Knowing what has already been established in the field is the first step in any research project. Thus, all researchers must combine a deep understanding of their subject with a broad knowledge of the discipline as a whole to push the boundaries of existing knowledge.
But reconceptualizing volumes of peer-reviewed literature over long periods of time is not a simple process. How can academic search engines (ESAs) help streamline a literature search and enable researchers to better formulate relevant research questions?
What is an academic search engine?
Academic search engines aim to combine the convenience and power of web search engines with the rigor of peer-reviewed scholarly sources. Unlike traditional academic databases, which often sit behind a paywall, most ASEs are freely accessible and often link to full-text research articles. ASE searches return publications sorted by topic and importance in the field, with the most frequently cited publications appearing higher in the default list. Researchers can strategically use ESAs to compile an extensive bibliography and streamline the literature review process.
How do academic search engines work?
The underlying algorithms used by search engines are often referred to as “web crawlers”; these index a steady stream of online traffic. The metadata generated by this pre-filtering process allows search engines to return immediate results in response to keyword queries. Metadata generated by search engine algorithms (and in some cases artificial intelligence tools) can be used to find networks of related articles, all of which can be saved in customizable reading lists or exported in batches to reference management software.
What is the best academic search engine for your needs?
ASEs with a broad multidisciplinary approach will naturally have the largest database of sources, and Google Scholar has always been the leader on this front. Other ASEs are all catching up, but Bielefeld Academic Search Engine (BASE), Semantic Scholar, and Refseek have all increased the number of papers housed in their databases. To generate metadata for millions of sources, Google Scholar leverages Google’s ubiquitous web crawling algorithm, while Semantic Scholar uses AI-based techniques. The proprietary nature of these tools can limit transparency and user control, and the iterative nature of these tools can compromise research reproducibility. In fact, even consecutive queries using identical search terms in Google Scholar can yield inconsistent results. In contrast, BASE uses an internationally standardized protocol to collect metadata while disclosing its list of content providers, and may be better suited for meta-analyses or systematic literature reviews.
While ASEs are generally free to end users, the availability of full-text research articles can be quite limited. CORE mitigates this by only hosting articles published in open access journals, but this may not be a viable option for your topic.
Access to ASEs can also vary depending on your location – for example, Google is blocked or censored in some parts of the world – and it can be difficult to rely on ASEs as your only information search tool. The ASE landscape can be quite volatile overall, with Microsoft Academic – Google Scholar’s former main competitor – ceasing operations in 2021. The best approach may still be to pair an ASE with a more traditional academic database ( such as Web of Science or Scopus) as well as databases specifically adapted to your discipline (ERIC, SSRN, Pubmed, CiteseerX).
Top Research Tips
Whichever ESA you choose, as a researcher you should use a consistent approach when planning a research.
- Summarize your topic or research questions in one or two sentences.
- Underline your subject keywords and list their synonyms as alternative search terms.
- Perform a search using different combinations of keywords and assess whether there are too many or too few relevant results.
- Sort results by publication period and number of citations, and save all relevant articles to a personalized reading list.
- Use the “cited by” or “related articles” feature of ASEs to extend the reach of your key references.
A common search mistake is not incorporating Boolean operators into your search strategy. Google Scholar, for example, uses the following Boolean operators:
- AND limits results by returning only relevant articles for all search terms (e.g. learn AND teachers)
- WHERE broadens your results by returning relevant articles for either search term (e.g. learning OR teachers)
- The minus sign (-) limits results by excluding keywords (thus, learning -teachers)
- -to place excludes results from a website (teachers -site:wikipedia.org)
- ~ expands your results by including synonyms for the key term in the search (~teachers)
- “” limits your results by only showing articles with the exact wording (“professional learning for teachers”).
make it work
ASEs are just one more tool in a researcher’s toolbox, and you can get creative with how you choose to use them. You can create a separate reading list for each new article you write and quickly share these reference lists with your co-authors to speed up the final review process. You can create email alerts whenever a prominent author in the field (including you!) publishes a new article, or whenever a new study cites your work. ESAs can be used strategically to improve the public accessibility of scholarly literature and to help you form new collaborations.
Jack Wang is an Associate Professor in the School of Chemistry and Molecular Biosciences at the University of Queensland. He was rewarded Australian University Professor of the Year 2020.
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