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24 results for “background modelling”
Background data 'Effect of biotic dependencies in species distribution models: The future distribution of Thymallus thymallus under consideration of Allogamus auricollis'
<p>Background data of the paper 'Effect of biotic dependencies in species distribution models: The future distribution of Thymallus thymallus under consideration of Allogamus auricollis'</p>
Data and code for gmd-2023-113 "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"
<p>Data and code for the paper "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"</p> <p>includes: </p> <p>The model is Community Earth System Model (v1.2.1) (provided by www.cesm.ucar.edu)</p> <p>Data assimilation code is initially provided by Data Assimilation Research Testbed (DART) (https://dart.ucar.edu/), some modifications are made to enable parameter estimation function of ocean background vertical diffusivity coefficients. And the programs and scripts for deal with OISST and EN4 profiles are also developed.</p> <p>The parameter sensitivity experiment results are saved as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012.nc">sensitive2008-2012.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012salt.nc">sensitive2008-2012salt.nc</a> for temperature and salinity, respectively. And the python script to draw the results is </p> <p>The state estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>The parameter estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>the estimated paremeter ensemble is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/parameters.nc">parameters.nc</a></p> <p>the python script for comparing the SE and PE results is <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/plot_analysis.py">plot_analysis.py</a></p> <p>the nino3.4 indices computed by the forecast experiment is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/fcst_correlation.nc">fcst_correlation.nc</a></p> <p> </p>
Main model fits and substitution rate predictions for: A quantitative genetic model of background selection in humans
<p>Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This can be thought of as a "linked selection signal" that reflects the impact of selection varying according to the placement of functional regions and recombination rates along the genome. Previous work has shown that negative selection against the steady influx of new deleterious mutations into conserved regions is the predominant mode of selection in humans. However, the theoretic model that underpins these results, classic Background Selection theory, is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of the linked selection, which models the effects of weak draft created according to how polygenic additive fitness variance is distributed along the genome. We use a recent model that jointly predicts the equilibrium fitness variance and substitution rates due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three human populations. While our model can accommodate weaker selection, we initially find evidence across three human populations of very strong selection against deleterious mutations consistent with previous work. However, the corollary predicted substitution rates for conserved regions are unreasonably low, and in disagreement with observed rates. We hypothesize this could be due to selected sites experiencing a further diminished population size due to selective interference. When we account for this in our method, we find evidence of weakly deleterious mutations in conserved regions which brings the predicted substitution rate into agreement with observations. However, these models lead to implausibly large mutation rate estimates. Overall, while our model of the genomic linked selection signal brings us a step towards uniting population and quantitative genetic selection models with the substitution process, our work suggests considerable uncertainty remains about the processes generating fitness variance in humans.</p>
Near-inertial wave energetics modulated by background flows in a global model simulation
<p>This data set contains data from a forward global HYCOM simulation (EXPT 19.2) with realistic atmospheric forcing. This is a 4-km simulation with 41 layers. All data is on the native tri-polar grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon).</p> <p>More details and context of this data can be found in the article: <a href="https://doi.org/10.1175/JPO-D-21-0130.1">https://doi.org/10.1175/JPO-D-21-0130.1</a>. Please cite this article along with any use of this data.<br> Raja, K. J., Buijsman, M. C., Shriver, J. F., Arbic, B. K., & Siyanbola, O. (2022). Near-Inertial Wave Energetics Modulated by Background Flows in a Global Model Simulation, <em>Journal of Physical Oceanography</em>, <em>52</em>(5), 823-840.</p>
Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements
<p>Time series comparisons between SIBaR, Brantley, and Apte background signals for all 312 time series in the Houston mobile monitoring campaign. Additionally, a R script demo (DemoData.R) of the SIBaR partitioning step on the demo datatset (DemoData.csv).</p>
hb or not hb - when and why accounting for background mortality in toxicological survival models matters?
