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2,113 results for “High resolution”

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zenodo40/100

High spatial resolution dataset of grapevine yield components at the within-field level

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. Raw data includes 9 shapefiles (.shp), one per data type and per field. <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> 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. A Data in Brief article is associated to this dataset.

opencc-by-4.0Jul 2023View details →

zenodo40/100

High spatial resolution dataset of downscaled LUH2 land use scenarios for Belgium (10 m and 100 m)

This dataset comprises high-resolution land use data downscaled from LUH2 scenarios for Belgium at both 10 m and 100 m resolutions. These datasets were generated based on research conducted by Rashidi et al. in 2023 and published in the Land Journal. We employed the GLOBIO land allocation routine to downscale fractional land use data, originally at a 0.25° resolution (approximately 25 km), into discrete land use maps at 10 m and 100 m resolutions. This process utilized three distinct reference land cover maps: ESA WorldCover at 10 m resolution, ESA WorldCover upscaled to 100 m resolution, and CORINE land cover at 100 m resolution. During the downsizing process, we considered three SSP-RCP scenarios to model land use trends for both the present and the year 2050 on a national scale in Belgium. Key components of the model included regional land use demand, an assessment of grid cells' suitability for various land use types, and a reference land cover map. It's important to note that the classification system used in the reference maps differs from that of LUH2. To ensure comparability for land use simulations, we conducted a reclassification process following the methodologies outlined by Pérez-Hoyos et al. (2012), Dong et al. (2018), and Liao et al. (2020). This reclassification consolidated land use classes, except for water, into seven general categories: 1) urban, 2) cropland, 3) pasture, 4) forestry, 5) secondary vegetation, 6) undefined, and 7) natural. The raw data consists of three folders corresponding to the three reference maps, each containing four TIFF files (.tif), one for each scenario type.

opencc-by-4.0Dec 2022View details →

zenodo40/100

Dataset of "High-resolution MHz trARPES based on a tunable VUV source"

This upload includes the experimentally measured data on Au111, Bi2Se3, and TaTe2 by time- and angle-resolved photoemission spectroscopy, as well as a laser spectrum of the Y-Fi VUV laser system.

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Dataset results

High spatial resolution dataset of grapevine yield components at the within-field level

High spatial resolution dataset of downscaled LUH2 land use scenarios for Belgium (10 m and 100 m)

Dataset of "High-resolution MHz trARPES based on a tunable VUV source"

Data from: Direct segmentation of cortical cytoarchitectonic domains using ultra-high-resolution whole-brain diffusion MRI

A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2017

Data from: Solanum pennellii (LA5240) backcross inbred lines (BILs) for high resolution mapping in tomato

A high-resolution and whole-body dataset of hand-object contact areas based on 3D scanning method

Data from: Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease

High-content high-resolution microscopy and deep learning assisted analysis reveals host and bacterial heterogeneity during Shigella infection

High-resolution CONUS-wide downscaled rainfall estimates (HRCDRE)

High-resolution 4DVAR-based Gulf Stream data-assimilative model product

Data for: Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data

Data from: Non-invasive age estimation based on fecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins

A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2018

Interactive 3D PDF file for the structures 10 hr juvenile of Oikopleura dioica and supplementary movie S1-20 with high resolution

Data from: High-resolution chromosome-level genome of Scylla paramamosain provides molecular insights into adaptive evolution in crab

A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics

High-resolution tropical rain-forest canopy climate data

Age estimation of captive Asian elephants (Elephas maximus) based on DNA methylation: An exploratory analysis using methylation-sensitive high-resolution melting (MS-HRM)

Data from: TC-GEN: Data-driven tropical cyclone downscaling using machine learning-based high-resolution weather model

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Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro