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1,600 results for “input”
Input and output files for WRF urban simulations for the metropolitan area of Tel-Aviv
Open the record for dataset details and reuse information.
Methyl bromide atmosphere-ocean box model code, inputs, and outputs
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Simulation input and out and data analysis for calculating partition coefficients of small molecules in octanol/water and cyclohexane/water
Open the record for dataset details and reuse information.
Hungarian pond aquaculture production and input use
<p>Country and region level time-series on production and input use from 2000 onwards. Harvested and stocked biomass quantity available for common carp and for other pond species. Input data contain: feed quantity, pond area, water and labor.</p>
GENE coupling inputs
<p>This is the simplified version of the ADIOS2 supported in-memory coupling input for GENE provided by Gabriele Merlo.</p>
Input data of phylogenetic field analysis for exotic seed plants in Japan
<p>This release includes the dataset for evaluating phylogenetic fields of exotic plant species in Japan: co-occurrence matrices between Japanese native species (exotic vs all native seed plants at 10 km grid cells; exotic vs endemic native seed plants at 10 km grid cells; exotic vs all native seed plants at vegetation plots; exotic vs endemic native seed plants at vegetation plots), exotic species list with their species attributes (geographical extent within Japan, climatic niche widths, minimum residential time, disturbance dependency, origin place, and mean phylogenetic distance from their native recipients), and phylogenetic tree for native and exotic seed plants. </p>
Data_Effects of input modality on vocal effector prioritization in manual-vocal dual tasks
<p>Dual-task costs in RTs and error rates compared between effector systems in vocal-manual dual-tasks using either only auditory or only visual stimuli for both responses</p>
Figure S1 - Time-series and trends of VIs, DIN-output and S-input for all catchments
<p>Figure S1: Trends (dotted lines) in long-term standard scores for measured sulfur deposition (S-input) and stream water total dissolved inorganic nitrogen export (DIN-output) during the observation period 1994-2016 and trends in vegetation indices extracted from Landsat satellite data for the observation period 1984-2016 for all catchments</p> <p> </p> <p>Table S1: Linear regression coefficients of trend lines in Figure S1</p>
Trajectory and input files for "Coating of magnetic nanoparticles affects their interactions with model cell membranes"
<p>Input and output files, including trajectories, for the study published in BBA - General Subjects :</p> <p>"Coating of magnetic nanoparticles affects their interactions with model cell membranes"</p> <p>https://doi.org/10.1016/j.bbagen.2020.129671</p>
Making the sun shine at night: Comparing the Cost of Dispatchable Concentrating Solar Power and Photovoltaics with Storage: Balmorel input data revised
<p><strong>Description of the dataset</strong></p> <p>This dataset holds all Balmorel model input data as well as the Balmorel code used for the scenarios of the paper 'Making the Sun Shine at Night: Comparing the Cost of Dispatchable Concentrating Solar Power and Photovoltaics with Storage' submitted to 'Energy Sources, Part B: Economics, Planning, and Policy.'</p> <p><strong>Data format</strong></p> <p>We provide the data in form of the data folders holding the .inc files for all scenarios.</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, Ea Energy Analyses and Jasper Geipel (TU Wien) with previously done works.</p>
Input forcing data for CLM-ml at Soltis Center in Costa Rica
<p>Forcing data for Modeling profiles of micrometeorological variables in a tropical premontane rainforest using multi-layered CLM (CLM-ml)</p>
Input data for Isca radiative kernel offline calculation
<p>Input data for calculating the radiative kernels (offline) for Isca, including:</p> <ul> <li>three hourly climatology from Isca with Byrne & O'Gorman (BOG) radiaiton scheme</li> <li>three hourly climatology from Isca with RRTM radiaiton scheme</li> <li>ozone file for RRTM</li> </ul>
Input dataset for gap filling and land-cover mapping using eumap Library - 2000 to 2020
<p>Benchmark dataset containing slope, elevation, Landsat temporal composites and night light raster layers, and the training samples (<a href="https://land.copernicus.eu/imagery-in-situ/lucas">LUCAS</a> and <a href="https://land.copernicus.eu/pan-european/corine-land-cover">CORINE</a> samples compilation) to map the land-cover in different areas of the European Union-EU.</p> <p>The slope and elevation refers to <a href="https://zenodo.org/record/4057883#.X3MJ0Nkmz0q">Digital Terrain Model for Continental Europe</a>, and the night light images are from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP46A1/">VNP46A1 product (VIIRS/NPP Daily Gridded Day Night Band 500m)</a>. The temporal composites were based on <a href="https://glad.geog.umd.edu/ard/glad-landsat-ard">GLAD Landsat ARD</a>, considering the 4 seasons and 3 percentiles per season (25, 50 and 75), for 6 spectral (blue, green, red, NIR, SWIR1, SWIR2) and 1 thermal band, resulting at end in 88 Landsat composites per year. The images for each season were selected using the same calendar dates for all period:</p> <ul> <li>Winter: December 2 of previous year until March 20 of current year</li> <li>Spring: March 21 until June 24 of current year</li> <li>Summer: June 25 until September 12 of current year</li> <li>Fall: September 13 until December 1 of current year</li> </ul> <p>The temporal composites were generated to <a href="https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html">Sentinel-2 L2A</a> for 2018, 2019 and 2020, using the same approach (4 seasons x 3 percentiles x 6 spectral bands).</p> <p>The benchmark areas were selected according to the EU tiling system, which consists of 7,042 regular tiles with 30 x 30 km. The dataset uses the ETRS89-extended / LAEA Europe as spatial reference system (<a href="https://epsg.io/3035">EPSG:3035</a>), and all the raster layers have 1,000 x 1,000 pixels and 30m of spatial resolution.</p> <p>For all the EU the training samples will have 32 land-cover classes, varying according to the benchmark area:</p> <ul> <li>111: Urban fabric</li> <li>122: Road and rail networks and associated land</li> <li>123: Port areas</li> <li>124: Airports</li> <li>131: Mineral extraction sites</li> <li>132: Dump sites</li> <li>133: Construction sites</li> <li>141: Green urban areas</li> <li>211: Non-irrigated arable land</li> <li>212: Permanently irrigated arable land</li> <li>213: Rice fields</li> <li>221: Vineyards</li> <li>222: Fruit trees and berry plantations</li> <li>223: Olive groves</li> <li>231: Pastures</li> <li>311: Broad-leaved forest</li> <li>312: Coniferous forest</li> <li>321: Natural grasslands</li> <li>322: Moors and heathland</li> <li>323: Sclerophyllous vegetation</li> <li>324: Transitional woodland-shrub</li> <li>331: Beaches, dunes, sands</li> <li>332: Bare rocks</li> <li>333: Sparsely vegetated areas</li> <li>334: Burnt areas</li> <li>335: Glaciers and perpetual snow</li> <li>411: Inland wetlands</li> <li>421: Maritime wetlands</li> <li>511: Water courses</li> <li>512: Water bodies</li> <li>521: Coastal lagoons</li> <li>522: Estuaries</li> <li>523: Sea and ocean</li> </ul> <p>The gap filling validation data was generated by creating a mask of all nodata pixels (gaps) for each temporal composite, and then transposing that mask. All valid pixels covered by the transposed nodata mask are considered validation pixels. This method was chosen to retain the diversity of spatiotemporal nodata patterns that occur in the data. Each gap filling validation file contains 3 directory:</p> <ul> <li>raw: original temporal composite</li> <li>validation: transposed data</li> <li>filled_tmwm8: the best gap filling method that was tested</li> </ul> <p>See the <a href="https://gitlab.com/geoharmonizer_inea/eumap">eumap library</a> for more information about the gapfiling approach and land-cover mapping using this dataset.</p>
Input files and scripts for Hamiltonian replica-exchange molecular dynamics simulations of intrinsically disordered proteins using a software GROMACS patched with PLUMED
<p>Here we share the necessary files and scripts to run Hamiltonian replica-exchange molecular dynamics simulations of intrinsically disordered protein studied in the preprint uploaded to bioRxiv (doi: https://doi.org/10.1101/2020.06.16.155374). It requires software GROMACS patched with PLUMED.</p>
The influence of dynamic topography, climate, and tectonics on the Nile River source-to-sink system – Model input data
<p>Input data for Badlands models used in 2020 Honours thesis at the University of Sydney.</p>
Input and output model data for Horvath et al. 2020 (https://doi.org/10.5194/bg-2020-149)
<p>Please see "readme" file. </p>
Demeter - Input and Output Data
<p>This dataset includes input and output data for 20 combinations of GCM/RCP scenarios for the Demeter model. The GCM and RCP selected for the study of Argentina Energy-Water-Land Systems are MIROC-ESM-CHEM and RCP 6.0.</p>
