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5,155 results for “Data Base”
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 2. Facial expression recognition in proposed method
<p>In this stage, a video is prepared using the color data captured from Kinect camera. The face region in each frame is obtained from the video using Viola-Jones algorithm (Figure 2). Because of different distance from the Kinect camera, the obtained images from the face must be re-sized, in order to have the same size. At the end, the colored images are converted to gray-scaled images.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 1. Peak of facial expression in database
<p>In this study, data are obtained from the Kinect camera that benefits from colorful images and depth data. Kinect can record colorful and depth data simultaneously at 30 frames per second. The data are collected from the person who initially pose in front of the camera with normal face mode and then the various modes are represented. It should be noted that data are obtained at different distances from the Kinect camera and in different lighting conditions. Figure 1 shows various facial modes in our database.</p>
Data to accompany the paper "Improved fragment-based protein structure prediction by redesign of search heuristics"
<p>This repository contains the older and newer input fragment sets and other data used for the analyses in our paper. The filenames for each tarball contain the PDB identifier of each protein along with a chain ID if applicable, followed by 'old' or 'new' for old and new fragments, respectively. Each tarball contains: a .fasta file of the input sequence, a matching PDB structure file, the relevant PSIPRED secondary structure prediction file, and the 9mer and 3mer fragment files. <br> <br> An additional tarball, ScoreRMSDplots_3protocols.tgz, contains extended versions of Figure 3 which show score and RMSD distributions clearly. Additionally, the same data is shown for equivalent experiments using the older fragment set.</p>
SUBSOL Knowledge Base data
<p>This dataset includes the content of the SUBSOL Knowledge Base and Marketplace, excluding all informations related to personal data. The same content is provided as:</p> <ul> <li>MySQL Database dump and</li> <li>Excel files exported from the Database</li> </ul>
Supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation
<p>These are supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation</p> <p>The folowing files are available:</p> <ul> <li>Supplement 1. Map of Mayotte with the sampling sites and the rivers.</li> <li>Supplement 2. <em>rbcL</em> primers, reaction mixture, and conditions used for the PCR of the 312-bp <em>rbcL</em> fragment. The information provided is for a single reaction with a final volume of 25µL.</li> <li>Supplement 3. The 20 fastq files containing the demultiplexed DNA reads.</li> <li>Supplement 4. Number of sequence reads for each sample before and after the trimming procedure.</li> <li>Supplement 5. The 20 OTU lists, corresponding to the 20 SSTs, including the number of DNA reads within the 90 samples and their assigned taxonomy.</li> <li>Supplement 6. Sampling site description with sample codes, names of rivers, year, number of raw DNA reads and GPS coordinates.</li> <li>Supplement 7. Values and summary statistics for the environmental variables.</li> <li>Supplement 8. The script used in Mothur for the bioinformatic analysis from trimming to the used OTU lists.</li> </ul> <p> </p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: VelocityProfilies_2018_Musumarra
<p>The experimental campaign was devoted to study the velocity profile inside the small scale flume for several bottom configurations. In particular, the following configurations were considered: thin sand (D<sub>50</sub>=0.25 mm); coarse sand (D<sub>50</sub>=0.56 mm); mixed sand: 10% coarse sand and 90% thin sand; mixed sand: 20% coarse sand and 80% thin sand; mixed sand: 30% coarse sand and 70% thin sand; mixed sand: 40% coarse sand and 60% thin sand; small gravel (diameter between 3 and 5 mm); gravel (diameter between 9 and 14 mm); small gravel and thin sand; gravel and thin sand.</p>
Bias correction of simulated Brazilian wind power generation based on reanalysis data
