Automated Author ProfileVlasov, Ilya
Saint Petersburg State University
Vlasov, Ilya
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: 2.7 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The set of the artefacts for a SIGCSE-2022 paper. The SIGCSE_atrifacts.pdf document contains detailed information about each folder, also this document has supplementary materials. Folders:
- comparison. This folder contains a public dataset in Java for comparison of our code assessment tool with the Tutor tool.
- dynamics. This folder contains two datasets of Python and Java public submissions from Stepik and Hyperskill platforms to check the influence of our tool on students code style.
- plots. This folder contains examples of charts that were plotted to analyze the tool thresholds. Supplementary materials:
- Examples of code quality issues categories. This section in the document contains code snippets with examples of issues for each code quality issue category for Python and Java.
- List of available subcategories in the tool.
- Tables with penalty coefficients for detection of recurring errors algorithm
- Examples of charts that were plotted to analyze the tool thresholds. This section is the same with the plots folder.
Authors
- Birillo, Anastasiia ;
- Vlasov, Ilya ;
- Burylov, Artyom ;
- Selishchev, Vitalii ;
- Goncharov, Artyom ;
- Tikhomirova, Elena ;
- Vyahhi, Nikolay ;
- Bryksin, Timofey
The set of the artefacts for a SIGCSE-2022 paper. The SIGCSE_atrifacts.pdf document contains detailed information about each folder, also this document has supplementary materials. Folders:
- comparison. This folder contains a public dataset in Java for comparison of our code assessment tool with the Tutor tool.
- dynamics. This folder contains two datasets of Python and Java public submissions from Stepik and Hyperskill platforms to check the influence of our tool on students code style.
- plots. This folder contains examples of charts that were plotted to analyze the tool thresholds. Supplementary materials:
- Examples of code quality issues categories. This section in the document contains code snippets with examples of issues for each code quality issue category for Python and Java.
- List of available subcategories in the tool.
- Tables with penalty coefficients for detection of recurring errors algorithm
- Examples of charts that were plotted to analyze the tool thresholds. This section is the same with the plots folder.
Authors
- Birillo, Anastasiia ;
- Vlasov, Ilya ;
- Burylov, Artyom ;
- Selishchev, Vitalii ;
- Goncharov, Artyom ;
- Tikhomirova, Elena ;
- Vyahhi, Nikolay ;
- Bryksin, Timofey