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

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

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

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`.

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

Step 1 Typical collection/folder in Zotero

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`.

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

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)

Output from step 3: word cloud in timeline

Output from step 3: cummulative word cloud

Output from step 3: N-grams

Output from step 3: words connection

Output from step 3: heat map

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

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

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

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.

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

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

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.

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

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.

For more infos Kustiati Kusno: kustiati(.)kusno(@)students(.)itb(.)ac(.)id Dasapta Erwin Irawan: @dasaptaerwin License: