A worldwide reproducibility crisis around published scientific studies has gained attention from academics, journalists, and concerned citizens in recent decades. The inability to reliably reproduce experiments from scholarly research—especially in areas of high- impact science—has far-reaching social and economic implications. Fraud may seem an obvious culprit, but in our data-intensive world, vague methods, unclear standards, and even accidental mismanagement of digital resources can all be contributing factors. Reproducibility is an area of increasing focus within the scientometrics community and looking to emerging technologies to help mitigate reproducibility challenges makes practical sense. In the Web 3.0 era, the promise of distributed computing, the maturation of cloud services, and other novel convergences point toward new ways to enable bibliometric reproducibility. Concurrently, research artifacts beyond the peer-reviewed article are growing in prominence—datasets, algorithms, pre-prints—all serve an expanding role in research dissemination and discovery. In this paper we present an overview of some new approaches—with particular focus on the benefits of blockchain-based software systems—for managing research information and improving scientometric reproducibility
