Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
477
datasets available to search
ShareScore release 0.9.0
Dataset results
477 results for “input data”
Transcriptional profiling of cells in the dorsal striatum of Drd1-TRAP (CP73) mice: input data
GEO Series GSE142142. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Input data: Can the Hermit Warbler serve as an old-forest indicator species in the Sierra Nevada?
<p>Input data used for occupancy modeling of Hermit Warblers across the Sierra Nevada in 2021.</p>
Example input data GLEAMv4: 2004-01
<p>Example input data for GLEAMv4 for January 2004. GLEAM is a global evaporation model, more info can be found at <a href="https://www.gleam.eu/">GLEAM | Global Land Evaporation Amsterdam Model</a>.</p>
SubsurfaceBreaks v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces: Input and Processed Data
<p>This companion dataset relates to the manuscript "<strong>SubsurfaceBreaks</strong> <strong>v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces"</strong>, by Michał Michalak, Christian Gerhards and Peter Menzel.</p> <p>There are several groups of files:</p> <ul> <li>a file with parameters (params.txt) of the generated homoclinal interfaces (slopes) such as dip angle, dip direction, level of noise).</li> <li>files 0-999 are generated using the code from GitHub. (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_synthetic_subsurface_slopes) for generating synthetic slopes. Every slope is in a separate file (.txt files) and it is possible to upload the slope to ParaView for further inspection: Delaunay triangulation, normal vectors and dip vectors have their own .vtu files. The .txt files (0-999) can be uploaded for training using the Python script (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_subsurface_slopes_training_testing_evaluating_revision.ipynb).</li> <li>KSH_input.txt corresponds to real data from Kraków-Silesian Homocline. Every row corresponds to a point representing a geological horizon separating Middle Jurassic geological units: Kościeliska sandstones from ore-bearing clays. This data set can be used to calculate geometric attributes using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes).</li> <li>KSH_input_output_0 corresponds to an output file from processing the KSH_input.txt file using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes). This file should be uploaded to the Python script to identify fault-related features on a real subsurface slope.</li> </ul>
Input data for the IMACLIM-R France model
<p>Data to run the IMACLIM-R France code</p> <p>https://github.com/CIRED/IMACLIM-R_France</p>
Yerrida Basin Geophysical Modeling - Input data and inverted models.
<p>This companion datasets relates to the manuscript "<strong>Integration of geological uncertainty into geophysical inversion by means of local gradient regularization</strong>", by J. Giraud, M. Lindsay, V. Ogarko, M. Jessell, R. Martin and E. Pakyuz-Charrier, submitted to Solid Earth. The archive contains the input and output geophysical data, starting and inverted models, probabilistic geological model and conditioning volume derived from the calculation of Shannon's entropy. </p>
Input data and R code for "Larger male Yellow Warbler ( Setophaga petechia ) occupy smaller home ranges over winter in natural and agricultural sites in western Mexico"
<p>Data used for modeling Yellow Warbler home ranges in western Mexico from 2012 to 2014.</p> <p> </p> <p> </p>
WRF-Chem configurations and input data sets for sensitivity tests of emission inventories
<p>WRF-Chem and WPS v4.4 source codes and their configurations with namelist files.</p> <p>Emission inventory data sets (EDGAR-HTAP v2 and v3) for 'anthro_emis' input are included.</p> <p>The KORUS v5 emission data are provided with 'wrfchemi' format.</p> <p>The 'namelist.input' contains physics and chemistry options that are used for WRF-Chem model.</p> <p>The model grid information is available in 'namelist.wps'.</p> <p> </p> <p>Kim, K.-M., Kim, S.-W., Seo, S., Blake, D. R., Cho, S., Crawford, J. H., Emmons, L., Fried, A., Herman, J. R., Hong, J., Jung, J., Pfister, G., Weinheimer, A. J., Woo, J.-H., and Zhang, Q.: Sensitivity of the WRF-Chem v4.4 ozone, formaldehyde, and precursor simulations to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-132, in review, 2023.</p>
Balmorel input data for comparative modelling of CSP + TES and PVS
<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 Concentrating Solar Power and Photovoltaics with battery 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 as well as the Balmorel model folder containing the GAMS code.</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> <p> </p>
Input data for a spatial urban sprawl model
<p>Input data for projecting global urban extent under future shared socioeconomic pathways (2010-2100)</p>
Clustering has a meaning: optimization of angular similarity to detect geometric anomalies in geological terrains - Input and processed data.
<p>This companion dataset relates to the manuscript "<strong>Clustering has a meaning: optimization of angular similarity </strong></p> <p><strong>to detect geometric anomalies in geological terrains</strong>", by</p> <p>Michał P. Michalak, Lesław Teper, Florian Wellmann, Jerzy Żaba, Krzysztof Gaidzik, Marcin Kostur, Yuriy P. Maystrenko, Paulina Leonowicz</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>
The East Asia Moho depth model and the input gravity data
Open the record for dataset details and reuse information.
Selenium_test_4_input_data
Open the record for dataset details and reuse information.
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>
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 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>
input data jrc-eraa
<p>Input hdfs for JRC-ERAA</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.