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575 results for “Modeling Codes”
Model code for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions model code as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p>Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.0eV-1024Mpc)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.0eV 1024Mpc simulation</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-fiducial)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the fiducial simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-ic-HR)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>initial condition data for the HR simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-ic-1024Mpc)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>initial condition data for the 1024Mpc simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-ic-fiducial)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>initial condition data for the fiducial simulations as well as the primordial phases used for all simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-HR-z0)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV HR simulation at z = 0</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.0eV-HR)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.0eV HR simulation</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-1024Mpc-z1)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV 1024Mpc simulation at z = 1</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-1024Mpc-z0)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV 1024Mpc simulation at z = 0</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-HR-z1)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV HR simulation at z = 1</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Impact of Code Language Models on Automated Program Repair (Dataset)
<p>This is the fine-tuning dataset used in the paper Impact of Code Language Models on Automated Program Repair</p>
Code for the population genetic models of the evolution of preference strength
<p>Sexual selection has a rich history of mathematical models that consider why preferences favor one trait phenotype over another (for population genetic models) or what specific trait value is preferred (for quantitative genetic models). Less common is exploration of the evolution of choosiness or preference strength: that is, by how much a trait is preferred. We examine both population and quantitative genetic models of the evolution of preferences, specifically developing "baseline models" of the evolution of preference strength during the Fisher process. Using a population genetic approach based on the classic model of Kirkpatrick (1982), we find selection for stronger and stronger preferences when trait variation is maintained by mutation. However, this force is quite weak and likely to be swamped by drift in moderately-sized populations. In a quantitative genetic model based on Lande (1981), unimodal preferences will generally not evolve to be increasingly strong without bounds when male traits are under stabilizing viability selection, but evolve to extreme values when viability selection is directional. Our results highlight that different shapes of fitness and preference functions lead to qualitatively different trajectories for preference strength evolution ranging from no evolution to extreme evolution of preference strength.</p>
Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing for "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea" paper.
<p>Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing as well as selected model output used for analysis and producing figures in the paper "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea". Contact Bronwyn Cahill if you have questions at: bronwyn.cahill@io-warnemuende.de</p>
Model data and code supporting "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "
<p>Model data and analysis code supporting the Geoscientific Model Development manuscript "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "</p> <p> </p> <p> </p>
Codes for ''The lithospheric loading model for large impact basin where mantle plug presents''
<p>This dataset contains the fortran codes and shell script used to generate data and figure for the article titled with "<strong>The lithospheric loading model for large impact basin where mantle plug presents</strong>".</p> <p>GMT and SHTOOLS is required for the operation of these code.</p> <p>The source code named LocalizedAdmitCorrV4.f95 is modified from the SHTOOLS example: LocalizedAdmitCorr.f95 written by Mark Wieczorek (April, 2005).</p> <p> </p>
CCSM4 model code and ozone files
<p>CCSM4 model code used in paper submission titled "The impact of model resolution on the Southern Hemisphere in CCSM4 idealized climate simulations" by Houraa Daher and Ben Kirtman from the University of Miami Rosenstiel School of Marine and Atmospheric Sciences. </p> <p>The tar.gz file contains the CCSM4 model code and the *.nc files are the two ozone files (year 1955 and year 2000) used in the study. </p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-figure)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>data needed for generating all figures</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Data and Code for "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application"
<p>This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application" authored by Juan José Price and Arne Henningsen and published in the Journal of Productivity Analysis (DOI: <a href="https://doi.org/10.1007/s11123-023-00684-1">10.1007/s11123-023-00684-1</a>).</p> <p>We conducted the empirical analysis with the "R" statistical software (version 4.3.0) using the add-on packages "combinat" (version 0.0.8), "miscTools" (version 0.6.28), "quadprog" (version 1.5.8), sfaR (version 1.0.0), stargazer (version 5.2.3), and "xtable" (version 1.8.4) that are available at CRAN. We created the R package "micEconDistRay" that provides the functions for empirical analyses with ray-based input distance functions that we developed for the above-mentioned paper. Also this R package is available at CRAN (https://cran.r-project.org/package=micEconDistRay).</p> <p>This replication package contains the following files and folders:</p> <ul> <li><strong>README</strong><br> This file</li> <li><strong>MuseumsDk.csv</strong><br> The original data obtained from the Danish Ministry of Culture and from Statistics Denmark. It includes the following variables: <ul> <li><em>museum</em>: Name of the museum. </li> <li><em>type</em>: Type of museum (Kulturhistorisk museum = cultural history museum; Kunstmuseer = arts museum; Naturhistorisk museum = natural history museum; Blandet museum = mixed museum).</li> <li><em>munic</em>: Municipality, in which the museum is located.</li> <li><em>yr</em>: Year of the observation.</li> <li><em>units</em>: Number of visit sites.</li> <li><em>resp</em>: Whether or not the museum has special responsibilities (0 = no special responsibilities; 1 = at least one special responsibility).</li> <li><em>vis</em>: Number of (physical) visitors.</li> <li><em>aarc</em>: Number of articles published (archeology).</li> <li><em>ach</em>: Number of articles published (cultural history).</li> <li><em>aah</em>: Number of articles published (art history).</li> <li><em>anh</em>: Number of articles published (natural history).</li> <li><em>exh</em>: Number of temporary exhibitions.</li> <li><em>edu</em>: Number of primary school classes on educational visits to the museum.</li> <li><em>ev</em>: Number of events other than exhibitions.</li> <li><em>ftesc</em>: Scientific labor (full-time equivalents).</li> <li><em>ftensc</em>: Non-scientific labor (full-time equivalents).</li> <li><em>expProperty</em>: Running and maintenance costs [1,000 DKK].</li> <li><em>expCons</em>: Conservation expenditure [1,000 DKK]. </li> <li><em>ipc</em>: Consumer Price Index in Denmark (the value for year 2014 is set to 1).</li> </ul> </li> <li><strong>prepare_data.R</strong><br> This R script imports the data set MuseumsDk.csv, prepares it for the empirical analysis (e.g., removing unsuitable observations, preparing variables), and saves the resulting data set as DataPrepared.csv.</li> <li><strong>DataPrepared.csv</strong><br> This data set is prepared and saved by the R script prepare_data.R. It is used for the empirical analysis.</li> <li><strong>make_table_descriptive.R</strong><br> This R script imports the data set DataPrepared.csv and creates the LaTeX table /tables/table_descriptive.tex, which provides summary statistics of the variables that are used in the empirical analysis.</li> <li><strong>IO_Ray.R</strong><br> This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions with the 'optimal' ordering of outputs, imposes monotonicity on this distance function, creates the LaTeX table /tables/idfRes.tex that presents the estimated parameters of this function, and creates several figures in the folder /figures/ that illustrate the results.</li> <li><strong>IO_Ray_ordering_outputs.R</strong><br> This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions, imposes monotonicity for each of the 720 possible orderings of the outputs, and saves all the estimation results as (a huge) R object allOrderings.rds.</li> <li><strong>allOrderings.rds</strong> (not included in the ZIP file, uploaded separately)<br> This is a saved R object created by the R script IO_Ray_ordering_outputs.R that contains the estimated ray-based Translog input distance functions (with and without monotonicity imposed) for each of the 720 possible orderings.</li> <li><strong>IO_Ray_model_averaging.R</strong><br> This R script loads the R object allOrderings.rds that contains the estimated ray-based Translog input distance functions for each of the 720 possible orderings, does model averaging, and creates several figures in the folder /figures/ that illustrate the results.</li> <li><strong>/tables/</strong><br> This folder contains the two LaTeX tables table_descriptive.tex and idfRes.tex (created by R scripts make_table_descriptive.R and IO_Ray.R, respectively) that provide summary statistics of the data set and the estimated parameters (without and with monotonicity imposed) for the 'optimal' ordering of outputs.</li> <li><strong>/figures/</strong><br> This folder contains 48 figures (created by the R scripts IO_Ray.R and IO_Ray_model_averaging.R) that illustrate the results obtained with the 'optimal' ordering of outputs and the model-averaged results and that compare these two sets of results.</li> </ul>
Code and Data to support "Atmospheric circulation-constrained model sensitivity recalibrates Arctic climate projections"
<p><a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/sic.sep.5member.dat">sic.sep.5member.dat</a> contains direct binary data of spatial monthly averaged sea ice concentrations for 1979 January to 2020 December from the CESM2 wind-nudging runs.</p> <p><a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/cism2.exp.smb.01.nc">cism2.exp.smb.01.nc</a> to <a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/cism2.exp.smb.01.nc">cism2.exp.smb.05.nc</a> contain netcdf files of annual averaged surface mass balance output from the CESM2-CISM2 wind-nudging runs between 1979 and 2020.</p> <p>topal&ding_code1.py - data preparation Python code</p> <p>topal&ding_code2.py - creating the main text and supplementary figures.</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.