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264 results for “grapevine”
Grapegenomics.com: a web portal with genomic data and analysis tools for wild and cultivated grapevines
<p><a href="https://grapegenomics.com">Grapegenomics.com</a> is a web portal that provides public access to genome references for grapevine cultivars (<em>Vitis vinifera</em> ssp. <em>vinifera</em>), wild grapevines (<em>Vitis vinifera</em> ssp. <em>sylvestris</em>), various wild grape species (<em>Vitis</em> spp. and <em>Muscadinia</em> spp.), and major fungal pathogens affecting grapes.</p> <p>All genomes are accessible through dedicated genome browsers, and published genomes are available for complete <a href="https://www.grapegenomics.com/download.php">download</a>.</p> <p>The site hosts all genomes produced by the laboratory of Dario Cantù in the Department of Viticulture and Enology at the University of California, Davis, along with published genome references generated by others, such as PN40024 and Pinot noir ENTAV115. Instructions for genome submission are provided <a href="https://www.grapegenomics.com/submit.php">here</a>. The portal is maintained by Noé Cochetel (ndcochetel[at]ucdavis.edu). In this version 2.0, all genome browsers utilize <a href="https://jbrowse.org/jb2/">jbrowse 2</a>. <br><br>Link to the website: <a href="https://www.grapegenomics.com">https://www.grapegenomics.com</a> </p>
Data for "Crop Diversification in Viticulture with Aromatic Plants: Effects of Intercropping on Grapevine Productivity in a Steep-Slope Vineyard in the Mosel Area, Germany"
<p>This dataset is corresponding to an open-access article named "Crop Diversification in Viticulture with Aromatic Plants: Effects of Intercropping on Grapevine Productivity in a Steep-Slope Vineyard in the Mosel Area, Germany" published in Agriculture (https://www.mdpi.com/2077-0472/11/2/95; <a href="https://doi.org/10.3390/agriculture11020095">https://doi.org/10.3390/agriculture11020095</a>), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
The complete reference genome for grapevine (Vitis vinifera L.) genetics and breeding
<div>PN40024, a highly homozygous inbred line originating from ‘Helfensteiner’, was used for T2T genome assembly. In total, 21 Gb (21 024 461 524 bp, ∼42× coverage) HiFi reads were generated by the PacBio platform. For the preliminary assembly, hifiasm was used to assemble the HiFi reads. We then used MUMmer and the 12X.v0 genome version (V. vinifera genome assembly 12X.v0 to order the 38 contigs into 19 chromosomes.</div> <p> The PN_T2T genome size was finally generated (494.87 Mb), being 69 Mb longer than 12X.v0 using the same statistical method. The k-mer metric was used to evaluate genomic homozygosity, estimated at 99.8%. The BUSCO for this genome is up to 98.5%.</p> <p>The PN40024.T2T genome assembly: PN.fa</p> <p>The PN40024.T2T gene annotation: PN_T2T.v5.1.gff3</p> <p>The PN40024.T2T TE annotation: PN_T2T_TE.gff</p> <p>The PN40024.T2T centromere annotation: PN.trf.gff3</p> <p>The PN40024.T2T protein sequence: PN_protein.fa</p> <p>The PN40024.T2T cds sequence: PN40024.cds.fa</p> <p>Comparison of gene annotation among PN_T2T and PN_T2T.v5.1, 12X.v0, 12X.v2, PN40024.v4, PN40024.v4.1: correlation.list.txt</p> <p>Mitochondrial assembly sequence of PN40024: PN_T2T_mit.fa</p> <p>Annotation of mitochondrial assembly for PN40024:PN_T2T_mit.gff3</p> <p>Chloroplast assembly sequence of PN40024: PN_T2T_chl.fa</p> <p>Annotation of chloroplast assembly for PN40024: PN_T2T_chl.gff3</p> <p>Citation: </p> <p>Please cite this paper when using the data of PN_T2T for your publications.</p> <p>Xiaoya Shi, Shuo Cao, Xu Wang, Siyang Huang, Yue Wang, Zhongjie Liu, Wenwen Liu, Xiangpeng Leng, Yanling Peng, Nan Wang, Yiwen Wang, Zhiyao Ma, Xiaodong Xu, Fan Zhang, Hui Xue, Haixia Zhong, Yi Wang, Kekun Zhang, Amandine Velt, Komlan Avia, Daniela Holtgräwe, Jérôme Grimplet, José Tomás Matus, Doreen Ware, Xinyu Wu, Haibo Wang, Chonghuai Liu, Yuling Fang, Camille Rustenholz, Zongming Cheng, Hua Xiao, Yongfeng Zhou, The complete reference genome for grapevine (<em>Vitis vinifera</em> L.) genetics and breeding, <em>Horticulture Research</em>, Volume 10, Issue 5, May 2023, uhad061, <a href="https://doi.org/10.1093/hr/uhad061">https://doi.org/10.1093/hr/uhad061</a></p>
Grapevine Aragon Data
