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392 results for “Grape”
December 2021 raw audio data at 9999 kHz using Grape 1 for radio station JJ1BDX
<p>This dataset contains all audio data (WAV, 8kHz S16_LE monaural) for the analysis of 10MHz standard frequency stations (mostly BPM, including WWVH and WWV) for HamSCI December 2021 Eclipse Festival recorded at radio station JJ1BDX in Setagaya City, Tokyo, Japan. </p> <p>Other references:</p> <ul> <li>See <a href="https://github.com/jj1bdx/hamsci-202112-freqdata">https://github.com/jj1bdx/hamsci-202112-freqdata</a> for the analysis results.</li> <li>See <a href="https://github.com/jj1bdx/dcrx-10MHz-design/">https://github.com/jj1bdx/dcrx-10MHz-design/</a> for the technical specification of the direct conversion receiver HamSCI Grape 1.</li> </ul> <p> </p>
Grape bunch and vine trunk dataset for Deep Learning object detection.
<p>Grape bunch and vine trunk dataset containing images and annotations for Deep Learning object detection.</p>
KD8CGH Grape 1 WWV 10 MHz
<p>KD8CGH, node N0000022 Grape 1 WWV 10 MHz 143 .csv files from 21-06-28 to 22-01-01</p>
Paninvasion severity assessment of a U.S. grape pest to disrupt the global wine market
<p class="MsoNormal">Economic impacts from plant pests are often felt at the regional scale, yet some impacts expand to the global scale through the alignment of a pest's invasion potentials. Such globally invasive species (i.e., paninvasives) are like the human pathogens that cause pandemics. Like pandemics, assessing paninvasion risk for an emerging regional pest is key for stakeholders to take early actions that avoid market disruption. <span>Here, we develop </span>the paninvasion severity assessment framework and use it to assess a rapidly spreading regional U.S. grape pest, the spotted lanternfly planthopper (<em>Lycorma delicatula; </em>SLF), to spread and disrupt the global wine market. We found that SLF invasion potentials are aligned globally because important viticultural regions with suitable environments for SLF establishment also heavily trade with invaded U.S. states. If the U.S. acts as an invasive bridgehead, Italy, France, Spain, and other important wine exporters are likely to experience the next SLF introductions. Risk to the global wine market is high unless stakeholders work to reduce SLF invasion potentials in the U.S. and globally.</p>
Grape V1 Data: Frequency Estimation and Amplitudes of North American Time Standard Stations
<p>Frequency and amplitude estimation data collected by Grape V1 stations in the Personal Space Weather Station network. </p>
Earrings with a grape pendant
ID no.: MAK/10157/71 Archaeological Museum in Kraków https://muzea.malopolska.pl/en/objects-list/1521 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
An advanced metabolomic approach untangles oviposition preference of grape skin by Drosophila suzukii
Open the record for dataset details and reuse information.
Fig. 1 in First record of Heliothis virescens (Lepidoptera: Noctuidae) damaging table grape bunches
Fig. 1. Larva of Heliothis virescens damaging grape bunches.
Fig. 3 in First record of Heliothis virescens (Lepidoptera: Noctuidae) damaging table grape bunches
Fig. 3. Holes made by Heliothis virescens in berries of a grape bunch.
Fig. 2 in First record of Heliothis virescens (Lepidoptera: Noctuidae) damaging table grape bunches
Fig. 2. Grape bunch damaged by Heliothis virescens worthless for the fresh market.
Fig. 2 in Establishment of the sea grape flatid, Petrusa epilepsis (Hemiptera: Fulgoroidea: Flatidae), in Florida
Fig. 2. Petrusa epilepsis feeding on stem of Hamelia patens (firebush).
Fig. 3 in Establishment of the sea grape flatid, Petrusa epilepsis (Hemiptera: Fulgoroidea: Flatidae), in Florida
Fig. 3. Petrusa epilepsis feeding on inflorescence of Stachytarpheta jamaicensis (blue porterweed).
Fig. 1 in Nocturnal vs. diurnal pollination of self-fertile peaches and muscadine grapes
Fig. 1. Fruit-set proportions by treatment groups in Redhaven peaches and various muscadine grapes.
