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696 results for “Dataset, test,”
FLOATECH WP3 experimental dataset : wave-tank hybrid testing of a 10 MW turbine based on a spar platform (ECN)
<p>This dataset presents the experimental measurements made in the Hydrodynamic and Ocean Engineering wave tank of Ecole Centrale de Nantes, in France, with the model of a 10 MW turbine supported by a spar platform at a scale 1:40. </p> <p>The tests were performed using a real-time hybrid testing method (or software-in-the-loop) called SoftWind presented and published in Ocean Engineering (the paper is available at this <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). </p> <p> </p> <p><strong>Presentation of the experimental model:</strong></p> <p>The model is presented in details in the provided Excel file "FLOATECH_C3_Project data and model description.xlsx". </p> <p> </p> <p><strong>In the dataset:</strong></p> <p>The measurement files of the tests are gathered in folders by "series", and each test file has a test number. The series and the test conditions of each run are detailed in the provided Excel file "FLOATECH_C3_Database_Matrix.xlsx". </p> <p>Decay tests, pull-out tests and hammer tests were performed and are given in the dataset. </p> <p> </p> <p><strong>Real-time simulation models</strong></p> <p>The numerical models used in the real-time OpenFAST simulations are also provided in the compressed file "RT Simulations files.zip". </p> <p> </p> <p><strong>Data used in the published paper:</strong></p> <p>Some of the tests were used in the paper (see <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). The corresponding test numbers are given in the table below. </p> <table> <tbody> <tr> <td><strong>Load cases</strong></td> <td><strong>Hs (m)</strong></td> <td><strong>Tp (s)</strong></td> <td><strong>Uhub (m/s)</strong></td> <td><strong>TI (%)</strong></td> <td><strong>Wave dir. (°)</strong></td> <td><strong>Wind dir(°)</strong></td> <td><strong>TestNum 1C</strong></td> <td><strong>TestNum 3C</strong></td> <td><strong>TestNum 5C</strong></td> </tr> <tr> <td>1.2</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>0</td> <td>269</td> <td>268</td> <td>270</td> </tr> <tr> <td>2.1</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>25</td> <td>275</td> <td>307</td> <td>281</td> </tr> </tbody> </table> <p> </p> <p> </p>
Test dataset for "Steam condensation scaled experiment in the presence of non-condensable gases for small modular reactor containment passive safety"
<p>This study presents scaled experiments using steam condensation with non-condensable gas (NCG)—helium (He), simulating hydrogen, and nitrogen (N<sub>2</sub>)—as these experiments are pivotal for water-cooled reactor passive containment cooling system (PCCS) design and analysis. Research into PCCSs for small modular reactors (SMRs) is especially important in light of SMR system design; however, studies in the literature reflect limitations due to test geometry and operational condition variations, without considering SMR prototypic design. To address these challenges, a scaled test facility was developed to accurately replicate SMR PCCSs. This facility includes vertical down-flow condensing test sections with 1-, 2-, and 4-in.-diameter condensing tubes, accompanied by annular water cooling. Experiments were conducted using both superheated and saturated steam, with steam mass flow rates in the presence of NCG varying from: (a) 55 to 66 kg/hr. of steam, and 1.8 to 22 kg/hr. of He (as the NCG); (b) 58 to 63 kg/hr. of steam, and 4.4 to 13.3 kg/hr. of N<sub>2</sub> (as the NCG). Test data were collected on (a) the axial temperatures of the annular cooling water; (b) the outer wall temperature of the condensers; and (c) the mass flow rate, temperature, and pressure at the test section inlets and outlets. These primary test data were used in conjunction with a standard data reduction methodology to estimate essential thermal parameters such as heat fluxes, heat transfer coefficients, and condensation rates. The effects of NCGs on steam condensation within the geometry of the scaled test sections were then presented in regard to various testing conditions.</p>
Test input dataset for simulation of supercapacitors
<p>Metalwalls is an application for simulating supercapacitors, developed by CNRS and used in the context of H2020 FET-HPC EXA2PRO project. </p> <p>This dataset contains application test input of various sizes. </p>
Multi-echo masking test dataset
Open the record for dataset details and reuse information.
