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69 results for “pattern detection”
Hypernym-LIBre: A free Web-based corpus from Hypernym Detection [ Hearst Pattern extractions from Hypernym-LIBre]
<p>Hypernym-LIBre ( DOI: 10.5281/zenodo.3662204 ) is a free Web-based corpus for Hypernym detection.</p> <p>Its part-of-speech tagged and dependency annotated version is present at this: (DOI: 10.5281/zenodo.3689303)</p> <p>Here we provide the hypernym-hyponym pairs that were extracted from Hypernym-LIBre using Hearst patterns. This is to further the usage of these extractions with more techniques and methods. We also provide the counts of each pattern in a separate file.</p> <p> </p> <p>Format:</p> <p>hyponym \t hypernym</p> <p> </p> <p>Format for the counts file:</p> <p>pair \t frequency of extraction</p> <p> </p> <p>There are 2 files, one with the pairs, one with unique pair and its counts. Both total ~430MB.</p>
Dataset from : Detection and Analysis of an Alternate Flow Pattern in a Radial Vaned Diffuser
<p>This dataset pertains to the publication 'Detection and Analysis of an Alternate Flow Pattern in a Radial Vaned Diffuser', V. Moënne-Loccoz, I. Trébinjac, N. Poujol, P. Duquesne. International Journal of Turbomachinery, Propulsion and Power. MDPI (2020).</p><p>Where possible, each figure is provided in CSV format.</p>
Geodetic anomaly detection and analysis in the Campi Flegrei caldera (Italy) deformation pattern of the 2021-2023 escalating unrest phase
<p>Data used within the manuscript: "<strong><span>First evidence of a geodetic anomaly in the Campi Flegrei caldera (Italy) ground deformation pattern revealed by DInSAR and GNSS measurements during the 2021-2023 escalating unrest phase</span>"</strong></p> <p> </p> <p>Archive content:</p> <ul> <li><code>DTSLOS_CNRIREA_20150325_20231021_FB9K</code>: Line of Sight displacement time series retrieved by applying the P-SBAS algorithm to Sentinel-1 data set acquired from ascending orbits (Track 44) over Campi Flegrei caldera in the 20150325 - 20231021 interval. Data format is according to the <a href="https://gitlab.com/epos-tcs-satdata/doc/-/blob/main/TCS_SATD_Product_Description.md#los-displacement-time-series-dtslos" target="_blank" rel="noopener noreferrer">EPOS specification</a>.</li> <li><code>DTSLOS_CNRIREA_20150324_20231020_UJBI</code>: Line of Sight displacement time series retrieved by applying the P-SBAS algorithm to Sentinel-1 data set acquired from descending orbits (Track 22) over Campi Flegrei caldera in the 20150324 - 20231020 interval. Data format is according to the <a href="https://gitlab.com/epos-tcs-satdata/doc/-/blob/main/TCS_SATD_Product_Description.md#los-displacement-time-series-dtslos" target="_blank" rel="noopener noreferrer">EPOS specification</a>.</li> <li><code>Campi_Flegrei_GNSS_Weekly_Timeseries</code>: Weekly displacement time series of Campi Flegrei caldera GNSS network from 2016 to 2023.</li> </ul>
Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
<p>1. Patterns in, and the underlying dynamics of, species cooccurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterisations have been published that could be used with the same fundamental modelling framework that accounts for imperfect detection, although each parameterisation has different advantages and disadvantages.</p> <p>2. We propose a parameterisation based on log-linear modelling that does not require a species hierarchy to be defined (in terms of dominance), and enables a numerically robust approach for estimating covariate effects.</p> <p>3. Conceptually the parameterisation is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, 'symmetric', interpretations of parameter estimates.</p> <p>4. The parameterisation can be applied to many species, in either a maximum-likelihood or Bayesian estimation framework. We illustrate the method using camera trapping data collected on three mesocarnivore species in South Texas.</p>
Fig. 6. Electrophoresis agarose gel showing RFLP pattern comparison between the F2 in Detection of maize bushy stunt phytoplasma in leafoppers collected in native corn crops grown at high elevations in southeast Mexico
Fig. 6. Electrophoresis agarose gel showing RFLP pattern comparison between the F2nR2 sequences amplified from Dalbulus elimatus and Idiodonus wickhami digested with AluI, BstUI, HaeIII, HinfI, and Tsp509I. Molecular weight (MW) marker, 1 kb plus.
