Automated Author ProfileGuo, Jin L.C.
McGill University0000-0003-1782-1545
Guo, Jin L.C.
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: 18.6 (sum of 11 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This artifact contains details of the resources used in the study methodology, and the data analyzed for the paper "How Programmers Interact with Multimodal Documentation" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard accepted in the Proceedings of the International Conference on Cooperative and Human Aspects of Software Engineering (CHASE) 2025.
Authors
- Arya, Deeksha M. ;
- Guo, Jin L.C. ;
- Robillard, Martin P.
This artifact contains details of the resources used in the study methodology, and the data analyzed for the paper "How Programmers Interact with Multimodal Documentation" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard accepted in the Proceedings of the International Conference on Cooperative and Human Aspects of Software Engineering (CHASE) 2025.
Authors
- Arya, Deeksha M. ;
- Guo, Jin L.C. ;
- Robillard, Martin P.
This artifact contains details of the resources used in the study methodology, and the data analyzed for the paper "The Software Documentor Mindset" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard.
Authors
- Arya, Deeksha M. ;
- Guo, Jin L.C. ;
- Robillard, Martin P.
This artifact contains details of the resources used in the study methodology, and the data analyzed for the paper "The Software Documentor Mindset" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard.
Authors
- Arya, Deeksha M. ;
- Guo, Jin L.C. ;
- Robillard, Martin P.
This artifact contains details of the resources used in the study methodology, and the data analyzed for the paper "The Software Documentor Mindset" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard.
Authors
- Arya, Deeksha M. ;
- Guo, Jin L. C. ;
- Robillard, Martin P.
This artifact contains details of the resource collection, analysis scripts, and data analyzed in the paper "Properties and Styles of Software Technology Tutorials" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard.
Authors
- Deeksha M. Arya ;
- Jin L.C. Guo ;
- Martin P. Robillard
This artifact contains details of the resource collection, analysis scripts, and data analyzed in the paper "Properties and Styles of Software Technology Tutorials" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard.
Authors
- Deeksha M. Arya ;
- Jin L.C. Guo ;
- Martin P. Robillard
This artifact contains the the documents needed to replicate our user study as well as the collected data to validate our observations presented in the paper "How Programmers Find Online Learning Resources" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard.
Authors
- Arya, Deeksha M. ;
- Guo, Jin L.C. ;
- Robillard, Martin P.
This artifact contains the the documents needed to replicate our user study as well as the collected data to validate our observations presented in the paper "How Programmers Find Online Learning Resources" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard.
Authors
- Arya, Deeksha M. ;
- Guo, Jin L.C. ;
- Robillard, Martin P.
Training and test data for the machine learning experiments described in the paper Deep API Learning Revisited paper. Trained models are also included. Deep API Learning Revisited paper: https://doi.org/10.1145/3524610.3527872 GitHub repository: https://github.com/hapsby/deepAPIRevisited
Authors
- Martin, James ;
- Guo, Jin L.C.