Research Data Curation and Long-Term Preservation Training Course

Course Introduction

Research Data Curation And Long-Term Preservation Training Course provides a comprehensive foundation for managing research data throughout its lifecycle, from initial planning and acquisition through documentation, quality assurance, preservation, discovery, access, and eventual reuse. The course examines how institutions can establish systematic approaches for ensuring that valuable research datasets remain understandable, authentic, accessible, and usable over extended periods.

The course explores the principles, practices, standards, workflows, and technologies required to curate research data effectively within universities, research organizations, libraries, archives, data centers, and institutional repositories. Participants learn how to assess datasets, develop appropriate documentation, apply metadata standards, identify preservation requirements, manage versions, and prepare research data for reliable long-term stewardship.

A major focus is placed on the relationship between data curation and digital preservation. Participants examine how file formats, metadata, provenance, fixity, storage environments, preservation strategies, identifiers, and repository architectures influence the long-term usability of research data. The training also addresses how curation decisions should consider the scientific, technical, administrative, legal, and contextual characteristics of different datasets.

The course addresses responsible stewardship of research data, including intellectual property, licensing, privacy, confidentiality, ethical restrictions, sensitive information, access controls, and data-sharing requirements. Participants learn how to preserve the evidentiary and research value of datasets while applying appropriate restrictions and documenting the conditions under which data can be accessed, interpreted, and reused.

Advanced topics cover FAIR data principles, preservation metadata, trusted digital repositories, persistent identifiers, research data provenance, emulation and migration, automated quality control, cloud preservation, distributed storage, machine-actionable metadata, artificial intelligence, and emerging preservation technologies. Participants also examine preservation risks such as technological obsolescence, media degradation, format changes, repository failure, incomplete documentation, and loss of contextual information.

Through practical exercises and an integrated curation and preservation project, participants develop the ability to design research data curation workflows, assess preservation risks, select appropriate preservation strategies, and create sustainable long-term stewardship plans. The course supports institutions seeking to strengthen research data services, comply with funder and policy expectations, improve research reproducibility, and preserve valuable scholarly assets for future generations.

Duration

10 days

Who Should Attend

• Research data librarians responsible for research data management, curation, and preservation services

• Institutional repository managers and administrators overseeing scholarly datasets and digital research collections

• Digital archivists responsible for preserving research data and other complex digital objects

• Research data managers supporting researchers throughout the data lifecycle

• Data curators responsible for assessing, documenting, organizing, and preparing research datasets for reuse

• Research support professionals assisting researchers with data management plans and repository deposits

• University librarians and information professionals developing digital scholarship and research data services

• Digital preservation specialists responsible for long-term access and sustainability of digital research materials

• Data stewards and governance professionals managing institutional research data assets

• Research information system professionals integrating datasets with publications, projects, researchers, and institutional systems

• Repository developers and systems administrators supporting research data storage and preservation infrastructure

• Metadata specialists and cataloguing professionals creating descriptive and preservation metadata for datasets

• Researchers and principal investigators seeking to improve the documentation, preservation, and reuse of their research data

• Research administrators developing institutional policies and frameworks for research data stewardship

• Records and information management professionals working with long-term digital information preservation

• Professionals involved in open science, scholarly communication, digital archives, research infrastructure, and data governance

Course Objectives

• Develop a comprehensive understanding of research data curation and its role in maintaining the quality, context, integrity, accessibility, and long-term value of research datasets.

• Understand the complete research data lifecycle and identify appropriate curation and preservation activities at planning, collection, processing, analysis, publication, sharing, and archival stages.

• Apply systematic approaches for evaluating research datasets, identifying curation requirements, documenting data characteristics, and preparing materials for sustainable institutional stewardship.

• Design high-quality metadata and documentation structures that enable future users to discover, interpret, validate, understand, and appropriately reuse preserved research data.

• Assess file formats, storage environments, technical dependencies, and technological risks that may threaten the long-term accessibility and usability of research datasets.

