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2,152 results for “outputs”
Bayesian models' outputs for: A new method to explicitly estimate the shift of optimum along gradients in multispecies studies
<p>This repository contains data to reproduce analysis presented in the paper:</p> <p>B. Mourguiart, B. Liquet, K. Mengersen, T. Couturier, J. Mansons, Y. Braud, A. Besnard . A new method to explicitly estimate the shift of optimum along gradients in multispecies studies. <em>Journal of Biogeography</em>, (in press).</p> <p>The paper introduces a new formulation of a Bayesian hierarchical linear model that explicitly estimates optimum shifts for multiple species having symmetrical response curves. This new formulation, called Explicit Hierarchical Model of Optimum Shifts (EHMOS), is compared to a mean comparison method and a Bayesian generalized linear mixed model (GLMM) using simulated and real datasets. Fitting the models to the simulated data took several days. Here we provide the models' outputs needed to reproduce the results presented in the paper without re-running the models. </p>
Abeshu-etal_2023_GMD: Model Input and Output data
<p>1. Input data required to conduct the experiment</p> <p>2. Model output data for reproducing the manuscript figures</p>
In- and output files for krill growth models intercomparison (Bahlburg et al. 2023)
<p>These datasets are required to re-run the krill growth model simulations presented in Bahlburg et al. (2023)</p> <p>The code repository can be found under https://github.com/dbahlburg/krillGrowthModelsComparison</p> <p>The ZIP file contains two folders: One containing the input data including the climatologies which should be deposited into inputData/climatologies in the project</p> <p>The other folder contains the simulation results which should be copied into the "simulationResults"-folder in order to run the "10ManuscriptPlots.R"-Script.</p>
TRENDYv10 DGVM output for: Process-oriented analysis of dominant sources of uncertainty in the land carbon sink
<p><strong>New datasets saved as NCDF files.</strong></p> <p><strong>Data</strong></p> <p>Post-processed TRENDYv10 DGVM data covering the period 1901-2020. Each file contains monthly/annual mean values for 19 models: CABLE-POP, CLASSIC, CLASSIC-N, CLM5.0, DLEM, IBIS, ISAM, ISBA-CTRIP, JSBACH, JULES-ES-1.1, LPJ-GUESS, LPJ, LPX-Bern, OCN, ORCHIDEE, ORCHIDEEv3, SDGVM, VISIT, YIBs. See https://blogs.exeter.ac.uk/trendy/</p> <p>List of variables can be found at https://blogs.exeter.ac.uk/trendy/documents/ . File name: 'trendy_listofvariables_GCP2021'.</p> <p>Data for three experiments (S1 - S3) is provided. For experiment details, see 'GlobalCarbonBudget-Protocol-2021-web' at https://blogs.exeter.ac.uk/trendy/documents/</p> <p><strong>Code</strong></p> <p>All data processing code is also provided. Files starting "raw#..." are run in order 1-4. Then the figure/manuscript files are executed, starting with "proc1.." - "proc3..". The remaining Figure scripts (Figures 1-5 and S1-S8) and "temp_changes.R" can be run in any order.</p> <p>Any enquiries, contact Mike O'Sullivan at m.osullivan@exeter.ac.uk</p>
Anti-Riok2 virtual screening output results
<p>Various investigations have revealed the role of RIOK2 in the carcinogenesis and potential of RIOK2 inhibitors as anticancer medicine. In this study, an in-silico approach have been utilized to find potential RIOK2 inhibitors and the uplouded file is the output of docking based virtual screening.</p>
Condensed GCHP-CAM simulation output
<p>Condensed output dataset from GCHP-CAM. This data was generated for the paper "Rapid estimation of climate-air quality interactions in integrated assessment using a response surface model", published in ACS Environmental Au in 2023. An analysis code which applies this dataset has the DOI 10.5281/zenodo.7618827. The full zip file contains five directories, one for each ensemble member (w10, w13...). For each ensemble member, there are 10 subdirectories, reflecting two different climate scenarios (pol3.7 and ref, for 3.7 W/m2 and 10 W/m2 of warming by 2100 respectively) and five different emissions perturbations (either no perturbation or a 10% reduction in NH3, NOx, SOX, or VOCs respectively). Each subdirectory contains a single file called "AQ_annual_2080.nc4". This file is a c24 output dataset with entries for 2080 to 2099.</p>
Model output for "Catchment coevolution and the geomorphic origins of variable source area hydrology"
<p>Model output supporting "Catchment coevolution and the geomorphic origins of variable source area hydrology" for submission to Water Resources Research. The Python package DupuitLEM v1.1-alpha (DOI: 10.5281/zenodo.7620978) contains the models and scripts used to generate and post-process output.</p>
Model output publication 'Mechanisms of salt overspill at estuarine network junctions explained with an idealized model'
<p>The model output for the publication in JGR: Oceans titled 'Mechanisms of salt overspill at estuarine network junctions explained with an idealized model'</p>
Atomistic Picture of Opening-Closing Dynamics of DNA Holliday Junction Obtained by Molecular Simulations: Simulations Topology, Coordinate, Parameters, Input and Output files
<p>The simulation data for the article: Atomistic Picture of Opening-Closing Dynamics of DNA Holliday Junction Obtained by Molecular Simulations.</p> <p>ck_metad.tar.gz: Includes the topology files, coordinates files and gromacs parameter input file (.mdp) used for WT-MetaD-HREX simulations with different c(K+), which are newly added runs for resubmission. The corresponding script files and Plumed files are in GitHub.</p> <p>eq_mini.tar.gz: Includes the parameter files required for the equilibration and minimization protocol.</p> <p>standard_md.tar.gz: Includes the topology files and coordinate files for all systems built in the article. Also include the hbfix parameters file required on the MD run, and the MD script file.</p> <p>metad.tar.gz: Includes the topology files, coordinates files and gromacs parameter input file (.mdp) used for WT-MetaD-HREX simulations. The corresponding script files and Plumed files are in GitHub.</p> <p>metad*fe*.tar.gz: Plumed HILLS files and metad.bias data used for drawing the free energy landscapes.</p> <p>ions.tar.gz: Data used for Figure.3 in the manuscript</p> <p>si_data.tar.gz: All data used for SI figures.</p>
Model output for analyses in Qian et al. (2023) "Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model".
