Science Mapping and Research Landscape Analytics Training Course

Course Introduction

Science Mapping and Research Landscape Analytics Training Course provides a comprehensive foundation for understanding how scientific knowledge, research communities, publications, technologies, institutions, and emerging themes can be systematically mapped and analyzed. The course equips participants with practical approaches for visualizing complex research ecosystems and transforming large volumes of scholarly information into meaningful intelligence for strategic decision-making.

The course explores the principles and applications of science mapping, bibliometric analysis, scientometric analysis, research landscape assessment, thematic mapping, citation networks, co-authorship analysis, keyword analysis, and knowledge structure visualization. Participants learn how relationships among publications, researchers, institutions, journals, concepts, and research areas can reveal the structure and evolution of scientific knowledge across disciplines and geographic regions.

Participants will examine how research landscapes can be analyzed to identify established research clusters, emerging topics, influential contributors, collaboration networks, knowledge gaps, technological developments, and rapidly expanding scientific fields. The training emphasizes the interpretation of visual and quantitative evidence, enabling participants to move beyond simple publication counts toward a deeper understanding of how research areas develop, interact, compete, converge, and generate new opportunities.

A practical component of the programme focuses on developing research maps and analytical outputs that can support research strategy, funding decisions, institutional planning, technology intelligence, partnership development, and evidence-based policy. Participants will explore methods for preparing scholarly datasets, constructing networks, identifying clusters, analyzing thematic relationships, interpreting temporal changes, and communicating research landscape findings through effective visualizations and analytical reports.

The programme also incorporates emerging developments in artificial intelligence, machine learning, natural language processing, semantic analysis, knowledge graphs, automated literature discovery, open science, research data analytics, and real-time scholarly intelligence. Participants will consider how these technologies are changing the way researchers and institutions discover emerging scientific fields, monitor competitive landscapes, identify knowledge gaps, and anticipate future research directions.

By the end of the course, participants will be able to conduct structured science mapping exercises, analyze research landscapes, interpret scholarly networks, identify emerging research themes, visualize knowledge structures, and develop actionable research intelligence. The training connects analytical methods with practical research management applications, enabling participants to use evidence from scientific landscapes to support stronger research strategies, collaborations, investments, innovation pathways, and measurable research impact.

Duration

10 days

Who Should Attend

• Researchers seeking to understand the structure, development, and emerging directions of their research fields.

• Research managers responsible for research strategy, portfolio analysis, institutional intelligence, and scientific performance monitoring.

• University leaders seeking evidence to identify research strengths, emerging disciplines, collaboration opportunities, and strategic priorities.

• Research librarians and information specialists supporting bibliometric analysis, scholarly discovery, and research intelligence services.

• Data analysts working with publication datasets, citation networks, research metadata, and scholarly information systems.

• Research funding professionals assessing scientific landscapes, research opportunities, emerging fields, and potential investment areas.

• Policy analysts studying scientific development, national research capacity, technology trends, and evidence for research policy.

• Innovation and technology intelligence professionals monitoring emerging scientific developments and knowledge-based opportunities.

• Research support officers responsible for literature analysis, research mapping, publication monitoring, and strategic research reporting.

• Academic departments seeking to understand their research positioning and relationships within wider scientific communities.

• PhD students and postgraduate researchers exploring research trends, knowledge gaps, influential literature, and potential research directions.

• Monitoring and evaluation professionals interested in mapping research outputs, knowledge development, and scientific programme landscapes.

• Research consultants conducting scientific landscape studies, portfolio reviews, competitive intelligence, and evidence-based research assessments.

• Journal and publishing professionals interested in understanding research communities, thematic developments, and scholarly communication patterns.

• Science and technology organizations seeking analytical intelligence to guide research priorities, partnerships, innovation, and investment decisions.

Course Objectives

• Explain the fundamental concepts, principles, methods, and applications of science mapping and research landscape analytics.

• Analyze publication, citation, authorship, keyword, institutional, and thematic data to understand the structure of scientific research fields.

• Develop practical research maps that visualize relationships among researchers, publications, institutions, concepts, journals, and scientific communities.

• Apply co-authorship and collaboration analysis to identify research networks, influential groups, institutional partnerships, and interdisciplinary connections.

• Use keyword and thematic analysis to identify established research areas, emerging topics, knowledge clusters, and changing scientific priorities.

• Analyze citation relationships to understand intellectual connections, influential publications, knowledge development, and research field structures.

• Apply temporal analysis techniques to examine how research landscapes evolve and how emerging scientific themes develop over time.

• Identify research gaps, underexplored areas, emerging opportunities, and potential collaboration pathways through systematic landscape analysis.

• Develop effective visualizations that communicate complex scientific relationships, clusters, trends, networks, and research patterns to diverse stakeholders.

• Evaluate the quality, completeness, limitations, biases, and contextual challenges associated with scholarly datasets used in science mapping.

• Explore artificial intelligence, machine learning, semantic analysis, and knowledge graph technologies for advanced research landscape intelligence.

• Translate science mapping findings into actionable intelligence that supports research strategy, funding, innovation, collaboration, policy, and institutional decision-making.

Comprehensive Course Outline

Module 1: Foundations Of Science Mapping

• Understanding the concepts, evolution, objectives, applications, and strategic importance of science mapping across research environments.

• Examining relationships between science mapping, bibliometrics, scientometrics, research intelligence, knowledge management, and research evaluation.

• Exploring different types of scientific maps and understanding how each method addresses specific research landscape questions.

• Identifying practical applications of science mapping for universities, research organizations, funders, policymakers, libraries, and innovation institutions.

Module 2: Research Landscape Analytics Fundamentals

• Understanding research landscapes as interconnected ecosystems of publications, researchers, institutions, technologies, concepts, and knowledge communities.

• Examining methods for defining research landscapes by discipline, topic, geography, institution, technology, funding area, or strategic research priority.

• Identifying important research landscape dimensions including productivity, influence, collaboration, specialization, emergence, and knowledge development.

• Developing structured analytical questions that guide research landscape studies and produce useful evidence for strategic decision-making.

Module 3: Scholarly Data Collection And Preparation

• Exploring publication databases, citation indexes, institutional repositories, researcher profiles, and other sources of scholarly research information.

• Understanding bibliographic metadata including authors, titles, abstracts, keywords, affiliations, journals, citations, subjects, and publication dates.

• Applying data cleaning methods to address duplicate records, inconsistent author names, changing affiliations, missing metadata, and classification problems.

• Developing reliable workflows for preparing high-quality scholarly datasets suitable for mapping, network analysis, visualization, and landscape intelligence.

Module 4: Publication And Citation Landscape Analysis

• Analyzing publication volumes, growth patterns, subject distributions, research productivity, and publication concentration within selected scientific landscapes.

• Examining citation relationships to identify influential publications, intellectual connections, knowledge structures, and areas of scholarly recognition.

• Applying citation-based approaches to identify highly connected research themes, publications, authors, institutions, and knowledge communities.

• Interpreting citation patterns carefully while accounting for disciplinary differences, citation practices, publication age, and database coverage.

Module 5: Keyword And Thematic Mapping

• Using keywords, titles, abstracts, and subject terms to identify major themes and conceptual structures within research landscapes.

• Applying keyword co-occurrence analysis to discover relationships among research concepts, topics, methods, technologies, and scientific problems.

• Developing thematic maps that distinguish established, emerging, specialized, central, and developing areas of research activity.

• Interpreting thematic relationships to identify knowledge clusters, interdisciplinary connections, research opportunities, and potential knowledge gaps.

Module 6: Co-Authorship And Research Collaboration Mapping

• Mapping co-authorship networks to understand relationships among researchers, research groups, institutions, and scientific communities.

• Identifying collaboration structures, influential contributors, central research groups, interdisciplinary partnerships, and international research connections.

• Examining institutional and geographic collaboration patterns to reveal strategic partnership opportunities and research capacity relationships.

• Interpreting collaboration networks while considering differences in research team structures, disciplinary cultures, and publication practices.

Module 7: Knowledge Structure And Intellectual Mapping

• Exploring co-citation and bibliographic coupling methods for understanding relationships among publications, authors, research schools, and knowledge communities.

• Mapping intellectual structures to identify foundational literature, influential research streams, conceptual connections, and developing areas of scholarship.

• Examining how research communities build upon previous knowledge and how new research areas emerge from existing intellectual structures.

• Translating knowledge structure analysis into clear visual and analytical outputs for researchers, managers, funders, and strategic decision-makers.

Module 8: Research Trend And Emerging Topic Analytics

• Identifying emerging scientific topics through publication growth, keyword evolution, thematic development, citation activity, and temporal research patterns.

• Applying trend analysis to monitor rapidly developing research areas, technologies, methodologies, scientific debates, and interdisciplinary opportunities.

