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2,152 results for “outputs”
Data from ROMS-CoSiNE-Fe model outputs at Station S1
<p>ROMS-CoSiNE-Fe model outputs at Station S1. Standard ROMS averaged files.</p>
Trees and Roary output for Adaptation by ancient horizontal acquisition of butyrate metabolism genes in Aggregatibacter actinomycetemcomitans
<p>Supporting data for publication. </p> <ol> <li>Raw Trees for Figure 1 and supplementary figures 2 and 3.</li> <li>Roary presence absence output.</li> </ol>
Multi-regional Input–output Table for the Global Emerging Economies (EMERGING V2.5)
<p><strong>EMERGING V2.5</strong>: This version removes the 27th sector, totaling 133 sectors, compared to EMERGING1.0. It has been optimized and calibrated to improve the intermediate input matrix (Z matrix), trade volumes (imports and exports), and total output for the electricity, transportation, and energy sectors across 245 economies, with a particular focus on small and medium-sized emerging economies.</p> <p><strong>EMERGING CO<sub>2</sub> Inventory</strong>: CO<sub>2</sub> emissions (Mt CO<sub>2</sub>eq) from fossil fuel combustion and energy consumption at the sectoral level in each region are available from the IEA database and CEADs emission inventory data. The EMERGING CO<sub>2</sub> Inventory includes a total of 7 energy types: 1. Coal; 2. Natural gas; 3. Oil products; 4. Crude, NGL, Ref Feeds.; 5. Other; 6. Oil shale & oil sands; 7. Peat & Peat products.</p> <p><strong>Announcements</strong></p> <p>These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. The availability of these data does not constitute publication of the data. The data providers rely on the ethics and integrity of the user to ensure that they receive fair credit for their work. If the data are obtained for potential use in a publication or presentation, we kindly ask you to cite them properly according to the instructions on the website.</p> <p>Queries about the EMERGING database and further collaboration can be addressed to: huojw20@hotmail.com</p>
China's input-output table
<p>The input-output table is classified into more detailed sectors, which can deeply study the various complex interdependence and main structures between industries and products and reveal the chain reaction between various economic activities in the operation of the national economy, so it is an essential means to analyze the intricate causal relationship and development law of an economic system in the process of operation.</p> <p>This data is based on the input-output table the Chinese Bureau of Statistics released. For the convenience of researchers, it is then organized into a dataset.</p>
Model output for "Climate variability leads to multiple oxygenation episodes across the Great Oxidation Event"
<div> <p>This repository contains all the model output presented in Garduno et al. (2024). Climate variability leads to multiple oxygenation episodes across the Great Oxidation Event. Submitted to Geophysical Research Letters.</p> <h1>Model output organization</h1> <p>There are seven zip files containing the model output from different simulations described in the main manuscript. See the main manuscript and <a href="https://github.com/DanyIvan/climate_goe_over_time" target="_blank" rel="noopener">code repository</a> for more details.:</p> <ul> <li>`o2_flux_constant_1.8e12.zip`: model output for simulation in which the O2 input flux is kept constant at a value of 1.8e12 molecules/cm^2/s</li> <li>`o2_flux_constant_2.2e12.zip`: model output for simulation in which the O2 input flux is kept constant at a value of 2.2e12 molecules/cm^2/s</li> <li>`linear_o2_flux_increase.zip`: model output for simulation in which the O2 input flux is linearly increased</li> <li>`linear_o2_flux_increase_change_during_glaciations.zip`: model output for simulation in which the O2 input flux is linearly increased, superimposing a 60% decrease during glaciations and a 60% increase after glaciations.</li> <li>`linear_ri_flux_decrease.zip`: model output for simulation in which the reductant input flux is linearly decreased and the O2 input flux is kept constant</li> <li>`linear_ri_flux_decrease_change_during_glaciations.zip`: model output for simulation in which the reductant input flux is linearly decreased and the O2 input flux is kept constant with a 60% decrease during glaciations and a 60% increase after glaciations.