CRAP: Colne River Assessment Project. This data was collected by Volunteers, myself, Drew Henderson, and Rob Furguson, a lecturer at the University of Essex, who is leading the project. (No date) Colne River Action Plan (CRAP). Available at: https://epigenetics.essex.ac.uk/shiny/crap/ (Accessed: 23 August 2024). Introduction- The Colne River Assessment Project (CRAP) is an ongoing initiative focused on monitoring and assessing the biodiversity and water quality in various waterways across East Essex and Suffolk. Over the past year, the project collected water samples from key water bodies, including the Colne, Stour, Hythe, and Tollesbury rivers. These samples were analyzed to evaluate the levels of E. coli, Enterococcus, and antimicrobial-resistant bacteria (AMR) such as Extended-Spectrum Beta-Lactamase (ESBL) and Vancomycin-Resistant Enterococci (VRE). The project involved volunteers and experts like Drew Henderson and Rob Furguson from the University of Essex. The primary goal was to understand how environmental factors, particularly tide conditions, impact bacterial contamination and to compare the microbial levels against UK inland bathing water standards.

Classifying images using keras

Assignment from John Hopkins' Developing Data Products course on Coursera. Mapping the top ten most populated cities in Michigan.

This study investigates the influence of macroprudential policy and financial stability announcements on government bond spreads, stock prices, and market volatility across Europe. Additionally, it explores whether the market response varies depending on the announcement's stance - tightening or loosening - and how other economic conditions play a role.

Daily: 1. Pull 100 news stories from Event Registry API       • Prompt: “2024 US presidential election” (no date range) 2. Chat-GPT-4o-mini uses the news to create election-result stories in 4 distinct voices       • Four characters, each representing different perspectives, will generate 100 stories: • Voice 1: Anonymous/Direct reporter • Voice 2: Fox Reporter Bret Baier • Voice 3: MSNBC Reporter Rachel Maddow • Voice 4: BBC Reporter Laura Kuenssberg 3. Generate election stories from each character’s perspective       • For each character, 100 stories are written about the election outcome in each state. 4. Extract the election winners from each story       • Use GPT to extract only the name of the winners from the stories for each character. 5. Save winners in a matrix       • Store winners as: • 1 = Trump/Republican • 0 = Harris/Democrat           • Create a matrix with 100 trials for each of the 50 states. 6. Run the steps daily       • Repeat the process daily, appending new results to the previous day’s data panel. 7. Present the results in a Shiny app       • Display the results in the app, showing: • Daily winner for each state and overall. • Percentage of daily trials that went to each party in each state.

Optimizing Wine Marketing: A Cluster Analysis