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9,680 results for “Leaf”
Global leaf chlorophyll content (LCC) product from MODIS imagery (2000-2020)
<p>The spatial and temporal distribution of leaf chlorophyll content (LCC) is critical for understanding the capacity of vegetation photosynthesis. Here, a global 8-day leaf chlorophyll content (LCC) dataset at 500-m resolution was generated from MODIS data using a matrix system with two pairs of vegetation indices. </p> <p><strong>The following paper should be cited when using the data:</strong></p> <p>Xu, M., Liu, R., Chen, J.M., Liu, Y., Wolanin, A., Croft, H., He, L., Shang, R., Ju, W., Zhang, Y., He, Y., Wang, R., 2022. A 21-year time-series of global leaf chlorophyll content maps from MODIS imagery. IEEE Trans. Geosci. Remote Sens. <a href="https://doi.org/10.1109/TGRS.2022.3204185">https://doi.org/10.1109/TGRS.2022.3204185</a>.</p> <p>Detailed description of data organization can be found in the uploaded document "1Readme.docx".</p> <p>The whole dataset of MODIS LCC product is from 2000 to 2020. <strong>Due to the data volume limitation of Zenodo, currently the data from 2000-2010 can be downloaded through Google Drive sharing link:</strong></p> <p><a href="https://drive.google.com/drive/folders/11eXetjsAB_ZjFqGs8SXEWzd6byr_8LoW?usp=sharing">https://drive.google.com/drive/folders/11eXetjsAB_ZjFqGs8SXEWzd6byr_8LoW?usp=sharing</a></p> <p><strong>Related dataset: </strong>Mingzhu, X., Liu, R., Chen, J. M., Shang, R., & Liu, Y. (2022). Global leaf chlorophyll content product from MERIS imagery (GLOBMAP MERIS LCC) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10467919">https://doi.org/10.5281/zenodo.10467919</a></p> <p>For any other questions, please send email to Mingzhu Xu (<a href="mailto:xumzhu@gmail.com">xumzhu@gmail.com</a>).</p>
diFUME Leaf Area Index V0.1
<p>Description:</p> <p>The Level 2A (L2A) product by the Theia Land Data Centre of CNES (Centre national d'études spatiales) is used, which provides georeferenced and orthorectified surface reflectance (SR), water vapor content (WVC), aerosol optical thickness (AOT), cloud and geophysical masks, processed by the MAJA atmospheric processing software. The 10 m SR bands in red (SRred : 665 nm) and near-infrared (SRNIR : 842 nm) are used to compute NDVI (Normalized Difference Vegetation Index) as (SRNIR – SRred)/(SRNIR + SRred) and the product is masked for clouds, cloud shadows and snow according to the L2A product flags. NDVI is converted to Leaf Area Index (LAI) values by applying an empirical exponential formula and is then resampled from 10 m to 5 m resolution, enhancing the initial LAI values, using the vegetation fraction derived by the 1 m Land Cover product.</p> <p> </p> <p>Data specifications:</p> <p>CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected</p> <p>Spatial Extent: 392120.0,5266860.0 : 395160.0,5269840.0</p> <p>Temporal Extent: 2018 - 2020</p> <p>Units: meters</p> <p>Width: 608</p> <p>Height: 596</p> <p>Bands: 1</p> <p>Pixel Size: 5,-5</p> <p>Data type: Float32 - Thirty two bit floating point</p> <p>GDAL Driver Description: GTiff</p> <p>GDAL Driver Metadata: GeoTIFF</p>
Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method
<p>This dataset contains a GitHub repository containing all the data, analysis, Nextflow workflows and Jupyter notebooks to replicate the manuscript titled "Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method".</p> <p>It also contains the Multiple Sequence Alignments (MSAs) generated and well as the main figures and tables from the manuscript.</p> <p>The repository is also available at GitHub (https://github.com/cbcrg/dpa-analysis) release `v1.2`.</p> <p>For details on how to use the regressive alignment algorithm, see the T-Coffee software suite (https://github.com/cbcrg/tcoffee).</p>
Nocturnal leaf respiratory CO2 release in different species measured at constant temperature
