Meta-analyses suggest that the published literature represents only a small minority of the total data collected in biomedical research, with most becoming ‘dark data’ unreported in the literature. Dark data is due to publication bias toward novel results that confirm investigator hypotheses and omission of data that do not. Publication bias contributes to scientific irreproducibility and failures in bench-to-bedside translation. Sharing dark data by making it Findable, Accessible, Interoperable, and Reusable (FAIR) may reduce the burden of irreproducible science by increasing transparency and support data-driven discoveries beyond the lifecycle of the original study. We illustrate feasibility of dark data sharing by recovering original raw data from the Multicenter Animal Spinal Cord Injury Study (MASCIS), an NIH-funded multi-site preclinical drug trial conducted in the 1990s that tested efficacy of several therapies after a spinal cord injury (SCI). The original drug treatments did not produce clear positive results and MASCIS data were stored in boxes for more than two decades. The goal of the present study was to independently confirm published machine learning findings that perioperative blood pressure is a major predictor of SCI neuromotor outcome (Nielson et al., 2015). We recovered, digitized, and curated the data from 1125 rats from MASCIS. Analyses indicated that high perioperative blood pressure at the time of SCI is associated with poorer health and worse neuromotor outcomes in more severe SCI, whereas low perioperative blood pressure is associated with poorer health and worse neuromotor outcome in moderate SCI. These findings confirm and expand prior results that a narrow window of blood-pressure control optimizes outcome, and demonstrate the value of recovering dark data for assessing reproducibility of findings with implications for precision therapeutic approaches.

荟萃分析表明，已发表的文献仅占生物医学研究收集的总数据的一小部分，其中大多数成为文献中未报告的“暗数据”。暗数据是由于发表偏向于证实研究者假设的新颖结果而遗漏了不能证实研究者假设的数据。发表偏见会导致科学的不可重复性和临床转化的失败。通过使其可查找、可访问、可互操作和可重用 (FAIR) 来共享暗数据，可以通过提高透明度来减轻不可重复科学的负担，并支持超出原始研究生命周期的数据驱动发现。我们通过从多中心动物脊髓损伤研究 (MASCIS) 中恢复原始数据来说明暗数据共享的可行性，这是一项由 NIH 资助的多中心临床前药物试验，于 20 世纪 90 年代进行，测试了脊髓损伤后多种疗法的疗效。科学索引）。最初的药物治疗并没有产生明确的阳性结果，MASCIS 数据在盒子里保存了二十多年。本研究的目标是独立证实已发表的机器学习发现，即围手术期血压是 SCI 神经运动结果的主要预测因素（Nielson 等，2015）。我们恢复、数字化并整理了来自 MASCIS 的 1125 只大鼠的数据。分析表明，SCI 时围手术期血压高与较严重 SCI 的健康状况较差和神经运动结果较差相关，而围手术期血压较低与中度 SCI 的健康状况较差和神经运动结果较差相关。 这些发现证实并扩展了先前的结果，即血压控制的狭窄窗口可以优化结果，并证明了恢复暗数据对于评估研究结果的可重复性的价值，以及对精准治疗方法的影响。