Automated Author ProfileWalther, Dominik
0000-0001-7450-6002
Walther, Dominik
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: 1.3 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset contains recordings of 100 welds with two different welding configurations. The dataset was recorded with three inductive probes which measure the absolute positional change of the metal sheet during the weld. A Long-wave Infrared (LWIR) camera was used to record the thermal radiation and receive a visual representation of the weld. The dataset can be utilized for forecasting purposes, such as gap prediction and classification tasks like quality monitoring.
Authors
- Walther, Dominik ;
- Schmidt, Leander ;
- Schricker, Klaus ;
- Junger, Christina ;
- Bergmann, Jean Piere ;
- Notni, Gunther ;
- Mäder, Patrick
This dataset contains recordings of 100 welds with two different welding configurations. The dataset was recorded with three inductive probes which measure the absolute positional change of the metal sheet during the weld. A Long-wave Infrared (LWIR) camera was used to record the thermal radiation and receive a visual representation of the weld. The dataset can be utilized for forecasting purposes, such as gap prediction and classification tasks like quality monitoring.
Authors
- Walther, Dominik ;
- Schmidt, Leander ;
- Schricker, Klaus ;
- Junger, Christina ;
- Bergmann, Jean Piere ;
- Notni, Gunther ;
- Mäder, Patrick