Today’s high-stakes, time- and cost-intensive scientific studies are carried out with increasingly complex tools. This include instrumentation, software tools, and sample complexity that make it difficult to achieve experimental reproducibility. Along the way, intermediary results are measured in multidimensional, seemingly unstructured, data formats. With such large volumes of variegated inputs and intermediaries that could impact successful outcomes, we turn to a novel platform for real-time analytics to uncover latent, and thus potentially overlooked contributors to unsuccessful experimental results. Identifying and removing these obstacles promptly intercepts continued downstream propagation of these issues and yields the types of successful, meaningful outcomes that drive scientific innovation. We present case studies showing how seemingly unrelated data affecting experimental procedures can point towards unexpected root causes of irreproducibility and errors, and how “unknown unknowns” can be more readily identified.
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