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1,600 results for “input”
D6.001 space Input dataset
Open the record for dataset details and reuse information.
Mass-balance model inputs for the Kaskawulsh River headwaters
<p>This dataset contains the input files necessary to run the mass-balance model (see Young et al. (2021), Robinson et al. (2024)) for the Kaskawulsh River headwaters from 1980–2022. Downscaled/bias corrected temperature/precipitation inputs will be uploaded separately due to the large file sizes.</p> <p><strong>Downscaling > CoarseNARR_KRH</strong></p> <p>This folder contains the coarse North American Regional Reanalysis (NARR) data that were downscaled to obtain the temperature/precipitation inputs used to drive the Kaskawulsh River headwaters mass-balance model (see Robinson, 2024). </p> <p>All files contain an array of 6 x 6 gridcells, centered on the Kaskawulsh Glacier, Yukon, Canada. The bottom left corner of the grid has coordinates 502592 E, 6624579 N and is located over Yakutat Bay, Alaska. </p> <p>Variables: </p> <ul> <li> <p>air: NARR 3-hourly multi-level air temperatures</p> </li> <li> <p>hgt: NARR 3-hourly multi-level geopotential height</p> </li> <li> <p>apcp: NARR daily total surface precipitation</p> </li> <li> <p>KRH_CE.nc: Time-invariant elevation of NARR gricells</p> </li> </ul> <p><strong>Input_geometry > KRH</strong></p> <p>This folder contains the input geometries needed to run the model for the Kaskawulsh River headwaters.</p> <p>KRH_Xgrid.txt / KRH_Ygrid.txt - contains the Easting/Northing (UTM 7N) of each gridcell in the model domain. </p> <p>Zgrids - a folder containing the annual surface elevation grids (m a.s.l.) for 1979–2022 (see Robinson et al. (2024) for details on how these were derived.</p> <p>KRH_SfcType.txt - a value of 0 indicates the gridcell is glacierized terrain, a value of 1 indicates that the gridcell is non-glacierized terrain. </p> <p>KRH_Tributaries.txt - gridcell value indicates which portion of the Kaskawulsh Glacier the gridcell belongs to (1 = South Arm, 2 = Stairway Glacier, 3 = Central Arm, 4 = North Arm, 5 = Trunk). </p> <p><strong>Solar</strong></p> <p>Contains one file per year with the 3-hourly potential direct clear-sky solar radiation, calculated using the shading program from the Hock (1999) Distributed Enhanced Temperature-Index Model (DETIM). For details see Hock (1999).</p> <p><strong>Debris</strong></p> <p>‘KRH_debrismap.txt’ is a text file with dimensions x,y = 230 gridcells x 329 grid cells. Gridcells are 200 m x 200 m. Each gridcell contains either a value representing the estimated debris thickness (in meters), or NaN if the gridcell is not debris covered. Debris thicknesses are from the Rounce et al. (2021) global debris thickness dataset, interpolated to the Kaskawulsh River headwaters 200 m domain by Robinson (2024).</p> <p>For details on the debris thickness estimate, see Rounce et al. (2021). For details on the interpolation of the original debris thickness estimate to the gridcell size of the model, see Robinson (2024).</p> <p><strong>Tuning > parameters</strong></p> <p>This directory contains a folder ‘initial_params’ with all the original values of a_ice, a_snow, and MF used to tune all the mass-balance models in Robinson et al. (2024) (in groups of 10,000 param combinations). </p> <p>Each additional directory contains the final 100 parameter combinations tuned for each model presented in Robinson et al. (2024), including:</p> <ol> <li> <p>The reference model.</p> </li> <li> <p>The debris-free model.</p> </li> <li> <p>The model with sub-debris melt-scaling from Rounce et al. (2021).</p> </li> <li> <p>The model with uncorrected accumulation.</p> </li> <li> <p>The model with accumulation bias corrected with precipitation gauge data.</p> </li> </ol> <p><strong>Tuning > snowlines</strong></p> <p>Contains 53 .npy files with the rasterized versions of observed snow cover on the Kaskawulsh Glacier delineated from satellite images (see Robinson, 2024). These rasters are used in the second stage of tuning to compare modelled and observed snowlines. </p>
Hdf5 input files for FICOS simulator
<p>This resource contains the hdf5 input files needed by the FICOS water quality simulator. To use them with the ficosUQ package, download the files below and place them in the ficosUQ/data folder:</p> <ul> <li>hd_files.hdf5 : Boundary conditions.</li> <li>hd_files_ma_5.hdf5 : Boundary conditions with moving average smoothing for solar radiation.</li> <li>loading.hdf5 : Nutrient loadings.</li> <li>loading_Aurajoki.hdf5 : Nutrient loadings based on the Aurajoki basin.</li> </ul>
FRACTESUS_UC_S275_T0_MCT_input
<p>Fractesus project. Fracture test mini-CT. Master curve input S275JR . UC. </p>
Data from: Akt-mTORC1 signaling regulates Acly to integrate metabolic input to control of macrophage activation
Macrophage activation/polarization to distinct functional states is critically supported by metabolic shifts. How polarizing signals coordinate metabolic and functional reprogramming, and the potential implications for control of macrophage activation, remains poorly understood. Here we show that IL-4 signaling co-opts the Akt-mTORC1 pathway to regulate Acly, a key enzyme in Ac-CoA synthesis, leading to increased histone acetylation and M2 gene induction. Only a subset of M2 genes is controlled in this way, including those regulating cellular proliferation and chemokine production. Moreover, metabolic signals impinge on the Akt-mTORC1 axis for such control of M2 activation. We propose that Akt-mTORC1 signaling calibrates metabolic state to energetically demanding aspects of M2 activation, which may define a new role for metabolism in supporting macrophage activation.