<p>RAW data for GCB publication by Dan Bruhn, Martijn Slot, and Lina M Mercado, '<span>Simple and accurate representation of cumulative night-time leaf respiratory CO<sub>2</sub>-efflux'</span></p> <p><span>Nocturnal leaf respiratory CO2 release in 14 different species measured at constant temperature measured in the field.</span></p>
Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach
<p>Main data used for the scientific paper entitled: "Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach".</p> <ol> <li>"Danta_dem_10cm_px.tif": orthomosaic-derived DEM</li> <li>"Danta_rgb_2.2cm_px.tif": ortophoto </li> <li>"GPS points": list of GPS samples points</li> <li>"Main data": field vegetation data and indexes used for the regressions</li> <li>"Raw PointCloud". Lidar original dataset</li> <li>"Pre-processed PointCloud": Lidar dataset after pre-processing (see paper's methods) </li> <li>"DTM_DantaGround_grid50cm_minimo": Output (TIFF); the LiDAR-derived DTM showed in the paper</li> <li>"LAI": Output (Shapefile); the LiDAR-derived LAI showed in the paper.</li> </ol> <p> </p>
Global variation in the fraction of leaf nitrogen allocated to photosynthesis
<p>ReadMe</p> <p>1. The datasets were produced based on the method described in Luo X. et al. Global variation in the fraction of leaf nitrogen allocated to photosynthesis. Nature Communications. doi: 10.1038/s41467-021-25163-9.</p> <p>2. Vcmax25_RF and fLNR_RF are the key output. Vcmax25_RF was estimated using random forest trained by ground observations, remote sensing leaf chlorophyll content and some ancillary environment variables. fLNR_RF was further calculated from Vcmax25_RF.</p> <p>3. Vcmax25_un and fLNR_un are the uncertainties of Vcmax25_RF and fLNR_RF. </p> <p>4. Note there are several gridded leaf nitrogen content maps (LNC; area-based) available for our derivation of fLNR from Vcmax25. In our study, we mainly use EB17, but also provide the results based on AMM18 and CB20 (see reference).</p> <p>5. Other Vcmax25 and fLNR datasets are provided for comparison. They are all driven by CRU TS4.01 climate data, soil grids soil data and EB17 leaf nitrogen/phosphorus datasets.</p> <p>If you have any questions about the dataset, please contact Xiangzhong (Remi) Luo at xzluo.remi@nus.edu.sg</p>
Leaf water and stem cellulose oxygen isotope ratios simulated with global dynamic vegetation model LPX-Bern
<p>Description of leaf water and stem cellulose oxygen isotope ratios simulated with LPX-Bern</p> <p>Citation of describing paper:</p> <p>Keel SG, Joos F, Spahni R, Saurer M, Weigt RB, Klesse S. 2016. Simulating oxygen isotope ratios in tree ring cellulose using a dynamic global vegetation model, Biogeosciences, 13, 3869–3886, 2016 doi:10.5194/bg-13-3869-2016</p> <p>download: www.biogeosciences.net/13/3869/2016/</p> <p>General Information: Format: NetCDF, gridded</p> <p>Model: Dynamic global vegetation model LPX-Bern Version 1.0 (Land surface Processes and eXchanges, Bern) (Spahni et al., 2013; Stocker et al., 2013)</p> <p>Resolution: 3.75° x 2.5° lat/lon global Time: Monthly from Jan 1960 to Dec 2012</p> <p>Variables:</p> <p>cellu18: monthly stem cellulose δ18O (per mil) lw18: monthly leaf water δ18O (per mil) -2 NPP: monthly net primary production (g C m ) FPC: monthly fractional plant cover</p> <p>Dimensions: i=longitude, j=latitude, l=time, k=plant functional type Codes for plant functional types (k):</p> <ol> <li> <p>1 tropical broad-leaved evergreen</p> </li> <li> <p>2 tropical broad-leaved deciduous (raingreen)</p> </li> <li> <p>3 temperate needle-leaved evergreen</p> </li> <li> <p>4 temperate broad-leaved evergreen</p> </li> <li> <p>5 temperate broad-leaved deciduous (summergreen)</p> </li> <li> <p>6 boreal needle-leaved evergreen</p> </li> <li> <p>7 boreal needle-leaved deciduous (summergreen)</p> </li> <li> <p>8 boreal broad-leaved deciduous (summergreen)</p> </li> <li> <p>9 temperate herbaceous</p> </li> <li> <p>10 tropical herbaceous</p> </li> </ol>
Metagenomic and metaproteomic insights into bacterial communities in leaf-cutter ant fungus gardens
