In the previous post we assumed we had a list of 100 papers to use as baseline for our percentile calculations. But what papers should be on this list? 

It matters: not to brag, but I’m probably a 90th-percentile chess player compared to a reference set of 3rd-graders. The news isn’t so good when I’m compared to a reference set of Grandmasters. This is a really important point about percentiles: they’re sensitive to the reference set we pick.

The best reference set to pick depends on the situation, and the story we’re trying to tell. Because of this, in the future we’d like to make the choice for total-impact reference sets very flexible, allowing users to define custom reference sets based on query terms, doi lists, and so on.

For now, though, we’ll start simply, with just a few standard reference sets to get going.  Standard reference sets should be:

• meaningful
• easily interpreted
• not too high impact nor too low impact, so gradations in impact are apparent
• applicable to a wide variety of papers
• amenable to large-scale collection
• available as a random sample if large

For practical reasons we focus first on the last three points.  Total-impact needs to collect reference samples through automated queries.  This will be easy for the diverse products we track: for Dryad datasets we’ll use other Dryad datasets, for GitHub code repositories we’ll use other GitHub repos.  But what about for articles?  

Unfortunately, few open scholarly indexes allow queries by scholarly discipline or keywords… with one stellar exception.  PubMed.  If only all of research had a PubMed!  PubMed’s eUtils API lets us query by MeSH indexing term, journal title, funder name, all sorts of things.  It returns a list of PMIDs that match our queries.  The api doesn’t return a random sample, but we can fix that (code).  We’ll build ourselves a random reference set for each publishing year, so a paper published in 2007 would be compared to other papers published in 2007.

What specific PubMed query should we use to derive our article reference set?  After thinking hard about the first three points above and doing some experimentation, we’ve got a few top choices:

• any article in PubMed
• articles resulting from NIH-funded research, or
• articles published in Nature,

All of these are broad, so they are roughly applicable to a wide variety of papers.  Even more importantly, people have a good sense for what they represent — knowing that a metric is in the Xth percentile of NIH-funded research (or Nature, or PubMed) is a meaningful statistic.  

There is of course one huge downside to PubMed-inspired reference sets: they focus on a single domain.  Biomedicine is a huge and important domain, so that’s good, but leaving out other domains is unhappy.  We’ll definitely be keeping an eye on other solutions to derive easy reference sets (a PubMed for all of Science?  An open social science API?  Or hopefully Mendeley will include query by subdiscipline in its api soon?).  

Similarly, Nature examines only on a single publisher—and one that’s hardly representative of all publishing. As such, it may feel a bit arbitrary.

Right now, we’re leaning toward using NIH-funded papers as our default reference set, but we’d love to hear your feedback. What do you think is the most meaningful baseline for altmetrics percentile calculations?

(This is part 5 of a series on how total-impact will give context to the altmetrics we report.)

Let’s take the definitions from our last post for a test drive on tweeted percentiles for a hypothetical set of 100 papers, presented here in order of increasing readership with our assigned percentile ranges:

• 10 papers have 0 tweets (0-9th percentile)
• 40 papers have 1 tweet (10-49th)
• 10 papers have 2 tweets (50-59th)
• 20 papers have 5 tweets (60-79th)
• 1 paper has 9 tweets: (80th)
• 18 papers have 10 tweets (81-98th)
• 1 paper has 42 tweets (99th)

If someone came to us with a new paper that had 0 tweets, given the sample described above we would assign it to the 0-9th percentile (using a range rather than a single number because we roll like that).  A new paper with 1 tweet would be in the 10th-49th percentile.  A new paper with 9 tweets is easy: 80th percentile.

If we got a paper with 4 tweets we’d see it’s between the datapoints in our reference sample — the 59th and 60th percentiles — so we’d round down and report it as 59th percentile.  If someone arrives with a paper that has more tweets than anything in our collected reference sample we’d give it a 100th percentile.

Does this map to what you’d expect?  Our goal is to communicate accurate data as simply and intuitively as possible.  Let us know what you think!  @totalimpactorg on twitter, or team@total-impact.org.

(part 4 of a series on how total-impact plans to give context to the altmetrics it reports. see part 1, part 2, and part 3.)

Normalizing altmetrics seem by percentiles seems so easy!  And it is. except when it’s not.

Our first clue that percentiles have tricky bits is that there is no standard definition for what percentile means.  When you get an 800/800 on your SAT test, the testing board announces you are in the 98th percentile (or whatever) because 2% of test-takers got an 800… their definition of percentile is the percentage of tests with scores less than yours.  A different choice would be to declare that 800/800 is the 100th percentile, representing the percentage with tests with scores less than or equal to yours.  Total-impact will use the first definition: when we say something is in the 50th percentile, we mean that 50% of reference items had strictly lower scores.

Another problem: how should we represent ties?  Imagine there were only ten SAT takers: one person got 400, eight got 600s, and one person scored 700.  What is the percentile for the eight people who scored 600?  Well…it depends.

• They are right in the middle of the pack so by some definitions they are in the 50th percentile.
• An optimist might argue they’re in the 90th percentile, since only 10% of test-takers did better.
• And by our strict definition they’d be in the 10th percentile, since they only beat the bottom 10% outright.

The problem is that none of these are really wrong; they just don’t include enough information to fully understand the ties situation, and they break our intuitions in some ways.

What if we included the extra information about ties? The score for a tie could instead be represented by a range, in this case the 10th-89th percentile.  Altmetrics samples have a lot of ties: many papers recieve only one tweet, for example, so representing ties accurately is important.  Total-impact will take this range approach, representing ties as percentile ranges. Here’s an example, using PubMed Central citations:

Finally, what to do with zeros?  Impact metrics have many zeros: many papers have never been tweeted.  Here, the range solution also works well.  If your paper hasn’t been tweeted, but neither have 80% of papers in your field, then your percentile range for tweets would be 0-79th.  In the case of zeros, when we need to summarize as a single number, we’ll use 0.

We’ll take these definitions for a test-drive in the next post.

(part 3 of a series on how total-impact plans to give context to the altmetrics it reports. see part 1, part 2, and part 4.)