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9,302 results for “DETECT”
Linking detected gravity modes to axisymmetric internal magnetic fields
<p>The spectropolarimetric techniques used to characterize surface magnetic fields do not probe the stellar interior, and therefore cannot be used to constrain internal magnetic fields. The most promising way to detect/characterize such fields is to consider their effect on stellar oscillations that probe deep stellar layers, the gravity modes. Conventional asteroseismic modelling ignores any effect of a (internal) magnetic field, however, Prat et al. (2019) developed a formalism that takes the perturbative effect into account for axisymmetric, dipolar, fossil magnetic fields confined to the stellar interior, allowing one to perform magneto-asteroseismic modeling for the first time. Most detected gravity modes in intermediate-mass main-sequence pulsating stars are dipolar, and propagate in the direction of rotation (e.g. Li et al. (2020)). In this talk I will discuss the typical signatures expected in period spacing patterns of dipolar gravity modes, according to the Prat et al. (2019) formalism, for a range of stellar models covering fundamental parameters typical for this mass range. We vary the initial metallicity and mass, the magnetic field strength, the rotation rate, and the degree of mixing in the envelope and near-core regions, throughout the evolution of the main sequence. We find detectable signatures that are significantly different from those due to rotation in the period spacing patterns of gravity modes in terminal age main sequence models if the near-core magnetic field strength is higher than 10^5 Gauss. Such signatures can be used in future magneto-asteroseismic modeling based on photometry from space missions such as Kepler (Borucki et al. (2010)), TESS (targets in the continuous viewing zone; Ricker et al. (2015)) and PLATO (Rauer et al. (2014)).</p>
2D-Raman-THz Spectroscopy with Single-Shot THz Detection
<p>We present a 2D-Raman-THz setup with multichannel (single-shot) THz detection, utilizing two crossed echelons, in order to reduce the acquisition time of typical 2D-Raman-THz experiments from days to a few hours. This speed-up is obtained in combination with a high repetition rate (100 kHz) Yb-based femtosecond laser system and a correspondingly fast array detector. The wavelength of the Yb-laser (1030 nm) is advantageous, since it assures almost perfect phase matching in GaP for THz generation and detection, and since dispersion in the transmissive echelons is minimal. 2D-Raman-THz test measurements on liquid bromoform (CHBr3) are reported. An enhancement of ∼5.8 times in signal-to-noise ratio is obtained for single-shot detection when comparing to conventional step scanning measurements in the THz time-domain, corresponding to speed up of acquisition time of 34.</p>
Extracted Ion Chromatograms for peaks detected by IDSL.IPA software
<p>Extracted Ion Chromatograms (EICs) for each detected peak by IDSL.IPA were generated using a custom R-code for the file 003.mzml. The dataset also contain EICs for peaks that were detected by MzMINE, XCMS and MS-DIAL software. </p>
DCNDS Project Dataset - P2P Botnet Detection Using Enhanced Peer Hunter
<p>This dataset represents the Enhanced Peer Hunter implementation that was deployed on two NREN within the AsiaConnect project in Malaysia and Indonesia. Based on a 30 days measurement from Saturday, June 19, 2021 4:00:00 PM GMT - Monday, July 19, 2021 3:59:55 PM GMT, the list of detections and all flows associated with the detected machines are listed in the dataset (2 files per country). The dataset would allow any future p2p botnet detection mechanisms to leverage this (flow) data to train and evaluate their detection mechanisms.</p> <p> Take note that all IP addresses have been anonymized using a hash function with salt to avoid leakage of information.</p> <p> </p>
SSH Username Enumeration Attack Detection Dataset
