Data envelopment analysis (DEA) is a methodology for evaluating the relative efficiencies of a set of decision-making units (DMUs), based on their multiple inputs and outputs. The original model is based on the assumption that DMUs operate independently of one another. However, this assumption may not apply in some situations, as in the case we present in this paper, in which DMUs can work together to produce joint outputs. What makes it more interesting is the situation in which this characteristic of sharing outputs among some DMUs differs from one DMU to another; this makes it more challenging to determine independent efficiency scores that cater for this phenomenon. To address this, the current paper presents a methodology for measuring efficiency in situations in which DMUs share outputs with other units. We examine the case of a set of research groups in a Mexican university. For this study, the inputs used are professors belonging to various groups, and outputs are the published journal articles, some of which are produced completely within a group, whereas others arise from collaboration with professors from other research groups. Jointly published articles form a link connecting the groups.