Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub
Mar 2026 – Mar 2026
1 Month active
Languages Used
MarkdownR
Technical Skills
data analysisdata visualizationmachine learningnatural language processingpsycholinguisticsstatistical modeling
Zee1230
PROFILE
Zee1230
Worked on the Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub repository to overhaul and consolidate documentation for a surprisal-based comparison study of human and large language model (LLM) text generation. Focused on clarifying the research question, dataset, and data processing plan, the work refined the project’s scope to emphasize the role of surprisal in psycholinguistics and natural language processing. Using R and Markdown, the developer improved documentation quality to better support onboarding and collaboration. No bugs were reported during this period, and the updates provided a clearer framework for future data analysis, statistical modeling, and machine learning contributions to the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
186,822
Activity Months1
Your Network
60 peopleShared Repositories
60
Work History
March 2026
3 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub focusing on documentation improvements for a surprisal-based comparison study between human and LLM language generation. The work consolidated and updated the project description to emphasize the research question, dataset, data processing plan, and expected contributions; refinements were made to reflect the surprisal focus and to clarify how surprisal relates to language models.
3 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub focusing on documentation improvements for a surprisal-based comparison study between human and LLM language generation. The work consolidated and updated the project description to emphasize the research question, dataset, data processing plan, and expected contributions; refinements were made to reflect the surprisal focus and to clarify how surprisal relates to language models.
March 2026
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage53.4%
Skills & Technologies
Programming Languages
MarkdownR
Technical Skills
data analysisdata visualizationmachine learningnatural language processingpsycholinguisticsstatistical modeling
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline