Bioart's hybrid nature spanning art, science, technology, ethics, and politics defies traditional single-axis categorization. I present BioArtlas, analyzing 81 bioart works across thirteen curated dimensions using novel axis-aware representations that preserve semantic distinctions while enabling cross-dimensional comparison. Our codebook-based approach groups related concepts into unified clusters, addressing polysemy in cultural terminology. Comprehensive evaluation of up to 800 representation-space-algorithm combinations identifies Agglomerative clustering at k=15 on 4D UMAP as optimal (silhouette 0.664 +/- 0.008, trustworthiness/continuity 0.805/0.812). The approach reveals four organizational patterns: artist-specific methodological cohesion, technique-based segmentation, temporal artistic evolution, and trans-temporal conceptual affinities. By separating analytical optimization from public communication, I provide rigorous analysis and accessible exploration through an interactive web interface (https://www.bioartlas.com) with the dataset publicly available (https://github.com/joonhyungbae/BioArtlas).

翻译：生物艺术兼具艺术、科学、技术、伦理与政治的混合特质，使得传统的单一轴线分类方式难以适用。我提出了BioArtlas，对81件生物艺术作品在13个精心设计的维度上进行分析，采用了新型的轴线感知表征方法，既保留语义区分，又支持跨维度比较。这一基于编码手册的方法将相关概念归入统一的聚类，以应对文化术语中的一词多义现象。通过对最多800种表征-空间-算法组合的全面评估，发现基于4D UMAP的凝聚式聚类（k=15）为最优方案（轮廓系数0.664 ± 0.008，可信度/连续性0.805/0.812）。该分析揭示了四种组织模式：艺术家特有的方法一致性、基于技术的分割、时间上的艺术演变，以及跨时间的概念亲和性。通过将分析优化与公众沟通分开，我提供了严谨的分析和可通过交互式网页界面（https://www.bioartlas.com）访问的探索方式，数据集也已公开（https://github.com/joonhyungbae/BioArtlas）。