FAIR Data Implementation for Institutional Repositories Training Course

Course Introduction

FAIR Data Implementation For Institutional Repositories Training Course provides a comprehensive framework for understanding and applying the principles of Findability, Accessibility, Interoperability, and Reusability to research data managed within institutional repositories. The course examines how repositories can be designed, configured, governed, and maintained to make research outputs more discoverable, accessible, technically interoperable, and reusable across disciplines and data environments.

The course explores practical approaches for translating FAIR principles into institutional repository policies, workflows, metadata structures, technical architectures, and data management practices. Participants examine how persistent identifiers, descriptive metadata, controlled vocabularies, standardized schemas, machine-readable formats, open protocols, and repository services contribute to FAIR data implementation while considering institutional requirements, disciplinary practices, and the needs of researchers and other data users.

Participants will develop an understanding of the relationship between FAIR data and institutional repository infrastructure, including repository platforms, metadata harvesting, interoperability protocols, authentication mechanisms, preservation systems, data catalogs, and research information systems. The training also addresses how repositories can support both human users and automated systems by providing structured metadata, persistent access mechanisms, standardized interfaces, and machine-actionable information.

The course places strong emphasis on data governance, responsible access, licensing, privacy, security, intellectual property, ethical considerations, and long-term stewardship. Participants learn that FAIR does not necessarily mean that every dataset must be openly accessible, and they explore practical approaches for making metadata discoverable while applying appropriate restrictions to sensitive, confidential, proprietary, or legally protected research data.

Advanced topics address FAIR assessment, FAIR maturity models, metadata quality, FAIR Digital Objects, persistent identifiers, semantic interoperability, linked data, APIs, machine-actionable metadata, repository interoperability, automated FAIR evaluation, and emerging technologies. Participants also examine the role of artificial intelligence, knowledge graphs, automated metadata enrichment, and machine-readable repository services in improving FAIRness at institutional scale.

Through practical exercises and an integrated implementation project, participants develop the ability to assess an institutional repository against FAIR requirements and design an actionable implementation roadmap. The course is intended to help research institutions, universities, libraries, data centers, and repository teams strengthen research data stewardship while improving discovery, interoperability, preservation, responsible access, and reuse of institutional research assets.

Duration

10 days

Who Should Attend

• Institutional repository managers and administrators responsible for repository services and research outputs

• University librarians and research data librarians supporting scholarly communication and digital collections

• Research data managers responsible for organizing, documenting, sharing, and preserving research datasets

• Research information and scholarly communication professionals implementing institutional data services

• Data stewards and governance professionals responsible for research data quality and stewardship

• Digital archivists and preservation specialists managing long-term access to research data

• Research support officers assisting researchers with data management and repository deposits

• Metadata specialists and cataloguing professionals developing repository metadata and discovery structures

• Information technology professionals responsible for repository platforms, APIs, integrations, and infrastructure

• Open science and research data professionals implementing FAIR and responsible data practices

• Academic researchers and principal investigators seeking to improve the FAIRness of research datasets

• Research administrators developing institutional research data management policies and workflows

• Data governance professionals establishing standards, ownership, access controls, and stewardship processes

• Repository developers and systems administrators supporting technical implementation and interoperability

• Research funders, programme managers, and policy professionals interested in FAIR data compliance

• Professionals involved in institutional data management, digital scholarship, research infrastructure, and scholarly publishing

Course Objectives

• Develop a comprehensive understanding of FAIR principles and their practical application within institutional repositories and research data management environments.

• Evaluate existing repository infrastructure, policies, metadata, workflows, and services against Findability, Accessibility, Interoperability, and Reusability requirements.

• Design repository metadata structures that support comprehensive discovery, machine readability, semantic consistency, persistent identification, and effective research data reuse.

• Implement persistent identifiers and standardized access mechanisms that improve the long-term discoverability and reliable referencing of institutional research datasets.

• Apply metadata standards, controlled vocabularies, ontologies, schemas, and semantic technologies to strengthen interoperability across repositories and research information systems.