• Detecting shifts in research priorities and identifying areas experiencing increased scholarly attention across defined periods.

• Developing horizon-scanning approaches that connect current research trends with potential future scientific and technological developments.

Module 9: Institutional And Geographic Research Landscapes

• Mapping research activity across universities, organizations, countries, regions, and geographic research communities to identify patterns of scientific capacity.

• Comparing institutional research portfolios to understand specialization, collaboration, publication concentration, and emerging research strengths.

• Analyzing geographic distributions of scientific research to identify international collaboration patterns and regional knowledge concentrations.

• Developing geographic and institutional intelligence that supports partnership development, investment planning, capacity building, and research strategy.

Module 10: Interdisciplinary Research And Convergence Analytics

• Identifying connections between disciplines by analyzing shared publications, keywords, researchers, citations, methods, and research themes.

• Mapping interdisciplinary research areas to understand where scientific fields converge and where new knowledge communities are developing.

• Examining emerging research intersections involving science, technology, social sciences, health, engineering, computing, and other knowledge domains.

• Using convergence intelligence to identify potential interdisciplinary projects, collaborations, research programmes, and innovation opportunities.

Module 11: Science Mapping Visualization And Interpretation

• Designing clear scientific maps that communicate research clusters, networks, themes, relationships, and temporal developments effectively.

• Exploring network graphs, thematic maps, density visualizations, overlay maps, strategic diagrams, and other research intelligence visual formats.

• Selecting appropriate visualization techniques based on analytical objectives, dataset structures, research questions, and stakeholder requirements.

• Interpreting visual outputs responsibly while avoiding misleading conclusions caused by scale, clustering algorithms, missing data, or inappropriate visual design.

Module 12: Research Portfolio And Competitive Landscape Intelligence

• Applying science mapping to understand institutional research portfolios, areas of specialization, emerging capabilities, and strategic positioning.

• Identifying research strengths, capability gaps, emerging competitors, potential collaborators, and areas requiring further investigation.

• Comparing research landscapes using contextual indicators rather than relying solely on publication volume or citation performance.

• Translating landscape intelligence into evidence for research investment, funding priorities, institutional strategy, partnership development, and programme design.

Module 13: Artificial Intelligence For Research Landscape Analytics

• Exploring artificial intelligence and machine learning applications for automated literature discovery, classification, clustering, summarization, and research mapping.

• Applying natural language processing techniques to identify concepts, themes, relationships, emerging topics, and semantic similarities within scholarly literature.

• Examining knowledge graphs and semantic research infrastructures for connecting publications, researchers, institutions, concepts, technologies, and research outputs.

• Evaluating AI-generated research intelligence for accuracy, bias, source verification, transparency, reproducibility, and appropriate human oversight.

Module 14: Open Science And Digital Research Landscapes

• Examining the influence of open access, repositories, preprints, open data, open methods, and digital scholarship on research visibility and landscape analysis.

• Analyzing how open research practices affect scholarly discovery, collaboration, knowledge reuse, transparency, and research dissemination.

• Exploring digital research outputs and alternative research indicators as complementary sources of information about scholarly reach and engagement.

• Assessing emerging open science infrastructures and their implications for research mapping, research intelligence, and institutional strategy.

Module 15: Emerging Issues In Science Mapping And Research Intelligence

• Examining real-time scholarly intelligence, automated landscape monitoring, predictive analytics, semantic search, and continuously updated research maps.

• Exploring challenges involving algorithmic bias, data ownership, platform dependency, privacy, automated evaluation, research integrity, and information quality.

• Assessing the growing role of AI-assisted discovery, research agents, knowledge graphs, and advanced analytics in scientific landscape monitoring.

• Investigating future directions in research landscape intelligence, including multidimensional mapping, predictive research analytics, and integrated knowledge ecosystems.

Module 16: Strategic Research Landscape Intelligence And Reporting

• Integrating publication, citation, thematic, collaboration, institutional, geographic, and emerging-topic analyses into comprehensive research landscape assessments.

• Developing research landscape reports that translate complex scientific data into clear findings, strategic insights, evidence, and actionable recommendations.

• Designing intelligence dashboards and visual reporting structures that support researchers, institutional leaders, funders, policymakers, and innovation stakeholders.

• Creating an end-to-end science mapping strategy that supports research planning, funding, collaboration, innovation, scientific positioning, and long-term research development.

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.