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4,356 results for “RED”
Positions for "First insights into migration routes and nonbreeding sites used by Red-rumped Swallows (Cecropis daurica rufula) breeding in the Iberian Peninsula"
<p><strong>Abstract</strong></p> <p>Using EURING data and geolocation, we describe migration routes and nonbreeding range of Red-rumped Swallows breeding in the Western Palearctic. One bird ringed in southern Spain and recovered in southern Morocco indicates southwestern migration; geolocator data from five birds from central and eastern Iberian Peninsula confirm migration to various nonbreeding sites in sub-Saharan west Africa between Senegal/Mauritania and Ghana. Two swallows showed non-breeding site itinerancy by using more than one nonbreeding site per season. Despite wide ranges in departure for autumn (August- October) and spring migration (February-March), all birds arrived at nonbreeding and breeding sites within ±1-week from each other.</p> <p><strong>Zusammenfassung</strong></p> <p>Erste Einblicke in Zugrouten und Überwinterungsgebiete von Rötelschwalben (<em>Cecropis daurica rufula</em>) der Iberischen Halbinsel.<br> In dieser Studie beschreiben wir Zugrouten und Überwinterungsgebiete westpaläarktischer Rötelschwalben basierend auf EURING- und Geolokations-Daten. Eine Rötelschwalbe, die in Südspanien beringt und im südlichen Marokko wiedergefunden wurde, spricht für einen südwestlichen Zug. Geolokalisation von fünf Vögeln der zentralen und östlichen Iberischen Halbinsel zeigen Überwinterungsorte im sub-Saharischen Westafrika zwischen Senegal/Mauretanien und Ghana. Zwei der getrackten Rötelschwalben nutzten mehrere Überwinterungsplätze pro Saison. Trotz der großen Schwankungsbreite der Abzugszeiten im Herbst (August-Oktober) und im Frühjahr (Februar-März) erreichten die getrackten Vögel ihre Nichtbrut- bzw. Brutplätze innerhalb von 1–2 Wochen.</p>
Phenotypic variation and quantitative trait loci for resistance to southern anthracnose and clover rot in red clover
<p>Red clover (<em>Trifolium pratense</em> L.) is an important forage legume of temperate regions, particularly valued for its high yield potential and its high forage quality. Despite substantial breeding progress during the last decades, continuous improvement of cultivars is crucial to ensure yield stability in view of newly emerging diseases or changing climatic conditions. The high amount of genetic diversity present in red clover ecotypes, landraces and cultivars provides an invaluable, but often unexploited resource for the improvement of key traits such as yield, quality, and resistance to biotic and abiotic stresses.</p> <p>A collection of 397 red clover accessions was genotyped using a pooled genotyping-by-sequencing approach with 200 plants per accession. Resistance to the two most pertinent diseases in red clover production, southern anthracnose caused by <em>Colletotrichum trifolii</em>, and clover rot caused by <em>Sclerotinia trifoliorum, </em>was assessed using spray inoculation. The mean survival rate for southern anthracnose was 22.9% and the mean resistance index for clover rot was 34.0%. Genome-wide association analysis revealed several loci significantly associated with resistance to southern anthracnose and clover rot. Most of these loci are in coding regions. One quantitative trait locus (QTL) on chromosome 1 explained 16.8% of the variation in resistance to southern anthracnose. For clover rot resistance we found eight QTL, explaining together 80.2% of the total phenotypic variation. The SNPs associated with these QTL provide, once validated, a promising resource for marker-assisted selection in existing breeding programs, facilitating the development of novel cultivars with increased resistance against two devastating fungal diseases of red clover.</p>
Data archive for "Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation"
<p>The archive contains the data files to reproduce the results presented in the article “Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation” published in the Journal of Applied Ecology.</p>
Water Body Checklists 2019: Red Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Red Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: Red Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Red Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Rumen_Microbial_Genomes_from_Cow_Fed_with_Red_Seaweed_Additives
