UX Caucus Database Tips

In previous posts, we showed you how to test your searches for inclusion of key articles in PubMed and in Ovid databases . You can recycle your PMID strings to create test sets for other databases, too! I search Web of Science for virtually every SR or scoping review I do, so I usually recycle my list of PMIDs. Here's the syntax: PMID=(12450163 or 15982428 or 27391569 or 27940902 or 28941542 or 29056764 or 31651628 or 31874458 or 32340564 or 32855234) While this list contains 10 PMIDs, only 6 of them are indexed in Web of Science. I need to keep track of how many records the string retrieves in Web of Science the same way I would in MEDLINE. For non-MEDLINE articles, you can use DOIs with the following syntax: DO=(10.1182/blood-2020-143231 OR 10.1093/jac/dkaa016 OR 10.1016/j.jadohealth.2019.11.163 OR 10.1089/trgh.2018.0061 OR 10.1093/ofid/ofz360.2167 OR 10.1016/j.apmr.2019.10.050 OR 10.1177/0333102419859835 OR 10.1111/head.13549 OR 10.1212/CPJ.0000000000000401 OR 10.1080/01658107.

Carrie Price recently contributed a tip about how to test for key article inclusion in PubMed . It's easy to do this on the Ovid platform as well: Ovid MEDLINE The syntax is a series of PMIDS combined with OR, nested in parentheses, and searched in the .ui. field (which is where PMIDs live in Ovid MEDLINE). The following example should retrieve 10 results: (12450163 or 15982428 or 27391569 or 27940902 or 28941542 or 29056764 or 31651628 or 31874458 or 32340564 or 32855234).ui. You'll notice Ovid automatically wraps each PMID in quotation marks: I have learned to sort the PMIDs in numeric order to make it easier to scan for and remove individual identifiers, which I often need to do during exploratory searching while the scope of the review question, as well as the inclusion and exclusion criteria, are still evolving.  Ovid Embase If you want to test your Embase strategy - never a bad idea - you can do the same by recycling this same string and changing the field to .pm., which

This week's tip is brought to you by the brilliant SRLibrarianProblems Twitter account:        What the heck is lemmatization (lemmatisation)? And why is it important to consider for sensitive (yet precise) searches? Lemmatization is a feature in many databases (we will demonstrate other examples in later blog posts) that attempts to make a relatively simple keyword search more robust (sensitive) behind the scenes. In the SRLibrarianProblems tweet above, you can see that their example shows that the Topic keyword search for aging with and without quotes produces significantly different results. So what is going on between the two versions of this simple search? According to the Web of Science help documentation : "Web of Science automatically applies lemmatization rules to search queries. Lemmatization reduces inflected forms of a word to their lexical root. With lemmatization turned on, a search term is reduced to its "lemma" and inflected forms of the word are