Location
Ngee Ann Kongsi Auditorium (NAKA)
Start Date
4-6-2026 3:00 PM
End Date
4-6-2026 3:30 PM
Description
The growing momentum for open research data in Asia intersects with global advances in artificial intelligence (AI). Open data policies and infrastructures are increasingly recognized as critical enablers of research equity, reproducibility, and innovation. This talk examines the current state of open academic data, highlights economic and scientific arguments for its adoption, and explores the transformative potential of Asian open data ecosystems in powering AI-driven discovery. Drawing on global examples such as the Protein Data Bank and emerging health datasets, the discussion positions Asian institutions to leverage open strategies that simultaneously meet compliance mandates, enhance visibility, and accelerate breakthroughs in health, agriculture, and beyond.
Data citation counts is a new area of research that looks at how often research data is used and mentioned in other studies, similar to how we track citations for research papers. Just as the citation of a paper can demonstrate the importance and influence of that paper in its field and beyond, citing data helps highlight the value of the research data itself. This approach is becoming more important as we recognize that data, like papers, contributes significantly to scientific progress. By keeping track of data citations, researchers can better understand how data is shared and used in the scientific community.
In Asia, the imperative for open data strategies is especially strong: a continent facing pressing societal challenges but also poised to contribute unique, diverse data resources to the global research commons. At the same time, the rapid evolution of AI makes the availability of structured, high-quality datasets more urgent than ever. This paper explores the drivers, barriers, and opportunities in advancing Asian open research data as a foundation for AI.
Asian Open Research Data and its Potential for AI: Bridging the Digital Divide through Strategic Data Sharing
Ngee Ann Kongsi Auditorium (NAKA)
The growing momentum for open research data in Asia intersects with global advances in artificial intelligence (AI). Open data policies and infrastructures are increasingly recognized as critical enablers of research equity, reproducibility, and innovation. This talk examines the current state of open academic data, highlights economic and scientific arguments for its adoption, and explores the transformative potential of Asian open data ecosystems in powering AI-driven discovery. Drawing on global examples such as the Protein Data Bank and emerging health datasets, the discussion positions Asian institutions to leverage open strategies that simultaneously meet compliance mandates, enhance visibility, and accelerate breakthroughs in health, agriculture, and beyond.
Data citation counts is a new area of research that looks at how often research data is used and mentioned in other studies, similar to how we track citations for research papers. Just as the citation of a paper can demonstrate the importance and influence of that paper in its field and beyond, citing data helps highlight the value of the research data itself. This approach is becoming more important as we recognize that data, like papers, contributes significantly to scientific progress. By keeping track of data citations, researchers can better understand how data is shared and used in the scientific community.
In Asia, the imperative for open data strategies is especially strong: a continent facing pressing societal challenges but also poised to contribute unique, diverse data resources to the global research commons. At the same time, the rapid evolution of AI makes the availability of structured, high-quality datasets more urgent than ever. This paper explores the drivers, barriers, and opportunities in advancing Asian open research data as a foundation for AI.