Automated Author ProfileTsoulias, Nikos
Leibniz Institute for Agricultural Engineering and Bioeconomy0000-0002-6248-4333
Tsoulias, Nikos
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author'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: 3.8 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The data set captures four measurements during fruit growth: 06/28/2022 (15:00), 07/12/2022 (15:00), 09/01/2022 (15:00), 09/06/2022 (13:00)Additionally, diurnal courses are provided for three days: DateTime09/2107:00, 08:00, 10:00, 12:00, 13:00, 18:0009/2207:00, 08:00, 10:00, 12:00, 13:00, 18:0010/0506:30, 07:00, 09:00, 10:00, 11:00, 16:00 The data set was measured in Blocks (A-D) of trees (T) on apple (A) fruit and is stored as compressed zip files, capturing raw, preprocessed, and manually recorded reference (ground truth) data.1. zip files entitled Raw_YYYY_MM_DD_Block[A-C]-[R, L] for seasonal data and Raw_YYYY_MM_DD_BlockD-[R, L]Hour:_ for diel data- raw data of LiDAR 3D point clouds - txt files- raw image data by thermal camera - txt files2. zip files entitled YYYY_MM_DD or DailyAcquisitions:- preprocessed (merged) sensor data of temperature-annotated 3D point clouds of canopies - csv files- preprocessed data, capturing manually segmented point clouds of temperature-annotated fruit - txt files3. Microsoft Excel files entitled References and Weather data:- raw data, representing reference data of fruit - xlsx file- raw data of weather conditions - xlsx file
Authors
- Zude-Sasse, Manuela ;
- Regen, Christian ;
- Jörissen, Sven ;
- Bignardi, Marco ;
- Tsoulias, Nikos
The data set captures four measurements during fruit growth: 06/28/2022 (15:00), 07/12/2022 (15:00), 09/01/2022 (15:00), 09/06/2022 (13:00)Additionally, diurnal courses are provided for three days: DateTime09/2107:00, 08:00, 10:00, 12:00, 13:00, 18:0009/2207:00, 08:00, 10:00, 12:00, 13:00, 18:0010/0506:30, 07:00, 09:00, 10:00, 11:00, 16:00 The data set was measured in Blocks (A-D) of trees (T) on apple (A) fruit and is stored as compressed zip files, capturing raw, preprocessed, and manually recorded reference (ground truth) data.1. zip files entitled Raw_YYYY_MM_DD_Block[A-C]-[R, L] for seasonal data and Raw_YYYY_MM_DD_BlockD-[R, L]Hour:_ for diel data- raw data of LiDAR 3D point clouds - txt files- raw image data by thermal camera - txt files2. zip files entitled YYYY_MM_DD or DailyAcquisitions:- preprocessed (merged) sensor data of temperature-annotated 3D point clouds of canopies - csv files- preprocessed data, capturing manually segmented point clouds of temperature-annotated fruit - txt files3. Microsoft Excel files entitled References and Weather data:- raw data, representing reference data of fruit - xlsx file- raw data of weather conditions - xlsx file
Authors
- Zude-Sasse, Manuela ;
- Regen, Christian ;
- Jörissen, Sven ;
- Bignardi, Marco ;
- Tsoulias, Nikos