DIVA User's Manual

DIVA is a set of MATLAB programs that can be used for mappping research specialties through samples of their journal literature. It was developed at Oklahoma State University, in Stillwater, Oklahoma, USA, by Steven Morris and other students in the Electrical Engineering and Industrial Engineering Departments there. The software is currently maintained by Steven Morris as a tool for research in descriptive bibliometrics.

To inquire about using DIVA, please contact Steven Morris (email link)

1. Introduction

• 1.1. Theory of specialty mapping
• 1.2. Introduction to DIVA

2. Getting started

• 2.1. Building a collection of papers
• 2.2. Using the project database
• 2.3. Creating an ODBC link

3. Introduction to DIVA

• 3.1. Installing DIVA
• 3.2. Starting DIVA

4. Clustering

• 4.1. Calculating co-occurrence matrices
• 4.2. Similarity calculation
• 4.3. Setting thresholds
• 4.4. Setting number of clusters
• 4.5. Dendrogram seriation
• 4.6. Clustering results

5. Mapping research fronts

• 5.1. Building the paper-reference matrix
• 5.2. Clustering
• 5.3. Making research front timelines
• 5.4. Timeline exploration tools
• 5.5. Research front report
• 5.6. Labeling research fronts
• 5.7. Timelines as interactive webpages
• 5.8. Interpretation of timelines

6. Mapping references

• 6.1. Co-citation analysis
• 6.2. RF to reference crossmap
• 6.3. Reference timeline
• 6.4. Reference usage map
• 6.5. Reference co-citation report
• 6.6. Interpretation of reference maps

7. Mapping paper authors

• 7.1. Co-authorship analysis
• 7.2. RF to paper author crossmap
• 7.3. Paper author usage map
• 7.4. Interpretation of paper author maps

8. Mapping reference authors

• 8.1. Author co-citation analysis
• 8.2. RF to reference author crossmap
• 8.3. Reference author usage map

9. Mapping reference journals

• 9.1. Journal co-citation analysis
• 9.2. RF to reference journal map

10. Mapping paper journals

• 10.1. Journal coupling analysis
• 10.2. RF to paper journal map

11. Mapping terms

• 11.1. Term definitions
• 11.2. Term co-occurrence analysis
• 11.3. RF to term crossmap

12. Mapping countries

• 12.1. Country entity extraction
• 12.2. Country co-occurrence analysis
• 12.3. RF to country crossmap

13. Institutions

• 13.1. Institution entity extraction
• 13.2. Institution co-occurrence analysis
• 13.3. RF to institution crossmap

14. Using DIVA for bibliometrics research

• 14.1 Database tables
• 14.2 Diva project variables
• 14.3 Catalog of DIVA mfiles
• 14.4 Adding User 2-D maps
• 14.6 Adding User timelines
• 14.7 Adding User map links
• 14.8 Adding User crossmaps
• 14.9 Adding user cluster routines
• 14.10 Adding User similarity functions