Docker Implementation for Reproducible Creative Commons Data Quantification Pipeline

## Description
This issue proposes implementing Docker containerization to enhance the reproducibility and consistency of Creative Commons data analysis workflows. The containerized infrastructure directly supports the project's mission to quantify the size and diversity of openly licensed works while addressing key technical challenges outlined in the project requirements.

## Relevance to Creative Commons Mission
• Docker containerization directly supports quantifying "the size and diversity of the
commons"
• Ensures reproducible analysis of Creative Commons works across different environments
• Aligns with open source principles by providing consistent, shareable infrastructure
• Docker ensures identical execution environment for all Creative Commons data sources
• Eliminates "works on my machine" issues affecting data consistency
Containerized services support persistent data volumes for multi-day operations

### Impact on Creative Commons Quantification

This infrastructure ensures that Creative Commons data analysis produces consistent, reproducible results regardless of the computing environment, supporting the project's open source mission and enhancing collaboration among contributors working to quantify the global commons.

## Implementation

- [x] I would be interested in implementing this feature.

ファイル、テスト、サービス、エントリポイントは指定されていません。まずリポジトリの構成と既存の分析ワークフローを調査し、その後、必要なコンテナ、データソース、永続ボリューム、実行手順を明確にしてください。文書化された Docker 環境で定量化パイプラインが再現可能な形で実行できれば、完了とします。

索引モデルが issue の本文から書いたものです。