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1,600 results for “input”
The East Asia Moho depth model and the input gravity data
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Test 1 using ReCom files as input
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Selenium_test_4_input_data
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FL fish recruitment analyses input output 1
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FL fish recruitment analyses input output 2
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Input Datasets for Estimating Gross Primary Productivity (GPP) 2020
<p>The four zip file contains the input files required for GPP estimation as proposed in https://doi.org/10.1016/j.scitotenv.2018.02.314</p> <p>All the input files are from the year 2020 over the European continent.</p> <p>Year: 2020</p> <p>Region: Europe</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>For further information please contact s.bandopadhyay@soton.ac.uk</p>
Simulation inputs to knowledge graph
<p>This dataset was created for the CISBAT conference to demonstrate using a knowledge graph to manage data for urban energy simulation. The data is compiled from sources including <a href="https://download.geofabrik.de/">Open Street Map</a>, <a href="https://www.swisstopo.admin.ch/de/geodata/landscape/buildings3d2.html">SwissBuildings3D</a>, and the <a href="https://www.housing-stat.ch/de/index.html">Federal Register of Buildings and Dwellings</a> in Switzerland. The data also makes reference to the <a href="https://h3geo.org/">H3 geospatial</a> indexing system.</p>
Input JSON data for the pipeline of the CLARA Knowledge Graph
<p><span>/!\</span> This deposit is deprecated; a more complete version of the deposit can be found here: <a href="https://zenodo.org/records/8403142">8403142</a>. <span>/!\</span></p> <p><strong>CLARA</strong><br>This deposit is part of the <a href="https://project.inria.fr/clara/">CLARA project</a>. The CLARA project aims to empower teachers in the task of creating new educational resources. And in particular with the task of handling the licenses of reused educational resources.</p> <p>The present deposit contains the JSON files extracted from the <a href="https://www.x5gon.org/">X5GON</a> Postgresql database. The files are fed to the pipeline of the CLARA project for the creation of 4 different RDF graphs. This is achieved through the use of RDF mappings (<a href="https://rml.io/">RML</a>, <a href="https://ceur-ws.org/Vol-2980/paper374.pdf">RML-star</a>).<br>That pipeline can be found on <a href="https://gitlab.univ-nantes.fr/clara/pipeline">Gitlab</a>.</p> <p>The results of this pipeline can also be found on Zenodo, on those four different deposits:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.8108855">Standard reification</a></li> <li><a href="http://doi.org/10.5281/zenodo.8108962">Singleton properties</a></li> <li><a href="http://doi.org/10.5281/zenodo.8108947">Named graphs</a></li> <li><a href="http://doi.org/10.5281/zenodo.8108970">RDF-star</a></li> </ul> <p> </p> <p><strong>Content</strong></p> <p>The JSON files contain information on a total of 45K educational resources, linked to a total of 135K subjects (extracted from DBpedia). Each educational resource is linked to the subjects it talks about. Each of those links has two corresponding scores which represent the certainty of the given link. Those scores are <em>"norm_cosine" </em>and <em>"norm_pageRank"</em>.</p> <p>The dataset was cut into multiple JSON files in order to make its processing easier. <br>There are two type of json files in this deposit:</p> <ul> <li><strong>authors_[</strong>X<strong>].json</strong> - Which lists the authors names</li> <li><strong>ER_[</strong>X<strong>].json</strong> - Which lists the educational resources and their related information.<br>That information contains: <ul> <li>their <em>title.</em></li> <li>their <em>description.</em></li> <li>their <em>language</em> (and <em>language_detected</em>, only the first one is used in the pipeline here).</li> <li>their <em>license.</em></li> <li>their <em>mimetype.</em></li> <li>the <em>authors.</em></li> <li>the <em>date</em> of creation of the resource.</li> <li>a <em>url</em> linking to the resource itself.</li> <li>and finally the subjects (named <em>concepts</em>) associated to the resource. With the corresponding scores.</li> </ul> </li> </ul>
Florida Scrub-Jay PVA-2 Vortex input files
<p>Vortex input files for PVA-2 of Florida scrub-jay, a collaboration of NASA, USFWS, TNC, SCTI, and others. Extended from PVA-1 for the species (also available on Zenodo) by the use of more recent demographic data and the linking of the population model to habitat projections made with an ST-Sim vegetation transition model. </p>
Mapping file used for input validation in Quest for Orthologs
<pre>Mapping file used in the validation step and the map_relation.py step of the uploaded file format in QfO 2018 bechmarking workflow (https://github.com/qfo/benchmark-webservice/tree/elixir).</pre>
A new methodology using borehole data to measure angular distances between geological interfaces - Input and processed data
<p>This companion dataset relates to the manuscript "<strong>A new methodology using borehole data to measure angular distances between geological interfaces</strong>", by</p> <p>Michał P. Michalak<sup>a,b,</sup><a href="#sdfootnote1sym"><sup>1</sup></a>, Paweł Marzec<sup>b,</sup><a href="#sdfootnote2sym"><sup>2</sup></a>, Filip Turoboś<sup>c,</sup><a href="#sdfootnote3sym"><sup>3</sup></a>, Paulina Leonowicz<sup>d,</sup><a href="#sdfootnote4sym"><sup>4</sup></a>, Lesław Teper<sup>a,</sup><a href="#sdfootnote5sym"><sup>5</sup></a>, Paweł Gładki<sup>e,</sup><a href="#sdfootnote6sym"><sup>6</sup></a>, Michael J. Pyrcz<sup>f</sup><sup>,</sup><a href="#sdfootnote7sym"><sup>7</sup></a>,</p> <p>Mariusz Szubert<sup>g,</sup><a href="#sdfootnote8sym"><sup>8</sup></a></p> <p><a href="#sdfootnote1anc">1</a> Michał Michalak devised the project, wrote the manuscript, performed the computations and discussed the results.</p> <p><a href="#sdfootnote2anc">2</a> Paweł Marzec conducted the geological interpretation and discussed the results.</p> <p><a href="#sdfootnote3anc">3</a> Filip Turoboś conducted the statistical analysis.</p> <p><a href="#sdfootnote4anc">4</a> Paulina Leonowicz prepared the chapter about stratigraphy.</p> <p><a href="#sdfootnote5anc">5</a> Lesław Teper prepared the chapter about regional geology.</p> <p><a href="#sdfootnote6anc">6</a> Paweł Gładki participated in the study conceptualisation (discussion about distance functions)</p> <p><a href="#sdfootnote7anc">7</a> Michael Pyrcz discussed the applications of the method and revised the statistical section.</p> <p><a href="#sdfootnote8anc">8</a> Mariusz Szubert was responsible for the data acquisition.</p> <p>The archive contains the input and processed data. The input data contains XYZ coordinates of points documenting the investigated interfaces. The output files contains calculated orientations and coordinates of vectors. The output files can be processed in RStudio.</p>
Input Dataset for susceptibility mapping in Mila Basin, Algeria (1985-2017)
<p>Input Dataset for susceptibility mapping in Mila Basin, Algeria from January 1985 to December 2017.</p>
Input data and MATLAB scripts used in the Master's project
<p>This folder contains the input data and MATLAB scripts used during the Master's project.</p> <p>The master's project investigates the effects of yaw misalignment on power production for a passively yawed floating MR system. High temporal resolution experimental wind data is analyzed and used for various analyses. An in-house Matlab code is developed to simulate the floating MR system's yawing motions based on wind speed and direction input time series. </p> <p>The input data is provided in ten ASCII TXT files containing experimental wind data during the year 2014. The experimental data is sampled at the Skipheia wind measurement station located at Frøya in Trøndelag. The ten ASCII TXT files represent each month during 2014, with the exception of January and February. The ASCII TXT files were provided by the data source as presented in the folder, which explains how the files are named.