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Dataset results
311 results for “new dataset”
Unsupervised New Physics detection at 40 MHz: h+ -> tau nu Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of h+ -> tau nu decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Unsupervised New Physics detection at 40 MHz: h^0 -> tau tau Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of h^0 -> tau tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Unsupervised New Physics detection at 40 MHz: LQ -> b tau Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of Leptoquarks -> b tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Unsupervised New Physics detection at 40 MHz: Black Box Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of the signal+background Black Box datasets, containing collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Unsupervised New Physics detection at 40 MHz: Training Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Training dataset, consisting of a cocktail of Standard Model collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Unsupervised New Physics detection at 40 MHz: A -> 4 leptons Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of A -> 4 leptons decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
A new merged dataset of global ocean chlorophyll-a concentration for better trend detection
<p>Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve as a climate data record (CDR), it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI, which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the intermission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the Sea-viewing Wide Field-of-view Sensor, Medium-spectral Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and Ocean and Land Colour Instrument with intermission differences corrected in this work. The fitness of the dataset as a CDR was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records. </p>
Dataset from "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist.
<p>Dataset used in publication "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist.</p> <p><br>This repository contains images from annual pollen trap samples mounted on slides and scanned under light microscopy; image annotation metadata; and the weights of the trained models from the YOLOv5 algorithm, saved after the last training epoch.</p> <p>More details can be found in the README file.</p>
New glacier thickness and bed topography maps for Svalbard - Dataset
<p>The dataset includes three Geotiff files that include:</p> <p>1) A bed topography map of Svalbard (heights in m a.s.l.) [Bed_map.tiff]</p> <p>2) An ice thickness map of Svalbard (in m) [Thickness_map.tiff]</p> <p>3) A mask file that with values from 0-3 indicating non-glacier areas (0), glaciers modelled with the Parallel Ice Sheet model (1), glaciers modelled with the Instructed Glacier Model (2), and surging glaciers (3). </p> <p>For a description of the methods used to generate the datasets, we refer to the manuscript "A new glacier thickness and bed map for Svalbard", to be submitted to The Cryosphere Discussions.</p>
CLDF dataset derived from Aaley and Bodt's "New Kusunda data: A list of 250 concepts" from 2020
<p>Cite the source of the dataset as:</p> <blockquote> <p>Uday Raj Aaley and Timotheus A. Bodt (2020): New Kusunda data: A list of 250 concepts. Computer-Assisted Language Comparison in Practice 3.4 (08/04/2020), URL: https://calc.hypotheses.org/2414.</p> </blockquote>
Supplementary Datasets for the Paper "A new view of seismicity under Mt. Etna volcano, Italy, 2014-2023 from multi-scale high-precision earthquake relocations"
<p>Supplementary Datasets for the Paper <br><strong>Mapping finite-fault earthquake slip with spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax, Tiziana Tuvè, Elisabetta Giampiccolo, Ornella Cocina<br>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/xxxx</a></p> <p><strong>20240724A_Etna_Seismicity_2014-2023_INGV-OE_NLL-SC.csv</strong> is the catalog of NLL-SC relocations presented in the paper in CSV (.csv) format.</p> <p><strong>File_S1_catalog_config_run.zip</strong> includes the relocated NLL-SC catalog in CSV (.csv) and NLL-Hypocenter (.hyp) formats, along with pick data, configuration and other files used to run the NLL-SC relocations presented in the paper.</p>
Dataset related to article "New in silico models to predict in vitro micronucleus induction as marker of genotoxicity"
<p>The .txt file contains the dataset of the in silico model for genotoxicity as induction of micronuclei.</p> <p>The .doc file contains the descriptors of the models and the structural alerts.</p>
Dataset of five years of in-situ and satellite derived chlorophyll a concentrations and its spatiotemporal variability in the Rotorua Lakes, New Zealand
<p><strong>rotorua_chl_fields_2015-2020.nc</strong> is a time series of 283 <em>Chl</em> fields of 13 of the lakes derived from Sentinel-2 MSI images with a regionalised parametrization of the C2RCC algorithm at 60 m pixel resolution. It also includes C2RCC and Idepix masks as well as a shoreline-and-shallow-water-buffer for flexible quality flagging.</p> <p><strong>rotorua_chl_spatial_variability.tif</strong> is a GeoTIFF that illustrates the representativeness of each grid cell for the <em>Chl</em> distribution in each lake and thus indicates recurring spatial patterns. The file contains three bands. Each band shows the relative frequency (in %) which <em>Chl</em> concentration was found near the median, or upper or lower quartile, respectively. The intervals around the median and quartiles are 5% to either side.</p> <p><strong>rotorua_insitu_chl_2015-2019.csv</strong> contains 831 in situ <em>Chl</em> measurements from 12 of the lakes collected between 2015 and 2019. The majority of these measurements (802) have been taken as part of the monthly Bay of Plenty lake water quality monitoring programme, in which 11 lakes are monitored. The data set also contains samples from field work under the <em>Eye on Lakes</em> project (University of Waikato) obtained by one of the authors (MKL). These 29 samples also include two measurements at Lake Rotokakahi, which is not part of the monthly monitoring program.</p> <p><strong>shoreline_shallow_water_buffer.zip</strong> contains a shapefile with polygons of the valid water pixels of all lakes to remove areas contaminated by bottom reflectance in remote sensing products. Each lake has a 120 m shoreline buffer to avoid mixed land-water pixels to reduce adjacency effects. It further excludes lake areas shallower than the 95%-quantile of all Secchi depth measurements of the Bay of Plenty lake water quality monitoring programme.</p>
Rees River, New Zealand - Geomorphic Change Detection - Example Dataset
<p>A simple<a href="https://gcd.riverscapes.net/Tutorials/example-data-sets.html"> Example GCD Dataset </a>illustrating topographic change detection on a long (31 km) dataset. Great for learning about analyses with DEMs produced from a hybrid of survey types. Used in <a href="https://gcd.riverscapes.net/Tutorials/ErrorModelling/multimethoderror.html">Multi-Method Error Estimation Tutorial</a> and <a href="https://gcd.riverscapes.net/Tutorials/GeomorphicInterpretation/morphological-approach.html">Morphological Approach Tutorial</a>. This is part of the dataset from:</p> <ul> <li>2011. Richard Williams, James Brasington, Damia Vericat, Murray Hicks, Fred Labrosse, Mark Neal. Chapter Twenty - Monitoring Braided River Change Using Terrestrial Laser Scanning and Optical Bathymetric Mapping. Editor(s): Mike J. Smith, Paolo Paron, James S. Griffiths. Developments in Earth Surface Processes. Elsevier, Volume 15, Pages 507-532, ISSN 0928-2025. DOI: <a href="https://doi.org/10.1016/B978-0-444-53446-0.00020-3">10.1016/B978-0-444-53446-0.00020-3</a>.</li> </ul> <p>Dataset is from:</p> <ul> <li>2km of braided river near <a href="https://www.google.com/maps/place/44%C2%B046'38.6%22S+168%C2%B024'17.9%22E/@-44.7767196,168.3891697,7451m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d-44.777379!4d168.404972">Queenstown, New Zealand</a></li> <li>Two LiDAR surveys</li> <li>0.5m cell resolution</li> <li><a href="https://s3-us-west-2.amazonaws.com/etalweb.joewheaton.org/GCD/GCD7/Tutorials/GeoTERM_Rees.zip">Download Full</a></li> <li><a href="https://s3-us-west-2.amazonaws.com/etalweb.joewheaton.org/GCD/GCD7/Tutorials/MaskOnly_MorphologicalApproach.zip">Download Morphological Only</a></li> </ul> <p>Dataset includes raw data to run exercises, as well as full *.gcd projects that can be opened. </p>
IEEE New England 39-bus test case: Dataset for the Transient Stability Assessment
