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9,671 results for “Leaf”
Minuartia yukonensis (Caryophyllaceae) - stem - showing leaf bases
Image of Minuartia yukonensis (Caryophyllaceae) - stem - showing leaf bases
Geranium erianthum (Geraniaceae) - stem - showing leaf bases
Image of Geranium erianthum (Geraniaceae) - stem - showing leaf bases
Minuartia yukonensis (Caryophyllaceae) - leaf - on upper stem
Image of Minuartia yukonensis (Caryophyllaceae) - leaf - on upper stem
Heracleum maximum (Apiaceae) - leaf - on upper stem
Image of Heracleum maximum (Apiaceae) - leaf - on upper stem
Heracleum maximum (Apiaceae) - stem - showing leaf bases
Image of Heracleum maximum (Apiaceae) - stem - showing leaf bases
Heracleum maximum (Apiaceae) - leaf - margin of upper + lower surface
Image of Heracleum maximum (Apiaceae) - leaf - margin of upper + lower surface
Heracleum maximum (Apiaceae) - stem - showing leaf bases
Image of Heracleum maximum (Apiaceae) - stem - showing leaf bases
Sanguisorba stipulata (Rosaceae) - stem - showing leaf bases
Image of Sanguisorba stipulata (Rosaceae) - stem - showing leaf bases
Sanguisorba stipulata (Rosaceae) - stem - showing leaf bases
Image of Sanguisorba stipulata (Rosaceae) - stem - showing leaf bases
Sanguisorba stipulata (Rosaceae) - leaf - basal or on lower stem
Image of Sanguisorba stipulata (Rosaceae) - leaf - basal or on lower stem
Sanguisorba stipulata (Rosaceae) - leaf - on upper stem
Image of Sanguisorba stipulata (Rosaceae) - leaf - on upper stem
Sanguisorba stipulata (Rosaceae) - leaf - basal or on lower stem
Image of Sanguisorba stipulata (Rosaceae) - leaf - basal or on lower stem
Sanguisorba stipulata (Rosaceae) - leaf - margin of upper + lower surface
Image of Sanguisorba stipulata (Rosaceae) - leaf - margin of upper + lower surface
Heracleum maximum (Apiaceae) - leaf - margin of upper + lower surface
Image of Heracleum maximum (Apiaceae) - leaf - margin of upper + lower surface
Ulmus serotina (Ulmaceae) - leaf - showing orientation on twig
Image of Ulmus serotina (Ulmaceae) - leaf - showing orientation on twig
China Earth Observation Data Cube: The 30m Seamless Annual Leaf-On Landsat Composites from 1985 to 2024
<p>The <strong>30m seamless annual leaf-on Landsat composites from 1985 to 2024</strong> were generated using a comprehensive framework designed to ensure high-quality, consistent data across decades. Starting with preprocessed Level-2 surface reflectance images from multiple Landsat sensors, the dataset is restricted to the Leaf-On season, with rigorous cloud and shadow masking applied based on quality assessment bands. To maintain consistency across sensors, spectral harmonization is conducted, followed by annual composite generation using the medoid method to capture peak vegetation conditions. The resulting composites are structured into a spatially consistent data cube, facilitating efficient analysis and monitoring of vegetation dynamics over time.</p> <p>The band naming convention follows Landsat TM standards, with bands designated as <strong>Blue (B1), Green (B2), Red (B3), NIR (B4), SWIR1 (B5), and SWIR2 (B7)</strong>. Both qualitative and quantitative evaluations were conducted to validate the data quality. Here, we provide 2023 image data covering southwestern forest regions of China as a sample for testing. For access to the full dataset, please visit <strong>Google Earth Engine</strong> at <a href="https://code.earthengine.google.com/6d1ea26ff4463277840eaf6a2662763c">this link</a>, and <strong>Earth Engine App (<a target="_blank">Landsat Yearly Composite Viewer</a>)</strong> at <a href="https://ee-caiyt33-catcd.projects.earthengine.app/view/landsat-yearly-composite-viewer">this link</a>.</p> <p><strong><a target="_blank">The dataset has now been updated to include data up to 2024.</a></strong></p> <p><a target="_blank"><strong>Data citation:</strong> Cai, Y., Li, X., Zhu, P., Nie, S., Wang, C., Liu, X., & Chen, Y. (2025). China Earth Observation Data Cube: The 30m Seamless Annual Leaf-On Landsat Composites from 1985 to 2023. <em>Journal of Remote Sensing</em>. </a><a href="https://doi.org/10.34133/remotesensing.0698">DOI: 10.34133/remotesensing.0698</a></p> <p>For data-related inquiries, please contact Dr. Yaotong Cai at <a href="mailto:caiyt33@mail2.sysu.edu.cn">caiyt33@mail2.sysu.edu.cn</a>.</p>
