Automated Author ProfileFranke, Katrin
Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
Franke, Katrin
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: 2.1 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Experimental and precomputed data for the paper "Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics" by Oesterle et al. 2020 (DOI: 10.7554/eLife.54997). The cone bipolar cell data has been described and published in the paper "Inhibition decorrelates visual feature representations in the inner retina" by Franke et al. 2017 (DOI: 10.1038/nature21394). This data is both a supplement to the Oesterle et al. paper and the code for this paper. The code is available in this GitHub repository. We recommend downloading the GitHub repository and to follow the instructions there.
Authors
- Oesterle, Jonathan ;
- Behrens, Christian ;
- Schröder, Cornelius ;
- Herrmann, Thoralf ;
- Euler, Thomas ;
- Franke, Katrin ;
- Smith, Robert G ;
- Zeck, Günther ;
- Berens, Philipp
Experimental and precomputed data for the paper "Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics" by Oesterle et al. 2020 (DOI: 10.7554/eLife.54997). The cone bipolar cell data has been described and published in the paper "Inhibition decorrelates visual feature representations in the inner retina" by Franke et al. 2017 (DOI: 10.1038/nature21394). This data is both a supplement to the Oesterle et al. paper and the code for this paper. The code is available in this GitHub repository. We recommend downloading the GitHub repository and to follow the instructions there.
Authors
- Oesterle, Jonathan ;
- Behrens, Christian ;
- Schröder, Cornelius ;
- Herrmann, Thoralf ;
- Euler, Thomas ;
- Franke, Katrin ;
- Smith, Robert G ;
- Zeck, Günther ;
- Berens, Philipp