• Implement appropriate digital preservation strategies including migration, normalization, emulation, replication, integrity monitoring, backup, and preservation planning.

• Apply provenance, version control, fixity, authenticity, and chain-of-custody practices to maintain confidence in the integrity and history of preserved research data.

• Develop responsible access and preservation approaches that address privacy, confidentiality, intellectual property, licensing, ethical restrictions, and sensitive research information.

• Apply relevant metadata standards, preservation frameworks, repository practices, and interoperability mechanisms to support sustainable research data management.

• Evaluate research data repositories and preservation environments using appropriate quality, trustworthiness, sustainability, security, and preservation criteria.

• Develop institutional curation and preservation policies, workflows, roles, service models, and monitoring mechanisms that support sustainable research data stewardship.

• Complete an integrated research data curation and long-term preservation project demonstrating practical planning, assessment, documentation, preservation strategy selection, and implementation.

Comprehensive Course Outline

Module 1: Foundations Of Research Data Curation And Preservation

• Concepts, principles, objectives, and scope of research data curation and digital preservation

• Research data lifecycle models and the relationship between active data management, curation, and preservation

• Roles of libraries, repositories, archives, researchers, institutions, funders, and data stewards in long-term stewardship

• Importance of preserving research evidence, contextual information, scientific integrity, reproducibility, and future reuse

Module 2: Research Data Lifecycle And Curation Planning

• Planning curation activities across data creation, collection, processing, analysis, publication, sharing, and preservation stages

• Identifying dataset characteristics, dependencies, stakeholders, expected users, and long-term preservation requirements

• Integrating curation and preservation considerations into research data management plans and institutional workflows

• Developing lifecycle-based curation policies, responsibilities, decision points, quality controls, and documentation requirements

Module 3: Research Data Appraisal And Selection

• Principles and criteria for evaluating the scientific, institutional, historical, administrative, and research value of datasets

• Data appraisal frameworks for determining what should be retained, preserved, transferred, restricted, or safely disposed

• Evaluating completeness, uniqueness, quality, provenance, contextual value, legal status, and expected future usefulness

• Developing transparent selection and retention decisions that support sustainable research data preservation

Module 4: Data Ingest, Transfer And Pre-Curation Processing

• Research data submission, transfer, intake procedures, deposit requirements, and repository ingest workflows

• Dataset inspection, file identification, structural analysis, virus scanning, validation, and pre-ingest quality checks

• Handling complex datasets, large research files, databases, geospatial data, multimedia, software, and computational research objects

• Designing controlled ingest workflows that preserve original materials while creating appropriate preservation and access copies

Module 5: Metadata, Documentation And Data Description

• Descriptive, structural, administrative, technical, provenance, and preservation metadata for research datasets

• Metadata standards, schemas, controlled vocabularies, identifiers, crosswalks, and discipline-specific documentation requirements

• Creating dataset documentation that explains variables, methodologies, collection procedures, processing, limitations, and contextual information

• Improving metadata completeness, consistency, machine readability, discoverability, and long-term interpretability

Module 6: Data Quality, Validation And Integrity Management

• Data quality dimensions including accuracy, completeness, consistency, validity, uniqueness, and contextual integrity

• Automated and manual validation procedures for files, metadata, structures, values, formats, and dataset relationships

• Checksums, cryptographic hashes, fixity verification, integrity monitoring, and detection of unauthorized or accidental changes

• Developing quality assurance workflows and documenting validation results throughout the research data lifecycle

Module 7: File Formats And Digital Preservation Strategies

• Identifying preservation-friendly, open, standardized, documented, and widely supported research data formats

• Format sustainability assessment, format identification, characterization, validation, and preservation risk analysis

• Migration, normalization, emulation, encapsulation, and other strategies for managing technological obsolescence

• Developing format policies and preservation decisions for structured data, documents, images, audio, video, software, and complex objects

Module 8: Research Data Provenance, Authenticity And Version Management

• Capturing provenance information about data creation, transformation, processing, analysis, ownership, and stewardship

