1. Bibliometric analysis on “Musca Resistance”
Using open source tools: Zotero, Paper Machines, and Vos Viewer
Kustiati Kusno and Dasapta Erwin Irawan

2. Objectives To classify references based on: frequencies of author/s
topics from keywords
To extract movement of topic through out time
To visualize research landscape

3. Tools
Scientific database: WoS, Scopus, Google Scholar, Microsoft Academics
References manager and plugins: Zotero and PaperMachines
Bibliometric tools: VosViewer

4. Gear up Download and install Zotero
Download and install `Zotero button`: plug in for easier look up and saving
Download and install `PaperMachines`: text mining plug in
Download and install `VosViewer`.

5. Step 1 Make a individual collection/folder in Zotero
Search your keywords in scientific databases
Be sure to check all the necessary metadata

6. Step 1
Typical collection/folder in Zotero

7. Step 2
Export the collection/folder in Zotero to: `RIS` formats. We’ll be needing the `xxx.RIS` later on with VosViewer.
Right click the collection/folder and choose `Extract text for PaperMachines`.

8. Step 2 Right click and select
If you see grey menus under it, don’t worry it will go black and functional after text extracting process finish

9. Step 3 Now the time to make some analysis.
Use the menus under `extract text` menu.
Word cloud: to build a word cloud
N-grams: normalized statistics of most frequently used words
Phrase Net: to see word’s connections
Mapping: to make a map based on references geolocation info
Topic modeling: make topic models (Personally we think this needs refinement)

10. Output from step 3: word cloud in timeline

11. Output from step 3: cummulative word cloud

12. Output from step 3: N-grams

13. Output from step 3: words connection

14. Output from step 3: heat map

15. Step 4.1
Open VosViewer
Create > create map based on bibliographic data

16. Step 4.2
Choose `RIS` tab
Load your xxx.RIS file

17. Step 4.3 Choose counting method
You can try each option and see the results
For this try out, choose defaults options

18. Step 4.4 You can see we have 1926 authors from 700 papers.
Choose min number of doc (paper) per author. The default is 5.
The bigger the number the less complicated the results. but it won’t give you a complete picture.
Apparently only 31 authors who have a min of 5 papers in the database.
So let’s try 2 min docs per author to give you 223 authors.

19. Step 4.5 Here’s the list of 223 authors.
For example: `ahmad, intan` has 10 papers with total of 19 co-authors.

20. Step 4.6 Apparently only 86 out of 223 authors are connected.
Let’s choose no to see all 223 items together.

21. Step 4.7 So here’s the results.
Red color shows author with large number of papers.
Orang and yellow shows less papers.
Green shows even less papers.

22. Step 4.8 You can see the cluster of author/s here
You can adjust the result from the panels on the right.

23. That’s about it
These are just graphs/visualization based on your existing database.
They are meaningless without some given context.
You still need to do some interpretations.

24. For more infos
Kustiati Kusno:
Dasapta Erwin Irawan: @dasaptaerwin
License: