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252 results for “variability modeling”
Modeling surface pCO2 variability in two contrasting basins of North Indian Ocean using advanced machine learning algorithms
<p>The dataset contains surface ocean <em>p</em>CO2, uncertainty and air-sea CO2 flux for the North Indian Ocean region. The data is available from 1993 to 2020 on a monthly time scale. Each of these data has a spatial resolution of 1/12º. Air-sea CO2 flux is calculated using a bulk parameterization, which is a function of wind speed. A positive CO2 flux value signifies CO2 outgassing, while a negative value indicates atmospheric CO2 uptake. </p> <p><strong>**It is recommended to use the latest version V3. Previous versions are depricated.</strong></p>
Glacier model simulations of moraine building forced by interannual variability in climate
<p>A set of 2,000-year simulations of moraine building by a glacier flowing through a synthetic alpine landscape forced by interannual variability in weather imposed on an otherwise stable climate. Moraine relief is shown for a standard deviation in mean annual air temperature (dT) of 0.5°C, 1.5°C, and 3.0°C around a long-term mean of 7.0°C. Simulations were made using the ice-flow model iSOSIA (Egholm et al., 2011, <em>Geomorphology</em>).</p>
Afterslip Model Database RC2022 (Afterslip Moment Scaling and Variability from a Global Compilation of Estimates)
<p>Churchill2022AfterslipDatabase.xlsx is a detailed database of aseismic afterslip models and corresponding mainshock information compiled by Robert Churchill (under the supervision of Maximilian Werner, Juliet Biggs and Åke Fagereng). The database contains afterslip models of mainshocks since 1979, with a publication cut-off at the end of 2018. The database is near complete, but not exhaustive. Descriptions of each column can be found as comments in the header field, as well as in the accompanying paper. Not all fields are not complete, some are also approximate or inferred values.</p> <p>This accompanies the paper:</p> <p>Churchill, R.M., Werner, M.J., Biggs, J. and Fagereng, Å., 2022. Afterslip Moment Scaling and Variability from a Global Compilation of Estimates. <em>Journal of Geophysical Research: Solid Earth</em>, p.e2021JB023897. <a href="https://doi.org/10.1029/2021JB023897">https://doi.org/10.1029/2021JB023897</a>.</p> <p>We hope the database serves as a useful resource to the afterslip community. Please reference our associated paper when using this database, as well as the database itself. References for individual afterslip papers can be found on the second sheet of the database, and references for additional data used in our study can be found in the third sheet. In the future, the database may be updated to include additional (missed) studies, however, this first version accompanies our study.</p> <p>*Headers for columns U and V are mislabelled Afterslip Upper Depth Limit (km) and Afterslip Lower Depth Limit (km), when these should be Coseismic Slip Upper Depth Limit (km) and Coseismic Slip Lower Depth Limit (km). The data in these columns is otherwise correct.</p> <p> </p>
Soil chemical variables improve models of understory plant species distributions
<div class="page"> <div class="section"> <div class="layoutArea"> <div class="column"><strong>Aim</strong></div> <div class="column">To determine the importance of soil variables relative to more commonly used topo-climatic or remotely sensed variables in species distribution models (SDMs) for understory plants.</div> <div class="column"> </div> <div class="column"><strong>Location</strong></div> <div class="column">White Mountain National Forest, New Hampshire, U.S.A.</div> <div class="column"> </div> <div class="column"><strong>Methods</strong></div> <div class="column">We fit models for presence of 41 forest understory plant species across 158 plots using soil, topographic, and spectral predictors to determine the relative contribution of different predictor types. We determined (a) if the potential importance of soil variables is greater than generally described in SDM literature, (b) which predictors are most important, and (c) if a standard subset of predictors can be used to effectively model all species.</div> <div class="column"> </div> <div class="column"><strong>Results</strong></div> <div class="column">Models containing all three predictor types performed best. Soil and topographic variables had comparable importance; spectral variables were of lesser importance. The best predictor variable was B horizon carbon to nitrogen ratio (B C:N), followed by topographic position index, elevation, and B horizon exchangeable calcium (B Ca). No standard subset effectively modeled all species.</div> <div class="column"> </div> <div class="column"><strong>Main conclusions</strong></div> <div class="column"> Our results and those of other SDMs that include in-situ soil geochemical data suggest that soil variables are increasingly important with more detailed descriptions of soils. Soil fertility data, such as B C:N and B Ca, are particularly important in acidic, forest soils where pH is a poor indicator of fertility. Commonly used topo-climatic variables provide meaningful predictions but are limited by their use of indirect predictor variables, inhibiting transferability and interpretability. The poor performance of models created using standard subsets of variables highlights the uniqueness of each species' niche and the need to combine flexible model building techniques with a variety of predictor variables.</div> </div> </div> </div>