<p>Dataset from Aragon Wineries</p> <p>"estacion": Name of the station to which the data belongs<br> "ubi": Name of the station to which the data belongs<br> "anio": Year to which the data belongs<br> "dia": Day of the year to which the data belongs<br> "fecha": Date to which the data belongs<br> "tmed_min": Minimum temperature reached in the station in that date.<br> "tmed_max": Maximum temperature reached in the station in that date.<br> "tmed_mean": Mean temperature reached in the station in that date.<br> "rad_min": Minimum solar radiation reached in the station in that date.<br> "rad_max": Maximum solar radiation reached in the station in that date.<br> "rad_mean": Mean solar radiation reached in the station in that date.<br> "hr_mean": Mean relative humidity measured in the station in that date.<br> "hourFrac_sum": As we are concerning on resumes from hourly data, this variable counts the number of hours included in such resume. If there are hors missing, it will be visible here.<br> "potentialDormancyDay": Binary variable. It takes value 0 if theoretically a grapevine ubicated near the station would be hibernating and 1 if not.<br> "season": Season to which the data belongs<br> "rad_sum": Solar radiation recived that date in the station<br> "precip_sum": Precipitations recived that date in the station<br> "wind_N": Mean wind speed in the nord component<br> "wind_NE": Mean wind speed in the nordeast component<br> "wind_E": Mean wind speed in the east component<br> "wind_SE": Mean wind speed in the southeast component<br> "wind_S": Mean wind speed in the south component<br> "wind_SW": Mean wind speed in the southwest component<br> "wind_W": Mean wind speed in the west component<br> "wind_NW": Mean wind speed in the northwest component<br> "SeasonDay_t0": Day of the year starting the 't0' accumulation (used in the cummulative variables that come before).<br> "SeasonDay_1": Day of the year starting the '1' accumulation (used in the cummulative variables that come before). It should be the 1 of January<br> "SeasonDay_2": Day of the year starting the '2' accumulation (used in the cummulative variables that come before). It should be the 1 of February<br> "rad__X__Cumm": Accumulated radiation for the station and date acording to the start in X (it should be 0 if 'dia'<X) <br> "precip__X__Cumm": Accumulated precipitation for the station and date acording to the start in X (it should be 0 if 'dia'<X) <br> "winkler_T_Tbase": New winklre's points added for that station on that date if winkler index starts increasing on temperature 'T'<br> "gdd_T_X_Tbase_sum": New GDD (Growing Degree Day) points added for that station on that date if GDD index starts increasing on temperature 'T' and day of the year 'X'<br> "chillingDD_T_X_Tbase_sum": New chilling index points added for that station on that date if chilling index starts increasing on temperature 'T' and day of the year 'X'<br> "gdd_T_X_Tbase_sum_Cumm": Total GDD (Growing Degree Day) points of the station on that date if GDD index starts increasing on temperature 'T' and day of the year 'X'<br> "chillingDD_T_X_Tbasemin_sum": Total chilling index points of the station on that date if chilling index starts increasing on temperature 'T' and day of the year 'X'<br> "winkler_T_X_Tbase_Cumm": Total winklre's points of the station on that date if winkler index starts increasing on temperature 'T' and day of the year 'X'</p>
Identification of grapevine clones via high-throughput amplicon sequencing: a proof-of-concept study VCF files
<p>VCF files used and cited in the article: Identification of grapevine clones via high-throughput amplicon sequencing: a proof-of-concept study</p>
Global predictions for the risk of establishment of Pierce's disease of grapevines
<p>Example to compute the risk of Pierce's Disease establishment from ERA5-Land temperature data. Related to the publication <em><a href="https://www.nature.com/articles/s42003-022-04358-w" target="_blank" rel="noopener">Global predictions for the risk of establishment of Pierce’s disease of grapevines</a></em></p> <p>See <a href="https://github.com/agimenezromero/PierceDisease-GlobalRisk-Predictions" target="_blank" rel="noopener">GitHub</a> repo.</p>
Increases in vein length compensate for leaf area lost to lobing in grapevine
