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1,952 results for “Testing Data”
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2008 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2009 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
Dataset and program scripts for the reproducibility of the hierarchical data structure file. Related to the manuscript entitled: Hierarchical Representation of Measurement Data, Metrological Uncertainty and Metadata for Calibrated Battery Tests
<p>We present an interoperable hierarchical data representation for battery tests, leading to improved scalability of data transmission and enhanced data accessibility and comprehensibility for both human interpretation and machine processing. The hierarchical data format includes the raw trace electrical measurement data, the metrological calibration and uncertainty data, the metadata such as experimental settings, instruments and software versions, as well as post-processed data such as electrochemical model fit parameters. This data representation allows repetition of the battery test under the exact same conditions such that identical results are achieved within defined error bounds. This is in line with the general F.A.I.R. data approach and provides repeatability and traceability in the battery value chain. As an application of the hierarchical data representation, we show the classification of cells as pass/fail being performed with quantitative confidence levels. We demonstrate the complete workflow of establishing the hierarchical data structure for electrochemical impedance spectroscopy (EIS), starting from metrological traceability of the calibration and uncertainty analysis towards the storage of the structured data as a single integrated file that preserves the hierarchical data format.</p>
Data for At-home testing to characterize SARS-CoV-2 seroprevalence among children and adolescents
<div> <div>This repository contains the data used to reproduce *At-home testing to characterize SARS-CoV-2 seroprevalence among children and adolescents* by Ahmed et al.</div> </div>
CATCH-EyoU: Exploiting European data and testing the integrated theory of youth active EU citizenship: EACEA subset analysis
<p>This dataset was created within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme, Grant Agreement No 649538. Work Package 4 of this project (Exploiting European data and testing the integrated theory of youth active EU citizenship) is focused on the re-analysis of existing European data. This dataset contains a subset of data originally collected within the project “<em>EACEA 2010/03: Youth Participation in Democratic Life</em>”, coordinated by the London School of Economic and Political Science. Specifically, an online questionnaire survey in seven European countries was conducted among young people age 15-30 in 2011. This dataset contains a subset of 22 variables that were employed for the reanalysis within the CATCH-EyoU project.</p>
GPR data used to test the efficient deconvolution method of Schmelzbach and Huber (2015)
<p>GPR data recorded with Pulse Ekko Pro from Sensors & Software on the river bed of the Tagliamento River (NE Italy).</p> <p>This data was used to test the efficient deconvolution scheme of Schmelzbar and Huber (2015):</p> <p>C. Schmelzbach, E. Huber (2015) Efficient Deconvolution of Ground-Penetrating Radar Data. IEEE Transactions on Geoscience and Remote Sensing, 53(9): 5209 - 5217<br> doi: <a href="http://dx.doi.org/10.1109/TGRS.2015.2419235">10.1109/TGRS.2015.2419235</a></p>
Flume Erosion Testing Data of Root-Permeated and Organic Matter Amended Soil Samples Using Three Streambank Boundary Conditions.