Modelling snowpack bulk density using snow depth, cumulative degree-days and climatological predictor variables -- data set
<p>This file constitutes the data set containing the snow course survey, North American Regional Reanalysis (NARR)-derived degree-day indices, and climatological variables data used to conduct the analysis, and generate the figures and tables in the manuscript titled "Modelling snowpack bulk density using snow depth, cumulative degree-days and climatological predictor variables" by Andras J. Szeitz and R. Dan Moore. The manuscript was submitted for publication in the journal 'Hydrological Processes'.</p> <p>Due to the size of the NARR data files used to derive the air temperature time series for each snow course location, we recommend acquiring them from the National Oceanic and Atmospheric Administration's data portal directly (<a href="https://psl.noaa.gov/data/gridded/data.narr.html">https://psl.noaa.gov/data/gridded/data.narr.html</a>).</p> <p>Likewise, the ClimateNA software application used to extract the climatological variables for each snow course location can be obtained from the Centre for Forest Conservation Genetics, Department of Forest and Conservation Sciences, UBC, directly (<a href="https://climatena.ca/">https://climatena.ca/</a>).</p>
CESM2 data for "Ocean complexity shapes sea surface temperature variability in a CESM2 coupled model hierarchy" - submitted to JCLI
<p><strong>CESM2 Experiment names:</strong></p> <ul> <li>FC = fully coupled model, CESM2 (variables freely available on https://esgf-node.llnl.gov/search/cmip6/)</li> <li>MD = mechanically decoupled model, CESM2</li> <li>SOM = slab ocean model, CESM2</li> </ul> <p>All datasets are for pre-industrial forcing (e.g., piControl), nominal 1-degree horizontal resolution </p> <p>---</p> <p>Decoding the files names:</p> <ul> <li><strong>climatology_monthly </strong>= 12 month climatology </li> <li><strong>climatology_annual</strong> = time mean climatology</li> <li><strong>variance</strong> = anomaly variance computed over time</li> </ul> <p>---</p> <p>Variables:</p> <ul> <li><strong>PRECL</strong> = large-scale convective precipitation</li> <li><strong>PRECC</strong> = convective precipitation</li> <li><strong>total precipitation (not provided but can be calculated)</strong> = PRECC + PRECL</li> <li><strong>HMXL</strong> = mixed layer depth</li> <li><strong>SST</strong> = sea surface temperature </li> </ul> <p><strong>Files for the CESM2 MD piControl run:</strong></p> <ol> <li>forcing_coupled.F90: POP2 (ocean) source code changes for cesm2.1.4-rc08 (search for "slarson" throughout code to find our changes</li> <li>cesm2.1.4-exp03-CTRL_B1850_f09_g17_hourlyclim_TAUX.nc: 6 hourly climatology for TAUX, from a FC run of CESM2. This file and the TAUY climatology are opened and read in the "rotate wind stress" subroutine in forcing_coupled.F90. This file is named "x2oavg_Foxx_taux_6hourly.nc" in forcing_coupled (we wanted a shorter file name in the code)</li> <li>cesm2.1.4-exp03-CTRL_B1850_f09_g17_hourlyclim_TAUY.nc: 6 hourly climatology for TAUY. This file is named "x2oavg_Foxx_tauy_6hourly.nc" in forcing_coupled (we wanted a shorter file name in the code) </li> </ol> <p> </p>
Model data to investigate wood frog abundance in 17-year post harvest variable retention mixed wood forests
<p>Variable retention forest harvesting aims to reduce negative effect of harvesting on forest biodiversity, but its effectiveness is not well understood for many taxa. To better understand the effects of variable retention forest management and environmental features on amphibians, we used pitfall traps to capture wood frogs (<em>Lithobates sylvaticus</em>) across 4 levels of retention harvest (clearcut [0%], 20%, 50%, and unharvested control [100%]), and 2 forest types (deciduous and coniferous), in 17-year post-harvest forests in northwest Alberta. We mapped breeding sites and used a terrain moisture index (Depth-to-Water) derived from airborne LiDAR to examine relationships between relative abundance, breeding site proximity and soil moisture. Retention level alone had no effect on relative abundance, but in late summer (July and August) there was a significant interaction between retention level and forest type: capture rates decreased with amount of retention for deciduous forests, but increased with amount of retention in conifer forests. During late summer, capture rates were higher in conifer forests than in deciduous forests, with soil moisture (lower Depth-to-Water) positively related to capture rates. Though timber retention may be beneficial to wood frogs in the short-term, any impacts of forest harvesting on wood frog abundance was undetectable in stands 17 years post-harvest. </p>
