Features#

FairDM offers a comprehensive, flexible platform designed to support all aspects of research data management—combining robust data modeling with rich community and collaboration features.

Structured Core Data Model#

At its foundation, FairDM organizes data into four interconnected levels—Project, Dataset, Sample, and Measurement—capturing essential metadata common across research domains. This core model provides a consistent framework while allowing domain-specific customization. (A detailed overview of the data model follows below.)

Community Engagement and Collaboration#

FairDM encourages active participation and collaboration among researchers through:

  • Personal accounts and profiles: Users can create personal accounts to manage their projects and datasets, and showcase their work through customizable profiles. Some portals may enable third-party login (such as ORCID).

  • Contribution tracking: FairDM tracks individual contributions across datasets and projects, helping users build a clear record of their research involvement.

  • Following and updates: Users can follow other researchers and projects to stay informed about new developments and progress.

  • Comments and discussions: Community members can engage by commenting on datasets and projects, fostering dialogue and collaboration.

  • Sharing and visibility: Researchers can share their datasets and projects publicly to encourage knowledge exchange and collaborative opportunities.

Project and Dataset Management#

FairDM supports comprehensive management of research projects and datasets:

  • Create, share, and manage research projects, giving visibility to ongoing, past, and future work.

  • Upload and publish datasets that conform to predefined metadata standards.

  • Manage contributors—both personal and organizational—to ensure proper attribution.

  • Publish datasets with support for external services (where configured).

  • Even datasets in development can be added early to track progress, organize metadata, and attract collaboration.

User-Friendly Data Access#

Researchers and community members can explore and interact with data via a clean, intuitive interface:

  • Search and filter datasets and projects using powerful list views and filtering tools.

  • Access “data collections” that can aggregate specific types of samples or measurements across multiple datasets (depending on portal configuration).

  • View detailed pages for individual projects, datasets, sites, and measurements.

  • Download data in common formats, supporting further analysis or reuse.

Programmatic Access#

FairDM can include a RESTful API (when enabled) to facilitate:

  • Automated data harvesting.

  • Integration into existing research workflows and software.

  • Programmatic querying and data retrieval for advanced applications.

Authentication and Access Control#

  • ORCID and third-party login integration (where enabled).

  • Email verification and optional two-factor authentication (where enabled).

  • Role-based access control supporting public and private dataset visibility.

  • Account and profile management tailored to community needs.

Administrative Tools#

  • Intuitive administration interface for managing portal content, contributors, and metadata.

  • Flexible schema extension to adapt to specific domain requirements.

  • Role management for contributors, moderators, and editors.

  • Clear separation of data and presentation layers for easy customization.

  • Deployment support through Docker Compose templates suited to varied hosting environments, including cloud-based object storage.

Standards and Interoperability#

  • Integration with research identifiers and standards such as ORCID, IGSN, and DataCite.

  • Use of controlled vocabularies to improve data consistency and interoperability.

  • Persistent identifiers for datasets and samples to support long-term citation and tracking.

  • Export-ready metadata and data formats for easy sharing with external repositories.

Modularity and Customization#

FairDM’s modular design allows enabling or disabling features as needed (e.g., account creation, discussion forums), providing portals that fit the unique requirements of different research communities.

Documentation and Support#

FairDM places a strong emphasis on comprehensive documentation and community support to ensure users and developers can get the most out of the framework.

  • User Guide: Clear, step-by-step instructions help researchers and data managers understand how to navigate the portal, manage datasets, and engage with community features.

  • Administrator Guide: Detailed resources assist portal administrators in configuring the system, managing users and permissions, and maintaining the platform efficiently.

  • Developer Guide: For developers extending or customizing FairDM, thorough technical documentation covers the data model, API usage, extension points, and deployment best practices.

  • Community and Collaboration: FairDM encourages an active user and developer community, offering forums, issue trackers, and contribution guidelines to foster collaboration, feedback, and continuous improvement.

  • Ongoing Maintenance: Regular updates and a clear roadmap ensure that FairDM evolves with emerging research data standards and technology trends, backed by responsive support channels.