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9,680 results for “leaf”
Leaf area index (LAI) recorded from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons
This file contains leaf area index (LAI) measurements from an nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016. LAI was recorded using a handheld plant canopy analyzer (LI-COR 2200C; LI-COR, Lincoln, NE, USA) Data spans 4 years from 2016 (when fertilization began) until 2019. Data was recorded once a year at the peak of each growing season.
Mangrove leaf physiological response to local climate at Key Largo, Watson River Chickee, Taylor Slough, and Little Rabbit Key, South Florida (FCE) from July 2001 to August 2001
Determine the red mangrove leaf physiological response to the local climate to understand the local controls on plant physiology. Data were collected in the Key Largo Ranger Station, Watson River Chickee and Taylor Slough research Sites, South Florida.
Thalassia leaf morphology and productivity measurements from arbitrary plots located in a Thalassia seagrass meadow in Rabbit Key Basin, Florida Bay (FCE) from March 2000 to April 2001
Thalassia leaf morphology and productivity were measured from six arbitrary 200 cm2 plots within a Thalassia seagrass meadow in Rabbit Key Basin, Florida Bay.
Leaf area index for Spartina alterniflora near the GCE-LTER Flux Tower in 2018 and 2019
Leaf area index (LAI) was measured at permanent vegetation plots located in the GCE-LTER Flux Tower site for short and medium form Spartina alterniflora. LAI data were collected using a handheld ceptometer in 2018 and 2019.
Leaf area index and above ground biomass for Juncus roemerianus in the Grand Bay National Estuarine Research Reserve from 2015 to 2019
Leaf area index (LAI) and above ground biomass were measured in 16 permanent Juncus roemerianus vegetation plots located in the Grand Bay National Estuarine Research Reserve in Mississippi. Data were collected from 2015 to 2019. LAI were measured using a handheld ceptometer. Above ground biomass for each plot were collected from core samples.
Hubbard Brook Experimental Forest: Watershed 6 Temporal Canopy Leaf Chemistry, 1992 - ongoing
Overstory foliage is collected in late summer from a reference forest to the west of Watershed 6 (also referred to as Bear Brook Watershed). Concentrations of C, N, P, K, Ca, Mn, Mg, and the natural abundance of N and C isotopes (delta-15N and delta-13C) in foliage are measured. These measurements, in combination with litterfall estimates of foliar biomass, allow us to estimate the pool of nutrients in foliage. They also allow us to estimate nutrient retranslocation, using measurements of leaf litterfall chemistry. Long-term measurements continue with the aim of detecting disturbances in nutrient cycling and trends in foliar chemistry over long time scales. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Leaf litter cover data on 1m x 1m plots from the long-term Small Mammal Exclusion Study (SMES) at Jornada Basin LTER, 1995-2020
This data package contains leaf litter cover data from plots with various levels of herbivore exclusion on the Jornada Experimental Range. Study sites were established in 1995; one in black grama grassland and the other in creosotebush shrubland to compare the impact of herbivores on ecosystem processes between these vegetation types. Parallel studies were established at the Sevilleta LTER site (New Mexico, USA) and Mapimi Biosphere Reserve (Durango, Mexico). Each study site is 1 km by 0.5 km in area. Four replicate experimental blocks were randomly located at the grassland study site to measure vegetation responses using exclusion treatments including a) all mammalian herbivores, including cattle, lagomorphs, and rodents, b) lagomorphs and cattle only, c) cattle only, and d) control accessible to all herbivores. Because grazing cattle are excluded from the entire creosote site, only three replicate experimental blocks were randomly located there including a) all mammalian herbivores, including lagomorphs, and rodents, b) lagomorphs only, and c) control accessible to all herbivores. Thirty-six sampling points were positioned at 5.8-meter intervals on a systematically located 6 by 6 point grid within each plot. A permanent one-meter by one-meter vegetation measurement quadrat is located at each of the 36 points. Each year in spring and fall from 1995-2005, the total percent cover of leaf litter in each quadrat was estimated by summing the percent of each 10 cm square within a quadrat (including 100 10-cm squares) containing leaf litter (See methods for a detailed explanation). After 2005, sampling frequency changed to every 5 years. This study is ongoing.
Leaf Area Index on the GLBRC Biofuel Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (2009 to 2017)
Dataset AbstractThe leaf area index was measured to estimate the phenology and growth patterns of the different biofuel crops.original data source http://lter.kbs.msu.edu/datasets/225
Forest Transition Experiment - Leaf Litter in a Coastal Virginia Forest
Leaf litter collected in basket-based litter traps in forest transition plots
Leaf Vein Network CNN Results
<p>Results for leaf vein networks extracted using the LeafVeinCNN software package. The original image data set is available from Blonder et al. (2019) <a href="https://doi.org/10.1002/ecy.2844">https://doi.org/10.1002/ecy.2844</a>. The LeafVeinCNN software used in the analysis is available at <a href="https://doi.org/10.5281/zenodo.4007731"> </a><a href="https://doi.org/10.5281/zenodo.4007730">https://doi.org/10.5281/zenodo.4007730</a></p> <ul> <li>The Results_xxx.zip files contain all the Excel results spreadsheets separated by the code for each field site.</li> <li>results.xls provides a summary of all the network metrics for each file that was analysable</li> <li>Results_figures.pdf provides a summary image of the processing steps and results for each leaf segment</li> <li>Network_images.pdf contains a colour-coded image of each network superimposed on the leaf segment</li> <li>HLD_plots shows the binary tree following Hierarchical Network Decomposition</li> <li>PR_results.zip contains the Excel spreadsheets for evaluation of different enhancement methods for each leaf segment.</li> <li>PR_summary.xls provides a summary of the performance of each enhancement method.</li> <li>PR_F1_images.pdf and PR_FBeta2_images.pdf show the pixel classification for each enhancement and segmentation method compared to the manual ground-truth using two different optimum criteria (F1 and FBeta2).</li> <li>PR_fullwidth_plots show the full Precision-Recall plots for the full-width binary image compared to the manual ground-truth using the FBeta2 metric.</li> <li>PR_skeleton_plots show the full Precision-Recall plots for the skeletonised binary image compared to the manual ground-truth using the FBeta2 metric.</li> <li>PR_threshold_plots.pdf show how a set of network metrics vary with the segmentation threshold for each enhancement method.</li> </ul>
Maize Phosphorus Leaf Deficiency (MPLD) Database | Compact Scientific Camera (original-processed)
<p>This database presents samples of maize leaves placed on a withe background, representing three levels of phosphorus deficiency: complete absence of the nutrient (labeled -P), half dose of the required phosphorus for normal plant development (-P50), and complete supply (C).</p><p>Its composed of two folders:</p><ul><li>Original_dataset: 722 jpg images of 1280 x 1020 pixels size divided into '_C', '-P' and '-P50' folders for class labels.</li><li>Processed_dataset: 2433 png images of 224 x 224 pixels size divided into '-C', '-P' and '-P50' folders for class labels.</li></ul>
Leaf moisture content (live-fuel moisture content) at global scale from passive microwave satellite observations of vegetation optical depth (VOD2LFMC)
<p><strong>Related paper:</strong> <a href="https://hess.copernicus.org/preprints/hess-2022-121/">Forkel et al. (2022)</a></p> <p>The VOD2LFMC dataset contains estimates of leaf moisture content as defined as live-fuel moisture content (LFMC) derived from passive microwave satellite observation of vegetation optical depth (VOD). LFMC is defined as the fresh mass of a leaf over the dry mass and is expressed in %:</p> <p><span class="math-tex">\(LFMC = {m_{fresh}-m_{dry}\over m_{dry}}*100\%\)</span></p> <p>LFMC was estimated from the <a href="https://doi.org/10.5281/zenodo.2575599">VODCA version 1</a> dataset of Ku-band VOD using the model approach “B” as described in Forkel et al. (2022).