Parameter variability across different timescales in the energy balance-based model and its effect on evapotranspiration estimation
<p>Our dataset is for the manuscript "Parameter variability across different timescales in the energy balance-based model and its effect on evapotranspiration estimation". It includes the instantaneous and daily <em>z<sub>0m</sub></em>, <em>z<sub>0h</sub></em>, <em>g<sub>s</sub></em>, and <em>EBR</em>, which are derived from FLUXNET2015 dataset. The training and test datasets for building the data-driven parameter models are also uploaded.</p>
Project files provided as supporting information to the manuscript "Fast, accurate, and system-specific variable-resolution modelling of proteins"
<p>Project files provided as supporting information to the manuscript "Fast, accurate, and system-specific variable-resolution modelling of proteins".</p> <p>The dataset contains the following files:</p> <p>- RMSD_adk: files containing the root-mean-square deviation computed on the C-alpha atoms/beads in the atomistic and CANVAS simulations of adk (Figure 6).</p> <p>- RMSF_adk: files containing the root-mean-square fluctuations of the C-alpha atoms/beads in the atomistic and CANVAS simulations of adk (Figure 6).</p> <p>- APBS_adk: PQR file of the system and DX file of the surface potential, for both the atomistic and CANVAS systems (Figure 7).</p> <p>- SASA_antibody: files containing the per-residue solvent accessible surface area of the atomistic region of the ake protein, in the atomistic and CANVAS simulations (Figure 8).</p> <p>- RMSF_antibody: files containing the root-mean-square fluctuations of the C-alpha atoms/beads in the atomistic and CANVAS simulations of antibody (Figure 9).</p> <p>- APBS_antibody: PQR file of the system and DX file of the surface potential, for both the atomistic and CANVAS systems (Figure 10).</p> <p>- SASA_antibody: files containing the per-residue solvent accessible surface area of the hinge region of the antibody for each conformational cluster, in the atomistic and CANVAS simulations (Figure 11).</p> <p>- RMSIP_antibody: RMSIP between the essential subspaces computed from the atomistic and CANVAS simulations (Figure S3).</p> <p>- rgyr_antibody: files containing the radii of gyration of the antibody for each conformational cluster, in the atomistic and CANVAS simulations (Figure S4).</p> <p>- ake_AA.mp4: video of the all-atom simulation of adenylate kinase</p> <p>- ake_canvas.mp4: video of the canvas simulation of adenylate kinase</p>
Variability of Global Fire Emissions - Data and Model Code
<p>Netcdf files including all relevant data for the manuscript entitled "Trends and variability of global fire emissions due to historical anthropogenic activities", submitted to Global Biogeochemical Cycles in 2017. </p> <p>FINALv2_presentday_2002-2009.nc: Monthly fire area burned and carbon emissions data from FINAL.2 for the years 2002 through 2009</p> <p>FINALv2C_*_1700-2009.ts.nc: Historical time series of monthly area burned and carbon emissions for natural, secondary, crop and pasture land cover for years 1700 to 2009</p> <p>vegn_fire.F90: The main module of FINAL.2 in the GFDL LM3</p>
Replication of manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates"
<p>Replication of manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates".</p> <p>This page contains datasets used to replicate figures in a manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates". These ascii or netcdf files can be easily read by NCL, Fortran and others.</p> <p> </p>
Data underlying the article: "Excuse me, there is a mutant in my bioactivity soup! A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modelling"
<p>This repository contains the data underlying the article: “Excuse me, there is a mutant in my bioactivity soup! A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modelling” available as a preprint on ChemRxiv.</p> <p>Main authors: Marina Gorostiola González & Olivier J.M. Béquignon (Leiden University)</p> <p>Senior author: Gerard J.P. van Westen (Leiden University)</p> <p>This analysis was performed using the code available at <a href="https://github.com/CDDLeiden/chembl_variants" target="_blank" rel="noopener">https://github.com/CDDLeiden/chembl_variants</a></p>
A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy (dataset)
<p>Data cleaned and used for the path analysis model estimated in the article</p> <h1>A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy</h1> <p>https://doi.org/10.3390/su13041879</p>
Diurnal variability of Black Carbon in Modena: Modeled concentration maps for winter 2020 and 2021
