Aesthetic Value of the Great Barrier Reef: Eye-Tracking, Survey, and AI Algorithm Data

Three inter-linked datasets from a 2017-2018 project by the Griffith Institute for Tourism Research studying the natural beauty of the Great Barrier Reef. The data includes eye-tracking experiment outcomes, online survey results, and algorithm development data for automated image attractiveness assessment. The project is part of the NESP TWQ 3.2.3 initiative and is filed in the eAtlas enduring data repository.

Use Cases

• Train AI models for automated scenic beauty assessment based on the algorithm development data.
• Analyze human visual attention to natural scenes using the eye-tracking experiment outcomes.
• Study public perception of reef aesthetics through conjoint analysis and picture rating from the online survey.
• Compare human-rated versus AI-predicted aesthetic values of environmental images.

Strengths

• Data originates from a formal research project under the NESP TWQ 3.2.3 program, suggesting structured methodology.
• Includes three distinct data types (eye-tracking, survey, algorithm) allowing for multi-modal analysis.
• Detailed data dictionaries are referenced in three separate Griffith Institute for Tourism Research reports.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file size, and specific data formats beyond PNG and HTML are unknown, which may limit suitability assessment.
• Data may reflect temporal and geographic bias inherent to the 2017 Great Barrier Reef study context.

Provenance

Source

Griffith Institute for Tourism Research, NESP TWQ 3.2.3 project.

Collection Method

Combined eye-tracking experiments, online surveys, and AI algorithm development.

Time Range

2017-2018

Freshness

Last updated 2026-07-14 08:10:52.065268; freshness should be verified.

Geography

Great Barrier Reef, Australia