How to Use SciMAT for Science Mapping Analysis

• ⏱️ 13 min read • 🧭 Bibliometrics • SciMAT

Have you ever seen a strategic diagram dividing topics into four quadrants — motor themes, niche themes, emerging/declining themes, basic themes — in a Q1 journal and wondered “how is this made?” The answer: SciMAT (Science Mapping Analysis Tool). Unlike VOSviewer or Biblioshiny which give you a snapshot, SciMAT is the king of longitudinal analysis — it maps theme evolution across decades and shows evolution paths (evolution map) no other tool can produce. The bad news: its UI looks like it’s from the 90s, Java installation is tricky, and the learning curve is steep. The good news: this article reveals all its secrets. And if you want professional results without battling Java, the Corvexum team is ready to execute it for you.

Why SciMAT Excels at Longitudinal Analysis

SciMAT was developed by the Soft Computing and Intelligent Information Systems group at the University of Granada. It has three flagship weapons:

  • Strategic Diagram: maps themes by centrality (external relevance) and density (internal development) into four classic quadrants — the “kitchen secret” of many Q1 bibliometric articles.
  • Evolution Map: shows how themes migrate between quadrants from one period to the next.
  • Overlapping Map: displays the degree of theme overlap between sub-periods.

When journal reviewers ask for “evidence of field dynamics over the last 20 years”, SciMAT is the best answer.

Step 1: Installation & Data Preparation

SciMAT is Java-based. Download the full package from the official site (scimatio.es), extract, then run the .jar. Prepare a folder structure: input, output, and thesaurus. SciMAT accepts ISI WoS plain text, Scopus CSV, and PubMed formats. For Scopus, use the built-in converter or bibliometrix’s convert2df() first. Mandatory: prepare a thesaurus file (.txt) with synonym pairs, e.g., artificial intelligence|AI|machine intelligence.

Step 2: Preprocessing & Normalization

In the Data Management menu, import the dataset then run preprocessing: remove duplicates, apply the thesaurus, choose the unit of analysis (keyword / author / reference). SciMAT offers several normalization methods: Cosine, Jaccard, and Inclusion Index. For keyword co-occurrence, Cosine is usually the safest choice. Then pick the clustering algorithm — the default Simple Center Clustering works well for most cases.

Step 3: Time Slicing & Strategic Diagram

Divide the dataset into several sub-periods (e.g., 2000-2005, 2006-2010, 2011-2015, 2016-2020, 2021-2026). Click Run Analysis for each slice. Main outputs:

  • Strategic Diagram per sub-period: four quadrants showing each theme’s position.
  • Cluster network: internal network of each theme with relevant documents.
  • Performance indicators: publication count, citations, h-index per theme.

Step 4: Evolution Map — The Secret Weapon

In the Evolution menu, combine results across sub-periods. SciMAT will produce an evolution map showing theme migration paths: which themes persisted, which grew from basic to motor, and which faded from motor to declining. This is what makes your article stand out — reviewers can see the narrative of your field’s evolution year by year, not a static snapshot.

Step 5: Export & Reporting

SciMAT exports results in PNG, CSV, and a complete HTML report. For journal articles:

  • Take the strategic diagram of each sub-period as the main figure.
  • Take the evolution map as the synthesis figure.
  • Take the performance indicator table for the results section.
  • Write an evolution narrative based on the maps: “Theme X was born as a niche in period 1, grew into emerging in period 2, and is now a motor theme in period 5.”
💡 Pro Tip: Compare SciMAT results with Biblioshiny’s Thematic Map and VOSviewer’s Overlay Visualization. If all three confirm the same theme as emerging/motor, you have layered evidence that is nearly irrefutable to Q1 reviewers.

🎯 When to Use SciMAT vs Other Tools?

  1. Want 10-30 year evolution analysis? → SciMAT wins clearly.
  2. Want a quick thematic structure snapshot? → VOSviewer or Biblioshiny thematic map is more practical.
  3. Want emerging topics with burst detection? → CiteSpace is more suitable.
  4. Best strategy for Q1 articles: combine SciMAT (evolution) + Biblioshiny (thematic) + VOSviewer (network) for evidence triangulation.

❓ FAQ

Q: Is SciMAT paid or free?
A: Free for academic use. A commercial version is available with a separate license.

Q: How long to master SciMAT?
A: 2-4 weeks intensive. Or hand it to Corvexum — professional results ready in days.

Q: Is SciMAT suitable for a master’s thesis?
A: Highly suitable, especially for bibliometric theses requiring long-term evolution evidence.

🚀 Don’t Let Java and a 90s UI Stall Your Publication

Learning SciMAT from scratch means weeks battling Java installation, runtime errors, and a 90s-era UI — while your submission deadline keeps approaching. A single thesaurus misconfiguration can corrupt your entire longitudinal analysis. Corvexum takes over the technical burden so you can focus on the academic argument:

  • ✅ SciMAT installation & configuration on our dedicated server
  • ✅ Professional thesaurus building + data preprocessing
  • ✅ Strategic time slicing (3-5 optimal sub-periods)
  • ✅ Strategic diagram + evolution map + overlapping map at 300+ DPI
  • ✅ Evolution narrative ready to paste in your results chapter + target journal consultation
💬 WhatsApp: +62 888-6329-421
📧 admin@corvexum.com
🌐 corvexum.com/#contact

Alternative email: corvexum@gmail.com

Crafted by the Corvexum bibliometrics team. Because longitudinal analysis deserves the right tools — and your time deserves protection.


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