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1,840 results for “Representative”
Data from: Jointly representing long-range genetic similarity and spatially heterogeneous isolation-by-distance
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Datasets for: PARNAS: Objectively selecting the most representative taxa on a phylogeny
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An abundant sea anemone from the Carboniferous Mazon Creek Lagerstӓtte, U.S.A.: Representative specimens examined
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Data from: The Chord-Normalized Expected Species Shared (CNESS)-distance represents a superior measure of species turnover patterns
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The last representatives of the Superfamily Wellerelloidea (Brachiopoda, Rhynchonellida) in the westernmost Tethys (Iberian paleomargins) prior to their demise in the Early Toarcian Mass Extinction Event
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A representative gel of ALAD following a restriction fragment length digestion
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Mitogenome alignment of 159 unique haplotypes representing 455 individual killer whales
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Frontopolar cortex represents complex features and decision value during choice between environments
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Data from: Analysis of genotyping data reveals the unique genetic diversity represented by the breeds of sheep native to the United Kingdom
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Geometric morphometric wing analysis represents a robust tool to identify female mosquitoes (Diptera: Culicidae) in Germany
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Set of European CO2 and CO emission grids representing emission uncertainties
<p>This dataset was prepared by TNO as a contribution to the H2020 project CHE and the H2020 project VERIFY. The basis is a high-resolution (~1x1 km) emission inventory providing CO<sub>2</sub> and CO (from fossil fuels and biofuels separately) over western Europe (2ºW - 19ºE, 47ºN - 56ºN). The reported emissions by European countries to UNFCCC (CO<sub>2</sub>) and to EMEP/CEIP (CO) have been used and where needed gap-filled or replaced with emission data from the GAINS model. These country-level emissions are disaggregated in space using a consistent spatial distribution methodology, whereas large point sources are listed with their exact locations. This approach is similar to the one described by Kuenen et al., (ACP, 2014). Emissions are reported per GNFR sector, with an extra split for road transport.</p> <p>The emission grids that are part of this dataset are a variation on the base grid, representing the uncertainty in the emission data. Each grid is equally plausible. The grids have been created using a Monte Carlo approach. The uncertainties in the underlying data used to create the base grid (emissions: activity data and emission factors, spatial proxies) have been collected (either from country reports or based on expert judgement). Through the Monte Carlo simulation these uncertainties, taking into account error correlations between some sub-sectors, are combined to create ten new emission grids. The spread in emissions between these emission maps gives an indication of the uncertainty in the emissions.</p> <p>The grid files (in .csv and .nc format) contain annual total emissions per grid cell for the year 2015. A separate file has been prepared for each ensemble member in the Monte Carlo simulation (indicated with M). The unit in the files is kg/yr.</p> <p>A detailed description of the Monte Carlo simulation is presented in:</p> <p>Super, I., Dellaert, S. N. C., Visschedijk, A. J. H., and Denier van der Gon, H. A. C.: Uncertainty analysis of a European high-resolution emission inventory of CO<sub>2</sub> and CO to support inverse modelling and network design, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-696, in review, 2019.</p> <p><strong>N.B. It is important to note that 10 maps are not sufficient to describe the sometimes complex uncertainty structures, for example in the case of lognormal uncertainty distributions. The interpretation of the uncertainty based on these 10 maps should therefore be done with care.</strong></p> <p><strong>NB. Despite efforts to prevent negative emissions to occur in the grid maps, some negative values are still present. In local studies this might cause some issues, and we recommend to set negative emissions to zero in those cases.</strong></p>
FIGURES 1–6. Quwatanabius chiaw, male specimen. 1. habitus, scale bar represents 1 in Taxonomic notes on the genus Quwatanabius in Taiwan (Coleoptera: Staphylinidae)
FIGURES 1–6. Quwatanabius chiaw, male specimen. 1. habitus, scale bar represents 1mm; 2. male sternite VIII; 3. male sternite IX; 4. male tergite X; 5. line drawing of aedeagus in lateral view; 6. line drawing of aedeagus in ventral view.
FIGURE 2 in Heissiella donguri sp. nov. (Hemiptera: Heteroptera: Tingidae) feeding on Quercus gilva (Fagaceae): First representative of the genus from Japan
FIGURE 2. Detail morphological images of Heissiella donguri sp. nov. A–C: cephalic and thoracic structures, dorsal, lateral and ventral views; D, E, hemelytron, dorsal and lateral views; F, apical part of abdomen of male, ventral view; G, same of female, ventral view. Scale bars: 0.2 mm.
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
FIGURE 1 in Redescription of Leptogyra bujnitzkii (Gorbunov, 1946) comb. nov., the first representative of the gastropod subclass Neomphaliones from the high Arctic
FIGURE 1. Shell and radulae of Leptogyra bujnitzkii, Arctic Ocean, 81°50'N, 136°14'E, 3700-3800 m, r/v "Sadko", 24 July 1938. A, C–E. Syntype 1: A. Shell. C–D. Protoconch details. E. Teleoconch details. B. Shell of syntype 2. F–G. Radulae of two syntypes. Scale bars: A–B: 2 mm, C: 100 µm, D: 50 µm, E: 200 µm, F–G: 50 µm.
Subspecies and Distribution. R.p. pusillus Temminck, 1834 - N Sumatra, N, E & S Borneo (including Banggi I), W Java, and Bali I. R. p. blythi K. Andersen, 1918 - N India (Uttarakhand, Sikkim, West Bengal, Assam, Meghalaya, and Arunachal Pradesh), Nepal, Bhutan, and SE Bangladesh. R.p. calidusG. M. Allen, 1923-SE China (Guizhou, Guangxi, Guangdong, and Fujian, along with a recent record from Beijing area that may represent this subspecies). R. p. gracilis K. Andersen, 1905 - S India (Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu). R.p. lakkhanae Yoshiyuki, 1990 - S China (Yunnan), Thailand, Laos, Vietnam (including Cat Ba I), Cambodia, and Peninsular Malaysia (including Tioman I). R.p. minutillusG. S. Miller, 1906 —Anambas Is (Siantan). R.p. pagi Tate & Archbold, 1939 — Mentawai Is (North Pagai). R.p. parcus G. M. Allen, 1928 - Hainan I, China. R. p. szechwanus K Andersen, 1918 - Myanmar and C China (Sichuan, Guizhou, Hubei, and probably Yunnan). in Rhinolophidae
Subspecies and Distribution. R.p. pusillus Temminck, 1834 - N Sumatra, N, E & S Borneo (including Banggi I), W Java, and Bali I. R. p. blythi K. Andersen, 1918 - N India (Uttarakhand, Sikkim, West Bengal, Assam, Meghalaya, and Arunachal Pradesh), Nepal, Bhutan, and SE Bangladesh. R.p. calidusG. M. Allen, 1923-SE China (Guizhou, Guangxi, Guangdong, and Fujian, along with a recent record from Beijing area that may represent this subspecies). R. p. gracilis K. Andersen, 1905 - S India (Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu). R.p. lakkhanae Yoshiyuki, 1990 - S China (Yunnan), Thailand, Laos, Vietnam (including Cat Ba I), Cambodia, and Peninsular Malaysia (including Tioman I). R.p. minutillusG. S. Miller, 1906 —Anambas Is (Siantan). R.p. pagi Tate & Archbold, 1939 — Mentawai Is (North Pagai). R.p. parcus G. M. Allen, 1928 - Hainan I, China. R. p. szechwanus K Andersen, 1918 - Myanmar and C China (Sichuan, Guizhou, Hubei, and probably Yunnan).
FIGURE4.Map of selected records of Siphamia species recorded in Queensland and Papua New Guinea waters in 2003–2005, including the type locality of Siphamia guttulata. Some symbols represent more than one specimen. in Redescription and distributional range extension of the Speckled Siphonfish, Siphamia guttulata (Pisces: Apogonidae)
FIGURE4.Map of selected records of Siphamia species recorded in Queensland and Papua New Guinea waters in 2003–2005, including the type locality of Siphamia guttulata. Some symbols represent more than one specimen.
Statistical model training data for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>Gzipped CSV files containing convection scheme inputs and outputs used for training.</p> <p>Column format of each file:</p> <p>THETA_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,Q_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DTHETA_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DQ_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28</p> <p>where THETA_IN are input values of potential temperature [K], Q_IN are input values of specific humidity [kg/kg], DTHETA are changes in potential temperature due to convection [K], DQ are changes in specific humidity due to convection [kg/kg].</p> <p>Key:</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>All are 30 day simulations either for January "jan" or July "jul".</p> <p> </p>
Fire and grazing determined grasslands of central Madagascar represent ancient assemblages
<p>The ecology of Madagascar's grasslands is under-investigated and the dearth of ecological understanding of how disturbance by fire and grazing shapes these grasslands stems from a perception that disturbance shaped Malagasy grasslands only after human arrival. However, worldwide, fire and grazing shape tropical grasslands over ecological and evolutionary timescales, and it is curious Madagascar should be a global anomaly. We examined the functional and community ecology of Madagascar's grasslands across 71 communities in the Central Highlands. Combining multivariate abundance models of community composition and clustering of grass functional traits, we identified distinct grass assemblages each shaped by fire or grazing. The fire-maintained assemblage is primarily composed of tall caespitose species with narrow leaves and low bulk density. In contrast, the grazer-maintained assemblage is characterized by mat-forming, high bulk density grasses with wide leaves. Within each assemblage, levels of endemism, diversity and grass ages support these as ancient assemblages. Grazer-dependent grasses can only have co-evolved with a now-extinct megafauna. Ironically, the human introduction of cattle likely introduced a megafaunal substitute facilitating modern day persistence of a grazer-maintained grass assemblage in an otherwise defaunated landscape, where these landscapes now support the livelihoods of millions of people.</p>
Dataset (.esg files) of a multiphase aggregate representative of a lower mantle composition compressed at High P/T in an R rDAC
<p>A collection of .esg files for use in the software package Materials Analysis Using Diffraction (MAUD; Lutterotti et al., 1997). Seven files are included, spanning the range used in data analysis. All are post-conversion from San Carlos olivine. Files are named in the following format:</p> <p>MPBrgFp: Stands for Multiphase, primary phases bridgmanite and ferropericlase<br> ###: Number of the diffraction pattern taken during the experiment<br> ##GPa: Approximate temperature, rounded to the nearest whole number. This is to cross-correlate .esg files with experimental Pt pressures cited within the paper.</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.