<p>This archive contains post-processed data for analyses in Qian et al. (2023) "<strong>Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model</strong>". These data are model output from the Perturbed Parameters Ensemble (PPE) simulations conducted with DOE's E3SM Atmosphere Model Version 1 (EAMv1). There are 256x12 PPE simulations with each simulation run for 5 days following the approach of Cloud Associated Parameterization Testbed (CAPT) and transpose Atmosphere Model Intercomparison Project (AMIP) paradigm. The diagnostic variables associated with the parameterizations of turbulence and shallow convection, deep convection, cloud microphysics, and gravity wave drag at day 5 are saved and used for analyses in this paper. </p>
Williams Flats WRF-Fire Output Contain for FMC
<p>Configuration and output files for a WRF-Fire simulation starting on August 6th for the contain-FMC sensitivity.</p> <p> </p>
Diverse carbon dioxide removal approaches could reduce energy-water-land impacts (output data)
<p>GCAM scenario output data</p>
Supporting CESM output for "The effect of plant physiological responses to rising CO2 on global streamflow" (Fowler et al.; 2019)
<p>Thirty years of CESM soil moisture, rain, and snow data is collected here based on each experiment contained within Fowler et al. (2019). These data were used to produce Figure 3 of that work. Other supporting data including daily runoff and river discharge is archived elsewhere, while analysis scripts and more information can be found here: https://github.com/megandevlan/Physiology-Streamflow.git. </p>
InSAR-derived horizontal velocities in a global reference frame - final output dataset and software codes
<p>The output dataset as described in the article in title, extracted from COMET LiCSAR dataset in March 2021.</p> <p>We also provide a snapshot of the python3 codes used to generate the output dataset.</p> <p>Contents and description of the dataset:</p> <p>uaz_values.csv<br> ==============<br> Contains u_az values and other relevant data in columns:<br> frame - ID of related frame (same as in frame_values.csv)<br> esd_master - reference acquisition epoch<br> epoch - date of acquisition epoch<br> daz_total_wrt_orbits - original extracted azimuth shift w.r.t. orbits<br> daz_cc_wrt_orbits - original extracted azimuth shift w.r.t. orbits from intensity cross-correlation (prior to spectral diversity)<br> drg_wrt_orbits - original extracted range shift w.r.t. orbits<br> orbits_precision - precision of applied orbits (P..precise, R..restituted)<br> version - orbits version<br> daz_iono_grad_mm - u_az from ionosphere propagation<br> tecs_A - estimated TECs at centre of hyphotetical burst A<br> tecs_A - estimated TECs at centre of hyphotetical burst B<br> daz_mm_notide - u_az after correction of solid-earth tides<br> daz_mm_notide_noiono_grad - u_az after correction of solid-earth tides and ionospheric gradient propagation<br> is_outlier_* - flag of outlier datapoint, as identified through Huber loss function (related to velocity estimates in frame_values.csv)</p> <p>frame_values.csv<br> ================<br> Contains along-track velocity estimates and other relevant data in columns:<br> frame - ID of related frame<br> master - reference acquisition epoch<br> center_lon - longitude coordinate of the frame centre<br> center_lat - latitude coordinate of the frame centre<br> heading - satellite heading angle (from the geograpic north)<br> azimuth_resolution - extracted azimuth pixel spacing (in metres)<br> range_resolution - extracted range pixel spacing (in metres)<br> avg_incidence_angle - average incidence angle of the frame<br> centre_range_m - approximate slant distance between the satellite and centre of the frame<br> centre_time - acquisition time (UTC) of centre of the frame at the reference epoch (appliable to other epochs)<br> s1AorB - flag of S-1A or B of the reference epoch<br> slope_plates_vel_azi_itrf2014 - along-track velocity estimated from ITRF2014 plate motion model<br> slope_daz_mm_mmyear - estimated along-track velocity from the original u_az values (in mm/year)<br> slope_daz_mm_notide_mmyear - estimated along-track velocity from u_az values after correction on solid-earth tides<br> slope_daz_mm_notide_noiono_grad_mmyear - estimated along-track velocity from u_az values after correction on solid-earth tides and ionosphere<br> intercept_* - corresponding intercept (in mm)<br> *_RMSE_selection - RMSE of outlier-free u_az data samples<br> *_count_selection - count of outlier-free u_az data samples used to estimate corresponding velocity<br> *_RMSE_mmy_full - RMSE