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Data now plays a central role in civic life and community practices. This has created a pressing need for new forms of translation and sense-making that can engage diverse publics. Research-based Theatre (RbT) has proven to be an effective approach to delivering qualitative data to community stakeholders. We extend this tradition by proposing “community-engaged data theatre”. This approach translates quantitative data into theatrical language to engage communities in deliberative conversations on relevant issues. Community-engaged data theatre requires bridging multiple disciplines and involves creating new definitions and shared vocabularies in discourses that formerly have had little overlap in meaning. In this article, we share key insights from our initial experiments in which we adapted quantitative and qualitative data to devise a pilot piece in collaboration with a local community partner. In this essay, we communicate our collaborative process in polyvocal, artistic form. We edit and adapt materials from our conversations and creative practices into scenes illustrating how we taught and learned from each other about data science, participatory modeling, material deliberation and Composition to pilot our lab’s first community-engaged data theatre prototype.

Understanding Food–Energy–Water (FEW) systems is crucial in order to plan for a resilient and sustainable future of interdependent urban–rural regions. While research tends to focus on urban transitions, the topic remains understudied relative to urban-rural regions. The often conflicting pressures in these regions (e.g., urbanization and growing crop production) may pose distinctive challenges where large urbanizations are adjacent to sparsely populated rural areas. These systems may further shift in response to local and global economic and demographic trends, as well as climate change. Identifying these complex system trajectories is critical for sustainability and resilience planning and policy, which requires the pooling of both urban and rural expertise across multiple disciplines and domains. We convened panels of subject matter experts within a participatory causal loop diagramming (CLD) approach. Our workshops were facilitated by our research team to collaboratively construct the web of connections among the elements in the urban–rural FEW system. The CLDs and the discussions around them allowed the group to identify potentially significant lever points in the system (e.g., support for minority farmers to enhance food security while reducing waste), barriers to sustainability (e.g., laws restricting the sale of water treatment biosolids), and potential synergies across sectors (e.g., food and green energy advocacy jointly pressing for policy changes). Despite the greater understanding of urban–rural interdependence afforded by participatory CLD, urban factors were consistently prioritized in the representation of the integrated system, highlighting the need for new paradigms to support sustainable urban–rural transitions.

Participatory Modeling (PM) is becoming increasingly common in environmental planning and conservation, due in part to advances in cyberinfrastructure as well as to greater recognition of the importance of engaging a diverse array of stakeholders in decision making. We provide lessons learned, based on over 200 years of the authors’ cumulative and diverse experience, about PM processes. These include successful and, perhaps more importantly, not-so-successful trials. Our collective interdisciplinary background has supported the development, testing, and evaluation of a rich range of collaborative modeling approaches. We share here what we have learned as a community of participatory modelers, within three categories of reflection: a) lessons learned about participatory modelers; b) lessons learned about the context of collaboration; and c) lessons learned about the PM process. First, successful PM teams encompass a variety of skills beyond modeling expertise. Skills include: effective relationship-building, openness to learn from local experts, awareness of personal motivations and biases, and ability to translate discussions into models and to assess success. Second, the context for collaboration necessitates a culturally appropriate process for knowledge generation and use, for involvement of community co-leads, and for understanding group power dynamics that might influence how people from different backgrounds interact. Finally, knowing when to use PM and when not to, managing expectations, and effectively and equitably addressing conflicts is essential. Managing the participation process in PM is as important as managing the model building process. We recommend that PM teams consider what skills are present within a team, while ensuring inclusive creative space for collaborative exploration and learning supported by simple yet relevant models. With a realistic view of what it entails, PM can be a powerful approach that builds collective knowledge and social capital, thus helping communities to take charge of their future and address complex social and environmental problems.