<p>The submitted protein sequences were compiled from two of our previous studies, 1) 'Metagenomic and metaproteomic insights into bacterial communities in leaf-cutter ant fungus gardens' (doi.org/10.1038/ismej.2012.10) and 2) 'Leucoagaricus gongylophorus Produces Diverse Enzymes for the Degradation of Recalcitrant Plant Polymers in Leaf-Cutter Ant Fungus Gardens' (doi.org/10.1128/AEM.03833-12).</p>
Microsatellite genotype data and leaf morphological data of the publication "Bidirectional gene flow between Fagus sylvatica L. and F. orientalis Lipsky despite strong genetic divergence"
<p>These data sets were used for analyses in the publication "Bidirectional gene flow between <em>Fagus sylvatica</em> L. and<em> F. orientalis</em> Lipsky despite strong genetic divergence" accepted in Forest Ecology and Management <a href="https://www.sciencedirect.com/journal/forest-ecology-and-management/vol/537/suppl/C">Volume 537</a>, 1 June 2023, 120947, <a href="https://doi.org/10.1016/j.foreco.2023.120947">https://doi.org/10.1016/j.foreco.2023.120947</a></p> <p>For details about the data, please read the corresponding ReadMe files.</p>
Dataset for "On the variability of the leaf relative uptake rate of carbonyl sulfide compared to carbon dioxide: insights from a paired field study with two soybean varieties"
<p>Data of measurements and model output of the publication "On the variability of the leaf relative uptake rate of carbonyl sulfide compared to carbon dioxide: insights from a paired field study with two soybean varieties". NO DOI YET</p> <p>The data consists of micrometeorological data, COS,CO<sub>2</sub> and H2O flux measurements and resistances of two soybean cultivars at an agricultural field in Italy.</p> <p>For additional information, please contact: <a href="mailto:felix.spielmann@uibk.ac.at">Felix.Spielmann@uibk.ac.at</a> or <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a>.</p>
Supporting information for "Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa"
<p><strong>Description</strong></p> <p>In this work, we use few-shot learning to segment the body and vein architecture of <em>P. trichocarpa</em> leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy and retain biologically realistic features. The leaf and vein segmentations are compared against a U-Net baseline model, and subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the "vein density" trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code, we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 <em>P. trichocarpa</em> genome for 1,419 genotypes.</p> <p><strong>Directories:</strong></p> <pre><code>Few-shot learning for p. trichocarpa leaf traits ├── data │ ├── genomes │ │ ├── Ptri_V4_Nisq1.[...].bed │ │ ├── Ptri_V4_Nisq1.[...].bim │ │ └── Ptri_V4_Nisq1.[...].fam │ ├── images │ │ └── *.jpeg │ ├── leaf_masks │ │ └── *.png │ ├── leaf_preds │ │ └── *.png │ ├── leaf_unet_preds │ │ └── *.png │ ├── results │ │ ├── digital_traits.tsv │ │ ├── gwas_results.csv │ │ ├── manual_traits.tsv │ │ ├── vein_density_blups.tsv │ │ └── vein_density_tps_adj.tsv │ ├── vein_bce_preds │ │ └── *.png │ ├── vein_bce_probs │ │ └── *.png │ ├── vein_fl_preds │ │ └── *.png │ ├── vein_fl_probs │ │ └── *.png │ ├── vein_masks │ │ └── *.png │ ├── vein_unet_bce_preds │ │ └── *.png │ ├── vein_unet_bce_probs │ │ └── *.png │ ├── vein_unet_fl_preds │ │ └── *.png │ ├── vein_unet_fl_probs │ │ └── *.png ├── figures │ └── *.png ├── logs │ ├── leaf_tracer_256.txt │ ├── leaf_unet_256.txt │ ├── vein_grower_bce_128.txt │ ├── vein_grower_fl_128.txt │ ├── vein_unet_bce_128.txt │ └── vein_unet_fl_128.txt ├── models │ ├── BuildCNN.py │ ├── BuildUNet.py │ ├── LeafTracer.py │ └── VeinGrower.py ├── notebooks │ ├── Figures.ipynb │ ├── GrowerInference.ipynb │ ├── GrowerTraining.ipynb │ ├── TracerInference.ipynb │ ├── TracerTraining.ipynb │ ├── UNetLeafSegmentation.ipynb │ └── UNetVeinSegmentation.ipynb ├── utils │ ├── GetLowestGPU.py │ ├── ImageLoader.py │ ├── LeafGenerator.py │ ├── ModelWrapperGenerator.py │ ├── TimeRemaining.py │ ├── TraceInitializer.py │ ├── UNetTileGenerator.py │ └── VeinGenerator.py └── weights ├── leaf_tracer_256_best_val_model.save ├── leaf_unet_256_best_val_model.save ├── vein_grower_bce_128_best_val_model.save ├── vein_grower_fl_128_best_val_model.save ├── vein_unet_bce_128_best_val_model.save └── vein_unet_fl_128_best_val_model.save </code></pre> <p><strong>Data:</strong></p> <p>The <code>data</code> folder includes all images, ground truth segmentations, predicted segmentations, and extracted leaf traits. All images encode the sample ID in the file name by indicating the treatment, block, row, position, and leaf side, respectively. For example, the file, <code>C_1_1_2_bot.jpeg</code>, indicates the control treatment, block 1, row 1, position 2, and the bottom side of the leaf. Tabulated results include position IDs as well as the corresponding genotype IDs.</p> <ul> <li>The <code>images</code> folder includes the 2,906 high-resolution leaf scans taken in the field.</li> <li>The <code>leaf_masks</code> folder includes 50 ground truth segmentations used for training the leaf tracing algorithm.</li> <li>The <code>leaf_preds</code> folder includes the 2,906 predicted segmentations from the leaf tracing algorithm.</li> <li>The <code>leaf_unet_preds</code> folder includes the 2,906 predicted segmentations from the U-Net model for leaf segmentation.</li> <li>The <code>vein_masks</code> folder includes 8 ground truth segmentations used for training the vein growing algorithm.</li> <li>The <code>vein_*_preds</code> folder includes the 1,453 predicted segmentations from the vein growing algorithm, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_*_probs</code> folder includes the 1,453 predicted probability maps from the vein growing algorithm before thresholding, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_unet_*_preds</code> folder includes the 1,453 predicted segmentations from the U-Net model for vein segmentation, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_unet_*_probs</code> folder includes the 1,453 predicted probability maps from the U-Net model for vein segmentation before thresholding, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>genomes</code> folder includes the set of SNPs called against the v4 <em>P. trichocarpa</em> genome for 1,419 genotypes with a README file detailing the steps taken.</li> <li>The <code>results</code> folder includes <ul> <li>Raw values of the 68 predicted leaf traits in <code>digital_traits.tsv</code></li> <li>Manually measured values of petiole length and width in <code>manual_traits.tsv</code></li> <li>Thin plate spline (TPS) adjusted values of the vein density trait in <code>vein_density_tps_adj.tsv</code></li> <li>Best linear unbiased prediction (BLUP) adjusted values of the vein density trait in <code>vein_density_blups.tsv</code></li> <li>GWAS results for the vein density trait, including chromosome positions and corresponding P values, in <code>gwas_results.csv</code></li> </ul> </li> </ul> <p><strong>Figures:</strong></p> <p>The <code>figures</code> folder includes all figures and videos used in the manuscript. See <code>notebooks/Figures.ipynb</code> for the methods used to generate these figures.</p> <p><strong>Logs:</strong></p> <p>The <code>logs</code> folder includes logs of CNN convergence for the training and validation sets during model training for the leaf tracing CNN vein growing CNN, and U-Net models. The file names include the model, loss function (bce: binary cross-entropy, fl: focal loss), and size of the input window for each method (e.g., 128 for the vein growing CNN).</p> <p><strong>Models:</strong></p> <p>The <code>models</code> folder includes the CNN implementations in PyTorch as well as the leaf tracing and vein growing algorithms at inference time.</p> <ul> <li><code>BuildCNN.py</code> defines the CNN architecture for leaf tracing or vein growing, with user-specified input shape, output shape, layers, and output activation functions.</li> <li><code>BuildUNet.py</code> defines the U-Net architecture for leaf and vein segmentation, with user-specified input/output shape, layers, and output activation functions.