<p>This dataset represents the 3180 non-redundant species-level rumen microbial genomes established through the integration of MAGs recovered from the rumen of cows that were fed with red seaweed or not, publicly available rumen MAGs and isolate genomes from Hungate collection. In order to match those old genome names with the new genome names used in the manuscript, please refer to supplementary table 3.</p>
Data from: Habitat suitability models reveal extensive distribution of deep warm water coral frameworks in the Red Sea
<p>Deep-sea coral frameworks are understudied in the Red Sea, where conditions in the deep are conspicuously warm and saline compared to other basins. Habitat suitability models can be used to predict the distribution pattern of species or assemblages where direct observation is difficult. Here we show how coral frameworks, built by species within the families Caryophylliidae and Dendrophylliidae, are distributed between water depths of 150 m and 700 m in the northern Red Sea and Gulf of Aqaba. To extrapolate the known (ground-truthed) positions of these deep frameworks, we use environmental and geomorphometric variables to inform well-performing maximum entropy models. Over 250 km2 of seafloor in our study area are identified as suitable for such frameworks, equivalent to at least 35% of the area of photic-zone coral reefs in the same region. We hence contend that deep-water coral frameworks are an important and underappreciated repository of Red Sea biodiversity.</p>
3D Archaeological Greek Pottery: MHNC-UP-020058, Apulian red-figure lekanis, Circle of the Patera and the Baltimore Painter
<p><strong>This 3D dataset is related to the publication</strong>:</p> <ul> <li>Moitinho de Almeida, V. (2022). "<a href="https://www.researchgate.net/publication/353038967_Contributions_of_3D_digital_methods_and_techniques_to_the_study_of_ancient_pottery">Contributions of 3D digital methods and techniques to the study of ancient pottery</a>". In <em>Myths, Gods, and Heroes. Greek vase collections in Portugal / Mitos, Deuses e Heróis. As coleções de vasos gregos em Portugal</em>. R. Morais, R. Centeno, D. Ferreira (eds.). Câmara Municipal de Santa Maria da Feira - Museu Convento dos Lóios; Reitoria da Universidade do Porto; Faculdade de Letras da Universidade do Porto; Imprensa da Universidade de Coimbra. Pp.269-291. (ISBN: 978-989-8183-25-5)</li> </ul> <p>3D processed dataset for object MHNC-UP-020058 (two parts: lid and vessel) in the Museu de História Natural e da Ciência da Universidade do Porto (MHNC-UP), Portugal. <strong>CC BY-NC-SA 4.0 license</strong>.</p> <p>MHNC-UP-020058 is an Apulian red-figure <em>lekanis, </em>Circle of the Patera and the Baltimore Painter, dating from c. 330 BCE and unknown provenance (Rocha Pereira & Morais, 2007; Morais, 2019; Morais et al., 2021).</p> <p><strong>Aims</strong>: 3D digital documentation; morphological characterization; technological and functional analysis of archaeological Greek pottery.</p> <p><strong>Data acquisition</strong>: at the MHNC-UP, with a portable non-contact structured white light scanner, Breukmann smartSCAN3D-HE, equipped with stereo colour cameras at 250 mm FOV. Additional metadata included in associated spreadsheet.<br><strong>Data processing</strong>: 44 (lid) + 41 (vessel) scans aligned and merged in 2 separate parts (a, b). MHNC-UP-020058_3D01.ply: non-manifold edges, self-intersections, small components, and small tunnels in the mesh automatically fixed, noise data removed; orientation and position normalised. MHNC-UP-020058_3D01-holesFilled.ply: holes filled for calculation of material density, filling volume, and centre of mass. Additional metadata included in associated spreadsheet.</p> <p>Access to the MHNC-UP-020058 was granted by the MHNC-UP.</p> <p>When citing this material: please include the original inventory ID (MHNC-UP-020058) reference to the physical object.</p>
3D Archaeological Greek Pottery: MDDS-2017-0299, Attic red-figure trefoil oinochoe, Class N, The Cook Class
<p><strong>This 3D dataset is related to the publication</strong>:</p> <ul> <li>Moitinho de Almeida, V. (2023). "<a href="https://www.researchgate.net/publication/353038967_Contributions_of_3D_digital_methods_and_techniques_to_the_study_of_ancient_pottery">Contributions of 3D digital methods and techniques to the study of ancient pottery</a>". In <em>Myths, Gods, and Heroes. Greek vase collections in Portugal / Mitos, Deuses e Heróis. As coleções de vasos gregos em Portugal</em>. R. Morais, R. Centeno, D. Ferreira (eds.). Câmara Municipal de Santa Maria da Feira - Museu Convento dos Lóios; Reitoria da Universidade do Porto; Faculdade de Letras da Universidade do Porto; Imprensa da Universidade de Coimbra. Pp.269-291. (ISBN: 978-989-8183-25-5)</li> </ul> <p>3D processed dataset for object 2017-0299, from the Museu de Arqueologia D. Diogo de Sousa (MDDS) in Braga, Portugal. <strong>CC BY-NC-SA 4.0 license</strong>.</p> <p>2017-0299 is an Attic red-figure trefoil <em>oinochoe</em>, Class N, The Cook Class, dating from c. 500-475 BCE and unknown provenance (Fundação Buehler Brockhaus / Museu D. Diogo de Sousa, 2020; Morais et al., 2021).</p> <p><strong>Aims</strong>: 3D digital documentation; morphological characterization; technological and functional analysis of archaeological Greek pottery.</p> <p><strong>Data acquisition</strong>: at the Museu de História Natural e da Ciência da Universidade do Porto (MHNC-UP), with a portable non-contact structured white light scanner, Breukmann smartSCAN3D-HE, equipped with stereo colour cameras at 250 mm FOV. Additional metadata included in associated spreadsheet.<br><strong>Data processing</strong>: 29 scans aligned and merged. MDDS-2017-0299_3D01.ply: non-manifold edges, self-intersections, small components, and small tunnels in the mesh automatically fixed, noise data removed; orientation and position normalised. MDDS-2017-0299_3D01-holesFilled.ply: holes filled for calculation of material density, filling volume, and centre of mass. Mesh is not watertight (inner surface not digitised due to occlusion). Additional metadata included in associated spreadsheet.</p> <p>Access to the 2017-0299 was granted by the MDDS.</p> <p>When citing this material: please include the original inventory ID (2017-0299) reference to the physical object.</p>
Supplementary material for "Productivity drives the dynamics of a red kite source population that depends on immigration"
<p>Data files, code and custom functions for all analyses and figures presented in the paper. The seven data files are provided in csv format (CMRJuvRing.csv, CMRJuvDraht.csv, CMRAdDraht.csv, CMRAdSat.csv, RingRecoveries.csv, Bruten.csv, Condition.csv). The code file (RedKiteCode.txt) and the function file (Custom_functions.txt) are space delineated text files. The code file is written for R, but some models are run in JAGS from R. The code file also contains descriptions of the data files and code for data management.</p> <p> </p>
Master and Landsat-8 simultaneous acquisition datacubes for the quantification of directional anisotropy in Thermal Infra-Red domain
<p>‎</p> <p>This dataset contains datacubes of simultaneous Landsat-8 and Master<sup><a href="#fn.1">1</a></sup> data as listed in table <a href="#org4c9ba67">1</a>. Those pairs have been identified by cross-searching Landsat-8 and Master archive for Master flight tracks with a Landsat-8 overpass during the flight. The dataset has been collected and analysed in the following paper:</p> <p><em>Julien Michel, Olivier Hagolle, Simon J Hook, Jean-Louis Roujean, Philippe Gamet. Quantifying Thermal Infra-Red directional anisotropy using Master and Landsat-8 simultaneous acquisitions. 2023. <a href="https://hal.science/hal-04073733">⟨hal-04073733⟩</a></em></p> <table> <caption>Table 1: List of valid Master and Landsat-8 pairs</caption> <thead> <tr> <th scope="col"><strong>Id</strong></th> <th scope="col"><strong>Master track id</strong></th> <th scope="col"><strong>Landsat L2 product