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7,370 results for “supplementation”
Occupancy model for Rattus spp. in high and low human human refuse supplementation conditions
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Supplemental data from: Inference of phylogenetic networks from sequence data using composite likelihood
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Data supplement to: Quality control of image sensors using gaseous tritium light sources
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Supplement: Multicenter validated detection of focal cortical dysplasia using deep learning
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Analytical and supplemental data from: Ammonites as paleothermometers: Isotopically reconstructed temperatures of the Western Interior Seaway track global records
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Data from: Food supplementing peregrine falcon (Falco peregrinus tundrius) nests increases reproductive success without changes in parental mean provisioning rate
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Supplementing enhanced weathering with organic amendments accelerates the net climate benefit in rangeland soils
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Effects of food supplementation and helminth removal on space use and spatial overlap in wild bank vole populations
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Supplemental data from: Exploring phylogenetic signal in multivariate phenotypes by maximizing Blomberg’s K
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Effects of supplemental feeding on nesting success and physiological metrics in Eastern Bluebirds (Sialia sialis)
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Supplemental data Effects of imidacloprid exposure on honey bee colony growth and activity
To better understand how exposure to a low (5 ppb) concentration of imidacloprid affects honey bee colonies we conducted five field experiments involving a total of 54 bee colonies. In each experiment, bee colonies in one treatment group were fed sugar syrup adulterated with 5ppb imidacloprid over six weeks, and colonies in the other group were fed unadulturated sugar syrup over the same time period. Treatments were intended to simulate nectar collection by the colonies. No commercial agriculture existed within 10 km of the sites. We collected data from hive assessments, including adult bee mass and mass of food resources, and the total surface area of capped brood; and we collected data from sensors, including continuous hive weight, internal temperature and, for two years of the study, internal CO2 concentration. We measured Varroa mite fall before and after treatment each year of the study, and for 3 years of the study we measured the dry weight of newly-emerged adult bees.
Supplemental Old Field Grasshopper Sampling:Successional Dynamics on a Resampled Chronosequence
The purpose of this observational study is to describe the dynamics of ecosystem succession. The change in the number, type, and amount of plant and grazing animal species is monitored in more than 20 fields. These fields were previously cultivated, but then abandoned from agriculture at various times in the past. The fields were left undisturbed for plants to develop from seeds within the soil or brought into the fields by wind or animals. Permanent transects have been established in these abandoned fields for purposes of sampling in a consistent location from year to year. Permanent plots along these transects have been used to sample soil nutrients, (in particular, nitrogen) abundance of vegetation, species composition and herbivore populations. The sampling occurs approximately every 6 years. In the initial survey, 100 quadrats of size 1 by 0.5 m were sampled per field in 23 different fields. Abandoned fields included in E014 are 4, 5, 10, 21, 24, 26, 27, 28, 32, 35, 39, 40, 41, 44, 45, 47, 53, 70, 72, 76, 77. Fields 22(B), 29(A), and 69(C) were originally included in E014 but used for other purposes shortly after the start of the study. This experiment was established in 1983 and 1989 by principal investigators Johannes Knops and David Tilman. Past work at CDR and elsewhere has demonstrated an overriding influence of fire frequency in maintaining prairie openings and oak savanna at the prairie-forest border. Fire regimes harm some types of species while favoring others and drive light and nutrient dynamics, which in turn drive community functional attributes and diversity levels. Ultimately, fire frequency interacts with climate, N deposition, land use, and biotic invasion to determine the outcomes of tree-grass interactions and the dynamics of vegetation at ecotones such as the prairie-forest border in Minnesota. In 2006 each field was divided in half, and one half randomly chosen for periodic prescribed burning (a fire every other year). We anticipate that th
Supplement data for Numerical investigation into fracture risk of bone following adaptation
<p>This dataset contains meshes, CT scans and scripts used to produce results in the paper titled: Numerical investigation into fracture risk of bone following adaptation.<br> </p>
Data supplement for Wind Energy Science Paper 'Implementation of the blade element momentum model on a polar grid and its aeroelastic load impact'
<p>Contains the data for most figures in the article, as well as a plotting file written in python that generates the figures.</p>
Supplemental material to manuscript "Enhancement of adhesives strength of wood-metal joints using atmospheric plasma treatments"
<p>This dataset includes the supplementary information<strong> </strong>related to the manuscript:</p> <p>Authors: Žigon J, Kovač J, Zaplotnik R, Saražin J, Šernek M, Petrič M, Dahle S</p> <p>Title: <strong>Enhancement of adhesives strength of wood-metal joints using atmospheric plasma treatments</strong></p>
Supplement material for the paper "On the Relationship between User Churn and So!ware Issues"
<p>The data represented as CSV files containing the alternatives, the comments, the reviews, and the issue reports studied in the paper.</p>
Supplement 5: Comparison of Ptolemy's and Strabo's coastline length data
<p>Supplement 5 to the paper "<strong>The Configuration of the Pontus</strong><strong> Euxinus in Ptolemy’s <em>Geography</em></strong>" submitted to the History of Geo- and Space Sciences (URL: <a href="https://www.history-of-geo-and-space-sciences.net/">https://www.history-of-geo-and-space-sciences.net</a>). The paper is a part of the research project “Development of the Methods for Analysis of the Historical Maps” supported by the Russian Foundation for Basic Research (grant 18-011-00258-a). <em>Correspondence</em>: Dmitry A. Shcheglov (shcheglov@yandex.ru).</p>
Supplement 4: Comparison of Ptolemy's and Pseudo-Arrian's coastline length data
<p>Supplement 4 to the paper "<strong>The Configuration of the Pontus</strong><strong> Euxinus in Ptolemy’s <em>Geography</em></strong>" submitted to the History of Geo- and Space Sciences (URL: <a href="https://www.history-of-geo-and-space-sciences.net/">https://www.history-of-geo-and-space-sciences.net</a>). The paper is a part of the research project “Development of the Methods for Analysis of the Historical Maps” supported by the Russian Foundation for Basic Research (grant 18-011-00258-a). <em>Correspondence</em>: Dmitry A. Shcheglov (shcheglov@yandex.ru).</p>
Supplement to: Electron energy partition across interplanetary shocks: III. Analysis
<p><strong>Quick Summary:</strong></p> <p>The PDF file herein provides additional example superposed epoch analysis (SEA) plots in addition to reference tables of the upstream values used to normalize the SEA data in this file and those in the paper this supplement supports. This is a supplement to Part 3 of a three-part study of the electron velocity distribution functions (VDFs) observed near interplanetary (IP) shocks by the <em>Wind</em> spacecraft. Paper I [<a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab22bd"><em>Wilson et al.</em>, 2019a</a>] introduced the methodology and data products [<a href="https://doi.org/10.5281/zenodo.2875806"><em>Wilson et al.</em>, 2019c</a>] for fitting the electron VDFs to the sum of three model functions. Paper II [<a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab5445"><em>Wilson et al.</em>, 2019b</a>] presents the statistics of the fit parameters produced and provided in the data products from Paper I. Paper III presents and summarizes the analysis of the fit parameters. The papers share the title <strong><em>Electron energy partition across interplanetary shocks</em></strong>.