<p><span></span></p> <p>There is considerable variation in leaf lobing and leaf size, including among grapevines, some of the most well-studied leaves. We examined the relationship between leaf lobing and leaf size across grapevine populations which varied in extent of leaf lobing. We used homologous landmarking techniques to measure 2,632 leaves across two years in 476 unique, genetically distinct grapevines from 5 biparental crosses which vary primarily in the extent of lobing. We determined to what extent leaf area could explain variation in lobing, vein length, and vein to blade ratio. Although lobing was the primary source of variation in shape across the leaves we measured, leaf area varied only slightly as a function of lobing. Rather, leaf area increases as a function of total major vein length, total branching vein length, and decreases as a function of vein to blade ratio. These relationships are stronger for more highly lobed leaves, with the residuals for each model differing as a function of distal lobing. For a given leaf area, more highly lobed leaves have longer veins and higher vein to blade ratios, allowing them to maintain similar leaf areas despite increased lobing. These findings show how more highly lobed leaves may compensate for what would otherwise result in a reduced leaf area, allowing for increased photosynthetic capacity through similar leaf size.</p>
Phenotypic data from: from buds to shoots: insights into grapevine development from the Witch's Broom bud sport
<p><strong>Background </strong></p> <p>Bud sports occur spontaneously in plants when new growth exhibits a distinct phenotype from the rest of the parent plant. The Witch's Broom bud sport occurs occasionally in various grapevine (<em>Vitis vinifera</em>) varieties and displays a suite of developmental defects, including dwarf features and reduced fertility. While it is highly detrimental for grapevine growers, it also serves as a useful tool for studying grapevine development. We used the Witch's Broom bud sport in grapevine to understand the developmental trajectories of the bud sports, as well as the potential genetic basis. We analyzed the phenotypes of two independent cases of the Witch's Broom bud sport, in the Dakapo and Merlot varieties of grapevine, alongside wild type counterparts. To do so, we quantified various shoot traits, performed 3D X-ray Computed Tomography on dormant buds, and landmarked leaves from the samples. We also performed Illumina and Oxford Nanopore sequencing on the samples and called genetic variants using these sequencing datasets.</p> <p><strong>Results</strong></p> <p>The Dakapo and Merlot cases of Witch's Broom displayed severe developmental defects, with no fruit/clusters formed and dwarf vegetative features. However, the Dakapo and Merlot cases of Witch's Broom studied were also phenotypically different from one another, with distinct differences in bud and leaf development. We identified 968–974 unique genetic mutations in our two Witch's Broom cases that are potential causal variants of the bud sports. Examining gene function and validating these genetic candidates through PCR and Sanger-sequencing revealed one strong candidate mutation in Merlot Witch's Broom impacting the gene GSVIVG01008260001.</p> <p><strong>Conclusions</strong></p> <p>The Witch's Broom bud sports in both varieties studied had dwarf phenotypes, but the two instances studied were also vastly different from one another and likely have distinct genetic bases. Future work on Witch's Broom bud sports in grapevine could provide more insight into development and the genetic pathways involved in grapevine.</p>
Fig. 2 in Flight ability and dispersal of European grapevine moth gamma-irradiated males (Lepidoptera: Tortricidae)
Fig. 2. Schematic representation of the vineyard and the pheromone traps positions at the experimental plot where the marked male moths were released.
Fig 1 in Flight ability and dispersal of European grapevine moth gamma-irradiated males (Lepidoptera: Tortricidae)
Fig 1. Schematic representation of the flight assessment cage used to measure the flight responses of Lobesia botrana males to calling females. In the female compartment, 2-day-old virgin females were confined inside a small cylindrical plastic mesh box with a 5% sucrose-wetted wick. Males irradiated either with 150 Gy or with 350 Gy and untreated males differentially marked with variously colored fluorescent powders were introduced into the male compartment. The number of males of each of the 3 kinds that flew through the open slit at the 45 cm height [the 2 lower openings (slits) were sealed] into the female compartment were recorded at 24, 48, 72 and 96 h. Air was drawn into the female compartment and exhausted from the male compartment.
