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13,800 results for “Testing”
Dataset for "Machine learning predictions on an extensive geotechnical dataset of laboratory tests in Austria"
<p>This dataset comprises over 20 years of geotechnical laboratory testing data collected primarily from Vienna, Lower Austria, and Burgenland. It includes 24 features documenting critical soil properties derived from particle size distributions, Atterberg limits, Proctor tests, permeability tests, and direct shear tests. Locations for a subset of samples are provided, enabling spatial analysis.</p> <p>The dataset is a valuable resource for geotechnical research and education, allowing users to explore correlations among soil parameters and develop predictive models. Examples of such correlations include liquidity index with undrained shear strength, particle size distribution with friction angle, and liquid limit and plasticity index with residual friction angle.</p> <p>Python-based exploratory data analysis and machine learning applications have demonstrated the dataset's potential for predictive modeling, achieving moderate accuracy for parameters such as cohesion and friction angle. Its temporal and spatial breadth, combined with repeated testing, enhances its reliability and applicability for benchmarking and validating analytical and computational geotechnical methods.</p> <p>This dataset is intended for researchers, educators, and practitioners in geotechnical engineering. Potential use cases include refining empirical correlations, training machine learning models, and advancing soil mechanics understanding. Users should note that preprocessing steps, such as imputation for missing values and outlier detection, may be necessary for specific applications.</p> <p><strong>Key Features</strong>:</p> <ul> <li><strong>Temporal Coverage</strong>: Over 20 years of data.</li> <li><strong>Geographical Coverage</strong>: Vienna, Lower Austria, and Burgenland.</li> <li><strong>Tests Included</strong>: <ul> <li>Particle Size Distribution</li> <li>Atterberg Limits</li> <li>Proctor Tests</li> <li>Permeability Tests</li> <li>Direct Shear Tests</li> </ul> </li> <li><strong>Number of Variables</strong>: 24</li> <li><strong>Potential Applications</strong>: Correlation analysis, predictive modeling, and geotechnical design.</li> </ul> <p><strong>Technical Details</strong>:</p> <ul> <li>Missing values have been addressed using K-Nearest Neighbors (KNN) imputation, and anomalies identified using Local Outlier Factor (LOF) methods in previous studies.</li> <li>Data normalization and standardization steps are recommended for specific analyses.</li> </ul> <p><strong>Acknowledgments</strong>:<br>The dataset was compiled with support from the European Union's MSCA Staff Exchanges project 101182689 Geotechnical Resilience through Intelligent Design (GRID).</p>
Live fuel in-flame flammability testing data
<p>Live fuel in-flame flammability testing data for white spruce in central Alberta collected in 2014</p>
Dataset for the validation of a Computational Thinking test for upper primary school (grades 3-4)
<p>This dataset contains quantitative student data acquired during the administration of a new computational thinking assessment for upper primary school (grades 3 and 4). Over 1500 students (approximately half in grade 3 and half in grade 4) participated in the data collection which took place in January 2021 in the Canon Vaud in Switzerland. The data was used to validate the psychometric properties of the instrument in the referenced article. </p> <p> </p> <p>If you use any of the resources provided in this repository, please cite the following</p> <p>• The Zenodo repository, DOI: 10.5281/zenodo.5865573</p> <p>• The corresponding journal article</p> <p>• Licence : CC-BY-NC</p> <p> </p> <p>In case of inquiries, please contact laila.elhamamsy@epfl.ch</p>
RvSpectML/EchelleCCFs.jl Test dataset
<p>Dataset used in continuous integration test for <a href="https://github.com/RvSpectML/EchelleCCFs.jl">RvSpectML/EchelleCCFs.jl</a>. </p> <p>Source: Gilbertson, Ford & Dumuseque (2020) Research Notes of the AAS, Volume 4, Issue 4, id.59 <a href="https://ui.adsabs.harvard.edu/link_gateway/2020RNAAS...4...59G/doi:10.3847/2515-5172/ab8d44">10.3847/2515-5172/ab8d44</a> with full data set at <a href="https://doi.org/10.5281/zenodo.3753254">https://doi.org/10.5281/zenodo.3753254</a>. <br> </p>
