Who Bears the Brunt?

Visualizing the Areas Most Impacted by Trump’s Grant Cuts

Nathaniel Cross

Introduction

  • Examination of grants terminated by the Trump Administration

  • Visualizations help understand who is most impacted by these terminations

    • Scientific domains

    • Geographic entities

  • Tracks grants housed under the National Science Foundation

National Science Foundation Grant Terminations

  • Derived from Grant Watch

    • Independent project tracking the termination of scientific research grants under the Trump Administration’s second term

    • Data is sourced largely by submissions from affected investigators

    • Tracks individual grant terminations, and includes information on the organization receiving the grant, date of termination, and value terminated

    • Time frame spans from April 18 to May 15

  • Supplementary data from the National Science Foundation and CNN Politics

Question 1

Which scientific domains saw the highest grant termination rates, and how do these terminations vary over time?

Introduction

  • With a wide impacts of grant terminations, I wanted to explore which scientific domains have been the most impacted

  • Local impacts here at the University of Arizona

  • NSF is divided into directorates, administrative units that manage grants in certain fields

Approach

  • Faceted line plots to best represent time-series data and differences between directorates

  • Use of cumulative summations to depict total grant terminations

  • For reference, both dollar value and quantity are plotted in the project

Question 2

Were grant terminations clustered by geography or motivated by states’ political leanings?

Introduction

  • Motivated by variation in state and federal policy, especially in how states implement policy

  • Era of record high interstate and partisan polarization

  • Map plots are cool :)

Approach

  • Color-mapped choropleth

    • Hue mapped to states’ partisanship

    • Value mapped to grant terminations

  • Grant terminations measured in dollar value as a proportion of average annual grant allocation through the NSF to standardize differences between states

  • Partisanship measured through proxy of 2024 presidential election results

Conclusion

  • Many losers, no winners

  • Universities and research institutions everywhere impacted

    • Especially in states like Massachusetts, D.C., Maryland, Alabama, and Arkansas

    • And institutions focusing on STEM Education or similar initiatives

  • Long-lasting consequences on research and scientific enquiry

  • Limitations

    • Presentation of raw or summarized data

    • Standardizing across states in Question 2

  • Further directions:

    • Continued analysis as terminations continue

    • Critical to track and document this data as it becomes available

Thank you