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2,152 results for “outputs”
Data from: Higher thermal plasticity in flowering phenology increases flowering output
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RepeatModeler and RepeatMasker output files
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LUNAR: Automated input generation and analysis for reactive LAMMPS simulations input and output files
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Community Land Model synthetic meteorology simulation model output
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Data from: Interplay of cooperative breeding and predation risk on egg allocation and reproductive output
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Statistical analysis code for output from a model used to simulate foot-and-mouth disease dynamics in the United Kingdom
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Outputs of current speed and sea otter abundance models in Glacier Bay, Alaska
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Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
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Model executable, output, drivers and parameters for modeling organism acclimation to changing availability of and requirements for substitutable and interdependent resources
Files used to generate the data for figures in: Rastetter, EB, Kwiatkowski, BL. An approach to modeling resource optimization for substitutable and interdependent resources. Ecological Modelling (2020). https://doi.org/10.1016/j.ecolmodel.2020.109033. This paper presents a hierarchical approach to modeling organism acclimation to changing availability of and requirements for substitutable and interdependent resources. Substitutable resources are resources that fill the same metabolic or stoichiometric need of the organism. Interdependent resources are resources whose acquisition or expenditure are tightly linked (e.g., light, carbon dioxide, and water in photosynthesis and associated transpiration). We illustrate the approach by simulating the development of vegetation with four substitutable sources of nitrogen that differ only in the cost of their uptake and assimilation.
Model output dataset used in "Assessment of Ch in the Noah-MP LSM over China"
<p>1. The Noah-MP LSM v3.6 was used in an offline standalone mode to execute single-site and regional-scale land surface experiments. Single-point experiments were executed at nine flux tower sites, while the simulated regional domain covered all of China.</p> <p>2. Three cases were designed: (1) the original M-O option with identical <em>Z<sub>om</sub></em> and <em>Z<sub>ot</sub></em> (Default), (2) a constant <em>C<sub>zil</sub></em> specified as 0.1 (Czil), and (3) a dynamic canopy-height-dependent <em>C<sub>zil</sub> </em>( Newczil).</p> <p>3. The model output data files for single-site land surface experiements are named in accordance with the following convention:</p> <p>SiteName_SchemeName_start{year/month/day/hour/minute}_end{year/month/day/hour/minute}UTC_noahmp.nc</p> <p>For regional-scale land surface experiments (UTC):</p> <p>NoahMP.{year/month/day}T{hour}0000.nc</p> <p>The file "Domain_regional-scale.nc" contains the static geographic information, such as land cover.</p>
Post-processed output from WACCM: historical simulation of 1957–2005 with medium energy electrons (compset B55TRWCN) and sensitivity studies (compset FW)
<p>This dataset contains post-processed monthly output from WACCM (version 4) simulations:</p> <ol> <li>historical simulation of 1957–2005 with medium energy electrons (compset B55TRWCN, B55TRWCN.zip)</li> <li>sensitivity study: 50-years experiments designed with different geomagnetic and solar conditions (compset FW, FW.zip)</li> </ol> <p>The postprocessing involved (1) zonal averaging, (2) Transformed Eulerian mean (TEM) budget calculation in log-pressure coordindates from model output (h0 files).</p> <p><strong>Contact</strong><br> Monika Szelag (monika.szelag@fmi.fi)</p> <p><strong>Raw data</strong><br> The raw, ungridded monthly data (2TB, h0 files) are currently available on FMI storage system, under path /ibrix/arch/waccm, and can be requested through the contact person. </p> <p><strong>Dataset contents</strong></p> <pre><code>B55TRWCN.zip files: vdk.zonal.002.1957-2005.nc vdk.zonal.003.1957-2005.nc vdk.zonal.004.1957-2005.nc tem.vdk.zonal.002.1957-2005.nc tem.vdk.zonal.003.1957-2005.nc tem.vdk.zonal.004.1957-2005.nc</code></pre> <p>WACCM output: historical simulations, zonal averages, 3 ensemble members of 49 years each (002,003,004).</p> <p>Variables are <strong>NOY, O3, OH, QJOULE, QRL_TOT, QRLNLTE, QRS_TOT, T, U</strong> (vdk.zonal.00[234].1957-2005.nc) and <strong>delf, fphi, fz, vres, wres, z3</strong> (tem.vdk.zonal.00[234].1957-2005.nc</p> <pre><code>FW.zip files: zonal_F2000_max.nc zonal_F2000_avg.nc zonal_F2000_min.nc tem_zonal_F2000_max.nc tem_zonal_F2000_avg.nc tem_zonal_F2000_min.nc</code></pre> <p>WACCM output: perpetual year 2000, zonal averages, 50 years experiments with 3 different solar and geomagnetic conditions (min, max, avg).</p> <p>Variables are <strong>NOY, NOX, O3, OH, QRL_TOT, QRS_TOT, T, U, V, CO, CO2, PS, TS</strong> (zonal_F2000_*.nc) and <strong>delf, fphi, fz, vres, wres, z3</strong> (tem_zonal_F2000_*.nc)</p> <p> </p>
NGS-MSTB - Example output Web report
<p><a href="https://github.com/ngs-mstb/micgent">NGS-MSTB</a> Docker image ships with the pre-computed example of assembling a set of complete RSV genomes. This dataset includes the Web report of the assembly results. It is used when building the Docker image.</p>
RAPID Model Output Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins
<p>This database contains the simulated output files of the RAPID model (river flow in m<sup>3</sup>/s); which were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins. If you use this RAPID Model Output Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins in your work, please cite: <em>Sikder et al.</em>, [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a>.</p> <p>The database contains;</p> <p>River Flow Files: Qout_<strong><em>res</em></strong>_MIGBM_20000101_20091231_<strong><em> prj</em></strong>_<strong><em>LSMsr</em></strong>_<strong><em>tr</em></strong>_utc.nc</p> <p>(Note: <strong><em>res</em></strong> = fine or coarse; <strong><em>prj</em></strong> = GLDAS or GLDAS.2.0 or GLDAS.2.1 or ECMWF; <strong><em>LSM</em></strong> = CLM, MOS, NOAH, VIC, ERAint; <strong><em>sr</em></strong> = 10 or 025; <strong><em>tr</em></strong> = 3H or D)</p> <p> </p> <p>Other necessary links associated with this database:</p> <p>RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS</a></p> <p>ECMWF outputs: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land</a></p> <p> </p> <p>References:</p> <p>Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, <a href="https://doi.org/10.5194/hess-19-389-2015">https://doi.org/10.5194/hess-19-389-2015</a></p> <p>David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p> <p>Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a></p>
GEOS-Chem v9-02 with simple SOA scheme for ATAL simulations & model output
<p>This code/data repository includes (1) GEOS-Chem (v9-02, <a href="http://www.geos-chem.org/">http://www.geos-chem.org/</a>) code modified to use the simple SOA scheme in the simulation of Asian Tropopause Aerosol Layer (ATAL), and (2) model ATAL simulation output. Please see README.txt and this paper for details:</p> <p>Citation: Fairlie, T.D., H. Liu, J.-P. Vernier, P. Campuzano-Jost, J. L. Jimenez, D.S. Jo, B. Zhang, M. Natarajan, M.A. Avery, and G. Huey, Estimates of regional source contributions to the Asian Tropopause Aerosol Layer using a chemical transport model, Journal of Geophysical Research-Atmospheres, in press, Jan. 2020.</p>
Model outputs: Vegetation biomass change in China in the 20th century: An assessment based on a combination of multi-model simulations and field observations
<p>This dataset contains six model outputs of vegetation biomass on plant functional type level and total vegetation biomass on grid cell with and without bias-correction in different sensitivity experiments, as described in Readme.txt.</p>
High-resolution climate model output for selected extreme precipitation events in Cyprus
<p>This dataset consists of high-resolution model output for selected past and future extreme precipitation events for Cyprus. It was generated in the framework of the BINGO Research Project (http://www.projectbingo.eu/) . BINGO has received funding from the European Union’s Horizon 2020 Research and Innovation programme, under Grant Agreement number 641739. More details about the dataset and the design of the simulations in:</p> <p>G. Zittis, A. Bruggeman, C. Camera, P. Hadjinicolaou, J. Lelieveld,<br> The added value of convection permitting simulations of extreme precipitation events over the eastern Mediterranean,<br> Atmospheric Research, Volume 191, 2017, Pages 20-33, https://www.sciencedirect.com/science/article/pii/S0169809516307153</p>
VR Experiences with wolves - SPSS-Output
<p>The file is the SPSS-Output for the publication "Virtual Reality Nature Experiences Involving Wolves in YouTube: Presence, Emotion, and Attitudes in Immersive and Non-Immersive Settings" - Sustainability</p>
GWAS output for early salt stress responses in Arabidopsis thaliana accessions
<p>The output of the Genome Wide Association Study for early responses to salt stress in Arabidopsis accessions. The "_gwas2029.rda" files contain associations found for each trait using single models, where the first part of file name describes the phenotype (e.g. ROUNDNESS), the first number describes the day after stress application, the subsequent C/S indicated whether the association was performed with the trait scored at Control / Salt Stress conditions respectively. The "mtmm_final.rda" files contain the associations found between two traits, indicated as above in the first part of the file name. </p>
Dataset with Curated Coronavirus-related R&D Outputs (1970 - March 2020)
<p><strong>The zip file includes metadata about R&D outputs related to all </strong><a href="https://www.niaid.nih.gov/diseases-conditions/coronaviruses"><strong>Coronaviruses</strong></a>.</p> <p>The two main data sources used for this work include:</p> <ul> <li> <p><a href="https://docs.microsoft.com/en-us/academic-services/project-academic-knowledge/introduction"><strong>Microsoft Academic Graph (MSA) API</strong></a>: Subset of 10.000+ coronavirus R&D outputs, with data from 1970, including patents and scientific publications.</p> </li> <li> <p><a href="https://www.grid.ac/"><strong>The Global Research Identifier Database (GRID)</strong></a><strong>:</strong> Used to enrich the organization data extracted from MSA.</p> </li> </ul> <p>After cleaning and enriching the data we extracted a total of <strong>1.100+ organization and</strong> <strong>26.700+ researchers</strong> spread across <strong>90+ countries</strong> and <strong>700+ cities</strong>.</p> <p>Inside the zip file you will find: </p> <ul> <li>documents.csv: Full list of documents</li> <li>topics.csv: list of topics connected to documents</li> <li>terms.csv: list of terms connected to documents</li> <li>people.csv: list of people (authors) connected to documents</li> <li>orgsplus.csv: list of organisations (incl their locations) connected to documents</li> </ul> <p>More information is available at <a href="https://app.gitbook.com/@dataverz/s/coronavirus-r-and-d/">https://app.gitbook.com/@dataverz/s/coronavirus-r-and-d/</a> </p>
DSS SS20 UE1 Experiment Comparison Output
<p>This is the output of the experiment run for the course data stewardship at TU Vienna during the summer term 2020.<br> The output includes two figures, one showing the loss over the training period, and the other one showing the accuracy over the training period. A text file includes the final accuracy and loss scores at the end of training.</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.