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26 results for “Maximum Entropy”
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Underlying data for "Interpretation of Hydrogen-Deuterium Exchange Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles"
<p>This dataset contains code, data, and figures used in the article "Interpretation of Hydrogen-Deuterium Exchange Data<br> by Maximum-Entropy Reweighting of Simulated Structural Ensembles".</p> <p>Contents:</p> <p>code/* - Underlying code used to analyze molecular dynamics trajectories and calculate predicted HDX-MS data, used to reweight structural ensembles to best fit target HDX-MS data, and used to structurally cluster simulation frames after reweighting</p> <p>data/* - Simulation trajectories of the TeaA protein, along with two sub-trajectories corresponding to only 'closed' or 'open' TeaA frames, and predicted HDX-MS deuterated fractions used as target data in simulation reweighting. Also simulation trajectories of the LeuT protein, in either 'outward-facing' or 'inward-facing' conformational states embedded in a DMPC bilayer, and experimental HDX-MS deuterated fractions used as target data in simulation reweighting</p> <p>figures/* - Underlying data and scripts used to create all figures and movies used in the article.</p> <p>Where appropriate, README files include instructions for regenerating data used in the article, and details of the Python packages used to run Python scripts are available in conda_environment.yml</p>
Global Surface Ozone Concentration Dataset 1990-2017 Mapped at Fine Resolution through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output
<p>This global surface ozone concentration dataset corresponds to the data developed in this paper:</p> <p>DeLang, M. N., J. S. Becker, K.-L. Chang, M. L. Serre, O. R. Cooper, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, S. Cleland, E. Collins, M. Brauer, and J. J. West (2021) Mapping yearly fine resolution global surface ozone through the Bayesian Maximum Entropy data fusion of observations and model output for 1990-2017, <em>Environmental Science & Technology</em>, 55, 4389-4398, doi: 10.1021/acs.est.0c07742.</p> <p>Ozone concentrations are estimated as described in the paper, with output shown for the Ozone Season Daily Maximum 8-hr metric (OSDMA8) for each year between 1990 and 2017, at 0.1 degree spatial resolution. Ozone is estimated through data fusion of output from several global models, with observations of ozone collected by TOAR. The data fusion involves application of the M3Fusion method to create a multi-model composite of several global models, followed by BME data fusion, as described in the paper. </p> <p>The *.nc file contains the latitude, longitude, ozone concentration estimate, and estimated variance for each 0.1 x 0.1 degree grid cell.</p> <p>Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We'd like to hear from you to know how you're using the data!</p> <p> </p> <p> </p>
Global Surface Ozone Concentration Dataset 1990-2017 Generated by Bayesian Maximum Entropy Data Fusion With RAMP Bias Correction
<p>This dataset reports estimates of surface ozone concentration at fine spatial resolution for 1990 to 2017, at 0.5 degree horizontal resolution. Also reported is the variance. Estimates correspond to this paper:</p> <p><span>Becker, J. S.</span><span>, DeLang, M. N., K.-L. Chang, M. L. Serre, O. R. Cooper, <u>H. Wang</u>, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, M. Brauer, and <span>J. J. West</span> (2023) Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration, <em>Elementa Science of the Anthropocene</em>, 11: 1, doi: 10.1525/elementa.2022.00025.</span></p> <p>The dataset reports estimates of surface ozone for the OSDMA8 metric (the 6-month ozone-season average of the daily maximum 8-hr concentration), estimated through a data fusion of ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and output from multiple global atmospheric models. Estimates are created in each year by a combination of M3Fusion to create a multi-model composite, Regional Air Quality Model Performance (RAMP) regional and nonlinear bias correction, and Bayesian Maximum Entropy (BME) data fusion in space and time. The estimates here are the final results using a weighted RAMP bias correction. </p>
FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept - Input Data