</p> <p>The file VOD2LFMC-B_v01_2000-2017.zip contains (unzipped ~ 57 GB):</p> <ul> <li>daily global data per month netCDF files</li> <li>a README file</li> <li>Ancillary file VOD2LFMC-B_v01_support-by-obs.nc</li> </ul> <p>Grid, time and variable definitions:</p> <ul> <li> <p>Grid-name: Geographic Lat/Lon</p> </li> <li> <p>Pixel-size: 1/4 degrees</p> </li> <li> <p>Size-x: 1440</p> </li> <li> <p>Size-y: 557</p> </li> <li> <p>Time period: February 2000 – July 2017</p> </li> <li> <p>Temporal resolution: daily</p> </li> <li> <p>Variable: Live-fuel moisture content (LFMC) in %</p> </li> <li> <p>Valid-range: 0-400%</p> </li> </ul> <p> </p>
MicroCT scans of a hybrid poplar leaf dehydrating, with annotated slices for model training
<p>Dataset of a leaf segment of a hybrid poplar (<em>P. maximowiczii x P. nigra</em> ‘Max3’) leaf scanned using microcomputed tomography (microCT) over time as it dehydrates.</p> <p> </p> <p><strong>Data acquisition methodology</strong></p> <p>Plants were brought to the TOMCAT tomographic beamline of the Swiss Light Source at the Paul Scherrer Institute (Villigen, Switzerland). Before microCT scanning, a young fully expanded leaf was detached from the plant and a short strip (0.4 x 1.5 cm) was cut between second-order veins. The base of the strip was wrapped in polyimide tape and inserted into a styrofoam block fixed on a sample holder. The strip was immediately scanned by imaging 1801 projections of 100 ms under a beam energy of 21 keV and a magnification of 40x, yielding a final voxel size of 0.1625 µm (field of view: ~416x416x312 µm). The leaf was left to dehydrate in the holder and additional scans were taken 10, 20, 25, and 30 minutes after the initial scan. Scanned projections were reconstructed to a transverse view using both absorption (gridrec; Marone <em>et al.</em> 2012) and phase contrast enhancement (Paganin <em>et al.</em> 2002) reconstruction.</p> <p> </p> <p><strong>Dataset description</strong></p> <p>On the reconstructed images a region of interest was identified using a paradermal view (i.e. top to bottom of the leaf) and used to manually align the scans of each time step. Thereafter, all images were cropped to that ROI, ensuring that the same region of the leaf was present in all image stacks.</p> <p>For all stacks, files start with:<br> <em>DEHYDRATION_small_Leaf4_time_N_</em><br> where N is the time point, with values from 1 to 5 equaling 0, 10, 20, 25, and 30 minutes.</p> <p>Following this prefix is either GRID (gridrec reconstruction), PAGANIN (phase contrast enhancement reconstruction), or LABELLED (hand labelled slices or ground truth). For GRID and PAGANIN, 8-bit grayscale stacks are provided. The AOI suffix indicates the region of interest.</p> <p>Stacks have been hand labelled over three orientations (for visual examples of the orientations see <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time1.png?versionId=26fc15aa-702e-4052-b162-702cc567634c">Labeled_Sections_order_time1.png</a> and <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time2.png">Labeled_Sections_order_time2.png</a>):</p> <ol> <li>CROSS (cross sectional, or transverse, view)</li> <li>LONGI (longitudinal view: similar to cross sectional view but starting normal to it, i.e. along the depth of the stack starting from the left of the cross-sectional view)</li> <li>PARADERMAL (top to bottom view: starting at the upper epidermis)</li> </ol> <p>A general idea of the slice range within one LABELLED stack is presented after the orientation, as:<br> <em>STARTtoENDbyRANGE</em><br> The exact position of the labelled slices for each time point can be found in the <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_slices_positions.txt?versionId=93d7e22f-9f07-4f98-8c49-d93e9a2e1ce5">Labeled_slices_positions.txt </a>file. <strong>Note that one-based indexing is used (as in ImageJ), not zero-based indexing (as in e.g. Python).</strong></p> <p> </p> <p><strong>References</strong></p> <p>Marone F, Stampanoni M. 2012. Regridding reconstruction algorithm for realtime tomographic imaging. Journal of Synchrotron Radiation 19: 1029–1037.</p> <p>Paganin D, Mayo SC, Gureyev TE, Miller PR, Wilkins SW. 2002. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. Journal of Microscopy 206: 33–40.</p>
