Scientists have access to more information in electronic form than at any other time in history. Despite this increased access scientists report information overload because the quantity of information far exceeds human processing capacity. This talk will draw examples from health care and the environment to demonstrate how informatics can help to address these grand challenges. Specifically, the talk will introduce the Claim Framework that reflects how authors across the sciences communicate findings in empirical studies. The Framework captures different levels of evidence by differentiating between explicit and implicit claims, and by capturing underspecified claims such as correlations, comparisons, and observations. The results from 29 full-text articles show that authors report fewer than 7.84% of scientific claims in an abstract, thus revealing the urgent need for text mining systems to consider the full-text of an article rather than just the abstract. The results also show that authors typically report explicit claims (77.12%) rather than an observations (9.23%), correlations (5.39%), comparisons (5.11%) or implicit claims (2.7%). Informed by the initial manual annotations, we introduce an automated approach that uses syntax and semantics to identify explicit claims automatically and measure the degree to which each feature contributes to the overall precision and recall. Results show that a combination of semantics and syntax is required to achieve the best system performance.