Standard bibliometric measures -- raw citation counts and average Field Citation Ratios -- are poorly suited for cross-country and cross-field comparison. Citation counts ignore field norms; average FCR collapses across vastly different publication volumes. Without field-sensitive measures, research landscape assessments produce a distorted picture: funders miss high-impact targets, institutions fail to recognize their own relative strengths, and potential partners go unidentified because volume-based rankings bury the contributors that matter most. This project develops and validates an Impact-Weighted Research Output metric that balances field-normalized citation impact with log-transformed publication volume, then applies it to benchmark country-level performance, map international collaboration networks, and compare research contributions across disciplines using the Dimensions COVID-19 public dataset via Google BigQuery.
The portfolio page includes a full project narrative, key findings, and figures.
Languages: Python | SQL
Tools: Google BigQuery | Jupyter
Packages: google-cloud-bigquery | pandas | numpy | plotly | networkx | pandas-gbq | bigframes
End-to-end research intelligence pipeline design -- from metric development and validation through network analysis to policy-relevant output for non-technical stakeholders.
- No local environment required -- analysis runs in Google BigQuery sandbox via Jupyter notebooks.
- Code and scripts © Kara C. Hoover, licensed under the MIT License.
- Data, figures, and written content © Kara C. Hoover, licensed under CC BY-NC-SA 4.0.