<p>The dataset is collected from a closed-environment network using network monitoring tools installed in the data collection point. The dataset generation was achieved through the use of common vulnerabilities and exposures (CVE) with the identification number CVE-2018-15473 retrieved from the public exploits database and pcap file of normal traffic obtained from public training repository. A total of 36,273 instances were collected with two classes <em>“username enumeration attack”</em> and “<em>non-username enumeration</em>”. We chose the terms <em>“username enumeration attack”</em> and “<em>non-username enumeration</em>” instead of the traditional <em>“attack”</em> and <em>“normal”</em> label notations since <em>“</em>normal<em>”</em> traffic data could contain attacks other than username enumeration attack.</p> <p>The username enumeration attack corresponds to the attack traffic while non-username enumeration traffic corresponds to the normal traffic. This traffic reflects different services including emails, DNS, HTTP, web, few to mention. Several data preprocessing techniques were carried out including categorical encoding. Both label encoding and one hot encoding techniques were used to transform categorical feature values into numerical feature values. Hence, two types of datasets were generated. </p>
Data in support of the publication "First Detection and Thermal Characterization of Terminator CO2 Ice Clouds with ExoMars/NOMAD" on Geophysical Research Letters
<p>This archive contains the data underlying the 3 Figures in the main manuscript, including the vertical profiles of retrieved aerosols, vertical profiles of CO2 saturation ratio and spatial distribution of the retrieved clouds.</p>
Inputs folder contents for DETECT model v1.0
<p>These files contain the inputs for running the soil CO2 and transport DETECT model v1.0. The model is described in the paper:</p> <p>Ryan, E. M., K. Ogle, H. Kropp, Y. Carrillo, K. E. Samuels-Crow, E. Pendall (in review) Modelling soil CO2 production and transport with dynamic source and diffusion terms: Testing the steady-state assumption using DETECT v1.0<em>. </em>In Geoscientific Model Development.</p>
Auditory Attention Detection Dataset Nanjing University
<p>Auditory Attention Detection Dataset</p> <p>The experiment is conducted by Yuanming, Zhang and Ziyan, Yuan, supervised by Jing, Lu.</p> <p> </p> <p>Two zip files are included in this dataset.</p> <p>The script.zip file contains several scripts to divide each trial into several parts. These scripts are useful if you want to split the dataset into training, validation and test set.</p> <p>The other zip file contains the preprocessed EEG and audio signals. (Not the raw EEG!)</p> <p>Each mat file contains EEG and audio signals from one subject. Their are two variables named <strong><em>expinfo</em></strong> and <strong><em>data</em></strong>.</p> <p>The <em><strong>expinfo </strong></em>contains the attended direction information and audio stimulus playback information of each trial of the experiments. The azimuth column in <em><strong>expinfo </strong></em>indicates the directions of the two competing speakers. The negative directions indicated the speaker coming from the left, and positive coming from the right.</p> <p>The <strong><em>data </em></strong>contains several essential fields relative to the EEG and audio signals. The <em><strong>data.eeg</strong></em> field is a cell array with 32 cell elements. Each cell contains the EEG signals in a single trial. And the index of the cell is the number of trial. The <em><strong>data.leftWav</strong></em> and <em><strong>data.rightWav</strong></em> field are original audio stimuli, and the <em><strong>data.leftEnv</strong></em> and <em><strong>data.rightEnv</strong></em> are the envelopes of the<em><strong> data.leftWav </strong></em>and <em><strong>data.rightWav</strong></em> respectively.</p> <p>The <em><strong>data.dim</strong></em> field tells you the physical meaning of each dimension of each signal, and the <em><strong>data.dim.chan.eeg</strong></em> contains the name of each channel of the EEG signal.</p>
REMODEL. WP4. Vision-Based Perception. T4-4. Functional component detection. Data related to a paper presented at 27th International Conference on Automation and Computing (ICAC) (2022)
<p>Dataset with evaluation parameters of the paper "Real-Time Instance Segmentation of Pedestrians using Transfer Learning", DOI: <a href="https://doi.org/10.1109/ICAC55051.2022.9911121">10.1109/ICAC55051.2022.9911121</a></p>
A deep learning dataset for underwater object detection of tropical freshwater fish species in northern Australia
<p>This dataset includes 44,112 images with 82,904 bounding box annotations for 23 tropical freshwater fish taxa from northern Australia. </p> <p>Images were derived from Remote Underwater Video (RUV) deployments in deep channel and shallow lowland billabongs, Kakadu National Park, Northern Territory Australia. RUV deployments were conducted during the <a href="https://www.dcceew.gov.au/science-research/supervising-scientist">Supervising Scientists</a> annual fish monitoring program in the 2016, 2017 and 2018 recessional flow period (dry season). More information can be found <a href="https://www.dcceew.gov.au/sites/default/files/documents/ss-atr-2020-21.pdf">here</a>.</p> <ul> <li>All images are in .jpg format and are 1920x1080 in dimension.</li> <li>Bounding box annotations are in COCO format. </li> </ul> <p>Two .zip files are included:</p> <ul> <li><a href="https://zenodo.org/api/files/990412db-e633-4f82-9b32-6990ef439ccd/202210-KakaduFishAI-CompactModel.zip">202210-KakaduFishAI-CompactModel.zip</a>: includes compact model weights in tensorflow format (.pb) trained using Azure's Custom Vision platform. This model is suitable for edge devices due to its reduced size. Code is provided to use the compact model for inferencing. </li> <li> <a href="https://zenodo.org/api/files/990412db-e633-4f82-9b32-6990ef439ccd/202210-KakaduFishAI-TrainingData.zip">202210-KakaduFishAI-TrainingData.zip</a>: includes all images and one COCO (.json) file with annotations. </li> </ul> <p>Fish taxa include: </p> <ol> <li><em>Ambassis agrammus</em></li> <li><em>Ambassis macleayi</em></li> <li><em>Amniataba percoides</em></li> <li><em>Craterocephalus stercusmuscarum</em></li> <li><em>Denariusa bandata</em></li> <li><em>Glossamia aprion</em></li> <li><em>Glossogobius</em> spp.</li> <li><em>Hephaestus fuliginosus</em></li> <li><em>Lates calcarifer</em></li> <li><em>Leiopotherapon unicolor</em></li> <li><em>Liza ordensis</em></li> <li><em>Megalops cyprinoides</em></li> <li><em>Melanotaenia nigrans</em></li> <li><em>Melanotaenia splendida inornata</em></li> <li><em>Mogurnda mogurnda</em></li> <li><em>Nemetalosa erebi</em></li> <li><em>Neoarius</em> spp.</li> <li><em>Neosilurus</em> spp.</li> <li><em>Oxyeleotris</em> spp.</li> <li><em>Scleropages jardinii</em></li> <li><em>Strongylura kreffti</em></li> <li><em>Syncomistes butleri</em></li> <li><em>Toxotes chatareus</em></li> </ol> <p>If you use this data for your own deep learning project we'd love to hear about how you used this dataset: andrew.jansen@environment.gov.au.</p>
Ngaruroro 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 Directional Masks in GCD.</p> <p>Dataset is from:</p> <ul> <li>31km river on the <a href="https://www.google.com/maps/place/39%C2%B035'58.6%22S+176%C2%B043'23.7%22E/@-39.6060374,176.6490462,27291m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d-39.599602!4d176.723239">north island of New Zealand</a></li> <li>Two LiDAR surveys</li> <li>2m cell resolution</li> </ul> <p>Dataset includes raw data to run exercises, as well as full *.gcd projects that can be opened. </p>
Sulphur Creek, California - 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 from a single flood event. Used in Tutorials (e.g. <a href="https://gcd.riverscapes.net/Tutorials/ChangeDetection/DoD-thresholding.html">DoD Thresholding</a>). Dataset is from </p> <ul> <li>300m of gravel bed river near <a href="https://www.google.com/maps/place/38%C2%B029'44.0%22N+122%C2%B028'09.0%22W/@38.4958086,-122.4803136,4904m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d38.49555!4d-122.469166">St. Helena California</a> that underwent a New Year's Eve flood in December 2005.</li> <li>Two surveys (Dec 2015 and Jan 2016)</li> <li>0.5m cell resolution</li> <li>Surveyed with hybrid of RTKGPS and Total Station</li> </ul>
Crowd simulation (CrowdSim2) for tracking and object detection
<p>CrowdSim2 is an extension of crowd simulation tool designed in Unity for the purpose of generation massive synthetic data. Such a generated data from crowd simulation enables to validate various methods in terms of tracking multiple people and detect objects (in that example pedestrians and cars). </p> <table align="center"> <caption>Information summarizing number of folders, seconds and frames of data for different weather conditions</caption> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Folders</th> <th scope="col">Seconds</th> <th scope="col">Frames</th> </tr> </thead> <tbody> <tr> <td>Sun</td> <td>2899</td> <td>86 970</td> <td>2 174 250</td> </tr> <tr> <td>Rain</td> <td>1633</td> <td>48 990</td> <td>1 224 750</td> </tr> <tr> <td>Fog</td> <td>1653</td> <td>49 590</td> <td>1 239 750</td> </tr> <tr> <td>Snow</td> <td>1646</td> <td>49 380</td> <td>1 234 500</td> </tr> </tbody> </table> <p>Due to the limitations of the Zenodo platform, we could only include part of data here. If you are interested in the entire collection - please visit the project website: <a href="https://crowdsim.aei.polsl.pl/">CrowdSim</a></p> <p><strong>Acknowledgments</strong> </p> <p>This work was supported by: European Union funds awarded to Blees Sp. z o.o. under grant POIR.01.01.01-00-0952/20-00 “Development of a system for analysing vision data captured by public transport vehicles interior monitoring, aimed at detecting undesirable situations/behaviours and passenger counting (including their classification by age group) and the objects they carry”); EC H2020 project “AI4media: a Centre of Excellence delivering next generation AI Research and Training at the service of Media, Society and Democracy” under GA 951911; research project (RAU-6, 2020) and projects for young scientists of the Silesian University of Technology (Gliwice, Poland); research project INAROS (INtelligenza ARtificiale per il mOnitoraggio e Supporto agli anziani), Tuscany POR FSE CUP B53D21008060008. Publication supported under the Excellence Initiative - Research University program implemented at the Silesian University of Technology, year 2022. This research was supported by the European Union from the European Social Fund in the framework of the project ”Silesian University of Technology as a Center of Modern Education based on research and innovation” POWR.03.05.00- 00-Z098/17 We are thankful for students participating in design of Crowd Simulator: Piotr Bartosz, Stanisław Wróbel, Marcin Wola, Angelika Gluch and Marek Matuszczyk. </p> <p> </p> <p><strong>Citing the Crowdsim 2</strong></p> <p>The Crowdsim 2 is released under a Creative Commons Attribution license, so please cite the Crowdsim 2 if it is used in your work in any form.<br> Published academic papers should use the academic paper citation for our Crowdsim 2 paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people</p> <pre><code class="language-markdown">TBA: Article citations using this dataset will appear here</code></pre> <p>and this Zenodo Dataset</p> <pre><code class="language-markdown">@dataset{crowdsim2_zenodo, title={Crowd simulation (CrowdSim2) for tracking and object detection}, DOI={10.5281/zenodo.7262220}, publisher={Zenodo}, author={Agnieszka Szczęsna and Paweł Foszner and Adam Cygan and Bartosz Bizoń and Michał Cogiel and Dominik Golba and Luca Ciampi and Nicola Messina and Elżbieta Macioszek and Michał Staniszewski}, year={2023}, month={Feb} }</code></pre> <p> </p>
Datasets for "MGTCOM: Community Detection in Multimodal Graphs"
<p>These are the datasets used in <em><strong>"MGTCOM: Community Detection in Multimodal Graphs"</strong></em></p> <p>The dataset preparation code can be found in <a href="https://github.com/EgorDm/MGTCOM">our repository</a>.</p> <p>Each dataset consists of a heterogenous graph with additional edge or node timestamps and preprocessed feature vectors.</p> <p>For each dataset, the files are split into raw and processed folders.<br> * `<em>raw</em>` folder: contains a set of parquet files with formatted raw dataset data. Files follow the naming convention `node_<name>` or `edge_<from>_<rel_name>_<to>`.<br> * `<em>processed</em>` folder: contains preprocessed datasets in <a href="https://pytorch-geometric.readthedocs.io/en/latest/">pytorch geometric</a> graph data format</p>
Supplementary material to "Habitat detection, habitat choice copying, or mating benefits: what drives conspecific attraction in a nomadic songbird?"