Agroforestry system with Pine trees and grapevine (vitiforestry system)
<p>An agroforestry system with 4 rows of grapevine (<em>Vitis vinifera</em>) between rows of stone pine trees (<em>Pinus pinea</em>) in plot B7 of Domaine de Restinclières (France) (coordinates 43.724202 , 3.859748), grapevine and trees were planted in 1996. Picture taken on 2020-06-16</p>
Tractor in a grapevine agroforestry system
<p>Small tractor working in grapevine (<em>Vitis vinifera</em>) in an agroforestry system (vitiforestry) with Pine trees (<em>Pinus pinea</em>) in plot B7 of Domaine de Restinclières (France) (coordinates 43.724202 , 3.859748), grapevine and trees were planted in 1996. Picture taken on 2019-09-30</p>
Vector biology of the soft scales Parthenolecanium corni (Bouché) and Parthenolecanium persicae (Fabricius) (Hemiptera: Coccidae) with grapevine leafroll-associated viruses and grapevine virus A
<p>Raw tables of Elisa and RT-PCR results of LR1 and GVA transmission tests by the soft scales Parthenolecanium corni (Bouché) and Parthenolecanium persicae (Fabricius) (Hemiptera: Coccidae) to grapevine</p>
Grapevine Dataset for plant organ segmentation
<p><br>This is a dataset for Grapevine segmentation for the purposes of winter pruning, created in a joint work between Istituto Italiano di Tecnologia and Università Cattolica del Sacro Cuore, as part of the Vinum project. The work was also cofounded by Italian Ministry of University and Research PRIN 20172HHNK5 Project, under the supervision of Matteo Gatti, Claudio Semini and Darwin Caldwell.</p> <p>The images were captured in the simulated grapevine garden in Università Cattolica, located in Piacenza, Italy, and it is split in two parts, following agronomic trials being run on the university. The first part is a group of seven specimen that is the Control group, and the second group of eight specimens is being performed shoot thinning.</p> <p>Each plant has around 5 spurs, and the photos are taken from both sides of the grapevine specimen, meaning that there is one picture where the plant grows from left to right, and the other side where the plant grows from right to left. The resolution of the images is 4608x3456, and we have a total of 149 images. </p> <p> </p> <p>It is annotated using the COCO Segmentation format, and some of the statistics of the dataset such as the number of annotations and number of images can be seen next.</p> <p><strong>Control Complex</strong></p> <ul> <li>Total <ul> <li>Images: 69</li> <li>Annotated Images: 69</li> <li>Annotations: 1838</li> <li>Categories: 5</li> </ul> </li> <li>Annotations Per Category <ul> <li>Main Cordon: 79</li> <li>Cane: 440</li> <li>Node: 1100</li> <li>Arm: 103</li> <li>Spur: 116</li> </ul> </li> <li>Annotated Images Per Category <ul> <li>Main Cordon: 69</li> <li>Cane: 69</li> <li>Node: 69</li> <li>Arm: 62</li> <li>Spur: 68</li> </ul> </li> </ul> <p><strong>Shoot Thining Simple</strong></p> <ul> <li>Total <ul> <li>Images: 79</li> <li>Annotated Images: 79</li> <li>Annotations: 1635</li> <li>Categories: 5</li> </ul> </li> <li>Annotations Per Category <ul> <li>Main Cordon: 84</li> <li>Cane: 341</li> <li>Node: 912</li> <li>Arm: 154</li> <li>Spur: 144</li> </ul> </li> <li>Annotated Images Per Category <ul> <li>Main Cordon: 79</li> <li>Cane: 79</li> <li>Node: 79</li> <li>Arm: 79</li> <li>Spur: 77</li> </ul> </li> </ul> <p> </p>
Trinity assembly of Downy mildew of Grapevine lesion samples DMG-C
<p>De novo Trinity assembly from RNAseq</p>
MicroCT scans of sun and shade grown leaves of Cabernet Sauvignon and Blaufränkisch grapevine (Vitis vinifera L.) cultivars