DATASET - AVALIAÇÃO EMPÍRICA DA GERAÇÃO AUTOMATIZADA DE TESTES DE SOFTWARE SOB A PERSPECTIVA DE TEST SMELLS
<p>A constante busca pela qualidade sempre está em destaque na área de Engenharia de Software. Dentre as diversas disciplinas dedicadas a essa temática, o teste de software tem se estabelecido como uma das mais importantes, dado sua eficácia na identificação de defeitos, em momento prévio à liberação de sistemas de software para o mercado. O teste de software é atividade-chave para o desenvolvimento de software de qualidade. Entretanto, desenvolver testes é tão ou mais custoso do que desenvolver o código de produção. Uma alternativa para a redução dos custos associados ao teste de software se dá pelo uso intensivo de ferramentas de automação de testes. A proposta dessas ferramentas é reduzir o tempo de produção sem afetar a qualidade do código. Apesar dessa premissa, não é comum encontrar abordagens que incluam uma camada de verificação de qualidade dos testes gerados automaticamente, o que pode reduzir a confiabilidade da eficácia desses testes. Neste cenário, a proposta dessa dissertação é analisar empiricamente massas de dados de teste, sob a perspectiva de test smells, no sentido de avaliar a qualidade dos testes produzidos por ferramentas de geração automatizada de testes de software. Test smells são más escolhas no design dos testes e tem características sintomáticas e podem acarretar diminuição na qualidade dos sistemas. Considerando os test smells em código de teste, o estudo analisa os testes gerados por duas ferramentas amplamente aceitas pela comunidade de testes: Evosuite e Randoop. Um conjunto de vinte e um projetos de software de código aberto, disponíveis na plataforma Github foram considerados no estudo. A análise considerou a dispersão de test smells no código de teste desses projetos, bem como a existência de potenciais correlações entre test smells e as relações com as métricas estruturais. Como principais resultados, encontramos fortes correlações entre os test smells e as métricas de cobertura do código, diferenças significativas entre os dados encontrados nas suítes de testes geradas automaticamente e com os testes pré-existentes nos projetos avaliados.</p>
Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: test datasets.
<p>We provide here the datasets used for the test and assessment of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>). </p> <p>The test dataset presented here includes different sets of co-located temperature and salinity vertical profiles: </p> <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units)</li> </ul> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, <em>submitted</em>.</p> <p>Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p> <p>Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.</p> <p> </p>
Cu dataset – A copper ore labeled images dataset for segmentation training and testing
<p>This dataset is composed of 121 pairs of correlated images. Each pair contains one image of a copper ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255).</p> <p>The sample came from a copper ore from Yauri Cusco (Peru) with a complex mineralogy, mainly composed of sulfides, oxides, silicates, and native copper. It was classified by size. The fraction +74-100 μm was cold mounted with epoxy resin and subsequently ground and polished.</p> <p>Correlative microscopy was employed for image acquisition. Thus, 121 fields were imaged on a reflected light microscope with a 20× (NA 0.40) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 1017×753 pixels with a resolution of 0.53 µm/pixel. As matter of fact, some images (the images No. 2, 3, 24, 25, 46, 47, 69, 91, and 113) have slightly smaller sizes because they were cropped during the registration procedure to correct co-localization errors of the order of a few pixels. Finally, the images from SEM were thresholded to generate the reference images.</p> <p>Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012).</p> <p>This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model (Chen et al., 2018) that reached mean values of 90.56% and 92.12% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights.</p> <p>For further questions and suggestions, please do not hesitate to contact us.</p> <p> </p> <p><strong>Contact email</strong>: ogomes@gmail.com</p> <p> </p> <p>If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5020566</p> <p> </p> <p>Please also cite this paper, which provides additional details about the dataset:</p> <p>Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.</p>
Test datasets
<p>This is a test</p>
Training and test datasets for the PredictONCO tool