<p>This document provides all the supplementary files we are providing in direct relationship with our manuscript entitled “hb or not hb - when and why accounting for background mortality in toxicological survival models matters?”, co-signed by Plantade, Baudrot and Charles and submitted to the Journal “MethodsX”.</p>
Data from: FST and genetic diversity in an island model with background selection
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Main model fits and substitution rate predictions for: A quantitative genetic model of background selection in humans
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A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos
<p>This dataset was used on the paper "A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos" for the comparison of three of the most common background modelling methods. The objective was to determine which of the models would be a better fit for the videos we had available at the time.</p> <p>Images are separated into folders, each corresponding to one of the videos used. To understand the naming convention, you can check <a href="https://arxiv.org/abs/1810.02835">the paper</a>, available at arXiv.</p>
Data for "Measurement report: Comparison of airborne in-situ measured, lidar-based, and modeled aerosol optical properties in the Central European background – identifying sources of deviations"
<p>A unique set of data is presented, derived from measurements conducted at the rural central European observatory at Melpitz, Germany. Data derived from remote sensing (lidar), airborne platforms (helicopter, balloon), and ground-based in-situ methods is included. Measured and Mie-modeled optical aerosol parameters are presented in the dry- and ambient state. Modeled optical parameters are based on Mie-theory. For ambient state hygroscopic growth simulations are utilized.</p>
Modeling the Extragalactic Background Light and the Cosmic Star Formation History (data)
<p>Paper: Modeling the Extragalactic Background Light and the Cosmic Star Formation History</p> <p>Authors: Justin D. Finke, Marco Ajello, Alberto Dominguez, Abhishek Desai, Dieter H. Hartmann, Vaidehi S. Paliya, Alberto Saldana-Lopez</p> <p>Description: These are the luminosity densities, EBL energy density/intensities, and gamma-ray absorption optical depths for "Model A" from this publication.</p> <p>Contents:</p> <p>lumdens.tar.gz: The model luminosity density, with redshift given in the title of the file. Each file contains the wavelength in microns, and the luminosity density in Watts/Mpc^3.</p> <p>EBL_energydensity.tar.gz: The model EBL energy density, with redshift given in the name of the file. Each file contains the photon energy in eV, and the energy density in erg/cm^3.</p> <p>EBL_intensity.tar.gz: The model EBL intensity, with redshift given in the name of the file. Each file contains the wavelength in angstroms, and the intensity in nW/(m^2 srad).</p> <p>tau.tar.gz: The model gamma-gamma absorption optical depths, with redshift given in the name of the file. Each file contains the photon energy in TeV, and the absorption optical depth.</p> <p>Distribution Statement A. Approved for public release. Distribution is unlimited.<br> </p>
Catalogues of LISA-detectable sBHB inspirals and confusion backgrounds following the GWTC-3 fiducial model posterior
<h2>Data set</h2> <p>The contents of <code>SNR_min_2_z1_LISA_SNR.tar.gz</code> consist of approximately 10k folders, each corresponding to each of the samples of the public GWTC-3 fiducial sBHB population model posterior, and containing the following files:</p> <ul> <li><code>population.yaml</code>: population parameters of this sample.</li> <li><code>background.txt</code>: frequencies and characteristic strain squared of the sBHB confusion noise in the LISA band for this population.</li> <li><code>population_detector_frame_SNR.h5</code>: subset of loud sBHB sources, including but not limited to those with LISA SNR larger than 4 (missing in some samples). </li> </ul> <p>For a description of the population parameters in <code>population.yaml</code> and the individual source parameters in <code>population_detector_frame_SNR.h5</code>, see the contents of the <code>Demo.ipynb</code> notebook.</p> <p>To be able to run the notebooks described below, uncompress the <code>SNR_min_2_z1_LISA_SNR.tar.gz</code> file inside a <code>data/</code> subfolder under that of the notebook.</p> <h2>Demo notebook</h2> <p>For examples of how to load and process the catalogues, see the <code>Demo.ipynb</code> notebook.</p> <p>This notebook requires the following Python packages:</p> <p> <code>numpy, scipy, pandas, matplotlib, pyyaml, tqdm</code><br> <br>Some of the plots in the notebook require the <a href="https://github.com/JesusTorrado/extrapops" target="_blank" rel="noopener">extrapops</a> simulation package:</p> <p> <code>$ git clone git@github.com:JesusTorrado/extrapops.git</code><br> <code>$ cd extrapops</code><br> <code>$ pip install .</code></p> <h2>References</h2> <p>For detailed descriptions of the generation of the data set, see the references mentioned in the Zenodo page.</p> <h2>Questions and comments</h2> <p>Please use the <a href="https://github.com/JesusTorrado/LISA_sBHB_catalogues/issues">GitHub issue tracker</a> or contact the corresponding authors of the papers cited under the "described by" header of the Zenodo entry.</p>
Harmonising the land-use flux estimates of global models and national inventories for 2000-2020: background data