Zostera marina leaf associated bacterial metagenome assembled genomes
<p>Metagenome assembled genomes (MAGs) associated with:</p> <p>A genomic resource for exploring bacterial-viral dynamics in seagrass ecosystems</p> <p>Analysis, code, intermediate and supporting files are archived here: <a href="https://doi.org/10.5281/zenodo.14226514">10.5281/zenodo.14226514</a></p> <p>Viral sequences from this work are archived here: <a href="https://doi.org/10.5281/zenodo.14226038">10.5281/zenodo.14226038</a><br><br>This archive contains:<br>(i) Fifty-six fasta files representing the MAGs described in the above titled work with > 80% completion and < 10% contamination based on CheckM2 metrics<br>(ii) Metadata file describing the MAGs (i.e., subset of Table S3 from the above work)</p>
TEMPEST 2021 to 2023 leaf water potentials
<p>This data contains pressure chamber derived leaf water potential measurements from <em>Fagus grandifolia</em>, <em>Liriodendron tulipifera</em>, and <em>Acer rubrum</em> trees in the TEMPEST (Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments) ecosystem manipulation experiment for years 2021 (pre-data), 2022 (first treatment year), and 2023 (second treatment year). More information on TEMPEST can be found in Hopple et al 2023.</p> <p><span>Hopple, Anya M. ; Doro, K. O. ; Bailey, V. L. ; Bond-Lamberty, B. ; McDowell, N. ; Morris, K. A. ; Myers-Pigg, A. ; Pennington, S. C. ; Regier, P. ; Rich, Roy L. ; Sengupta, A. ; Smith, R. ; Stegen, J. ; Ward, N. D. ; Woodard, S. C. ; Megonigal, J. Patrick. </span><span>(2023). </span><span><a href="https://link.springer.com/content/pdf/10.1007/s10661-022-10807-0.pdf">Attaining freshwater and estuarine-water soil saturation in an ecosystem-scale coastal flooding experiment</a> .</span> <span>Environmental monitoring and assessment, </span><span>195 </span><span>(3) </span><span><a href="http://dx.doi.org/10.1007/s10661-022-10807-0" target="_blank" rel="noopener">http://dx.doi.org/10.1007/s10661-022-10807-0</a></span><br> </p>
Vertical profiles of leaf photosynthesis and leaf traits, and soil nutrients in two tropical rainforests in French Guiana before and after a three-year nitrogen and phosphorus addition experiment
<p>We provide a comprehensive dataset of vertical profiles of photosynthetic capacity and important leaf traits, including leaf N and P concentrations, from two three-year, large-scale fertilisation experiments conducted in two tropical rainforests in French Guiana. These data present a unique source of information to further improve model representations of the roles of N, P, and other leaf nutrients, in photosynthesis in tropical forests. To further facilitate the use of our data in syntheses and model studies, we provide an elaborate list of ancillary data, including important soil properties and nutrients, along with the leaf data. As environmental drivers are key to improve our understanding of carbon (C)-nutrient cycle interactions, this comprehensive dataset will aid to further enhance our understanding of how nutrient availability interacts with C uptake in tropical forests.</p>
Phylogenetic α- and β-diversities jointly reveal leaf-litter ant community assembly mechanisms along a tropical elevational gradient