<p>This dataset was used for training and validating the <a href="https://loschmidt.chemi.muni.cz/predictonco/">PredictONCO </a>web tool, supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning. The dataset consists of 1073 single-point mutants of 42 proteins, whose effect was classified as Oncogenic (509 data points) and Benign (564 data points). All mutations were annotated with a clinically verified effect and were compiled from the ClinVar and OncoKB databases. The dataset was manually curated based on the available information in other precision oncology databases (The Clinical Knowledgebase by The Jackson Laboratory, Personalized Cancer Therapy Knowledge Base by MD Anderson Cancer Center, cBioPortal, DoCM database) or in the primary literature. To create the dataset, we also removed any possible overlaps with the data points used in the PredictSNP consensus predictor and its constituents. This was implemented to avoid any test set data leakage due to using the PredictSNP score as one of the features (see below).</p> <p>The entire dataset (<strong>SEQ</strong>) was further annotated by the pipeline of PredictONCO. Briefly, the following six features were calculated regardless of the structural information available: essentiality of the mutated residue (yes/no), the conservation of the position (the conservation grade and score), the domain where the mutation is located (cytoplasmic, extracellular, transmembrane, other), the PredictSNP score, and the number of essential residues in the protein. For approximately half of the data (<strong>STR</strong>: 377 and 76 oncogenic and benign data points, respectively), the structural information was available, and six more features were calculated: FoldX and Rosetta ddg_monomer scores, whether the residue is in the catalytic pocket (identification of residues forming the ligand-binding pocket was obtained from P2Rank), and the pKa changes (the minimum and maximum changes as well as the number of essential residues whose pKa was changed – all values obtained from PROPKA3). For both <strong>STR </strong>and <strong>SEQ </strong>datasets, 20% of the data was held out for testing. The data split was implemented at the position level to ensure that no position from the test data subset appears in the training data subset. </p> <p>For more details about the tool, please visit the <a href="https://loschmidt.chemi.muni.cz/predictonco/help">help page</a> or <a href="https://loschmidt.chemi.muni.cz/peg/contact/">get in touch with us</a>.</p> <p>14-Dec-2023 update: the file with features<em> PredictONCO-features.txt</em> now includes UniProt IDs, transcripts, PDB codes, and mutations.</p>
Datasets with typos for testing LEA (https://arxiv.org/abs/2307.02912)
<p>Datasets for testing the generalization capacity of LEA in the presence of typos. Link to the paper: https://arxiv.org/abs/2307.02912</p> <p>Below is a list of raw public datasets and different versions of test splits to which automatically synthetically generated typos have been added by deleting and replacing characters.</p> <ul> <li>Abt-Buy</li> <li>Amazon-Google</li> <li>WDC-Computers (small, medium, large and xlarge)</li> <li>WDC-All (xlarge)</li> <li>RTE</li> <li>MRPC</li> </ul> <p> </p> <p>Reference:</p> <p>Almagro, M., Almazán, E., Ortego, D., & Jiménez, D. (2023, August). LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias. In <em>Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining</em> (pp. 36-46).</p>
DATASET: TESTING NICHE EQUIVALENCE IN AMPHIDROMOUS FISH POPULATIONS
<p>Presence records of<em> Galaxias maculatus</em> were collected from 12 locations across five river basins in central-southern Chile during March, May, August, and November of 2019 as part of a study on fish sampling and processing (Ramírez-Álvarez et al. 2022. doi.org/10.1038/s41598-022-06936-8)</p> <p>Isotopic niches defined using a standard ellipse area (SEAc) in isotopic space, represented by a 2D ellipsoidal space (δ13C - δ15N) (see Supporting Information: Standard ellipse area functions - Ramírez-Álvarez et al. 2024. doi:10.1007/s10750-024-05738-5) - Empirical Bayesian Kriging</p> <p>Database of varibles used for niche modelling: isotopic niche and seven abiotic variables selected from 23 predictor variables: (1) 19 climate variables representing 1950–2000 climate averages from WorldClim (http://www.worldclim.org/). (2) Four spatially continuous topographic and hydrological variables from the EarthEnv Project adjusted to the HydroSHEDS river network (http://www.earthenv.org/) (Domisch et al. 2015). Selection of variables was accomplished by analysis of covariance and multicollinearity, using the ENMeval R package (Muscarella et al. 2014): (1) principal component analysis (PCA) to explore relationships among all predictors, evaluating the composition of components (component variables) that accounted for ≥65% of variance explained, (2) pairwise comparisons to detect pairs of variables with strong correlations (Pearson correlation coefficients <0.8), groups with a correlation of less than 0.8 were considered independent, and (3) variance inflation factor (VIF) <10, to reduce the effect of collinearity between predictors (Listed below). A VIF greater than 10 indicates collinearity problems in the model. The vifcor and vifstep functions were employed by calculating two different strategies to exclude highly collinear variables using a stepwise procedure (Muscarella et al. 2014).