Science in the cloud (SIC): A use case in MRI connectomics.

Area of Science:

• Computational Science
• Data Science
• Scientific Computing

Background:

• Modern scientific endeavors generate vast, complex datasets, shifting focus from data collection to analysis.
• Reproducibility and extensibility of scientific results are hindered by a lack of standardized data sharing mechanisms.
• Advancements in data organization and code portability present opportunities for improved scientific communication.

Purpose of the Study:

• To propose an accessible and extensible framework for reproducible research.
• To leverage existing technologies for enhanced scientific discovery and collaboration.

Main Methods:

• Utilizing scientific containers for code portability.
• Implementing cloud computing for scalable data analysis.
• Employing cloud data services for data accessibility.
• Developing a web service for interactive data and tool engagement.

Main Results:

• Demonstrated the capability to perform computations within the cloud environment.
• Successfully deployed a web service for interactive exploration of scientific tools and data.
• Established a model for reproducible and extensible scientific research.

Conclusions:

• The 'science in the cloud' (SIC) model offers a practical solution for reproducible research.
• SIC facilitates community-driven acceleration of scientific breakthroughs through replication and extension.
• The framework promotes enhanced interaction with scientific tools and data, fostering collaborative advancement.