Workspace

The Workspace is a collaborative environment designed to support a wide range of projects, including AI and Machine Learning (ML) workflows, exploratory data analysis (EDA), scientific research projects and educational projects. Each workspace operates within JupyterHub and provides integration with data resources from the catalog and external GitHub repositories.

Use Cases

Integrated Workflow Development

The workspace is the main unit for assembling and delivering complete research workflows by integrating datasets from the data catalog, source code from GitHub, and connections to computing resources. Researchers use workspaces to combine data, code, and computation in a unified environment, enabling streamlined exploration, analysis, and experimentation.

Classrooms and Data Challenges

Within a classroom or data challenge environment, workspaces function as foundational units that support both structured learning and exploratory, project-based activities. These workspaces align with course or data challenge objectives and provide students with interactive, hands-on modules, assessments, and access to curated datasets and computing resources.

As part of the learning experience, students and participants are encouraged to develop their own workspaces.

Community Training

Research groups and agencies that contribute datasets or services to Wildfire Commons have the opportunity to develop dedicated workspaces designed as demos or tutorials. These workspaces act as practical tools to train the broader community on how to effectively access, process, analyze, and visualize their resources. For example, a workspace might guide users through working with a sample dataset, demonstrating data utilization workflows and showcasing techniques such as live streaming analysis or real-time data visualization, helping users fully leverage the contributed resources in their own work.

Key Features of the workspace

Metadata Form

This form helps users provide all the relevant information about their workspace, including a clear description, step-by-step execution instructions for running it in JupyterHub, any prerequisites (for educational purposes), tags to improve discoverability, and links to additional resources or references.

Data from Catalog

The workspace supports the addition of resources from the catalog, making it easy for users to find and utilize datasets relevant to their project.

Models Integration

Users can integrate models from open-source platforms like HuggingFace to enrich their workspaces with advanced machine learning capabilities.

GitHub Integration

With GitHub integration, the workspace allows users to connect to external repositories, ensuring that source code, configuration files, and dependencies are easily managed.