<p>This repository contains driving data used by training and evaluation of FLAME in the "FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept" paper. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over Brazil. </p> <div>Not all variables were used in the final analysis<br> <table> <tbody> <tr> <td><strong> NetCDF File</strong></td> <td> <p><strong> Variable</strong></p> </td> <td> <p><strong>Used/not Used</strong></p> </td> <td> <p><strong>Source/Reference</strong></p> </td> </tr> <tr> <td> <p>burned_area.nc</p> </td> <td> <p>Burned area</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018)</td> </tr> <tr> <td> <p>burned_area_nat_veg.nc</p> </td> <td> <p>Burned area in natural vegetation</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> <p>burned_area_non_nat_veg.nc</p> </td> <td> <p>Burned area in non natural vegetation</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> <p> tas_max.nc</p> </td> <td> <p> Maximum Temperature</p> </td> <td> <p>Used</p> </td> <td><br><br> <p>ISIMIP3a</p> <p>FRIELER et al. (2023)</p> </td> </tr> <tr> <td> <p>precip.nc</p> </td> <td> <p>Precipitation</p> </td> <td> <p>Used</p> </td> </tr> <tr> <td> <p>vpd.nc</p> </td> <td> <p>Vapor pressure deficit</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>rhumid.nc</p> </td> <td> <p> Relative Humidity </p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td><br>consec_dry_days.nc</td> <td><br> <p>Consecutive number of dry days </p> </td> <td>Not Used</td> </tr> <tr> <td> <p>soilM.nc</p> </td> <td> <p>Soil Moisture</p> </td> <td> <p>Not Used</p> </td> <td> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>lightn.nc </p> </td> <td> <p> Lightning</p> </td> <td> <p>Not Used</p> </td> <td><br> <p> ISIMIP3a</p> <p>FRIELER et al. (2023)</p> </td> </tr> <tr> <td> <p>popDen.nc</p> </td> <td> <p> Population density</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>road_density.nc</p> </td> <td> <p>Road density</p> </td> <td>Used</td> <td> <p> GRIP global</p> <p>(MEIJER et al., 2018)</p> </td> </tr> <tr> <td> <p>cveg.nc</p> </td> <td> <p>Vegetation carbon</p> </td> <td> <p>Not Used</p> </td> <td><br> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>csoil.nc</p> </td> <td> <p>Carbon in dead vegetation</p> </td> <td>Used</td> <td><br> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>forest.nc</p> </td> <td> <p> Forest</p> </td> <td> <p>Used</p> </td> <td><br><br><br> <p> MAPBIOMAS, 2022</p> </td> </tr> <tr> <td> <p>grassland.nc</p> </td> <td> <p> Grassland</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>savanna.nc</p> </td> <td> <p> Savanna</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>cropland.nc</p> </td> <td> <p> Cropland</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>pasture.nc</p> </td> <td> <p> Pasture</p> </td> <td> <p>Used</p> </td> </tr> <tr> <td> <p>np.nc</p> </td> <td> <p>Number of patches </p> </td> <td> <p>Not Used</p> </td> <td><br><br> <p>Calculated from MAPBIOMAS,<br>2022</p> <br><br></td> </tr> <tr> <td> <p>ed.nc </p> </td> <td>Edge density</td> <td>Used</td> </tr> </tbody> </table> </div> <p> </p>
Fig. 1 in Maximum entropy niche-based modeling (Maxent) of potential geographical distribution of Coreura albicosta (Lepidoptera: Erebidae: Ctenuchina) in Mexico
Fig. 1. Model of potential distribution of Coreura albicosta with enhancement of the favorable climatic regions for this species, and superposition with the network of protected areas of México. Gray: lower probability of appropriate environmental conditions for distribution of the species. Light gray sections represent the decision threshold (0.2426) in which the grids are favorable for the distribution of the species. Darker sections inside light gray: areas with high probability of presence of the species. Black dots indicate the known distribution of the species. The protected areas are represented with a black line.
Fig. 1 in Actual and potential distribution of five regulated avocado pests across Mexico, using the maximum entropy algorithm
Fig. 1. Potential distribution of 5 insect pests of quarantine importance in Mexican avocados, based on ecological niche modeling. A) Conotrachelus aguacatae; B) Conotrachelus perseae; C) Copturus aguacatae; D) Heilipus lauri; and E) Stenoma catenifer. Current and potential distribution in Mexico was projected according to biogeographic provinces (Morrone 2005, 2014a), 1 = Baja California, 2 = California, 3 = Sonora, 4 = Sierra Madre Occidental, 5 = Mexican Plateau, 6 = Tamaulipeca, 7 = Mexican Pacific Coast, 8 = Trans-Mexican Volcanic Belt, 9 = Sierra Madre Oriental, 10 = Veracruzana, 11 = Balsas Basin, 12 = Sierra Madre del Sur, 13 = Chiapas, 14 = Yucatan. Scale values: 0 = absence, 1 = presence.
Fig. 2 in Actual and potential distribution of five regulated avocado pests across Mexico, using the maximum entropy algorithm
Fig. 2. Geographic areas in Mexico where both the insect pest and avocados are found. Shading indicates hectares affected. A) Conotrachelus aguacatae; B) Conotrachelus perseae; C) Copturus aguacatae; D) Heilipus lauri; and E) Stenoma catenifer.