<p>The <strong>dataset</strong> contains <strong>350</strong> <strong>features</strong> engineered from the phasor measurements (PMU-type) signals from the <strong>IEEE New England 39-bus power system</strong> test case network, which are generated from the 9360 systematic MATLAB®/Simulink electro-mechanical transients simulations. It was prepared to serve as a convenient and open database for experimenting with different types of <strong>machine learning</strong> techniques for <strong>transient stability assessment</strong> (TSA) of electrical power systems.</p> <p>Different load and generation levels of the New England 39-bus benchmark power system were systematically covered, as well as all three major types of short-circuit events (three-phase, two-phase and single-phase faults) in all parts of the network. The consumed power of the network was set to 80%, 90%, 100%, 110% and 120% of the basic system load levels. The short-circuits were located on the busbar or on the transmission line (TL). When they were located on a TL, it was assumed that they can occur at 20%, 40%, 60%, and 80% of the line length. Features were obtained directly from the time-domain signals at the pickup time (pre-fault value) and at the trip time (post-fault value) of the associated distance protection relays.</p> <p>This is a <strong>stochastic dataset</strong> of 3120 cases, created from the population of 9360 systematic simulations, which features a statistical distribution of different fault types, as follows: single-phase (70%), double-phase (20%) and three-phase faults (10%). It also features a <strong>class imbalance</strong>, with less than 20% of cases belonging to the unstable class. Dataset is a compressed CSV file.</p> <p><strong>List of feature names in the dataset:</strong></p> <ul> <li><em>WmGx</em> - rotor speed for each generator Gx, from G1 to G10,</li> <li><em>DThetaGx</em> - rotor angle deviation for each generator Gx, from G1 to G10,</li> <li><em>ThetaGx</em> - rotor mechanical angle for each generator Gx, from G1 to G10,</li> <li><em>VtGx</em> - stator voltage for each generator Gx, from G1 to G10,</li> <li><em>IdGx</em> - stator d-component current for each generator Gx, from G1 to G10,</li> <li><em>IqGx</em> - stator q-component current for each generator Gx, from G1 to G10,</li> <li><em>LAfvGx</em> - pre-fault power load angle for each generator Gx, from G1 to G10,</li> <li><em>LAlvGx</em> - post-fault power load angle for each generator Gx, from G1 to G10,</li> <li><em>PfvGx</em> - pre-falut value of the generator active power for each generator Gx, from G1 to G10,</li> <li><em>PlvGx</em> - post-falut value of the generator active power for each generator Gx, from G1 to G10,</li> <li><em>QfvGx</em> - pre-falut value of the generator reactive power for each generator Gx, from G1 to G10,</li> <li><em>QlvGx</em> - post-falut value of the generator reactive power for each generator Gx, from G1 to G10,</li> <li><em>VAfvBx</em> - pre-fault bus voltage magnitude in phase A for each bus Bx, from B1 to B39,</li> <li><em>VBfvBx</em> - pre-fault bus voltage magnitude in phase B for each bus Bx, from B1 to B39,</li> <li><em>VCfvBx</em> - pre-fault bus voltage magnitude in phase C for each bus Bx, from B1 to B39,</li> <li><em>VAlvBx</em> - post-fault bus voltage magnitude in phase A for each bus Bx, from B1 to B39,</li> <li><em>VBlvBx</em> - post-fault bus voltage magnitude in phase B for each bus Bx, from B1 to B39,</li> <li><em>VClvBx</em> - post-fault bus voltage magnitude in phase C for each bus Bx, from B1 to B39,</li> <li><em>Stability</em> - binary indicator (0/1) that determines if the power system was stable or unstable (0 - stable, 1 - unstable); this is the label variable.</li> </ul> <p><strong>License</strong>:<strong> </strong>Creative Commons CC-BY.</p> <p><strong>Disclaimer</strong>: This dataset is provided "as is", without any warranties of any kind.</p>
Dataset for "New Perspectives for Nonlinear Depth-inversion of the Nearshore Using Boussinesq Theory"
<pre>This dataset gathers all cross-spectral, spectral and bispectral data produced and used in the manuscript accepted for publication in Geophysical Research Letters: "New Perspectives for Nonlinear Depth-inversion of the Nearshore Using Boussinesq Theory", by K. Martins, P. Bonneton, O. de Viron, I. L. Turner, M. D. Harley and K. Splinter The sharing of the processed data is motivated by research reproductibility purposes and with the hope that it will foster efforts in improving the newly-proposed depth-inversion procedure. Three laboratory experiments are considered, namely the experiments reported in van Noorloos (2003), Michallet et al. (2011) and GLOBEX (e.g., see Ruessink et al., 2013). The processed data is organised in separate self-explanatory .mat files (generated with MATLAB software), gathering: - "cel_data" : wave phase velocities obtained from cross-spectral and cross-correlation analyses between adjacent wave gauges - "bulk_data" : range of bulk wave parameters computed across the different wave flumes - "bispectrum_data" : spectral and bispectral estimates computed across the different wave flumes The scripts for performing the depth-inversion as well as a for plotting the results are provided. The new Boussinesq depth-inversion