Automated Author ProfileLuis Dias
Luis Dias
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: 3.2 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This repository incorporates a benchmark dataset for the multi-asset system resource definition and allocation problem. A thorough description of the dataset is provided in a data article, aiming at extending the instances used to study the approach proposed in the paper "A co-evolutionary matheuristic for the multi-asset system maintenance budget definition and allocation problem". In total, the provided dataset contains 216 problem instances that cover different characteristics of the tackled problem, such as the problem size and degradation variability. Researchers can use the dataset to evaluate future algorithms for this problem and benchmark the performance with the existing algorithms. The dataset includes a program developed in Python that can be used to generate new problem instances. To enable the reproducibility of the showcased results and since the assets degradations are stochastic, we also provide access to the generated scenarios.
Authors
- Luis Dias
This repository incorporates a benchmark dataset for the multi-asset system resource definition and allocation problem. A thorough description of the dataset is provided in a data article, aiming at extending the instances used to study the approach proposed in the paper "A co-evolutionary matheuristic for the multi-asset system maintenance budget definition and allocation problem". In total, the provided dataset contains 216 problem instances that cover different characteristics of the tackled problem, such as the problem size and degradation variability. Researchers can use the dataset to evaluate future algorithms for this problem and benchmark the performance with the existing algorithms. The dataset includes a program developed in Python that can be used to generate new problem instances. To enable the reproducibility of the showcased results and since the assets degradations are stochastic, we also provide access to the generated scenarios.
Authors
- Luis Dias