id</strong></th> </tr> </thead> <tbody> <tr> <td>1</td> <td><code>2013-03-29_18:06:53</code></td> <td><code>LC08_L2SP_038037_20130329_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>2</td> <td><code>2013-04-11_18:14:46</code></td> <td><code>LC08_L2SP_041036_20130411_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>3a</td> <td><code>2013-05-22_18:13:09</code></td> <td><code>LC08_L2SP_040036_20130522_20200913_02_T1</code></td> </tr> <tr> <td>3b</td> <td><code>2013-05-22_18:13:09</code></td> <td><code>LC08_L2SP_040037_20130522_20200913_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>4</td> <td><code>2013-12-05_18:23:35</code></td> <td><code>LC08_L2SP_043035_20131205_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>5a</td> <td><code>2014-03-31_18:11:16</code></td> <td><code>LC08_L2SP_039035_20140331_20200911_02_T1</code></td> </tr> <tr> <td>5b</td> <td><code>2014-03-31_18:11:16</code></td> <td><code>LC08_L2SP_039036_20140331_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>6a</td> <td><code>2014-04-14_18:27:14</code></td> <td><code>LC08_L2SP_041036_20140414_20200911_02_T1</code></td> </tr> <tr> <td>6b</td> <td><code>2014-04-14_18:27:14</code></td> <td><code>LC08_L2SP_041037_20140414_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>7</td> <td><code>2014-04-28_18:22:43</code></td> <td><code>LC08_L2SP_043035_20140428_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>8a</td> <td><code>2014-06-06_18:25:35</code></td> <td><code>LC08_L2SP_044033_20140606_20200911_02_T1</code></td> </tr> <tr> <td>8b</td> <td><code>2014-06-06_18:25:35</code></td> <td><code>LC08_L2SP_044034_20140606_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>9a</td> <td><code>2014-10-21_18:35:15</code></td> <td><code>LC08_L2SP_043034_20141021_20200910_02_T1</code></td> </tr> <tr> <td>9b</td> <td><code>2014-10-21_18:35:15</code></td> <td><code>LC08_L2SP_043035_20141021_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>10a</td> <td><code>2015-05-28_18:13:05</code></td> <td><code>LC08_L2SP_040036_20150528_20200909_02_T1</code></td> </tr> <tr> <td>10b</td> <td><code>2015-05-28_18:13:05</code></td> <td><code>LC08_L2SP_040037_20150528_20200909_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>11</td> <td><code>2018-06-19_18:28:30</code></td> <td><code>LC08_L2SP_042034_20180619_20200831_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>12a</td> <td><code>2021-03-30_18:32:40</code></td> <td><code>LC08_L2SP_043033_20210330_20210409_02_T1</code></td> </tr> <tr> <td>12b</td> <td><code>2021-03-30_18:32:40</code></td> <td><code>LC08_L2SP_043034_20210330_20210409_02_T1</code></td> </tr> </tbody> </table> <p>Variables of interest are resampled on a common UTM grid at 100m. The resulting datacubes are distributed as netCDF files, and contains the variables listed in table <a href="#org09b0cd2">2</a>. Landsat-8 pixels flagged as cloud and missing pixels are set to NaN.</p> <table> <caption>Table 2: Description of variables in netCDF files</caption> <thead> <tr> <th scope="col"><strong>Variable Name</strong></th> <th scope="col"><strong>Description</strong></th> </tr> </thead> <tbody> <tr> <td><code>ls8_lst</code></td> <td>Landsat-8 Land Surface Temperature (K)</td> </tr> <tr> <td><code>ls8_bt</code></td> <td>Landsat-8 Surface Brightness temperature (K)</td> </tr> <tr> <td><code>ls8_b2</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b3</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b4</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b5</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_emis</code></td> <td>Landsat-8 emissivity (unitless)</td> </tr> <tr> <td><code>ls8_water</code></td> <td>Landsat-8 water mask (1 = water, 0 = no water)</td> </tr> <tr> <td><code>ls8_snow</code></td> <td>Landsat-8 snow mask (1 = snow, 0 = no snow)</td> </tr> <tr> <td><code>ls8_view_zenith</code></td> <td>Landsat-8 view zenith angle (degrees)</td> </tr> <tr> <td><code>ls8_view_azimuth</code></td> <td>Landsat-8 view azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> <tr> <td><code>ls8_sun_zenith</code></td> <td>Landsat-8 sun zenith angle (degrees)</td> </tr> <tr> <td><code>ls8_sun_azimuth</code></td> <td>Landsat-8 sun azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> </tbody> <tbody> <tr> <td><code>master_lst</code></td> <td>Master Land Surface Temperature (K)</td> </tr> <tr> <td><code>master_bt</code></td> <td>Master Surface Brightness Temperature (K)</td> </tr> <tr> <td><code>master_emis3</code></td> <td>Master B47 emissivity (unitless)</td> </tr> <tr> <td><code>master_emis4</code></td> <td>Master B48 emissivity (unitless)</td> </tr> <tr> <td><code>master_emis</code></td> <td>Master interpolated emissivity (unitless)</td> </tr> <tr> <td><code>master_view_zenith</code></td> <td>Master view zenith angle (degrees)</td> </tr> <tr> <td><code>master_view_azimuth</code></td> <td>Master view azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> <tr> <td><code>master_sun_zenith</code></td> <td>Master sun zenith angle (degrees)</td> </tr> <tr> <td><code>master_sun_azimuth</code></td> <td>Master sun azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> </tbody> </table> <p>Landsat-8 products were downloaded from the collection 2 level 2 archive from the EarthExplorer portal<sup><a href="#fn.2">2</a></sup>. Master L1B products, containing radiances and viewing angles, as well as L2 products, containing LST and geo-location grids, were requested on the Master website<sup><a href="#fn.1">1</a></sup>. Landsat-8 viewing angles have been computed by using a C program publicly available on USGS website<sup><a href="#fn.3">3</a></sup>.