procedure relies on the function "fun_compute_krms_terms.m", which is provided and originates from the bispectral analysis library accessible from the first author GitHub repository at https://github.com/ke-martins/bispectral-analysis. Acknowledgements: Kévin Martins greatly acknowledges the financial support from the European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant Agreement 887867 (lidBathy). We warmly thank Ap van Dongeren and Hervé Michallet for providing the raw data for the experiments described in van Noorloos (2003) and Michallet et al. (2011), respectively. The raw data from GLOBEX used in this research can be accessed on Zenodo at https://zenodo.org/record/4009405 and can be used under the Creative Commons Attribution 4.0 International license. The GLOBEX project was supported by the European Community’s Seventh Framework Programme through the Hydralab IV project, EC Contract 261520. References: Michallet, H., Cienfuegos, R., Barthélemy, E., & Grasso, F. (2011). Kinematics of waves propagating and breaking on a barred beach. European Journal of Mechanics - B/Fluids, 30 (6), 624 – 634. doi: 10.1016/j.euromechflu.2010.12.004 Ruessink, G. B., Michallet, H., Bonneton, P., Mouazé, D., Lara, J. L., Silva, P. A., & Wellens, P. (2013). GLOBEX: Wave dynamics on a gently sloping laboratory beach. In Coastal Dynamics ’13: Proceedings of the Seventh Conference on Coastal Dynamics, Arcachon, France. van Noorloos, J. C. (2003). Energy transfer between short wave groups and bound long waves on a plane slope (Master’s thesis, Delft University of Technology, Delft, The Netherlands). Retrieved from http://resolver.tudelft.nl/uuid:13616ff0-407d-43de-9954-ba707cd40d27</pre>
Small-scale farming in drylands: New models for resilient practices of millet and sorghum cultivation - Dataset and code
<p>This repository contains the primary research data and R code used for data analysis for the article "<em>Small-scale farming in drylands: New models for resilient practices of millet and sorghum cultivation"</em> Published in the journal PLOS ONE (<a href="https://doi.org/10.1371/journal.pone.0268120">https://doi.org/10.1371/journal.pone.0268120</a>)</p> <p>N.B. To run the code unzip the folder 9-RData.zip and save it in the same working directory as the datasets</p> <p>V 2.0 changes:</p> <p>A. Datasets 1-2-3 - small formatting corrections</p> <p>B. Dataset 7 - fixing some errors in the calculations</p> <p>C. Code - minor fixes and seimplicifaction</p> <p> </p>
CLDF dataset derived from a preprint of 'Új magyar etimológiai szótár' [New Hungarian Etymological Dictionary] by Károly Gerstner (ed.)
<p>Cite the source of the dataset as:</p> <blockquote> <p>Gerstner, Károly (ed.) (2011-2023). Új magyar Etimológiai Szótár. Hungarian Academy of Sciences, Budapest. http://uesz.nytud.hu/.</p> </blockquote>
Dataset of the article "A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs"
<p>Dataset of the article "A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs" (https://doi.org/10.5281/zenodo.7927968), recommended by PCI Evol Biol (https://evolbiol.peercommunityin.org/articles/rec?id=651)</p>
A new inventory of High Mountain Asia surging glaciers derived from multiple elevation datasets since the 1970s
<p>Glacier surging is an unusual undulation instability of ice flow and complete surging glacier inventories are important for regional mass balance studies and assessing glacier-related hazards. Glacier surge events in High Mountain Asia (HMA) are widely reported. Through the estimated elevation changes from multiple DEMs sources that acquired from 1970s to 2020, and morphologic changes from 1986 to 2021, here we present a new surging glacier inventory across HMA. The inventory has incorporated 890 surging and 336 surge-like glaciers, each glacier is assigned with indicators of surging feature and surge possibility. Compared to previous surging glacier inventory in HMA, our inventory is theoretically more complete because of the much longer observation period. This data repository contains the surging glacier inventory and glacier elevation change maps. The inventory is stored in the format of GeoPackage (.gpkg) and ESRI Shapefile format (.shp), which is represented by glacier polygon (from GAMDAM2) or surface point with geometric attributes. The multi-temporal elevation change maps of identified surging glaciers were divided into 1×1° tiles, storing in the format of GeoTiff(*.tif). Detailed description of the dataset including the file contents and attributes information can be found in the metadata file (README.txt).</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.