TalentRank is a scientometrics.ai properietery ranked database which ranks talent based on research outputs and other sources of innovation such as patent registers and preprint servers.
TalentRank is a flagship product by scientometrics.ai that offers a proprietary, ranked database of global talent in deep tech, with a core emphasis on research output, innovation indicators, and domain relevance. Designed to serve enterprise, academic, and public-sector stakeholders, TalentRank aggregates and ranks individual researchers and inventors by integrating multiple validated sources - including peer-reviewed publications, patent filings, and preprint repositories. This multi-source approach provides a more comprehensive and future-forward view of scientific and technological innovation than citation-based metrics alone.
Where traditional bibliometric systems rely solely on journal-indexed research outputs and metrics like the h-index, TalentRank incorporates structured data from diverse innovation sources. This includes global patent databases (e.g., USPTO, EPO, WIPO) and preprint platforms like arXiv and bioRxiv, offering early signals of innovation not yet visible in the slow academic publishing cycle. Such integration is aligned with findings in innovation studies (e.g., Breschi et al., Research Policy, 2004) that patents and preprints are strong predictors of translational impact and future R&D value.
The underlying ranking methodology applies field-normalized citation impact, authorship disambiguation, and source-type weighting, allowing for meaningful cross-discipline and cross-sector comparison. This enables clients to distinguish between an AI researcher producing state-of-the-art conference papers and a robotics innovator with granted patents - both of whom may be critical hires, collaborators, or grant recipients depending on institutional goals.
TalentRank is designed to be API-accessible and fully queryable, allowing seamless integration into enterprise workflows, applicant tracking systems, academic recruitment portals, or national innovation dashboards. Use cases include real-time candidate screening, scouting for grant funding or fellowships, institutional benchmarking, and identifying emerging leaders in fast-moving subfields like generative AI, autonomous systems, or neuromorphic computing.
Moreover, the searchable database supports custom filters across research domain, impact percentile, affiliation history, and innovation type, enabling precise talent discovery tailored to mission-critical objectives. For example, a tech firm building AI chips can filter for researchers with both high-impact computing papers and semiconductor patents - a task not possible with conventional recruitment tools.
In essence, TalentRank transforms fragmented innovation data into actionable intelligence, offering a strategic lens on global talent that’s grounded in verifiable output. It fills a critical gap in the innovation ecosystem - where the ability to identify and rank knowledge creators is as essential as the ability to fund or hire them.