</p> <div><em>Variable description and ecological question associated, selected variables are marked in bold.</em></div> <div><em>Series 1: Temperature, temperature variations and interaction with precipitation.</em></div> <div>bio1: Annual Mean Temperature; Is the temperature usually suitable? </div> <div><strong>bio2: Mean Diurnal Range; Are the days too warm or too cold?</strong></div> <div>bio3: Isothermality; Do temperatures fluctuate greatly over the course of a month? </div> <div>bio4: Temperature Seasonality (standard deviation); Do temperatures fluctuate greatly over the course of a year?</div> <div>bio5: Min Temperature of Coldest Month; Is the maximum temperature too high?</div> <div>bio6: Min Temperature of Coldest Month; Is the temperature constantly too high?</div> <div><strong>bio7: Temperature Annual Range; Do temperatures fluctuate greatly over the course of a year?</strong></div> <div><strong>bio8: Mean Temperature of Wettest Quarter; Is it too cold or too warm during the rainy season?</strong></div> <div>bio9: Mean Temperature of Driest Quarter; Is it too cold or too warm during the dry season?</div> <div>bio10: Mean Temperature of Warmest Quarter; Are the warmer months too cold?</div> <div><strong>bio11: Mean Temperature of Coldest Quarter; Are the colder months too warm?</strong></div> <div><em>Series 2: Precipitation and Rainfall Patterns</em></div> <div>bio12: Annual Precipitation; Does it rain enough in a year?</div> <div>bio13: Precipitation of Wettest Month; Does it rain a lot during the wettest month?</div> <div><strong>bio14: Precipitation of Driest Month; Does it rain poorly during the driest month?</strong></div> <div>bio15: Precipitation Seasonality (Coefficient of Variation); Would rainfall fluctuate much between seasons?</div> <div>bio16: Precipitation of Wettest Quarter; Does it rain a lot in the rainy season?</div> <div>bio17: Precipitation of Driest Quarter; Is rainfall low during dry seasons?</div> <div>bio18: Precipitation of Warmest Quarter; Does it rain enough during the warmer months?</div> <div>bio19: Precipitation of Coldest Quarter; Does it rain enough during the colder months?</div> <div><em>Series 3: Topology and hydrology</em></div> <div><strong>dem: Average elevation; Does altitude play a role as a topological factor?</strong></div> <div><strong>slope_av: Average slope; Is the slope suitable for the accumulation of small ponds?</strong></div> <div>flow_ac_ac: Accumulation flow; Does enough water accumulate or does it drain too quickly?</div> <div>flow_ac_le: Accumulation flow direction; Does the flow direction support the formation of small ponds?</div> <p>Domisch, S., G. Amatulli & W. Jetz, 2015. Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Scientific Data 2(1):150073 doi:10.1038/sdata.2015.73.</p> <p>Muscarella, R., P. J. Galante, M. Soley‐Guardia, R. A. Boria, J. M. Kass, M. Uriarte & R. P. Anderson, 2014. ENM eval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in ecology and evolution 5(11):1198-1205</p>
Extended dataset for the validation the competent Computational Thinking test in grades 3-6
<p>Extended dataset for the validation the competent Computational Thinking test in grades 3-6<br>=======================================================</p> <p>• If you publish material based on this dataset, please cite the following :</p> <p> • The Zenodo repository : Laila El-Hamamsy, Barbara Bruno, Jessica Dehler Zufferey, & Francesco Mondada (2023). Extended dataset for the validation of the competent Computational Thinking test in grades 3-6 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7983525 </p> <p> • The article on the validation of the computational thinking test for grades 3-6 : El-Hamamsy, L., Zapata-Cáceres, M., Martín-Barroso, E., Mondada, F., Zufferey, J. D., Bruno, B., & Román-González, M. (2025). The competent Computational Thinking test (cCTt): A valid, reliable and gender-fair test for longitudinal CT studies in grades 3–6. <em>Technology, Knowledge and Learning</em>, 1-55. https://doi.org/10.1007/s10758-024-09777-8 </p> <p>• License : This work is licensed under a Creative Commons Attribution 4.0 International license (CC-BY-4.0)</p> <p>• Creators : El-Hamamsy, L., Bruno, B., Dehler Zufferey, J., and Mondada, F.</p> <p>• Date May 30th 2023</p> <p>• Subject : Computational Thinking (CT), Assessment, Primary education, Psychometric validation</p> <p>• Dataset format : CSV. The dataset contains four files (one per grade, see detailed description below). Please note that the spreadsheets may contain missing values due to students not being present for a part of the data collection. To have access to the specific cCTt questions please refer to the original publication [1] and Zenodo repository [2] which provide the full set of questions and correct responses.