The Empirical Foundation for High-Impact Research

Academic research is facing a data crisis. High-quality financial and ESG data is usually locked behind prohibitively expensive terminals, forcing faculties to limit access to a few seats or rely on outdated datasets. Worse, for PhD candidates and researchers, the data itself is often unsuitable for rigorous analysis. Commercial vendors provide opaque "scores" or "ratings" based on proprietary methodologies that change without notice, making longitudinal analysis impossible and regression results unstable.

The alternative - manual collection - is a massive drain on human capital. Research Assistants waste thousands of hours hand-coding data from PDFs instead of focusing on analysis. Furthermore, the lack of traceability in commercial datasets creates a crisis of reproducibility: if you cannot prove where a number came from or share the underlying data due to restrictive licenses, your research findings are harder to publish and impossible for the community to verify.