In this tutorial, I'm going to show you how to apply the search related concepts we learned in the previous tutorials to the Web of Science and Scopus, two very popular databases. So, again, the first thing to do when dealing with a new database is to locate its "help" document. So here I am in the Web of Science, and the "Help" document is right here. This is where you will most likely find answers to your questions related to searching this database. We're going to come back to this document from time to time, so I'm going to leave it open in the browser. Let me go back to the Web of Science's search interface. So, as usual, the first thing we're going to look at is "Fielded Search". This drop-down menu has all the fields you can search in, or combinations of fields if you will. So, you can search by "Topic", "Title", "Author", et cetera. The help document explains exactly how each type of fielded search is performed in the database. So we can see that the topic search includes the "Title" and the "Keywords" fields. Unlike the databases we looked at in the previous tutorials, the Web of Science does not support a controlled vocabulary system, which means it's up to you to think of all the possible synonyms for the concepts in your search. For those, I'd refer you back to the concept table you developed for your project, which we had talked about in Episode Three of this series of tutorials. Because we don't have a controlled vocabulary system here, the truncation and wildcard search feature is especially important. There are some simple examples of them right here, under the search box. In the help document, there's a whole page dedicated to the truncation and wildcard feature of the database. So this gives you a summary of what each of the symbols does. Notice that even though different databases may use the same truncation or wildcard symbols, their meanings could be different. So, for example, in PubMed and OvidSP, a dollar sign or and asterisk is a truncation symbol, which means it only works at the end of a search term. And in OvidSP, a question mark is a wildcard symbol representing zero or one character. In the Web of Science however, all these symbols are wildcard symbols, which means they can be used in any part of the search term. A question mark here represents exactly one character, and a dollar sign represents zero or one character. These differences can be confusing, and they also make search strategies not easily portable among different databases. Like most databases, "Phrase Search" in the Web of Science is indicated by double quotes. Like the example here the query "energy conservation" in quotes will retrieve records that contain the exact phrase "energy conservation" As in most databases, "phrase search" means "exact match", which means phrase search turns off lemmatization, a really handy feature in the Web of Science, and some other databases too. Lemmatization is the automatic mapping of word inflections, such plurals, verb conjugations, some spelling variations, et cetera. So "napkin" finds vocabulary variants such as napkin and serviette. "Serfs" finds phonological variants such as serfs and serves with a "v". "Defense" finds spelling variants such as defense and defence with a "c". We did not talk about this in previous tutorials, but you might want to check to see if your database supports lemmatization. Just look for the word "lemmatization" or "stemming" in the database's help document. Like the other databases, the Web of Science also supports various search operators including Boolean operators, and proximity operators. You can see that they give you some examples of using Boolean operators right under the search box. You can also use the Boolean operators with the drop-down menus in the search form. Like all the other databases, you can also use Boolean operators to combine search history sets. So if I want the intersection of searches one and two, I can combine those sets with "and". This page in the help document tells you all about search operators in the Web of Science. The general usage syntax and meanings of boolean operators are similar in other databases. The "Proximity Operator" in a database is "NEAR", and you can specify how NEAR with a slash and a number. As the example here shows beverage NEAR slash five bottle finds records containing both beverage and bottle and the two words must be within five words of each other. Notice that the Web of Science has an additional proximity operator called SAME, which is primarily used to limit terms in the same address field. So the example here McGill University SAME Quebec SAME Canada finds records in which McGill University appears in the same address field along with Quebec and Canada. The limit options on the search page of the Web of Science are pretty much only Timespan and the choice of different citation databases. But, just as many other databases, in the "Search Result" page, you do have these facets on the left which you can use for limiting purposes. Just as all the other databases, the Web of Science has its own "Command Line Search" syntax. Command line search is available via the "Advanced Search" interface here. So here is an example of what a command line search looks like. On the right-hand side to assist you with the syntax, there is a list of possible search operators, which we have seen, and a list of field tags. If you want more examples, click here. So this gives you a list of all possible types of searches via command line and detailed explanations of each one. The Web of Science is, of course, better known for its citation search feature - how many articles have cited a particular article, or a particular author, et cetera. We'll cover that in a later tutorial. Now, let's look at another database, Scopus. Compared with the other databases we've looked at, Scopus has a much broader coverage. And just as the Web of Science, Scopus does not support a controlled vocabulary system, either. And by now you should know what that means. You have to find all the synonyms to every single concept in your search. Again, I would refer to this concept table we talked about in Episode Three. Again, the first thing to do when you have a new database is to look for its "Help" document, and it is right here for Scopus. And here you can find all the details about searching Scopus. As usual, we're going to look at how Scopus implements these search features that we saw in the other databases: fielded search, truncation