Data from: Insect brain plasticity: effects of olfactory input on neuropil size
Insect brains are known to express a high degree of experience-dependent structural plasticity. One brain structure in particular, the mushroom body, has been attended to in numerous studies as it is implicated in complex cognitive processes such as olfactory learning and memory. It is, however, poorly understood to what extent sensory input per se affects plasticity of the mushroom bodies. By performing unilateral blocking of olfactory input on immobilized butterflies, we were able to measure the effect of passive sensory input on the volumes of antennal lobes and mushroom body calyces. We showed that the primary and secondary olfactory neuropils respond in different ways to olfactory input. Antennal lobes show absolute experience-dependency and increase in volume only if receiving direct olfactory input from ipsilateral antennae, while mushroom body calyx volumes were unaffected by the treatment and instead show absolute age-dependency in this regard. We therefore propose that cognitive processes related to behavioural expressions are needed in order for the calyx to show experience-dependent volumetric expansions. Our results indicate that such experience-dependent volumetric expansions of calyces observed in other studies may have been caused by cognitive processes rather than by sensory input, bringing some causative clarity to a complex neural phenomenon.
Data from: Increased nitrogen input enhances Kandelia obovata seedling growth in the presence of invasive Spartina alterniflora in subtropical regions of China
Mangroves in China are severely affected by the rapid invasion of the non-native species Spartina alterniflora. Although many studies have addressed the possible impacts of S. alterniflora on the performance of mangrove seedlings, how excessive nitrogen (N) input due to eutrophication affects the interactions between mangrove species and S. alterniflora remains unknown. Here, we report the results from a mesocosm experiment using seedlings of the native mangrove species Kandelia obovata and the exotic S. alterniflora grown in monoculture and mixed culture under no nitrogen addition and nitrogen (N) addition treatments for 18 months. Without N addition, the presence of S. alterniflora inhibited the growth of K. obovata seedlings. Excessive N addition significantly increased the growth rate of K. obovata in both cultures. However, the positive and significantly increasing relative interaction intensity index under excessive N input suggested that the invasion of S. alterniflora could favour the growth of K. obovata under eutrophication conditions. Our results imply that excessive N input in southeastern China can increase the competitive ability of mangrove seedlings against invasive S. alterniflora.
Merged HLS2 (L30), ERA5-Land inputs and sample predictions of land surface temperature for the IBM granite-geospatial-land-surface-temperature model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and ERA5-Land data following the UTM CRS:WGS84. It has been assembled for predicting land surface temperature with a fine-tuned granite geospatial foundation model developed by IBM Research. In addition, we include sample predictions of land surface temperature derived from this model. Please see https://huggingface.co/ibm-granite/granite-geospatial-land-surface-temperature for more information on data preparation and model use.</p> <p><strong>HLS:</strong></p> <p>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <h4><strong>ERA5-Land:<br></strong></h4> <p>Copernicus Climate Change Service, Climate Data Store, (2024): ERA5-land post-processed daily-statistics from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.e9c9c792">10.24381/cds.e9c9c792</a> (Accessed on 05-11-2024)</p> <h4><strong>LST-predictions:</strong></h4> <p>These predictions of land surface temperature are derived from the IBM granite-geospatial-land-surface-temperature model and have been made available for Abidjan, Côte d’Ivoire and Johannesburg, South Africa for the period 2013-2023. </p> <h4>Attribution</h4> <p>Copernicus programme:</p> <p>Contains modified Copernicus Climate Change Service information [2024]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Data</strong></p> <p>Muñoz Sabater, J., Comyn-Platt, E., Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N., Cagnazo, C., Cucchi, M. (2024): ERA5-land post-processed daily-statistics from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.e9c9c792">10.24381/cds.e9c9c792</a> (Accessed on 05-11-2024)</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.