Traditional evaluation tools are oftentimes ill-suited for use in community settings where intervention outcomes of interest may occur at multiple levels and are influenced by interacting factors. Ripple effects mapping (REM) is a participatory technique engaging stakeholders to visually map project/program efforts and results to collect impact data. Using appreciative inquiry, the method helps participants and evaluators understand context for changes resulting from program experiences and is particularly well suited for community-based, participatory programs where impacts often occur beyond the individual level. This article describes the REM method and how it was applied to explore impact and refine program theory of the Extension Wellness Ambassador Program (EWAP), a community-based health-focused master volunteer program, from the perspective of program implementers ( n = 10). Insights emerging from the REM session indicate EWAP promotes health behavior change, contributes to community development due to increased leadership capacity, and sustains and grows implementing organizations. The program theory shaping evaluation was refined to better capture impact beyond individual levels. Application of the REM method to a community health program demonstrates feasibility; health promotion practitioners should consider REM to understand program context and capture outcomes that typically evade measurement using traditional techniques.

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Digital technology is having a major impact on many areas of society, and there is equal opportunity for impact on science. This is particularly true in the environmental sciences as we seek to understand the complexities of the natural environment under climate change. This perspective presents the outcomes of a summit in this area, a unique cross-disciplinary gathering bringing together environmental scientists, data scientists, computer scientists, social scientists, and representatives of the creative arts. The key output of this workshop is an agreed vision in the form of a framework and associated roadmap, captured in the Windermere Accord. This accord envisions a new kind of environmental science underpinned by unprecedented amounts of data, with technological advances leading to breakthroughs in taming uncertainty and complexity, and also supporting openness, transparency, and reproducibility in science. The perspective also includes a call to build an international community working in this important area.

Environmental data science is uniquely placed to respond to essentially complex and fantastically worthy challenges related to arresting planetary destruction. Trust is needed for facilitating collaboration between scientists who may share datasets and algorithms, and for crafting appropriate science-based policies. Achieving this trust is particularly challenging because of the numerous complexities, multi-scale variables, interdependencies and multi-level uncertainties inherent in environmental data science. Virtual Labs-easily accessible online environments provisioning access to datasets, analysis and visualisations-are socio-technical systems which, if carefully designed, might address these challenges and promote trust in a variety of ways. In addition to various system properties that can be utilised in support of effective collaboration, certain features which are commonly seen to benefit trust-transparency and provenance in particular-appear applicable to promoting trust in and through Virtual Labs. Attempting to realise these features in their design reveals, however, that their implementation is more nuanced and complex than it would appear. Using the lens of affordances, we argue for the need to carefully articulate these features, with consideration of multiple stakeholder needs on balance, so that these Virtual Labs do in fact promote trust. We argue that these features not be conceived as widgets that can be imported into a given context to promote trust; rather, whether they promote trust is a function of how systematically designers consider various (potentially conflicting) stakeholder trust needs. CCS CONCEPTS• Human-centered computing → Computer supported cooperative work; HCI theory, concepts and models.