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575 results for “Modeling Codes”
Data and Code to support COVID-19 - exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study
<p>Data and code to support paper published in Wellcome Open research on years of life lost among people who died with COVID-19.</p>
Model input and output, performance measures and modifications in the source code for PALM simulations on Mäkelänkatu in Helsinki, Finland
<p>This dataset is for air quality simulations conducted on Mäkelänkatu in Helsinki, Finland, using the PALM model system 6.0.</p> <p>By default, simulations use modelled data as boundary conditions. This includes modelled meteorological data from MEPS (MetCoOp Ensemble Prediction System) and air pollutant background concentrations from the ADCHEM model. Alternatively, measured meteorology from the Kivenlahti mast in Espoo, Finland, and aerosol size distribution from the SMEAR III station in Kumpula, Helsinki, is applied.</p> <p>Simulations:</p> <ul> <li>9 June morning: <ul> <li>0609_morning: use modelled boundary conditions for meteorology and air pollutants</li> <li>0609_morning_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution</li> <li>0609_morning_smear: use modelled boundary conditions for meteorology and measured for aerosol size distribution</li> <li>0609_morning_wd_smear: use modelled boundary conditions for meteorology, but modify the wind direction by using the measured wind direction at Kivenlahti. For aerosol size distribution, use the measured boundary conditions.</li> <li>0609_morning_wdk_smear: use modelled boundary conditions for meteorology, but modify the wind direction by using the measured wind direction at SMEAR III. For aerosol size distribution, use the measured boundary conditions.</li> <li>precursor_0609_morning: precursor with modelled boundary conditions for meteorology</li> <li>precursor_0609_morning_allmet: precursor with measured boundary conditions for meteorology</li> <li>precursor_0609_morning_wd: precursor with modelled boundary conditions, but the wind direction is modified by using the measured wind direction at Kivenlahti.</li> <li>precursor_0609_morning_wdk: precursor with modelled boundary conditions, but the wind direction is modified by using the measured wind direction at SMEAR III.</li> </ul> </li> <li>9 June evening: <ul> <li>0609_evening: use modelled boundary conditions for meteorology and air pollutants</li> <li>0609_evening_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution</li> <li>precursor_0609_evening: precursor with modelled boundary conditions for meteorology</li> <li>precursor_0609_evening_allmet: precursor with measured boundary conditions for meteorology</li> </ul> </li> <li>12 December morning: <ul> <li>1207_morning: use modelled boundary conditions for meteorology and air pollutants</li> <li>1207_morning_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution </li> <li>precursor_1207_morning: precursor with modelled boundary conditions for meteorology</li> <li>precursor_1207_morning_allmet: precursor with measured boundary conditions for meteorology</li> </ul> </li> </ul> <p> </p> <p>Datasets are given separately for the root (no suffix), parent (suffix _N02) and child (_N03) domain. The content is following:</p> <ul> <li>input_monitoring_output_usercode <ul> <li>Input data <ul> <li><run_identifier>_chemistry: emission data for gases</li> <li><run_identifier>_dynamic: initialisation and forcing data for meteorological variables and air pollutants</li> <li><run_identifier>_p3d: parameter file for model steering</li> <li><run_identifier>_salsa: emission data for aerosol particles</li> <li><run_identifier>_static: topography information</li> </ul> </li> <li>Simulation performance information <ul> <li><run_identifier>_cpu: information on the CPU time consumed</li> <li><run_identifier>_header: information about the selected model parameters</li> <li><run_identifier>_rc: time step control output</li> </ul> </li> <li>Output data <ul> <li><run_identifier>_av_masked_N03_M01.nc: temporally averaged wind speed data close to the ground</li> <li><run_identifier>_av_masked_N03_M04.nc: temporally averaged aerosol particle concentration data close to the ground</li> <li><run_identifier>_av_masked_N03_M06.nc: temporally averaged aerosol particle concentration data in a vertical column next to the air quality monitoring station on Mäkelänkatu</li> <li><run_identifier>_av_masked_N03_M07.nc: temporally averaged aerosol particle concentration data in a vertical column on the other side of the street from the air quality monitoring station on Mäkelänkatu</li> <li><run_identifier>_pr.nc: temporally vertical profile data on meteorological variables</li> <li><run_identifier>_ts.nc: flow statistics data</li> </ul> </li> <li>Modifications made to the source code (PALM revision, https://palm.muk.uni-hannover.de/trac/browser?rev=4416, last access: 10 Sept 2019) <ul> <li>chem_gasphase_mod.f90: chemical mechanism salsa+simple</li> <li>chem_emissions_mod.f90 (modifications indicated with "MONA")</li> <li>user_module.f90 (modification listed under "Current revisions")</li> <li>Makefile (modification listed under "Current revisions")</li> </ul> </li> </ul> </li> </ul> <p>See the PALM model webpage (https://palm.muk.uni-hannover.de) for details.</p> <p> </p>
Data and code for publication "The role of history and strength of the oceanic forcing in sea level projections from Antarctica with the Parallel Ice Sheet Model"
<p>Data and code underlying the publication <a href="https://tc.copernicus.org/preprints/tc-2019-330/">"The role of history and strength of the oceanic forcing in sea level projections from Antarctica with the Parallel Ice Sheet Model"</a>.</p> <p>Journal: The Cryosphere</p> <p>Authors: <em>Ronja Reese<sup>1*</sup></em><em>, Anders Levermann</em><sup><em>1,2,3</em></sup><em>, Torsten Albrecht</em><sup><em>1</em></sup><em>, Hélène Seroussi<sup>4</sup></em><em>, Ricarda Winkelmann<sup>1,2 </sup></em></p> <p>(1) Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany</p> <p>(2) Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany</p> <p>(3) LDEO, Columbia University, New York, USA</p> <p>(4) Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA</p> <p>(*) email ronja.reese@pik-potsdam.de</p> <p>Abstract:<br> Mass loss from the Antarctic Ice Sheet constitutes the largest uncertainty in projections of future sea level rise. Ocean-driven melting underneath the floating ice shelves and subsequent acceleration of the inland ice streams is the major reason for currently observed mass loss from Antarctica and is expected to become more important in the future. Here we show that for projections of future mass loss from the Antarctic Ice Sheet, it is essential (1) to better constrain the sensitivity of sub-shelf melt rates to ocean warming and (2) to include the historic trajectory of the ice sheet. In particular, we find that while the ice sheet response in simulations using the Parallel Ice Sheet Model is comparable to the median response of models in three Antarctic Ice Sheet Intercomparison projects – initMIP, LARMIP-2 and ISMIP6 – conducted with a range of ice sheet models, the projected 21st century sea level contribution differs significantly depending on these two factors. For the highest emission scenario RCP8.5, this leads to projected ice loss ranging from 1.4 to 4.0 cm of sea level equivalent in the ISMIP6 simulations where the sub-shelf melt sensitivity is comparably low, opposed to a likely range of 9.2 to 35.9 cm using the exact same initial setup, but emulated from the LARMIP-2 experiments with a higher melt sensitivity based on oceanographic studies. Furthermore, using two initial states, one with and one without a previous historic simulation from 1850 to 2014, we show that while differences between the ice sheet configurations in 2015 are marginal, the historic simulation increases the susceptibility of the ice sheet to ocean warming, thereby increasing mass loss from 2015 to 2100 by about 50 %. Our results emphasize that the uncertainty that arises from the forcing is of the same order of magnitude as the ice dynamic response for future sea level projections.</p> <p>Large zip files contain data, small zip file python notebooks for data analysis and PISM code. Please contact ronja.reese@pik-potsdam.de if you have any further questions.</p>
Stan code from: Simulation modeling reveals the evolutionary role of landscape shape and species dispersal on genetic variation within a metapopulation
Different shapes of landscape boundaries can affect the habitat networks within them and consequently the spatial genetic-patterns of a metapopulation. In this study, we used a mechanistic framework to evaluate the effects of landscape shape, through watershed elongation, on genetic divergence among populations at the metapopulation scale. Empirical genetic data from four, sympatric stream-macroinvertebrates having aerial adults were collected from streams in Japan to determine the roles of species-specific dispersal strategies on metapopulation genetics. Simulation results indicated that watershed elongation allows the formation of river networks with fewer branches and larger topographic constraints. This results in decreased interpopulation connectivity but a lower level of spatial isolation of distal populations (e.g., those found in headwaters) occurring in the landscapes examined. Distal populations had higher genetic divergence when their downstream-biased dispersal (relative to upstream- and/or overland-biased dispersal) was high. This underscores the importance of distal populations influencing genetic divergence at the metapopulation scale for species having downstream-biased dispersal. In turn, lower genetic divergence was observed under watershed elongation when the genetic isolation of distal populations was decreased in such species. This strong association between landscape shape and evolutionary processes highlights the importance of natural, spatial architecture in assessing the effectiveness of conservation and management strategies.
Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding-Model setup and source code
<p>Source code and model setup/inputs for the paper titled "Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding" article. Simulations were conducted using a modified version of ADCIRC+DLB (ADCIRC + Dynamic Load Balancing) on unstructured triangular meshes.</p> <p>Contains:</p> <ol> <li>Model input files. <ol> <li>ADCIRC model input files for the ideal channel setup and Hurricane Irene simulation (*.13, *.14, *.15)</li> </ol> </li> <li>Zipped archive of the ADCIRC code (adcirc-cg-DLB.zip) used to produce the simulations for the paper.</li> <li>Step-by-step compilation and usage instructions for ADCIRC+DLB. <ol> <li>Installation.html </li> <li>Usage.html</li> </ol> </li> </ol>
Python Annotated Code Search (PACS) Datasets & Pretrained Models
<p>This upload contains datasets and pre-trained models used for the paper <em>Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent. </em>The code for easily loading these datasets and models will be made available here: <a href="http://github.com/nokia/codesearch">http://github.com/nokia/codesearch</a> </p> <p><strong>Datasets</strong><br> There are three types of datasets:</p> <ul> <li>snippet collections (code snippets + natural language descriptions): so-ds-feb20, staqc-py-cleaned, conala-curated</li> <li>code search evaluation data (queries linked to relevant snippets of one of the snippet collections): so-ds-feb20-{valid|test}, staqc-py-raw-{valid|test}, conala-curated-0.5-test</li> <li>training data (datasets used to train code retrieval models): so-duplicates-pacs-train, so-python-question-titles-feb20</li> </ul> <p>The staqc-py-cleaned snippet collection, and the conala-curated datasets were derived from existing corpora:</p> <ul> <li>staqc-py-cleaned was derived from the Python StaQC snippet collection. See <a href="https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset">https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset</a>, <a href="https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset/blob/master/LICENSE.txt">LICENSE</a>. </li> <li>conala-curated was derived from the conala corpus. See <a href="https://conala-corpus.github.io/">https://conala-corpus.github.io/</a> , <a href="https://creativecommons.org/licenses/by-sa/4.0/">LICENSE</a></li> </ul> <p>The other datasets were mined directly from a recent Stack Overflow dump (https://archive.org/details/stackexchange, <a href="https://creativecommons.org/licenses/by-sa/4.0/">LICENSE</a>). <br> <br> <strong>Pre-trained models</strong><br> Each model can embed queries and (annotated) code snippets in the same space. The models are released under a BSD 3-Clause License.</p> <ul> <li>ncs-embedder-so-ds-feb20</li> <li>ncs-embedder-staqc-py</li> <li>tnbow-embedder-so-ds-feb20</li> <li>use-embedder-pacs</li> <li>ensemble-embedder-pacs</li> </ul>
Data and R Code for publication: Should dispersers be fast learners? Modelling the role of cognition in dispersal
<p><span><span><span><span><span><span><span><span><span><span><span>Both cognitive abilities and dispersal tendencies can vary strongly between individuals. Since cognitive abilities may help dealing with unknown circumstances it is conceivable that </span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>dispersers may rely more heavily on learning abilities than residents. However, cognitive abilities are costly and leaving a familiar place might result in losing the advantage of having learned to deal with local conditions. Thus, individuals which invested in learning to cope with local conditions may be more reluctant to leave their natal place. In order to disentangle the complex relationship between dispersal and learning abilities we implemented individual-based simulations. By allowing for developmental plasticity, individuals could either develop a "resident" or "dispersal" cognitive phenotype.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>In line with our expectations, the correlation between learning abilities and dispersal could take any direction, depending how much time individuals had to recoup their investment in cognition. Both, longevity and the timing of dispersal within lifecycles determine the time individuals have to recoup that investment and thus crucially influence this correlation. We therefore suggest that species' life-history will strongly impact the expected cognitive abilities of dispersers, relative to their resident conspecifics, and that cognitive abilities might be an integral part of dispersal syndromes.</span></span></span></span></span></span></span></span></span></span></span></p>