<p>Image data set of sun and shade-grown leaves of Cabernet Sauvignon (CS) and Blaufränkisch (BF) grapevine (Vitis vinifera L.) cultivars.</p> <p>When using this dataset, please cite:</p> <blockquote> <p>Théroux-Rancourt G, Herrera C, Voggeneder K, Luijken N, Nocker L, Savi T, Scheffknecht S, Schneck M, Tholen D. (accepted) Analyzing anatomy over three dimensions unpacks the differences in mesophyll diffusive area between sun and shade Vitis vinifera leaves. AoB Plants</p> </blockquote> <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) in pots (Cabernet Sauvignon (CS)) or as cut shoots with the cut end placed in water (Blaufränkisch (BF)). Before scanning, a leaf was cut from the stem and a thin strip of ~1.5 mm width and 1.5 cm length was cut in between apparent higher-order veins, immediately wrapped in polyimide tape and inserted into a styrofoam block glued with wax onto a holder. Three (CS) or two (BF) strips were cut at different locations on the leaf surface to ensure within-leaf replications and to get better leaf-level averages. The strip was immediately scanned by imaging 1801 projections of 100 ms under a beam energy of 21 keV and magnified using a 40x (CS) or 20x (BF) objective, yielding respective final voxel sizes of 0.1625 µm (field of view: ~416x416x312 µm) and 0.325 µm (field of view: ~832x832x624 µm). Scanned projections were reconstructed to cross-sectional view using both absorption (gridrec; Marone <em>et al.</em> 2012) and phase contrast enhancement (Paganin <em>et al.</em> 2002) reconstructions.</p> <p><br> <br> <strong>Dataset description</strong></p> <p>After aligning the stacks to be parallel to the image edges using ImageJ (Schneider <em>et al.</em> 2012), at least nine slices were hand labeled using a graphics pen display tablet to precisely segment the background, the epidermis, and the vasculature. The mesophyll cells and the intercellular airspace were segmented by thresholding each absorption and phase contrast scan to maximize airspace volume (background) and taking care to avoid false segmentation within the cells (i.e. false segmentation of airspace). The hand-labeled slices were then used to automatically segment the whole stack using a Python-based random-forest machine learning approach (Théroux-Rancourt, Jenkins, <em>et al.</em> 2020).</p> <p> </p> <p><strong>File naming convention</strong></p> <p>For all stacks, files start with:<br> <em>Cultivar_Treatment_Plantn_leaf_N_</em></p> <ul> <li>Cultivar: <em>CS</em> for Cabernet Sauvignon, <em>BF</em> for Blaufränkisch</li> <li>Treatment: <em>Sun</em> for plants grown under high light, <em>Shade</em> for plants grown under low light</li> <li>Plantn: Plant number; 1-6 for CS, 1-5 for BF</li> <li>leaf: Leaf number; 1-4</li> </ul> <p><em>N</em> specifies the type of stack:</p> <ul> <li><em>GRID</em> (gridrec reconstruction)</li> <li><em>PAGANIN</em> (phase contrast enhancement reconstruction)</li> <li><em>labelled-stack</em> (hand labeled slices / ground truth; stacks have been hand labeled in cross-sectional view.)</li> <li><em>SEGMENTED</em> (automatically segmented stack using random-forest machine learning approach)</li> <li><em>STOMATAL_REGIONS_BBOX_CROPPED</em> (Stack with individually segmented stomatal vaporsheds, i.e. airspace closest to a stoma. The stack has been cropped in paradermal view around the stomata closest to the stack's edges, i.e. a bounding box (BBOX) with stomatal vaporsheds fully enclosed).</li> </ul> <p>All stacks are provided as 8-bit grayscale TIF files. Stacks have been hand labeled in cross-sectional view.</p> <p> </p> <p><strong>Plant material and growth conditions </strong></p> <p>The experiment was carried out over two consecutive years, in 2018 and 2019, at the facilities of BOKU UFT (Tulln, Austria). In the first year, rooted grafts of <em>Vitis vinifera </em>‘Cabernet Sauvignon’ (clone 191E) on 101-14 rootstock (hereafter named CS) were acquired from a local nursery (<em>Reben Iby</em>, Neckenmarkt, Austria). In the second year, rooted grafts of <em>Vitis vinifera </em>‘Blaufränkisch’ (clone 13-3 GM) on 5 BB rootstock (hereafter named BF) were acquired from the same nursery. Blaufränkisch is known to have originated from Lower Styria (present day Styria, Slovenia, Maul <em>et al. </em>2016) and to be genetically different from Cabernet Sauvignon (Magris <em>et al. </em>2021), which originated in the Bordeaux region in France.