</p> <p>A short overview of how the scripts were used:</p> <ul> <li>April_30days.m and June_30days.m were used for providing Weibull distributions and a histogram in the Theory chapter</li> <li>Ideal_conditions_9months.m was used for providing a Gaussian distribution in the Theory chapter</li> <li>Yaw_misalignment.m was used for a percentage power-loss overview for yaw misalignments up to 90 degrees</li> <li>Ideal_conditions_9months.m was used for power production analyses in ideal conditions (no yaw misalignment)</li> <li>Low_speed_yawmis.m was used for an aero-hydro-dynamic analysis of a low wind speed situation</li> <li>High_speed_yawmis.m was used for an aero-hydro-dynamic analysis of a high wind speed situation</li> <li>Extreme_speed_yawmis.m was used for an aero-hydro-dynamic analysis of an extreme wind speed situation</li> <li>Low_speed_yawmis.m, High_speed_yawmis.m, and Extreme_speed_yawmis.m were used for an angular rate of yaw correction analysis</li> <li>June_30days.m and March_8days.m were used for illustrating the variance in experimental wind data</li> <li>WindRose.m was used for distributing the wind speeds and wind directions of the experimental data (developed by other researchers)</li> </ul>
SPV3 reference input files
<p>- nzTabSPV3.dat: n(z) for the SPV3 reference scenario (columns: z, n1, n2, ...., n13)<br> <br> - nuiTabSPV3.dat: nuisance parameters (columns: mean redshift, number density, linear galaxy bias, LF logarithmic slope, photo - z mean shift, error on photo - z mean shift) </p>
Input Dataset for Landslide Susceptibility Mapping in Sihjhong Basin, Taiwan (2004-2014)
<p>Input Dataset for Landslide Susceptibility Mapping in Sihjhong Basin, Taiwan (2004-2014)</p>
Supporting input files for "GB1 Dimerization in Crowders: A Multiple Resolution Approach"
<p>All necessary input files to run coarse-grained Martini and Readdy simulations are provided.</p>
input data jrc-eraa
<p>Input hdfs for JRC-ERAA</p>
Inputs sufficient to recapitulate WGD results in both species
<p>Inputs sufficient to recapitulate WGD results in both species</p>
MCScanX inputs, outputs, and relevant scripts for analysis
<p>MCScanX inputs, outputs, and relevant scripts for analysis</p>
DSCOVR EPIC L2 Ozone (O3), Sulfur Dioxide (SO2) Aerosol Index (AI) with Epic L1B V03 Input, Version 2
Robust cloud products are critical for the Deep Space Climate Observatory (DSCOVR) to contribute significantly to climate studies. Building on our team’s track record in cloud detection, cloud property retrieval, oxygen band exploitation, and DSCOVR-related studies, we propose to develop a suite of algorithms for generating the operational Earth Polychromatic Imaging Camera (EPIC) cloud mask, cloud height, and cloud optical thickness products. Multichannel observations will be used for cloud masking; the cloud height will be developed with information from the oxygen A- and B- band pairs (780 nm vs. 779.5 nm and 680 nm vs. 687.75 nm); for the cloud optical thickness retrieval, we propose an approach that combines the EPIC 680 nm observations and numerical weather model outputs. Preliminary results from radiative transfer modeling and proxy data applications show that the proposed algorithms are viable.Product validation will be conducted by comparing EPIC observations/retrievals with counterparts from coexisting Low Earth Orbit (LEO) and Geosynchronous Earth Orbit (GEO) satellites. The proposed work will include a rigorous uncertainty analysis based on theoretical and computational radiative transfer modeling that complements standard validation activities with physics-based diagnostics. We also plan to evaluate and improve the calibration of the EPIC O2 A- and B-band absorption channels by tracking the instrument performance over known targets, such as cloud-free ocean and ice sheet surfaces.The deliverables for the proposed work include an Algorithm Theoretical Basis Document (ATBD) for peer review, products generated with the proposed algorithms, and supporting research articles. The data products, archived at the Atmospheric Science Data Center (ASDC) at the NASA Langley Research Center, will provide essential inputs needed for the community to apply EPIC observations to climate research and better interpret The National Institute of Standards and Technology Advanced Radiometer (NISTAR) observations.The proposed work directly responds to the solicitation to “develop and implement the necessary algorithms and processes to enable various data products from EPIC sunrise to sunset observations once on orbit” and improve “the calibration of EPIC based on in-flight data.”
ScienceDex guides
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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.