<p>This video presents concentration maps of Black Carbon over the city of Modena during the winters of 2020 and 2021. The concentrations are categorized into Fossil Fuel, Biomass Burning, and their combined totals (Fossil Fuel + Biomass Burning). This supplementary material supports the paper titled "Measurement report: Source attribution and estimation of black carbon levels in an urban hotspot of the central Po Valley: An integrated approach combining high-resolution dispersion modelling and micro-aethalometers," published in EGUsphere (https://doi.org/10.5194/egusphere-2023-2641).</p>
Seasonal and Interannual Variability of Areal Extent of the Gulf Hypoxia from a Coupled Physical-Biogeochemical Model: A New Implication for Management Practice
<p>netcdf data and code for JGR manuscript: seasonal and interannual variability of areal extent of the Gulf Hypoxia from a coupled physical-biogeochemical model: A new implication for management practice</p>
Atmospheric and sea ice model fields from the perturbed parameter ensemble E3SMv0-HILAT used to examine emergent relationships among climate variables in the Arctic
<p>These files contain time series of several sea ice ad atmospheric fields produced in an ensemble of perturbed parameter simulations using the E3SMv0-HiLAT model. The time series are used to produced seasonal means, which are used to examine emerging relationships in the ensemble discussed in our manuscript, as of September 2019, in review at JGR</p>
Supplementary files: Machine Learning Insights into Türkiye's Climate Variability: Predictive Modelling and Spatial Analysis
<p>This dataset and python code were used in the study titled "Machine Learning Insights into Türkiye's Climate Variability: Predictive Modelling and Spatial Analysis".</p>
Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005-2017 based on multi-variable random forest model
<p>The aim of our study was to construct random forest models with high-performance, and estimate daily average PM<sub>2.5</sub> concentration and O<sub>3</sub> daily maximum 8h average concentration (O<sub>3</sub>-8hmax) of China in 2005-2017 at a spatial resolution of 1km×1km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on ten-fold cross validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly simulations of PM<sub>2.5</sub> gave average model fitting R<sup>2</sup> values of 0.85, 0.88 and 0.90, respectively; these R<sup>2</sup> values were 0.77, 0.77, and 0.69 for O<sub>3</sub>-8hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM<sub>2.5</sub> and O<sub>3</sub>-8hmax simulations. During 2005-2017, PM<sub>2.5</sub> exhibited an overall downward trend, while ambient O<sub>3</sub> experienced an upward trend. Whilst the spatial patterns of PM<sub>2.5</sub> and O<sub>3</sub>-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic.</p> <p>Each dataset is the annual mean concentration of PM<sub>2.5</sub> or O<sub>3</sub>-8hmax based on the standard grid (Grid.csv) for that year. The coordinate system of the grid is WGS-84.</p>
Imprints of Ocean Chaotic Intrinsic Variability on Bottom Pressure and Implications for Data and Model Analyses
<p>These data are used for the manuscript "Imprints of Ocean Chaotic Intrinsic Variability on Bottom Pressure and Implications for Data and Model Analyses" to be submitted to Geophysical Research Letter. Uploaded data include:</p> <p>[1] ext_int_quater_deg.nc: atmospherically driven and intrinsic variations for subseasonal, intra-annual and mean seasonal bottom pressure signals at model original resolution.</p> <p>[2] ext_int_3deg.nc: atmospherically driven and intrinsic variations for smoothed subseasonal and intra-annual bottom pressure signals at 3*3 degree resolution.</p> <p>[3] ext_int_10deg.nc: atmospherically driven and intrinsic variations for smoothed intra-annual bottom pressure signals at 10*10 degree resolution.</p> <p>[4] meanseason_timeseries.nc: Time series of mean seasonal bottom pressure signals from all 50 ensemble members over Agulhas Current region and Argentine Basin.</p>
The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content - ACCESS-CM2 data and code
<p>This dataset contains post-processed ACCESS-CM2 PI control CMIP6 climate model output and code used to produce Figs. 5, 6 and 9 in the published article:</p> <p>McDougall, T., J., Barker, P.M., Holmes, R.M., Pawlowicz, R., Griffies, S. and Durack, P. (2021): The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content, <strong>Geoscientific Model Development</strong>, 14, 1–21, <a href="https://doi.org/10.5194/gmd-2020-426">https://doi.org/10.5194/gmd-2020-426</a></p> <p>The processed ACCESS-CM2 data is included as .mat files and is accompanied by Matlab processing routines (including code from the TEOS-10 Gibbs SeaWater Oceanographic Toolbox, https://www.teos-10.org/software.htm#1) to produce the figures. A more detailed description of the data are included in README.md. The code and data is also available under version control at <a href="https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published">https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published</a>.</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.