Automated Organization ProfileUniversität Bayreuth
Universität Bayreuth
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: 22.2 (sum of 18 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Measurement Data for Investigation of the influence of active cooling on battery cell testing.A detailled description of the tests and the arrangement of the numbered cells (see filenames) is given in the report, chapter II: Experimental.
Authors
- Röder, Fridolin ;
- Birner, Dominic ;
- Schweiger, Martin ;
- Danzer, Michael A.
Measurement Data for Investigation of the influence of active cooling on battery cell testing.A detailled description of the tests and the arrangement of the numbered cells (see filenames) is given in the report, chapter II: Experimental.
Authors
- Röder, Fridolin ;
- Birner, Dominic ;
- Schweiger, Martin ;
- Danzer, Michael A.
No description available
Authors
- Neuberger, Julian
No description available
Authors
- Neuberger, Julian
No description available
Authors
- Neuberger, Julian
Dataset and trained model used in the publication:Neural force functional for non-equilibrium many-body colloidal systems, T. Zimmermann, F. Sammüller, S. Hermann, M. Schmidt, and D. de las HerasarXiv 2406.03606 (2024).
Authors
- Zimmermann, Toni ;
- Sammüller, Florian ;
- Hermann, Sophie ;
- Schmidt, Matthias ;
- de las Heras, Daniel
Dataset and trained model used in the publication:Neural force functional for non-equilibrium many-body colloidal systems, T. Zimmermann, F. Sammüller, S. Hermann, M. Schmidt, and D. de las HerasarXiv 2406.03606 (2024).
Authors
- Zimmermann, Toni ;
- Sammüller, Florian ;
- Hermann, Sophie ;
- Schmidt, Matthias ;
- de las Heras, Daniel
No description available
Authors
- Hartel, Johannes ;
- Kraft, Marvin ;
- Till, Paul Simon ;
- Zeier, Wolfgang ;
- Strotmann, Kyra ;
- Faka, Vasiliki ;
- Maus, Oliver Marcel ;
- Banik, Ananya ;
- Ali, Mohammed Yusuf ;
- Helm, Bianca ;
- Li, Cheng ;
- Wiggers, Hartmut
The date sets contain the grain size data and EPMA data for the article "Bridgmanite grain size variation accounts for the mid-mantle viscosity jump" by H. Fei et al.
Authors
- Fei, Hongzhan ;
- Ballmer, Maxim ;
- Faul, Ulrich ;
- Walte, Nicolas ;
- Cao, Weiwei ;
- Katsura, Tomoo
The date sets contain the grain size data and EPMA data for the article "Bridgmanite grain size variation accounts for the mid-mantle viscosity jump" by H. Fei et al.
Authors
- Fei, Hongzhan ;
- Ballmer, Maxim ;
- Faul, Ulrich ;
- Walte, Nicolas ;
- Cao, Weiwei ;
- Katsura, Tomoo