Minimal dataset to test multiplexed DNA imaging (Hi-M) software pipelines
<p>This is a dataset of nuclei (DAPI), and 3 multiplexed DNA imaging cycles to test and validate processing software packages, such as pyHiM (https://github.com/marcnol/pyHiM). This dataset was acquired in a nc14 Drosophila embryo.</p> <p>File contents:</p> <p>scan_001_RT27_001_ROI_converted_decon_ch00.tif barcode 27, fiducial <br> scan_001_RT27_001_ROI_converted_decon_ch01.tif barcode 27<br> scan_001_RT29_001_ROI_converted_decon_ch00.tif barcode 29, fiducial <br> scan_001_RT29_001_ROI_converted_decon_ch01.tif barcode 29 <br> scan_001_RT37_001_ROI_converted_decon_ch00.tif barcode 37, fiducial <br> scan_001_RT37_001_ROI_converted_decon_ch01.tif barcode 37 <br> scan_006_DAPI_001_ROI_converted_decon_ch00.tif DAPI <br> scan_006_DAPI_001_ROI_converted_decon_ch01.tif DAPI, fiducial <br> scan_006_DAPI_001_ROI_converted_decon_ch02.tif RNA</p> <p> </p> <p>To test this dataset please refer to <a href="https://github.com/marcnol/pyHiM">pyHiM documentation page</a>.</p>
HyPer SMM wind tunnel tests: PIV pictures
<p>In this study, windblown sand transport on flat ground is reproduced by means of Wind-Sand Tunnel Tests (WSTT) carried out in the wind tunnel L-1B of von Karman Institute for Fluid Dynamics. The aim of WSTT is twofold. On one hand, they are intended to characterize the incoming sand flux in open field conditions. On the other hand, they allow to properly tune cheaper Wind-Sand Computational Simulations. The wind tunnel setup implements a uniform 5-meter-long sand fetch as sand source. The wind speed boundary layer is characterized through 2D Particle Image Velocimetry (PIV) technique. Wind flow state variables are assessed along the sand fetch by setting the wind speed equal to 1.3, 1.5, 2 times the threshold one. For the complete wind tunnel setup and data analysis please refer to: Raffaele L., Coste, N., and Glabeke G. "Life-Cycle Performance and Cost Analysis of Sand Mitigation Measures: Toward a Hybrid Experimental-Computational Approach." Journal of Structural Engineering 148.7 (2022): 04022082.</p> <p>The study has been developed in the framework of the MSCA-IF-2019 research project Hybrid Performance Assessment of Sand Mitigation Measures (HyPer SMM, https://hypersmm.vki.ac.be/). This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant Agreement No. 885985</p>
HyPer SMM wind tunnel tests: PTV pictures
<p>In this study, windblown sand transport on flat ground is reproduced by means of Wind-Sand Tunnel Tests (WSTT) carried out in the wind tunnel L-1B of von Karman Institute for Fluid Dynamics. The aim of WSTT is twofold. On one hand, they are intended to characterize the incoming sand flux in open field conditions. On the other hand, they allow to properly tune cheaper Wind-Sand Computational Simulations. The wind tunnel setup implements a uniform 5-meter-long sand fetch as sand source. The sand flux saltation layer are characterized through Particle Tracking Velocimetry (PTV) technique. Sand transport is assessed along the sand fetch by setting the wind speed equal to 1.3, 1.5, 2 times the threshold one. For the complete wind tunnel setup and data analysis please refer to: Raffaele L., Coste, N., and Glabeke G. "Life-Cycle Performance and Cost Analysis of Sand Mitigation Measures: Toward a Hybrid Experimental-Computational Approach." Journal of Structural Engineering 148.7 (2022): 04022082.</p> <p>The study has been developed in the framework of the MSCA-IF-2019 research project Hybrid Performance Assessment of Sand Mitigation Measures (HyPer SMM, https://hypersmm.vki.ac.be/). This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant Agreement No. 885985</p>
Data from Test House in Porto
<p>Project: Hybrid-BioVGE</p> <p>The Hybrid – BioVGE project is proposed with the primary objective to develop, design and demonstrate a highly integrated solar/biomass hybrid air conditioning system for space cooling and heating of residential and commercial buildings that is affordable, operating with improved efficiency and with a strong market potential.</p> <p>Project details at https://hybrid-biovge.inegi.up.pt/index.asp</p> <p>File 01: Rawdata from PortoTestHouse: Hybrid-BioVGE_PortoTestHouse_RawData_WT7_INEGI_v1_31052022</p> <p>File 02: Variable information and meta data</p>
Cortical slice labelled with anti GFP and VAMP2 antibodies - sample image for software testing of "Contacting synapse" protocol
