||Squishy Amoeba-Like Objects
||May 9 2019
||On June 19th, 1970, a group of computer scientists who were inventing the internet referred to key pieces of its proposed design as "squishy amoeba-like objects". Amoebas are porous yet have well-defined boundaries. Thinking about these creatures gives us new ways to think about networks and communities and technology. This talk makes a case for the squishy amoeba-like object as an organizing principle for what is broadly being called "the decentralized web", a web outside of monolithic, monopolistic actors.
||Version Controlled Stakeholder Reporting: Building an End-to-End Data Reporting Infrastructure
||Jose M Hernandez
||May 9 2019
||Daisy Bingham Room
||King County, Washington is currently undergoing complex social and economic changes that have both positive and negative impacts on local residents. With rising rents displacing low-income households to outlying areas or into homelessness, there is a critical need to understand the prevalence and mechanisms of housing insecurity for government organizations tasked to address these issues. Currently, our team of Data and social scientists at the University of Washington, eScience Institute are collaborating with stakeholders across the King County Housing and Homelessness prevention agencies to derive meaningful insights from their data. While their aim is not to produce academic research, our findings may have significant and immediate impact for their organizational practices and the communities they are tasked to serve. In this context and where there is an iterative and constant feedback loop present, reproducibility of the results we present to them, from figures, tables, and even written language is critical. To ensure a successful collaboration, our team has built an end to end data reporting infrastructure to produce reports for our stakeholders that are reproducible and version controlled from raw data to final product. We employ some common open source tools to accomplish this, including R/Rstudio, Python, Rmarkdown, and git.
||A Love Letter to the Boxplot
||May 9 2019
||We'll briefly cover what the boxplot is, why it's so great to look at distributions instead of single statistics, and common boxplot variations. I'll spend at least half the talk showing boxplots of real data and comparing them to other summary methods. The talk will wrap up with some quick info on how to create boxplots in many common chartings/statistics/BI tools. I hope this talk will make people more likely to use my favorite chart!