Fig. 1 in The detection of Japanese encephalitis virus in Megachiropteran bats in West Kalimantan, Indonesia: A potential enzootic transmission pattern in the absence of pig holdings
Fig. 1. Buffer map overlaying estimated flight range of Culex mosquitoes and hunting ranges of bats.
Figure 4 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 4. The SVM structure used in application one approach. The weights determined during the superoised part of the training are {w0, w1, ..., wX̅1}. The output element linearly combines the outputs of the hidden layer with the weights.
Figure 2. The paraconsistent plane where the axes G1 and G2 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 2. The paraconsistent plane where the axes G1 and G2 represent the degrees of certainty and contradiction, respectioely. P = (G1, G2) = (α ̅ β, α + β ̅ 1), drawn in blue just to exemplify, is an important element for our analysis: The closer it is to the corner (1,0), the weaker the classifier associated with the features oector can be. The oalues of α and β are derioed from intra-class and inter-class analyses, respectioely, as detailed in [17].
Figure 1 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 1. Quesada gigas. On the left, male emitting acoustic signals. On the right, lateral oiew of male resting.
Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
Open the record for dataset details and reuse information.
Data from: Spatial and temporal patterns of environmental DNA detection to inform sampling protocols in lentic and lotic systems.
<p>The development of efficient sampling protocols for the capture of environmental DNA (eDNA) could greatly help improve accuracy of occupancy monitoring for species that are difficult to detect. However, the process of developing a protocol in situ is complicated for rare species by the fact that animal locations are often unknown. We tested sampling designs in lake and stream systems to determine the most effective eDNA sampling protocols for two rare species: the Sierra Nevada yellow-legged frog (<i>Rana sierrae</i>) and the foothill yellow-legged frog (<i>R. boylii</i>). We varied water volume, spatial sampling, and seasonal timing in lakes and streams; in lakes we also tested multiple filter types. We found that filtering 2 L versus 1 L increased the odds of detection in streams 5.42X (95% CI: 3.2-9.19X) in our protocol, from a probability of 0.51 to 0.85 per technical replicate. Lake sample volumes were limited by filter clogging and we found no effect of volume or filter type. Sampling later in the season increased the odds of detection in streams by 1.96X for every 30 days (95% CI: 1.3 - 2.97X) but there was no effect for lakes. Spatial autocorrelation of the quantity of yellow-legged frog eDNA captured in streams between 100 and 200 m, indicating that sampling at close intervals is important.</p>
Review-based Comparison of Design Pattern Detection Tools
<p>Replication package containing the data obtained from the Systematic Literature Review of design pattern detection tools and the instances obtained from the execution of the tools available.</p> <p>Contains a README file introducing each of the files in this replication package.</p>
Unsupervised detection of Large-scale Weather Patterns in the Northern Hemisphere via Markov State Modelling: from Blockings to Teleconnections
<p><em><span>Data Source</span></em></p> <p>This dataset is derived from the <em><span>NCEP-NCAR Reanalysis 1</span> data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href="https://psl.noaa.gov/">https://psl.noaa.gov</a></em>., a robust atmospheric dataset that includes a wide range of climatic measurements essential for comprehensive climate analysis. The original data can be accessed at the NOAA Physical Sciences Laboratory website: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html.</p> <p> </p>
Replication Package for "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis"