</li> <li><code>LeafTracer.py</code> defines the leaf tracing algorithm at inference time.</li> <li><code>VeinGrower.py</code> defines the vein growing algorithm at inference time.</li> </ul> <p><strong>Notebooks:</strong></p> <p>The <code>notebooks</code> folder includes Jupyter notebooks used for model training, model inference, and figure generation.</p> <ul> <li><code>Figures.ipynb</code> is used to generate all of the manuscript figures.</li> <li><code>GrowerTraining.ipynb</code> is used to train the vein growing CNN.</li> <li><code>GrowerInference.ipynb</code> is used to apply the vein growing algorithm to the 1,453 leaf bottom images.</li> <li><code>TracerTraining.ipynb</code> is used to train the leaf tracing CNN.</li> <li><code>TracerInference.ipynb</code> is used to apply the leaf tracing algorithm to the 2,906 leaf top and bottom images.</li> <li><code>UNetLeafSegmentation.ipynb</code> is used to train and apply U-Net for leaf segmentation.</li> <li><code>UNetVeinSegmentation.ipynb</code> is used to train and apply U-Net for vein segmentation.</li> </ul> <p><strong>Utils:</strong></p> <p>The <code>utils</code> folder includes utility scripts implemented in Python that assist in model training and inference.</p> <ul> <li><code>ImageLoader.py</code> loads image/mask pairs for sampling training/validation tiles.</li> <li><code>LeafGenerator.py</code> generates inputs/outputs for the leaf tracing CNN.</li> <li><code>VeinGenerator.py</code> generates inputs/outputs for the vein growing CNN.</li> <li><code>UNetTileGenerator.py</code> generates inputs/outputs for the U-Net model.</li> <li><code>GetLowestGPU.py</code> identifies available GPUs using the <code>nvidia-smi</code> command and selects the one with lowest memory usage, if none available the device is set to CPU.</li> <li><code>ModelWrapperGenerator.py</code> wraps the PyTorch CNN and data loaders with similar functionality to the Keras Model class in TensorFlow (e.g., model.fit(...)).</li> <li><code>TimeRemaining.py</code> is used by the model wrapper to estimate remaining time left per epoch.</li> <li><code>TraceInitializer.py</code> is used by the tracing algorithm at inference time to initialize the leaf trace using automatic thresholding.</li> </ul> <p><strong>Weights:</strong></p> <p>The <code>weights</code> folder includes the CNN parameters from the epoch resulting in the best validation error. The file names include the model, loss function (bce: binary cross-entropy, fl: focal loss), and size of the input window for each method (e.g., 128 for the vein growing CNN). The weights are loaded into the CNN models for inference.</p> <p><strong>Citation:</strong></p> <pre><code>@article{ doi:10.34133/plantphenomics.0072, author = {John Lagergren and Mirko Pavicic and Hari B. Chhetri and Larry M. York and Doug Hyatt and David Kainer and Erica M. Rutter and Kevin Flores and Jack Bailey-Bale and Marie Klein and Gail Taylor and Daniel Jacobson and Jared Streich }, title = {Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa}, journal = {Plant Phenomics}, volume = {0}, number = {ja}, pages = {}, year = {}, doi = {10.34133/plantphenomics.0072}, URL = {https://spj.science.org/doi/abs/10.34133/plantphenomics.0072}, eprint = {https://spj.science.org/doi/pdf/10.34133/plantphenomics.0072}, }</code></pre>
Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020 (V1.2)
<p><strong>Brief Introduction:</strong></p> <p> </p> <p>The fourth generation GIMMS Leaf Area Index product (GIMMS LAI4g, version 1.2) provides spatiotemporally consistent global LAI data in half-month and 1/12° from 1982 to 2020. It is created to address two major uncertainties presented in current global long-term LAI products, i.e., (1) the effects of NOAA satellite orbital drift and AVHRR sensor degradation and (2) insufficient LAI reference data to build robust LAI model particularly before the late 1990s.