How FAIR is VIS?

Area of Science:

• Data Science
• Scientific Visualization
• Information Management

Background:

• The findable, accessible, interoperable, and reusable (FAIR) principles are widely adopted across scientific disciplines for robust data management.
• While many fields mandate FAIR principles, the scientific visualization community is actively developing strategies for their implementation.
• Existing research highlights the importance of FAIR data for reproducibility and collaboration in scientific endeavors.

Purpose of the Study:

• To examine the intersection of the visualization community and FAIR data principles.
• To identify successful FAIR principle implementations in other scientific disciplines.
• To propose actionable recommendations for adopting FAIR principles within the visualization field.

Main Methods:

• Literature review of FAIR data principles and their application in various scientific domains.
• Case study analysis of disciplines that have successfully integrated FAIR principles.
• Comparative analysis to identify transferable strategies for the visualization community.

Main Results:

• Successful FAIR principle adoption in other fields offers transferable models for visualization.
• Key challenges and opportunities for FAIR data implementation in visualization were identified.
• Specific strategies for enhancing data findability, accessibility, and reusability in visualization were explored.

Conclusions:

• Integrating FAIR principles into the visualization community is feasible and beneficial.
• Adoption of FAIR principles can enhance the rigor and impact of scientific visualization.
• Further research and community engagement are recommended to fully realize FAIR data in visualization.