Making NIH Common Fund (CF) datasets FAIR is but the first step in realizing their potential within the “big data” revolution. Science progresses through the accumulation of knowledge, which achieves a wide reach only if it is accessible to a diverse spectrum of researchers. While computer scientists have made substantial strides in modeling knowledge within “knowledge graphs” (KGs), non-computational scientists can find it hard to interpret the graph-based reasoning tools and visualizations that accompany KGs because such tools use logical reasoning that does not account for scientific context or uncertainty and can produce a plethora of scientifically invalid inferences. Our CFDE KC will aim to present scientifically valid knowledge produced by CF projects. We will represent this knowledge as a KG, compliant with existing CFDE and external knowledge curation efforts. But we will focus on scientific validity through both (a) careful knowledge extraction, by ensuring that each edge in the KG is either a primary experimental finding or the result of an expert-applied analysis, and (b) careful knowledge presentation, by building a portal that de-emphasizes general-purpose graph traversal in favor of single-purpose visualizations. To implement this KC, we will draw from our experience managing four large-scale NIH-funded projects that have faced similar challenges in related settings.