The data published here is expected to accompany one publicly available dissertation (Chapter 6 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Artificial Roots and Soil Microorganisms Increase Soil Resistance to Fluvial Erosion
Phindr3D: Test Data Set 1 (primary mouse cortical neurons)
<p>3D confocal image stacks of primary cortical neurons under different treatment conditions to test the functionality of Phindr3D. Explanatory .txt file contained in the ZIP file.</p> <p>Please see the manuscript for details and on how to access the full data set:</p> <p> </p> <p><strong>Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning</strong></p> <p>Philipp Mergenthaler*, Santosh Hariharan*, James M. Pemberton, Corey Lourenco, Linda Z. Penn, David W. Andrews</p> <p><em>PLOS Computational Biology, DOI: <a href="https://dx.doi.org/10.1371/journal.pcbi.1008630">10.1371/journal.pcbi.1008630</a></em></p> <p> </p> <p><strong>Phindr3D is available on GitHub</strong>: <a href="https://github.com/DWALab/Phindr3D">GitHub - DWALab/Phindr3D</a></p> <p> </p>
Phindr3D: Test Data Set 2 (human MCF10A breast cancer organoids)
<p>3D confocal image stacks of human MCF10A breast cancer organoids expressing different oncogenes to test the functionality of Phindr3D. Explanatory .txt file contained in the ZIP files.</p> <p>Please see the manuscript for details and on how to access the full data set:</p> <p> </p> <p><strong>Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning</strong></p> <p>Philipp Mergenthaler*, Santosh Hariharan*, James M. Pemberton, Corey Lourenco, Linda Z. Penn, David W. Andrews</p> <p><em>PLOS Computational Biology, DOI: <a href="https://dx.doi.org/10.1371/journal.pcbi.1008630">10.1371/journal.pcbi.1008630</a></em></p> <p> </p> <p><strong>Phindr3D is available on GitHub</strong>: <a href="https://github.com/DWALab/Phindr3D">GitHub - DWALab/Phindr3D</a></p>
Test data for the transverse Mercator projection
<p>This is a set of 287000 geographic points together with their coordinates in the transverse Mercator projection. The WGS84 ellipsoid (equatorial radius <em>a</em> = 6378137 m, flattening <em>f</em> = 1/298.257223563) is used, with central meridian 0°, central scale factor 0.9996 (the UTM value), false easting = false northing = 0 m.</p> <p>Each line of the test set gives 6 space delimited numbers</p> <ul> <li>latitude, φ (degrees, exact)</li> <li>longitude, λ (degrees, exact — see below)</li> <li>easting (meters, accurate to 0.1 pm)</li> <li>northing (meters, accurate to 0.1 pm)</li> <li>meridian convergence (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>scale (accurate to 10<sup>−20</sup>)</li> </ul> <p>These are computed using high-precision calculations using the exact formulas for the projection, see Lee (1976). The latitude and longitude are all multiples of 10<sup>−12</sup> deg and should be regarded as exact, except that λ = 82.63627282416406551° should be interpreted as exactly (1 − <em>e</em>) 90°, where <em>e</em> is the eccentricity given by <em>e</em><sup>2</sup> = <em>f</em> (2 − <em>f</em> ).</p> <p>The contents of the file are as follows:</p> <ul> <li>250000 entries randomly distributed in φ ∈ [0°, 90°], λ ∈ [0°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [0°, 90°], λ = 0°</li> <li>1000 entries randomly distributed on φ = 0°, λ ∈ [0°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [0°, 90°], λ = 90°</li> <li>1000 entries close to φ = 90° with λ ∈ [0°, 90°]</li> <li>1000 entries close to φ = 0°, λ = 0° with φ ≥ 0°, λ ≥ 0°</li> <li>1000 entries close to φ = 0°, λ = 90° with φ ≥ 0°, λ ≤ 90°</li> <li>2000 entries close to φ = 0°, λ = (1 − <em>e</em>) 90° with φ ≥ 0°</li> <li>25000 entries randomly distributed in φ ∈ [−89°, 0°], λ ∈ [(1 − <em>e</em>) 90°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [−89°, 0°], λ = 90°</li> <li>1000 entries randomly distributed on φ ∈ [−89°, 0°], λ = (1 − <em>e</em>) 90°</li> <li>1000 entries close to φ = 0°, λ = 90° (φ < 0°, λ ≤ 90°)</li> <li>1000 entries close to φ = 0°, λ = (1 − <em>e</em>) 90° (φ < 0°, λ ≤ (1 − <em>e</em>) 90°)</li> </ul> <p>The entries for φ < 0° and λ ∈ [(1 − <em>e</em>) 90°, 90°] use the “extended” domain for the transverse Mercator projection explained in Sec. 5 of Karney (2011). The first 258000 entries have φ ≥ 0° and are suitable for testing implementations following the standard convention.</p>
Experimental data of dissipative embedded column base connections tested under cyclic lateral loading
