Our community brings together researchers who've been displaced, disrupted or disheartened with the current state of science and its support.¹ It gives them a virtual home where they can keep their important work going despite the curveballs life may throw their way. We've built this digital space where brilliant minds can connect, share ideas, and support each other through transitions—whether they're dealing with relocations, institutional changes, or other disruptions that might otherwise slow their research momentum.

Find more information about Terminus at terminus.praxisscience.com

We are helping to facilitate the trend of increased multi-organizational publications, including unaffiliated scientists. This can disintermediate to a degree both institutions and labs/PIs to find a new homeostasis for optimizing scientific output in an environment with increased unaffiliated and small org affiliated authors working together. We don't seek to increase the rate of publications, but to improve the quality, comprehensiveness and complexity by facilitating the development of rigorous, novel and impactful multi-party and multi-perspective manuscripts across scientific fields.²

At the heart of this push ahead with artificial intelligence (AI) is the notion of Sci-Slop – the concept of flawed science and research rooted in the training of an AI model on unvalidated data.⁴ While there is validity to the use of AI in research efforts and there has been an acceleration of biomarker identification and drug discovery⁵, it is crucial to recognize that the acceleration of science can lead to a loss of validation, the creation of too many papers and issues with reproducibility as the black box of AI doesn't always allow for transparency in the science.⁴

This issue is not only the data coming out, but the data going in. Unvalidated and unverified data that comes from sources scraped from the internet can lead to confusing hallucinations generated by large language models. The need for validated, verified datasets is becoming more critical as more users depend on the results of these models for their healthcare and research decisions.

Our prototype is a blockchain-based solution that provides an immutable system for credentialing, validation, and verification. This solution relies on decentralization and cryptography as well as human in the loop reviews aided by a credentialing system that identifies authors and tracks the provenance of data over time to ensure quality. The prototype system was the first place winner of the 2026 Hackathon 4 Humanity hosted by the Duquesne University Grefenstette Center.

Scientia machina is a proposed conceptual framework for a technology-accelerated system of biomedical science. It identifies key layers of trust in the framework of biomedical science and explores how technologies and can be evaluated and introduced into the process in an efficient and functional manner. The entire framework can be found here in the Frontiers in Systems Biology..³