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
During March 2026, this developer focused on enhancing the documentation for the Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub repository, specifically for a surprisal-based comparison study of human and large language model (LLM) text generation. They consolidated and updated the project description to clarify the research question, dataset, and data processing plan, ensuring the documentation reflected the central role of surprisal in the analysis. Working primarily in Markdown and R, they improved the clarity and structure of the documentation to support onboarding and collaboration. The work demonstrated depth in natural language processing, psycholinguistics, and statistical modeling, though no code-level bugs were addressed.
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
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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
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