The goal of this three-day short course, held May 13–15, 2020, is to introduce quantitative scientists interested in socio-environmental synthesis to Shiny/R as a way of shrinking the gap between information generation in science and policy making or public outreach through the creation of web-based tools.
In trying to close the disconnect between science and decision making, an important challenge is translating scientific findings into interpretable results and tools that decision makers can use. Because traditional scientific articles often cannot effectively communicate the practical implications of complex analyses to policy makers and practitioners, there is emerging interest in using interactive graphics to better communicate scientific results. However, the potential for developing interactive graphics into prototypes of decision-support tools that can bridge this communication gap is still underappreciated.
Web-based interactive tools can facilitate stakeholder engagement and provide greater transparency in decision making, which can be particularly useful for controversial topics. By promoting greater involvement as well as a shared understanding of trade-offs that are often unavoidable among different scenarios, these decision support tools can help forge a path forward for wicked problems.
This course aims to introduce quantitative scientists interested in socio-environmental synthesis to Shiny/R as a way of shrinking the gap between information generation in science and policy making or public outreach through the creation of web-based tools.
Short Course Details
This will be a three day short course held from May 13–15, 2020 in Annapolis, MD. This course will:
- Introduce participants to web-based tools, covering the basics of Shiny for the interactive display of data and model results
- Explore how quantitative researchers can transition from statistical models to policy-relevant decision making. We will also discuss some potential pitfalls associated with developing interdisciplinary decision support tools.
- Provide an introduction to structured decision-making process
- Discuss how the incorporation of value-based parameters within web-apps can be useful for decision making
- Discuss best practices for app accessibility, development, and maintenance.
Throughout this course, considerable time will be provided for participants to practice and develop their own web-based tools. For this reason, participants are highly encouraged to bring their own ideas regarding interactive tools they would like to develop, together with any relevant data, to start creating these tools during the course
Quantitative scientists from various backgrounds (e.g., ecologists, environmental scientists, geographers, social scientists, epidemiologists), either in training (graduate students and postdocs) or working professionals in academia, government agencies, private sector, or other non-governmental organizations should apply. As a pre-requisite, participants should have a solid understanding of R syntax and basic programming logic.
All participants should bring their own laptops, with R and RStudio pre-installed.
Costs and Travel
There is no cost to attend this short course, but participants will be responsible for their own travel and accommodations. There will be limited support for travel and accommodations for those with a high need as determined on a case-by-case basis.
How To Apply
Please complete the application at the link below:
Lyndsie Wszola, University of Nebraska-Lincoln
Chad Palmer, University of Florida
For questions, please contact one of the instructors above.
Valle, Denis, Kok Ben Toh, and Justin Millar. "Rapid prototyping of decision support tools for conservation." Conservation Biology 33, No. 6 (March 2019). https://doi.org/10.1111/cobi.13305
Wszola, Lyndsie S., et al. "Translating statistical species-habitat models to interactive decision support tools." PLOS One 12, No. 12 (2017): e0188244. https://doi.org/10.1371/journal.pone.0188244
Gerber, Leah R., et al. "Endangered species recovery: a resource alloction problem." Science 362, No. 6412 (2018):284–6. https://doi.org/10.1126/science.aat8434
The University of Maryland Is an Equal Opportunity Employer.
Minorities and Women Are Encouraged to Apply.