I introduced the concept of databases in section 3. Databases, or sometimes bibliographic databases, offer a number of unique advantages over search engines, and some disadvantages, too. The main advantages are that databases offer specialized collections on a variety of topics; they offer many sources that are invisible to search engines; and they provide greater control over the search process. The main disadvantage is that they are a bit complicated to use well, there are many databases to choose from (and find), each has their own user interface, and they are often only accessible via a library.
Many databases are only accessible via a library because a library pays to use them. Google and other search engines operate on different revenue models, like serving ads.
Information retrieval (search) in databases works similarly and differently than it does with the web. Like with the web, database information retrieval works on documents in a corpus. We search that corpus using queries, and how we construct our queries is important.
Documents in databases though are a bit different. As discussed in the previous section, while web pages on the web exist in a fairly organized hierarchy (with respect to top level domains, etc.), web pages themselves are not always very structured. Search engines have become really good at taking all that unstructured text and making sense of it.
Databases, on the other hand, in particular, the ones we access through libraries, generally index fairly structured documents: i.e., bibliographic records. They may also index full text documents if those documents are accessible to the database, but the focus is on those bibliographic records. You can see examples of bibliogrpahic records at the Library of Congress. If only bibliographic records are indexed, the database is usually called an abstract and indexing database (A&I database). Otherwise it's just called a full text database. Many of the databases that we have access to at our library are a mix of the two.
By bibliographic information, I generally mean metadata, and by bibliographic records, I generally mean metadata about specific items (books, articles, photos, etc.). Metadata is broadly defined as data about data, or sometimes as information about information. For example, a title of a book is metadata about a book. The author name of a journal article is metadata about the journal article. And so forth. All the metadata about a specific item (book, journal article, etc) is a record. In a database, this metadata is controlled, and therefore, well structured (see the Libary of Congress link above). As searchers, this basically means that there are pre-set fields that we can search in these databases and that these pre-set fields specifically search the corresponding metadata. For example, in Academic Search Complete (ASC), we can search the following fields:
- Subject Terms
- Abstract text
- Author supplied keywords
- Geographic terms
- People (names)
- Journal Name
And more. We can also filter by publication date, full text availability, document type, language, number of pages, and images (depending on the database and its content). In the end, this means we have greater control over the search process than we do in a search engine because the corpus is better defined.
For example, in the previous section,
I showed how we can search the web
by using the
:filetype operator to limit results
to PDFs, DOCX, XLSX, etc files.
In a bibliographic database like ASC, however,
we can often specify that we want PDFs, but
we can limit results by document type.
That means we can restrict results to items like:
- book chapters
- book reviews
- case studies
- film reviews
And much more.
Otherwise, all the same principles apply to searching databases as searching the web with a search engine. Specifically:
- Document-centered (bibliographic records are documents)
- Documents exist within a corpus
- Query construction is important
And many of the same techniques apply, too:
- We can use quoting to make sure words are included in the results
- Term order matters
- We can use OR between terms to focus on one term or the other or both
- We can use other operators, like NOT and AND
The AND operator between two query terms means that both terms must be present for each result in a search. For example, if I search for
dogs AND catsin a database like ASC, then each result must include both the terms dogs and cats. We usually have to specify this AND in a database. This not the case with Google and other search engines. In search engines, the AND is assumed between terms. So the equivalent search in, for example, Google, Bing, DuckDuckGo, etc is simply
Many (but not all) databases offer the ability to search by subject or thesauri term. Subject/thesauri terms are kinds of controlled vocabulary. If a database uses these kinds of vocabulary terms, it means that each record in the database includes a list of these terms that should well describe the contents of the item it describes. Further, this means that all bibliographic records that share a specific subject term are linked together.
For example, the ASC database uses subject terms. One subject term is Forest animals, and if I use that as my search query, then each record that is returned must include that subject term, and that record should match the contents of the item. I can peruse the results and identify other subject terms that help narrow my results. For example, the subject term BIRD habitats appears in records with the subject term Forest animals, since records often have multiple subject terms. If I combine those terms with an AND operator, then I narrow my results down to two journal articles, which is pretty precise. ASC is a multi-disciplinary database, and so feel free to explore subject terms related to your own interests.
Although database search can be more precise than searching in search engines, databases are also good for browsing.
We all browse (online, in stores, as we page through books, and so on) but as a type of search process, browsing can become a highly useful tool when applied systematically and strategically. The result is not simply a way to scan through search results. Rather, the result of intentional browsing, (reading or skimming a list of titles and abstracts) can be the accumulation of highly relevant source material, relevant to our information needs and queries.
Although we make a distinction between browsing and searching, it is oftentimes helpful to begin a browsing session with a keyword search, and then use something from the search results, something like an author's name or subject term, to find and collect related information. We call this type of browsing pearl growing.
Below is an image of the ERIC Database. ERIC stands for Education Resources Information Center. It is provided by the U.S. Department of Education, and it is an important access point for millions of bibliographic records to journal articles, books, research reports, white papers, government and other organizational reports, and more on education related topics.
ERIC, like other databases, offers a thesaurus of controlled terms to help aid search. For example, let's say I'm interested in research on academic libraries. In this screen shot, I'm looking at the page that describes the thesaurus descriptor for academic libraries, and as is usual with thesauri, it not only describes how the term is defined in the database, but it also links to related terms, including terms that are conceptually broader than academic libraries, conceptually narrower than academic libraries, or that are conceptually related to academic libraries. I can click on any of these terms, and then click on the link that says to Search collection using this descriptor. And in doing so, I engage in subject browsing.
