When a paper flags your training data, the warning lives in a PDF

说明与用途

一篇同行评议论文指出两个流行的 Kaggle 数据集真实性无法验证，但警告仅存在于 PDF 中。创建了机器可读的注册表，以跟踪此类警告并防止其在下游模型中的使用。

问题

Clinical prediction models may continue to be used even after their training data is flagged as unverifiable, because the warning is embedded in a PDF and not reflected in the dataset metadata.

结果

The registry provides a transparent, citation‑based record of datasets flagged for authenticity or provenance issues, preventing their unverified use in downstream models.

工作流

• 1. Identify datasets cited in peer‑reviewed literature.
• 2. Review literature for authenticity or provenance warnings.
• 3. Create a JSONL row with dataset id, status, flags, source, and downstream impact.
• 4. Host the registry on GitHub with CC0 license and snapshot semantics.
• 5. Encourage contributions via pull requests with citations.
• 6. Use the registry to filter datasets before model training or deployment.

证据

The authenticity of both datasets could not be verified ...

来源

125 clinical prediction model studies in peer-reviewed publications