Faunal input at host plants: can Camelthorn trees use nutrients imported by resident Sociable weavers?
"Islands of fertility" result from the focussing of water and nutrients around many shrub- or tree species due to plants foraging for resources. Plant-animal feedbacks may amplify the development of such islands through environmental modification due to, for example, faunal deposition of nutrients and seeds. Fauna residing within vegetation clumps are likely to exert stronger feedbacks on their hosts than itinerant species. We studied the interaction between camelthorn trees (Vachellia erioloba) and the colonial nests of sociable weavers (Philetairus socius) in the Kalahari. We hypothesised that the accumulation of biological material below the nests will alter the nutrient status of the soil beneath the nest trees, in relation to unoccupied trees and the surrounding grassland. We also suggested that this association will have both positive and negative effects on the camelthorn trees. We found that soil concentrations of N, P and K were respectively 4, 4.6 and 1.2 times higher below trees with nests compared to control trees, indicating faunal concentration of nutrients. Soil δ15N values were higher below trees with than below control trees without nests. Foliar δ15N values were also higher in nest trees than in control trees, showing the trees accessed faunally-derived N. Furthermore, foliar biomass per diameter of terminal branches was 27% higher in nest trees, suggesting that trees respond to nutrient input from the weavers with increased growth. Large barren areas in the sub-canopy vegetation directly beneath the colonies were attributed to decreased water infiltration rates as a result of accumulation of organic matter due to continuous deposition of faeces, possibly limiting competitive species from establishing in the sub-canopy. On the other hand, canopy volume was reduced in trees with nests due to nests occupying large volumes within the canopy and nests frequently causing branch fall, indicating costs associated with hosting weaver colonies.
Data supporting: Multiple input tangential interpolation-driven damage detection of a jet trainer aircraft
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Sample input data for beamModelTester
<p>Sample data for use with beamModelTester. the HDF5 file represents an observation from LOFAR station SE607 of CasA on 16th-17th March 2018. The CSV file represents a dreamBeam model of that observation. </p>
Input Files and Results for Benchmarking the iMD-VR Framework.
<p>Sets of input files and result for benchmarking the iMD-VR framework presented in the thesis Accelerated Sampling of High Dimensional Molecular Systems by Mike O'Connor. </p> <p>The benchmarks folder contains the input files and results of the benchmarks for testing the scaling of the NarupaXR framework (in 2018).</p> <p>The interactions_test folder contains the input files and results of the benchmarks testing the group interaction potentials and velocity reinitialisation procedures with the knotted protein MJ0366.</p> <p> </p>
Input data and analysis codes for "Reversal of trends in global fine particulate matter air pollution"
<p>This dataset contains Input data and analysis codes used for the following article:</p> <p>Li, C., A. van Donkelaar, M. S. Hammer, E. E. McDuffie, R. T. Burnett, J. V. Spadaro, D. Chatterjee, A. J. Cohen, J. S. Apte, V. A. Southerland, S. C. Anenberg, M. Brauer, & R. V. Martin, Reversal of trends in global fine particulate matter air pollution, submitted, 2023.</p> <p> </p> <p><strong>Input Data</strong></p> <p>Baseline mortality data (204 countries and territories, 17 age groups, 6 diseases, 22 years)</p> <p>Concentration-response functions (GEMM and MRBRT)</p> <p>PM<sub>2.5</sub> exposure for 204 territories and 22 years</p> <p>Age-specific population for 204 territories and 22 years</p> <p> </p> <p><strong>Derived Data</strong></p> <p>Age- and disease-specific PM<sub>2.5</sub>-attributable Mortality estimates for 204 territories and 22 years.</p> <p>Sensitivity of PM<sub>2.5</sub>-attributable Mortality to marginal PM<sub>2.5</sub> reduction for 204 territories and 22 years.</p> <p>Attributable of changes in PM<sub>2.5</sub>-attributable Mortality (and its sensitivity to marginal PM<sub>2.5</sub> reduction) to four driving factors.</p> <p> </p> <p><strong>Code</strong></p> <p>Necessary python scripts to verify and replicate analysis results in the manuscript.</p>
Aquila and Aquila_stLFR input files
<p>Aquila and Aquila_stLFR input uniqueness files</p>
