Recently, there was an article in Scientometrics about main path analysis by Liu et al. It’s supposed to help trace the development path of a scientific or technological field. Before hearing this, I was just being content with the capabilities of CitNetExplorer in showing the trends in my field of interest. However, after reading the technique’s capabilities. I was quite intrigued as it may make analyzing the overarching trend in a field of interest simpler to visualize. The only problem is that there is really no tutorial on how to do it. The only thing I found was this youtube video using Pajek, which honestly was not very informative. To add to that, I did not have experience with Pajek, and with its very intimidating interface, I really had to tinker with it. Nonetheless, after playing with it, I hacked my way into generating my own main path analysis plots.
In the following, I will explain the process. Note that I do not have much experience with Pajek so there might be easier ways to do it.
The workflow I engineered was this (more explanation in the coming days):
Download articles from Web of Knowledge
Import articles to CitNetExplorer
Export the citation network file from CitNetExplorer
Reformat the file into a Pajek .net file
Import Pajek net file to Pajek
Run Network -> Acyclic Network -> Create Weighted Network + Vector -> Trasversal Weights -> Search Path Link Count (SPLC). Note that you can choose others weights such as SPC and SPNP. In the article above however, they recommended SPLC as they said that it somehow reflects how knowledge diffuse in real life.
Run Network -> Acyclic Network -> Create (Sub)Network -> Main Paths -> Global Search -> Key-Route
Enter an arbitrary number of routes. I tried 1-50.
Run Draw -> Network
Run Layout -> Kamada Kawai -> Fix first and last vertex
This is a sample map for the field of Fragment-based drug discovery.
Literature review can be a tedious process. With so many articles to read, new researchers in a field can find themselves stuck, trying to stay on top of all the readings required. In an effort to streamline the process, bibliometrics can be a powerful tool to make the article selection more efficient, adding a visual component to it.
Last November 10-11, I gave a talk on bibliometric methods at the 8th joint PhD workshop of VU Amsterdam and FH Munster. I got really great response from my talk, with people asking me to make a manual on the topic. Though I only started using bibliometrics three months ago, I found that learning the basics to be a very useful investment. In this post, I will try to create a simple manual on the basics of the method.
Benefits of Bibliometrics
Especially for researchers, here are some things you would be able to do after reading this post:
Get an overview of the important publications in your field of study
Generate a database of important researchers and institutes in your field
Visualize how your field is connected
Though there are many ways to do this, I found using the Web of Science as database and the bibliometric software VosViewer and CitiNetExplorer to have the easiest learning curve. The process generally is composed of the following steps:
Downloading the articles from the database
Generating the maps using the software
Formulating the Keywords
The first part is just the regular literature search on the Web of Science. Most scientists would be knowledgeable already on this area, having done literature search in the past. Though the basic search would usually suffice, it would be more efficient to learn how to use the advanced search with the Boolean operators.
For example, if you are researching on entrepreneurship in the Netherlands. You want to search the terms entrepreneurship and Netherlands together. At the same time, you might want to include related words like business or industry and even the words Holland and Dutch. With these in mind, your keyword search could be:
TS = ((entrepreneurship OR business OR industry) AND(Netherlands OR Holland OR Dutch))
This yielded 3,381 results as of Nov 2016. A preliminary look at the results can then be done. At this point, you can decide to reformulate the keywords or stick with the results. The good thing is that you can easily change your keywords if the list of articles fail to reflect your intended outcome.
Downloading the articles
This part is the easiest yet most tedious. The problem with the Web of Science is that you can only download 500 article data at a time. Thus, if you have 3,000 articles, then you have to repeat the saving process 6 times.
At the results page, what you want to do is click the down arrow beside ‘Save to EndNote online’ and click ‘Save to Other File Formats’
Afterwards, save the first 500 records by typing at the records space 1 to 500. Also, for the record content should be with the cited references. And finally, click send.
You will then have a text file containing information about the first 500 records.
To save the next 500, click again the down arrow and save records 501-1000, 1001-1500 and so on.
Using the Software
With the articles downloaded, it is now possible to analyze them with the software. Download CitNetExplorer. It’s just a matter of loading the text files into the program. It automatically generates a map of the most cited papers in your set of papers. This software is smart such that even if an article does not have the keywords you used, it can still be included if it is cited a lot by the papers in your database.
More importantly, it also shows the connection among these papers. Through this, one can infer how the field developed and how ideas have evolved over time. By being able to visualize how these papers are related to one another, doing literature review then becomes a little bit easier.