Numerical model code, input files and output data for publication ``Mixing and Transformation in a Deep Western Boundary Current: a case study''
<p>Contains numerical model data (code, input files, selected output) to supplement publication ``Mixing and Transformation in a Deep Western Boundary current'', by Spingys and co-authors. All umerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations used in the above-referenced paper.</p>
Amory et al. (2021), Geoscientific Model Development : data, model outputs and source code
<p><strong>Data and model outputs for the replication of the analysis made in:</strong><br> (see the published version of this article in Geoscientific Model Development, 2021 - please cite this version if you use these data)<br> C. Amory, C. Kittel, L. Le Toumelin, C. Agosta, A. Delhasse, V. Favier, and X. Fettweis: Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adelie Land, East Antarctica, Geoscientific Model Development, accepted, 2021. </p> <p>See README.txt for a full description of the dataset content</p> <p>Please contact me at amory.charles@live.fr if you need other half-hourly outputs or for more details on the dataset</p>
Modeling of a chain of three plasma accelerator stages with the WarpX electromagnetic PIC code on GPUs
<p>This archive contains the data used for plots 1, 2, 3, 6 and 7 of the Physics of Plasma paper "Modeling of a chain of three plasma accelerator stages with the WarpX electromagnetic PIC code on GPUs" by J.-L. Vay et al (2021).</p> <p> </p> <p>To produce plots 1, 2 and 3, cd into the corresponding directory and run the python script.</p> <p> </p> <p>To produce plots 6 and 7, cd in the directory Figures_6_7 and run the script mkzplots.</p> <p> </p> <p>Simulations were performed on Summit (OLCF) with WarpX Version 19.10-2404-gafa3d4808ff0-dirty (code with slight modification from repo is given in archive warpx_source.tar.gz).</p> <p> </p> <p>Loaded Modules:</p> <p> 1) hsi/5.0.2.p5 3) lsf-tools/2.0 5) DefApps 7) spectrum-mpi/10.3.1.2-20200121 9) ccache/3.7.9 11) boost/1.66.0 13) hdf5/1.10.4 15) openblas/0.3.9-omp 17) nano/2.6.3</p> <p> 2) xalt/1.2.1 4) darshan-runtime/3.1.7 6) gcc/6.4.0 8) cuda/10.1.243 10) fftw/3.3.8 12) cmake/3.18.2 14) adios2/2.5.0 16) netlib-lapack/3.8.0 18) python/3.6.6-anaconda3-5.3.0</p>
Data from: Heterogeneity in genetic diversity among non-coding loci fails to fit neutral coalescent models of population history
Inferring aspects of the population histories of species using coalescent analyses of non-coding nuclear DNA has grown in popularity. These inferences, such as divergence, gene flow, and changes in population size, assume that genetic data reflect simple population histories and neutral evolutionary processes. However, violating model assumptions can result in a poor fit between empirical data and the models. We sampled 22 nuclear intron sequences from at least 19 different chromosomes (a genomic transect) to test for deviations from selective neutrality in the gadwall (Anas strepera), a Holarctic duck. Nucleotide diversity among these loci varied by nearly two orders of magnitude (from 0.0004 to 0.029), and this heterogeneity could not be explained by differences in substitution rates. Using two different coalescent methods to infer models of population history and then simulating neutral genetic diversity under these models, we found that the among-locus heterogeneity in nucleotide diversity was significantly higher than expected for these simple models. Defining more complex models of population history demonstrated that a pre-divergence bottleneck was also unlikely to explain this heterogeneity. However, both selection and interspecific hybridization could account for the heterogeneity observed among loci. Regardless of the cause of the deviation, our results illustrate that violating key assumptions of coalescent models can mislead inferences of population history.
