Learning how to find relevant information sources is an important part of solving novel problems and mastering new domains. This paper introduces work on developing a lessons learned system that supports task-driven research by (1) automatically storing cases recording which information resources researchers consalt during their decision-making; (2) using these cases to proactlvely suggest information resources to consalt in similar future task contexts; and (3) augmenting existing information resources by providing tools to support users in elucidating and capturing records of useful information that they have found, for future reuse. Our approach integrates aspects of case-based reasoning, "just-in-time" task-based information retrieval, and concept mapping. We describe the motivations for this work and how lessons learned systems for suggesting research resources complement those that store task solutions. We present an initial system implementation that illustrates the desired properties, and close with a discussion of the primary questions and open issues to address.