I can certainly browse using other access points, like author names. After perusing the results from above, I can click on an author's name to narrow results.
Knowing that authors tend to write and research on a specific range of topics (i.e., are specialists) is helpful because it allows me to browse by author and subject topic.
I've described abstracting & indexing (A&I) databases, but there's another special type of A&I database called a citation database. Three useful ones available to us are
The first two are available via UK Libraries, and the latter is available freely on the web. A citation database is a database that shows who has cited an article (as known by their database) and provides a link to those articles that have cited an article. Citation theory says that when any two articles (or books, or other documents) are cited in this way, they are more likely to be about the same thing. In fact, this is how Google search works, in part. Google's original Page Rank Algorithm posited that if a web page links to another web page, then the two pages are likely to be about the same topic. Because of this theory, we can follow citations to find more relevant articles.
Pictured here is a record in Web of Science on information literacy. To the far right you can see that it has 4 Citations. If we click on that 4 Citations link, we can begin to browse those 4 articles or documents. Per citation theory, it's highly probable that those 4 citing documents are also about information literacy; and thus, browsing them would be of considerable help if we were interested in reading more about information literacy.
After clicking on the 4 Citations link, we can see that the term information literacy appears in the title of all four citing works. This is good evidence for our citation theory, but it's also a useful trick for us.
Google Scholar works in much the same way. Instead of Times Cited, it says Cited by, and the search results default by generally listing (we think) the most highly cited works rather than the most recent, as is the default in Web of Science. But if we click on the Cited by link, we'll be taken to a page that lists the citing articles and documents, and like the Web of Science example, it's likely that many of the citing articles will be relevant in our search on this topic.
Like with most other searches, we can combine terms and use those combinations to focus our browsing sessions. The available combinations depend on the database we use. Here's a screen shot of an item from the Communication & Mass Media Complete (CMMC) database. I searched this database using the thesauri term DIFFUSION of innovations AND also the term regression in the abstract. Basically, this tells the database to retrieve any record tagged with the thesauri term DIFFUSION of innovations and where also the term regression appears in the record's abstract. If it contains regression in the abstract, then the source likely used or refers to a statistical technique called linear regression, logistic regression, or like. Once I have this initial query, I can begin browsing the 11 titles and abstracts that are listed in the results.
Remember that database searching is more structured at the document level, and that this structure is reflected in the ability to do field searches. In the above example, for instance, we use two fields. The first field is a subject term search for the subject DIFFUSION of innovations, and it's marked as a subject field with the DE at the beginning. The second field is an abstract search, and this is shown in the drop down box to the right of the query term. In between these two fields is a Boolean AND operator. The AND operator tells the database that both query terms must be present in the results. We've seen this AND in prior examples.
I've mentioned two other Boolean operators: NOT and OR. Many bibliographic databases offer all three. The NOT operator instructs the database to exclude the assigned term. Thus, if we had chosen NOT "regression", then the CMMC database would have returned results where the term regression surely did NOT appear in the abstract for records with the subject term DIFFUSION of innovation.
The OR Boolean operator is a bit tricky. It means, basically, one or the other or both. Thus, if we had used it here, then CMMC would have returned all records having the subject term DIFFUSION of innovations, as well as those records that did or did not have regression in the abstract. The OR operator is more useful when querying terms in the same search fields. For instance, we might want to use the OR operator to search for two different terms that might appear in the abstract fields, or the subject term fields, such as the following related terms:
DIFFUSION of innovations theory" OR INFORMATION dissemination"
We can see how this plays out in the results. In the first record in the following screenshot, both terms appear in the subjects list. But in the second record, only one of the terms appears.
When we browse, therefore, we are attempting to locate key qualities from our results or our initial results lists (e.g., authors, subjects, etc.). These lists include the titles, the abstracts, the thesauri, and so forth. And these key terms will help capture what our search is about.
Many databases will offer a way to create, save, and export lists or individual records based on browsing and searching. This helps us easily manage the documents that we highlight as initially important. We can curate these lists as they grow and our search becomes more focused.
Creating a list in a specific database usually requires us to create an account on that database. I already have an account with EBSCOhost, the vendor that provides the CMMC database as well as many others, and in the following screenshot, I've already signed in to that account. To the right of the image, you can see a folder icon. As I browse through records that look relevant to my needs, I can click on that icon and save the result to a folder. I can also create multiple folders and email, download, or print the records for later use.
Of course, I prefer to save records in Zotero rather than use a database folder or list. This way I keep the records with me even if I lose access to the database.
In this section, we learned the following:
- Databases and search engines are different
- Each have advantages and disadvantages
- Search engines are well structured at the file system level
- Databases are well structured at the record level
- Searching in a database means search structured bibliographic records
- Records are structured by pre-set fields
- Subject terms or thesauri descriptors help create precise searches
- Systematic browsing can be a rigorous way to engage in search
- Pearl growing is a browsing strategy that involves collecting items
based on an initial aspect of a bibliographic record. Such as as:
- subject term
- author name
- Because databases search structured bibliographic records with pre-set fields, we can create very precise queries by combining fields
- We can combine fields using Boolean logic: AND, OR, NOT
- We can create and save lists as we browse
- Or we can save items to our reference manager (RM).
In the end, don't simply browse absentmindedly. Rather, browse with smarts: systematically and strategically. Make the systems work for you. And save your results in Zotero or your chosen RM as you go.