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Multi-user & Collaboration Support

Overview

The ALTERNATIVE platform is designed with robust multi-user support and collaboration features, recognizing the diverse needs of project researchers, partner company scientists, and designated regulators. These features are integral to the platform's architecture, ensuring secure and efficient teamwork across various user groups.

Key Collaboration Features

  1. Secure Data Sharing: The platform enables controlled access to datasets and analytical workflows, promoting transparency while maintaining data security.

  2. Multi-tenant Jupyter Notebooks: Users can work in isolated environments with selective content visibility, allowing for personalized workspaces that still support collaboration.

  3. CKAN Customization: Through CKAN extension points, the platform offers tailored experiences for different user groups, enhancing usability and efficiency.

  4. Single Sign-On (SSO): Seamless authentication across CKAN and JupyterHub components, powered by Keycloak, ensures a smooth user experience.

  5. Flexible Organization Hierarchies: Reflective sharing permissions allow for nuanced access control, such as department-specific visibility or project-based collaboration spaces.

Dataset Collaboration

Dataset Collaborators

The Collaborators tab provides granular control over dataset access:

  • Viewing Collaborators: Lists all users with special permissions for a dataset, including those outside the dataset's owning organization.
  • Managing Roles: Allows for adding, removing, or modifying collaborator roles directly from the interface.

Collaborator Roles and Permissions

  1. Member

    • Can access private datasets
    • Cannot edit dataset content

  2. Editor

    • Full access to private datasets
    • Can edit dataset content and resources

  3. Admin

    • All Editor permissions
    • Can manage collaborator list (add/remove users, change roles)

Collaboration Workflow

  1. Data Publishing: Organizations can publish datasets with specified visibility (public or private).
  2. Access Control: Admins can set granular permissions for datasets and resources.
  3. Collaboration Spaces: Users can create shared workspaces for specific projects or teams.
  4. Version Control: Built-in support for tracking changes and maintaining data integrity.
  5. Feedback Mechanisms: Users can rate, comment on, and discuss datasets within the platform.

Best Practices for Collaboration

  • Regularly review and update collaborator permissions to maintain security.
  • Utilize tagging and metadata to improve discoverability of shared resources.
  • Leverage Jupyter notebooks for interactive data exploration and sharing of analytical workflows.
  • Encourage the use of version control features to track changes and facilitate collaboration on datasets and code.