发表机构
Advanced Institute for Materials Research (WPI-AIMR), Tohoku University; The University of Tokyo; Institute for Materials Research, Tohoku University（东北大学材料研究高等研究院（WPI-AIMR）; 东京大学; 东北大学材料研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文以固体电解质电导率数据为案例，揭示文献数据存在的各类误差会作为标签噪声误导AI，提出需重视科学数据的可追溯报告、整理与验证以保障AI驱动材料发现的可靠性。
AI 中文摘要
人工智能（AI）日益将科学文献视为构建数据库、训练预测模型及指导发现的数据源。然而，源自文献的数据集常假设所报告的实验值内部一致且可直接复用。本文以固体电解质（SE）电导率数据为代表性材料科学案例，分析该假设：通过追溯从源文献到整理后数据集的数值，识别出文本与图表不匹配、坐标轴标注模糊、单位不一致及测量背景缺失等问题。这些差异在数值上常具合理性，故难以通过常规预处理检测，但会在数据库构建及机器学习复用中作为结构化标签噪声传播。跨数据库实例显示，模糊报告可造成100倍的电导率误差。本分析将数据准确性重新定义为AI驱动发现的基础设施要求，并推动可复用科学数据的可追溯报告、整理与验证实践。
英文摘要
Artificial intelligence (AI) increasingly treats scientific literature as a data source for building databases, training predictive models, and guiding discovery. Yet literature-derived datasets often assume that reported experimental values are internally consistent and directly reusable. Here, we analyze this assumption using solid electrolyte (SE) conductivity data as a representative materials-science case. By tracing values from source articles to curated datasets, we identify recurrent text-figure mismatches, ambiguous axis annotations, unit inconsistencies, and missing measurement context. These discrepancies are often numerically plausible and therefore difficult to detect through routine preprocessing, but they can propagate as structured label noise during database construction and machine-learning reuse. A cross-database example shows how ambiguous reporting can create a 100-fold conductivity error. Our analysis reframes data accuracy as an infrastructure requirement for artificial-intelligence-driven discovery and motivates traceable reporting, curation, and validation practices for reusable scientific data. Keywords: AI for science; Data reliability; Scientific databases; Structured label noise; Literature-derived data; Materials informatics; Solid electrolytes
Comments3 main figures, 2 supplementary tables, research article on data reliability for AI-driven materials discovery
