Research Data Lifecycle Management: Design, Storage, and Curation Course

Course Introduction
The Research Data Lifecycle Management: Design, Storage, and Curation Course provides a comprehensive foundation for understanding how research data is generated, organized, protected, and preserved across its entire lifecycle. Participants gain practical knowledge of industry-standard methodologies, emerging technological tools, and compliance frameworks that ensure data integrity, accessibility, and long-term usability within research environments.
This course emphasizes strategic planning for data collection, documentation, governance, and metadata development to ensure accuracy, reproducibility, and transparency of research outputs. Participants explore modern techniques that support high-quality dataset design, including ethical considerations, data-sharing protocols, version control, and quality assurance processes essential for both academic and applied research institutions.
As data volumes continue to increase, the course equips professionals with the skills required to adopt robust data storage architectures ranging from institutional repositories to secure cloud environments and implement effective backup, security, and preservation systems. Learners also explore evolving data sovereignty requirements, intellectual property issues, and FAIR Data principles.
Participants are introduced to contemporary digital tools supporting data management, such as electronic lab notebooks, research data management (RDM) workflows, collaborative platforms, and automated data pipelines. Emphasis is placed on selecting and integrating tools that reduce operational inefficiencies while supporting long-term curation and multi-disciplinary research needs.
The course also highlights best practices in data curation and archiving, focusing on methodologies to enhance data discoverability, interoperability, and reusability. Through case studies, practical demonstrations, and guided exercises, learners deepen their understanding of how curated datasets strengthen research quality, institutional credibility, and evidence-based decision-making.
By the end of the program, participants will be able to develop a complete Research Data Management Plan (DMP), implement sustainable data governance structures, and apply advanced curation strategies aligned with global standards. This holistic approach positions organizations to better manage risks, support collaboration, and maintain compliance with emerging research policies and funding requirements.

Who Should Attend

• Research professionals managing datasets throughout the research process.
• University lecturers, academic researchers, and early-career scientists responsible for generating and storing research data.
• Monitoring and evaluation specialists seeking stronger data governance and documentation skills.
• ICT officers, system administrators, and digital archivists supporting institutional data infrastructure.
• Data analysts, data stewards, and data curators working on structured and unstructured datasets.
• Project managers and coordinators involved in donor-funded research programs requiring strict data compliance.
• Records management and information governance officers.
• Librarians and knowledge management specialists responsible for repositories.
• Government and non-profit professionals overseeing research regulation and evidence-based policy development.
• Anyone interested in modern approaches to research data quality, preservation, and curation.

Course Objectives

• Equip participants with the skills to design effective research data collection frameworks that enhance integrity, reproducibility, and long-term usability of research outputs.
• Enable learners to develop detailed Data Management Plans (DMPs) aligned with institutional policies, funder requirements, and international data governance standards.
• Strengthen participant capacity to implement secure, scalable, and cost-efficient data storage systems using both local and cloud-based infrastructures.
• Provide practical knowledge on metadata standards and documentation techniques that enhance dataset organization, discoverability, and future interoperability.
• Build skills in applying FAIR Data Principles Findable, Accessible, Interoperable, and Reusable to improve data quality and reusability across research domains.
• Train participants on effective data curation, archiving strategies, and preservation workflows that safeguard long-term research value.
• Enhance understanding of ethical, legal, and regulatory considerations, including data privacy, consent, intellectual property, and compliance obligations.
• Strengthen capacity to manage large datasets using digital tools and automated workflows that streamline processing, version control, and updates.
• Improve participants’ ability to communicate data insights, documentation needs, and governance requirements across multidisciplinary teams.
• Empower organizations to adopt sustainable data lifecycle management practices that reduce risk, enhance collaboration, and support evidence-driven decision-making.

Comprehensive Course Outline

Module 1: Introduction to Research Data Lifecycle

• Understanding the full data lifecycle from design to preservation.
• Types of research data and classification techniques.
• Importance of lifecycle planning in research integrity.
• Common challenges and emerging best practices in data governance.

Module 2: Research Data Design and Planning

• Developing robust data collection frameworks and protocols.
• Creating effective Data Management Plans for research projects.
• Identifying ethical, legal, and regulatory requirements early.
• Integrating reproducibility and transparency measures in design.

Module 3: Metadata, Documentation, and Standards

• Key metadata standards used across disciplines and funders.
• Methods for documenting qualitative and quantitative datasets.
• Tools for automating metadata creation and version control.
• Ensuring interoperability through standard vocabularies.

Module 4: Data Storage, Security, and Backup Systems

• Comparing institutional, cloud, and hybrid storage options.
• Building secure access controls and data protection systems.
• Backup and disaster recovery planning for research data.
• Storage challenges for large or sensitive datasets.

Module 5: Data Processing, Quality, and Validation

• Methods for cleaning, validating, and transforming datasets.
• Tools for managing structured and unstructured data.
• Data quality indicators and continuous quality assurance.
• Automating workflows and ensuring reproducibility.

Module 6: Data Curation and Long-Term Preservation

• Curation models and their relevance to different research fields.
• Ensuring long-term access through repository and archival systems.
• Preservation formats, standards, and sustainability planning.
• Managing evolving technologies and digital obsolescence risks.

Module 7: Data Sharing, Access, and Collaboration

• Data sharing policies, funder requirements, and global standards.
• Licensing, intellectual property, and user rights.
• Tools and repositories for open science and collaboration.
• Managing sensitive, confidential, or embargoed datasets.

Module 8: Ethical and Legal Considerations

• Privacy, consent, and responsible data use.
• National and international data protection laws.
• Handling sensitive human-subject and proprietary data.
• Navigating institutional compliance frameworks.

Module 9: Digital Tools and Emerging Technologies

• Research data platforms, RDM automation tools, and ELNs.
• AI-assisted data processing and intelligent curation systems.
• Blockchain applications in data traceability and integrity.
• Trends in research data analytics, visualization, and integration.

Module 10: Implementing Institutional Data Governance

• Building organizational data governance structures and policies.
• Staff roles, responsibilities, and capacity-building strategies.
• Monitoring, evaluation, and continuous improvement frameworks.
• Crafting institutional roadmaps for sustainable data management.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808 

Training Venue

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.