As undergraduate students, most researchers are taught to use their university library’s journal databases for researching assignments, projects and papers. The best database for your needs varies by discipline, because most cover a subset of ALL academic journals based on disciplinary area.
Journal databases are great, and I strongly recommend researchers talk to their library liaison person to work out the best databases to use for their research. Seriously, librarians are awesome and know things about research tools that many academics don’t.
But sometimes journal databases don’t cut the mustard. I’ve become quite a fan of Google Scholar for a few reasons. GScholar is not just another professional social media for researchers; it’s a complementary research tool with huge benefits.
There are two main reasons why I love GScholar and why I have it on my web favourites list:
GScholar identifies if free online .pdf versions of articles exist, with links appearing on the right of the search result. It’s a go-to for me for this reason. If I come across a paywalled article online that I want to read, via social media, emails, or standard Google searching, the first thing I do is search the title on GScholar. If there’s no copy available online or through my library access, then I file a copy request from my uni library, or (if it’s a recent publication) I email the corresponding author.
GScholar is absolutely awesome if you’re looking for specific phrasing. Do you want to find other papers that have used a specific sampling or analytical technique? Do you want to know how many other researchers found a particular problem with their analysis? GScholar can tell you this. And you can limit timeframes to find old papers or new papers, or other search criteria, depending on your research goals.
Here are a few examples (searched on April 6 2017):
|Search term||# Scopus results||# Google Scholar results|
|“concept of ecosystem services”||270||5250|
|“pan traps on the ground”||1||18|
|“definition of meta-analysis”||5||222|
|“negative binomial regression”||3,072||32,100|
|“zero inflated negative binomial regression”||227||2,590|
|“we searched google scholar”||13||280|
Sure, a few of Google Scholar’s results can be dodgy or irrelevant. But with variance like that, it’s definitely worth a try.
And if you want to make sure your open access copy of your latest paper gets onto GScholar, read this.
© Manu Saunders 2017