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Related Experiment Video

Updated: Jul 17, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

Knowledge sharing and discovery across heterogeneous research infrastructures.

Siamak Farshidi¹, Xiaofeng Liao², Na Li²

¹Department of Information and Computer Science, Utrecht University, Utrecht, The Netherlands.

Open Research Europe

|August 30, 2023

Summary

A new knowledge management system was developed for environmental research infrastructures to address fragmented data. This system uses AI to integrate and deliver knowledge, aiding environmental scientists in understanding the Earth System.

Keywords:

Knowledge base knowledge management research infrastructure search engine software development lifecycle

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Published on: June 13, 2025

Area of Science:

Environmental Science
Information Science
Knowledge Management

Background:

Research infrastructures are vital for scientific data but are often domain-specific and disconnected.
Fragmented knowledge from heterogeneous sources poses challenges for researchers.
The rapid growth of knowledge exceeds human capacity for efficient capture.

Purpose of the Study:

To present the development of a knowledge management system for Environmental Research Infrastructures (ENVRI).
To address challenges in managing and integrating knowledge from diverse environmental science sources.
To facilitate knowledge discovery, acquisition, integration, and delivery for environmental scientists.

Main Methods:

Development of a knowledge management system tailored for ENVRI.
Application of artificial intelligence (AI) approaches for automated knowledge acquisition.
Integration of knowledge from heterogeneous and autonomous data sources.

Main Results:

Successful development of a functional knowledge management system for environmental research.
Demonstrated ability to discover, acquire, and integrate knowledge from fragmented sources.
Facilitated consistent knowledge delivery to researchers and end-users.

Conclusions:

Knowledge management systems are essential for overcoming fragmentation in research infrastructures.
AI-driven approaches can effectively automate knowledge acquisition and integration.
The developed system provides a valuable tool for environmental scientists studying the Earth System.