Automated Organization ProfileJulius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Grapewine Breeding, Siebeldingen, Germany
Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Grapewine Breeding, Siebeldingen, Germany
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.8 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Dataset contains high resolution images collected with a moving field phenotyping platform, the Phenoliner. The collected images show 3 different varieties (Riesling, Felicia, Regent) in 2 different training systems (VSP=vertical shoot positioning and SMPH= semi minimal pruned hedges), collected in 2 points in time (before and after thinning) in 2018. For each image we provide a manual masks which allow the identification of single berries.The folder contains: 1. List with image details (imagename, acquisition date, year, variety, training system and variety number)and 2. Dataset folder with 2 subfolders, namely 1. img – 42 original RGB images and 2. lbl – 42 corresponding labels (manual annotation, with berry, edge, background definition)The data were used to train a neural network with the main goal to detect single berries in images. The method is described in detail in the specified papers.
Authors
- Zabawa, Laura ;
- Kicherer, Anna