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2,152 results for “outputs”
Model output for "On the length and intensity of the West African Summer Monsoon during the Last interglacial Green Sahara Period"
<p>This dataset gives the model outputs from a pre-industrial (PI) and a last interglacial (LIG) simulation based on AWIESM-wiso. A more detailed description of the individual file is presented below:</p> <p>lig_isotope_daily.nc: climatological daily isotope composition in precipitation for LIG. The second level represents delta18O and the the third level for deltaD.</p> <p>pi_isotope_daily.nc: climatological daily isotope composition in precipitation for PI. The second level represents delta18O and the the third level for deltaD.</p> <p>lig_precip_daily.nc: climatological daily precipitation for LIG. </p> <p>pi_precip_daily.nc: climatological daily precipitation for PI. </p> <p>lig_temp2_daily.nc: climatological daily surface air temperature for LIG. </p> <p>pi_temp2_daily.nc: climatological daily surface air temperature for PI. </p> <p>lig_wind_composite.nc: composited LIG wind by using as the West African Summer Monsoon (WASM) onset as reference date (zonal wind: var131; meridional wind: var132; vertical wind: var133). </p> <p>pi_wind_composite.nc: composited PI wind by using as the West African Summer Monsoon (WASM) onset as reference date (zonal wind: var131; meridional wind: var132; vertical wind: var133). </p> <p> </p>
WRF-Chem output- concentrations for the two simulations: Business as Usual and COVID for the two periods, March-April and May
<p>The WRF-Chem model was utilized to calculate the changes in O3 chemistry during the COVID lockdown period. We ran two simulations: (1) Business As Usual (BAU) and (2) COVID for the lockdown (30 March to 12 April 2020) and relaxation (18- 25 to 30 May 2020) periods. </p> <p>Here, we provide modelled concentrations for several chemical and physical species for 30 March to 12 April (only weekdays) and 18 to 30 May (only weekdays) for both simulations, BAU and COVID, over the AMB area during the morning (6-8 UTC), afternoon (13-15 UTC) and evening (19-21 UTC) hours. </p> <p>We also provide the WRF-Chem output used to create the air parcel trajectories and the ozone concentrations from the Metropolitan Area of Barcelona to the Pyrenees. </p>
Numerical Output for Analysis of Atlantic Water Pathways in Fram Strait
<p>This dataset contains data for the publication: Shifts of the Recirculation Pathways in central Fram Strait drive Atlantic Intermediate Water Variability on Northeast Greenland shelf (McPherson et al., JGR Oceans).</p>
Model input files and output data for Pluto clathrate formation modeling
<p>Geochemical and thermal modeling input files and data for "Timing and abundance of clathrate formation controls ocean evolution in outer solar system bodies"</p>
Hifieval output data
<p>This folder contains a CHM13 subset the raw output of hifieval and PAF alignment files of reads using minimap2. More information could be found in https://github.com/magspho/hifieval. We hope those creating new EC tools may find it more efficient to run hifieval on their own tool rather than having to run the other assemblers during benchmarking.</p>
Simulation outputs of DynSoM model and instrumental data for Samoylov Island's polygonal tundra
<p>These datasets include the observational data, the input parameters, and the Dynamic Soil Model (DynSoM) outputs of a polygonal tundra arctic environment located in the quaternary sediments of Samoylov island, with and without considering heave due the segregation of ice. The data is gathered (SamValidation.mat) and simulated at the centre and rim of one of the polygons, which correspond to wet and dry tundra conditions respectively. This dataset Is used as a primary data input for the simulation and output for the figures of the article "Mechanistic Modelling of Segregated Ice and Soil Heave Dynamics in Artic Soils" by Xavier Rodriguez-Lloveras, Melanie A. Thurner Philipp Porada & Christian Beer, Universität Hamburg. Submitted to "The Journal of Advances in Modeling Earth Systems (JAMES)" on February 2023.</p> <p>The datasets are published as MATLAB matrix format and require adequate MATLAB (or equivalent) software for visualization.</p> <p>The validation data (SamValidation.mat) is a subset of the data published by Boike et al. (2019, doi:10.1594/PANGAEA.905236) adapted to the DynSoM model requirements.</p>
