Freva (Free Evaluation System Framework) is a comprehensive platform designed to support researchers, especially in the atmospheric and climate science communities, in managing, searching, and analyzing large-scale datasets. It bridges the gap between data centers and user-defined tools, promoting efficient, reproducible, and collaborative research workflows.

Intended Audience

Freva is ideal for:

• Researchers and Scientists: Streamline the search and evaluation of datasets hosted at various data centers.
• Data Analysts: Integrate user-defined tools into a unified analysis framework.
• System Administrators: Deploy scalable, reliable services to support data-heavy research.
• Research Institutions: Enable reproducible data analysis workflows and foster collaboration among scientists.

Whether you are analyzing climate model output, satellite observations, or observational data, Freva simplifies your workflow with its intuitive interface and robust backend services.

Core Features

• Data Discovery: Quickly and intuitively search large datasets across distributed data centers.
• Tool Integration: A unified interface to register, manage, and execute user-defined analysis tools.
• Reproducibility: Apply tools in a consistent and reproducible manner, with a focus on scientific rigor.
• Extensibility: Customize and expand the platform to meet the unique needs of your research team.

Setup

Setting up Freva involves deploying the necessary services and configuring them for your environment. Below is a high-level overview:

Prerequisites ~~~~~~~~~~~~~

• A Linux-based system with administrative privileges.
• Conda installed to manage dependencies.
• Access to required systemd services like Apache Solr and MongoDB.

Running the Freva REST Server ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

1. Start the REST server using:

   freva-rest-server

   This command launches the API endpoints that power Freva’s services, enabling you to interact with data and tools seamlessly.

Why Choose Freva?

Freva is designed for:

• Seamless access to multi-terabyte datasets stored at data centers.
• A scalable, modular architecture that adapts to diverse research needs.
• An emphasis on fostering collaboration through reproducibility and interoperability.