Agave
Agave is an open source, science-as-a-service platform that facilitates computational research. It enables scientists to manage data, run simulations, build models, and collaborate more effectively.
open-source
science
data-management
simulations
modeling
collaboration
Agave: Open Source Science-as-a-Service Platform
Facilitate computational research with Agave, an open source science-as-a-service platform that manages data, runs simulations, builds models and enhances collaboration among scientists.
What is Agave?
Agave is an open source, science-as-a-service platform developed by the Texas Advanced Computing Center. It aims to facilitate computational research across a wide range of scientific disciplines including physics, biology, engineering, and more.
Some key capabilities and features of Agave include:
- Data management - Agave enables researchers to store, share, and manage large datasets across storage systems like Dropbox, Google Drive, Amazon S3, and HPC storage.
- Application deployment - Scientists can deploy and manage applications without needing to worry about the computational infrastructure.
- Access to HPC resources - Agave allows coupling of storage and computational infrastructure to high performance computing systems.
- Collaboration features - Teams of researchers can share data, workflows, and other resources to promote collaboration.
- RESTful API - Programmatic control of the platform is enabled via a comprehensive API.
- Access control and security - Fine-grained user and group permissions allow scientists to securely share resources with colleagues.
By providing an integrated platform for computational research, Agave aims to increase scientific productivity and accelerate the pace of discovery across many scientific fields.
Agave Features
Features
- Storage and data management
- High performance computing
- Web-based interface
- RESTful API
- Integration with other platforms
- Collaboration tools
Pricing
- Open Source
Pros
Open source and free to use
Scalable storage and computing resources
Simplifies research workflows
Enables collaboration
Integrates with other platforms
Good documentation and community support
Cons
Steep learning curve
Limited native analysis tools
Can be complex to set up and manage
Requires technical expertise
Not as feature rich as some commercial platforms
Official Links
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