</li> <li>`stability_analysis.zip`: model output for stability analysis of steady states.</li> </ul> <p><br>Each of these folders contains model output every 1e5 years. The files are numbered from 1 to 4998. `1` corresponds to the output at 1e5 years, `2` corresponds to the output at 2e5 years, and so on.</p> <p>There are also files containing the O2 fluxes (`o2_flux.txt`) and reductant input (`ri_flux.txt`) used at each 1e5 output step.</p> <h1>Reading data</h1> <p>The data files are Fortran binary files. You can read the with the <a href="https://github.com/Nicholaswogan/PhotochemPy/blob/5bf449fa26370eb2d21d45ae59eacbfebf9705a4/PhotochemPy/io.py#L45" target="_blank" rel="noopener">read_evolve_output</a> function implemented in <a href="https://github.com/Nicholaswogan/PhotochemPy/" target="_blank" rel="noopener">PhotochemPy</a>.</p> <p>You can also use the Python functions provided in the paper's code repository: <a href="https://github.com/DanyIvan/climate_goe_over_time/blob/main/read_output.py" target="_blank" rel="noopener">https://github.com/DanyIvan/climate_goe_over_time/blob/main/read_output.py</a></p> <h1>Names and units</h1> <p>The model output files contain the mixing ratios for all modeled species species. They also contain information about:</p> <p>- 'T_time': temperature profile (K) at the output time step.<br>- 'edd_time': eddy diffusivity profile (cm^2/s) at the output time step.<br>- 'press_time': pressure profile (bar) at the output time step.<br>- 'h2osat': saturation vapor pressure profile (bar) at the output time step.<br>- 'time': time (s) at the output time step<br>- 'den': air number density (molecules/cm^3)</p> </div>
RHESSys model output - Millennium Drought case study
<p>Dataset for teaching case study on Millennium Drought at Walshes Creek catchment. RHESSys model results for a range of relevant ecohydrologic variables at the daily timescale.</p>
Model output for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"
<p>This dataset contains the numerical model output files used in the analysis described in "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"</p>
Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM2.5: Model Simulation Outputs
<p>This data sets are model outputs from CMAQ simulations. The output contains only PM2.5 variable after combining related aerosol species. File format is netCDF binary. File naming convention for Domain 1 (D1) is D1_EXP_DATE_TIME_e000.nc where EXP is the control experiment (CTR) or the assimilation experiments (DA_icbc), DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. Also, file naming convention for Domain 2 (D2) is D2_EXP_CASE_DATE_TIME_e000 where EXP is the control experiment (CTR) or the assimilation experiments (DA_ic and DA_icbc), CASE is the simulation cases for ANL or PRD, DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. For the processed and assimilated observation data in this study for D1 and D2, the file names are D1_OBS_DATA_YYYYMMDDhh.txt and D2_OBS_DATA_YYYYMMDDhh.txt, respectively, where YYYYMMDD is date format and hh is UTC.</p>
CESM2(WACCM6) Earth system prediction framework forecast output for "Limited surface impacts of the January 2021 sudden stratospheric warming"
<p>Included in this repository is CESM2(WACCM6) Earth system prediction framework forecast output necessary to replicate the results of "Delayed surface impacts of the January 2021 sudden stratospheric warming".</p> <p>Standard forecast output is formatted as "ssfcst_ddMMM", where ddMMM is either 04jan or 08feb.</p> <p>Forecasts initialized January 4th, 2021 with scrambled initial conditions are formatted as: "15to20kmRampBot_", for scrambled tropospheric initial conditions; "9to12kmRamp_" for scrambled stratospheric initial conditions; and "2017noSSW_" for scrambled atmospheric initial conditions started from a January 2017 atmospheric restart.</p> <p>The forecast initialized February 8th, 2021 with scrambled tropospheric initial conditions is formatted "15to20kmRampBot_update_".</p> <p>Forecasts initialized February 1st and 8th, 2021, with scrambled stratospheric initial conditions are formatted "9to12kmRamp_updated_".</p> <p>All files contain relevant dimensions. "mXX", where XX is between 0 and 20, denotes the forecast member. Geopotential height is in the "Z3" files, surface temperature is in the "tas" files, and zonal mean zonal wind and tropopause pressure are in the "h2" files.</p>
