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768 results for “Populus”
Growth parameters and resistance to Sphaerulina musiva-induced canker are more important than wood density for increasing genetic gain from selection of Populus spp. hybrids for northern climates
<p>The data was collected from a common garden genetics trial established in 2008 in northern Alberta, Canada. The trial represents 1978 (initial number) hybrid poplar clones from 63 families and includes interspecific crosses between <em>Populus deltoides</em> (D), <em>Populus nigra</em> (N), <em>Populus balsamifera</em> (B), <em>P. maximowiczii</em> (M), and <em>P. × petrowskyana</em> (<em>P. laurifolia</em> × <em>P. nigra</em>). Female clone 24 (‘Walker’ = (<em>Populus deltoides </em>× (<em>P. laurifolia × P. nigra</em>))) and male progeny clone 2403 (‘Okanese’ = (‘Walker’ × (<em>P. laurifolia × P. nigra</em>))) were used as reference clones. The study design was a randomized complete block design, with one ramet per clone in each of four blocks. Measurements were carried out after three, eight, and 10 growing seasons on the genetics trial. Results presented in ‘HybridPoplarsTrial.csv’ file, show is the raw data, while ‘Summary data.csv’ contains the mean values for clones obtained from the four blocks. Measured and calculated traits include: DBH (diameter at breast height; 1.3 m); H (height); canker (canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)); MAI (mean annual increment), V (volume).</p> <p>Description of headings:</p> <p>Trait [unit] - Description</p> <p>DBH_Age_3 [cm] - diameter at breast height at age 3</p> <p>H_Age_3 [m] - height at age 3</p> <p>DBH_Age_8 [cm] - diameter at breast height at age 8</p> <p>H_Age_8 [m] - height at age 8</p> <p>H_Age_10 [m] - height at age 10</p> <p>DBH_Age_10 [cm] - diameter at breast height at age 10</p> <p>Canker_Age_8 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>Canker_Age_10 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>V_Age_8 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 8</p> <p>MAI_Age_8 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 8</p> <p>V_Age_10 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 10</p> <p>MAI_Age_10 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 10</p> <p>WD_Age_10 [kg m<sup>-3</sup>] - wood density at age 10</p> <p> </p>
Herbarium specimen image of Populus nigra L., part of the collection of Natural History Museum, University of Tartu
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Herbarium specimen image of Populus trichocarpa Torr. & A.Gray, part of the collection of Natural History Museum, University of Tartu
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Herbarium specimen image of Populus laurifolia Ledeb., part of the collection of Natural History Museum, University of Tartu
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
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>
Dataset paper "N2 use in perennial swards intercropped with young poplars, clone I-214 (Populus × euramericana (Dode) Guinier) in the Mediterranean area under rainfed conditions"
<p>These files contain the data produced within a 2-yr field experiment conducted in Pisa, Central Italy, to assess N dynamics in a young silvopastoral system (i.e., where two forage crops, sulla and ryegrass, were grown in intercropping with an alley row of poplar trees) compared to a pure pastoral system (i.e., where teh afore-mentioned forage crops were grown without trees).</p> <p>Specifically:</p> <p>- "15N_poplars_Ntransfer.csv" contains the data on N2-transfer from sulla to poplar trees</p> <p>- "Averaged cumulate values.csv" contains the data on aboveground biomass, N yield and N fixed of the forage crops, cumulated over the two years of experimentation;</p> <p>- "Poplars growth.csv" contains the data on plant height and trunk diameters ( at the foot and at 130 cm) collected on poplar plants at different dates (at plantation time and at the end of each experimental year);</p> <p>- "Root_N_Nfix.csv" contains the data on delta 15N, %N derived from fixation and N concentration in poplar tree roots sampled at the end of each experimental year;</p> <p>- "Seasonal Data_AGB_N_Nfix.csv" contains the data on aboveground biomass, N yield, delta 15N, %N derived from fixation and N concentration and N fixed of forage species observed at each sampling time (mowing date) within the two experimental years;</p> <p>- "Soil_N_only SIPAST.csv" contains the data of soil total Nitrogen and nitric Nitrogen observed only in the silvopastoral system at different positions on the field;</p> <p>-"Soil_N.csv" contains the data of soil total Nitrogen and nitric Nitrogen observed in the two cropping systems at different positions on the field.</p>