<p><strong>Image 1.tif is a Brain slice</strong>. This 16 bits confocal stack of pictures ((801x711 pixels x33 z slices - pixel size 78.17 nm) of a brain slice has been taken at 93x (LeicaHC PL APO CS2 93x/1.30 GLYC) in sequential mode with two channels : one dedicated to the GFP detection, and the other one to synpatic boutons labelled with VAMP2 protein. VAMP2 protein are expressed at glutamatergic presynaptic sites and is usually found apposed to Post Synaptic Density. This is a good sample to test "contacting synapse" software. Here GFP cells were electroporated with various plasmid. The aim of the software is to identify if expression of those plasmid within the GFP labelled cell, influence the density of synapse contacting this GFP cells. Here presynaptic contact are identified through the use of antibodies to VAMP2 proteins.</p>
An External Replication on the Effects of Test-driven Development Using a Multi-site Blind Analysis Approach
<p>This dataset contains the <strong>unblinded </strong>version of the data collected and analyzed for the experiment reported in the paper. </p> <p>The semantics of the data can be found in the spreadsheet. For the formulas on how to obtain this data from the raw data, please see the paper. </p>
Test datasets for Hi-C scaffolding
<p>We provided two datasets for testing Hi-C scaffolding tools. For the CHM13 test dataset, we randomly chunked the first 10Mb of chr1, chr2 and chr3 of the T2T-CHM13v1.1 human genome assembly (Nurk et al. 2022) into 57 contigs. The Hi-C data downloaded from the telomere-to-telomere consortium GitHub repository (https://github.com/marbl/CHM13) were mapped to the reference genome and the reads mapped to these regions were extracted to generate Hi-C alignment files. For the LYZE01 test dataset, the Saccharomyces cerevisiae strain W303 genome assembly (Matheson et al. 2017) was split at positions with gaps (‘N’) to get the original contigs. An independent Hi-C data library was downloaded from the NCBI repository (GEO Accession GSM2417297) and downsampled to approximately 20X. The downsampled Hi-C data were mapped to the contigs to generate Hi-C alignment files.</p> <p>We provided five files for each test dataset: the contig file in FASTA format, the FASTA index file generated with SAMtools faidx command, and the Hi-C alignment file in BAM format sorted by coordinate, in BAM format sorted by query names (with the identifier 'qn' in the file name), and in BED format.</p>
Test-Retest qt-dMRI datasets for "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time"
<p>We release these four diffusion MRI data sets as part of our recent journal publication; Fick, Rutger H.J., et al. "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time." <em>Medical Image Analysis</em> (2017). More detailed information about the use of these data sets can also be found in the publication.</p> <p>We acquired test-retest diffusion MRI spin echo sequences from two C57Bl6 wild-type mice on an 11.7 Tesla Bruker scanner. The test and retest acquisition were taken 48 hours from each other. The data consists of 80x160x5 voxels of size 110x110x500<span class="math-tex">\(\mu\)</span>m. Each data set consists of 515 Diffusion-Weighted Images (DWIs) spread over 35 acquisition shells. The shells are spread over 7 gradient strength shells with a maximum gradient strength of 491 mT/m, 5 pulse separation shells between [10.8 - 20.0]ms, and a pulse length of 5ms. We manually created a brain mask and corrected the data from eddy currents and motion artifacts using FSL's eddy. We then drew a region of interest in the middle slice in the corpus callosum, where the tissue is reasonably coherent.</p> <p>- The diffusion MRI data are contained in the files with 'dwis' in the name.<br> <br> - The corpus callosum masks are contained in the files with 'mask' in the name.</p> <p>- The acquisition parameters are contained in the .txt files.</p>
Testing absolute plate reference frames and the implications for the generation of geodynamic mantle heterogeneity structure
