The secret to bibliometric analysis: generating a list of PMIDs

By now, it’s probably no secret that I love crunching bibliometric data. I find that analysing my results — both during search strategy formation and after downloading final results — gives me a broader perspective and see trends that I might otherwise miss.

However, analysing data can sometimes be time consuming and clunky. Data never seems to be in the format that you want it when you need it; the precise tool that you need at that moment hasn’t been invented yet or is otherwise proprietary; the right software for the job requires a programming language you haven’t yet learned, and so forth. Sometimes you want a quick and dirty answer to help develop a strategy and it doesn’t have to be tidy or perfect, but you need it now!

Here’s my quick and dirty trick for analysing your bibliometric [medline] data:

  1. Generate a list of PMIDs from your results (whether your strategy is finalised or not!)
  2. Pop into the data analysis program of your choosing…

The beauty of this trick is that you can copy-paste whatever you are working on at this very moment (provided you’re working with medline data, of course…) and get real-time feedback. No need to mess with clunky software interfaces or retype your strategy.

Generate a list of PMIDs


If you’re using PubMed, this part is easy. Click the “Format: Summary” drop down menu just below the search bar, then select “PMID”. Et voila! The resulting page is a plain text list of PMIDs, taken from the results on the previous page.


Note that the resulting PMID list will show only the citations from the previous page, so you may want to scroll to the bottom of the screen to show the max number of citations per page (200 at the time of this writing).


To extract PMIDs from Ovid:

  • select all citations (or a range if there’s a lot!)
  • click “export”
  • select “excel” under the drop-down menu “Export To:”
  • select “custom fields”
  • under “select fields” (beside the “custom fields” radio button), unselect everything except “unique identifier” (this is the field that contains the PMID in Ovid)
  • Then select “export citations”

An excel file should download with a column of PMIDs, which can then be copied/pasted.

(Thanks to Michelle Fiander for the excel tip!)


Analyse your data

Once you have your list of PMIDs, you can pop them into a variety of different tools to crunch the data in different ways. For example, try pasting your list into:

  • PubReminer  – for a word count analysis of authors, journals, MeSH, title/abstract words…
  • Medline Trends – for an analysis of citations over time
  • GoPubMed – for a variety of filters (maps! bar graphs! frequency charts!)
  • Yale MeSH Analyser – for a side-by-side comparison of MeSH usage

And more! Someday I intend to write up a full list of medline data analysis tools freely available online, but that day is not today…

It’s not necessary to input a full search strategy into most bibliometric analysis programmes… simply paste in your PMIDs!

Why would a person bother to do this?

Building a search strategy is an iterative process and it requires using a lot of different tools. For example, you can use your own common sense and intuition, but other tried-and-true strategies include: backwards/forwards citation chaining, talking to experts in the field, or looking at highly cited papers/journals in the field.

Using quick data analysis strategies throughout the process of building a search strategy will help ensure that important concepts aren’t missed. They provide a more objective picture of what’s happening, what’s missing, and how you can better refine your strategy.

That’s it for this week!

PS This is my first proper blog and I must say… keeping a blog up to date is not as easy as I thought. Please do let me know if you find this content useful and I will try my utmost to keep ’em coming! You can use the site contact form or find me on twitter at @v_woolf.



761bf8f77c17cc26a07f837501f75850913c192227b19aabaec2a3910e5c6f99No, it’s not a food that will give you a slimmer stomach or boost your manly prowess.
I’m talking, of course, about the ability to find the total number of citations in the Medline database. Why on earth would someone want to know how many citations are in a database, you ask?
  • To compare and contrast the size with other databases
  • For FUN, because you’re a nerd like me
  • Um… because?
It’s relatively straightforward to find the total number of citations in PubMed. Their documentation helpfully tells us: “To search for the total number of PubMed citations, enter all [sb] in the search box.”
However, a few days ago I was struck with an awkward problem. I needed to find the total number of citations in Ovid Medline. Why? I had conducted a straightforward scoping search for a researcher and created a basic frequency analysis of the number of citations retrieved in the search per year to show the publishing trends in the topic over time.
frequency analysis, non-normalised (raw count of citations)
The researcher asked me to normalise the data…. say what?? Do I look like a statistician?
I knew I couldn’t use the numbers from PubMed, because the two have slight differences in content. And I couldn’t translate my strategy into PubMed because it relied heavily on the adjacent operator (which is absent in PubMed).
After some frantic searching, I found out that this was not such a difficult task: all I needed to do was take the number of citations retrieved from the search in a given year, and divide this by the number of total citations published in the database that year. This would even out any potential errors in the chart from anomalies in the database as a whole.
The problem: I could not find an equivalent operation in Ovid Medline to PubMed’s all[sb] command. After combing through Ovid’s documentation, I finally broke down and tweeted them… and received a response within a few hours.
I know everyone’s been waiting with bated breath to find the answer: it’s
What does the .dz field code stand for? No idea. But anyway, it seems to get the trick done, and now I have my nicely normalised graph. In the second image, below, you can see that the downtick in citations for the year 2016 has vanished, because the number of citations retrieved from the search is proportionate to the total citations published this year.


frequency analysis, normalised (results as a percentage of total citations in database)
Happy story! The end.
PS Cheers to Ovid’s social media team! They are totally on the ball.