<p><strong>Abstract</strong></p> <ol> <li>Conspecific attraction during habitat selection is common among animals, but the ultimate (i.e., fitness-related) reasons for this behavior often remain enigmatic.</li> <li>We aimed to evaluate the following three hypotheses for conspecific attraction during the breeding season in male Wood Warblers (<em>Phylloscopus</em> <em>sibilatrix</em>): the habitat detection hypothesis, the habitat choice copying hypothesis, and the female preference hypothesis. These hypotheses make different predictions with respect to the relative importance of social and non-social information during habitat assessment, and whether benefits accrue as a consequence of aggregation.</li> <li>We tested the above hypotheses using a combination of a two-year playback experiment, spatial statistics and mate choice models.</li> <li>The habitat detection hypothesis was the most likely explanation for conspecific attraction and aggregation in male Wood Warblers, based on the following results: 1) males were attracted to conspecific song playbacks, but fine-scale habitat heterogeneity was the better predictor of spatial patterns in the density of settling males; 2) male pairing success did not increase, but instead slightly decreased, as connectivity with other males (i.e., the number and proximity of neighboring males) increased.</li> <li>Our study highlights how consideration of the process by which animals detect and assess habitat, together with the potential fitness consequences of resulting aggregations, are important for understanding conspecific attraction and spatially clustered distributions.</li> </ol>
Super resolution enhancement of Landsat imagery and detections of high-latitude lakes
<p>This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m<sup>2</sup> for 2017; 13 m<sup>2</sup> for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication.</p> <p>All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in <strong>Table 1</strong>. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets.</p> <p>Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey</p> <p>For an interactive map demo of these datasets via Google Earth Engine Apps, visit: <a href="https://ekyzivat.users.earthengine.app/view/super-resolution-demo">https://ekyzivat.users.earthengine.app/view/super-resolution-demo</a></p> <p><strong>Table 1</strong>: File naming scheme based on region, with some regions requiring two-scene mosaics.</p> <table> <tbody> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p><strong>Landsat ID</strong></p> </td> <td> <p><strong>Mosaic name</strong></p> </td> </tr> <tr> <td> <p><strong>Yukon Flats Basin</strong></p> </td> <td> <p>LC08_L2SP_068014_20170708_20200903_02_T1</p> </td> <td> <p>LC08_20170708_yflats_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_068013_20170708_20201015_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Old Crow Flats</strong></p> </td> <td> <p>LC08_L2SP_067012_20170903_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Mackenzie River Delta</strong></p> </td> <td> <p>LC08_L2SP_064011_20170728_20200903_02_T1</p> </td> <td> <p>LC08_20170728_inuvik_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_064012_20170728_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield Margin</strong></p> </td> <td> <p>LC08_L2SP_050015_20170811_20200903_02_T1</p> </td> <td> <p>LC08_20170811_cshield-margin_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_048016_20170829_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield near Baker Creek</strong></p> </td> <td> <p>LC08_L2SP_046016_20170831_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield near Daring Lake</strong></p> </td> <td> <p>LC08_L2SP_045015_20170723_20201015_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Peace-Athabasca Delta</strong></p> </td> <td> <p>LC08_L2SP_043019_20170810_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes North 1</strong></p> </td> <td> <p>LC08_L2SP_041021_20170812_20200903_02_T1</p> </td> <td> <p>LC08_20170812_potholes-north1_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_041022_20170812_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes North 2</strong></p> </td> <td> <p>LC08_L2SP_038023_20170823_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes South</strong></p> </td> <td> <p>LC08_L2SP_031027_20170907_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Fairbanks </strong></p> </td> <td> <p>LT05_L2SP_070014_19850831_20200918_02_T1</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><strong>References:</strong></p> <p>Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. <em>Remote Sensing</em>, <em>11</em>(18), 2163. <a href="https://doi.org/10.3390/rs11182163">https://doi.org/10.3390/rs11182163</a></p> <p>Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. <a href="https://doi.org/10.3334/ORNLDAAC/1707">https://doi.org/10.3334/ORNLDAAC/1707</a></p> <p>Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. <a href="https://doi.org/10.1080/07038992.2021.1924646">https://doi.org/10.1080/07038992.2021.1924646</a></p> <p>Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021b). Super-resolution surface water mapping on the canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. <a href="https://doi.org/10.1080/07038992.2021.1924646">https://doi.org/10.1080/07038992.2021.1924646</a></p> <p>Walter Anthony, K.., Lindgren, P., Hanke, P., Engram, M., Anthony, P., Daanen, R. P., Bondurant, A., Liljedahl, A. K., Lenz, J., Grosse, G., Jones, B. M., Brosius, L., James, S. R., Minsley, B. J., Pastick, N. J., Munk, J., Chanton, J. P., Miller, C. E., & Meyer, F. J. (2021a). Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. <em>Environ. Res. Lett</em>, <em>16</em>, 35010. <a href="https://doi.org/10.1088/1748-9326/abc848">https://doi.org/10.1088/1748-9326/abc848</a></p> <p>Walter Anthony, K., and P. Lindgren. 2021b. ABoVE: Historical Lake Shorelines and Areas near Fairbanks, Alaska, 1949-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. <a href="https://doi.org/10.3334/ORNLDAAC/1859">https://doi.org/10.3334/ORNLDAAC/1859</a></p>
Data for: Occupancy–detection models with museum specimen data: Promise and pitfalls
<p>Historical museum records provide potentially useful data for identifying drivers of change in species occupancy. However, because museum records are typically obtained via many collection methods, methodological developments are needed in order to enable robust inferences. Occupancy-detection models, a relatively new and powerful suite of statistical methods, are a potentially promising avenue because they can account for changes in collection effort through space and time.</p> <p>We use simulated datasets to identify how and when patterns in data and/or modelling decisions can bias inference. We focus primarily on the consequences of contrasting methodological approaches for dealing with species' ranges and inferring species' non-detections in both space and time. </p> <p>We find that not all datasets are suitable for occupancy-detection analysis but, under the right conditions (namely, datasets that are broken into more time periods for occupancy inference and that contain a high fraction of community-wide collections, or collection events that focus on communities of organisms), models can accurately estimate trends. Finally, we present a case-study on eastern North American odonates where we calculate long-term trends of occupancy by using our most robust workflow. </p> <p>These results indicate that occupancy-detection models are a suitable framework for some research cases and expand the suite of available tools for macroecological analysis available to researchers, especially where structured datasets are unavailable.</p>
HRPlanesv2 - High Resolution Satellite Imagery for Aircraft Detection
<p>The HRPlanesv2 dataset contains 2120 VHR Google Earth images. To further improve experiment results, images of airports from many different regions with various uses (civil/military/joint) selected and labeled. A total of 14,335 aircrafts have been labelled. Each image is stored as a ".jpg" file of size 4800 x 2703 pixels and each label is stored as YOLO ".txt" format. Dataset has been split in three parts as 70% train, %20 validation and test. The aircrafts in the images in the train and validation datasets have a percentage of 80 or more in size.</p>
Detection of Atmospheric Rivers in the Northern Hemisphere based on ERA5 reanalysis data and the IPART algorithm, 1979-2020
