Training Course on Implementing and Managing Research Data Management Services

Course Title: Training Course on Implementing and Managing Research Data Management Services

Executive Summary

This two-week intensive course equips participants with the knowledge and practical skills to implement and manage effective Research Data Management (RDM) services. The program covers the entire RDM lifecycle, from data creation and storage to preservation and sharing. Participants will learn about relevant policies, infrastructure, tools, and best practices necessary to support researchers in managing their data effectively. The course includes hands-on exercises, case studies, and group discussions to foster a collaborative learning environment. By the end of this course, participants will be prepared to develop and implement RDM strategies that align with institutional goals and promote open science principles, ultimately enhancing research impact and reproducibility.

Introduction

Research data is a critical asset in today’s research landscape. Effective Research Data Management (RDM) is essential for ensuring the integrity, accessibility, and long-term preservation of research outputs. As research becomes increasingly data-intensive, institutions must provide robust RDM services to support researchers throughout the data lifecycle. This course is designed to provide participants with a comprehensive understanding of RDM principles and practices, equipping them with the skills to develop and implement sustainable RDM services within their organizations. The course will cover key topics such as data management planning, metadata standards, data storage and preservation, data sharing and reuse, and relevant policies and infrastructure. Participants will learn from expert instructors and engage in practical exercises to develop their RDM expertise. By fostering a culture of good data management, institutions can enhance research quality, promote collaboration, and maximize the impact of their research investments.

Course Outcomes

• Understand the principles and benefits of Research Data Management (RDM).
• Develop and implement data management plans (DMPs) that meet funder requirements.
• Apply metadata standards to enhance data discoverability and reuse.
• Establish secure and reliable data storage and preservation solutions.
• Promote data sharing and reuse while adhering to ethical and legal considerations.
• Develop RDM policies and guidelines that align with institutional goals.
• Advocate for RDM within their organizations and the broader research community.

Training Methodologies

• Interactive lectures and presentations.
• Case study analysis of successful RDM implementations.
• Hands-on workshops on data management planning and metadata creation.
• Group discussions and peer learning activities.
• Guest lectures from RDM experts and practitioners.
• Role-playing exercises to address RDM challenges.
• Practical exercises using RDM tools and platforms.

Benefits to Participants

• Enhanced knowledge of RDM principles and best practices.
• Improved skills in developing and implementing data management plans.
• Ability to select and apply appropriate metadata standards.
• Increased confidence in managing research data effectively.
• Expanded network of RDM professionals.
• Career advancement opportunities in the growing field of RDM.
• Certification of completion recognizing RDM competence.

Benefits to Sending Organization

• Improved research data quality and integrity.
• Enhanced compliance with funder mandates and regulations.
• Increased visibility and impact of research outputs.
• Reduced risk of data loss or corruption.
• Improved efficiency in data sharing and reuse.
• Strengthened institutional reputation for research excellence.
• Better return on investment in research infrastructure.

Target Participants

• Research Data Managers
• Librarians
• IT Professionals supporting research
• Research Administrators
• Data Scientists
• Researchers
• Graduate Students

WEEK 1: Foundations of Research Data Management

Module 1: Introduction to Research Data Management

1. Overview of Research Data Management (RDM).
2. Importance of RDM for research integrity and reproducibility.
3. The research data lifecycle: creation, storage, access, preservation.
4. Key stakeholders in RDM: researchers, institutions, funders.
5. RDM policies and regulations.
6. Ethical considerations in RDM.
7. Introduction to FAIR data principles (Findable, Accessible, Interoperable, Reusable).

Module 2: Data Management Planning

1. What is a Data Management Plan (DMP)?
2. Benefits of creating a DMP.
3. Key components of a DMP: data description, storage, preservation, access.
4. Funder requirements for DMPs.
5. Tools and resources for DMP creation.
6. Writing effective DMPs.
7. Case studies of successful DMPs.

Module 3: Metadata Standards

1. What is metadata?
2. Importance of metadata for data discovery and reuse.
3. Common metadata standards: Dublin Core, DDI, Darwin Core.
4. Selecting appropriate metadata standards for different data types.
5. Creating high-quality metadata.
6. Tools for metadata creation and management.
7. Metadata and the FAIR principles.

Module 4: Data Storage and Preservation

1. Best practices for data storage.
2. Data security and access control.
3. Data backup and recovery strategies.
4. Long-term data preservation.
5. Selecting appropriate storage media.
6. Data versioning and provenance.
7. Cloud storage solutions for research data.

Module 5: Data Security and Ethics

1. Importance of data security in RDM.
2. Data encryption and anonymization techniques.
3. Access control and authentication mechanisms.
4. Data breach prevention and response.
5. Ethical considerations in data sharing.
6. Protecting sensitive data.
7. Legal aspects of data security.

WEEK 2: Implementing and Managing RDM Services

Module 6: Data Sharing and Reuse

1. Benefits of data sharing.
2. Data licensing options: Creative Commons, Open Data Commons.
3. Data repositories and archives.
4. Finding and accessing research data.
5. Citing research data.
6. Data sharing policies and guidelines.
7. Addressing concerns about data sharing.

Module 7: Building an RDM Infrastructure

1. Assessing institutional RDM needs.
2. Developing an RDM strategy.
3. Identifying key RDM services.
4. Selecting RDM tools and technologies.
5. Building an RDM team.
6. Developing RDM training and outreach programs.
7. Integrating RDM into existing institutional workflows.

Module 8: RDM Policies and Governance

1. Developing institutional RDM policies.
2. Aligning RDM policies with funder requirements.
3. Establishing an RDM governance structure.
4. Assigning roles and responsibilities for RDM.
5. Enforcing RDM policies.
6. Monitoring and evaluating RDM implementation.
7. Updating RDM policies as needed.

Module 9: RDM Advocacy and Outreach

1. Communicating the benefits of RDM to researchers.
2. Developing RDM training materials.
3. Organizing RDM workshops and seminars.
4. Creating an RDM website and online resources.
5. Engaging with stakeholders across the institution.
6. Promoting RDM best practices.
7. Advocating for RDM at the national and international level.

Module 10: RDM Case Studies and Future Trends

1. Case studies of successful RDM implementations at different institutions.
2. Lessons learned from RDM initiatives.
3. Emerging trends in RDM.
4. The role of artificial intelligence in RDM.
5. Open science and RDM.
6. The future of research libraries and RDM.
7. Developing a personal RDM action plan.

Action Plan for Implementation

1. Conduct a needs assessment of current RDM practices within your organization.
2. Develop a strategic plan for implementing or improving RDM services.
3. Identify key stakeholders and build partnerships to support RDM initiatives.
4. Prioritize RDM training and outreach for researchers and staff.
5. Select and implement appropriate RDM tools and technologies.
6. Establish clear RDM policies and guidelines.
7. Monitor and evaluate the effectiveness of RDM services on an ongoing basis.