Integrate arXiv processing script with standardized architecture

[Goziee-git]

Description

A fully integrated arXiv processing script that maintains its unique academic data processing capabilities while following the project's established architectural patterns and performance standards.

Technical Overview

The arXiv processing script arxiv_process.py processes fetched data from the arXiv API from the arxiv_fetch.py and ensures consistency with the project's processing pipeline architecture identifying optimization opportunities.

Similarities with Other Process Scripts

The arXiv processing script follows the established patterns found in GCS and GitHub processing scripts:

Shared Architecture:

• Standard argument parsing with --quarter, --enable-save, --enable-git flags
• Common imports and setup using shared.py module
• Consistent error handling with pygments-formatted tracebacks
• Git integration for fetch, merge, add, commit, and push operations
• CSV output with csv.QUOTE_ALL and Unix line terminators
• Pandas-based data processing and aggregation

Common Processing Pattern:

1. Parse arguments and setup paths
2. Git fetch and merge
3. Load CSV data from fetch phase
4. Process data into multiple aggregated views
5. Save processed data to CSV files
6. Git add, commit, and push changes

Key Differences and Unique Features

arXiv-Specific Processing:
• License categorization: Processes Creative Commons licenses with academic focus (CC0, CC BY, CC BY-
SA, etc.)
• Academic metadata: Handles arXiv-specific fields like categories, publication years, and author
counts
• Multi-dimensional analysis: Creates 7 different processed outputs:
• License totals
• Free Cultural Works categorization
• Restriction level analysis (0-3 scale)
• Category-based totals (academic disciplines)
• Year-based totals (publication timeline)
• Author count analysis

Data Processing Efficiency:
• Uses itertuples(index=False) for memory-efficient row iteration
• Implements domain-specific license classification logic
• Processes 4 separate input files vs. 3 for GCS

Areas for Integration Review

1. Code Standardization: Ensure consistent function naming and documentation patterns
2. Performance Optimization: Review data loading and processing efficiency compared to GCS script
3. Error Handling: Verify comprehensive error coverage for academic data edge cases
4. Output Consistency: Align CSV column naming conventions across all processing scripts
5. Git Integration: Ensure commit messages follow project standards

Expected Outcome

A fully integrated arXiv processing script that maintains its unique academic data processing capabilities while following the project's established architectural patterns and performance standards.

Implementation

• I would be interested in implementing this feature.

[TimidRobot]

Note: significant progress on this issue may not be possible until the parent issue is resolved:

• Integrate arXiv as data source for academic commons quantification #178