Complex collaborative activities such as treating patients, co-authoring documents and developing software are often characterized by teamwork that is loosely coupled and extends in time. To remain coordinated and avoid conflicts, team members need to identify dependencies between their activities - which though loosely coupled may interact - and share information appropriately. The loose-coupling of tasks increases the difficulty of identifying dependencies, with the result that team members often lack important information or are overwhelmed by irrelevant information. This paper formalizes a new multi-agent systems problem, Information Sharing in Loosely-Coupled Extended-Duration Teamwork (ISLET). It defines a new representation, Mutual Influence Potential Networks (MIP-Nets) and an algorithm, MIP-DOI, that uses this representation to determine the information that is most relevant to each team member. Importantly, because the extended duration of the teamwork precludes team members developing complete plans in advance, the MIP-Nets approach, unlike prior work on information sharing, does not rely on a priori knowledge of a team's possible plans. Instead, it models collaboration patterns and dependencies among people and their activities based on team-members' interactions. Empirical evaluations show that this approach is able to learn collaboration patterns and identify relevant information to share with team members.