I have long admired – and, I’ll admit – been a bit fearful of cool technology projects that make use of APIs. To be honest, I’m still not *entirely* sure how an API works. It feels a bit like magic. You need keys and secret keys and bits of code and all those things need to be in the right place at the right time and I might even have to use scary things like the command line!
So you can imagine, I’ve been looking at all the cool Twitter bots launched over the past few years with much wistfulness… some examples of my favourites:
As it turns out, a lovely fellow has made the process of creating Twitter bots super easy by coding all the hard stuff and launching a user-friendly template with step-by-step instructions, freely available for anyone to use. Special thanks to Zach Whalen for creating and putting this online!
So: without further ado, I present to you a Twitter bot that randomly generates a new healthcare review project every hour. You’re welcome!
The beauty of this bot is that some of the project names are so ridiculous… any yet you wouldn’t be surprised to see many of them actually published. I am endlessly entertained by the combinations that it comes up with, and I hope you are too!
Ah, grey literature! Confronted with a vast void of faceless, nameless literature, it’s easy to quickly become overwhelmed. Where do I start? What do I search? What am I even looking for?
As a medical librarian, I’m used to structured searches in curated databases, and going into the unknown can be a frightening thought. However, it is possible to add structure to a grey literature search!
First: What are you looking for?
Too often, the idea of “grey literature” is lumped into one monolithic term. In reality, grey literature is a broad umbrella term and encompasses a lot of different document types whose main commonality is that they are unpublished or published outside traditional publishers: basically, anything that’s not a traditional published research article.
Think about the research project at hand and what types of literature would best support it. For example, in a qualitative synthesis or realist review of a social sciences topic, a lot of robust evidence might come from book chapters with unpublished studies. In a mapping review in health services research, government white papers/reports about local health initiatives might be most relevant. What do you expect the evidence to look like, and where might you go about finding it?
Reports or white papers
Theses and dissertations
Clinical trials registers
Second: Make a plan!
Next, make a detailed plan for searching the literature. Your searching plan should contain information about what sources will be searched, how they will be searched, how the searches will be documented, and how/where the potentially relevant documents will be downloaded/stored.
Some strategies to consider including in your plan might be:
Traditional database searches that will include grey lit such as conference abstracts (e.g. PyscINFO, Embase, Proquest Theses and Dissertations)
Specialised databases (e.g. “grey” databases such as OpenGrey or HMIC, or small subject-specific databases without sophistocated search mechanisms)
Hand searching of key subject websites (e.g. the main associations or government departments in that topic area)
Consultation with experts (who may have ideas about papers you have missed)
For each strategy, document all the details you will need to conduct the search:
Who is going to conduct the search?
What search terms or search strategies will be used?
For more sophistocated sites, a full boolean strategy might be used, but for a site with a simple search box, perhaps one term or a few terms at a time might need to be used. Strategies should be similar, but adapted for the searching capabilities of that resource.
Think also about the context: if your search topic is “yoga for substance abuse”, and you’re searching the NIDA International Drug Abuse Research Abstract Database, you won’t need to include substance abuse terminology in your searches, because everything in that subject database is already about substance abuse.
How will the searches be documented? Oftentimes, an excel spreadsheet will suffice with information such as the person searching, the date, the searching strategy, number of items looked at, and the number of items selected as potentially relevant. Bear in mind that for some resources, the searching strategy might be narrative, such as “clicked the research tab and browsed the list of publications”.
How many results will you look at? The first 50? The first 100? Until there are diminishing returns?
Third: Execute the plan!
Make sure to have a strategy in place for recording your searches and downloading your citations. Due to the transient nature of the web, grey literature searches generally aren’t replicable. When you search google one week, and conduct the same search a year later, you might get different results. However, searches for grey literature can and should be transparent and well-documented, such that someone else could conduct the same searches at a later point, even if they would get different results.
For more information, check out the following papers:
In the “multi-line” vs “single line” searches debate, one point that is often thrown around is: multi-line searches are more cumbersome to edit and run. Even with Ovid’s new “edit” button, it still takes a few clicks and a few page refreshes to edit a strategy and see the results. When making lots of changes quickly to a strategy, this time can really add up.
One underappreciated and little known tool is Ovid’s mutli-line launcher. It’s beautiful! The multi-line launcher allows a user to copy/paste a multi-line strategy directly into the search box, press enter, and view the search results – with hits for each line – as normal.
When making edits to a strategy I tend to do the following:
paste the strategy into the multi-line launcher box
ensure that the line numbers are still correct or changed if needed
Have you ever tried to convert a search strategy from PubMed to Ovid or vice versa? It can be a real pain. The field codes in Ovid don’t always nicely match up with the tags in PubMed and it can be difficult to wrap your head around the auto-expode in PubMed vs manual explode in Ovid for indexing terms. Not to mention that there is some functionality that exists in Ovid but not PubMed (such as proximity operators) and in PubMed that doesn’t exist in Ovid (such as the supplementary concepts tag). Yikes!
Why would you want to convert a search strategy between the two, you ask? Don’t they have the same content?
You might want to use features that are available in both databases! Maybe you’re working on a strategy in Ovid MEDLINE, but realise partway through you’d really like to use one of the PubMed subject filters, for example.
Sometimes, you might find a search filter or hedge, but it is written in the syntax of a different interface. Translating a strategy isn’t always easy or intuitive, so automated the process can reduce errors and save time.
Over the past few months, I’ve been working with a colleague to build a tool that automatically converts searches between the two interfaces, and we recently presented our work at the EAHIL/ICML conference in Dublin.
During the conference week, we had dozens of excellent conversations in person and on Twitter, and 138 unique website visitors! Thanks to everyone who provided feedback and suggestions for improvements. We are working hard to incorporate many of them over the coming months.
The tool is freely available at medlinetranspose.github.io. Please feel free to check it out and let us know how it works for you!
Have you ever been asked to find a random set of citation from EndNote? This happens most often to me when researchers are testing out screening procedures, and want to ensure they are all interpreting the screening guidelines the same way. The researchers will all screen the same random set of 10-20 articles and compare results before screening the entire set.
So: what’s the best way to go about this? Sorting from a-z on any given field and selecting the top 10-20 articles isn’t likely to be truly random. For example, sorting by date will retrieve only very new or old articles. Sorting by record number is one possible way to do it, but also isn’t truly random as it will retrieve articles added to the database most or least recently.
Here’s how I take a truly random sample of citations from EndNote.
First, create an output filter in EndNote
The output filter will include only the citation record numbers. Don’t worry, you only have to do this once, and in the future it will all be set up for you!
In EndNote, go to Edit –> Output Styles –> New Style
In the resulting screen, click “templates” under the heading “bibliography”
Then, put your curser in the box below “generic”. Then, click “insert field” –> “Record Number” –> then press enter so that you curser goes to the next line in the text box.
Go to “file” –> “save as” and save it to something descriptive like “record-number-only”.
Next, export your record numbers.
Back in the main EndNote screen, click the dropdown box at the top of the screen, then “select another style”, and search for your previously created Output Style.
Then click “choose”. Ensure that your output style name is displaying in the dropdown box!
Select “all references” to make sure all your references (that you want to create a subset from) are displayed. Then click one of the references and press ctrl + a (or cmd + a on a mac) to select all references.
Right-hand click and select “copy formatted”.
Create your random subset!
Open excel, and press ctrl + v (or cmd + v on a mac) to paste all your record numbers.
in the cell to the right of your first record number, insert the formula =rand(). This will create a random number from 0 to 100.
Hover the cursor over the bottom-right corner of the cell until it makes a cross. Then click and drag all the way down to the last row that contains a record number
Insert a row at the top and click “sort & filter” –> “filter” on the menu bar.
Then, sort the second row (with the random numbers) from smallest to largest (or largest to smallest).
You now have a randomly sorted list! Select and copy the top x number of cells in the first column (however large you want your sample to be).
Format your record numbers to put back into EndNote.
Paste your subset of record numbers into word (paste as text, not a table!)
Click “replace” on the main toolbar to bring up the find and replace box.
Beside the box “find what”, write ^p (the up-carrot symbol followed by “p”).
Beside the box “replace with”, insert a semi-colon followed by one space.
Then click “replace all”.
You should have a string of record numbers separated by semi-colons.
Put them back into EndNote!
Go back to your EndNote Library.
Right-hand click in the sidebar and select “create smart group”
Give it a nice title, like “random set” 😃
In the first dropdown box, select “record number”, then “word begins with”, then paste in your formatted record numbers separated by semi-colons.
I hope you found this useful. It might sound complicated, but this process really only takes a few seconds once you have gone through it a few times.
Do you have a more efficient or a different way of doing it? What kinds of formatting and database problems do you come across in your position? Feel free to send me a message or tweet at me.
Did you know that Ovid’s search bar can be used like a command line? Its most common use is to type in search queries, but it can also be used to execute several time-saving commands.
Each command is preceded by two dots (..). These are what tell the database that you don’t want to search for terms, but do something different. Remember that there is no space between the two dots (..) and the command!
Part 1: Save and execute searches
..sv ps(search name) will save your search permanently. For example, “..sv ps(Heart-Disease)” (without the quotes) to save the current search. The parenthesis are important — without them, the search will only be saved temporarily (24 hours). I like to periodically type in the same command above while working to save any updates to the search that I’m working on.