and wildcard symbols, phrase search, Boolean operators, proximity operators, limits, and advanced command-line search options. So here's the Scopus homepage. As you might be able to guess here, the searchable field or combinations of fields, are in this drop down menu, so the default "Article Title, Abstract, and Keywords" search is probably the closest thing to a topic search in the other search engines. Just as the Web of Science, Scopus' wildcard symbols can also be used anywhere in the search term. This help page has all the details about wildcard symbols in Scopus. Again, they're slightly different from those in the other databases we've talked about. This is something you should be very careful with. The same syntax and symbols may mean very different things in different databases. So, for example here, the question mark means exactly one single character, and the asterisk means zero or more characters. Like in most databases, Scopus is capable of phrase searching. Again, there's a little bit of a difference here--in most other databases, phrase search are indicated by quotes, which usually means "search exactly AS-IS". However, in Scopus, quotes represent what's called "a loose or approximate phrase" search, in which punctuation is ignored. Wildcard symbols are searched as wildcard symbols, and plurals are included. As the example here shows: heart hyphen attack and heart attack in quotes are exactly the same search. So this gives us some flexibility, but what if we do want strict phrase searching. Well, in Scopus, you have to use curly brackets. So heart hyphen attack and heart attack in curly brackets return very different results. In Scopus, the phrase search also includes "stop words" which are the common, frequently-used words typically ignored by search engines, such as personal pronouns, articles, conjunctions, or even some verbs. Most databases implement a stop-word list, but some don't, such as OvidSP MEDLINE. This is something we didn't talk about in the previous tutorials. To look for the stop word list in a database, searching the help document for "stop words" is a good idea. Boolean operators in Scopus are generally the same as other databases, except that you have to use AND NOT to mean what usually is represented by just NOT in other databases. You can also construct a Boolean search using the interface here. So if I add a search field, I will have another row here, and I can connect these rows by AND, OR, or AND NOT. Boolean operators can also be used to combine searches in the "Search History" table down here. So, following the example here, if I want to combine my first two searches with AND, I can just say number one and number two. Scopus' proximity operators are also different from the other databases. The "PRE" operator indicates that the first term must precede the second term by the specified number of terms, and the "W" operator is similar to the proximity operators we've seen seen in the other databases, which indicate that the terms connected by it must be within a specified number of words of each other. As the example shows here: pain W slash fifteen morphine finds articles in which "pain" and "morphine" are no more than fifteen terms apart. And behavioural PRE slash three disturbances finds articles in which "behavioural" precedes "disturbances" by three or fewer words. There are more details about these operators. I cannot possibly cover everything in this tutorial. I would recommend reading the help document, or ask your medical librarian for assistance. The search page has some simple limits. For example, date ranges, document types, and some very broad subject areas. But in the search result page, you have a lot more facets or limit options. Interestingly, you can either "Limit to" your choices, or "Exclude" your choices, which is a feature not commonly available in the other databases. Last but not least, Scopus also has a command line search option under "Advanced Search", so you can type in your queries following the example down here. One interesting feature here is that the command line here is not that intimidating, thanks to the "code suggestion" feature here. So if I start typing, you can see that these field names started to be highlighted, and if I hit enter, they will be in my query box with the proper syntax. Just as the Web of Science, Scopus is also known for its citation search, and we'll cover that in a later tutorial. So in this tutorial, we covered the basics of building search strategies in the Web of Science and Scopus. Both databases are very popular, have broad coverage, and they do not support controlled vocabulary systems. Therefore, a well-built concept table for your search is critical for constructing an exhaustive search. By now we've looked at PubMed, OvidSP MEDLINE, the Web of Science, and Scopus, four of the most widely used databases in conducting systematic reviews in biomedicine. By now you are probably familiar with the general process of getting used to a new database. Even though these databases may have slightly different syntax, or support a slightly different set of features, you almost always want to figure out these things: How do I do a fielded search? How do I do a truncation or wildcard search? How do I do a phrase search? How do I search whatever I type in AS IS? How do I combine terms or search result sets with Boolean operators? How do I specify adjacency or proximity in my search? How do I place a limit on my search result? What limit options are available? Does the database have a stop word list? Does the database support stemming or lemmatization? Does the database support a controlled vocabulary system? Is there a command line search option? Usually you can find answers to of these questions in the help documents of the databases and don't forget, your medical librarian is your friend in database searches. Also note, that the slight differences in search syntax could mean a big difference in your search result. Therefore when you try to migrate your existing search strategies in one database to a new database, you will inevitably need to revise them a little bit. That's what I'm going to talk about in terms of building search strategies. It is not possible for me to give you detailed lessons on each search feature of each database you will be using, but by now you should have enough knowledge to learn searching with the databases of your choice on your own. Thanks for watching. I'll see you in the next video.