</p> <p> </p> <p>The GIMMS LAI4g was generated based on biome-specific BPNN models that employed the latest PKU GIMMS NDVI product and 3.6 million high-quality global Landsat LAI samples. It was then consolidated with the Reprocess MODIS LAI to extend the temporal coverage to 2020 via a pixel-wise Random Forests fusion method.</p> <p> </p> <p>The GIMMS LAI4g exhibits overall high accuracy and low underestimation evaluated by field LAI measurements and Landsat LAI samples. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency before and after the year 2000 and a more reasonable global vegetation trend. It could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and benefit the model development in Earth and environmental sciences.</p> <p> </p> <p>Here we provide two versions of GIMMS LAI4g for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the Reprocess MODIS LAI (1982−2020). We strongly recommend an adequate use of the quality control (QC) layer in the product. Please refer to the Readme file for more details.</p> <p> </p> <p><strong>Major updates:</strong></p> <p>Version 1.0 (February 17, 2023):</p> <p>· The original version of the product.</p> <p> </p> <p>Version 1.1 (June 14, 2023):</p> <p>· The GIMMS LAI4g is now validated by ground LAI measurements.</p> <p>· A pixel-wise Random Forests consolidation method is used to replace the linear one.</p> <p>· Two versions of GIMMS LAI4g are now available, one solely based on AVHRR data and one consolidated with MODIS LAI.</p> <p> </p> <p>Version 1.2 (August 25, 2023):</p> <p>· The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate LAI values during 1982−1984 for all biomes, October−April for EBF, and winters for ENF, when the Landsat NDVI samples were absent or relatively scarce.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN</p> <p>Projection: Geographic</p> <p>Spatial Resolution: 1/12 degree</p> <p>Temporal Resolution: Half month</p> <p>Temporal Coverage: January 1982 to December 2020</p> <p>Image Dimension: Rows-2160; Columns-4320</p> <p>Units: m<sup>2</sup>/m<sup>2</sup></p> <p>Fill Value: 65535</p> <p>Data Type: uint16</p> <p>Valid Range: 0-7000</p> <p>Scale Factor: 0.001</p> <p>File Format: TIFF(.tif)</p> <p>File Size: ~8Mb each file</p> <p> </p> <p><strong>References:</strong></p> <p>Cao, S., Li, M., Zhu, Z., Wang, Z., Zha, J., Zhao, W., Duanmu, Z., Chen, J., Zheng, Y., Chen, Y., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-68, in review, 2023.</p>
Study of Leaf Wilt in Soybean Plants
<p>This dataset was produced in collaboration with the Crop and Soil Science Department of North Carolina State University and the United States Department of Agriculture (USDA). It comprises of 1892 rgb images of plots of soybean fields. The images represent soybean plants having 5 different levels of wilting, each image being assigned a value between 0 and 4 by expert annotators. 0 represents leaves with least wilting while 4 represents the most wilted leaves.</p> <p>The full_data.zip file consists of all images in the dataset. The annotation file dataAnns_goodFiles.csv has all image IDs and their corresponding annotations ranging from 0-4. The last column Annotation has all labels stored from 0-4 for the corresponding image. The treatment_camera column in the csv file refers to the plot number associated with each image. This number is included in the full image id as well. The first part of the number denotes the camera ID while the second part identifies a particular plot ID. For example, 3-106 indicates camera 3, plot 106. The remaining numbers in the image ID denote the date and time when the particular image was captured. For example image 3-106_12_08_2019_06_30_40.jpg denotes camera 3, plot 106 and was taken on 12/08/2019 at 06 hours, 30 mins and 40 seconds.</p>
Data for Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates
<p>This repository contains data and scripts to reproduce results that are presented in the manuscript: Van Laere, J., Merckx, R., Hood-Nowotny, R., Dercon, G. (2023) Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates. <em>Front. Plant Sci</em>. 14:1222558 doi: 10.3389/fpls.2023.1222558</p>
Leaf temperature of northeastern US tree species