Grape Bunch Video Dataset with Berry Annotations and Tracking Data
<p>The dataset consists of videos of Bonarda grape bunches in a mature state. A total of 100 grape bunches were collected from five consecutive rows of the same plot at the "Finca de Sancho" located in Lavalle, Mendoza, Argentina, on March 21, 2023. Each bunch was assigned a unique identifier after collection.</p> <p>For video capture, two setups were built, referred to as setup 1 and setup 2. Each consisted of a stand to hold a bunch against a smooth white background. This background was carefully chosen to facilitate precise segmentation of the grapes in the images, ensuring more efficient and accurate object detection. Additionally, a curved structure with a 10x7 grid of QR codes was placed behind the bunch. These codes were not used in this dataset but were included for potential future studies. The setups were mounted outdoors, taking advantage of natural lighting to obtain a more realistic representation of the grape bunches. Video capture sessions were conducted over three consecutive days, lasting between 4 to 7 hours each day. The first session took place on the same day the bunches were harvested. Throughout the sessions, natural light fluctuated due to intermittent cloud cover, introducing variations in ambient lighting.</p> <p>Video recording was performed by two individuals referred to as capturer A and capturer B. The recording devices were the cameras of two smartphones, a Samsung Galaxy S20 FE and a Motorola G200. The Open Camera application (<a href="https://opencamera.org.uk/" target="_new" rel="noopener">https://opencamera.org.uk/</a>) was used, configured to automatically capture 5-second videos at a resolution of 720 pixels wide by 1280 pixels high (portrait orientation), at 30 frames per second. A fixed focal distance of 30 cm was maintained, and the white balance was manually adjusted according to the natural light variations to maintain good image quality. </p> <p>Three camera movements were defined for video capture: two systematic movements named "horizontal 180º" and "vertical 180º", and a third movement called "freestyle." The horizontal and vertical movements involved sweeping the camera from left to right and from bottom to top, respectively, while keeping the bunch centered in the frame and at a distance of approximately 30 cm. The freestyle movement consisted of random movements, maintaining the bunch centered as best as possible within the frame.</p> <p>The capture protocol was as follows:</p> <p>First, a grape bunch was placed in each setup. Capturer A at setup 1 captured five videos for each of the three defined camera movements, while capturer B did the same at setup 2. Afterward, the capturers switched positions, with capturer B taking videos at setup 1 and capturer A at setup 2. The bunches were then replaced with new ones, and the process was repeated until all 100 bunches were captured.</p> <p>As a result, the dataset contains five videos for each of the three camera movements, captured by both individuals for each of the 100 bunches. This produces a dataset consisting of 3,000 videos: 1,000 videos for each camera movement and 30 videos per bunch. </p> <p>It is worth noting that, due to the manual nature of the capture process, some variations occurred in the number of videos recorded per bunch. In some cases, 9 videos were recorded instead of 10, in one instance only 8 were captured, and in one case 11 videos were obtained. These minor discrepancies are mainly due to human error in counting the recorded videos, but they do not affect the overall quality or integrity of the dataset.</p> <p> </p> <p>The dataset also includes berry annotations for each video, provided in JSON files. These files specify, for each frame of the video, the pixel coordinates of each berry's center and the radius it occupies in the image, also in pixels. The berry detections were obtained through inference using a deep learning architecture called CircleNet, which was specifically trained for this dataset.</p> <p>In addition, the dataset contains berry tracking data, provided in CSV files. These files indicate which berries are the same across different frames of the video, through a unique berry identifier. The tracking was generated using a custom algorithm developed specifically to produce these tracks.</p> <p> </p>
Biodiversity effects on grape quality depend on variety and management intensity
<p>Interactions between plants can be beneficial, detrimental or neutral. In agricultural systems competition between crop and spontaneous vegetation is a major concern. We evaluated the relative support for three non-exclusive ecological hypotheses about interactions between crop and spontaneous plants based on competition, complementarity or facilitation.</p> <p>The study was conducted in Swiss vineyards with different vegetation management intensities. Thirty-three vineyards planted with two different grape varieties were studied over 3 years to determine whether low-intensity vegetation management might provide benefits for grape quality parameters. Management intensity varied with the degree of control of spontaneous inter-row vegetation. Features of spontaneous vegetation measured included total cover, total species richness and abundance of nitrogen fixing plants. Grape quality parameters of known importance to wine-making (yeast assimilable nitrogen, sugars, tartric acid, and malic acid) were determined by Fourier-transform infrared spectroscopy (FTIR). Using structural equation modeling, we evaluated hypotheses about the multivariate responses of grape quality parameters as well as the direct and indirect (plant-mediated) effects of management. </p> <p>Observed effects of management differed between grape varieties. Management intensity and abundance of N-fixing plants significantly influenced grape quality parameters, while total richness of spontaneous plants did not have detectable effects. Abundance of N-fixing plants was enhanced by low-intensity management resulting in increased N content in the red grape variety (<i>Pinot noir</i>), potentially enhancing grape quality, while measured soil N content did not explain the increase.</p> <p>Synthesis: Our study shows that crop quality can be enhanced by spontaneous plants, in this case by the abundance of a key functional group (N-fixers), most likely through plant-plant or plant-microbe facilitation. However, beneficial interactions may have a high specificity in terms of facilitation partners and may have contrasting effects at low taxonomic resolutions such as crop varieties. Generally increasing plant biodiversity in agricultural systems may increase competition with crops. Thus, the identification of suitable interaction partners and a careful balance between crop variety and spontaneous plant species may be necessary to utilize beneficial interactions and to reduce the trade-off between agricultural production and biodiversity to achieve a sustainable ecological benefit in agricultural systems.</p>
Chemistry-weather interacted model system GRAPES_Meso5.1/CUACE CW V1.0
<p><span>The Chinese Meteorology Administration chemistry model CUACE is online integrated into the mesoscale operational weather prediction (NWP) model (GRAPES_Meso5.1) and aerosol-cloud-radiation interaction is achieved to establish the first version (V1) of chemistry-weather (CW) interacted model GRAPES-Meso5.1/CUACE CW V1. The most polluted winter 2016-2017 is selected to study the meteorology impacts on haze/fog prediction, the impact of aerosol-radiation, aerosol-cloud, and CW interaction (ARI, ACI, CWI) on haze/fog prediction, and NWP. Single way model without CWI displays reasonable PM<sub>2.5</sub> and visibility prediction in general. However, modeled PM<sub>2.5</sub> peaks are underestimated and visibility valleys are overestimated</span> <span>during haze/fog pollution, the underestimation of relative humidity (RH) contributes major to this misestimation; CWI model cut the negative errors of PM<sub>2.5</sub> peaks and the positive errors of visibility valleys. The improvement of 5km and 3km low visibility by CWI during severe haze/fog period is more obvious than that of 10 km, which just compensates for the largest deficiency in low visibility prediction related to severe haze/fog by single way model; The NWP including sea level pressures, relative humidity(RH), temperature, wind speed are also improved by CWI from surface to upper troposphere; ARI contributes larger to the predicted PM<sub>2.5</sub>, visibility and NWP improvement than that of ACI, their relative contributions varies with model vertical height and the overlapping condition of cloud and aerosols. Due to the joint contribution of RH and PM<sub>2.5</sub>, CWI's improvement in visibility is larger than PM<sub>2.5</sub>. This study illustrates the importance of including CWI in the air quality prediction model.</span></p>
Figure 2 in A phylogenetic analysis of the grape genus (Vitis L.) reveals broad reticulation and concurrent diversification during neogene and quaternary climate change
Figure 2 (See legend on next page.)
Red Grape Polyphenol Oral Administration to Women Affected by Nickel-mediated Allergic Contact Dermatitis
ClinicalTrials.gov study NCT03902392. IPD Sharing: NO. Countries: 1. Publications: 3.
The Effect of Concord Grape Polyphenol-soy Protein Isolate Complex (GP-SPI) on Gut Microbiota
ClinicalTrials.gov study NCT04018066. IPD Sharing: NO. Countries: 1. Publications: 1.
Muscadine Grape Skin Extract in Treating Patients With Malignancy That Is Metastatic or Cannot Be Removed by Surgery
ClinicalTrials.gov study NCT02583269. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
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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.