<div>Description of Resources - Shephard et al. (2012)</div> <div> </div> <div>This file provides a detailed description of all of the files that make up the data collection associated with the publication: Shephard, G. E., Bunge, H. P., Schuberth, B. S., Müller, R. D., Talsma, A. S., Moder, C., & Landgrebe, T. C. W. (2012). Testing absolute plate reference frames and the implications for the generation of geodynamic mantle heterogeneity structure. Earth and Planetary Science Letters, 317, 204-217. doi: <a href="https://doi.org/10.1016/j.epsl.2011.11.027" target="_blank" rel="noopener">10.1016/j.epsl.2011.11.027</a></div> <div> </div> <div>Note: For information on file formats and what programs to use to interact with various file formats, see "File Formats and Recommended Programs”.</div> <div> </div> <div>This data collection includes both the rotations and topologically closed polygons* for each of the 5 absolute reference frames that were tested in the publication. They are to be loaded in GPlates (<a href="http://www.gplates.org" target="_blank" rel="noopener">http://www.gplates.org</a>).</div> <div> </div> <div>*Topologically closed plate polygons are constructed from the intersection of ridges, transforms, subduction zones and other plate boundary geometries. These 'resolved topologies' are valid at 1 Myr intervals. The plate boundary geometries and plate polygons have been assigned plate reconstruction IDs to allow them to be reconstructed using the supplied rotation files. </div> <div> </div> <div>The files associated with this data collection include:</div> <div>• <strong>Hybrid hotspot model (Moving and Fixed hotspots) (HHS)</strong></div> <div>* Caltech_Global_20110311HHS.gpml (37 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412HHS.rot (287 KB)- global rotation model</div> <div> </div> <div>• <strong>Fixed hotspot model (FHS)</strong></div> <div>* Caltech_Global_20110311FHS.gpml (36.8 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412FHS.rot (291 KB) - global rotation model</div> <div> </div> <div>•<strong> Hybrid hotspot and palaeomagnetic model (PMG)</strong></div> <div>* Caltech_Global_20110311PMG.gpml (35.9 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412PMG.rot (287 KB) - global rotation model</div> <div> </div> <div>• <strong>Subduction reference frame model (SUB)</strong></div> <div>* Caltech_Global_20110311SUB.gpml (36 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412SUB.rot (287 KB) - global rotation model</div> <div> </div> <div>• <strong>Hybrid hotspot and TPW-corrected palaeomagnetic model (TPW)</strong></div> <div>* Caltech_Global_20110311TPW.gpml (36.8 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412TPW.rot (287 KB)- global rotation model</div> <div> </div> <div>Project files (.gproj) are included for each .gpml/.rot pair.</div> <div> </div> <div>This article has additional supplementary data available with the online publication.</div> <div> </div> <div> </div> <div>Additional notes:</div> <div>*.rot contains the rotations for all plates and topological polygons.</div> <div>Each model is specific according to the African Plate (Plate ID 701) rotations. The rotations for all other plates are the same across each of the five models with the exception of cross-overs involving Pacific/Panthalassa plates for times earlier than 83.5Ma; these must be absolute reference frame specific and were re-calculated for each model. Programs used to calculate the new finite rotations include "adder" and "seaflow" </div> <div> </div> <div>*.gpml and .shp files contain continuously closing plate polygons i.e. from plate boundaries, from 140 Ma to present-day in 1 million year increments. </div> <div>These files differ slightly from those used in the paper, but are the most up-to-date version (as at May 2011) and are based on an updated model, Seton et al. (2012).</div> <div>They are specific to each of the five absolute reference frames. </div> <div> </div> <div>Note on velocity calculations in GPlates:</div> <div>GPlates calculates the velocity within each plate based on the stage rotation for that time period and averages for that respective period. For this reason, the velocities of a plate do not change incrementally within the time period and then abruptly change according to the next time period/stage rotation. </div> <div>This is also why there appears to be a "jump" in velocity magnitude and direction between 140 and 139 Ma.</div>
DIPROMATS 2024 - Shared Task 2: testing data for narrative identification