Plant Atlas 2020 — British and Irish phenological data (flowering and leafing ranges)
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data behind the phenological diagrams (flowering and leafing) presented on the Plant Atlas 2020 website (<a href="http://www.plantatlas2020.org"><span>www.plantatlas2020.org</span></a><span>) and in the <em>Plant Atlas 2020</em> book. </span><span>Note that for non-flowering plants included in the atlas (e.g. ferns, horsetails etc.), the “flowering” fields in the phenology file included here are equivalent to the months when spore-bearing structures are visible.</span></p>
Experimental data aquisition of the U-LEAF Linear Fresnel Collector of the Cyprus Institute
<p>The file contains the experimental data for 50+ (non continuous) days of operation of the <a title="Linear Fresnel Reflector of the Cyprus Institute" href="https://energy.cyi.ac.cy/facilities/fresnel/">LFR</a> at the Cyprus Institute. This is a csv file type. The data set is divided in several columns: </p> <ul> <li><strong>Date</strong>: Year, Month, Day, Hours, Minutes, Seconds <em>as per the aquisition time-steps of the Master PLC of the Linear Fresnel Collector<br></em></li> <li><strong>Solar position</strong>: Azimuth of the sun (Azimuth =0 at South), Elevation of the sun (0 from the horizon) <em>calculated based on NOAA algorithm fitting the Master PLC aquisition time-steps for the location of the Linear Fresnel collector <br></em></li> <li><strong>IAM (Incidence Angle Modifiers)</strong>: calculated from ray tracing software (Tonatiuh) <em> based on PLC aquisition steps</em></li> <li><strong>DNI (Direct Normal Irradiance):</strong> as measured by the pyrheliometer (LP Pyhre 16 AC with EKO STR 21G tracker)</li> <li><strong>Weather station data</strong>: Ambient Temperature, Pressure, GHI, Humidity, Wind velocity (<em>Davis Vantage pro 2</em>)</li> <li><strong>Reflectometry data</strong> as measured several time per week and interpolated in between (<em>D&S Portable Specular Reflectometer Model 15R-USB</em>)</li> <li><strong>Absorber measurements</strong>: measured inlet, measured outlet and average calculated temperatures <em>in the absorber as the the aquisition of the PLCs (TC MISURE E CONTROLLI PT100, Class 1/3)</em></li> <li><strong>Heat transfer Fluid characteristics</strong>: Cp and Density, Massflow obtained from the volumetric flow (Prowirl F200, 7F2B25, DN25 1"), Power absorbed <em>as calculated based on the Duratherm 450 datasheet</em></li> </ul> <p>The date is set by the aquisition by the master PLC that registers the date, the volumetric flowrate of the HTF, the inlet temperature and the outlet temperature. </p> <p>They are 4 data base merged together:</p> <ul> <li>The master PLC </li> <li>The weather station</li> <li>DNI</li> <li>Reflectometer</li> </ul> <p>The available data matches periods of time when the 4 database had data registered. Time-steps vary between 15 seconds and 30 seconds. </p> <p>For more information you can send an email to a.montenon@cyi.ac.cy</p>
Leaf carbonyl sulfide flux data from Stunt Ranch in 2013