<p># 1. Overview</p> <p>This is a catalogue of atmospheric river (AR) detections over the Northern Hemisphere, based on 6-hourly ERA5 reanalysis dataset and the Image-Processing based Atmospheric River Tracking (IPART) algorithm.</p> <p>Time domain of the data:</p> <ul> <li>From 1979-Jan-01 to 2020-Dec-31</li> <li>Temporal resolution is 6-hourly</li> </ul> <p>Spatial domain of the data:</p> <ul> <li>Northern Hemisphere, land and ocean</li> <li>Spatial resolution is 0.25 * 0.25 degrees latitude/longitude</li> </ul> <p>Input data from ERA5 include:</p> <ul> <li>Vertical integral of northward water vapour flux, in kg/(m s).</li> <li>Vertical integral of eastward water vapour flux, in kg/(m s).</li> </ul> <p>Data in the Northern Hemisphere domain (0 - 90 N), at 0.25 * 0.25 degrees latitude/longitude resolution are obtained from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5.</p> <p>Version v3.0.8 of the IPART Python module used for detection and tracking of atmospheric rivers is preserved at <strong>10.5281/zenodo.4164826</strong>, available via Creative Commons Attribution 4.0 International license and developed openly at the Github repository https://github.com/ihesp/IPART.</p> <p># 2. File naming convention</p> <p>The data files are named using the following convention:</p> <p>ar_YYYYMM.nc</p> <p>where:</p> <ul> <li>YYYY: 4-digit year number</li> <li>MM: 2-digit month number</li> </ul> <p>E.g. `ar_199902.nc` means detections in Feb of 1999.</p> <p>Months are calendar months, including Feb-29th in leap-years.</p> <p># 3. Data format</p> <p>Data are saved in netCDF format.</p> <p>Each data file contains one 3-dimensional array, of a shape `(t, 360, 1440)`, where:</p> <ul> <li>`t`: length of the time dimension. Since data are 6-hourly, t equals 4 * num_of_days_in_month.</li> <li>`360`: latitude dimension, from 0 - 90N, with a 0.25-degree step.</li> <li>`1440`: longitude dimension, from 80 - 440 E (shifted eastward by 80 degrees to put both the Pacific and Atlantic oceans within the domain), with a 0.25-degree step.</li> </ul> <p>Each time slice of the data contains maps of the Northern Hemisphere, with integer values in grid cells. Possible values are:</p> <ul> <li>0: meaning no AR is detected in the grid cell.</li> <li>1, 2, ... ,n: integer labels, each corresponding to the region of an AR entity.</li> </ul> <p># 4. Important parameters in the IPART algorithm</p> <p>Here are the most important parameters used when detecting ARs from ERA5 data using the IPART python module:</p> <ul> <li> THR filtering kernel: `[16, 13, 13]`. `16` means 16 time slices, or equivalently 4 days given 6-hourly input data. `13` means 13 grid cells, or equivalently ~325 km, given 0.25 degrees latitude/longitude input data. Note that both of these temporal and spacial lengths are half of the sizes of the filtering kernel.</li> <li> minimum area: `50 * 1e4`, in km^2, minimum size of AR region candidates.</li> <li> maximum area: `1800 * 1e4`, in km^2, maximum size of AR region candidates.</li> <li> minimum L/W: `2.0`, minimum length/width ratio of AR region candiates.</li> <li> minimum length: `2000`, in km, minimum length of AR region candidates.</li> <li> minimum latitude: `20`, minimum latitude of the geometrical centroid of an AR region candidate.</li> <li> maximum latitude: `80`, maximum latitude of the geometrical centroid of an AR region candidate.</li> </ul> <p>For more details regarding these parameters, as well as the IPART algorithm, please refer to our published works:</p> <ul> <li>Xu, G., Ma, X., Chang, P., and Wang, L.: Image-processing-based atmospheric river tracking method version 1 (IPART-1), Geosci. Model Dev., 13, 4639–4662, https://doi.org/10.5194/gmd-13-4639-2020, 2020.</li> </ul> <p>Or the Github repository that houses the IPART module:</p> <ul> <li>https://github.com/ihesp/IPART</li> </ul>
Procalcitonin detection in human plasma specimens using a fast version of proximity extension assay.
<p>Proximity Extension Assay (PEA), an homogeneous, dual-recognition immunoassay, has proven to be sensitive, specific and convenient for detection or quantitation of one or multiple analytes in human plasma. In this paper, the PEA principle was applied to the detection of procalcitonin (PCT), a widely used biomarker for the identification of bacterial infection. A simple, short PEA protocol, with an assay time suitable for point-of-care diagnostics, is presented here as a proof of concept. Pairs of oligonucleotides and monoclonal antibodies were selected to generate tools specifically adapted to the development of an efficient PEA for PCT detection. The assay time was reduced by more than 13-fold compared to published versions of PEA, without significantly affecting assay performance. It was also demonstrated that T4 DNA polymerase could advantageously be replaced by other polymerases having strong 3’>5’ exonuclease activity. The sensitivity of this improved assay was determined to be about 0.1 ng/mL of PCT in plasma specimen.</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.