<p>This online repository includes all the relevant data used in the paper "Harmonizing the land-use flux estimates of global models and national inventories for 2000-2020" (Grassi et al. 2023), plus some additional methodological information, organised in the following files:</p> <p>1) "<strong>Global model</strong><strong>s</strong> <strong>land CO2 </strong><strong>data 2000-2020</strong>" (MS Excel Format), including for each country data for:</p> <p>a. Land-use CO2 fluxes from each of three Bookkeeping Models (BMs) used, and for different categories (net LULUCF, deforestation, forest, other transitions, organic soils). </p> <p>b. The ensemble mean of the ‘natural terrestrial sink’ estimated by 16 Dynamic Global Vegetation Models (DGVMs), filtered with maps of intact/non-intact forest.</p> <p>The global model data included here are consistent with those included in the Global Carbon Budget 2022 (Friedlingstein et al., 2022).</p> <p>2) “<strong>National inventories LULUCF data 2000-2020</strong>” (version Dec 2022, MS Excel Format), including a comprehensive collection of LULUCF CO2 data based on countries' submissions to the United Nations Framework Convention on Climate Change (UNFCCC). The data here represent a slight update of the dataset included in Grassi et al. (2022).</p> <p>3) “<strong>Processing steps for DGVM results”,</strong> describing the protocol used to filter the results of DGVMs with maps of intact/non-intact forest and further details on the maps (PDF Format). </p> <p>4) “<strong>Intact and non-intact forest maps</strong>”, available in two files with different resolutions (0.5 and 0.05 degrees) in NetCDF format. Grassi et al. (2023) used the 0.5 degree resolution.</p> <p>5) "<strong>IntactAndNonIntactForest_0.5deg_script.js</strong>", the Google Earth Engine Java script to produce the forest maps (.js/text format)</p> <p>For further details, please refer to:</p> <p>Grassi et al. (2023) Harmonising the land-use flux estimates of global models and national inventories for 2000-2020. Earth Syst. Sci. Data.</p> <p>Other references:</p> <p>Friedlingstein et al. (2022) Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900.</p> <p>Grassi et al (2022) Carbon fluxes from land 2000–2020: bringing clarity to countries' reporting. Earth Syst. Sci. Data, 14, 4643-4666.</p> <p> </p> <p> </p>
Data from: Bunching up the background betters bias in species distribution models
Sets of presence records used to model species' distributions typically consist of observations collected opportunistically rather than systematically. As a result, sampling probability is geographically uneven, which may confound the model's characterization of the species' distribution. Modelers frequently address sampling bias by manipulating training data: either subsampling presence data or creating a similar spatial bias in non-presence background data. We tested a new method, which we call "background thickening," in the latter category. Background thickening entails concentrating background locations around presence locations in proportion to presence location density. We compared background thickening to two established sampling bias correction methods — target group background selection and presence thinning — using simulated data and data from a case study. In the case study, background thickening and presence thinning performed similarly well, both producing better model discrimination than target group background selection, and better model calibration than models without correction. In the simulation, background thickening performed better than presence thinning when the number of simulated presence locations was low, and vice versa. We discuss drawbacks to target group background selection, why background thickening and presence thinning are conservative but robust sampling bias correction methods, and why background thickening is better than presence thinning for small sample sizes. Particularly, background thickening is advantageous for treating sampling bias when data are scarce because it avoids discarding presence records.
Model data for: Analysis of the global atmospheric background sulfur budget in a multi-model framework
<p>The present dataset contains all model data used in the model intercomparison in ACP. All data is provided as monthly means. For more data, please contact the first author. V2 addresses inconsistencies in the time axes, vertical coordinates, and variable names between models.</p>
Data from: Bunching up the background betters bias in species distribution models
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ECCO Ocean 3D Gent-Mcwilliams, Redi, and Background Vertical Diffusivity Coefficients for the Lat-Lon-Cap 90 (llc90) Native Model Grid (Version 4 Release 4)
This dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Ocean 3D Gent-Mcwilliams, Redi, and Background Vertical Diffusivity Coefficients for the 0.5 degree Lat-Lon Model Grid (Version 4 Release 4)
This dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
Deep dynamical modelling of developmental trajectories with temporal transcriptomics [BACKGROUND_DATA]
GEO Series GSE236508. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Geometries, Driving Factors, And Connectivity Of Background Fractures At The Latemar Carbonate Platform (N. Italy): Relevance For Subsurface Reservoir Modelling_DATASETS
<p>The datasets (original field photographs and drone images) presented here are drawn from the extensive fieldwork by the author(s) at the Latemar carbonate platform in northern Italy. These datasets show different rock units in the Latemar platform riddled with diverse structural elements, including fractures (veins, joints, faults) and stylolites. In addition, the datasets are part of the paper titled "<strong>Geometries, Driving </strong><strong>F</strong><strong>actors, And Connectivity Of Background Fractures At The Latemar Carbonate Platform (N. Italy): Relevance For Subsurface Reservoir Modelling", </strong>submitted for publication in the Tectonics Journal. </p> <p>The drone images are taken with DJI Phantom 4® and processed using Agisoft PhotoScan® - a photogrammetry tool. The workflow in processing the drone images is publicly available in the published work of Bisdom et al. (2017).</p>
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