• Version control strategies for datasets, metadata, documentation, derived products, and preservation objects

• Authenticity, chain of custody, audit trails, fixity information, and evidence required to establish trust in preserved data

• Managing relationships between original, processed, derived, published, corrected, and superseded research data versions

Module 9: Repository Architecture And Preservation Infrastructure

• Institutional repository components supporting storage, metadata management, access, preservation, discovery, and administration

• Storage architectures including local infrastructure, cloud environments, distributed storage, replication, and hybrid preservation models

• Repository interoperability, APIs, harvesting protocols, persistent identifiers, and integration with research information systems

• Designing scalable infrastructure that supports availability, resilience, security, preservation, and sustainable long-term access

Module 10: Long-Term Storage, Backup And Disaster Recovery

• Distinguishing backup, replication, archival storage, and digital preservation within long-term research data stewardship

• Developing geographically distributed storage, redundancy, replication, recovery, and business continuity strategies

• Disaster preparedness for hardware failure, software failure, cyber incidents, natural disasters, human error, and institutional disruption

• Testing recovery procedures, documenting restoration processes, monitoring storage health, and maintaining preservation readiness

Module 11: Access, Rights, Privacy And Ethical Preservation

• Intellectual property, copyright, licensing, ownership, data-use agreements, and rights management for preserved research datasets

• Privacy, confidentiality, sensitive information, personal data, controlled access, anonymization, and ethical preservation practices

• Designing access policies that balance research reuse, public benefit, legal obligations, institutional requirements, and participant protection

• Documenting restrictions, embargoes, permissions, conditions of use, and responsible access procedures for long-term collections

Module 12: FAIR Data, Interoperability And Reusability

• Applying Findability, Accessibility, Interoperability, and Reusability principles throughout research data curation and preservation

• Persistent identifiers, machine-readable metadata, standardized protocols, linked data, and interoperable repository services

• Connecting datasets with publications, researchers, projects, institutions, instruments, software, and other research outputs

• Designing curated datasets that remain discoverable, interpretable, accessible, and reusable across changing technical environments

Module 13: Trusted Digital Repositories And Preservation Standards

• Principles and characteristics of trustworthy digital repositories and sustainable research data preservation services

• Preservation frameworks, reference models, certification approaches, audit requirements, and repository maturity assessment

• Policies, governance, staffing, technical controls, documentation, security, sustainability, and accountability within trusted repositories

• Evaluating repository preservation capabilities and developing improvement strategies for institutional research data services

Module 14: Automation, AI And Emerging Preservation Technologies

• Automated format identification, metadata extraction, quality assessment, validation, characterization, and preservation workflow management

• Artificial intelligence and machine learning applications for metadata enrichment, anomaly detection, classification, and curation support

• Cloud preservation, distributed architectures, intelligent storage, knowledge graphs, and machine-actionable preservation metadata

• Emerging approaches including computational preservation, research software preservation, FAIR Digital Objects, and intelligent digital archives

Module 15: Preservation Planning, Governance And Sustainability

• Developing institutional preservation policies, governance structures, responsibilities, workflows, and service-level expectations

• Preservation risk assessment, technology monitoring, dependency analysis, succession planning, and proactive preservation management

• Financial, organizational, technical, and human-resource sustainability for long-term research data preservation services

• Establishing performance indicators, preservation monitoring, review cycles, continuous improvement, and institutional accountability

Module 16: Integrated Research Data Curation And Preservation Practicum

• Conducting a complete assessment of a research dataset and identifying its curation, documentation, access, and preservation requirements

• Developing metadata, provenance records, quality controls, preservation strategies, storage requirements, and responsible access conditions

• Designing an end-to-end curation and preservation workflow covering ingest, validation, storage, monitoring, preservation actions, and reuse

• Presenting an integrated long-term preservation plan with risks, resources, governance requirements, implementation priorities, and sustainability measures

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.