Tracing the history and tractability of datasets is critical for confident target identification, yet dataset provenance and suitability are often poorly understood. This poster examines how historical context, curation methods, and original experimental intent influence the reliability of datasets used in AI driven target discovery. Key challenges include hidden bias, inconsistent annotations, and reuse of data beyond its intended scope. The discussion will explore how assessing dataset lineage and model tractability can improve target prioritization, reduce false positives, and strengthen biological confidence. Emphasis will be placed on aligning data selection with target biology and downstream translational relevance.