Insights into FAIR and Analyzable Data
Quality-controlled biomedical data, analyzed by AI/ML, holds the key to unlocking new treatments and cures.
It starts with the data.
FAIRLYZ Blog: Data Sharing in the Era of AI
-
Part 2: Enhancing EHR Data Quality: Key Findings from Recent Studies
This post is Part 2 of our 2-part series on recent research studies looking at EHR data quality. Part 1 presented 3 studies from 2025, and Part 2 presents 2 more recent studies and a proposed technological shift that avoids…
-
Part 1: Reactive vs. Proactive: The High Cost of Unvalidated EHR Data
Electronic Health Records were meant to be the backbone of modern healthcare and AI clinical prediction system. Instead, the 2025 research landscape paints a sobering picture. EHR data is incomplete, inconsistent, and often wrong at a scale that threatens clinical…
-
Autonomous Agents Visiting Data
The Google AI Agents Intensive Course (5DGAI) debuted in March 2025 (First 5DGAI) and returned in November 2025 (second 5DGAI), offering developers a front-row seat to the rapid evolution of agentic systems. The First 5DGAI course focused on foundational skills:…
-
Data Visiting and the Future of Inclusive Biomedical Research
In the age of AI-driven discovery, biomedical research is undergoing a quiet revolution—one that’s not just about faster algorithms or bigger datasets, but about rethinking how we access and share data across borders, institutions, and communities. The Geographies of Trust…
-
Data Visiting in Patient-Controlled Health Wallets
Back in 2019, we ran a project we called “Symphony” alongside three blockchain pioneers, driven by a bold vision: to empower patients with full control over their health data through a personal data wallet. The goal was to let individuals…
-
FAIR Data Trains
The FAIR Data Train is a data analysis approach that combines two key elements: As described in the LIFES Networking Meeting from December 2024: The FAIR Data Train approach was introduced as a domain-agnostic system designed to enhance automated interoperability…
-
Has Generative AI Killed the PID Star? – PIDs Part 2
The role of Persistent Identifiers (PIDs) – those enduring signposts in the digital landscape – is facing a fascinating challenge from the rise of Generative AI. At a recent meeting of the Research Data Alliance (RDA) PID Interest Group, experts…
-
The PID Paradox – PIDs Part 1
The digital age has ushered in an unprecedented era for scientific discovery, promising accelerated progress through open data sharing. Yet, beneath the surface of progressive policies and sophisticated technological solutions lies a potent psychological barrier: the fear of being “scooped.”…
-
Federated Data Platforms vs. Data Visiting Technologies
What technologies and methods enable the access and analysis of sensitive data using AI/ML, while preserving data privacy and avoiding centralization? That’s the challenge federated data and data visitation technologies aim to solve. But are they the same thing? Not…
-
Dataset Digital Twin in Biomedical Research
Imagine a holographic view of a patient—a Biomedical Digital Twin—reflecting their unique physiology, lifestyle, and medical history. This isn’t science fiction; it’s a rapidly evolving field transforming healthcare and research. This article explores various types of digital twins and their…
FAIRLYZ Registry
Our comprehensive data sharing registry caters to a diverse group of researchers, ranging from investigators doing laboratory experiments to physician researchers and CROs running clinical trials.
FAIRLYZ Registry & Data QC
- Collaborate with fellow researchers
- Showcase your quality-controlled studies and data.
- Get funding for studies that reuse your data
- Run quality-control on your data or collaborators’ data
Join other FAIRLYZ subscribers
Stay in the loop with the newest scientific studies, potential collaborations, and AI/ML research of quality-controlled data