<p>Version 1.0.1: Added citation to the publication. Note that if you are using Version 1.0, there is no need to switch because no change has been made to the data.</p> <p>This repository contains data and code used to reproduce results in the following work:</p> <p>Sun, W., Maseyk, K., Lett, C., & Seibt, U. (2024). Restricted internal diffusion weakens transpiration–photosynthesis coupling during heatwaves: Evidence from leaf carbonyl sulphide exchange. <em>Plant, Cell & Environment</em>, <em>47</em>(5), 1813–1833. <a href="https://doi.org/10.1111/pce.14840" target="_blank" rel="noopener">https://doi.org/10.1111/pce.14840</a></p> <p>The data product contains leaf chamber measurements of water vapor, carbon dioxide (CO2), and carbonyl sulfide (COS) fluxes and auxiliary biometeorological variables. For details, see README.md.</p> <p>This repository is licensed under either the MIT License or the CC-BY 4.0 License. You may choose the license that best suits your intended use case. By accepting the license, you are granted permission to use this repository without requiring additional written consent from the authors. If you use this data set, we kindly ask that you give fair credit to the authors by citing or acknowledging this work as appropriate.</p>
Novel estimates of the leaf relative uptake rate of carbonyl sulfide from optimality theory
<p>Data and Matlab scripts for repeating the analysis presented in the paper. In addition, global monthly climatological LRUs are provided at 0.05° resolution for the period 2001-2010 as nc-files. </p>
Black Rock Forest Spring Freeze Defoliation Radial Growth and Leaf-Level Gas Exchange
These data are from a study conducted at Black Rock Forest in Cornwall, New York, USA during 2020 and 2021. This study was conduct to assess the ecophysiological responses of red oak (Quercus rubra) and red maple (Acer rubrum) trees in a temperate broadleaf forests to a spring frost in 2020 that that defoliated red oak trees, but not red maples. We used 2021—a year without a defoliation event—as a reference year. The datasets include tree-level measurements of (1) basal area increment for the early growing season, late growing season, and entire growing season and (2) leaf-level gas exchange (Amax, gsw, and WUE) for red oak (Quercus rubra) and red maple (Acer rubrum). These data are associated with the manuscript “Compensatory Responses of Leaf Physiology Reduce Effects of Spring Frost Defoliation on Temperate Forest Tree Carbon Uptake” by Reinmann et al.2023 in Frontiers in Forests and Global Change.
Regional and local variation in chemical, structural, and physical leaf traits for tree species in the northeastern United States, 2016-2023.
This dataset is a compilation of leaf trait measurements for 25 different Northern American tree species in the northeastern United States collected between 2016 and 2023 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains measurements for 2,006 samples across 18 chemical, physical, and structural traits. Measured traits include stable isotopes for carbon (C) and nitrogen (N), chlorophyll estimates, leaf and petiole dimensions, and leaf and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions has been included. Additional leaf physiological and optical traits have been measured concurrently on many of these samples and have been or will be published separately. This is a continuous dataset that will be updated on an as needed basis.
What the heart wants: adaptive significance of cordate leaf morphology in Arnica (Asteraceae)
We studied how the leaf inclination of basal leaves of two species, heartleaf arnica (Arnica cordifolia Hook.) and broadleaf arnica (Arnica latifolia Bong.) varied with canopy cover in the Greater Yellowstone Ecosystem, Wyoming, USA in July and August, 2022. Basal leaves of heartleaf arnica possess cordate leaf bases while those of broadleaf arnica do not, leading to potential biomechanical limitations of the latter to persist in shaded forest understories. Leaf inclination was measured as the angle (degrees) between the petiole and leaf planes of basal leaves for each species; cordateness was measured as the ratio of leaf length on either side of the petiole insertion point in basal leaves of heartleaf arnica. Data collection are complete.
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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.