Web scraping is revolutionizing academic and professional research by enabling the collection of big data.
Advanced collection practices allow higher levels of data extraction at faster rates, enabling new research opportunities in healthcare, finance, ecology, politics, and economics.
The Digital Landscape Makes New Research Possible
New data sources from across the world are continuously being created as people increasingly conduct business, personal, and professional transactions online. As these sources expand, researchers are finding new opportunities to develop their research and obtain new insights.
Advanced insights can also lead to new questions, creating a cycle that drives further research and increases understanding of the subject matter. As a result, researchers improve their findings, derive increasingly accurate conclusions, and produce better solutions to problems affecting people, businesses, and governments.
Legacy data sources include journals, purchased data sets, and information collected manually from the internet. Besides being resource-intensive, these methods typically require hours of manual entry into spreadsheets that are tedious, time-consuming, and prone to error.
Today’s research landscape is vastly superior. Researchers now access a trove of online data covering nearly every subject. Examples include financial websites with historical stock information, public databases with clinical drug trials, and online marketplaces with detailed product and pricing information.
Modern data gathering methods enable researchers to extract that information at scale and automatically update their databases. For example, imagine an online resource with thousands of stocks, including historical pricing information, current news, and trading volumes. Web scraping makes it possible to make thousands of data requests from that website per second and deliver the information in a spreadsheet format that analysts can easily read.
Advanced web scraping requires the creation of scripts (or “bots”) written in a programming language like Python to crawl websites and extract data. Alternatively, smaller or personal data extraction projects can be executed using browser extensions that parse website HTML and export the information in a spreadsheet format.
Another alternative is a web scraping API that can be easily customized. Researchers opting for this solution can quickly extract information at scale and avoid many common process challenges, allowing them to focus on obtaining insights for research purposes.
Web scraping enables new research into economics, healthcare, ecology, and politics by allowing researchers to gather data from emerging online resources. Without automation, some of these projects would have been impossible to complete without hundreds of hours of manual data collection, entry, and processing.
Oxford researchers downloaded over 3000 PDF documents to study opioid deaths in the United Kingdom. Web scraping made it possible to scale the project considerably so they could focus on other research-related tasks. “We could manually screen and save about 25 case reports every hour,” reads an article in Nature describing the project. “Now, our program can save more than 1,000 cases per hour while we work on other things, a 40-fold time saving.”
Automating data collection also opened up collaboration. By publishing the database and frequently re-running the program, researchers enriched the project by sharing findings with the academic community.
