The following map uses data from the IMF Direction of Trade. IMF Trade Map body { font-family: sans-serif; margin: 20px; } #container { max-width: 800px; margin: auto; } #map { height: 700px; width: 100%; } #slider-container { margin-top: 20px; width: 159%; } input[type="range"] { width: 100%; } #slider-label { text-align: center; margin-top: 5px; font-size: 1.1em; } let allData = []; let periods = []; fetch('/data.

Use IMF API to automatically collect relevant data soures Process data as needed Create metrics to define majority import (in this case which ever of the three countries the share of the imports are from) Combine geojson file with data so that there is a proper geospatial file with the neceesary information Creating visuals of the dataset Start to finish process in one Jupyter Notebook GitHub Repository

Define metrics: Availability = Bike Paths (km)/ Roads (km) Quality = ( Seperated Bikepaths / All Bike Paths )*100 Utilize API to collect data from OpenStreetMap Acquire appropriate shapefiles from the city authorities Clean and process data Create data visualizations and report findings GitHub Repository

Extracted features from the Media Exposure and Opinion Formation (MEOF) survey. Processed large quantities (millions of rows) of user trace data from YouGov Utilized Python and R for proper data cleaning Segmented and documented code for modularized implementation Optimized Machine Learning models, neural networks, and utilized natural language processing for topic modeling The model performance for ideology is significantly better. GitHub Repository Paper

This project expands on the work by Sewerin, S., Kaack, L.H., Küttel, J. et al. Towards understanding policy design through text-as-data approaches: The policy design annotations (POLIANNA) dataset. Sci Data 10, 896 (2023). https://doi.org/10.1038/s41597-023-02801-z. The POLIANNA is a dataset of policy texts from the European Union (EU) that are annotated based on theoretical concepts of policy design, which can be used to develop supervised machine learning approaches for scaling policy analysis.