Data from: Modelling vaccination strategies against Rift Valley fever in livestock in Kenya: model code
The impacts of vaccination on the transmission of Rift Valley fever virus (RVFV) have not been evaluated. We have developed a RVFV transmission model comprising two hosts -- cattle as a separate host and sheep and goats as one combined host (herein after referred to as sheep) -- and two vectors -- Aedes species (spp) and Culex spp -- and used it to predict the impacts of: (1) reactive vaccination implemented at various levels of coverage at pre-determined time points, (2) targeted vaccination involving either of the two host species, and (3) a periodic vaccination implemented biannually or annually before an outbreak. Methodology/Principal Findings: The model comprises coupled vector and host modules where the dynamics of vectors and hosts are described using a system of difference equations. Vector populations are structured into egg, larva, pupa and adult stages and the latter stage is further categorized into three infection categories: susceptible, exposed and infectious mosquitoes. The survival rates of the immature stages (egg, larva and pupa) are dependent on rainfall densities extracted from the Tropical Rainfall Measuring Mission (TRMM) for a Rift Valley fever (RVF) endemic site in Kenya over a period of 1827 days. The host populations are structured into four age classes comprising young, weaners, yearlings and adults and four infection categories including susceptible, exposed, infectious, and immune categories. The model reproduces the 2006/2007 RVF outbreak reported in empirical surveys in the target area and other seasonal transmission events that are perceived to occur during the wet seasons. Mass reactive vaccination strategies greatly reduce the potential for a major outbreak. The results also suggest that the effectiveness of vaccination can be enhanced by increasing the vaccination coverage, targeting vaccination on cattle given that this species plays a major role in the transmission of the virus, and using both periodic and reactive vaccination strategies. Conclusion/Significance: Reactive vaccination can be effective in mitigating the impacts of RVF outbreaks but practically, it is not always possible to have this measure implemented satisfactorily due to the rapid onset and evolution of RVF epidemics. This analysis demonstrates that both periodic and reactive vaccination ought to be used strategically to effectively control the disease.
Integration of protein and coding sequences enables mutual augmentation of the language model
<p><strong>The file structure is as follows:</strong></p> <p>Project Root<br>├── TE_MRL<br>│ ├── MRL_dataset.zip<br>│ └── TE_dataset.zip<br>│<br>├── finetuned_model<br>│ ├── FoldP<br>│ ├── LocP<br>│ ├── SSP<br>│ └── SolP<br>│<br>├── tax_tsne<br>│ └── emb_3models.zip<br>│<br>└── training_data<br> ├── FoldP.csv<br> ├── LocP.csv<br> ├── SolP.csv<br> ├── SSP.pkl<br> └── pretrain_source_GCF.txt</p>
scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution - Supplementary data and code
<p>Data and code to reproduce the analyses from the study: "scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution". </p>
Data and code for "Large language models identify causal genes in complex trait GWAS"
<p><span>This file contains the data and scripts for the preprint "Large language models identify causal genes in complex trait GWAS"</span></p>
CFD Modeling Results and Related Data and Codes for Plotting of "A Mesoscale-to-LES Modeling of Tornado-like Vortex and Associated Local Strong Winds in Urban Area"
<p>The CFD modeling outputs, derived maximum wind fields in the analysis area, the topography data, the Python codes used to produce the figures, as we as the namelist of WRF simulation are available. The CFD modeling outputs are in binary format. The ctl. files of corresponding binary data (or dataset if ordered chronologically) are available in each directory (named after each experiment in our study).</p>
Soil dissolved organic carbon (DOC) machine learning model code
Open the record for dataset details and reuse information.
Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory"
<p>Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory" published in the Journal of Geophysical Research Oceans. <br>URL of the manuscript: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JC020101<br>DOI of the manuscript: https://doi.org/10.1029/2023JC020101</p>
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>
Model codes, data, and plot scripts for the paper "Quantifying the Role of Model Internal Year-to-Year Variability in Estimating Anthropogenic Aerosol Radiative Effects".
<p>The model codes, data, and plot scripts used in the paper "Quantifying the Role of Model Internal Year-to-Year Variability in Estimating Anthropogenic Aerosol Radiative Effects".</p><ul><li>Figs&NCL: the NCL scripts and figures used in the paper.</li><li>Mods: modified CAM model code.</li><li>PostFortran: the Fortran code for analysing the model results (post-processing) that produces the final results used for making plots.</li><li>Results4Plots: the final results used for making plots.</li></ul><p> </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.