from all u_az data samples applying corresponding velocity and intercept (in mm/year)</p> <p><br> decomposed_grid.csv<br> ===================<br> Contains decomposed velocities (in 250x250 km spacing grid) and other relevant data in columns:<br> count - count of frames used for the decomposition<br> opass - orbital pass codes of the input frames (D..descending, A..ascending)<br> centroid_lon - longitude coordinate of the grid cell centre<br> centroid_lat - latitude coordinate of the grid cell centre<br> VEL_N_noTI - northward velocity component from data corrected for solid-earth tides and ionosphere<br> VEL_E_noTI - eastward velocity component from data corrected for solid-earth tides and ionosphere<br> VEL_N_noT - northward velocity component from data corrected for solid-earth tides<br> VEL_E_noT - eastward velocity component from data corrected for solid-earth tides<br> ITRF_N - northward velocity component from averaged ITRF2014 plate motion model<br> ITRF_E - eastward velocity component from averaged ITRF2014 plate motion model<br> *RMSE_* - RMSE of corresponding data</p>
Output files of the theoretical calculation done for Pro-Glu-Glu and Pro-Val-Glu
<p>The output files of the theoretical calculation done in HbA and HbS where they are structurally different.</p>
Benchmark denoising strategies on fMRIPrep output - input data
<p>All inputs required to build the figures and statistics in the jupyter book.<br> Including: summary statistics for the figures and average connectomes per dataset.<br> Untar the file and put it under the `inputs/` directory.</p> <p>Repository: https://github.com/SIMEXP/fmriprep-denoise-benchmark</p> <p>Book: https://simexp.github.io/fmriprep-denoise-benchmark/</p> <p> </p>
Open access and international co-authorship: a longitudinal study of the United Arab Emirates research output
<p>Enriched data from Scopus used for the article: Open access and international co-authorship: a longitudinal study of the United Arab Emirates research output</p>
Supporting CESM1-SOM Output for "Regional Precipitation Sensitivity is Sensitive to Changes in Cross-Equatorial Ocean Heat Transport"
<p>Supporting CESM1-SOM experiment output.</p>
Code and model outputs for article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"
<p>Scripts from the scientific article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"<br> Submitted to Global Biogeochemical Cycles<br> Camila Serra-Pompei, Anna Hickman, Gregory L. Britten, Stephanie Dutkiewicz</p> <p>This repository contains all the files needed to run the MITgcm Biogeochemical and Optical model (MITgcmBgc, a.k.a. The Darwin model) and generate the figures in the paper.<br> Model outputs are also provided, so there is no need to run the entire model to generate the figures.</p> <p>The main folder "codes_phyto_bbp_paper.zip" contains all the files to process model output and reproduce the figures from the paper. The main files are:<br> - "Script_main.m" loads and processes model outputs and generates figures 2 to 9.<br> - "Script_new_optical_params" generates the optical parameters that are used as inputs in the MITgcmBgc model. It also generates figure 1.<br> - "Script_sensitivity_analysis" Performs offline sensitivity analysis and figure 10.</p> <p>The "mitgcm_darwin_3.zip" folder contain the code of the MITgcm with the Biogeochemical and Optical modules (Darwin package), with the configurations used in this study. Explanations of the model can be found in https://darwin3.readthedocs.io/en/latest/phys_pkgs/darwin.html </p> <p>These folders also contain the BGC-Argo floats data and climatological bbp and Chl NASA MODIS-Aqua GIOP data used in this study.</p> <p>The BGC-Argo data used in this article (download date: 27/10/2021) is available in "Other_extra_files/Argo_matrix.mat". The BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org, https://doi.org/10.17882/42182). The Argo Program is part of the Global Ocean Observing System. BGC-Argo float data was extracted using the BGC-Argo-Mat Matlab toolbox (Frenzel et al. 2021).</p> <p>The MODIS-Aqua satellite remote sensing data used in this article (download date: 23/12/2022) is available here in the folder "nasa_data". Satellite remote sensing data was extracted from NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group; (2014): MODIS-Aqua Ocean Color Data; NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. http://dx.doi.org/10.5067/AQUA/MODIS_OC.2014.0</p> <p> </p>
Output Data: gcam_v6.0_seasia
<p>Output databases from the Southeast Asia branch of GCAM.</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.