Automated Organization ProfileInstitut für Prozessdatenverarbeitung und Elektronik
Institut für Prozessdatenverarbeitung und Elektronik
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: 3.0 (sum of 5 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Microgrids are a promising solution for providing electricity access to rural populations in the Global South. To ensure such microgrids are affordable and effective, careful planning and dimensioning is required. Numerous tools exist to assist with this planning and dimensioning, however, they generally require microgrid electricity data, such as load profiles, as an input. Unfortunately, such electricity data is scarce for microgrids in the Global South and the little data that is available has a low temporal resolution. Therefore, we introduce high resolution real-world electricity data from three microgrids in the global south. The data is collected from microgrids in the Democratic Republic of Congo, Rwanda, and Haiti, and has a temporal resolution of up to five seconds. Furthermore, we include data from both residential and industrial microgrids, and consider microgrids with renewable generation from hydropower and photovoltaic arrays. We describe and analyse the characteristics of the recorded data and show how it can be used to derive flexible load profiles with varying temporal resolutions, useful for affordable microgrid planning and dimensioning.
Authors
- Luh, Matthias ;
- Phipps, Kaleb ;
- Britto, Anthony ;
- Wolf, Matthias ;
- Lutz, Marek ;
- Kraft, Johann
3D USCT II (2nd generation) experimental data. The following datasets are included: 1: Gelatin phantom 1 (plastic cup with gelatin an water inclusions) 2: Empty measurement with the same parameters as the gelatin phantom 1
Authors
- Ruiter, Nicole V. ;
- Zapf, Michael ;
- Hopp, Torsten
3D USCT II (2nd generation) experimental data. The following datasets are included: 1: Gelatin phantom 2 with embedded metal thread. 2: Empty measurement with the same parameters as the gelatin phantom 2, 1 aperture position.
Authors
- Ruiter, Nicole V. ;
- Zapf, Michael ;
- Hopp, Torsten
3D USCT III (3rd generation) experimental data. The following datasets are included: 1: Gelatin phantom with four inclusions made from PVC (spheres of different size 8 mm to 22 mm) 2: Empty measurement with the same acquisition parameters as the gelatin phantom.
Authors
- Ruiter, Nicole V. ;
- Zapf, Michael ;
- Hopp, Torsten
3D USCT II (2nd generation) experimental data. The following datasets are included: 1: Organic phantom: olive embedded in turkey steak and gelatin. 2: Empty measurement with the same parameters as the organic phantom.
Authors
- Ruiter, Nicole V. ;
- Zapf, Michael ;
- Hopp, Torsten