Cynthia's Project Page

Who:  I am from South Jordan, Utah. I decided to go to the University of Utah because there are numerous opportunities here to expand my knowledge and grow. In my free time, I enjoy running my photography business and playing tennis.

My scientific/engineering interests:  I have loved math and science ever since I was little. I owe a lot of my passion to my dad, who always helped me with my math homework and pushed me in these subjects. I started coding this year and I hope to continue developing my skills in Java and R.

Academic goals:  I am currently pursuing a quantitative analysis of markets and organizations major with an emphasis in information systems along with a computer science minor. I hope to continue working in my lab and in different internships over the next few years to increase my coding and analytic skills.

Career goals:  I would like to apply my computer and data science skills to economics and produce ways to make data more accessible to the general public. I also want to utilize my skills to empower others within my community.

The Waterisotopes Database (wiDB) collects water isotope data from a multitude of contributors around the world that can be applied to hydrology, climatology, ecology, and forensics. The challenge with adding these data to wiDB, however, is that the formatting of the metadata is arbitrary: there are no standards for terminology or what characters (commas, slashes, etc.) can be used. The data must also be examined for duplicates and errors. This process typically requires taking each dataset individually and writing a R script specific to that dataset; however, to streamline this process, we started to develop an app using the Shiny R package that would allow the contributors to perform the data preparation and increase efficiency. The program is based on previous code that was written for specific data sets and utilized concepts that could be applied more broadly. Our implementation includes “for-loops'' to iterate through the selected columns to check that the input is the correct data type and within the correct range, logical operators to match the user-selected columns to our fields, and reactive functions that allow for inputs to be altered in the server and rendered in the user interface. With the app, the user will be able to process their own data, visualize and save the altered data frame, ultimately decreasing the discrepancies on how their dataset fits our guidelines.  This will allow for the processing of relational data sets for insertion to be more efficient, accessible to those who are not familiar with data science tools.

A Shiny App to Increase the Accessibility & Quantity of Openly Shared Data Download A Shiny App to Increase the Accessibility & Quantity of Openly Shared Data