Creating a long-term future for big data in obesity research.

Area of Science:

• Obesity research
• Public health
• Data science

Background:

• Big data is increasingly recognized as crucial for advancing obesity research.
• A series of papers has illustrated the utility of big data in understanding obesity's drivers.
• This article concludes the series, reflecting on progress and future directions.

Purpose of the Study:

• To summarize the progress in utilizing big data for obesity research.
• To identify key areas for the continued integration of big data in the field.
• To provide practical guidance for researchers new to big data.

Main Methods:

• Review of a series of published papers on big data in obesity research.
• Synthesis of findings regarding the utility of big data for social and environmental drivers of obesity.
• Development of a 'getting started with big data' checklist.

Main Results:

• Big data has demonstrated utility in identifying large-scale social and environmental drivers of obesity.
• Four critical areas requiring attention for continued big data integration have been identified.
• A practical checklist is provided to facilitate researcher engagement with big data.

Conclusions:

• Continued progress in obesity research relies on the effective integration of big data.
• Addressing identified areas will promote the sustained role of big data in the field.
• Encouraging broader researcher engagement with big data resources is essential for future advancements.