Stock Data Scraper Output Example
<p>The uploaded dataset is a sample output of the share data scraper for Swiss Equity Indices published at https://github.com/ncotie/Share-data-scraper. It serves to illustrate the parsed output the scraper can provide, in CSV format.</p> <p>Included are two trading days' worth of data for the SMI Index.</p>
PISCES-BYONIC example output
<p>Annual mean outputs from the PISCES-BYONIC configuration: https://github.com/atagliab/BYONIC-PISCES</p>
Analysis of Plumed output by Metadynminer - Plumed Masterclass 2022
<p>We provide input data for the Plumed Masterclass "Analysis of Plumed output by Metadynminer". Input and output data are available for metadynamics simulations of alanine dipeptide in water using Gromacs and Plumed. It was calculated with two collective variables phi and psi (10 ns), or with a single collective variable phi or psi (1 ns).</p> <p> </p>
Regional model (ACM) output used to elucidate coastal ocean carbon transport processes in the northwest North Atlantic (Rutherford and Fennel, 2022; GRL)
<p>Key variables from the regional Atlantic Canada model (ACM) used to elucidate coastal ocean carbon transport in the northwest North Atlantic. The dataset includes all model variables required to reproduce the results in Rutherford & Fennel (2022, <em>GRL</em>). See RutherfordandFennel_GRL_ACM_data_README.txt for more details.</p>
Post-processed output for paper "Spatial and interannual variability of the Antarctic Slope Current in an eddying ocean-sea ice model"
<p>This dataset contains processed output of the ACCESS-OM2-01 global ocean-sea ice model.</p> <p>The processed output was used for analyses of the paper:</p> <p>Huneke W. G. C., Morrison A. K., Hogg A. McC. Spatial and interannual variability of the Antarctic Slope Current in an eddying ocean-sea ice model, 2022, Journal of Physical Oceanography, doi:10.1175/JPO-D-21-0143.1</p> <p>The Github repository github.com/wghuneke/ASC_SpatialTemporalVariability contains Jupyter notebooks and instructions for how to produce these files from the raw model output.</p> <p>Contact wilma.huneke@anu.edu.au if you want access to the ACCESS-OM2-01 raw model output itself.</p>
Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"
<p># Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"<br> This data was prodused using the the MadRat framework and the mrsoil R-library by the R-script SOCBudget.R, which is stored together with the data. mrsoil is based on the R-libraries mrcommons, mrmagpie and mrvalidation.</p> <p>### REFERENCES<br> Dietrich J, Baumstark L, Wirth S, Giannousakis A, Rodrigues R, Bodirsky B, Kreidenweis U, Klein D (2020). _madrat: May All Data be<br> Reproducible and Transparent (MADRaT)_. doi: 10.5281/zenodo.1115490 (URL: https://doi.org/10.5281/zenodo.1115490), R package version<br> 1.86.0, <URL: https://github.com/pik-piam/madrat>.</p> <p>rstens K, Dietrich J (2020). _mrsoil: MadRat Soil Organic Carbon Budget Library_. doi: 10.5281/zenodo.4317933 (URL:<br> https://doi.org/10.5281/zenodo.4317933), R package version 1.1.0, <URL: https://github.com/pik-piam/mrsoil>.</p> <p>Bodirsky B, Karstens K, Baumstark L, Weindl I, Wang X, Mishra A, Wirth S, Stevanovic M, Steinmetz N, Kreidenweis U, Rodrigues R, Popov<br> R, Humpenoeder F, Giannousakis A, Levesque A, Klein D, Araujo E, Beier F, Oeser J, Pehl M, Leip D, Molina Bacca E, Martinelli E,<br> Schreyer F, Dietrich J (2020). _mrcommons: MadRat commons Input Data Library_. doi: 10.5281/zenodo.3822009 (URL:<br> https://doi.org/10.5281/zenodo.3822009), R package version 0.11.10, <URL: https://github.com/pik-piam/mrcommons>.</p> <p>Karstens K, Dietrich J, Chen D, Windisch M, Alves M, Beier F, v. Jeetze P, Mishra A, Humpenoeder F (2020). mrmagpie: madrat based MAgPIE Input Data Library. doi: 10.5281/zenodo.4319612 (URL: https://doi.org/10.5281/zenodo.4319612), R package version 0.31.0, <URL: https://github.com/pik-piam/mrmagpie>.</p> <p>Bodirsky B, Wirth S, Karstens K, Humpenoeder F, Stevanovic M, Mishra A, Biewald A, Weindl I, Chen D, Molina Bacca E, Kreidenweis U, W. Yalew A, Humpenoeder<br> F, Wang X, Dietrich J (2020). _mrvalidation: madrat data preparation for validation purposes_. doi: 10.5281/zenodo.4317826 (URL:<br> https://doi.org/10.5281/zenodo.4317826), R package version 2.5.0, <URL: https://github.com/pik-piam/mrvalidation>.</p> <p>## LICENSE<br> This data is open-source: you can redistribute it and/or modify it under the terms of the **CC Attribution 4.0 International** as published by the Creative Commons Corporation at https://creativecommons.org/licenses/by/4.0/legalcode.</p> <p>## CONTACT<br> karstens@pik-potsdam.de</p>
Input and Output for CDGRe
<p>The input directory contains DREAM5 datasets for GRN inference and single-cell datasets including hESC and mDC.</p> <p>The output directory contains GRN inference results for each dataset.</p>
Output files for 'Multiscale topology classifies cells in subcellular spatial transcriptomics'
<p>This data set contains output files for the paper 'Multiscale topology classifies cells in subcellular spatial transcriptomics'. See the READMEs included for each experiment subdirectory.</p>
Outputs of the "Passenger demand mobility model" developed for South Asia
<p>This dataset comprises outputs and intermediate calculations of the passenger mobility demand model developed for South Asian regions. The dataset includes the following: </p> <ul> <li>'Total_PDT.zip' folder contains files of the type <em><strong>total_pdt_scename_j_m.csv</strong></em>: <ul> <li> These CSV files are the model output that contain passenger distance travelled (PDT) by mode in each area type and distance category for daily travel; and PDT by mode for long distance travel in different scenarios (scen_name), type of urbanisation (j), and mode share trajectory (m).</li> <li><strong>scename</strong>- This refers to the scenarios considered which are as follows - 'Base', 'NP', 'BP', 'TOD and 'TECH'</li> <li><strong>j</strong>- This refers to the type of urbanisation trajectories considered and are defined as follows: <ul> <li>1 - <a href="../api/records/10303828/draft/files/Urbanisation_type_trajectory_1.csv/content">Urbanisation_type_trajectory_1.csv</a></li> <li>2 - <a href="../api/records/10303828/draft/files/Urbanisation_type_trajectory_2.csv/content">Urbanisation_type_trajectory_2.csv</a></li> </ul> </li> <li><strong>m</strong>- This refers to the mode share trajectories considered in this study and are defined as follows: <ul> <li>1- Business as Usual (BaU)</li> <li>2- Car-oriented future (COF)</li> <li>3- Sustainable future (SF)</li> </ul> </li> </ul> </li> </ul> <ul> <li>Trip rates calculated for SSP 2- <a href="../api/records/10303828/draft/files/trip_rate_2.csv/content">trip_rate_2.csv</a></li> </ul> <p> </p>
Meningioma_DNA_methylation_output_predictor
<p><strong><span>Validation and second-generation update of a DNA methylation-based recurrence predictor for meningioma: a multicentre prospective study </span></strong></p>
Data, code, and outputs for: groundwater-dependent ecosystem map exposes global dryland protection needs
<p>Contains all data and code used to produce results for:</p> <p>Rohde, M.M.<strong>,</strong> C.M. Albano, X. Huggins, K.R. Klausmeyer, C. Morton, A. Sharman, E. Zaveri, L. Saito, Z. Freed, J.K. Howard, N. Job, H. Richter, K. Toderich, A. Rodella, T. Gleeson, J. Huntington, H.A. Chandanpurkar, A.J. Purdy, J.S. Famiglietti, M.B. Singer, D.A. Roberts, K. Caylor, J.C. Stella. 2024<em>.</em> Groundwater-dependent ecosystem map exposes global dryland protection needs. <em>Nature</em>, doi: 10.1038/s41586-024-07702-8.</p> <p>The resultant global groundwater-dependent ecosystem interactive webmap is available at <a href="https://codefornature.projects.earthengine.app/view/global-gde">https://codefornature.projects.earthengine.app/view/global-gde</a>.</p> <p>The code is also available at <a href="https://github.com/XanderHuggins/global-gde-map">https://github.com/XanderHuggins/global-gde-map</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.