</p> <p>Footnotes:</p> <p><sup><a href="#fnr.1">1</a></sup></p> <p><a href="https://masterprojects.jpl.nasa.gov/">https://masterprojects.jpl.nasa.gov/</a>, consulted on 2023.03.01</p> <p><sup><a href="#fnr.2">2</a></sup></p> <p><a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a>, consulted on 2023.03.01</p> <p><sup><a href="#fnr.3">3</a></sup></p> <p><a href="https://www.usgs.gov/landsat-missions/solar-illumination-and-sensor-viewing-angle-coefficient-file">https://www.usgs.gov/landsat-missions/solar-illumination-and-sensor-viewing-angle-coefficient-file</a>, consulted on 2022.09.12</p>
Naphthalimide-Annulated [n]Helicenes: Red Circularly Polarized Light Emitters
<p>Original data to report (Abstract):<br> Two [<em>n</em>]heliceno-bis(naphthalimides) <strong>1</strong> and <strong>2</strong> (<em>n</em> = 5 and 6, respectively) where two electron-accepting naphthalimide moieties are attached at both ends of helicene core were synthesized by effective two-step strategy, and their enantiomers could be resolved by chiral stationary-phase high-performance liquid chromatography (HPLC). The single-crystal X-ray diffraction analysis of enantiopure fractions of <strong>1</strong> and <strong>2</strong> confirmed their helical structure, and together with experimental and calculated circular dichroism (CD) spectra, the absolute configuration was unambiguously assigned. Both <strong>1</strong> and <strong>2</strong> exhibit high molar extinction coefficients for the S<sub>0</sub>–S<sub>1</sub> transition and high fluorescence quantum yields (73% for <strong>1</strong> and 69% for <strong>2</strong>), both being outstanding for helicene derivatives. The red circularly polarized luminescence (CPL) emission up to 615 nm for <strong>2</strong> with CPL brightness (<em>B</em><sub>CPL</sub>) up to 66.5 M<sup>–1</sup> cm<sup>–1</sup> demonstrates its potential for applications in chiral optoelectronics. Time-dependent density functional theory (TD-DFT) calculations unambiguously showed that the large transition magnetic dipole moment |<em>m</em>| of <strong>2</strong> is responsible for its high absorbance dissymmetry (<em>g</em><sub>abs</sub>) and luminescence dissymmetry (<em>g</em><sub>lum</sub>) factor.</p>
The APO-K2 Catalog. I. ~7,500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
<p><strong>Abstract: </strong>We present a catalog of fundamental stellar properties for ~7,500 evolved stars, including stellar radii and masses, determined from the combination of spectroscopic observations from the Apache Point Observatory Galactic Evolution Experiment (APOGEE), part of the Sloan Digital Sky Survey IV (SDSS), and asteroseismology from K2. The resulting APO-K2 catalog provides spectroscopically derived temperatures and metallicities, asteroseismic global parameters, evolutionary states, and asteroseismically-derived masses and radii. Additionally, we include kinematic information from <em>Gaia</em>. We investigate the multi-dimensional space of abundance, stellar mass, and velocity with an eye toward applications in Galactic archaeology. The APO-K2 sample has a large population of low metallicity stars (~288 at [M/H] ≤ -1), and their asteroseismic masses are larger than astrophysical estimates. We argue that this may reflect offsets in the adopted fundamental temperature scale for metal-poor stars rather than metallicity-dependent issues with interpreting asteroseismic data. We characterize the kinematic properties of the population as a function of α-enhancement and position in the disk and identify those stars in the sample that are candidate components of the <em>Gaia-Enceladus</em> merger. Importantly, we characterize the selection function for the APO-K2 sample as a function of metallicity, radius, mass, νmax, color, and magnitude referencing Galactic simulations and target selection criteria to enable robust statistical inferences with the catalog.