehydrated state and the breaking of dormancy and germination, which distinguishes it from that of most organisms. Seed germination and seedling establishment are critical ontogenetic stages in the plant life cycle and both are fueled by respiratory metabolism. However, the scaling of metabolic rate with respect to individual traits remains poorly understood. Here, we tested metabolic scaling theory during seed germination and early establishment growth using a recently developed model and empirical data collected from 41 species. The results show that (i) the mass-specific respiration rate (Rm) is weakly correlated with body mass, mass-specific N, and C content, (ii) Rm conformed to a single Michaelis-Menten curve as a function of tissue water content, and (iii) the central parameters in the model were highly correlated with DNA content and critical enzyme activities. The model offers new insights and a more integrative scaling theory that quantifies the combined effects of tissue water content and body mass on respiratory metabolism during early plant ontogeny.
Data release: Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors
<p>This contains the data release associated to "Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors".</p> <p>We provide the figures, machine-readable json summary files associated to Tables I-IV, scripts and data products used to produce the hyperparameter inference results in this publicatioln. A "lightweight" version is provided which excludes the pickled data products. To reproduce the results, download the full tar file, unzip, enter the scripts directory, and use the Makefile commands. These results where created using bilby v1.1.3 at commit hash <a href="https://git.ligo.org/lscsoft/bilby/-/commit/63c7aacaf30d721e77599bd11f3a9fa2447915cb">63c7aaca</a>.</p>
Model data in support of the E3SMv1-Arctic-OSI GMD manuscript (2021)
<p>This data set contains the time series and climatological fields that were computed from the E3SMv1 Arctic-OSI and LR-OSI simulations that are described in a manuscript submitted for review to GMD in October 2021.</p>
Projected shifts in deadwood bryophytes in Sweden, data used for species distribution modelling and for climate and forest scenario analysis
<p>Climate change and habitat loss are main threats to forest biodiversity. We fitted ensembles of single species distribution models for 23 deadwood-living bryophyte species in Sweden, based on species records from the Swedish Lifewatch website. This data set comprises the species and environmental data used for species distribution modelling, and coefficients of the fitted single species distribution models (GLM, Poisson point-process, MaxEnt).</p> <p>Based on the fitted species distribution models, we conducted simulations of future species distributions given realistic climate and forest projection scenarios at the national scale of Sweden. The data used for the scenario analysis are stored here.</p>
Source code and data for Ou et al. 2021 (US state-level capacity expansion pathways with improved modeling of the power sector dynamics within a multisector model)
<p>For details, please check the "readme" file.</p>
IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations
<p>IT-SNOW is a serially complete and multi-year snow reanalysis for Italy. The dataset includes daily maps of Snow Water Equivalent (SWE), snow depth (HS), bulk-snow density (RhoS), and liquid water content (Theta_W). </p> <p>Data are organized in monthly netCDF files, each providing time and lat/lon information for georeference. Units are as follows: HS is in cm, SWE is in mm w.e., RhoS is in kg/m3, and Theta_W is in %. Note that maps are instantaneous snapshots at 11AM UTC, here assumed as representative values for the day. </p> <p>As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 1000+ on-the-ground snow-depth sensors. Additional information are available in the following paper submitted to Earth System Science Data: </p> <p>"IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations (2009-2021)", Francesco Avanzi et al., 2022. </p> <p>The initial time span of data is September 1, 2010 to August 31, 2021, with future updates envisaged on an annual basis (see updates below).</p> <p><strong>UPDATES</strong></p> <ul> <li>September 29, 2025: released v5 with the complete 2025 water year (September 2024 - August 2025).</li> <li>November 12, 2024: released v4 with the complete 2024 water year (September 2023 - August 2024).</li> <li>September 02, 2024: released v3.1 with the complete 2023 water year (September 2022 - August 2023) AND all previous water years (which were inadvertently NOT carried over while creating v3).</li> <li>September 02, 2024: released v3 with the complete 2023 water year (September 2022 - August 2023).</li> <li>December 20, 2023: released v2 with the complete 2022 water year (September 2021 - August 2022).</li> </ul> <p>LICENSE INFORMATION</p> <p>IT-SNOW is distributed under a CC BY-NC 4.0 license. you are free to: </p> <p>1. Share — copy and redistribute the material in any medium or format; <br>2. Adapt — remix, transform, and build upon the material;</p> <p>under the following terms: </p> <p>a. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.<br>b. NonCommercial — You may not use the material for commercial purposes.</p> <p><br>DATA ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THESE DATA, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</p> <p>For details about the CC BY-NC 4.0 license, see: https://creativecommons.org/licenses/by-nc/4.0/deed.en</p>
Data files for "Stripe correlations in the two-dimensional Hubbard-Holstein model" by S. Karakuzu et al.
<p>Data files for the manuscript "Stripe correlations in the two-dimensional Hubbard-Holstein model" by S. Karakuzu et al. To appear in Communications Physics. Preprint available at https://arxiv.org/abs/2205.15464 (2022).</p> <p>This work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award Number<br> DE-SC0022311.</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.