The big picture: Scientific data and models are and will continue to be used and developed by the scientific community.  Scientists are aware that providing these data and models to the community is essential in securing a better and more sustainable future.  In regions such as the Southwestern US with high water stress, it is imperative that scientists learn to effectively communicate these data and models, enabling forward-looking strategies and communication to achieve water sustainability informed by evidence. 

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The problem:  Due to the complexity of the issues involved in addressing water stress and their nature to cross environmental, political, and economic variables, interactive simulations are often used to promote communication and translation between experts and decision-makers.  This being only one mechanism for presenting data and models I will be looking at.

 

How am I addressing it:  My research focuses on how people understand, communicate, and reason about future scenarios of food and water availability using scientific data and models.  We want to know to what extent scientific data and models can facilitate understanding, decision making, and impact user knowledge and competencies about food and water sustainability. Our study questions focus on data and model reasoning competencies, including understanding, knowledge, systems thinking, strategic thinking, futures thinking, collaboration skills, data skills, and values thinking.  The mechanisms we will be investigating for presenting scientific data and models include static graphs, canned models, interactive models, and conceptual models.  This will be done through a series of workshops where participants will be exposed to more complex data and models each time.  They will also be using a variety of online tools. 

 

Importance: These findings will prove useful to improving our understanding of how non-scientists engage with scientific models and data.  We will also be able to assess the effectiveness of the various mechanisms used to present scientific data and models to these non-scientific users.