<p>This repository provides the data sets and scripts used in the paper "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis" by Sebastian Nielebock, Robert Heumüller, Kevin Michael Schott, and Frank Ortmeier from the Faculty of Computer Science of the Otto-von-Guericke University Magdeburg, Germany. This paper is published in Springer's "Automated Software Engineering - An International Journal" in August 2021. The article is available as open access at <a href="https://dx.doi.org/10.1007/s10515-021-00294-x">https://dx.doi.org/10.1007/s10515-021-00294-x</a>. A preprint is available under <a href="https://arxiv.org/abs/2008.00277">https://arxiv.org/abs/2008.00277</a>.</p> <p>All scripts and data sets are provided by the authors and come without any guarantee. For any issues regarding replication do not hesitate to contact us ({sebastian.nielebock,robert.heumueller, kevin.schott, frank.ortmeier} <at> ovgu.de)</p> <p>If you use or refer to these datasets, please cite our paper using the following BibTex entry.</p> <pre>@article{NielebockAPIFilterSearch2021, title = {Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis}, author = {Sebastian Nielebock and Robert Heum\"{u}ller and Kevin Michael Schott and Frank Ortmeier}, editor = {Springer}, journal = {Springer Automated Software Engineering - An International Journal}, number = {15}, pages = {1-48}, volume = {28}, url = {https://arxiv.org/abs/2008.00277}, doi = {10.1007/s10515-021-00294-x}, year = {2021}, } </pre>
Distinct latitudinal community patterns of Arctic marine vertebrates along the East Greenlandic coast detected by environmental DNA
<p><strong><span>Aim: </span></strong><span>Greenland is one of the places on Earth where the effects of climate change are most evident. The retreat of sea ice has made East Greenland more accessible for longer periods during the year. East Greenland fjords have been notoriously difficult to study due to their remoteness, dense sea ice conditions and lack of infrastructure. As a result, biological monitoring across latitudinal gradients is scarce in East Greenland and relies on sporadic research cruises and trawl data from commercial vessels. We here aim to investigate the transition in fish and marine mammal communities from South to Northeast Greenland using environmental DNA (eDNA).</span></p> <p><strong><span>Location: </span></strong><span>South to Northeast Greenland.</span></p> <p><strong><span>Methods: </span></strong><span>We investigated the transition in fish and marine mammal communities from South to Northeast Greenland using eDNA metabarcoding of seawater samples. We included both surface and mesopelagic samples, collected over approximately 2400 km waterway distance, by sampling from Cape Farewell to Ella Island in August 2021.</span></p> <p><strong><span>Results:</span></strong><span> We demonstrate a clear transition in biological communities from south to northeast, with detected fish and mammal species matching known distributions. Samples from the southern areas were dominated by capelin (<em>Mallotus villotus</em>) and redfish (<em>Sebastes</em>), whereas northeastern samples were dominated by polar cod (<em>Boreogadus saida</em>), sculpins (<em>Myoxocephalus</em>) and ringed seal (<em>Pusa hispida</em>). We provide newly generated 12S rRNA barcodes from 72 fish species, bringing the public DNA database closer to full taxonomic coverage for Greenlandic fish species for this locus.</span></p> <p><strong><span>Main conclusions: </span></strong><span>Our results demonstrate that eDNA sampling can detect latitudinal shifts in marine biological communities of the Arctic region, which can supplement traditional fish surveys in understanding species distributions and community compositions of marine vertebrates. Importantly, sampling of eDNA can be a feasible approach for detecting northward range expansions in remote areas as climate change progresses.</span></p>
Detecting Patterns In UML Diagrammatic Readings - An Experimental Study
<p>This experiment was developed within the scope of the thesis "Constructing An Auditory Notation In Software Engineering - Understanding UML Models with voice and sound", with the main purpose of understanding if there any emerging patterns in how different people carry out diagrammatic readings of UML diagrams (Class and State Machine Diagrams, specifically). This is achieved by observing the most common actions amongst the participants.<br> <br> The transcriptions of this study are included here.</p>
Figure 8 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 8. Cross-oalidation algorithm.
Figure 6 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 6. The experimental setup for the proposed application two.
Figure 5 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 5. Examples of images used: high, low, and zero density, respectioely.
Figure 3 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 3. The experimental setup for the proposed application one.
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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.