AAI stands for Authentication and Authorization Infrastructure. It is a framework or system that manages the processes of authentication (verifying the identity of users) and authorization (granting users permission to access resources or perform actions) within a network or system. Within the Europe's open science and EOSC ecosystems, it is a critical component designed to manage and streamline the access of researchers, institutions, and services to the EOSC's vast array of digital resources and services.

• Federated Identity Management:
  • Single Sign-On (SSO): AAI enables researchers to use their existing institutional credentials to access multiple services and resources across Europe and the EOSC ecosystem without needing to log in separately to each service.
  • Interoperability Across Institutions: The AAI framework in EOSC is built to work across various research institutions and service providers, allowing seamless access to resources regardless of the user's home institution.
• Access Control and Permissions:
  • Role-Based Access: AAI assigns roles and permissions based on the user's profile, ensuring that they have appropriate access to the resources they need while maintaining security and compliance with institutional policies.
  • Policy Enforcement: It enforces policies related to data access, sharing, and privacy, ensuring that resources are accessed only by authorized users under the agreed-upon conditions.
• User-Centric Approach:
  • Simplified Access: AAI is designed to be user-friendly, providing a simplified and consistent user experience across the various services within EOSC, reducing the complexity typically associated with accessing multiple platforms.
  • Support for Collaboration: AAI facilitates collaboration by allowing users from different institutions and countries to access and share resources easily, promoting open science and cross-border research.
• Security and Privacy:
  • Data Protection: AAI includes robust mechanisms to protect user credentials and personal information, ensuring compliance with European regulations such as GDPR.
  • Trust Framework: It operates within a trust framework that ensures all participating organizations adhere to common standards of security and data protection.

A Data Management Plan (DMP) is a formal document that outlines how data will be handled both during and after a research project. It serves as a roadmap for managing data, detailing the procedures and practices that will be followed to ensure that data is collected, stored, shared, and preserved in a way that is secure, compliant, and sustainable.

Key components of a Data Management Plan typically include:

1. Data Collection:
   • Describes the types of data to be collected or generated during the project.
   • Explains the methodologies and tools that will be used for data collection.
2. Data Storage and Security:
   • Outlines how data will be stored, including the physical and digital storage solutions.
   • Details the measures that will be taken to ensure data security and protect sensitive information.
3. Data Sharing and Access:
   • Specifies how and when data will be shared with others, including policies for open access.
   • Identifies any restrictions or conditions for data sharing, such as licensing or ethical considerations.
4. Data Documentation and Metadata:
   • Describes the documentation that will accompany the data to ensure that it can be understood and used by others.
   • Includes information on the metadata standards that will be used to describe the data.
5. Data Preservation and Long-Term Storage:
   • Details the plans for preserving data after the project ends, including the repositories where the data will be stored.
   • Discusses strategies for ensuring long-term access to the data.
6. Compliance and Ethical Considerations:
   • Ensures that the data management practices comply with legal, ethical, and funding agency requirements.
   • Addresses issues such as informed consent, privacy, and intellectual property.
7. Roles and Responsibilities:
   • Identifies the individuals or teams responsible for managing the data throughout the project.

A well-crafted DMP is essential for ensuring that data is handled in a way that maximizes its value, facilitates collaboration, and meets the expectations of funders and other stakeholders. It also helps to enhance the reproducibility and transparency of research by making data available for verification and reuse.

An Output Management Plan (OMP) is a strategic document that outlines how the various outputs of a research project will be managed, disseminated, and preserved throughout and after the project's lifecycle. Outputs can include a wide range of materials, such as publications, datasets, software, protocols, presentations, and any other deliverables generated during the course of the research.

Key components of an Output Management Plan typically include:

1. Identification of Outputs:
   • Clearly defines what constitutes an output in the context of the project, including data, publications, software, and any other forms of deliverables.
2. Dissemination Strategy:
   • Describes how the outputs will be shared and made accessible to the intended audience, including through open access platforms, conferences, journals, and repositories.
   • Plans for engaging with stakeholders and promoting the use and impact of the outputs.
3. Licensing and Intellectual Property:
   • Specifies the licensing arrangements for the outputs, ensuring that intellectual property rights are protected while facilitating appropriate sharing and reuse.
   • Addresses any restrictions on the use of the outputs and how these will be communicated to users.
4. Documentation and Metadata:
   • Outlines how the outputs will be documented to ensure that they are understandable and usable by others.
   • Includes the use of metadata standards to describe and categorize the outputs for easier discovery and access.
5. Preservation and Long-Term Access:
   • Details the strategies for preserving the outputs after the project ends, including where and how they will be archived to ensure long-term accessibility.
   • Discusses plans for maintaining the usability and relevance of the outputs over time.
6. Compliance and Ethical Considerations:
   • Ensures that the management of outputs complies with relevant legal, ethical, and funder requirements.
   • Includes considerations such as data protection, privacy, and adherence to open science principles.
7. Roles and Responsibilities:
   • Identifies who within the research team is responsible for managing the different outputs, including their dissemination, documentation, and preservation.
8. Monitoring and Reporting:
   • Outlines how the management of outputs will be monitored throughout the project and how progress will be reported to stakeholders, including funders.

An Output Management Plan is essential for ensuring that all research outputs are effectively managed, shared, and preserved, maximizing their impact and ensuring that they contribute to the broader scientific community. By having a clear plan in place, research teams can enhance the visibility and usability of their work, comply with funder and institutional requirements, and support the principles of open science.

A Software Management Plan (SMP) is a document that outlines the processes and practices for managing software throughout its lifecycle within a research project or organization. Similar to a Data Management Plan (DMP), an SMP addresses key aspects of software development, maintenance, sharing, and preservation. It is designed to ensure that the software developed during a project is well-documented, reliable, and sustainable, meeting both the immediate needs of the project and future reuse by others.

Key components of a Software Management Plan typically include:

1. Development Practices: Guidelines on coding standards, version control, and testing protocols to ensure high-quality, maintainable software.
2. Licensing and Intellectual Property: Information on the software’s licensing terms and how intellectual property rights will be managed.
3. Documentation: Plans for creating and maintaining comprehensive documentation to ensure that the software can be easily understood and used by others.
4. Sustainability and Maintenance: Strategies for maintaining and updating the software after the initial project ends, including long-term support and potential funding for future development.
5. Sharing and Dissemination: Plans for distributing the software, including open-source release, repository hosting, and guidelines for user support.
6. Compliance and Security: Measures to ensure that the software complies with relevant legal, ethical, and security standards.

A "template" refers to a structured form or set of guidelines designed to assist researchers in creating Data Management Plans (DMPs) for specific research outputs. These templates are typically customized to meet the specific requirements of different funding bodies, research disciplines, or institutional policies. Each template includes predefined sections and prompts that guide the user through the necessary components of a DMP, such as data collection methods, storage solutions, data sharing plans, and long-term preservation strategies. Templates simplify the DMP creation process by ensuring that all essential aspects of data management are addressed, helping researchers produce comprehensive and compliant plans.