</p> <p>• Dataset size < 500 kB</p> <p>• Data collection period : January and November 2021</p> <p>• Abbreviations :<br> - CT : Computational Thinking<br> - cCTt: competent CT test</p> <p>• Funding : This work was funded by the the NCCR Robotics, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 51NF40_185543)</p> <p># References</p> <p>[1] El-Hamamsy, L., Zapata-Cáceres, M., Barroso, E. M., Mondada, F., Zufferey, J. D., & Bruno, B. (2022). The Competent Computational Thinking Test: Development and Validation of an Unplugged Computational Thinking Test for Upper Primary School. Journal of Educational Computing Research, 60(7), 1818–1866. https://doi.org/10.1177/07356331221081753 </p> <p>[2] El-Hamamsy, L., Zapata-Cáceres, M., Marcelino, P., Dehler Zufferey, J., Bruno, B., Martín Barroso, E., & Román-González, M. (2022). Dataset for the comparison of two Computational Thinking (CT) test for upper primary school (grades 3-4) : the Beginners' CT test (BCTt) and the competent CT test (cCTt) (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5885034 </p> <p>[3] El-Hamamsy, L., Zapata-Cáceres, M., Martín-Barroso, E. <em>et al.</em> The Competent Computational Thinking Test (cCTt): A Valid, Reliable and Gender-Fair Test for Longitudinal CT Studies in Grades 3–6. <em>Tech Know Learn</em> (2025). https://doi.org/10.1007/s10758-024-09777-8</p> <p>[4] Brennan, K. and Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. page 25</p> <p>[5] El-Hamamsy, L., Zapata-Cáceres, M., Marcelino, P., Bruno, B., Dehler Zufferey, J., Martín-Barroso, E., & Román-González, M. (2022). Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: The Beginners’ CT test (BCTt) and the competent CT test (cCTt). Frontiers in Psychology, 13. https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1082659</p>
Helsinki Deblur Challenge 2021 test dataset
<p>This dataset was primarily designed and captured to be used for the testing part of the Helsinki Deblur Challenge 2021 but it can be used for any testing and benchmarking purposes of image deblurring algorithms.</p> <p>The dataset contains photographs of random strings of text (including numbers), natural images and QR codes with varying levels of blur caused by misfocusing the camera. Each photo has both a blurred and sharp version.</p> <p>The images are split into 4 separate zip files each having 5 steps of different blur. Altogether there are 20 steps. Each one of the zip files contains two folders, one folder named CAM1_focused (Camera 1) with the sharp images and one named CAM2_blurred (Camera 2) with the blurred images. For each step there are 40 images of text character targets, 15 natural image targets, one QR-code target and 3 images of technical targets. Each one of the text target images is accompanied by a text file (same file name but .txt extension) containing the correct transcription of that particular text target.</p> <p>As a difference to the HDC training data (<a href="https://doi.org/10.5281/zenodo.4916176">https://doi.org/10.5281/zenodo.4916176</a>) the test data character targets include also numbers. </p> <p>A detailed description of the training dataset that was acquired in the exact same way as this test dataset can be found here: <a href="http://arxiv.org/abs/2105.10233">http://arxiv.org/abs/2105.10233</a></p> <p>Here is a link to the official webpage of the Helsinki Deblur Challenge 2021: <a href="https://www.fips.fi/HDC2021.php">https://www.fips.fi/HDC2021.php</a></p>
Twitter Account Dataset - 10,000 accounts tested through the Botometer API
<p>This dataset contains 10,420 accounts extracted from Twitter from tweets containing the word "vaccine". We verified each one through the Botometer API.</p> <p>Includes:</p> <ul> <li>Account ID</li> <li>Echo Chamber Score (0-5)</li> <li>Fake Follower Score (0-5)</li> <li>Financial Score (0-5)</li> <li>Self Declared Score (0-5)</li> <li>Spammer Score (0-5)</li> <li>Other Score (0-5)</li> <li>Overall Score (0-5)</li> </ul> <p>Those appearing with an 'X' correspond to deleted, modified or private accounts.</p>
Dataset con datos de la evolución del coronavirus en España y las pruebas de test realizadas
<p>El dataset es un fichero csv que contiene los datos de coronavirus diarios desde Enero de 2020 hasta Enero de 2021, clasificados además de por fecha, por comunidad autónoma. Se indican además del volumen de nuevos casos, los positivos registrados en función de distintas pruebas diagnósticas.</p>
Periodic Hydraulic Testing Dataset for "Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)"
<p>This dataset is associated with the SNSF-SPARK project “Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)”. Please read the ReadMe file for more information.</p>
Crop classification dataset for testing domain adaptation or distributional shift methods