</p> <p><strong><em>Wind</em> Spacecraft:</strong></p> <p>The <em>Wind</em> spacecraft (<a href="http://wind.nasa.gov/">https://wind.nasa.gov</a>) was launched on November 1, 1994 and currently orbits the first Lagrange point between the Earth and sun. It holds a suite of instruments from gamma ray detectors to quasi-static magnetic field instruments, <strong>B</strong><sub>o</sub>. The instruments used in this study and these datasets are the fluxgate magnetometer (<a href="https://doi.org/10.1007/BF00751330">MFI</a>), the radio receivers (<a href="https://doi.org/10.1007/BF00751331">WAVES</a>), ion Faraday cups (<a href="https://doi.org/10.1007/BF00751326">SWE</a>), and the electron and ion electrostatic analyzers (<a href="https://doi.org/10.1007/BF00751328">3DP</a>). The MFI measures 3-vector <strong>B</strong><sub>o</sub> at ~11 samples per second (sps); the SWE measures reduced VDFs of the thermal proton and alpha-particle populations from which velocity moments are derived and used herein; WAVES observes electromagnetic radiation from ~4 kHz to >12 MHz which provides an observation of the upper hybrid line (also called the plasma line) used to define the total electron density; and 3DP observes full 4π steradian VDFs of electrons and ions from a few eV to ~30 keV which provide both ion velocity moments and the electron VDFs modeled herein.</p> <p><strong>PDF Supplement Description:</strong></p> <p>Definitions:</p> <ul> <li>VDF = velocity distribution function</li> <li>Electron Components/Populations [taken from <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab22bd"><em>Wilson et al.</em>, 2019a</a>,<a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab5445">b</a>] <ul> <li>Core (<em>s</em> = ec): cold, dense population with energies <span class="math-tex">\(E_{ec} \lesssim \text{15 eV}\)</span></li> <li>Halo (<em>s</em> = eh): hot, tenuous population with energies <span class="math-tex">\(E_{eh} \gtrsim \text{20 eV}\)</span></li> <li>Beam/Strahl (<em>s</em> = eb): anti-sunward propagating, magnetic field-aligned beam (or strahl) with <span class="math-tex">\(E_{eb} \sim \text{a few tens of eV}\)</span></li> <li>Effective (<em>s</em> = eff): effective total electron population, i.e., used for approximate moments rather than integrating entire VDF</li> </ul> </li> <li>Ion Components/Populations [taken from <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab22bd"><em>Wilson et al.</em>, 2019a</a>,<a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab5445">b</a>] <ul> <li>Proton (<em>s</em> = p): core solar wind proton beam, i.e., main proton population streaming away from sun</li> <li>Alpha-particles (<em>s</em> = <span class="math-tex">\(\alpha\)</span>): alpha-particle magnetic field-aligned beam</li> </ul> </li> <li><span class="math-tex">\(k_{B}\)</span> = the Boltzmann constant [J K<sup>-1</sup>]</li> <li><span class="math-tex">\(\mu_{o}\)</span> = permeability of free space [T m A<sup>-1</sup>]</li> <li><span class="math-tex">\(n_{s}\)</span>= number density of species <em>s</em> [cm<sup>-3</sup>] (s = ec for core, eh for halo, eb for beam/strahl, p for proton, etc.)</li> <li><span class="math-tex">\(B_{o, j}\)</span>= j<sup>th</sup> component (GSE coordinate basis) of quasi-static magnetic field vector [nT]</li> <li><span class="math-tex">\(V_{Ts, j}\)</span> = j<sup>th</sup> component (relative to <strong>B</strong><sub>o</sub>) of thermal speed of species <em>s</em> [km/s] <ul> <li><span class="math-tex">\(V_{Ts, j} = \sqrt{ \tfrac{ 2 \ k_{B} \ T_{s, j} }{ m_{s} }}\)</span>, where <span class="math-tex">\(T_{s, j}\)</span> is the j<sup>th</sup> component (relative to <strong>B</strong><sub>o</sub>) of the temperature of species <em>s</em> [eV]</li> </ul> </li> <li><span class="math-tex">\(V_{os, j}\)</span> = j<sup>th</sup> component (relative to <strong>B</strong><sub>o</sub>) of drift speed of species <em>s</em> [km/s] in ion rest frame</li> <li><span class="math-tex">\(V_{s, j}\)</span> = j<sup>th</sup> component (GSE coordinate basis) bulk velocity of species <em>s</em> [km/s] in spacecraft frame</li> <li><span class="math-tex">\(T_{s, tot} = {1 \over 3} (T_{s, \parallel} + 2 \ T_{s, \perp})\)</span>, where <span class="math-tex">\(\parallel(\perp)\)</span> is the parallel(perpendicular) component relative to <strong>B</strong><sub>o</sub></li> <li><span class="math-tex">\(P_{s, j} = n_{s} \ k_{B} \ T_{s, j}\)</span> = partial thermal pressure [eV cm<sup>-3</sup>] of the <em>j</em><sup>th</sup> component of species <em>s</em></li> <li><span class="math-tex">\(P_{t, j} = \sum_{s} \ P_{s, j}\)</span> = total thermal pressure [eV cm<sup>-3</sup>] of the <em>j</em><sup>th</sup> component summed over all species including ions</li> <li><span class="math-tex">\(\mathcal{A}_{s} = \left(\tfrac{ T_{\perp} }{ T_{\parallel} } \right)_{s}\)</span> = temperature anisotropy [N/A] of species <em>s</em></li> <li><span class="math-tex">\(\xi_{s, j} = \tfrac{1}{2} m_{s} \ n_{s} \ V_{os, j}^{2}\)</span> = ram