<p>This experimental dataset is comprised of the following items:</p> <p>(a) the deduced experimental data of conventional/dissipative embedded column base connection specimens, which contains base moment, column drift ratio, and axial shortening responses (TestData.xlsx);</p> <p>(b) photos of each specimen taken during cyclic loading (C-N-0_Test_Photos.7z, D-M1-1_Test_Photos.7z, D-M1-3_Test_Photos.7z, D-M1-5_Test_Photos.7z, D-M2-2_Test_Photos.7z);</p> <p>(c) characteristic videos for each specimen that demonstrate the cyclic behavior (Test_Video.7z);</p> <p>(d) Digital image correlation (DIC) images taken during cyclic loading to obtain strain fields near the steel column/reinforced concrete foundation interface (C-N-0_DIC_Photos.7z, D-M1-1_DIC_Photos.7z, D-M1-3_DIC_Photos.7z, D-M1-5_DIC_Photos.7z, D-M2-2_DIC_Photos.7z); </p> <p>(e) Videos that demonstrate strain fields of column flanges of both conventional and dissipative embedded column base connection specimens (DIC_Video.7z) </p> <p>Please read the "README" file contained in each folder for more detailed information regarding each data.</p> <p> </p>
test data for gliderad2cp
<p>Data files used for the demonstration and testing of the gliderad2cp library https://github.com/bastienqueste/gliderad2cp</p><p>Includes hydrographic and ADCP observations in the Gulf of Oman. Subset from https://zenodo.org/doi/10.5281/zenodo.10075773</p><p> </p>
Test-bed PV system performance data
<p>The data is generated from the on-site data acquisition devices installed at the outdoor testing facilities of the Smart Energy Infrastructure | PHAETHON CoE. </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>
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>
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>
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>
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>
Orbital-radar.py test data repositroy
<p>The data set provided in this data repository contains the data set used to run the python notebook of the orbital-radar tool - <a href="https://github.com/igmk/orbital-radar">See GitHub page of the orbital-radar.py (DOI </a><a href="../doi/10.5281/zenodo.13375013">10.5281/zenodo.13375013</a><a href="https://github.com/igmk/orbital-radar">).</a><br>The data set consists mainly from ground-based w-band radar at JOYCE avilable at the ACTRIS database CLU (JOYCE data 2021-04-06), and the data set for the ground-based w-band radar at Mindelo during the ASKOS campaign, too (Mindelo data 2022-07-15). The GEOMS data filed from the ground-based w-band radars at JOYCE and Mindelo are provided by the University of Cologne and stored in this ZENODO database. In addition the data base contains a day of ground-based ARM radar data from the Cap Verdes, presented in Rémillard and Tselioudis, 2015, <em>J. Climate</em>, <a href="https://doi.org/10.1175/JCLI-D-15-0066.1" target="_blank" rel="noopener">https://doi.org/10.1175/JCLI-D-15-0066.1</a>. The airborne data set from the AFLUX campaign can be found in the PANGAEA database (AFLUX data 2022). The forward-modelled radar data using ICON output and the PAMTRA tool for the NyAlesund is provided by the University of Cologne and stored in the ZENODO database. </p> <ul> <li>JOYCE data 2021-04-06: <a href="https://doi.org/10.60656/e8c4957887854659">https://doi.org/10.60656/e8c4957887854659</a></li> <li>Mindelo data 2022-07-15: <a href="https://doi.org/10.60656/c5e09106ba0246bc">https://doi.org/10.60656/c5e09106ba0246bc</a> </li> <li>AFLUX data 2022: <a href="https://doi.org/10.1594/PANGAEA.944506">https://doi.org/10.1594/PANGAEA.944506</a> </li> <li>ARM data set from Cap Verdes: <a href="https://doi.org/10.1175/JCLI-D-15-0066.1" target="_blank" rel="noopener">https://doi.org/10.1175/JCLI-D-15-0066.1</a></li> </ul> <p>JOYCE and Mindelow data should be sorted in a YYYY/MM/DD. folder structure to be read by the tool. Other data need a simple path to the folders data are placed in.</p>
Lithium-ion battery charge and discharge testing data - current, voltage, soc, ta - at constant levels of power
<p>This dataset helped in the composition of a battery testing and modelling validation, of a lithium-ion battery. The data has the charge and discharge testing acquisition data - current, voltage, soc, ta - at constant levels of power.</p>
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.