Theory of Maximum Entropy Production (MEP) and Its Application to Microwave Remote Sensing - Simultaneous Retrieval of Soil Moisture and Vegetation Water Content
<p>A theory of maximum entropy production (MEP) for electromagnetic wave propagation in dielectric materials is proposed and applied to simultaneously retrieving soil moisture (SM) and vegetation water content (VWC) from L-band microwave brightness temperature (TB). One representation of the MEP principle states that a non-equilibrium system corresponds to such a configuration of energy fluxes that minimizes a dissipation function under the constraint of energy conservation. The dissipation function for radiative transfer is formulated as an analogy of that for heat transfer. A new physical parameter, radiative inertia as an analogy of thermal inertia, is introduced to characterize radiative attenuation in dielectric media. The radiative inertia is parameterized in terms of the penetration depth of electromagnetic waves as a function of the complex dielectric constant. The MEP based retrieval algorithm predicts SM and VWC by minimizing the dissipation function under the constraint of the conservation of radiative energy. The retrievals of SM and VWC based on the MEP theory were validated against field observations in tropical and temperate forested regions of the Amazon and North America. The proof-of-concept analysis demonstrates the capability of the MEP algorithm for simultaneous retrievals of SM and VWC even for dense canopy (e.g. VWC > 5 kg m-2). The MEP method is a new theoretical framework for developing innovative remote sensing algorithms of the Earth system not limited to just microwave observations.</p><p>Note: We would appreciate if users contact us for the use of the data.</p>
Sensitivity analysis of the maximum entropy production method to model evaporation in boreal and temperate forests
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Data from: Land use change through the lens of macroecology: insights from Azorean arthropods and the Maximum Entropy Theory of Ecology
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FIGURE 9. Maximum entropy model developed for D in A new species of Desmopachria Babington (Coleoptera: Dytiscidae) from Cuba with a prediction of its geographic distribution and notes on other Cuban species of the genus
FIGURE 9. Maximum entropy model developed for D. andreae sp. n. in Cuba. Values range from high (red areas) to low environmental suitability (blue areas).
Data for "Deciphering the code of viral-host adaptation through maximum entropy models"
<p>Data needed to reproduce the figures of the paper "Deciphering the code of viral-host adaptation through maximum entropy models", following the instructions provided in <a href="https://github.com/adigioacchino/MENB_snakemake">this GitHub repository</a>.</p>
Selected data analysed in the JGR Atmosphere manuscript " An application of the maximum entropy production method in the WRF Noah land surface model"
<p>The control experiment (hereafter WRF-CTL) and the MEP experiment (hereafter WRF-MEP) simulations results interpolated to the observation stations. The simulation period was 1 June to 31 August 2015 with 30 hours from 12:00 UTC (20:00 Beijing time (BJT)) each day, and the latest 24-hour outputs are provided.</p>
Fig. 6. Maximum entropy models for A in Weak Genetic Differentiation among Populations of the Andean Ground Beetle Pelmatellus columbianus (Reiche, 1843) (Coleoptera: Carabidae)
Fig. 6. Maximum entropy models for A) the past (21,000 years) and B) present distribution of Pelmatellus columbianus, using five bioclimatic variables. Maps show the limit of the montane forest (above 2,450 m) in green and páramo (above 3,000 m) in light brown.
Maximum entropy model estimates functional connectivity
<p>Tools to estimate brain connectivity have been useful to improve our understanding of brain functioning. Reduced models ofcultured neurons are often used to study the behavior of neuronal networks, including functional connectivity and how it mightbe affected by external stimuli. Cultured neurons tend to be active in ensembles, and when pairs of neurons show significantsynchronicity in their firing patterns they are said to be functionally connected. The most common methods to infer functionalconnections are based on pair-wise cross correlation between activity patterns of (small groups of) neurons. However, thesemethods are not designed to be used during external stimulation, and they are relatively insensitive to inhibitory connections.Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity, with thepotential benefit to estimate functional connectivity in the presence of an external stimulus and to infer excitatory as well asinhibitory connections. These models do not use pairwise comparison, but are based on probability distributions of sets ofneurons that are synchronously active in discrete time bins. We investigate the ability of the MaxEnt models to infer functionalconnectivity, using electrophysiological recordings fromin vitroneuronal cultures on micro electrode arrays. We comparefunctional connectivity as inferred by MaxEnt models to that obtained by conditional firing probabilities (CFP), an establishedcross-correlation based method. We show that MaxEnt models provide connectivity estimates that correlate well with CFPoutcomes. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the samemagnitude as those detected by CFP.</p>
Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European marine species based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.5° Resolution. The data report, for each 0.5° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell.</p>
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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.