<p>Narratives are causally connected sequences of events that are selected and evaluated as meaningful for a particular audience. They make sense of the world by identifying the significance of people, places, objects, and events in time. In international relations, international actors create strategic narratives to “construct a shared meaning of the past, present, and future of international politics to shape the behavior of domestic and international actors”</p> <p>DIPROMATS 2024 Task 2 is a multiclass multilabel classification problem. Given a series of predefined narratives of each international actor, systems must determine which narrative the tweets belong to. Systems will receive the description of each narrative and a few examples of tweets in both languages (English and Spanish) that belong to each of them (few-shot learning). A tweet may be associated with one, several or none of the narratives.</p> <p>The few-shot training data can be found here: <a href="https://doi.org/10.5281/zenodo.10820961" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10820961</a></p> <p>These are the testing datasets for Englsih and Spanish. They are provided without the keys so the large language models can't be contaminated. If you are interested on testing your system, write anselmo@lsi.uned.es for details on submission and leaderboards.</p>
Survey of participant experience in workshop for testing IGP software setup: supplemental dataset for SimAUD 2018
<p>This is a supplemental dataset for a SimAUD 2018 paper. For the context of the dataset, plots, and description text given here, please refer to the paper:</p> <blockquote> <p><strong>Heinrich, M.K., Zahadat, P., Harding, J., et al. Using interactive evolution to design behaviors for non-deterministic self-organized construction. In <em>Proc. of SimAUD</em> (2018). <em>In print</em>.</strong></p> </blockquote> <p>These survey results <em><strong>(see attached file dataset_survey-responses)</strong></em>, are regarding the experience of participants in a workshop testing the <em>Integrated Growth Projection</em> software setup, including an implementation of the <em>Vascular Morphogenesis Controller</em>, and the Interactive Evolution software <em>Biomorpher</em>.</p> <p>The full-time one-week workshop was held as part of the normal coursework of the Master's degree program <em>CITAstudio: Computation in Architecture</em>, in the Institute of Architecture and Technology, at [KADK] The Royal Danish Academy, School of Architecture, Copenhagen, Denmark. It was part of the first semester of the 2017-2018 school year. Workshop participants were current Master's students in the <em>CITAstudio </em>program. The workshop teaching was led by Mary Katherine Heinrich and Phil Ayres, with guest teaching by Payam Zahadat and John Harding, overall program teaching supervision by Paul Nicholas, and teaching assistance by Sebastian Gatz.</p> <p><strong>Survey method:</strong></p> <p>The workshop participants gave survey responses anonymously.</p> <p>Survey responses were collected via Google Forms (https://www.google.com/forms/about/). At the start of the survey, participants gave permissions for use and publication, and verified that they participated in the workshop and had not previously taken the survey. The platform discourages duplicate responses by requiring an email sign-in (which is not visible to the surveyor).</p> <p>Although workshop participants gave permission for survey results to be published before taking the survey, the participants were unaware of the specific intended context and purpose of publishing, prior to taking the survey. Authors of the related paper who were workshop participants had no contact with the process of survey preparation, analysis of its results, or writing of related paper sections. </p> <p>There were 26 workshop participants. Participants were architects or architectural designers. </p> <p>Participants were asked about 1) their prior experience, 2) their understanding of topics before and after the workshop, 3) the helpfulness of specific software aspects for their understanding and their project work, and 4) their likelihood to use specific software aspects in the future.