<p>In this upload we share processed crop type datasets from both France and Kenya. These datasets can be helpful for testing and comparing various domain adaptation methods. The datasets are processed, used, and described in this paper: <a href="https://doi.org/10.1016/j.rse.2021.112488">https://doi.org/10.1016/j.rse.2021.112488</a> (arXiv version: <a href="https://arxiv.org/pdf/2109.01246.pdf">https://arxiv.org/pdf/2109.01246.pdf</a>). </p> <p>In summary, each point in the uploaded datasets corresponds to a particular location. The label is the crop type grown at that location in 2017. The 70 processed features are based on Sentinel-2 satellite measurements at that location in 2017. The points in the France dataset come from 11 different departments (regions) in Occitanie, France, and the points in the Kenya dataset come from 3 different regions in Western Province, Kenya. Within each dataset there are notable shifts in the distribution of the labels and in the distribution of the features between regions. Therefore, these datasets can be helpful for testing for testing and comparing methods that are designed to address such distributional shifts.</p> <p>More details on the dataset and processing steps can be found in <a href="https://doi.org/10.1016/j.rse.2021.112488">Kluger et. al. (2021)</a>. Much of the processing steps were taken to deal with Sentinel-2 measurements that were corrupted by cloud cover. For users interested in the raw multi-spectral time series data and dealing with cloud cover issues on their own (rather than using the 70 processed features provided here), the raw dataset from Kenya can be found in <a href="https://openreview.net/forum?id=5HR3vCylqD">Yeh et. al. (2021)</a>, and the raw dataset from France can be made available upon request from the authors of this Zenodo upload.</p> <p>All of the data uploaded here can be found in "CropTypeDatasetProcessed.RData". We also post the dataframes and tables within that .RData file as separate .csv files for users who do not have R. The contents of each R object (or .csv file) is described in the file "Metadata.rtf".</p> <p><strong>Preferred Citation:</strong></p> <p>-Kluger, D.M., Wang, S., Lobell, D.B., 2021. Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sens. Environ. 262, 112488. https://doi.org/10.1016/j.rse.2021.112488.</p> <p>-URL to this Zenodo post https://zenodo.org/record/6376160</p>
ConFiRMa dataset_01: simulation of CRM characterization tests with the OOFEM code (detailed level modelling)
<p>The Dataset collects the input files developed for the simulation of characterization tests performed on Composite Reinforced Mortar samples with the free open-source code OOFEM (detailed level modelling). The description of the numerical models and the analysis and comparison of the results can be found in paper "Characterization of Textile Reinforced Mortar: state of the art and detailed modelling with a free open source finite element code" (<a href="https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240">https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240</a>).</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provide a description of the different input files.</p> <p> </p>
ConFiRMa dataset_02: simulation of tests on CRM strengthened masonry elements with the OOFEM code (detailed level modelling)
<p>The Dataset collects the input files developed for the simulation of tests on masonry elements strengthened through Composite Reinforced Mortar with the free open-source code OOFEM (detailed level modelling). The description of the numerical models and the analysis and comparison of the results can be found in paper "Masonry elements strengthened through Textile-Reinforced Mortar:application of the detailed level modelling with a free open-source Finite-Element code".</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provide a description of the different input files.</p>
Earthquake Response of Reinforced Concrete Frames with Infill and Active External Confinement: Tests and Dataset
<p>One option to retrofit reinforced concrete (RC) frames is the construction of infill walls. Many studies have shown that infill increases lateral strength and stiffness but tends to reduce drift capacity relative to bare frames. Fewer studies have quantified reductions in drift demand attributed to infills prior to failure. This report summarizes experiments designed to compare drift demands of frames with and without infill. Included data comes from two theses completed at Purdue University which focused on the dynamic response of one-third scale, non-ductile RC frames to uniaxial simulated earthquake ground motions. Tests were conducted on bare frames, frames with masonry infill walls, and frames with timber infill walls. In 11 of 14 test series, active confinement was applied to columns using external post-tensioned reinforcement</p> <p> </p> <p>This dataset summarizes two experimental programs that studied the dynamic, in-plane response of one-third scale RC frames with full-height infills and active external column confinement. Theses summarized were written by Monical (2021) and Kerby (2022), and included data from 254 in-plane dynamic tests of non-ductile RC frames with various seismic retrofits.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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