Preprints

Early Detection of Impactful Research

In rapidly evolving fields like computer science, artificial intelligence, and computational biology, groundbreaking discoveries can't wait for traditional peer review cycles. Tomorrow's transformative technologies often emerge first in preprints, offering a window into cutting-edge research months or even years before formal publication. Our mission is to help you identify these potential breakthroughs early, using advanced bibliometric analysis to assess research quality and potential impact.

Reference Network Analysis

We analyze the citation network formed by a preprint's references, treating it as a Directed Acyclic graph. Our algorithms evaluate network centrality, diversity, and temporal relevance to gauge the paper's theoretical foundation and potential influence. This helps identify work that builds meaningfully on established research while pushing boundaries into new territories.

Reference Network Score

Author Impact Assessment

Research quality often correlates with author expertise and track record. We aggregate comprehensive metrics for all co-authors, including h-indices, citation patterns, and publication history. This multi-dimensional analysis helps predict the potential impact and reliability of new research, even before it accumulates its own citations.

Co-authors Cumulated H-Index and Citations