</p> <p>In addition to looking at the full surveyed group, we compare experience sub-groups. Participants select relevant tasks that they have previously completed, from a provided list. They are placed in the <em>Less Experience</em> sub-group if they select one or no tasks, and in the <em>More Experience</em> sub-group if they select two or more. </p> <p><strong>Survey results:</strong></p> <p>Close to two-thirds of workshop participants submitted survey responses (16 of 26, or 61.5%), with at least two respondents per group. One respondent indicated workshop absence; their responses were removed. One respondent indicated that they did not understand two questions, so those two responses were removed. All responses were submitted within 18 days of workshop end.</p> <p>Attached file:<em><strong> Plot_1</strong></em>, caption:</p> <blockquote> <p>Plot 1: <em>Participants' scoring of their understanding of the topics "self-organization" and "Interactive Evolution" respectively, comparing scores before and after the workshop.</em></p> </blockquote> <p>Attached file:<em><strong> Plot_2</strong></em>, caption:</p> <blockquote> <p>Plot 2: <em>(Left) Participants' scoring of their likelihood to use certain aspects of the software setup again, if they were to design a non-deterministic self-organizing behavior, and (right) participants' indications of the helpfulness of those same software aspects.</em></p> </blockquote> <p>Responses regarding understanding <em><strong>(see attached file, Plot_1)</strong></em> give evidence that the <em>Integrated Growth Projection</em> software setup helped participants of both experience levels improve their understanding of related topics. Those with less prior experience improved their understanding more than others, and understanding of "Interactive Evolution" improved slightly more than understanding of "self-organization." </p> <p>Responses regarding the usefulness of certain software aspects <em><strong>(see attached file, Plot_2)</strong></em> give evidence that: 1) Interactive Evolution helped participants to understand and design a non-deterministic self-organizing behavior <em><strong>(see Plot_2, a)</strong></em>; 2) visualization of the environment and simultaneous viewing of multiple results helped them to understand and design such behaviors <em><strong>(see Plot_2, b and c)</strong></em>; and 3) the <em>Integrated Growth Projection</em>'s features of environment visualization and simultaneous results <em>inside</em> the artificial selection preview windows of the IE setup helped them to evolve behaviors to solve their chosen tasks<em> <strong>(see Plot_2, d and e)</strong></em>. </p> <p>____________________________________</p> <p>The research work involved here is part of EU project<em> flora robotica</em>.<br> <a href="http://www.florarobotica.eu/">http://www.florarobotica.eu/</a><br> Project<em> flora robotica</em> has received funding from the European Union's Horizon 2020 research and innovation program under the FET grant agreement, no. 640959.</p>
CENTAUR project laboratory testing data
<p>This dataset contains results from testing carried out at a laboratory facility at the University of Sheffield (UK) as part of the <a href="https://www.sheffield.ac.uk/centaur">CENTAUR project</a>. CENTAUR is an EC funded Horizon 2020 Innovation Action. The project has developed a system to reduce flood risk in urban areas by utilising existing available storage capacity in urban drainage networks through the use of a gate installed in an existing manhole. The gate is controlled by Fuzzy Logic, using data from level sensors.</p> <p>The laboratory facility is described in the 'CENTAUR_Lab_facility.pdf '. Further details of the sensors and logging system are provided in 'Data_File_Column_Descriptions.csv'.</p> <p>The file 'Test_Record.csv' describes all tests carried out. This dataset contains 83 csv data files in for days when good data was collected, these are zipped into 'DataFiles.zip'. Each csv file within the .zip contains the test results for one day, the files are named with the date of testing in the format yymmdd. The csv data files do not include column headers, but a full description of the data in each column is provided in 'Data_File_Column_Descriptions.csv'. The csv files contain data from all sensors, but the time period of the data from each sensor (or sensor set) and timesteps are not the same, hence for each sensor / sensor set there is a separate time column. The sampling interval for the level sensors is given in column 26 of 'Test_Record.csv', this will be correct for the test period, but outside the tests the interval was often increased and this may be seen in the data files. The gate / FCD sampling interval is the same as the Fuzzy Logic interval in column 27 of 'Test_Record.csv', although the position is only reported when the gate / FCD is active - i.e. not fully open. At the end of a test the FCD will return to the fully open position (100%), but this final datapoint is not recorded. The flow rate and downstream valve position sampling interval are given in column 12 of 'Test_Record.csv'.