Labeled 17 Hardwood Species and 55 Genotypes of Populus Stomatal Images Datasets
<p>Research has indicated the potential of using machine learning algorithms to detect and measure stomata automatically. However, the current limitation for further improving and fine-tuning machine learning-based stomatal study methods is due to the small, inconsistent, and monotypic nature of stomatal datasets, which are also not easily accessible. To address this issue, our collection comprises about 11,000 unique images of hardwood leaf stomata gathered from projects conducted between 2015 and 2020-2022. The dataset includes over 7,000 images of 17 frequently encountered hardwood species, including oak, maple, ash, elm, and hickory, as well as over 3,000 images of 55 genotypes from seven Populus taxa (as detailed in Table 1). Each image has been labeled as either <em>inner guard cell walls</em> or <em>whole_stomata</em> (stomatal aperture and guard cells) and has a corresponding YOLO label file that can be transformed to other annotation formats. These images and labels are publicly available, making it easier to train machine-learning models and examine leaf stomatal traits. By utilizing our dataset, users can (1) use state-of-the-art machine learning models to identify, count, and quantify leaf stomata; (2) investigate the diverse range of stomatal characteristics across different types of hardwood trees; and (3) create new indices for measuring stomata.</p>
Tree-ring width measurements and isotope data for riparian Populus species, Santa Clara River, 2019
This data set comprises tree-ring data collected from 114 cottonwood trees (Populus trichocarpa and Populus fremontii) within the floodplain of the Santa Clara River, CA. Tree-ring data include annual ring width measurements for all rings of each individual as well as semi-annual (earlywood and latewood) measurements of stable carbon and oxygen isotopes for pure alpha cellulose extracted from annual growth rings corresponding to calendar years 2010-2019 for a subset of 48 individuals. This data set is completed. Carbon and oxygen isotope ratios are reported using “delta” notation (i.e. δ13C and δ18O) calculated by the equation: δ13C (or δ18O) = (Rsample/Rstandard - 1) x1000 where R is the molar ratio of 13C/12C (or 18O/16O), with Rsample being that of tree ring cellulose and Rstandard that of Vienna Pee Dee Belemite (VPDB) for δ13C and Vienna Standard Mean Ocean Water (VSMOW) for δ18O. These data were used for the following publication: Williams, J., J.C. Stella, S.L. Voelker, A.M. Lambert, L. Pelletier, J.E. Drake, J.M. Friedman, D.A. Roberts, M.B. Singer. (2022). Local groundwater decline exacerbates response of dryland riparian woodlands to climatic drought. Global Change Biology.
Data from: Populus euphratica WRKY1 binds the promoter of PeHA1 to enhance gene expression and salt tolerance
<p>Plasma membrane proton pumps play a crucial role in maintaining ionic homeostasis in salt-resistant <i><span>Populus euphratica</span></i> under saline conditions<i><span>. </span></i>High levels of NaCl (200 mM) induced <i><span>PeHA1</span></i> expression in <i><span>P. euphratica</span></i> roots and leaves. We isolated a 2022-bp promoter fragment upstream of the translational start of <i><span>PeHA1 </span></i>from<i><span> P. euphratica</span></i>. The promoter-reporter construct <i><span>PeHA1-pro</span></i>::<i><span>GUS</span></i> was transferred to tobacco plants, demonstrating that β-glucuronidase activities increased in root, leaf, and stem tissues under salt stress. DNA affinity purification sequencing revealed that PeWRKY1 protein targeted the<i><span> PeHA1</span></i> gene. We assessed the salt-induced transcriptional response of PeWRKY1 and its interaction with <i><span>PeHA1</span></i> in <i><span>P. euphratica</span></i>. PeWRKY1 binding to the <i><span>PeHA1 </span></i>W-box in promoter region was verified by a yeast one-hybrid assay, electrophoretic mobility shift assay, luciferase reporter assay, and virus-induced gene silencing. Transgenic tobacco plants overexpressing <i><span>PeWRKY1</span></i> had improved expression of <i><span>NtHA4, </span></i>which has a cis-acting W-box in the regulatory region, and H<sup><span>+</span></sup> pumping activity in both in vivo and in vitro assays. We conclude that salt stress upregulated <i><span>PeHA1</span></i> transcription due to the binding of PeWRKY1 to the W-box in the promoter region of <i><span>Pe</span></i><i><span>HA1</span></i>. Thus, we conclude that enhanced H<sup><span>+</span></sup> pumping activity enabled salt-stressed plants to retain Na<sup><span>+</span></sup> homeostasis.</p>
Text-fig. 5. Lauraceae, Platanaceae, Cercidiphyllaceae/Trochodendraceae. a: Sassafras hespera with 2 lobes, UAPC-ALTA S6556. b: cf. Lindera leaf. UAPC-ALTA S 67687. c: Macginitiea gracilis, UAPC-ALTA S 25748. d: Macginicarpa capitulum showing florets grouped in fives, UAPC-ALTA S 59507. e, g: Platanaceous fruitlets with basal tufts of dispersal hairs, UAPC-ALTA S 25748B, S S275238. f: Macginicarpa infructesence with five attached capitula, UAPC-ALTA S 59507A. h: Leaf similar to Populus and Trochodendroides, BBM-PAL-P000010. i: Leaf similar to Populus and Trochodendroides, UAPC-ALTA S 59516. j: cf. Trochodendroides, UAPC-ALTA S 59516. k: Jenkinsella infructesence; Figured in Penhallow 1908, plate 33. l: cf. Leaf similar to Cercidiphyllum and Trochodendroides, BBM-PAL-P000010. Scale bars: a–c, f, h, j, l = 2 cm, d, i, k = 1 cm, e, g = 0.5 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 5. Lauraceae, Platanaceae, Cercidiphyllaceae/Trochodendraceae. a: Sassafras hespera with 2 lobes, UAPC-ALTA S6556. b: cf. Lindera leaf. UAPC-ALTA S 67687. c: Macginitiea gracilis, UAPC-ALTA S 25748. d: Macginicarpa capitulum showing florets grouped in fives, UAPC-ALTA S 59507. e, g: Platanaceous fruitlets with basal tufts of dispersal hairs, UAPC-ALTA S 25748B, S S275238. f: Macginicarpa infructesence with five attached capitula, UAPC-ALTA S 59507A. h: Leaf similar to Populus and Trochodendroides, BBM-PAL-P000010. i: Leaf similar to Populus and Trochodendroides, UAPC-ALTA S 59516. j: cf. Trochodendroides, UAPC-ALTA S 59516. k: Jenkinsella infructesence; Figured in Penhallow 1908, plate 33. l: cf. Leaf similar to Cercidiphyllum and Trochodendroides, BBM-PAL-P000010. Scale bars: a–c, f, h, j, l = 2 cm, d, i, k = 1 cm, e, g = 0.5 cm.
Figure 2 in Epiphytic Bryophyte And Lichen Communities In Relation To Tree And Forest Stand Variables In Populus Tremula Forests Of South-East Latvia
Figure 2. Epiphytic bryophyte and lichen species in the studied territories. Tade Micr – Microreserve in Tadenava, Augs land – Augšzeme Protected Landscape Area, Star Rese – Starinas mežs Nature Reserve. Signal species include all WKH indicator species and red-listed species.
Figure 1. Studied territories. 1 in Epiphytic Bryophyte And Lichen Communities In Relation To Tree And Forest Stand Variables In Populus Tremula Forests Of South-East Latvia
Figure 1. Studied territories. 1 – Microreserve in Tadenava, 2– Augšzeme Protected Landscape Area, 3 – Starinas mežs Nature Reserve.
Populus deltoides (Salicaceae) - inflorescence - whole - male
Image of Populus deltoides (Salicaceae) - inflorescence - whole - male
Populus deltoides (Salicaceae) - inflorescence - whole - female
Image of Populus deltoides (Salicaceae) - inflorescence - whole - female
Populus deltoides (Salicaceae) - inflorescence - whole - female
Image of Populus deltoides (Salicaceae) - inflorescence - whole - female
Populus deltoides (Salicaceae) - inflorescence - whole - female
Image of Populus deltoides (Salicaceae) - inflorescence - whole - female
Populus deltoides (Salicaceae) - inflorescence - whole - male
Image of Populus deltoides (Salicaceae) - inflorescence - whole - male
Populus deltoides (Salicaceae) - inflorescence - whole - male
Image of Populus deltoides (Salicaceae) - inflorescence - whole - male
Populus deltoides (Salicaceae) - inflorescence - whole - male
Image of Populus deltoides (Salicaceae) - inflorescence - whole - male
Populus deltoides (Salicaceae) - twig - close-up winter leaf scar/bud
Image of Populus deltoides (Salicaceae) - twig - close-up winter leaf scar/bud
ScienceDex guides
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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.