• Develop responsible accessibility strategies that balance FAIR data objectives with privacy, confidentiality, intellectual property, ethical restrictions, and legal requirements.

• Establish data licensing, usage rights, provenance, and documentation practices that provide sufficient information for researchers to understand and appropriately reuse datasets.

• Develop repository workflows that embed FAIR principles throughout dataset submission, curation, metadata creation, quality assurance, publication, preservation, and maintenance.

• Apply FAIR assessment methods, maturity indicators, metrics, and evaluation frameworks to identify weaknesses and prioritize repository improvement activities.

• Explore machine-actionable metadata, APIs, harvesting protocols, linked data, knowledge graphs, and other technologies that enable automated discovery and reuse of research data.

• Develop institutional FAIR implementation strategies that incorporate governance, organizational responsibilities, technical infrastructure, researcher engagement, training, and continuous improvement.

• Complete an integrated FAIR repository implementation project that demonstrates the ability to assess, design, implement, and evaluate practical FAIR data services.

Comprehensive Course Outline

Module 1: Foundations Of FAIR Data And Institutional Repositories

• Principles and objectives of Findability, Accessibility, Interoperability, and Reusability in research data environments

• Evolution of institutional repositories, open science, research data management, and digital scholarly infrastructure

• Relationship between FAIR data principles, open data, responsible data sharing, and research transparency

• Roles of institutional repositories in preserving, discovering, accessing, and enabling reuse of research outputs

Module 2: FAIR Data Requirements And Institutional Readiness

• Translating FAIR principles into institutional policies, strategies, standards, workflows, and repository requirements

• Assessing organizational readiness, technical capacity, skills, governance structures, and researcher support services

• Identifying institutional barriers to FAIR implementation and developing practical strategies for addressing them

• Establishing FAIR implementation priorities across research disciplines, repository collections, and institutional data services

Module 3: Findability And Research Data Discovery

• Designing rich metadata and indexing structures that improve dataset discovery across institutional and external services

• Persistent identifiers including DOIs, ORCID, Handles, and other mechanisms for stable research asset identification

• Search engine optimization, metadata harvesting, catalogs, registries, and discovery services for institutional datasets

• Machine-readable discovery mechanisms and automated approaches for identifying FAIR research data resources

Module 4: Accessibility And Repository Access Management

• Designing reliable access mechanisms using standardized protocols, persistent URLs, APIs, and repository interfaces

• Authentication, authorization, embargoes, controlled access, and differentiated data access requirements

• Applying FAIR principles to restricted, confidential, sensitive, proprietary, and ethically protected research datasets

• Developing repository access policies that balance openness, security, privacy, legal obligations, and research reuse

Module 5: Metadata Standards And FAIR Metadata Design

• Core metadata principles and standards for describing research datasets in institutional repository environments

• Metadata elements for identification, description, provenance, ownership, licensing, access, methodology, and reuse

• Crosswalks, metadata mappings, controlled vocabularies, taxonomies, and semantic consistency across repository systems

• Designing high-quality, complete, persistent, and machine-actionable metadata for FAIR research data

Module 6: Interoperability, Standards And Semantic Technologies

• Technical interoperability through standardized schemas, protocols, formats, APIs, and repository integration mechanisms

• Semantic interoperability using controlled vocabularies, ontologies, identifiers, taxonomies, and linked data principles

• Repository interoperability with research information systems, data catalogs, publisher platforms, and disciplinary infrastructures

• Applying machine-readable standards to enable automated processing, integration, exchange, and interpretation of research data

Module 7: Reusability, Documentation And Research Data Provenance

• Dataset documentation requirements for supporting meaningful interpretation and responsible reuse of research data

• Data provenance, processing histories, methodological descriptions, version information, and contextual documentation

• Licensing frameworks, usage conditions, attribution requirements, and rights information for research data reuse

• Developing reusable datasets through appropriate formats, documentation, quality information, and contextual metadata

Module 8: FAIR Data Governance And Institutional Policies

• Establishing institutional governance structures, roles, responsibilities, stewardship models, and accountability mechanisms