ChatGPT impacts in programming education: A short list of input questions and output answers from ChatGTP
<p><strong>Five questions (in red) were addressed to ChatGTP ver3.5 between the period July 24<sup>th</sup> – July 28<sup>th</sup> 2023, in order to identify the opinion of ChatGTP in the field of computer science programming and programmers training. It is chosen to pose questions related to the main applications, advantages, disadvantages, future implications, and ethical considerations.</strong></p>
The Future Evolution of Global Natural Gas Trade Output Data
<p>This data repository contains the GCAM output databases for the paper: Yarlagadda, B., G. Iyer, M. Binsted, P. Patel, M. Wise, and J. McLeod. (2023). The Future Evolution of Global Natural Gas Trade. </p>
Model output data to "Land surface modeling in the Himalayas: on the importance of evaporative fluxes for the water balance of a high elevation catchment"
<p>We provide i) gridded initial conditions (.tif), ii) modeled gridded monthly outputs (.tif), and iii) modeled hourly outputs at the station locations (.txt) for the hydrological year 2019. Information about the variables and units can be found in the figures (.png) associated to each dataset. Details about the datasets can be found in the original publication by Buri and others (2023).</p><p> </p><p>Buri, P., Fatichi, S., Shaw, T. E., Miles, E. S., McCarthy, M. J., Fyffe, C. L., ... & Pellicciotti, F. (2023). Land Surface Modeling in the Himalayas: On the Importance of Evaporative Fluxes for the Water Balance of a High‐Elevation Catchment. <i>Water Resources Research</i>, <i>59</i>(10), e2022WR033841. DOI: <a href="https://doi.org/10.1029/2022WR033841"><strong>10.1029/2022WR033841</strong></a></p>
Terrestrial laser scanning data of urban trees in Milton Keynes, UK: individual trees and Treegraph outputs
<p>This dataset was used for the analysis of the following publication:</p> <p>Yang, W., Wilkes, P., Vicari, M.B., Hand, K., Calders, K. and Disney, M., 2024. Treegraph: tree architecture from terrestrial laser scanning point clouds. <em>Remote Sensing in Ecology and Conservation</em>.</p> <p><strong>Any use of this dataset should cite the paper above </strong>(Creative Commons Attribution NonCommercial License).</p> <p> </p> <p>================================================<br> Dataset<br>================================================</p> <p><strong>TLS_point_clouds:</strong></p> <p>- Data collection: Terrestrial laser scanning data acquired in leaf-off condition in April 2021.</p> <p>- Scanning instrument: We used a RIEGL VZ-400 with a wavelength of 1550 nm, 0.35 mrad beam divergence and 0.04˚ angular resolution.</p> <p>- Locations: The data were collected from three sites in Milton Keynes, UK: Avebury Blvd (Ave), Dansteed Way (Dan), and Overgate (Ove).</p> <p>- File information: Each file is an individual leaf-off tree point cloud in Polygon File Format (.ply), which can be viewed in software such as CloudCompare.</p> <p> </p> <p><strong>Treegraph_outputs:</strong></p> <p>- Description: Model outputs from <em>Treegraph</em> for individual trees.</p> <p>- File naming convention: [TreeID]-[downsample_voxel_length]-[tip_diameter_if_known].*</p> <ul> <li>*.centres.ply: Skeleton nodes of individual trees.</li> <li>*.mesh.ply: Cylinder model of individual trees.</li> <li>*.json: Geometrical and topological attributes at varying scales, from internode and branch to the whole tree.</li> <li>*.txt: Summary of input parameters, logs of intermediate steps, and a statistical overview of whole-tree structural attributes.</li> </ul>
Introduction to HPC: molecular dynamics simulations with GROMACS: output files - Devana
<p> Introduction to HPC: molecular dynamics simulations with GROMACS: log files corresponding to the exercises 1.1, 1.2 2.1 3.1 and 3.2</p>
Selected L-Galaxies and binary_c output data (Yates+23)
<p>Dataset to accompany the publication entitled <a href="https://arxiv.org/abs/2310.15218"><strong>"The impact of binary stars on the dust and metal evolution of galaxies"</strong></a> by R. M. Yates, D. Hendriks, A. P. Vijayan, R. G. Izzard, P. A. Thomas, and P. Das.</p><p> </p><p>The L-Galaxies files included in this dataset were generated from the MM+binaryC and MM+singleStars versions of L-Galaxies, run in snapshot mode on representative sub-volumes of the Millennium-I and Millennium-II simulations, with the LITE_OUTPUT switch turned on. In detail:</p><p> </p><p><i><strong>MM+binaryC version:</strong></i></p><p><strong>SA_MM+binaryC_MRI_z0.00_0-5: </strong>Zip file containing output files (in binary format) for the MM+binaryC version of L-Galaxies in snapshot mode, run on treefiles 0-5 of Millennium-I at redshift 0.00.</p><p> </p><p><strong>SA_MM+binaryC_MRI_z0.00-8.22_0-5: </strong>Zip file containing output files (in binary format) for the MM+binaryC version of L-Galaxies in snapshot mode, run on treefiles 0-5 of Millennium-I at nine selected redshifts from 0.00 to 8.22.</p><p> </p><p><strong>SA_MM+binaryC_MRII_z#.##_40-79: </strong>Zip files containing output files (in binary format) for the MM+binaryC version of L-Galaxies in snapshot mode, run on treefiles 40-79 of Millennium-II at redshift #.## = [0.00,1.04,2.07,3.11,3.95,5.03,5.92,6.97,8.22].</p><p> </p><p> </p><p><i><strong>MM+singleStars version:</strong></i></p><p><strong>SA_MM+singleStars_MRI_z0.00_0-5:</strong> Zip file containing output files (in binary format) for the MM+singleStars version of L-Galaxies in snapshot mode, run on treefiles 0-5 of Millennium-I at redshift 0.00.</p><p> </p><p><strong>SA_MM+singleStars_MRI_z0.00-8.22_0-5:</strong> Zip file containing output files (in binary format) for the MM+singleStars version of L-Galaxies in snapshot mode, run on treefiles 0-5 of Millennium-I at nine selected redshifts from 0.00 to 8.22.</p><p> </p><p><strong>SA_MM+singleStars_MRII_z#.##_40-79: </strong>Zip files containing output files (in binary format) for the MM+singleStars version of L-Galaxies in snapshot mode, run on treefiles 40-79 of Millennium-II at redshift #.## = [0.00,1.04,2.07,3.11,3.95,5.03,5.92,6.97,8.22].</p><p> </p><p> </p><p><i><strong>Other:</strong></i><br><strong>L-Galaxies_and_binaryC_processing:</strong></p><p><strong>a) </strong>A set of simple python scripts to read the L-Galaxies outputs, process them into samples (as python "dictionaries"), and make some plots (including those presented in Yates+23). To use, place the desired output files into the /output folder, and execute main_lgals.py<i>. </i>Switches are provided at the top of main_lgals.py to modify functionality. The relevant data structure for python is also provided (LGalaxy_snapshots_liteOutput.py), as are conversions between snapshot number, redshift, and other properties for Millennium-I and Millennium-II snapshots for reference (Millennium_Planck1_Snapshots_Redshifts.txt and MillenniumII_Planck1_Snapshots_Redshifts.txt). For catalogues containing star formation histories, the corresponding times and widths of each bin are read from Database_SFH_table.fits.</p><p><strong>b)</strong> The binaryC ensemble files used in Yates+23 are included, along with a script to read these ensemble files and generate L-Galaxies yield tables from them (process_binaryc_outputs.py). Switches are provided at the top of process_binaryc_outputs.py to modify functionality.</p><p> </p><p> </p>
Corticosteroids in Postoperative Critically Ill Neonates With Low Cardiac Output Syndrome With Congenital Heart Disease
ClinicalTrials.gov study NCT00590018. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Intravenous AII for the Treatment of Severe Hypotension in High Output Shock: A Pilot Study
ClinicalTrials.gov study NCT01393782. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Targeting Personalized Brain States Reflecting Strong and Weak Corticospinal Tract Output in Real-time
ClinicalTrials.gov study NCT06365086. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparison Between NICOM-Cheetah and Flotrac-Vigileo for Cardiac Output Monitoring.
ClinicalTrials.gov study NCT03394768. IPD Sharing: NO. Countries: 1. Publications: 4.
Effect of Intravenous and Topical Tranexamic Acid on Drain Output in Breast Reduction Surgery
ClinicalTrials.gov study NCT07131592. IPD Sharing: NO. Countries: 1. Publications: 14.
Coupled PPE model output - land parameter impacts on the mean climate state
Open the record for dataset details and reuse information.
Arctic seabirds show individual variation in foraging responses to glacial conditions without consequences for reproductive output
Open the record for dataset details and reuse information.
Disruption of host-associated and benthic microbiota affects reproductive output and settlement of a habitat-forming macroalga
Open the record for dataset details and reuse information.
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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.