</p> <p>Test numbers and fuzzy logic version ids are simplified for the journal paper 'Demonstrating a Fuzzy Logic algorithm for real-time flow control in a full-scale laboratory environment' which is currently under review with the Urban Water Journal. A correlation between the information in the paper and in 'Test_Record.csv' can be found in 'Paper_Test_Numbers.csv'.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641931.</p>
Simulated radio maps for CAESAR source finder testing
<p>A dataset of simulated astronomical radio maps (fits format, size 2500 x 2500) with both compact and extended sources that can be used to test automated source extraction algorithms. Maps were produced with the CASA tool as described in S Riggi et al, PASA (2019) to test CAESAR source finding performances. The dataset includes:</p> <p>- Simulated maps in FITS format ("recmap_vis-RUNXXX.fits")</p> <p>- List of generated sources in Caesar ROOT format ("sources-RUNXXX.root") and corresponding DS9 regions ("ds9regions-RUNXXX.reg")</p> <p>- List of generated sources (convolved by the clean beam) in Caesar ROOT format ("sources-RUNXXX_conv_rec.root") and corresponding DS9 regions ("ds9regions_sources-RUNXXX_conv.reg")</p>
Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory
<p>This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork.</p> <p>In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections.</p> <p>This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4.</p> <p>See accompanying technical note for more Information.</p>
LYON19- Lymphocyte Detection Test Set
<p><strong>LYON19</strong></p> <p>The provided test set includes 441 ROIs saved in the .<em>png</em> files, and it is a test set of LYON grand challenge: <a href="https://lyon19.grand-challenge.org/">https://lyon19.grand-challenge.org</a></p> <p> </p> <p><strong>Data Description</strong></p> <p>The test set contains Region of Interests (ROIs) selected from whole-slide images (WSI) of immunohistochemistry (IHC) stained specimens of breast, colon and prostate. Data came from eight different medical centers in the Netherlands. All slides were stained with an antibody against CD3 or CD8. Slides were subsequently digitized with a Pannoramic 250Flash II scanner (3DHistech, Hungary), resulting in WSIs with a spatial resolution of 0.24μm/px. Selected ROIs were saved with full resolution in the .<em>png</em> files. </p> <p>Selected ROIs were representative for most different types of lymphocyte distributions that occur in slides, namely (1) area with regular lymphocyte distribution, (2) clustered cells, and (3) staining or tissue artifacts.</p> <p> </p> <p><strong>Citation:</strong></p> <p>Please reference the following paper if you use LYON19 data for a scientific publication:</p> <p>Swiderska-Chadaj, Zaneta, et al. "<em><strong>Learning to detect lymphocytes in immunohistochemistry with deep learning</strong></em>." Medical Image Analysis (2019): 101547.</p> <p>Link to the paper: <a href="https://www.sciencedirect.com/science/article/pii/S1361841519300829">https://www.sciencedirect.com/science/article/pii/S1361841519300829</a></p>
AquaMaps: AquaMaps species list (test)
from <p></p>http://www.aquamaps.org/. AquaMaps are computer-generated predictions of natural occurrence of marine species, based on the environmental tolerance of a given species with respect to depth, salinity, temperature, primary productivity, and its association with sea ice or coastal areas. These __environmental envelopes__ are matched against an authority file which contains respective information for the Oceans of the World. Independent knowledge such as distribution by FAO areas or bounding boxes are used to avoid mapping species in areas that contain suitable habitat, but are not occupied by the species. Maps show the color-coded likelihood of a species to occur in a half-degree cell, with about 50 km side length near the equator. Experts are able to review, modify and approve maps.<p></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.