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6,175 results for “Composite”
FIGURES 57–64 in Coleoptera from the middle-upper Eocene European ambers: generic composition, zoogeography and climatic implications
FIGURES 57–64. Recent distribution (shaded area) and Eocene fossils (black dot) of genera: 57) Prostomis; 58) Ischalia; 59) Dicentrus; 60) Dorcaschema; 61) Japanopsimus; 62) Nothorhina; 63) Stenhomalus; 64) Anisodera.
FIGURES 25–32 in Coleoptera from the middle-upper Eocene European ambers: generic composition, zoogeography and climatic implications
FIGURES 25–32. Recent distribution (shaded area) and Eocene fossils (black dot) of genera: 25) Dryophilus; 26) Eucrada; 27) Gastrallus; 28) Homophthalmus; 29) Microbregma; 30) Diplocoelus; 31) Seidlitzella; 32) Cymatodera.
FIGURES 1–8 in Coleoptera from the middle-upper Eocene European ambers: generic composition, zoogeography and climatic implications
FIGURES 1–8. Recent distribution (shaded area) and Eocene fossils (black dot) of genera: 1) Cupes; 2) Micromalthus; 3) Derovatellus; 4) Hydrotrupes; 5) Coptodera; 6) Ipelates; 7) Dictyon; 8) Heterlimnius.
Chemical and isotopic composition data
<p><span><span><span>This dataset is accompanying the submission of the manuscript "Rock weathering characteristics and carbon sink in the Liujiang Basin, North China". </span></span></span><span><span><span>It contains experimental data describing the concentration of ion and isotopic composition of river water in the study area basin. </span></span></span><span><span><span>These data are used for the calculation of chemical weathering carbon sinks and tracing cation sources described in the paper. </span></span></span><span><span><span>The data is provided in the form of an Excel file.</span></span></span></p>
Data for the figures in paper 'Variations in thermosphere composition and ionosphere total electron content under extremely weak geomagnetic activity conditions at solar-minimum'
This data set include the simulated percentage difference of O to N2 column density ratio between DOY 111 and 110 (Figure 3 of paper), and the absolute difference of neutral wind vector at pressure level -1.375 (Figure 3 of paper).They are from 0:10 UT to 23:55 UT on DOY 111 in 2019 with a temporal resolution of 15-min. And the absolute difference of diagnostic terms (horizontal advection, vertical advection and molecular diffusion) of O at pressure level -1.375 at 13:10 UT (Figure 4 of the paper)
Long-term climate trends and urbanization reveal human impacts and decoupling of dissolved organic carbon quantity and composition in streams
<p>This repository contains data, R code, graphical objects, and outputs in Kelley et al. (2025) entitled <strong><span lang="EN-US">Climate Change and Urbanization Decouple Dissolved Organic Carbon Quantity and Composition in Streams</span></strong><span lang="EN-US"> accepted for publication in <em>Global Biogeochemical Cycles</em></span><span lang="EN-US">. </span><a href="https://doi.org/10.1029/2025GB008534">https://doi.org/10.1029/2025GB008534</a></p> <p><span lang="EN-US">In this finalized repository, we provide a detailed meta data file (1_repository_meta_data.xlsx) containing lists and descriptions of all data files, variables, figures, and scripts within.</span> Note that previous versions contained water quality parameters for forms of nitrogen and phosphorus, which have been removed from this final version. These parameters were neither used in any analyses in this work nor underwent QA/QC protocols; thus, we suggest caution in their use from previous versions.</p> <p>Additionally, this repository contains a publication license for a graphical object created using the BioRender platform.</p>
Simulation results for "Deciphering clues regarding magma composition encoded in quartz-hosted embayments and melt inclusions through direct numerical simulations"
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ChinaHighPMC: Daily Seamless 1 km Ground-Level PM₂.₅ Composition Dataset for China (2000–Present)
<p>ChinaHighPMC is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM<sub>2.5</sub> chemical composition (i.e., <strong>SO<sub>4</sub><sup>2-</sup>, NO<sub>3</sub><sup>-</sup>, NH<sub>4</sub><sup>+</sup>, and Cl<sup>-</sup></strong>) dataset for Eastern China <strong>from 2013 to 2020</strong>. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R<sup>2</sup>) of 0.74, 0.75, 0.71, and 0.66, and root-mean-square errors (RMSEs) of 6.0, 6.6, 4.3, and 2.3 µg m<sup>-3</sup> for SO<sub>4</sub><sup>2-</sup>, NO<sub>3</sub><sup>-</sup>, NH<sub>4</sub><sup>+</sup>, and Cl<sup>-</sup>, respectively, on a daily basis.</p> <p>If you use the ChinaHighPMC dataset in your scientific research, please cite the following reference (Wei et al., EST, 2023):</p> <ul> <li>Wei, J., Li, Z., Chen, X., Li, C., Sun, Y., Wang, J., Lyapustin, A., Brasseur, G., Jiang, M., Sun, L., Wang, T., Jung, C., Qiu, B., Fang, C., Liu, X., Hao, J., Wang, Y., Zhan, M., Song, X., and Liu, Y. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EST-2023.pdf">Separating daily 1 km PM2.5 inorganic chemical composition in China since 2000 via deep learning integrating ground, satellite, and model data</a>. <em>Environmental Science & Technology</em>, 2023, 57(46), 18282–18295. https://doi.org/10.1021/acs.est.3c00272</li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
Is the isotopic composition of precipitation a robust indicator for reconstructions of past tropical cyclones frequency? A case study on Réunion Island from rain and water vapor isotopic observations
<p>Isotopic composition of precipitation and water vapor and LMDZ-iso and ECHAM6-wiso simulations associated with the manuscript:</p> <p>Françoise Vimeux, Camille Risi, Christelle Barthe, Sören François, Alexandre Cauquoin, Olivier Jossoud, Jean-Marc Metzger, Olivier Cattani, Bénédicte Minster, and Martin Werner (2024). Is the isotopic composition of precipitation a robust indicator for reconstructions of past tropical cyclones frequency? A case study on Réunion Island from rain and water vapor isotopic observations. Journal of Geophysical Research: Atmospheres, 129, e2023JD039794. https://doi.org/10.1029/2023JD039794</p>
Data of "Effects of bacteria-embedded polylactic acid (PLA) capsules on fracture properties of strain hardening cementitious composite (SHCC)"
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Species TX OK Distribution Map FH Hosts Phloeotribus liminaris (Harris) 1* 1* SENA 15 ph Prunus Phloeotribus pseudoscabricollis Atkinson 1 MEX+NT 16 ph Zanthoxylum Phloeotribus texanus Schaeffer 1 1* SE+MEX 17 ph Celtis Phloeosinina Chramesus chapuisii LeConte 1 1 SE+MEX 18 ph Celtis Chramesus hicoriae LeConte 1* 1* SENA 19 ph Carya Chramesus mimosae Blackman 1 MEX+NT 18 ph Legume trees Chramesus subopacus Schaeffer 1 MEX+NT 20 ph Celtis Chramesus varius Wood 1 MEX+NT 19 ph Legume trees Phloeosinus cristatus (LeConte) 1 SWNA 21 ph Cupressus Phloeosinus dentatus (Say) 1 1 SENA 22 ph Juniperus Phloeosinus hoferi Blackman 1 SWNA 25 ph Juniperus Phloeosinus scopulorum neomexicanus Blackman 1 SWNA 23 ph Juniperus Phloeosinus serratus (LeConte) 1 SWNA 24 ph Juniperus Phloeosinus taxodii Blackman 1 SE+MEX 21 ph Taxodium Hypoborina Chaetophloeus fasciatus (Blackman) 1 SWNA 26 ph Prosopis Chaetophloeus heterodoxus (Casey) 1 SWNA ph Rosaceae Chaetophloeus mexicanus Wood 1* MEX+NT 27 ph Eysenhardtia Chaetophloeus sulcatus Wood 1* MEX+NT 28 ph Composite shrubs Liparthrum squamosum (Blackman) 1*? SENA 29 ph Maclura pomifera Polygraphina Carphobius arizonicus Blackman 1* SWNA 30 ph Juniperus Carphoborus bicornus Wood 1* 1* SENA 31 ph Pinus Carphoborus bifurcus (Chapuis) 1* SENA 32 ph Pinus Carphoborus convexifrons Wood 1 SWNA 31 ph Pinus in Atlas and checklist of the bark and ambrosia beetles of Texas and Oklahoma (Curculionidae: Scolytinae and Platypodinae)
Species TX OK Distribution Map FH Hosts Phloeotribus liminaris (Harris) 1* 1* SENA 15 ph Prunus Phloeotribus pseudoscabricollis Atkinson 1 MEX+NT 16 ph Zanthoxylum Phloeotribus texanus Schaeffer 1 1* SE+MEX 17 ph Celtis Phloeosinina Chramesus chapuisii LeConte 1 1 SE+MEX 18 ph Celtis Chramesus hicoriae LeConte 1* 1* SENA 19 ph Carya Chramesus mimosae Blackman 1 MEX+NT 18 ph Legume trees Chramesus subopacus Schaeffer 1 MEX+NT 20 ph Celtis Chramesus varius Wood 1 MEX+NT 19 ph Legume trees Phloeosinus cristatus (LeConte) 1 SWNA 21 ph Cupressus Phloeosinus dentatus (Say) 1 1 SENA 22 ph Juniperus Phloeosinus hoferi Blackman 1 SWNA 25 ph Juniperus Phloeosinus scopulorum neomexicanus Blackman 1 SWNA 23 ph Juniperus Phloeosinus serratus (LeConte) 1 SWNA 24 ph Juniperus Phloeosinus taxodii Blackman 1 SE+MEX 21 ph Taxodium Hypoborina Chaetophloeus fasciatus (Blackman) 1 SWNA 26 ph Prosopis Chaetophloeus heterodoxus (Casey) 1 SWNA ph Rosaceae Chaetophloeus mexicanus Wood 1* MEX+NT 27 ph Eysenhardtia Chaetophloeus sulcatus Wood 1* MEX+NT 28 ph Composite shrubs Liparthrum squamosum (Blackman) 1*? SENA 29 ph Maclura pomifera Polygraphina Carphobius arizonicus Blackman 1* SWNA 30 ph Juniperus Carphoborus bicornus Wood 1* 1* SENA 31 ph Pinus Carphoborus bifurcus (Chapuis) 1* SENA 32 ph Pinus Carphoborus convexifrons Wood 1 SWNA 31 ph Pinus
Fig. 2. Climogram showing average month temperature and rainfall from June 1996 in Aquatic Coleoptera In The Subtropical-Pampasic Ecotone (Argentina, Buenos Aires): Species Composition And Temporal Changes
Fig. 2. Climogram showing average month temperature and rainfall from June 1996 to July 1998 (Data from Estación Meteorológica La Plata, Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata).
Evolution of thermal and mechanical properties of PP-based composites subjected to mechanical recycling
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Figure 2 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 2. Bayesian consensus tree resulting from the LW-Rh and Wg gene alignments (871 bp). Coloured dots on the branches indicate the values of posterior probability (PP): green dots represent values between 1.00 and 0.95, yellow dots between 0.94 and 0.90, and red dots ≤ 0.89. The nodes are indicated with numbers. Values above and below the branches represent the ancestral genome size (GS; 1C-values, in picograms) at particular nodes: in blue is the value generated by the maximum likelihood (ML) [asterisks are related to confidence interval (CI) values shown in Supporting Information, Table S4]; orange is the value generated by maximum parsimony (MP); and black, given below the branches, is the value generated by Bayesian inference (BI). Genome size data (1C-values) were obtained in the present work (pink dots) or taken from the literature (grey dots).
Figure 1 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 1. Fluorescence intensity histograms obtained from three different species, with Drosophila melanogaster as internal standard, stained with propidium iodide (PI; A–C) or 4,6-diamidino-2-phenylindole (DAPI; D–F). The x-axis corresponds to the scale of fluorescence intensity, and the y-axis represents the number of nuclei with that fluorescence intensity.
Figure 3 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 3. Mean genome size (in picograms and megabase pairs) estimated for Formicidae subfamilies. The phylogenetic tree generated in the present study was redrawn, with collapsed branches corresponding to species of the same subfamily.
Isotopic analysis: How our community analyzes soil and plant water samples for their isotopic composition
<p>As part of a project to develop a common methodological framework for water sampling, extraction, and isotopic analysis to study vegetation water use, we conducted an informal survey of current practices. A simple questionnaire via Google form was sent out to all members of the WATer isotopeS in the critical zONE (WATSON) European Cooperation in Science and Technology (COST) Action (#19120, 2020-2024) between July and September 2021. In this repository, we've included a brief write-up of this survey and the survey data. </p>
FIGURE 4 in Freshwater decapod crustaceans from the state of Minas Gerais, Brazil: species composition and distribution
FIGURE 4. Caridean shrimps from the state of Minas Gerais, Brazil: A, Macrobrachium amazonicum (Heller, 1862); B, Macrobrachium brasiliense (Heller, 1862); C, Macrobrachium iheringi (Ortmann, 1897); D, Macrobrachium jelskii (Miers, 1877); E, Macrobrachium pantanalense Dos Santos, Hayd & Anger, 2013; F, Macrobrachium potiuna (Müller, 1880); G, Macrobrachium veredensis Rossi, Magalhães & Mantelatto, 2020. (Photos: A by D. F. R. Alves; B, D, E by V. T. Carrijo; C by L. G. Pileggi; F by F. L. Carvalho; G by G. B. Jacobucci). Scale bars: 10 mm.
FIGURE 2 in Freshwater decapod crustaceans from the state of Minas Gerais, Brazil: species composition and distribution
FIGURE 2. Anomuran and brachyuran crabs from the state of Minas Gerais, Brazil: A, Aegla franca Schmitt, 1942; B, Trichodactylus aff. fluviatilis Latreille, 1828; C, Trichodactylus petropolitanus (Göldi, 1886); D, Dilocarcinus pagei Stimpson, 1861. (Photos: A–C by E.C. Mossolin; D by V. T. Carrijo). Scale bars: 10 mm.
FIGURE 1 in Freshwater decapod crustaceans from the state of Minas Gerais, Brazil: species composition and distribution
FIGURE 1. Watersheds in the state of Minas Gerais: 1, Rio Paraíba do Sul; 2, Rio Alcobaça ou Itanhém; 3, Rio Buranhém; 4, Rio Doce; 5, Rio Grande; 6, Rio Itabapoana; 7, Rio Itapemirim; 8, Rio Itaúnas; 9, Rio Jequitinhonha; 10, Rio Jucuruçu; 11, Rio Mucuri; 12, Rio Paranaíba; 13, Rio Pardo; 14, Rio Peruíbe; 15, Rio Piracicaba/Jaguari; 16, Rio São Francisco; 17, Rio São Mateus.
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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.