When we use keywords to find relevant articles, we know that we may find some but not all.
To find 'all' relevant articles, we build exhaustive search queries using a wide range of keywords and their synonyms. This strategy easily retrieves 1000s of other articles, making it time-consuming and inefficient.
CoCites uses scientific articles to find related articles. These scientific articles can be articles that you already know or that you found through simple best-guess keyword searches.
The CoCites browser plugin adds the CoCites logo for each scientific article in PubMed.
The number in the logo tells how often the article was cited. A click on the CoCites logo finds the article's co-citations. A grey button means that the article is not in the citation database.
Co-citations is the frequency with which two articles are cited together in the reference lists of other articles.
Across many reference lists, we find that some articles are co-cited more frequently than others. Articles that are frequently cited together tend to be on a similar topic.
CoCites retrieves articles that cite an article of interest (the 'query article') and extracts all titles in their reference lists. CoCites counts how often each title appears in all reference lists and ranks them in descending order.
CoCites finds articles that are frequently cited together with an article of interest. This makes CoCites an ideal method for:
Scientific reviews, including systematic reviews, meta-analyses, and rapid reviews: projects where the goal is to find similar articles
Finding the best-known or key articles on niche topics: the CoCites search results are presented with the articles' citation counts. This is helpful information for research on topic that are outside one's expertise, or for journalists to find experts on niche topics.
This is a beta version of CoCites. In the full version, you will be able to:
- Perform a co-citation search using multiple query articles.
- Find recently published articles through a citation search.
- Filter search results using a similarity score
- Save search queries for re-use and search updates