<p>Available data:</p> <p>- Brazilian wind power generation time series derived from MERRA-2 reanalysis data with wind speed and wind power bias correction.</p> <p>- Wind speed correction factors derived from INMET wind speeds (http://www.inmet.gov.br/portal/) as well as wind power correction factors dervied from ONS wind power generation time series are also provided.</p> <p>- Simulation of about 38 years of wind power generation with fixed capacity.</p> <p>Data used for validation:</p> <p>- Historical wind power generation data, which were used for validation of simulated time series, can be found at the ONS homepage (http://ons.org.br/Paginas/resultados-da-operacao/historico-da-operacao/geracao_energia.aspx).</p> <p> </p> <p>Other Links:</p> <p>- Information on this will soon be found here: https://refuel.world/</p> <p>- Code for generating time series, validation and analysis: https://github.com/KatharinaGruber/BrazilWind</p> <p>- Master thesis belonging to data: https://doi.org/10.5281/zenodo.1471221</p>
Multi-publication data set on experienced-based and description-based risky choice
<p>The data of 28 publications studying the description-experience gap, involving experience- and description-based risky choices, that served as the basis for the meta analysis reported in Wulff, D. U., Mergenthaler-Canseco, M., & Hertwig, R. (2018). A meta-analytic review of two modes of learning and the description-experience gap. <em>Psychological Bulletin, 144</em>(2), 140-176.</p>
Data assimilation-based surface temperature reconstructions over the last two millennia over Antarctica
<p>This dataset contains data assimilation-based temperature and δ<sup>18</sup>O reconstructions in 10 Antarctic regions over the last two millennia, presented in :</p> <blockquote> <p><a href="https://www.clim-past-discuss.net/cp-2018-90/">Klein, F., Abram, N. J., Curran, M. A. J., Goosse, H., Goursaud, S., Masson-Delmotte, V., Moy, A., Neukom, R., Orsi, A., Sjolte, J., Steiger, N., Stenni, B., and Werner, M.: Assessing the robustness of Antarctic temperature reconstructions over the past two millennia using pseudoproxy and data assimilation experiments, Clim. Past Discuss., https://doi.org/10.5194/cp-2018-90, in review, 2018. </a></p> </blockquote> <p>We use a new database of stable oxygen isotopes in ice cores compiled in the framework of Antarctica2k (Stenni et al., 2017) to constrain model ensembles derived from two simulations: one performed using ECHAM5-MPI-OM that covers the period 800-1999 CE with a horizontal resolution of 3.75° by 3.75° (Sjolte et al., 2018), and the other performed with ECHAM5-wiso, spanning 1871-2011 CE at 1.125° spatial resolution (Steiger et al., 2017). This latter simulation is available <a href="https://zenodo.org/record/1249604#.XHa824Uo_RY">here</a>.</p> <p>Four netCDF files are available:</p> <ol> <li>d18O_DA_ECHAM5-MPI-OM_1-2015.nc: data assimilation-based δ<sup>18</sup>O reconstructions using the model ensemble derived from ECHAM5-MPI-OM</li> <li>ts_DA_ECHAM5-MPI-OM_1-2015.nc: data assimilation-based surface temperature reconstructions using the model ensemble derived from ECHAM5-MPI-OM</li> <li>d18O_DA_ECHAM5-wiso_1-2015.nc: data assimilation-based δ<sup>18</sup>O reconstructions using the model ensemble derived from ECHAM5-wiso</li> <li>ts_DA_ECHAM5-wiso_1-2015.nc: data assimilation-based surface temperature reconstructions using the model ensemble derived from ECHAM5-wiso</li> </ol> <p>The variables included in the NetCDF files are:</p> <ul> <li>region: integers from 1 to 10 corresponding to the ID of the ten reconstructions targets, that were defined in Stenni et al. (2017): <ul> <li>1: East Antarctic Plateau</li> <li>2: Wilkes Land Coast</li> <li>3: Weddell Sea Coast</li> <li>4: Antarctic Peninsula</li> <li>5: West Antarctic Ice Sheet</li> <li>6: Victoria Land Coast-Ross Sea</li> <li>7: Dronning Maud Land Coast</li> <li>8: West Antarctica</li> <li>9: East Antarctica</li> <li>10: Antarctica</li> </ul> </li> <li>time: integers from 1 to 2015, corresponding to the years CE covered by the reconstructions</li> <li>DA_ts (or DA_d18O): data assimilation-based reconstructed surface temperature (or δ<sup>18</sup>O). The values are annual means and are given in anomalies computed over full period. The units are degrees celsius (or permil). </li> <li>DA_ts_std (or DA_d18O_std): Weighted standard deviation of the particles used for reconstructing temperature (or δ<sup>18</sup>O). The units are degrees celsius (or permil).</li> </ul> <p>For a detailed description of the experimental design, please see the associated publication (Klein et al., 2018). Don't hesitate to contact <a href="mailto:francois.klein@uclouvain.be">François Klein</a> for more information.</p> <p>References</p> <p>Klein, F., Abram, N. J., Curran, M. A. J., Goosse, H., Goursaud, S., Masson-Delmotte, V., Moy, A., Neukom, R., Orsi, A., Sjolte, J., Steiger, N., Stenni, B., and Werner, M.: Assessing the robustness of Antarctic temperature reconstructions over the past two millennia using pseudoproxy and data assimilation experiments, Clim. Past Discuss., https://doi.org/10.5194/cp-2018-90, in review, 2018.