Privacy reproducibility package

Full reproducibility package for "Benchmarking Privacy Compliance in EU Crypto-Asset Markets: A Quantitative Analysis of GDPR and MiCA Implementation" by Cordoba Otalora, Khalid, and Themistocleous. The paper is under journal review; an earlier version was accepted at AMCIS 2026 and withdrawn before the proceedings, so no proceedings paper or DOI exists. The study is a systematic quantitative benchmark of Crypto-Asset Service Provider (CASP) privacy disclosure quality in the early post-MiCA period. Privacy policies of 24 CASPs across 12 EU member states were retrieved and frozen between 3 and 16 March 2025 and coded against a 14-item rubric (5 categories: Transparency and Clarity, Consent and User Rights, Crypto-Specific Measures, Data Security and Transfers, Accountability; category weights 20/25/30/15/10 percent). The initial purposive pool contained 27 CASPs; the analytic sample of 24 retains no more than five CASPs per country. Every scored provider was matched to its MiCA authorisation outcome in the ESMA register. The package is the end-to-end trail from raw policy text to final composite score: the scoring rubric with regulatory anchors (01_rubric.csv); 24 structured analysis files with score, rationale, and verbatim justification quote per item (analysis/); the 24 x 14 item-level matrix (07_item_level_matrix.csv); the archived policy texts (documents/); entity composites and ranks (02_entity_scores.csv); category, country, correlation, and weighting-sensitivity tables (03-06); the paper's figures; and analysis notes. Version 2.0 (August 2026) corrects the summary tables and documentation against the item-level matrix, which is unchanged: regenerated category descriptives, country aggregates (the sample's twelfth state is Estonia; an earlier aggregate file listed Croatia in error), and weighting-sensitivity table; a corrected README (sample description, coding-protocol wording, citation status); and a new bootstrap script (scripts/ci_category_means.py) that computes the 95 percent confidence intervals reported in the manuscript and verifies the published composites before printing. All 24 composite scores reproduce exactly (to 0.01) from the item-level matrix. Released under CC-BY-4.0.