Automated Author ProfileOthmer, Carsten
Othmer, Carsten
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: 0.4 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We publish a small companion dataset containing ten single–segment matchinginstances with $n\in{3,\dots,12}$ surfer–breaker pairs. For each $n$ weprovide two \texttt{NumPy} arrays,\texttt{$n-vehicles-breakers.npy} and \texttt{$n-vehicles-surfers.npy}. Thebreaker array stores, per vehicle, its class label, cruising velocity, anddeparture time, while the surfer array stores the surfer’s class, preferredvelocity, departure time, and individual flexibility intervals for speed anddeparture time. These instances are used to construct the edge weights$w_{s,b}$ and the corresponding QUBO matrices $Q$ for all experiments reportedin the companion paper.
Authors
- Onah, Chinonso ;
- Guin, Agneev ;
- Montanez Barrera, Alejandro ;
- Othmer, Carsten ;
- Michielsen, Kristel
We publish a small companion dataset containing ten single–segment matchinginstances with $n\in{3,\dots,12}$ surfer–breaker pairs. For each $n$ weprovide two \texttt{NumPy} arrays,\texttt{$n-vehicles-breakers.npy} and \texttt{$n-vehicles-surfers.npy}. Thebreaker array stores, per vehicle, its class label, cruising velocity, anddeparture time, while the surfer array stores the surfer’s class, preferredvelocity, departure time, and individual flexibility intervals for speed anddeparture time. These instances are used to construct the edge weights$w_{s,b}$ and the corresponding QUBO matrices $Q$ for all experiments reportedin the companion paper.
Authors
- Onah, Chinonso ;
- Guin, Agneev ;
- Montanez Barrera, Alejandro ;
- Othmer, Carsten ;
- Michielsen, Kristel
We publish a small companion dataset containing ten single–segment matchinginstances with $n\in{3,\dots,12}$ surfer–breaker pairs. For each $n$ weprovide two \texttt{NumPy} arrays,\texttt{$n-vehicles-breakers.npy} and \texttt{$n-vehicles-surfers.npy}. Thebreaker array stores, per vehicle, its class label, cruising velocity, anddeparture time, while the surfer array stores the surfer’s class, preferredvelocity, departure time, and individual flexibility intervals for speed anddeparture time. These instances are used to construct the edge weights$w_{s,b}$ and the corresponding QUBO matrices $Q$ for all experiments reportedin the companion paper.
Authors
- Onah, Chinonso ;
- Guin, Agneev ;
- Montanez Barrera, Alejandro ;
- Othmer, Carsten ;
- Michielsen, Kristel