</p> <p>Sjolte, J., Sturm, C., Adolphi, F., Vinther, B. M., Werner, M., Lohmann, G., and Muscheler, R.: Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, Climate of the Past, 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018, 2018.</p> <p>Steiger, N. J., Steig, E. J., Dee, S. G., Roe, G. H., and Hakim, G. J.: Climate reconstruction using data assimilation of water isotope ratios from ice cores, Journal of Geophysical Research: Atmospheres, 122, 1545–1568, https://doi.org/10.1002/2016JD026011, 2017.</p> <p>Stenni, B., Curran, M. A. J., Abram, N. J., Orsi, A., Goursaud, S., Masson-Delmotte, V., Neukom, R., Goosse, H., Divine, D., van Ommen, T., Steig, E. J., Dixon, D. A., Thomas, E. R., Bertler, N. A. N., Isaksson, E., Ekaykin, A., Werner, M., and Frezzotti, M.: Antarctic climate variability on regional and continental scales over the last 2000 years, Climate of the Past, 13, 1609–1634, https://doi.org/10.5194/cp-13-1609-2017, 2017.</p>
Data for figures in "Reproducibility in Benchmarking Parallel Fast Fourier Transform based Applications"
<p>FFT benchmark data and Python plotting programs</p>
Supporting data for: A method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits
<p>The three following datasets are related to the article <em>A method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits</em> (Santos, Guyomarc'h, Rmoutilova, & Bruzek, 2019):</p> <ul> <li><strong>data_refPELVIS_Santos2019AJPA.csv</strong>: is described as the "reference sample" of 592 ossa coxae in the article. This is the learning dataset available in the <a href="https://gitlab.com/f.santos/pelvis">PELVIS R package</a></li> <li><strong>data_518RightBones_Santos2019AJPA.csv</strong>: the dataset of 518 right ossa coxae used to discuss asymmetry and the impact of lateralization on the sex estimates produced by PELVIS</li> <li><strong>data_3D_Santos2019AJPA.csv</strong>: the virtual coxal data acquired through 99 CT-scan images</li> </ul>
Supplementing data and code for: "Correlation of mRNA delivery timing and protein expression in lipid-based transfection"
<p>Supplementary data and code for Reiser <em>et al.</em>: Correlation of mRNA delivery timing and protein expression in lipid-based transfection. 2019, <a href="https://doi.org/10.1093/intbio/zyz030">doi:10.1093/intbio/zyz030</a>.</p> <p>See `README.pdf` for further details.</p>
One-dimensional hydrodynamic solution data of wavelet-based adaptive finite volume and discontinuous Galerkin shallow water solvers
<p>Raw data for a series of idealised, one-dimensional hydrodynamic test cases:</p> <ul> <li>dambreakwet (SWASHES 4.1.1)</li> <li>dambreakdry (SWASHES 4.1.2)</li> <li>dambreakmanning (SWASHES 4.1.3)</li> <li>dambreakupslope, dambreakdownslope (<a href="http://doi.org/10.1061/(ASCE)HY.1943-7900.0000494">Kesserwani and Liang 2011</a>)</li> <li>dambreakonehump (<a href="https://doi.org/10.1080/19942060.2011.11015393">Ozmen-Cagatay and Kocaman 2011</a>)</li> <li>lakeatrest (<a href="https://doi.org/10.29007/vm3q">Kesserwani et al. 2018</a>)</li> <li>parabolicbowlswashes (SWASHES 4.2.1)</li> <li>parabolicbowlliangmarche (<a href="https://doi.org/10.1016/j.advwatres.2009.02.010">Liang and Marche 2009</a>)</li> <li>steadysubcritical (SWASHES 3.1.3)</li> <li>steadysupercritical (<a href="https://doi.org/10.2166/hydro.2015.039">Haleem et al. 2015</a>)</li> <li>steadytranscriticalshockless (SWASHES 4.1.4)</li> <li>steadytranscriticalshock (SWASHES 4.1.5) </li> </ul> <p>SWASHES refers to <a href="https://doi.org/10.1002/fld.3741">Delestre et al. 2013</a> </p>
Data for "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"
<p>This dataset contains all the annotations done by either citizen scientists or experts of the publication: "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"</p> <p><strong>CSP.csv</strong> -> CS annotations of the Citizen Science Primer-experiment</p> <p><strong>CSPExpert.csv</strong> -> Expert annotations of the Citizen Science Primer-experiment</p> <p><strong>CSS.csv</strong> -> CS annotations of the Citizen Science Study</p> <p><strong>CSSExpert.csv</strong> -> Expert annotations of the Citizen Science Study</p> <p>Visual exploration of the image data is possible in the BIIGLE 2.0 image annotation system at <a href="https://biigle.de/projects/139">https://biigle.de/projects/159</a> using the login <em><a href="mailto:cs@example.com">cs@example.com</a></em> and the password <em>plosonecs</em>.</p>
