Exploring 16000 Frontiers in Neuroscience Articles
By: Marko Arezina
Tags: Data Science, Neuroscience

Photo by jesse orrico on Unsplash

Recently I’ve been working a lot on voice apps. One thing that really sticks out for me is how natural a conversational interface is. If the technology advances enough it will allow us to complete tasks more effortlessly since we can simply say what it is we want to do. However, no matter how efficiently a device is surfacing information, your ability to digest information will always be limited by the rate at which you can read, listen, or watch.

This is what makes the idea of advanced Brain Computer Interfaces (BCIs) so great. They would allow people to learn, retain knowledge, and communicate more efficiently than ever before. Elon Musk has been talking about how his mysterious Neuralink startup is developing high bandwidth BCIs to connect humans and computers. The idea behind Neuralink is that if the brain can be both interpreted and manipulated in real time you could “extend” the brain. This could add a new layer of capability, understanding, and communication. People will be able to transfer ideas directly to another person without needing to communicate with written / verbal language. However, BCIs have been researched for a while now, with many applications focusing on helping people suffering from paralysis.

The recent hype behind Neuralink is the inspiration behind this analysis. I wanted to explore what are the some of the trends in brain computer interface research. So after setting my goals I got ready to spend the day battling Pandas and Matplotlib to see if I can glean out any interesting information from my dataset.

Preparing the Dataset

Frontiers Media is an academic publisher of peer-reviewed open access scientific journals. They have numerous journals in science, technology, and medicine. For my analysis I used articles from the Frontiers in Neuroscience journal series which contains some of the world’s top neuroscience journals in terms of influence and quality. Some of these journals include Frontiers in Neuroscience, Frontiers in Neuroinformatics, and Frontiers in Cellular Neuroscience.

My first step was collecting the data. In this case I wrote a Python script to scrape all of the neuroscience articles. The web scraper saved the article’s text as well as their metadata. To collect the URLs of all the articles, I used a bit of regex to parse the sitemap.xml file for the URLs in the neuroscience series. Once my web scraper finish running, my dataset contained 15803 articles stored in a JSON format. These articles came with a rich array of metadata including publication countries, authors, citation dates, and keywords.

Converting the JSON dataset to a CSV is no problem with Pandas. You can simply use the normal dataframe constructor if the JSON object is flattened out (only one level of keys). However looking a bit closer reveals that there are empty dict objects in fields that were missing a value. Here’s a neat trick you can use to replace these empty dicts with NaN:

df = df.mask(df.applymap(str).eq(‘{}’))

Now that everything looks good, let’s call df.to_csv(), upload the dataset to Kaggle, and do some exploratory data analysis.

Check out the Kernel: https://www.kaggle.com/markoarezina/frontiers-in-neuroscience-article-eda

You can find the dataset on Kaggle: https://www.kaggle.com/markoarezina/frontiers-in-neuroscience-articles

Getting to Know the Data

I wanted to get a feel of the types of articles in the dataset before taking a deep dive into BCI specific research. Apart from the journal text, the dataset also contained a wide range of interesting metadata. This included useful fields like the “citation_journal_title“ which specified the article’s specific neuroscience journal. For example, Frontiers in Cellular Neuroscience. Since the metadata also contained the citation country, I thought a heat map of countries and neuroscience journals for number of articles might be a good way to get an overview of the the dataset.

From the heat map you can see some of the largest journals in the series are Frontiers in Cellular Neuroscience, and Frontiers in Neuroscience, and Frontiers in Human Neuroscience.

Next, I wanted to explore some of the common themes in the articles. To do this I found the most common unigrams, bigrams, and trigrams of the citation abstracts. The following plot shows some common themes.