<p>This study was conducted along the eastern slope of the Cofre de Perote mountain, in Veracruz, Mexico. This region is located at the junction of the Trans-Mexican volcanic belt and the Sierra Madre Oriental. We selected eight study sites spanning an elevational gradient of 3500 meters of altitude. Regardless of the geographical distance, all sites were systematically separated with an elevational difference of 500 meters on average between each other. We placed our study sites at the following elevations above sea level: 30-50 m, 610-670 m, 900-1010 m, 1470-1650 m, 2020-2230 m, 2470-2600 m, 3070-3160 m and 3480-3540 m, however, for simplicity, we will refer to each site as discrete units (i.e. 0, 600, 1000, 1500, 2100, 2500, 3100, 3500 m).</p> <p>Sampling sites were old-growth forests characterized by no obvious forest use and highly dominance of mature forests, except in the case of the lowest site (i.e., La Mancha), where most of its original vegetation has been transformed. To overcome the effect of perturbation in the studied patterns, we sampled La Mancha in a secondary forest with up to 30 years of regeneration. All sampling sites were closed-canopy forests in which a leaf-litter layer could be guaranteed. During the rainy season (July-September) of 2018 one 300-m transversal transect was located at each one of the eight study locations where we established 10 equidistantly sampling points (i.e., 30 meters between each other). Two independent 1-m2 samples were taken perpendicularly to each sampling point: one 10 meters on the right side and the other 10 meters from the left side. This procedure was repeated in a second transect placed during the dry season (March-May) of 2019 to increase community characterization as well as reduce any seasonality effect on our diversity patterns. Transects within an elevational site were separated at least 1 km away from each other. Thus, we obtained 320 m<sup>2</sup> leaf-litter samples characterized the whole mountain (8 study sites x 20 m<sup>2 </sup>per transect x 2 transects = 320 m<sup>2</sup>). In each 1-m<sup>2</sup> quadrat, we collected the leaf litter inside and sifted it through a coarse mesh screen of 1-cm grid size to remove the largest fragments and concentrate the fine litter. The concentrated fine litter from each sample was suspended in independent mini-Winkler sacks for 3 days in the laboratory. Falling arthropods were collected into a container with 95% ethanol. Ant workers were removed from each container for identification. When possible, specimens were identified at the species level. If not, we assigned a morphospecies number.</p> <p> </p> <p><strong>Phylogenetic tree constructions</strong></p> <p>Ideally, one would use a complete, species-level phylogeny of all ant species present in your study area to calculate phylogenetic diversity, yet our current understanding of ant relationships is still limited. As an alternative, we built a genus-level phylogeny based on the tree by Moreau & Bell, (2013), but using the phylogenetic relationships and divergence times within Myrmicinae from Ward et al. (2015). This phylogeny was then pruned to keep only a single species per genus to generate a genus-level phylogeny. To maximize taxonomic coverage, we replaced genera that were missing from those studies by closely-related lineages that were not present in our dataset using other phylogenetic studies (Borowiec, 2016; Lapolla et al., 2010; Schmidt & Shattuck, 2014). We then used the list of species (Supporting Information Table S1) in our dataset to simulate a species-level phylogeny in which the relationships within genera were obtained from a Yule (pure-birth) process using the <em>genus.to.species.tree</em> function in the “phytools” package (Revell, 2012). A total of 1000 simulated trees were obtained to account for phylogenetic uncertainty [see Arnan et al. (2018) and Divieso et al. (2020) for similar approach]. Additionally, we constructed a maximum clade credibility tree (hereafter MCC tree) which was used to summarize the uncertainty of the 1000 simulated trees. The MCC tree was constructed from the sample of the 1000 trees with the <em>maxCladeCred</em> function incorporated in the “ape” package (Paradis et al., 2019). Both the 1000 hypothetical trees and the MCC tree were used in downstream analyses (Supporting Information Fig. 1).</p>
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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.