• Developing FAIR data policies aligned with research governance, funding requirements, ethics, privacy, and institutional priorities

• Integrating FAIR principles into research data management plans, repository deposit requirements, and institutional workflows

• Monitoring policy implementation, compliance, governance performance, and continuous institutional improvement

Module 9: Repository Architecture And FAIR Technical Infrastructure

• Repository architecture components supporting metadata management, storage, preservation, discovery, access, and interoperability

• Integrating repository platforms with persistent identifier services, authentication systems, data catalogs, and research information systems

• APIs, OAI-PMH, linked data services, harvesting mechanisms, and machine-to-machine repository communication

• Designing scalable and sustainable technical infrastructure for FAIR data services and institutional research repositories

Module 10: FAIR Digital Objects, Persistent Identifiers And Machine-Actionability

• Concepts and characteristics of FAIR Digital Objects and their role in structured research data environments

• Persistent identifiers for datasets, files, metadata, researchers, organizations, instruments, publications, and related research entities

• Machine-actionable metadata, standardized object descriptions, repositories, and automated research data discovery

• Connecting datasets, publications, researchers, projects, instruments, and other research outputs through persistent relationships

Module 11: Data Quality, Validation And FAIR Assessment

• Data quality dimensions and their relationship to reliable, interpretable, interoperable, and reusable repository resources

• FAIR assessment frameworks, indicators, maturity models, benchmarks, and institutional evaluation approaches

• Automated and manual assessment of metadata completeness, accessibility, interoperability, provenance, and reuse conditions

• Developing FAIR improvement plans based on assessment results, performance indicators, and institutional priorities

Module 12: Preservation, Sustainability And Long-Term FAIRness

• Digital preservation principles for maintaining persistent access, usability, integrity, and interpretability of research datasets

• File formats, preservation metadata, version management, fixity checking, storage strategies, and technological obsolescence

• Sustainability models for institutional repositories including funding, staffing, infrastructure, governance, and service continuity

• Maintaining FAIRness throughout dataset lifecycle changes, repository migrations, platform upgrades, and organizational transitions

Module 13: Responsible FAIR Data, Privacy And Ethical Access

• Applying FAIR principles to personal, sensitive, confidential, health, social, cultural, and other restricted research data

• Privacy protection, anonymization, pseudonymization, access controls, and responsible data-sharing mechanisms

• Intellectual property, copyright, licensing, consent, data sovereignty, and ethical considerations in repository implementation

• Balancing discoverability and responsible access through FAIR metadata for datasets that cannot be openly distributed

Module 14: Automation, AI And Emerging FAIR Repository Technologies

• Automated metadata extraction, enrichment, classification, validation, and FAIRness assessment using modern technologies

• Artificial intelligence and machine learning applications for metadata generation, semantic mapping, entity recognition, and repository curation

• Knowledge graphs, semantic repositories, linked research data, and intelligent discovery services for connected scholarly ecosystems

• Emerging developments in machine-actionable data, autonomous research infrastructure, FAIR Digital Objects, and intelligent repository services

Module 15: FAIR Implementation Planning, Monitoring And Continuous Improvement

• Developing institutional FAIR implementation roadmaps with priorities, responsibilities, milestones, resources, and measurable outcomes

• Establishing FAIR metrics, key performance indicators, monitoring frameworks, assessment cycles, and reporting mechanisms

• Researcher engagement, training, communication, incentives, support services, and organizational change management

• Managing continuous improvement through stakeholder feedback, technology reviews, FAIR reassessment, and repository service optimization

Module 16: Integrated FAIR Institutional Repository Implementation Practicum

• Conducting a structured FAIR assessment of an institutional repository, including metadata, policies, infrastructure, workflows, and access mechanisms

• Designing a practical FAIR repository architecture incorporating identifiers, metadata standards, interoperability, governance, preservation, and responsible access

• Developing a phased implementation roadmap with institutional roles, technical requirements, resources, risks, performance indicators, and sustainability measures

• Presenting and evaluating an integrated FAIR implementation strategy for improving institutional research data discovery, access, interoperability, and reuse

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 a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, 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.