Example input and output data for RegCFinder
<p>Example input and output for RegCFinder and example workflow</p>
Datalog subsetting input files (Wikidata NTriple subsets)
<p>Two RDF susbets of Wikidata in NTriple format for being used in Souffle engine.</p>
HiPACE++ input scripts for "Temperature effects in plasma-based positron acceleration schemes using electron filaments"
<p>This dataset presents the <a href="https://github.com/Hi-PACE/hipace">HiPACE++</a> input scripts required to re-create the data of the figures in the article "Temperature effects in plasma-based positron acceleration schemes using electron filaments" by S. Diederichs, C. Benedetti, M. Thévenet, A. Sinn, E. Esarey, J. Osterhoff, and C. B. Schroeder.</p>
16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-14: model 13293 to 14293)
<p><em><strong>16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-14: model 13293 to 14293)</strong></em></p> <ul> <li>16807 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files for any of the 16807 models, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 39MB.</li> <li>An excel file: "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for 16807 virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "how_to_replace_point_coordinates.mp4". It shows how you can replace point coordinates here to the mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> <p><em><strong>Notes:</strong></em></p> <p>1- Model number in "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: 10.5281/zenodo.7715658; stl.part01.rar to stl.part09.rar).</p> <p>2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3.</p> <p>3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files.</p> <p><em><strong>Developed by: </strong></em>Morteza Rasouligandomani (Ph.D. student in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain).</p> <p>Email contact: morteza.rasouli@upf.edu</p>
input and output files for clustering
<p>input and output files for clustering</p>
CREST_input
<p>It is the inputs of CREST MODEL, sharing which for publication.</p>
Geospatial Dataset Estimating Corn and Soy Inputs for Biofuel Production
<p>This dataset provides estimates for the corn and soybean biomass used for biofuel production (corn ethanol and soybean biodiesel, respectively) based on the geographic locations of biofuel plants provided by US Energy Information Administration for the 2017 USDA crop census data. County-level biomass was allocated to each biofuel plant based on distance to the plant assuming all biomass is located at each county’s centroid. If two plants were competing for the same county’s biomass, priority was given to the biofuel plant with the highest production capacity. County-level biomass estimation was validated using reported state-level biomass to biofuel values. Reported values could only be found for 11 states, but the mean difference between reported and estimated biomass used for biofuel production was 1.4% for corn ethanol. </p>
[VISIR-2: input data for benchmark runs] Analitiche
<p>Namelists needed for the VISIR-2 ship weather routing model current and wave Analitiche runs.</p>
Data for "Unraveling the Atmospheric Energy Input and Ionization due to EMIC-Driven Electron Precipitation from ELFIN's Observations"
<p>Dataset relative to the paper "Unraveling the Atmospheric Energy Input and Ionization due to EMIC-Driven Electron Precipitation from ELFIN's Observations"</p><ol><li>case_study.zip: ELFIN&POES conjunction<ol><li>POES flux is in #/s/cm2/sr/keV</li><li>ELFIN flux is in #/s/cm2/sr/MeV</li></ol></li><li>EMICdriven_events_atmospheres_temps: background atmosphere density & temperature for all 144 events<ol><li>Density is in g/cm3</li></ol></li><li>EMICdriven_events_nflux_PA_en: ELFIN events number flux 0°-90°<ol><li>ELFIN flux is in #/s/cm2/sr/MeV (...nflux.csv files)</li><li>ELFIN pitch-angle is in degrees (...nflux_PA.csv files)</li></ol></li><li>EMICdriven_events_nflux_PA_en: ELFIN events number flux 0°-180°<ol><li>ELFIN flux is in #/s/cm2/sr/MeV (...nflux.csv files)</li><li>ELFIN pitch-angle is in degrees (...nflux_PA.csv files)</li></ol></li><li>energy_deposition.csv: energy deposition vs altitude</li><li>ionization_rates.csv: ionization rate vs altitude</li><li>lat_lon_ionization.csv: time of EMIC-driven events with lat/lon, ionization&altitude peak and energy flux</li><li>avg_atm_density.csv: average background density</li><li>mean_PAD: mean PAD in #/s/cm2/sr/eV</li><li>median_PAD: median PAD in #/s/cm2/sr/eV</li></ol>
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.