Methodology

Shadow Network Intelligence is built on transparency and trust. Our reports are rooted in official data, processed through a rigorous, automated pipeline, and reviewed to ensure accuracy and clarity. This page explains how we generate our reports, what data we rely on, and how we handle limitations.

Data Sources

All reports are based on publicly available data from the Federal Election Commission (FEC), accessed through the official FEC API. We currently pull data from the following endpoints:

This data is refreshed daily to reflect the latest available information as campaigns report new activity.

Ingestion Process

Our data collection began in April 2025 and includes records dated January 1, 2025 and later. Our backend pipeline is built in Python and structured to automatically pull, transform, and store campaign finance data in a PostgreSQL database. Each ingester is tailored to a specific FEC endpoint and includes:

All data is linked by candidate and committee IDs, allowing for accurate cross-referencing across schedules and reports.

Candidate Selection Criteria

Shadow Network Intelligence selects candidates for reporting using a consistent, data-driven approach designed to prioritize transparency and public relevance.

Reports are generally initiated based on total receipts raised by a candidate’s principal campaign committee, with higher-receipt campaigns prioritized for analysis. This approach ensures that reporting resources are focused on races where financial activity—and potential influence—is greatest.

Additional factors that may inform selection include:

Candidate selection criteria may evolve over time as data availability, reporting capacity, and public accountability needs change. Any material updates to this methodology are documented publicly.

Report Generation

Once the data is processed, we generate reports using a modular notebook-based pipeline. Each report:

This pipeline allows us to produce reports quickly while maintaining consistency, traceability, and transparency across every section.

Limitations and Caveats

While we strive for accuracy and thoroughness, we acknowledge the inherent limitations of public campaign finance data:

We apply clustering techniques (e.g., employer normalization, ZIP code grouping) to help surface possible coordination, but our findings should be understood as patterns of interest, not proof of illegality.

Ethical Commitments

We are committed to:

Collaboration and Transparency

We welcome inquiries from journalists, watchdog organizations, researchers, and others who want to:

To discuss potential collaboration or request access to documentation or schema details, please reach out to us.

We believe that democracy thrives in sunlight. Our methodology is designed to make that light as clear and focused as possible.