..e <saved search name> will execute a search. For example, if you have a saved search called Heart-Disease, type “..e Heart-Disease” (without the quotes) to execute the search.
..pg all to clear the search history. If your search is saved, it will stay saved, but this allows you to clear the slate and start something new. Similarly, use “.. pg #,#” (without the quotes) to purge specific lines.
..dedup # to remove any duplicates from a specific line in the search history.
..ps to view the entire search history in a printable format
Part 2: Look up information about MeSH
..scope <subject heading> will look up the scope note for the indicated subject heading. For example “..scope heart diseases” (without the quotes).
..tree <subject heading> will look up the subject heading in the tree hierarchy. For example, “..tree heart diseases” (without the quotes).
..sh <subject heading> to look up the subheading selection window for the subject heading.
(Note: The three commands above can be used with out without the dot dot (..) syntax preceding the command. I like to use it for all commands for consistency).
I hope you find these commands as useful as I do. If you can master these, you’ll be well on your way to becoming a database master (and also wow those around you with you efficient navigating ability!).
When developing a systematic search, it’s important to use an iterative approach, constantly tweaking and reevaluating your strategy to ensure relevant articles are captured (and hopefully, non-relevant articles are minimised).
Today, I’d like to share a trick that I frequently use when building my searches. First, develop a set of articles which are relevant to your topic. These are articles which should definitely be picked up by your search. The articles might come from researchers or your patrons, other team members on the systematic review, background scoping searches, google scholar, or any other number of places. The more variety in the set of articles, the better. These articles will comprise your “gold standard set” by which you will test your search strategy.
PART 1: Formatting your PMIDs
First, put each of these articles into your citation management system (ideally EndNote). Next, ensure that each article contains a PMID (PubMed ID) in the accession number field (or whichever one you choose). In EndNote, this can often be easily done by clicking “references”, then “find reference updates”. However, do check through all the citations for any that are missed; it may be necessary to manually find the PMID in PubMed.
After you have your gold set all tidied up in EndNote, export the set of references using a custom output filter containing only the accession number field. To set this up in EndNote v7 (only required the first time you do this!):
go to Edit -> Output Styles -> New Style.
in the sidebar, find “Bibliography” heading and click the “Templates” subheading.
in the box that says “Generic”, click “Insert Field”, then “Accession Number”. Save and close your output filter with a descriptive name such as “PMID”.
To export the references using your new filter, first make sure that your newly created output filter is selected (the name should appear in the dropdown box on the top header; if not select the dropdown box, then “select another style”). Next, press ctrl + A to select all references, then right-hand click and select “copy formatted”.
Open a word document and press ctrl + v to paste your formatted references. Your document ought to contain a list of PMIDs – one per line. From here, I use the find and replace tool to automatically format the list of PMIDs for Ovid Medline:
Click “find and replace”.
In the “find what” box, enter ^p (this stands for the paragraph character)
In the “replace with box”, enter “_OR_” (the underscores represent spaces)
Press “Replace all”.
Okay! Still with me? Your word document should be formatted most of the way. Now, I finish by adding an open parenthesis at the beginning of the document and replacing the final ” OR sequence with ).ui. The .ui at the end refers to the Ovid Medline field code for accession number (where the PMID is stored). The text of your document should now look something like this:
(“19901971” OR “22214755” OR “22214756” OR “24169943” OR “24311990” OR “18794216” OR “25491195” OR “16931779” OR “9727760” OR “22529271” OR “18757621” OR “25536072” OR “24838102” OR “25025477” OR “23460252” OR “26888209” OR “24381228” OR “25154608” OR “21889426” OR “24165853” OR “25315132” OR “26819213” OR “26936902” OR “27492817” OR “27531721” OR “27522246” OR “27067893”).ui
This process might take a little while to set up the first time, but once everything is automated through your custom output file, it will only take a few seconds in the future. I’m a big fan of front-loading my work to make things easier down the line.
PART 2: Testing your gold standard set
Now, navigate to your draft search strategy in Ovid Medline and paste the full query from part 1 into a new line below the search.
Take the line of your final search results and the line containing your gold standard set and OR them together. If the last two lines in Ovid contain the same number, you’re in luck! All the citations in your gold standard set will be picked up in your draft search. If not, NOT out your original search results to see which ones have been missed; by looking at these citations, you can strategise ways to pick up articles with similar wording or indexing.
I sometimes find that researchers are concerned about whether the relevant articles they have found will be captured by my search strategies, so I sometimes include this “gold standard search” in draft strategies that I send. I also annotate my process to make it more clear.
The beauty of this method is that as new relevant papers are discovered from additional sources, you can add them to the gold standard set, and continually check your strategy throughout the drafting process.