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Dataset results
477 results for “input data”
LUTO v2.1-beta Input Data
<p>Input data required to run the Land-Use Trade-Offs model (LUTO v2.1-beta). Copy entire dataset into the 'input' folder to run LUTO.</p>
Data from: Topography of inputs into the hippocampal formation of a food-caching bird
<p>The mammalian hippocampal formation (HF) is organized into domains associated with different functions. These differences are driven in part by the pattern of input along the hippocampal long axis, such as visual input to the septal hippocampus and amygdalar input to temporal hippocampus. HF is also organized along the transverse axis, with different patterns of neural activity in the hippocampus and the entorhinal cortex. In some birds, a similar organization has been observed along both of these axes. However, it is not known what role inputs play in this organization. We used retrograde tracing to map inputs into HF of a food-caching bird, the black-capped chickadee. We first compared two locations along the transverse axis: the hippocampus and the dorsolateral hippocampal area (DL), which is analogous to the entorhinal cortex. We found that pallial regions predominantly targeted DL, while some subcortical regions like the lateral hypothalamus (LHy) preferentially targeted the hippocampus. We then examined the hippocampal long axis and found that almost all inputs were topographic along this direction. For example, the anterior hippocampus was preferentially innervated by thalamic regions, while posterior hippocampus received more amygdalar input. Some of the topographies we found bear resemblance to those described in the mammalian brain, revealing a remarkable anatomical similarity of phylogenetically distant animals. More generally, our work establishes the pattern of inputs to HF in chickadees. Some of these patterns may be unique to chickadees, laying the groundwork for studying the anatomical basis of these birds' exceptional hippocampal memory.</p>
GLORY - Input and Output Data
<p>This is the input and output dataset for the study "Representing reservoir water storage in the Global Change Analysis Model (GCAM)".</p>
Scrutinizing the protein hydration shell from molecular dynamics simulations against consensus small-angle scattering data (Simulation input files)
<p>Simulation input files for gromacs to reproduce the data from the manuscript "Scrutinizing the protein hydration shell from molecular dynamics simulations against consensus small-angle scattering data" (submitted to Comm. Chem.)</p>
HANZE v2.4 flood impact model input data
<p>This dataset provides input data needed to run HANZE v2.4 model. The ZIP files need to be downloaded and unpacked in the same directory, which has to be defined in "get_file.py" of the HANZE model (variable "repo_path" at the beginning of the file).</p>
Input data from: Mammalian forelimb evolution is driven by uneven proximal-to-distal morphological diversity
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Input data to model multiple effects of large-scale deployment of grass in crop-rotations at European scale
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R scripts, input and output data for: Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs
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Data from: Associative nitrogen fixation (ANF) in switchgrass (Panicum virgatum) across a nitrogen input gradient
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Data and input supporting: Evolutionary dynamics of counter-helical magnetic flux ropes
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Data from: Topography of inputs into the hippocampal formation of a food-caching bird
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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.
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>
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 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>
Data from: Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs
Leaf traits are frequently measured in ecology to provide a 'common currency' for predicting how anthropogenic pressures impact ecosystem function. Here, we test whether leaf traits consistently respond to experimental treatments across 27 globally distributed grassland sites across 4 continents. We find that specific leaf area (leaf area per unit mass)—a commonly measured morphological trait inferring shifts between plant growth strategies—did not respond to up to four years of soil nutrient additions. Leaf nitrogen, phosphorus and potassium concentrations increased in response to the addition of each respective soil nutrient. We found few significant changes in leaf traits when vertebrate herbivores were excluded in the short-term. Leaf nitrogen and potassium concentrations were positively correlated with species turnover, suggesting that interspecific trait variation was a significant predictor of leaf nitrogen and potassium, but not of leaf phosphorus concentration. Climatic conditions and pretreatment soil nutrient levels also accounted for significant amounts of variation in the leaf traits measured. Overall, we find that leaf morphological traits, such as specific leaf area, are not appropriate indicators of plant response to anthropogenic perturbations in grasslands.
Case Studies analysis of prospects for different CSP technology concepts - Input data
<p><strong>Description of the dataset</strong></p> <p>This dataset contains the input data (.inc files) for Balmorel, used for the Case Studies analysis of prospects for different CSP technology concepts conducted within Deliverable 8.1 in the MUSTEC project.</p> <p>For description of the modelled scenarios, results and findings, see: Schöniger, F., Resch, G. (2019):<em> Case Studies analysis of prospects for different CSP technology concepts. </em>Deliverable 8.1 MUSTEC project, TU Wien, Wien.</p> <p>For information on the project see: https://www.mustec.eu/</p> <p><strong>Data format</strong></p> <p>We provide the data in form of the data folders holding the .inc files for the scenarios described in the report above.</p> <p>The original Balmorel source code is available under https://github.com/balmorelcommunity/Balmorel under the ISC license. It was adapted in order to include a new technology generating electricity from heat (GETOH). The inputs for this development were kindly supported by DTU and Ea Energy Analyses with previously done works.</p>
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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.