Challenges in biomarker discovery and biorepository for Gulf-war-disease studies: a novel data platform solution

TL;DR

This paper presents ROSALIND, a novel data platform designed to address complex challenges in biomarker discovery for Gulf War Illness, enhancing data integrity, collaboration, and research productivity.

Contribution

Introduction of ROSALIND, a researcher-centered, study-specific data platform that supports complex biomarker research and fosters collaborative, integrity-driven scientific inquiries.

Findings

Accelerated data analysis and experimentation in GWI study

Reduced errors and improved research quality

Enhanced data and research integrity through platform deployment

Abstract

Aims: Our Gulf War Illness (GWI) study conducts combinatorial screening of many interactive neural and humoral biomarkers in order to establish predictive, diagnostic, and therapeutic targets. We encounter obstacles at every stage of the biomarker discovery process, from sample acquisition, bio-marker extraction to multi-aspect, multi-way interaction analysis, due to the study complexity and lack of support for complex data problem solutions. We introduce a novel data platform, named ROSALIND, to overcome the challenges, foster healthy and vital collaborations and advance scientific inquiries. Main methods: ROSALIND is a researcher-centered, study-specific data platform. It provides vital support of individual creativity and effort in collaborative research. We follow the principles etched in the platform name - ROSALIND stands for resource organisms with self-governed accessibility, linkability, integrability, neutrality, and dependability. We translate, encode and implement the principles in the platform with novel use of advanced concepts and techniques to ensure and protect data integrity and research integrity. From a researcher's vantage point, ROSALIND embodies nuance utilities and advanced functionalities in one system, beyond conventional storage, archive and data management. Key findings: The deployment of ROSALIND in our GWI study in recent 12 months has accelerated the pace of data experiment and analysis, removed numerous error sources, and increased research quality and productivity. Significance: ROSALIND seems the first to address data integrity and research integrity in tandem with digital measures and means. It also promises a new type of distributed research networks with individualized data platforms connected in various self-organized collaboration configurations.