Leaf temperature measurements were collected during the summer of 2020 within forested areas at the Thompson Farm Earth Systems Observatory in Durham, New Hampshire, USA. Located within the property is a registered Ameriflux site, Thompson Farm Forest (US-TFF), as well as experimental throughfall exclusion plots that are part of DroughtNet (experiment running since 2015). Leaf temperature measurements were made within the footprint of the eddy covariance flux tower as well as within both control and throughfall exclusion treatment plots. Upper canopy foliage was accessed using a bucket lift and in situ measurements made using a handheld thermal IR sensor. All data were paired with concurrent meteorological measurements from US-TFF or data from a co-located NOAA CRN station (NH Durham 2 SSW). Additionally, leaf chemical, physical, structure, and physiological traits have been measured at this site as well as canopy scale measures of structure and UAV-based spectral, thermal, and lidar imagery. Specific to this leaf temperature dataset, leaf-level light, temperature, and vpd photosynthetic response curves were measured.
Leaf angle measurements for temperate tree species in northeastern USA
Leaf angle distribution (LAD) measurements were made during the growing season in 2021 at the Harvard Forest in Petersham, MA, USA, and in 2022 at the Thompson Farm Earth Systems Observatory in Durham, NH, USA. At both sites, a level-calibrated digital angle tool was used to measure LAD in upper canopy foliage of common northeastern temperate tree species accessed using a mobile canopy lift. Additionally, at Thompson Farm, measurements were made at multiple heights to characterize differences of LAD in high, middle, and low canopy positions. Here, we have published those measurements, including a summary table of species average leaf angles and calculated parameters for fitted beta distributions. Processing scripts can be made available upon request to the authors. Additionally, leaf chemical, physical, structure, optical and physiological traits have been measured at these site as well as canopy scale measures of structure and UAV-based spectral, thermal, and lidar imagery.
Seasonal trends in leaf level physiological parameters, obtained through gas exchange, reflectance spectroscopy and, functional trait analysis
This data package contains leaf level gas exchange, reflectance spectroscopy, and functional trait measurements collected in six common deciduous tree species across the full 2021 growth season (May -October) at the Black Rock Forest in Cornwall, New York, USA. Branches were sampled predawn using the shotgun method of branch retrieval, and re-cut under water to preserve hydraulic function before transport to the lab. Gas exchange data included in this package are stomatal response curves (irradiance response) which can be used to estimate stomatal slope and intercept. Spectroscopic data are full-range (350 – 2500 nm) leaf reflectance spectra collected on all leaves sampled for gas exchange and traits. Leaf level trait measurements include leaf mass per area (LMA), leaf dry matter content (LDMC), elemental nitrogen and carbon expressed on a per mass basis, and fitted values of Asat, Vcmax, and Rdark scaled to a reference temperature of 25C. Data from these three data tables (stomatal responce, spectra, leaf traits) can be cross referenced using the unique SampleID. Additional data tables include stomatal anatomy (stomatal density, length, and width of the guard cells), hydraulic properties estimated from pressure volume curves (relative water deficit at the turgor loss point), and predawn water potential for all sampled branches. Site level data includes the dGPS location of each sampled tree, its species, and DBH. Each tabular data file (*.csv) is accompanied by a data description (*_dd.csv) which includes relevant metadata (unit, definition, data type). Copies of all raw instrument output (spectroradiometer, LICOR, pressure chamber) and included as .zip files.
