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1,600 results for “input”
Alpine range by species input to simulations that reveal climate and legacy effects
Open the record for dataset details and reuse information.
Data from: Topography of inputs into the hippocampal formation of a food-caching bird
Open the record for dataset details and reuse information.
High frequency soil sensor data for SOM input - Complex drivers of riparian soil oxygen variability revealed using self-organizing maps
The provided datasets contain the original (non-normalized) high-frequency soil and meteorological observations that were fed to the Self-Organizing Map (SOM) in order to identify ranges of values associated with low and high soil O2 conditions. For the Champlain Valley (CV) site we used the natural breaks algorithm to subset the data into high and low O2 datasets. O2 values were consistently low at the Green Mountains (GM) site, so we ran a single SOM for all O2 values at this site. The original values were then range-normalized before they were fed to the SOM.
Effects of stem canker disease on N fixation inputs by Alnus tenuifolia to early-successional floodplains in interior and south-central Alaska. I. Nitrogen fixation rates, leaf chemistry and soil chemistry.
This dataset contains data on nitrogen fixation rates, leaf chemistry, soil temperature and moisture, and soil chemistry on trees selected for studying disease-mediated declines in N-fixation inputs by Alnus tenuifolia to early-successional floodplains in interior and south-central Alaska
Effects of stem canker disease on N fixation inputs by Alnus tenuifolia to early-successional floodplains in interior and south-central Alaska. II. Nodule biomass and incidence of canker for individual genets.
This dataset contains nodule biomass and incidence of canker infection for individual genets of Alnus tenuifolia as part of a project studying disease-mediated declines in N-fixation inputs by Alnus tenuifolia to early-successional floodplains in interior and south-central Alaska
Murphy Dome: annual litter inputs from 2012-2018
This dataset contains annual litter inputs collected in 2012-2018 from the Murphy Dome study site.
McMurdo Dry Valleys Glacier melt modeling: Inputs and example m-file reader
This is the data and metatada for the micromet inputs - the parameter and support data to produce six modeled parameters that comprise the Taylor Valley Galcier Melt modeling datasets Data contained and described in this document correspond to the physically-based surface energy balance model for the glaciers of Taylor Valley developed by the dataset owners. The spatial variability in ablation (ice melt and sublimation), runoff, and climate sensitivity of the glaciers was modeled using 16 years of meteorological and surface mass balance (the net mass gain or loss of ice on the surface of the glacier) observations collected in Taylor Valley (see figure).  An unusual aspect of the model is the inclusion of transmission of solar radiation into the ice and subsequent drainage of some subsurface melt .  Melt model was applied to the ablation zones of the glaciers of Taylor Valley, identified by colored areas. Mass balance stakes, meteorological stations, and stream gages shown for reference. This dataset package includes input files necessary to run the simulations, see the companion output dataset packages to re-use micromet data. In here you will find: The 250m Digital Elevation Model (DEIM) used in the modeling process as ascii - spotdem250.txt, by Matthew Hoffman The landcover data used in the modeling process as ascii - tv_landcover_met.txt -by Matthew Hoffman. The locations of met stations used to inform the MicroMet model can be found in met_station_locations.xlsx (Excel format) An example MATLAB script for visualizing MicroMet generated met data grids is also included, here. grid_viz_example.m The parameter file used to run MicroMet through snowmodel is snowmodel.par.Â
Chronos-inputs
<p>Test inputs to use in Chronos FPGA Acceleration Framework</p>
Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS-v.2.1
<p>This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS-v.2.1 [<em>Rodell et al.,</em> 2004] LSM outputs, available at;</p> <p><a href="http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi">http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi</a></p> <p>The GLDAS-v.2.1 outputs (from NOAH Land Surface Models) are available in 1º, 0.25º with 3-hour temporal resolution. The database contains the following files;</p> <p> GLDAS.2.1_NOAH<em><strong>res</strong></em>_3H_<em><strong>yyyy</strong></em>.tar.gz (Note: <em><strong>res</strong></em> = 10 or 025; <em><strong>yyyy</strong></em> = 2000 to 2009)</p> <p> </p> <p>Note: These runoff data 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.</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> </p> <p>References:</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>
Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS
<p>This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS [<em>Rodell et al.,</em> 2004] LSM outputs, available at;</p> <p><a href="http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi">http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi</a></p> <p>The GLDAS outputs (from four different Land Surface Models) are available in 1º with 3-hour temporal resolution. The database contains the following files;</p> <p> GLDAS_<strong><em>mod</em></strong>10_3H_<strong><em>yyyy</em></strong>.tar.gz (Note: <em><strong>mod</strong></em> = CLM or MOS or NOAH or VIC; <strong><em>yyyy</em></strong> = 2000 to 2009)</p> <p> </p> <p>Note: These runoff data 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.</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> </p> <p>References:</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>
Input data for the analysis of changes in functional structures of Japanese tree species by species loss simulation
<p>The dataset was used in Kusumoto, Shiono & Kubota (2020). It includes functional structure indices (community means, functional richness, and Rao's quadratic entropy) for 514 Japanese timber and non-timber tree species at 10-km grid cell level. The community means were based on specific leaf area and leaf nitrogen content, respectively. Functional richness and Rao's Q were based on wood density and tree height. There functional metrics were calculated for the observed species assemblages and simulated assemblages at 10-km grid cell level. The simulated assemblages were computed by removing species in each grid cell at 5 levels of species loss (10%, 20%, 30%, 40% and 50%) with two scenarios: random loss and ordered loss depending on species successional niche score (i.e. later successinal species are preferentially lost). See "README" sheet for detailed explanations of the contents.</p> <p>Kusumoto, Shiono & Kubota (2020) Ethnobotany-informed trait ecology: measuring vulnerability of timber provisioning services across forest biomes in Japan. Biodiversity and Conservation. DOI: 10.1007/s10531-020-01974-y</p>
Demeter Input Files for 0.05 degree LULCC from GCAM SSP/RCP/GCM Scenario Runs
<p>Global future land use (LU) is an important input for Earth system models for projecting Earth system dynamics and is critical for many modeling studies on future global change. Here we generated a new global gridded LU dataset using the Global Change Analysis Model (GCAM ) and a geospatial downscaling model (Demeter) under diverse Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) scenarios. Compared to existing similar datasets, the presented dataset has a higher spatial resolution (0.05°×0.05°) and is spread under more diverse SSP-RCP scenarios (in total 15 scenarios), and considers uncertainties from the forcing climates. The presented dataset will be useful for global Earth system modeling studies, especially for the analysis of the impacts of land use and land cover change and socioeconomics, as well as the characterizing the uncertainties associated with these impacts.</p> <p>The dataset includes the inputs for Demeter to produce projected global gridded land cover (excluding the Antarctic) for the period of 2015-2100 at 0.05-degree resolution and 5-year time step under fifteen SSP-RCP scenarios driven by five GCMs (i.e., gfdl, hadgem, ipsl, miroc, and noresm), using the Global Change Analysis Model (GCAM) and a geospatial downscaling model (Demeter). See <a href="https://github.com/JGCRI/chen_et_al_2020a">https://github.com/JGCRI/chen_et_al_2020a</a> for details on how to reproduce this experiment.</p> <p> </p>
Inputs for computational electrophysiology of the Glycine Receptor with GROMACS 19
<p>Inputs for computational electrophysiology of the Glycine Receptor (D&B-open model, doi:10.5281/zenodo.3476169) with GROMACS 19 and the CHARMM36 force-field, using:</p> <p>1- a single membrane system with the application of a constant electric field.</p> <p>2- a double membrane system with the application of the charge imbalance protocol.</p> <p>The model of the glycine receptor was reduced to its transmembrane domain and simulated with atomic positional restraints.</p> <p>Related to the published article: "On the functional annotation of open-channel structures in the glycine receptor".</p>
NEMO Reference configurations inputs
<p>This page refers to input archives for running the configurations included in NEMO 4.2 (beta version) and earlier. Subsequent releases have been served from an alternative site: <a href="https://gws-access.jasmin.ac.uk/public/nemo/sette_inputs">SETTE input files delivered through JASMIN</a>. Users are advised to download from that site for versions 4.2.0 onwards. The <a href="https://sites.nemo-ocean.io/user-guide/sette.html#obtaining-configuration-input-files">NEMO User Guide</a> contains a detailed section on running SETTE, including further information on obtaining the input files. </p> <p>This site is retained to provide a DOI for citing these datasets but remember to state clearly the exact version used.