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1,838 results for “location”
Locations of black bear (Ursus americanus) reproduction in Nevada from camera-trap data
<p>Understanding factors creating species range boundaries is a fundamental goal of ecology and biogeography. American black bears recolonized the western Great Basin from the Sierra Nevada in the late 1900s but this expansion has not proceeded further into the Great Basin despite the presence of suitable habitat. We deployed 100 camera traps across the occupied range of black bears in the U.S. state of Nevada and tracked bear detections across 3 years. A scent lure was applied in camera trap viewsheds to increase bear detections. We classified detections of bear cubs separately from detections of only adult bears, to serve as an indicator of black bear reproduction occurring at sites. Data are provided in the format necessary for a analysis with multistate occupancy model. Analysis of these data revealed low incidence of reproduction at the periphery of black bear range in the western Great Basin, which likely contributes to range boundary formation.</p>
Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans
<h3>Contact:</h3><h3>Ilya Verzhbinsky</h3><h3>ilya@health.ucsd.edu</h3><p> </p><p>This is the processed data used to generate the results in the manuscript:</p><p>Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans. <i>PNAS</i> (2023).</p><p>To analyze this data, please first access the code at the following repository: <a href="https://github.com/iverzh/coripple-prediction"><strong>https://github.com/iverzh/coripple-prediction</strong></a></p><p>All downloaded zip files should be uncompressed and placed in a directory named <i>out/ </i>in the <i>CoRipplePredictionPNAS/</i> folder.</p><p> </p><p> </p><p> </p>
Genotypes and geographic positions of 5797 European white oaks from 636 locations genotyped at 355 nuclear SNPs and 28 maternally inherited SNPs of the chloroplast and mitochondria
<p class="MsoNormal"><span>The data set is the result of genetic inventory on 5797 white oaks collected at 636 locations all over Europe. The oaks trees were assigned in forest inventories as <em>Quercus robur</em> </span><em>L.</em> <span>(3342), <em>Quercus petraea </em></span><em>Matt</em>. <span>(2090), <em>Quercus pubescens </em></span><em>Willd</em>. <span>(170) or as unspecified <em>Quercus</em>. spp. (195). The sampling had a focus on central and east Europe as well as the Black Sea and Caucasus region. All individuals were genotyped at 355 nuclear SNPs and 28 maternally inherited SNPs of the chloroplast and mitochondria. The combination of the maternally inherited SNPs resulted in 26 different haplotypes. </span></p> <p class="MsoNormal"><span>The genotype of each individual is one row in the csv-file "genotypes". The genotypes at the nuclear markers are diploid and represented by two columns per gene marker. The genetic information at the organelle genome is haploid. For each of these gene markers one column is used. Genotypes are coded by Arabic numbers. The meaning of the numbers is explained in the table "coding genotypes" in a second csv-file. Each Individual has a unique "Genotype_ID" and a "Thuenen_Sample_ID". The "Thuenen_Sample_ID" is a unique ID that serves to identify the sample in our depository at the Thuenen Institute of Forest Genetics. Each individual has data on the geographic origin given as "Longitude" and "Latitude" in decimal degrees. For each individual the putative oak species ("Putative species") as it has been assigned in the forest inventories is given. The numbers of the "Haplotype" represent the multilocus combination of the mitochondrial and chloroplast SNPs of that individual.</span></p>
Magnolia Warbler (Setophaga magnolia) flight calls demonstrate individuality and variation by season and recording location
<p><span>Flight calls are short vocalizations frequently associated with migratory behavior that may maintain group structure, signal individual identity, and facilitate intra- and interspecific communication. In this study, Magnolia Warbler (<em>Setophaga magnolia</em>) flight call characteristics varied significantly by season and recording location, but not age or sex, and an individual's flight calls were significantly more similar to one another than to calls of other individuals. To determine if flight calls encode traits of the signaling individual during migration, we analyzed acoustic characteristics of the calls from the nocturnally migrating Magnolia Warbler. Specifically, we analyzed calls recorded from temporarily captured birds across the northeastern United States, including Appledore Island in Maine, Braddock Bay Bird Observatory in New York, and Powdermill Avian Research Center in Pennsylvania to quantify variation attributable to individual identity, sex, age, seasonality, and recording location. Overall, our findings suggest that Magnolia Warbler flight calls may show meaningful individual variation and exhibit previously undescribed spatiotemporal variation, providing a basis for future research.</span></p>
RecView: An interactive R application for locating recombination positions using pedigree data
<p><span>We present <em>RecView</em>, an interactive R application and </span><span>its homonymous R package</span><span>, to facilitate locating recombination positions along chromosomes or scaffolds using whole-genome genotype data of a three-generation pedigree. </span><span>We demonstrate applicability of <em>RecView </em>using the genotype data from two offspring, as well as their grandparents and parents, of the great reed warbler (<em>Acrocephalus arundinaceus</em>).</span></p>
Coordinates of jungle cat vehicle collision locations and background points
Open the record for dataset details and reuse information.
CFS model monthly mean diurnal cycles of ocean and atmosphere variables at TAO mooring locations
<p>v0.1.3</p> <p>cfsm501_ocn_2002_2006_TAOpoints_hourly.tgz -- contains netCDF files of hourly ocean variables: one ocean file per month over 4 years (2002-2006).</p> <p>Each file contains water temperature with dimensions (time, depth, lat, lon) at TAO locations. If joining multiple files together, concatenate along the time axis.</p> <p>-------------------------</p> <p>v0.1.2</p> <p>cfsm501_atmo_2002_2006_TAOpoints_3D_monthlyMeanDiurnalCycle.tgz -- contains netCDF files of monthly mean diurnal cycle of atmosphere variables: one atmosphere file per month over 4 years (2002-2006).</p> <p>Each file contains multiple variables with dimensions (time, hour, plev, lat, lon) at TAO locations. The dimension "time" is of length 1 in all files. If joining multiple files together, concatenate along the time axis. The "hour" dimension represents the 24 hours of the diurnal cycle.</p> <p>Note that this version of the data has NOT had the 3-day high pass filter applied before the diurnal cycle calculation.</p> <p>-------------------------</p> <p>v0.1.1</p> <p>cfsm501_atmo_2002_2006_TAOpoints_hourly.tgz -- contains netCDF files of hourly atmosphere variables: one atmosphere file per month over 4 years (2002-2006).</p> <p>Each file contains multiple variables with dimensions (time, lat, lon) at TAO locations. If joining multiple files together, concatenate along the time axis.</p> <p>-------------------------</p> <p>v 0.1.0</p> <p>cfsm501_atmo_ocn_2002_2006_TAOpoints_monthlyMeanDiurnalCycle.tgz -- contains netCDF files of monthly mean diurnal cycle of ocean and atmosphere variables: one atmosphere and one ocean file per month over 4 years (2002-2006).</p> <p>Each file contains multiple variables with dimensions (time, hour, [depth,] lat, lon) at TAO locations. The dimension "time" is of length 1 in all files. If joining multiple files together, concatenate along the time axis. The "hour" dimension represents the 24 hours of the diurnal cycle.</p> <p>Note that this version of the data has NOT had the 3-day high pass filter applied before the diurnal cycle calculation.</p>
3D representation of meso-voids location and morphology within the fibrous pore space of three carbon fabrics
<p>Segmented meso-voids from a tomography scan of preforms impregnated under capillary dominated conditions, combined to 3D scans of the initial prefrom dry state.</p>
India Onshore Wind Energy Atlas Accounting for Altitude and Land Use Restrictions and Co-Located Solar
<p>India faces the simultaneous challenges of meeting rising energy demand and reducing carbon emissions. To address these, India must transition to renewable energy sources. These high-resolution maps are used to quantify available areas for wind farms, after accounting for restrictions, including airports, buildings, protected land use, military zones, railways, roads, water bodies, waterways, wildlife and nature, high elevation and slope, and existing solar farms, to which policy-informed setback distances are applied. This study finds the wind and solar potential within available areas considering three altitudes (100 m, 150 m, 200 m) and four wind speed thresholds (5-8 m/s), and modern wind turbine and solar array dimensions. The raster files included here indicate available areas after aggregating restrictions for different combinations of altitude and wind speed threshold. Availability is indicated with a binary system in which available land is designated with a value of zero and restricted land is designated with a value of one.</p>
Table (A): Phase 3 Prior Testing Summary Table for Individual Source in Location Nine: Robert's Building Cafe
<p>Summary table detailing the peak frequency before and after using the source with the corresponding non-ionising radiation levels. The final column details if there has been a significant or minimal change in the readings. For this testing, a variety of sources were used. </p>
Table (1) and (2): Determining Maximum Power Density for Locations One to Nine and Associating Frequencies to Sources
<p>Table 1 details the maximum NIR level in dBm, the corresponding phase, the area for that location which are all used to determine the maximum power density.</p> <p>Table 2 details the frequency range used for this project and denotes all the sources available within each frequency band along with the accuracy of the sources. The accuracy includes being a confirmed source or a tested source.</p>
Dataset for: A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces
<p>This dataset is part of "A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces" available <a href="https://www.arxiv.org/abs/2402.19037" target="_blank" rel="noopener">online</a>.</p> <p>The source code for testing the dataset is available on <a href="https://github.com/hardware-fab/DL-to-locate-COs-for-SCA">GitHub</a>.</p> <p>The dataset is organized as follows:</p> <ul> <li><strong>\training</strong>: contains three subsets, i.e., train, valid, and test. <br> Each subset consists of two .npy files: <ul> <li><em>_set</em>: it contains the side-channel traces that are preprocessed accordingly.</li> <li> <em>_labels</em>: itcontains the target labels for training the CNN, labeling each data as <em>cipher start</em>, <em>cipher rest</em>, or <em>noise</em>.</li> </ul> </li> <li><strong>\inference</strong>: contains two files as a demo of the inference pipeline.<br> One file is the is the side-channel trace containing an undefined number of AES encryptions. The other file is a list of plaintexts matching the AES encryptions to test a CPA attack.</li> </ul> <p><strong>Cite:</strong></p> <blockquote> <pre><code>@INPROCEEDINGS{10546758, author={Chiari, Giuseppe and Galli, Davide and Lattari, Francesco and Matteucci, Matteo and Zoni, Davide}, booktitle={2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)}, title={A Deep- Learning Technique to Locate Cryptographic Operations in Side-Channel Traces}, year={2024}, pages={1-6}, doi={10.23919/DATE58400.2024.10546758}}</code></pre> </blockquote> <p>This repository is protected by copyright and licensed under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.</p> <p>© 2024 hardware-fab</p>
F I G U R E 3 Mass specific growth rates among locations, 2016–2017 in Movement and habitat shift responses of juvenile Atlantic Salmon (Salmo salar) to annually permanent stream flooding
F I G U R E 3 Mass specific growth rates among locations, 2016–2017.
Dataset for: Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces Using Deep-Learning
<p>This dataset is part of "Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces Using Deep-Learning" [1] available <a href="https://arxiv.org/pdf/2408.06296">online</a>.</p> <p>The source code for testing the dataset is available on <a href="https://github.com/hardware-fab/Hound">GitHub</a>.</p> <p>The dataset is organized as follows:</p> <ul> <li><strong>/training</strong>: Contains three subsets: <em>train</em>, <em>valid</em>, and <em>test</em>. Each subset consists of two <em>.npy</em> files: <ul> <li><em><strong>_set</strong></em>: Contains the preprocessed side-channel traces.</li> <li><strong><em>_labels</em></strong>: Contains the target labels for training the CNN, labeling each data as `CP start`, `CP spare`, or `noise`.</li> </ul> </li> <li><strong>/inference</strong>: Contains files for two demos: consecutive AES executions and AES executions interleaved with noisy applications. Each demo consists of two <em>.npy</em> files: <ul> <li><strong>aes_</strong>: Contains the side-channel traces to input into Hound.</li> <li><strong>gt_</strong>: Contains the ground truth for checking the correctness of Hound segmentation.</li> </ul> </li> </ul> <p>This repository is protected by copyright and licensed under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.</p> <p>© 2024 hardware-fab</p> <blockquote> <p>[1] D. Galli, G. Chiari and D. Zoni, "Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces using Deep-Learning," 2024 IEEE 42nd International Conference on Computer Design (ICCD), Milan, Italy, 2024, pp. 114-121, doi: 10.1109/ICCD63220.2024.00027.</p> </blockquote>
Location and information files
<p>These are the files containing other information used for this thesis from which the various maps showing locations of quarries, samples, and artefacts were created. Each is in .csv format. The files are:</p> <ul> <li>Artefact data file.csv</li> <li>Quarries.csv</li> <li>Trace element map sample locations.csv</li> </ul> <p><strong>Artefact data file.csv:</strong> On line databases for 10 Swedish museums were searched for their steatite artefacts, and the information was compiled to this file. For those artefacts where the location was given only in the form of a place name I consulted online maps, including the Swedish National Map service, https://minkarta.lantmateriet.se/ and https://www.google.com/maps to determine a latitude and longitude for the address/location described. If the exact location could not be determined, or was not listed, but the database listed the parish in which it was found, then the location of the parish was determined from https://socknar.se/, and the location of the parish church was recorded. If there was not even a parish listed, then the approximate centre of the province was recorded. These three levels of accuracy for the location are recorded under the variable name “accuracy”.</p> <p><strong>Quarries.csv: </strong>The data for these steatite quarry, outcrop, and prospect locations was primarily obtained from the Norwegian Geological Survey (https://geo.ngu.no/kart/mineralressurser_mobil/) for the quarries located in Norway, and the Swedish Geological Survey (https://apps.sgu.se/kartvisare/kartvisare-malm-mineral.html) for those located in Sweden. This information was supplemented with some data from my own field work when collecting samples, and from the Swedish national archaeological database (https://app.raa.se/open/fornsok/). </p> <p><strong>Trace element map sample locations.csv:</strong> This file contains information specific to the samples analysed with LA-ICP-MS for this thesis, with location and geologic information coming from the quarry locations file (see above), and the information pertaining to the sample and trace-element composition map number, which epoxy mount it is in, and the date of analysis is recorded from working notes.</p> <p>Note: It was discovered that version 1 of this data set didn't have all three files, so version 2 was uploaded.</p>
Analysis data for location- and scale-invariant power transformations
<p>This repository contains various files and folders related to the machine learning experiments in a forthcoming manuscript on location- and scale-invariant power transformations.</p>
Landslide location, surface roughness, and age for the Teanaway basin, USA
<p>Excel database of mapped landslides in the Teanaway basin, Washington State, US. Landslides were mapped from 1m lidar provided by Quantum Spatial and publicly available at https://lidarportal.dnr.wa.gov/. The surface roughness was calculated with the MAD metric available at https://github.com/cageo/Trevisani-2015. </p>
River microplastic field data locations associated with publication
<p>Field data locations associated with Piehl et al (2020) Can Water Constituents Be Used as Proxy to Map Microplastic Dispersal Within Transitional and Coastal Waters? doi: <a href="https://www.frontiersin.org/articles/10.3389/fenvs.2020.00092/full">10.3389/fenvs.2020.00092</a>. Full dataset table, including water quality measurements and sampled microplastic concentrations, can be found in the Supplemental Material of that publication. </p>
Data from: Location, but not defensive genotype, determines ectomycorrhizal community composition in Scots pine (Pinus sylvestris L.) seedlings
<p class="western"><span><span><span>1. For successful colonisation of host roots, ectomycorrhizal (EM) fungi must overcome host defence systems, and <span>defensive phenotypes have previously been shown to affect the community composition of EM fungi associated with hosts</span>. Secondary metabolites, such as terpenes, form a core part of these defence systems, but it is not yet understood whether variation in these constitutive defences can result in variation in colonisation of hosts by specific fungal species.</span></span></span></p> <p class="western"><span>2. We planted seedlings from twelve maternal families of Scots pine (<i>Pinus sylvestris</i>) of known terpene genotype reciprocally in the field in each of six sites. After three months we characterised the mycorrhizal fungal community of each seedling using a combination of morphological categorisation and molecular barcoding, and assessed the terpene chemodiversity for a subset of the seedlings. We examined whether parental genotype or terpene chemodiversity affected the diversity or composition of a seedling's mycorrhizal community.</span></p> <p class="western"><span><span><span><span>3. While we found that terpene chemodiversity was highly heritable, w</span>e found no evidence that parental defensive genotypeor defensive phenoytpeaffected associations with EM fungi. Instead, we found that the location of seedlings, both <span>within and between sites</span>, was the only determinant of the diversity and makeup of EM communities.</span></span></span></p> <p class="western"><span><span><span>4. These results suggest that <span>while EM community composition varies within Scotland at both large and small scales</span>, variation in constitutive defensive compounds does not determine the EM communities of closely cohabiting pine seedlings. Patchy distributions of EM fungi at small scales may render any genetic variation in associations with different species unrealisable in field conditions. <span>The case for selection on traits mediating associations with specific fungal species may thus be overstated, at least in seedlings.</span></span></span></span></p>
Marsquake locations and 1-D seismic models for Mars from InSight data
<p>Data used to draw the figures in the paper 'Marsquake locations and 1-D seismic models for Mars from InSight data'.</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.