canopy herbivory and leaf traits of tree communities in tropical montane rainforests of southern Ecuador
This dataset contains herbivory data estimated as leaf area loss [cm²] and [%] and several leaf traits measured either conventionally or via spectral sensing-based techniques from canopies of tree communities in tropical montane rainforests of the Andes in southern Ecuador between February and March in 2019. The data were used by Schön et al. (in prep) to evaluate whether leaf traits are valuable indicators of canopy herbivory mainly caused by arthropods and further, whether chemical leaf traits estimated via spectral sensing-based techniques have similar strong relations to herbivory as leaf traits measured conventionally. Herbivory was estimated with the software WinFOLIA ™ 2019a from scanned mature and sun-exposed leaves of tree canopies. Spectral sensing-based leaf traits comprising secondary plant metabolites and both structural and nutritional cell components were estimated from leaves with an OceanOptics spectrometer HDX. Conventionally measured leaf traits comprising morphological and nutritional traits were derived by applying both elemental and morphometrical analyses (e.g., ICP analysis, a digital micrometer and penetrometer). For detailed descriptions of the methodology see Schön et al. (in prep), Homeier et al. (2021), and Limberger et al. (2021). Research was conducted by the subprojects A1, B1, and B4 within the framework of the RESPECT project (Environmental changes in biodiversity hotspot ecosystems of South Ecuador: RESPonse and feedback effECTs) funded by the DFG with the grant numbers: BE1780/51-1, BE1780/51-2, Ho3296/6-1, FA 925/11-1, FA 925/11-2, FA 925/16-1, BR1293/17-1.
Leaf traits plus growth and dieback data for cloud forest epiphytes in a cloud exclusion treatment at Wayqecha Biological Station, Peru
Morphological traits [specific leaf area, stomatal length and density, leaf thickness, cuticle thickness and hydrenchymal layer thickness] and physiological traits [stomatal conductance, minimum leaf conductance and foliar water uptake capacity] along with pressure volume curves were measured at the Wayqecha Biological Station, Peru. Vascular epiphytes were sampled from a control plot and fog reduction treatment plot constructed from two 30 m tall aluminum towers, 40 m apart with panels of polyethylene mesh strung between the towers. Measurements of leaf morphological traits were taken in June-July 2022 in the control and treatment plots to assess plasticity to fog reductionin the fifth year of the experiment. Samples of branches and leaves for each individual approximately 1-2 meters off the ground were collected in the plot, close enough to the curtain to maximize any effects of fog interception. Growth and dieback data were also collected species of vascular and non-vascular epiphytes attached to wooden transplant boards in the control and treatment plot in 2017. Rates of growth and dieback were recorded for vascular epiphytes from 2017-2019 and for non-vascular epiphytes from 2018-2019. The 2017 data collection took place in November while data for 2018 and 2019 were collected in June.
Leaf litter quality induces morphological changes in wood frog (Lithobates sylvaticus) metamorphs, Oakland University Biological Preserve (MI, USA) 2010.
For organisms that exhibit complex life cycles, resource conditions experienced by individuals before metamorphosis can strongly affect phenotypes later in life. Such resource-induced effects are known to arise from variation in resource quantity, yet little is known regarding effects stemming from variation in resource quality (e.g., chemistry). For larval anurans, we hypothesized that variation in resource quality will induce a gradient of effects on metamorph morphology. We conducted an outdoor mesocosm experiment in which we manipulated resource quality by rearing larval wood frogs (Lithobates sylvaticus) under 11 leaf litter treatments. The litter species represented plant species found in open- and closed-canopy wetlands and included many plant species of current conservation concern (e.g., green ash, common reed). Consistent with our hypothesis, we found a gradient of responses for nearly all mass-adjusted morphological dimensions. Hindlimb dimensions and gut mass were positively associated with litter nutrient content and decomposition rate. In contrast, forelimb length and head width were positively associated with concentrations of phenolic acids and dissolved organic carbon. Limb lengths and widths were positively related with the duration of larval period, and we discuss possible hormonal mechanisms underlying this relationship. There were very few, broad differences in morphological traits of metamorphs between open- and closed-canopy litter species or between litter and no-litter treatments. This suggests that the effects of litter on metamorph morphology are litter species-specific, indicating that the effects of changing plant community structure in and around wetlands will largely depend on plant species composition.
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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.