Library Help: Office For Open Research

Understanding research classification 

Research outputs are grouped into categories or topics using various classification systems. These systems underpin many types of analysis—supporting strategic decision-making, identifying research strengths, uncovering emerging fields, and enabling benchmarking across disciplines or institutions.

Classification schemes vary in structure, granularity, and methodology. Some are based on journals; others use full-text, citations, or machine learning to group similar papers. Outputs may be classified differently across platforms, so it's important to understand how each system works.

Major classification systems 

ASJC (All Science Journal Classification) – Scopus / SciVal

• Journal-based system developed by Elsevier

• ~330 subject areas arranged hierarchically

• Every journal indexed in Scopus is assigned one or more ASJC codes

• Used in SciVal for field-normalised metrics (e.g. FWCI), author comparisons, and custom analyses.

Web of Science categories 

• Journal-based classification used in Clarivate platforms

• Covers ~250 subject categories

• Built on the journal in which an article is published

• Used in InCites to support field-normalised metrics like CNCI.

OpenAlex Research Topics 

• Article-level classification using machine learning

• Assigns topics based on full-text metadata and citation networks

• More granular and dynamic than journal-based systems

• Suited for exploring cross-disciplinary research and emerging areas 

• Supports the Overton policy database and is used by many open infrastructure tools

ANZSRC Fields of Research (FoR)

• Developed by the Australian Bureau of Statistics and Stats NZ

• Hierarchical system with 2-, 4-, and 6-digit codes

• Widely used in research funding and assessment exercises in Australia and New Zealand

• Supports cross-country comparisons and high-level mapping
   

FoR codes are available in a number of platforms used at Manchester, including:

• Altmetric Explorer – used to classify University of Manchester research outputs

• InCites – appears as an optional classification overlay (based on journal data)

• SciVal – available as an additional field-based filter alongside ASJC codes
   

While FoR isn’t the native system for these platforms, it can be useful for exploring disciplinary spread, SDG alignment, or cross-national comparisons. 

SciVal Topics and Topic Clusters 

SciVal (Elsevier’s analytics platform) uses citation patterns and machine learning to create a dynamic topic-based classification system.

SciVal Topics (~94,000)

• Smallest unit of analysis

• Defined using citation patterns and natural language processing

• Continuously updated to reflect emerging research areas

• Each topic includes a group of papers sharing closely related themes 

Topic Clusters (~1,500)

• ~1,500 clusters of related topics

• Offers a higher-level view of broader research areas

• Useful for strategic decision-making or interdisciplinary mapping
   

• Read Elsevier's guide to Topics and Topic Clusters
   

Additional Features in SciVal

Topic Prominence

• Measures a topic’s momentum using a blend of recent citations and usage metrics

• Helps identify fast-growing or influential areas of research

Keyphrase Analysis

• Extracts key concepts and terms from a set of publications using Elsevier Fingerprint Engine

• Can be visualised as word clouds or mapped across topics or researchers

• Can be applied at the topic, author, or institutional level

How these tools can help

Research classification systems can be useful for:

• Strategic planning: Spot gaps, growth areas, or underexplored themes

• Benchmarking: Compare disciplines, researchers, or groups fairly

• Collaboration mapping: Find overlaps and potential synergies across fields or faculties

• Narrative building: Support REF, SDG alignment, grant proposals, or promotion cases

E.g. We have used SciVal Topic Clusters to map UoM research in Artificial Intelligence and Health Equity, helping to identify previously unconnected researchers who were working on related themes.

Important limitations to consider

• Methodological variation: Classifications differ by platform; the same paper may fall under different fields depending on the system used

• Journal bias: Systems like ASJC or WoS categories may misrepresent papers in newer or multidisciplinary journals

• Granularity mismatch: Topics can be too broad or narrow for effective evaluation

• Machine learning limitations: Automated systems (e.g. OpenAlex, SciVal) may introduce classification errors—especially for interdisciplinary work

• Update cycles: Coverage and freshness vary; not all tools reflect real-time publication data

Classifications should be viewed as tools to support insight—not definitive labels. We recommend combining classification analysis with local knowledge and qualitative input.

Need support?

The Research Indicators team can help you understand classification systems, generate tailored analyses, or explore how your work is represented in topic-based or field-based systems across different platforms.

If you’re unsure where to begin, or would like help selecting or interpreting research categories, please contact the Office for Open Research. 

If you already know the type of classification data you need—such as SciVal Topics, ASJC Fields, or CNCI by Web of Science Category—you can use our Research Indicators Gateway to submit a request.
 

• Unit-Level Analysis – For departments, schools, or faculties

• Personal Insights – For individual topic/category analysis based on your research outputs

Reports are produced in line with the University's Responsible Metrics Statement and support both strategic and narrative uses of research intelligence.