Supplementary data to Schmiester et al. *Efficient parameterization of large-scale dynamic models based on relative measurements*
<p>This archive contains Supplementary data to the manuscript <em>Efficient parameterization of large-scale dynamic models based on relative measurements</em> by Leonard Schmiester, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer and Daniel Weindl.</p>
Extended data for the paper "Reliable generation of native-like decoys limits predictive ability in fragment-based protein structure prediction"
<p>Extended data for the paper:<br> Reliable generation of native-like decoys limits predictive ability in fragment-based protein structure prediction</p> <p>Authors:<br> Shaun M Kandathil, Mario Garza-Fabre, Simon C Lovell and Julia Handl</p> <p>--------------------------------</p> <p>Contents of the zip file:</p> <p> </p> <p>Directory 'ECDFplots':<br> ----------------------<br> Data corresponding to Figure 3 for all targets, for the bilevel and ILS protocols. Data are available following stages 3 and 4 of the low-resolution protocol.</p> <p>Directory 'ScoreRMSDplots_3archivers':<br> --------------------------------------<br> Data corresponding to Figures 6 and 9 for all targets. Data corresponding to decoys obtained after low-resolution stages 3 and 4 can be found in subdirectories 'Stage3' and 'Stage4', respectively.<br> </p>
Wikidata's linked data for cultural heritage digital resources: an evaluation based on the Europeana Data Model
<p>Wikidata is an open data source with many potential applications. Our study aims to evaluate the usability of Wikidata as a linked data source for acquiring richer descriptions of digital objects within the context of Europeana, a data aggregator from the cultural heritage domain. Specifically, we aim to crawl and convert Wikidata using the standard approaches and operations developed for the (Semantic) Web of Data, i.e. using technologies like linked data consumption and RDF(S)/OWL ontology expression and reasoning. We also seek to re-use existing “semantic” specifications, such as conversions to and from generic data models like Schema.org and SKOS. We have developed an experimental set-up and accompanying software to test the feasibility of this approach. We conclude that Wikidata’s linked data is able to express an interesting level of semantics for cultural heritage, but quality can still be improved and a human operator still must assist linked data applications to interpret Wikidata’s RDF.</p>
MinION sequence data: MinION sequencing of colorectal cancer tumor microbiomes – a comparison with amplicon-based and RNA-Sequencing
<p>MinION sequencing data that was unmapped by minimap2 for the 11 samples using in the "MinION sequencing of colorectal cancer tumor microbiomes – a comparison with amplicon-based and RNA-Sequencing" paper.</p>
VLF phase and amplitude data recorded at Scott Base Antarctica
<p>This dataset contains the phase and amplitude data from VLF radio transmitter NPM (Hawaii, 21.4 kHz, 21.4⁰N, 158.2⁰E) recorded at the field-site for New Zealand's Scott Base, Arrival Heights, in Antarctica (77.8⁰S, 166.8⁰E). The propagation path is ~11 Mm long, oriented nearly north-south, with the mid-point at 28.9⁰S, 164.4⁰W.</p> <p>The data files are formatted as ASCII text files with columns of time (s), amplitude (dB) & phase (degrees) data. The MATLAB script plotUltraMSK.m can be used to plot the data files and an example data plot has been included in the dataset.</p>
Seed based phase synchronization data
<p>The aim of this research was to investigate how fear predicts the phase synchronization of regions within the brain, and how fear affects the synchronization of functional connectivity between individuals.</p> <p>Participants viewed one of two horror movies in the fMRI scanner (The Conjuring 2 or Insidious). A separate sample of participants viewed the movies and provided continuous measures of experience fear.</p> <p>Seed-based phase synchronization (SBPS): The phase synchronization between each of 116 region pairs of the AAL atlas was computed and correlated with the fear ratings. The result matrix of the correlation coefficients reveal the extent to which fear predicts phase synchronization between regions.</p> <p>Inter-subject phase synchronization (ISBPS): The phase synchronization of the SBPS was conducted between individuals and correlated with the fear ratings. The result matrix of the correlation coefficients reveal the extent to which fear predicts between-individual synchronization of regional phase synchronization.</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.