energy density [eV cm<sup>-3</sup>] <em>j</em><sup>th</sup> component of species <em>s</em></li> <li><span class="math-tex">\(\epsilon_{j} = \tfrac{ B_{o}^{2} }{ 2 \ \mu_{o} } + \sum_{s} \left[ P_{s, j} + \xi_{s, j} \right]\)</span> = total energy density [eV cm<sup>-3</sup>] of the <em>j</em><sup>th</sup> component of the system in the plasma bulk flow rest frame</li> <li><span class="math-tex">\(\zeta_{s, j} = \tfrac{ \xi_{s, j} }{ \epsilon_{j} }\)</span> = ratio of the ram energy density of the <em>j</em><sup>th</sup> component of species <em>s</em> to the total energy density [N/A]</li> <li><span class="math-tex">\(\psi_{s, j} = \tfrac{ P_{s, j} }{ \epsilon_{j} }\)</span> = ratio of the thermal energy density of the <em>j</em><sup>th</sup> component of species <em>s</em> to the total energy density [N/A]</li> <li><span class="math-tex">\(\Pi_{s, j} = \tfrac{ P_{s, j} }{ P_{t, j} }\)</span> = ratio of the partial thermal pressure of the <em>j</em><sup>th</sup> component of species <em>s</em> to the total thermal pressure [N/A]</li> <li><span class="math-tex">\(s_{es}\)</span> = exponent for the symmetric self-similar model VDF of species <em>s</em></li> <li><span class="math-tex">\(\kappa_{es}\)</span> = kappa value for the bi-kappa VDF of species <em>s</em></li> <li><span class="math-tex">\(p_{es}(q_{es})\)</span> = parallel(perpendicular) exponent for the asymmetric self-similar model VDF of species <em>s</em></li> <li><span class="math-tex">\(n_{eff} = \sum_{s} \ n_{s}\)</span> = effective number density of all electron populations</li> <li><span class="math-tex">\(T_{eff, j} = \tfrac{ \sum_{s} \ n_{s} \ T_{s, j} }{ n_{eff} }\)</span> = effective temperature of the <em>j</em><sup>th</sup> component of all electrons populations</li> <li><span class="math-tex">\(\beta_{s, j} = \tfrac{ 2 \ \mu_{o} \ n_{s} \ k_{B} \ T_{s, j} }{ B_{o}^{2} }\)</span> = plasma beta [N/A] of the <em>j</em><sup>th</sup> component of species <em>s</em></li> </ul> <p> </p> <p>This PDF supplement contains the following SEA plots:</p> <ul> <li><span class="math-tex">\(T_{s, j}\)</span> vs <span class="math-tex">\(\Delta\)</span>t (for <em>s</em> = ec, eh, and eb and <em>j</em> = <span class="math-tex">\(\parallel \text{ or } \perp \text{ or tot}\)</span>)</li> <li><span class="math-tex">\(\mathcal{A}_{s}\)</span> vs <span class="math-tex">\(\Delta\)</span>t (for <em>s</em> = ec, eh, and eb)</li> <li><span class="math-tex">\(\left( \tfrac{ T_{s} }{ T_{eff} } \right)_{j}\)</span> vs <span class="math-tex">\(\Delta\)</span>t (for <em>s</em> = ec, eh, and eb and j = <span class="math-tex">\(\parallel \text{ or } \perp \text{ or tot}\)</span>)</li> <li> <p><span class="math-tex">\(\psi_{s, j}\)</span> vs <span class="math-tex">\(\Delta\)</span>t (for <em>s</em> = ec, eh, eb, p, and <span class="math-tex">\(\alpha\)</span> and j = <span class="math-tex">\(\parallel \text{ or } \perp \text{ or tot}\)</span>)</p> </li> <li> <p><span class="math-tex">\(\Pi_{s, j}\)</span> vs <span class="math-tex">\(\Delta\)</span>t (for <em>s</em> = ec, eh, eb, p, and <span class="math-tex">\(\alpha\)</span> and j = <span class="math-tex">\(\parallel \text{ or } \perp \text{ or tot}\)</span>)</p> </li> </ul> <p>The PDF supplement contains tables of upstream median values for each shock used for normalizing the SEA plots, where the parameters listed include:</p> <ul> <li><span class="math-tex">\(T_{s, j}\)</span> (for <em>s</em> = ec, eh, and eb and j = <span class="math-tex">\(\parallel \text{ or } \perp \text{ or tot}\)</span>)</li> <li><span class="math-tex">\(n_{s}\)</span> (for <em>s</em> = ec, eh, eb, and eff and j = <span class="math-tex">\(\parallel \text{ or } \perp \text{ or tot}\)</span>)</li> <li><span class="math-tex">\(\tfrac{ n_{s} }{ n_{eff} }\)</span> (for <em>s</em> = ec, eh, and eb and j = <span class="math-tex">\(\parallel \text{ or } \perp \text{ or tot}\)</span>)</li> <li><span class="math-tex">\(\beta_{s, j}\)</span> (for <em>s</em> = ec, eh, and eb and j = <span class="math-tex">\(\parallel \text{ or } \perp \text{ or tot}\)</span>)</li> <li><span class="math-tex">\(s_{ec}\text{, }\kappa_{eh}\text{, and }\kappa_{eb}\)</span></li> <li><span class="math-tex">\(\mathcal{A}_{s}\)</span> (for <em>s</em> = ec, eh, eb, and eff)</li> <li><span class="math-tex">\(\left( \tfrac{ T_{s} }{ T_{eff} } \right)_{j}\)</span> (for <em>s</em> = ec, eh, and eb and j = <span class="math-tex">\(\parallel \text{ or } \perp \text{ or tot}\)</span>)</li> </ul>
Supplemental data
<p>Supplement 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.