</p> <p>The rooted grafts were planted in 7-L pots and allowed to grow in a glasshouse without any environmental control. For CS, nutrient-rich, sieved vineyard soil mixed with perlite (3:1 ratio) was used, while for BF pots were filled with commercial pot substrate containing slow release fertilizer (10 g pot-1, 15-5-20 NPK “Entec vino”). Pots were watered to pot capacity automatically every day. Clones were planted June 1 and April 1 in the first and second year, respectively. When all the plants had at least three mature leaves on one shoot, plants were pruned so that only one dominant shoot remained. To ensure that only leaves fully developed under different light conditions were used for further analyses, the last developing leaf below the tip was marked before moving half of the plants to the shaded environment (on June 29, 2018 for CS and on April 18, 2019 for BF). For the shade treatment, a tent of about 2 m (height) x 1.5 m (width) x 1.5 m (depth) was made from black polypropylene cloth (HaGa-Welt GmbH & Co. KG, Elze, Germany), resulting in a 60% reduction in photosynthetic photon flux density (PPFD). A spectrometer (FLAME-S-VIS- ES, Ocean Optics Inc. Largo, USA) was used to confirm that under both light conditions, relative differences in the contribution of red, green blue and far-red light to the total PPFD were below 5% (i.e. spectrally neutral shade).</p> <p>During the week before synchrotron microCT scanning (last week of August in 2018 and first week of September in 2019), one mature leaf per plant was selected at least three leaves above the previously mentioned mark indicating the last developing leaf at the start of the shade treatment. The measured leaves were estimated to be about one month old, resulting in an average daily light integral (DLI) of 30 (CS sun), 12 (CS shade), 24 (BF sun), and 10 (BF shade) mol m<sup>-2</sup> day<sup>-1</sup>. These estimates were computed using solar radiation measured at a weather station a few meters from the glasshouse, and using PPFD values measured inside the glasshouse. Average daily PPFD was below 700 μmol m<sup>-2</sup> s<sup>-1</sup> under full light, with maximum recorded values at leaf level of ~1200 μmol m<sup>-2</sup> s<sup>-1</sup> under full light and ~500 μmol m<sup>-2 </sup>s<sup>-1</sup> under shade, i.e. ~60% reduction.</p> <p> </p> <p><strong>References</strong></p> <p><strong>Marone F, Stampanoni M</strong>. <strong>2012</strong>. Regridding reconstruction algorithm for real-time tomo- graphic imaging. <em>J. Synchrotron Radiat. </em><strong>19</strong>: 1029–1037.</p> <p><strong>Paganin D, Mayo SC, Gureyev TE, Miller PR, Wilkins SW</strong>. <strong>2002</strong>. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. <em>J. Microsc. </em><strong>206</strong>: 33–40.</p> <p><strong>Schneider CA, Rasband WS, Eliceiri KW</strong>. <strong>2012</strong>. NIH Image to ImageJ: 25 years of image analysis. <em>Nat. Methods </em><strong>9</strong>: 671–675.</p> <p><strong>Théroux-Rancourt G, Jenkins MR, Brodersen CR, McElrone A, Forrestel EJ, Earles JM</strong>. <strong>2020</strong>. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning. <em>Appl. Plant Sci. </em><strong>8</strong>: e11380.</p>
FIGURE 2 in Phylogenetic Relationship Among Wild and Cultivated Grapevine in Sicily: A Hotspot in the Middle of the Mediterranean Basin
FIGURE 2 | Analyses of Sicilian sativa and sylvestris germplasm. Discriminant Analysis of Principal Components (DAPC) (A); Principal coordinates analysis (PCoA) (B); first round of STRUCTURE (C) with percentage (pies) for each cluster and population (D); second round of STRUCTURE for cluster A (E) and cluster B (F).
FIGURE 3 in Phylogenetic Relationship Among Wild and Cultivated Grapevine in Sicily: A Hotspot in the Middle of the Mediterranean Basin
FIGURE 3 | Analyses of Sicilian, Mediterranean and Central Asian sativa and sylvestris germplasm. Discriminant Analysis of Principal Components (DAPC) (A); Principal coordinates analysis (PCoA) (B); first round of STRUCTURE (C) with percentage (pies) for each cluster and population (D); Second round of STRUCTURE
Biometeorological Dataset for 'Novel algorithms for high resolution prediction of canopy evapotranspiration in grapevine'
<p>A head trained <strong><em>Vitis vinifera</em></strong> L. cv. Zinfandel vine was grafted on St. George rootstock (<em>V. rupestris</em>) then planted in a 1.1 m<sup>3</sup> plastic container filled with Yolo County, CA sourced sandy loam.<br> <br> To estimate evapotranspiration, we measured the wind speed, air temperature and relative humidity in vine canopies by mounting each vine with a suite of research grade sensors. We measured wind speed (units m ᐧ s<sup>-1</sup>) inside the vine canopy using a single needle anemometer (<em>East 30 Sensors</em>; Pullman, WA) that took instantaneous wind speed measurements every 10 seconds and recorded the average of the previous 12 instantaneous measurements for every 2-minute interval.</p> <p>We measured temperature (units <sup>o</sup>C) and relative humidity (units %) using HMP60L sensors (Campbell Scientific; Logan, UT) mounted both inside and outside of each vine canopy and recorded instantaneous measurements at each 2-minute interval. We filtered all biometeorological data using a 3-hour moving average to remove noise without causing any significant over or under-approximation of daily maxima and minima.</p> <p>We automated all data collection using two CR1000 data loggers (<em>Campbell Scientific</em>; Logan, UT), with 1 or 2 vines and associated sensors per logger, using custom CR1 programs. A single 30W solar cell and 12V lead acid battery powered the entire vine-sensor system.</p> <p>This dataset represents all sensor data from a single vine, as measured in August 2020. Columns are named accordingly and include units.</p> <p><strong>Please Note</strong>: The column named 'load_cell_kg' is not named accurately. The values given are in units of millivolts, and need to be translated from millivolts to kilograms. The 2020 calibration coefficient is 0.00330693663 millivolts per kilogram.</p>
High spatial resolution dataset of grapevine yield components at the within-field level
<p>This dataset comprises a comprehensive mapping of vine yield at the plant scale over two vine fields located in the southern region of France. Both vine fields were planted with the Vitis vinifera : cv. Syrah. The first field (Field 1) occupies 0.8 ha and data were collected in 2022, while the second field (Field 2) has an area of 0.5 ha and data were collected in 2008. Throughout the growing season, information regarding unproductive vines, inflorescence number, and bunch weight was collected for both vine fields. For both fields, at the flowering stage, the location of each productive and unproductive vines (dead and missing vines) was georeferenced, and the number of inflorescences was manually counted for all productive vines. For Field 1, at harvest, all bunches of the field were manually weighed with an accuracy of ±1 gram and georeferenced precisely (one point per vine). For each vine, total yield (grams per vine) was then computed as as the sum of the weight of its bunches. For Field 2, at harvest, the total yield per vine was estimated based on the weighing of representative bunches obtained from several regularly spaced set of 5 vines. In addition to the yield data, two ancillary data, including soil apparent resistivity measurements and common vegetative index derived from remote sensed imagery, are provided for both vine fields. Overall, the dataset consists of 3644 vines, with 2151 being productive, along with a total count of 33354 inflorescences and 19635 manually weighed bunches at harvest.</p> <p>Raw data includes 9 shapefiles (.shp), one per data type and per field.</p> <ul> <li>“Field1_Dead_Missing_Vines.shp” (Figure 1.A) contains the location of missing and dead vine identified in Field 1;</li> <li>“Field1_Inflorescences.shp” and “Field2_Inflorescences.shp” (Figure 1.B and Figure 1.C) both contain the location and the number of inflorescences per vine counted during flowering;</li> <li>“Field1_Final_Yield.shp” and “Field2_ Final_Yield.shp” (Figure 1.D and Figure 1.E) both contain location and measured values of yield weight per vine at harvest. For Field 1, the list of the bunch weight is also available;</li> <li>“Field1_Soil_Resistivity.shp” and “Field2_ Soil_Resistivity.shp” (Figure 1.F and Figure 1.G) contain the electrical resistivity measurements of the soil on each field;</li> <li>“Field1_Vegetation_Index.shp” and “Field2_ Vegetation_Index.shp” (Figure 1.H and Figure 1.I) contain vegetation index values, NDVI (Normalized Difference Vegetation Index, without unit) for Field 1 and FCover (Fraction of vegetation Cover, in %) for Field 2.</li> </ul> <p>Aggregated data are composed of three csv files, two for Field 1 and one for Field 2. “Field1_Yield.csv” and “Field2_Yield.csv” aggregate all available yield data for each vine plant (either productive or other types). In both these csv files, each line represents a planted vine. Another file named "Field1_Bunches.csv" contains another representation of the data for Field 1. In this file, each row corresponds to a weighed bunch.</p> <p>A Data in Brief article is associated to this dataset.</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.