</p> <p>Input archives for running the configurations included in <strong>NEMO 4.2 (beta version) -not suitable for releases 4.2.0 and later</strong>:</p> <ul> <li>AGRIF_DEMO: <em>AGRIF_DEMO_v4.0.tar</em> and <em>ORCA2_ICE_v4.0.tar</em></li> <li>AMM12: <em>AMM12_v4.0.tar</em></li> <li>C1D_PAPA: <em>INPUTS_C1D_PAPA_v4.0.tar</em></li> <li>ISOMIP+: ISOMIP+_v4.0.tar, input for the new ISOMIP+ test case on ice shelf module and the coupling with an ice sheet model</li> <li>ORCA2_ICE_PISCES: <em>ORCA2_ICE_v4.2.tar</em>, <em>INPUTS_PISCES_v4.2.tar and ORCA2_ABL_4.2.tar</em></li> <li>ORCA2_OFF_PISCES: <em>ORCA2_OFF_v4.0.tar</em> and <em>INPUTS_PISCES_v4.0.tar</em></li> <li>ORCA2_OFF_TRC: <em>ORCA2_OFF_v4.0.tar</em></li> <li>ORCA2_SAS_ICE: <em>ORCA2_ICE_v4.0.tar</em> and <em>INPUTS_SAS_v4.0.tar</em></li> <li>SPITZ12: <em>SPITZ12_v4.0.tar</em></li> <li>WED025: WED025_v4.2.tar, input for the new WED025 reference configuration, now replacing SPITZ12 to demonstrate ice shelf module, SI3, bdy with tide and sea ice capabilities<br> </li> </ul>
Flow of Agricultural Nitrogen, version 2 (FANv2): Model input and output data
<p>This upload includes data associated with the manuscript "An improved mechanistic model for ammonia volatilization in Earth system models: Flow of Agricultural Nitrogen, version 2 (FANv2)" submitted to Geoscientific Model Development. The dataset includes an input file for use with the Community Land Model, and an output file with the simulated ammonia emissions for the agricultural sector. The emissions are monthly averages from the simulation for 2010-2015. Additional information is given in the readme file.</p>
Input files and scripts for creating PALM simulation input files on Mäkelänkatu in Helsinki, Finland
<p>Datasets and scripts to create input files for running PALM simulations around Mäkelänkatu, Helsinki. The dataset contains:</p> <ul> <li>input_data_to_palm: raster maps and final input files to be applied by PALM</li> <li>scripts: scripts used to create the input files</li> <li>source_data: source data for creating the input files</li> <li>user_code: PALM user code modifications</li> </ul> <p> </p>
Inputs for Galaxy Trainig ATAC-seq
<p>The fastq.gz are a subset of SRR891268 but enriched into pairs which map to chr22.</p> <p>The bed is from ENCODE.</p>
Bowdoin Glacier input files HiDEM
<p>This dataset contains the input files for HiDEM simulations presented in the article "Numerical modelling shows increased fracturing due to melt-undercutting prior to major calving at Bowdoin Glacier", ECH van Dongen, JA Åström, G Jouvet, J Todd, DI Benn, M Funk, Frontiers in Earth Sciences.</p> <p>GeometryControl.dat contains the input geometry. Andrea Walter conducted the UAV survey for surface elevation data. Izumi Asaji and Shin Sugiyama provided bed elevation data.</p> <p>inpHiDEM.dat contains the values of model parameters.</p> <p>The code of HiDEM is available on <a href="http://github.com/joeatodd/HiDEM">GitHub</a>.</p>
Input and output data from numerical simulations associated to paper Biggs and Annen (2019)
<p>Input and output files produced by code Heat2D_Car and used to produce the results published in Biggs and Annen (2019)</p>
Data set for "Anatomically and functionally distinct thalamocortical inputs to primary and secondary mouse whisker somatosensory cortices"
<p>Data set for: El-Boustani S, Sermet BS, Foustoukos G, Oram TB, Yizhar O, Petersen CCH (2020) Anatomically and functionally distinct thalamocortical inputs to primary and secondary mouse whisker somatosensory cortices. Nature Communications 11: 3342. doi: 10.1038/s41467-020-17087-7</p> <p>There are 4 files in this upload:</p> <p>1. The file named "2020_El-Boustani_NCOMMS.pdf" is the Open Access pdf file of the manuscript published in Nature Communications.</p> <p>2. The file named "2020_El-Boustani_NCOMMS_SupMovie1.avi" is Supplementary Movie 1 in .avi format, accompanying the Nature Communications publication.</p> <p>2. The file named "2020_El-Boustani_NCOMMS_SupMovie2.avi" is Supplementary Movie 2 in .avi format, accompanying the Nature Communications publication.</p> <p>4. The file named "El-Boustani_data_code.zip" (~36 GB) is a zipped version of a folder "El-Boustani_data_code" (~45 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. In the main folder, data for all experiments are stored within folders starting by the prefix “SB”. The code for generating population data and plotting figures from the paper are in the folder “Matlab_code”. In this folder, several Matlab scripts are named after the panels or figures they will plot such as “Plot_Fig3d_Axon_GCaMP6s_traces_example.m”. After opening each file, executing the script will automatically plot the panels and name them accordingly. In some files, the type of data to plot should be specified at the very beginning of the script: “VPM” for VPM data, “POMf” for POm-FO data and “Layer1” for POm-HO data in layer 1. For figure 2, the code is located in a dedicated folder where a Matlab file “Plot_Fig2d_g_Populatin_Plot_POm_FO_HO.m” is used to generate the figures. Finally, other Matlab files are included that are used to create population .mat files or for additional analysis related to the manuscript.</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.