</p> <p><strong>Included Files:</strong></p> <ul> <li>The publicly available APO-K2 catalog, the is provided in the publication.</li> <li>The APO-K2 catalog without truncation to any numbers. </li> <li>The selection function relative density tables for the mass-radius parameter space. </li> <li>The selection function relative density tables for the metallicity-mass parameter space. </li> <li>The selection function relative density tables for the magnitude-color parameter space. </li> <li>The selection function relative density tables for the ν<sub>max</sub>-mag parameter space. </li> </ul>
Macroscale Variation in Red Maple (Acer rubrum) Foliar Carbon, Nitrogen, and Nitrogen Resorption
Project Description The primary goal of this project was to investigate intraspecific variation of foliar nitrogen resorption for Acer rubrum (red maple). In particular, we are interested in examining whether foliar nutrient resorption is related to climatic factors such as mean annual temperature and/or precipitation. The approach used to collect green and fallen leaf samples was through a community science project where participants sent leaves to our lab at Boston University for analysis. In the spring/summer of 2019 plant and naturalist organizations throughout the range of red maple in the United States were contacted to request information about this project be sent to their members regarding the collection of red maple leaves for the study. Interested parties were prompted to complete a google form that included basic contact information. Each participant was then sent a sampling kit which included gloves, sampling protocols, and data sheets. For each set of leaves collected from a single tree they were assigned the following identification “tasper-###” where the numbers were uniquely assigned. Green and fallen leaves were assigned different “tasper-###” numbers. Within a single identification (e.g., tasper-120) each leaf was individually assigned a letter a-n, where n corresponds to the letter of how many leaves were sent from that tree. For most samples a-j was obtained because we asked participants to collect 10 leaves. Individual leaves were scanned for area analysis using a flatbed scanner at 300 dpi and weighed. For C&N analysis each leaf blade from a set of green or fallen leaves from a single tree was hole-punched and the samples were combined yielding one C and N concentration per tree for both each green and fallen leaves. Punches were ground and homogenized to a powder using a mortar and pestle. Approximately 3 mg of dried sample was analyzed for C and N concentration using a NC2500 elemental analyzer (CE Elantech, Lakewood, NJ, USA). NIST Apple Le
Taxonomic Composition of Red Knot Fecal Samples on the Virginia Coast
Taxonomic Composition of Red Knot Fecal Samples on the Virginia Coast Understanding which prey birds use and how prey selection is related to prey availability is important to understanding avian ecology and for conservation planning. Abundant prey at stopovers during migration is a key to shorebird survival and breeding success. We determined which prey were available to foraging red knots (Calidris canutus rufa) using Virginia's barrier islands during spring migration by collecting substrate core samples containing prey on sand and peat substrates in May 2017 - 2019. We also collected red knot feces during the same period and used fecal DNA metabarcoding to determine which invertebrates red knots consumed. We used compositional analysis to determine which prey red knots selected on these islands. Crustaceans (Orders Amphipoda and Calanoida) were the most abundant prey on both sand and peat. Red knots consumed bivalves (Orders Venerida and Mytiloida), crustaceans (Orders Amphipoda and Calanoida), and insect larvae (Order Diptera). Red knots selected bivalves over non-bivalve prey, though non-bivalve prey may still be an important portion of the total caloric intake on Virginia's stopover, given their abundance and use. It is important that coastal conservation practices in the Western Mid-Atlantic stopover region continue to be designed to promote natural barrier island movement which leads to the formation of the peat banks used by many prey.
Red, yellow, green and blue are not particularly colorful
<p>This is supplementary material accompanying the article:</p> <p>Witzel, C., Maule, M., & Franklin, A. (2019) Red, yellow, green and blue are not particularly colorful. <em>Journal of Vision</em>.</p> <p>focsat_data.xlsx contains all the individual data, including adjustments of typical and unique hues (+ super-saturated condition), detection (JND0) and discrimination (JND) data for red, yellow, green, and blue; and average saturation matches (subjective saturation) from Witzel and Franklin (2014). Nine sheets overall. IMPORTANT: All data is matched by participants (rows); but participant ids are not provided for reasons of data protection. Empty rows correspond to missing data. Also note that detection thresholds are provided in the first 10 columns, discrimination thresholds in the following 10 columns (11-20).</p> <p>focsat_tables.xlsx contains the exact data from tables in the article (Table 2) and the Supplementary Tables S1-S10. Eleven sheets in total.</p> <p>weberfechner.m is a Matlab function that allows for calculating Weber fractions and discriminable saturation as reported in the article.</p>
Fig. 3 in Population and reproductive parameters of the red-tailed catfish, Phractocephalus hemioliopterus (Pimelodidae: Siluriformes), from the Xingu River, Brazil
Fig. 3. Size at first sexual maturity in the red-tailed catfish Phractocephalus hemioliopterus specimens collected from the Xingu River in Pará, Brazil.
Fig. 5 in Population and reproductive parameters of the red-tailed catfish, Phractocephalus hemioliopterus (Pimelodidae: Siluriformes), from the Xingu River, Brazil
Fig. 5. Relative frequency (%) of the different gonadal maturation stages of the red-tailed catfish Phractocephalus hemioliopterus specimens collected from the Xingu River in Pará, Brazil. a. males; and b. females.
MRI and unbiased averages of wild muskrats (Ondatra zibethicus) and red squirrels (Tamiasciurus hudsonicus)
<p>Preprocessed data from:</p> <p>Amuno, S., Rudko, D.A., Gallino, D., Tuznik, M., Shekh, K., Kodzhahinchev, V., Niyogi, S., Chakravarty, M.M., Devenyi, G.A., 2020. Altered neurotransmission and neuroimaging biomarkers of chronic arsenic poisoning in wild muskrats (Ondatra zibethicus) and red squirrels (Tamiasciurus hudsonicus) breeding near the City of Yellowknife, Northwest Territories (Canada). Sci. Total Environ. 707, 135556. https://doi.org/10.1016/j.scitotenv.2019.135556</p> <p> </p> <p>As well as averages and brain masks generated during DBM processing. Files in both MINC2 and NIFTI format.</p>
Figure 2 in Zoeal stages of Hiplyra variegata (Rüppell, 1830) (Crustacea: Brachyura: Leucosiidae) reared in the laboratory and collected from plankton at Al-Kharrar creek, central Red Sea
Figure 2. Hiplyra variegata (Rüppell, 1830), maxillule: (a) zoea I; (c) zoea II; (e) zoea III. Maxilla: (b) zoea I; (d) zoea II; (f) zoea III.
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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.