Science-Maps

Overview

This project takes as input a search for something that would be the subject of scientific research and a number of articles to scrape from, and returns a csv of locations found in the abstracts of the articles returned by the desired search (using sciencedirect.com ). These locations are determined by checking the text of all abstracts found against a dictionary of ~7,000 cities and countries. The user imports the csv to google maps. An example map with results from a few different searches can be found here.

Usage

I wrote this script as a tool to discover trends in research practices. These insights are meaningful on a few levels. For one, it's fun to check out patterns in research for certain animals, plants, or geographical features (figure 1 ) . On a more serious note, the script can illuminate imbalances in research location(figure 2 ), which can have significant consequences for the integrity of the research.

Fig. 1

Combined results from searches on "volcanoes" and "earthquakes" clearly outline some tectonic plate boundaries

Fig. 2

A search on "music cognition"(900) shows a clear imbalance where the vast majority of results are in North America and Europe.

Next Steps

improving existing functions

• Integrate a named entity recognition tool, such as the Stanford Natural Language Processing group's Named Entity Recognizer coupled with the geocoding function of the Google Maps API to detect and get coordinates for locations that aren't loaded into the dictionary.
• Add support for protected areas, mountain ranges, and forests around the world(only U.S. National parks are supported as of now)
• Automate the mapping step
• Look into an issue where the scraper returns 404 errors on ~25% of links it runs
• Link location results to the title of the article that they came from

Literature mapping project

I'm currently working on a project that is designed to take in pieces or collections of literature (or just text files for that matter), determine locations mentioned throughout the work, and map them(with D3 or the Google Maps API) with respect to mention frequency and their relation to each other in time.