Data Sharing and Collaboration in Research Consortia Training Course

Data Sharing and Collaboration in Research Consortia Training Course

Introduction

In today’s dynamic research ecosystem, the ability to share data and collaborate effectively within and across research consortia is pivotal to scientific innovation, transparency, and impact. Data Sharing and Collaboration in Research Consortia Training Course equips researchers, data managers, project coordinators, and institutional leaders with the practical tools and strategic insights necessary to navigate the complexities of data governance, interoperability, FAIR data principles, and cross-institutional communication. This course provides hands-on learning with real-world case studies to foster collaborative excellence and ensure ethical, efficient data sharing in diverse research settings.

As the demand for open science and interdisciplinary research grows, understanding how to align data sharing protocols with international standards and stakeholder expectations becomes crucial. This course enables participants to master effective consortium governance, intellectual property considerations, data access agreements, and collaborative technology platforms. Through expert-led sessions and problem-based learning, participants will develop robust frameworks for managing data-intensive collaborations, safeguarding research integrity, and amplifying the societal and scientific value of shared data.

Course Objectives

By the end of the course, participants will be able to:

1. Understand the principles and best practices of research data sharing within consortia.
2. Apply FAIR data principles (Findable, Accessible, Interoperable, Reusable) in real research contexts.
3. Implement effective data governance policies across multiple institutions.
4. Establish collaboration frameworks that foster trust, transparency, and innovation.
5. Manage intellectual property rights and licensing in shared research outputs.
6. Navigate legal and ethical considerations in cross-border data sharing.
7. Create data management plans (DMPs) aligned with funder and consortium requirements.
8. Utilize collaboration tools and platforms for data sharing, visualization, and version control.
9. Build interdisciplinary communication skills for successful joint research.
10. Promote open science practices in collaborative environments.
11. Analyze case studies of successful and failed research consortia to extract actionable lessons.
12. Design and implement effective data access and sharing agreements.
13. Evaluate data security, anonymization, and compliance strategies for sensitive data.

Target Audience

1. Research Scientists & Principal Investigators
2. Data Stewards & Research Data Managers
3. Project Managers in Research Consortia
4. University Research Officers
5. Health, Environmental, and Social Science Researchers
6. IT Professionals Supporting Research Infrastructure
7. Legal and Compliance Officers in Research Organizations
8. Graduate Students and Early Career Researchers

Course Duration: 10 days

Courses Modules

Module 1: Introduction to Research Consortia and Collaborative Frameworks

• Definition and structure of research consortia
• Importance of collaboration in multi-institutional research
• Key elements of a successful consortium
• Common challenges and mitigation strategies
• Building trust and shared governance
• Case Study: The Human Genome Project – Global Research Collaboration

Module 2: Data Sharing Policies and Regulatory Frameworks

• Overview of international and national data policies
• GDPR, HIPAA, and other regulations
• Understanding funder requirements
• Institutional and cross-border data policy alignment
• Ethical approval processes and documentation
• Case Study: Data sharing in the EU Horizon 2020 Program

Module 3: Implementing FAIR Data Principles in Research Consortia

• Overview of FAIR principles
• Metadata standards and ontologies
• Data repositories and persistent identifiers
• Making data discoverable and reusable
• FAIRness assessment tools
• Case Study: FAIRification of data in the GO FAIR initiative

Module 4: Data Governance, Security, and Confidentiality

• Consortium-wide data governance frameworks
• Roles and responsibilities in data stewardship
• Data security protocols and risk management
• Encryption, access control, and backup strategies
• Managing sensitive and personal data
• Case Study: Secure sharing in the International Cancer Genome Consortium

Module 5: Intellectual Property, Licensing, and Data Ownership

• IP rights in collaborative research
• Choosing appropriate licenses (Creative Commons, etc.)
• Managing ownership and authorship disputes
• Legal frameworks and MOUs
• Open vs restricted data access
• Case Study: IP challenges in the Open COVID Pledge

Module 6: Developing Data Management Plans (DMPs)

• Purpose and components of a DMP
• Aligning DMPs with consortium goals
• Dynamic and machine-actionable DMPs
• Tools for collaborative DMP creation
• Monitoring and updating DMPs
• Case Study: DMP alignment in the ELIXIR research infrastructure

Module 7: Platforms and Technologies for Data Collaboration

• Collaborative tools (e.g., GitHub, OSF, Zenodo)
• Data storage and version control
• Interoperability and APIs
• Data integration platforms
• Visualization and shared analysis environments
• Case Study: Using DataVerse in global agricultural research

Module 8: Ethical and Legal Considerations in Data Sharing

• Informed consent and data re-use
• Cultural sensitivity in data collection
• Managing risks of data misuse
• Legal review processes and documentation
• Accountability and transparency
• Case Study: Ethics of genomic data sharing in Africa

Module 9: Communication and Coordination in Research Consortia

• Building effective communication workflows
• Conflict resolution strategies
• Collaborative writing and reporting
• Use of communication tools (Slack, Trello, etc.)
• Stakeholder engagement and dissemination
• Case Study: Coordination success in the Global Water Partnership

Module 10: Data Access and Use Agreements (DAUAs)

• Components of a DAUA
• Negotiating terms of access and use
• Conditional sharing and embargo periods
• Templates and legal considerations
• Enforcing compliance and audit trails
• Case Study: DAUAs in the NIH dbGaP project

Module 11: Data Quality, Standardization, and Interoperability

• Data harmonization approaches
• Common data models and dictionaries
• Validation and cleaning processes
• Standards for interoperability
• Quality assurance protocols
• Case Study: Data standardization in the ENCODE project

Module 12: Capacity Building and Training in Collaborative Data Sharing

• Skills development in data literacy
• Train-the-trainer models
• Incentivizing participation
• Building local infrastructure
• Evaluating training outcomes
• Case Study: H3Africa data sharing training programs

Module 13: Monitoring, Evaluation, and Impact Assessment

• KPIs for data sharing performance
• Feedback loops and continuous improvement
• Impact metrics: citations, reuse, innovation
• Evaluation tools and dashboards
• Benchmarking against best practices
• Case Study: Impact assessment in the UK Biobank consortium

Module 14: Sustainability and Long-Term Data Stewardship

• Planning for post-project data preservation
• Funding and maintenance strategies
• Institutional support mechanisms
• Migration and archival formats
• Repository certification and trustworthiness
• Case Study: Long-term archiving in the Dryad Digital Repository

Module 15: Building Open Science and Citizen Science in Consortia

• Principles of open science
• Engaging citizen scientists
• Public sharing and communication of data
• Co-creation and participatory research
• Tools for open and transparent collaboration
• Case Study: Open science in the Galaxy Zoo project

Training Methodology

• Interactive expert-led lectures
• Case study analysis and group discussion
• Hands-on practice with data sharing tools
• Peer collaboration and feedback
• Live demonstrations and Q&A sessions
• Evaluation through project-based assessments

• Bottom of Form

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.