Automated Author ProfileAlencar da Costa, Daniel
University of Otago0000-0003-4525-3266
Alencar da Costa, Daniel
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: 1.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Reproduction Package for the Paper "How do Machine Learning Projects use Continuous Integration Practices? An Empirical Study on GitHub Actions"This reproduction package contains the necessary materials to replicate the findings presented in the paper published at the Mining Software Repositories (MSR) conference in 2024.Folder Structure:- datasets: Contains all datasets used in the analysis of the Research Questions (RQs) of the paper.- plots: Contains plots generated to present the results of the investigated RQs of the study.- r-script: Contains all R scripts used in the analysis of the RQs, as well as scripts used to compute metrics such as build duration, time to fix broken builds, and test coverage.- RQ3-neovis-network-graph: Contains a README.txt file providing instructions to create the network graph used to present the results of RQ3 using the neovisjs library.Please refer to the specific folders for detailed information on how to reproduce the analysis and results presented in the paper.Furthermore, the code we used to retrieve data for the studied projects is available in the following GitHub repository: https://github.com/joaohelis/ml-ci-project-miner
Authors
- Bernardo, João Helis ;
- Alencar da Costa, Daniel ;
- Queiroz de Medeiros, Sérgio ;
- Kulesza, Uira
Reproduction Package for the Paper "How do Machine Learning Projects use Continuous Integration Practices? An Empirical Study on GitHub Actions"This reproduction package contains the necessary materials to replicate the findings presented in the paper published at the Mining Software Repositories (MSR) conference in 2024.Folder Structure:- datasets: Contains all datasets used in the analysis of the Research Questions (RQs) of the paper.- plots: Contains plots generated to present the results of the investigated RQs of the study.- r-script: Contains all R scripts used in the analysis of the RQs, as well as scripts used to compute metrics such as build duration, time to fix broken builds, and test coverage.- RQ3-neovis-network-graph: Contains a README.txt file providing instructions to create the network graph used to present the results of RQ3 using the neovisjs library.Please refer to the specific folders for detailed information on how to reproduce the analysis and results presented in the paper.Furthermore, the code we used to retrieve data for the studied projects is available in the following GitHub repository: https://github.com/joaohelis/ml-ci-project-miner
Authors
- Bernardo, João Helis ;
- Alencar da Costa, Daniel ;
- Queiroz de Medeiros, Sérgio ;
- Kulesza, Uira