Ewan Birney Profile picture
Aug 26, 2019 10 tweets 2 min read Read on X
Metrics on science (people, institutes) are crude tools and should be used with extreme care - my basic rules:
1. Never be lazy and use metrics when you can just read the science (even the goddamn abstracts is better!). We've all been there - don't do it. I have to book in time to read the papers / assessment, but this is potentially travel time
(its also fun reading science - that's why we love our science right - and broadens your own knowledge, so there are positives about being 'forced' to read papers)
Even if you are not an expert, presumably the committee *has* someone experts / written reviews on this topic (if not - what is going on? Complain loudly). I'd take 5 subjective unbiased opinions and average them over 1 hetregenous, hard to normalise, poorly discriminant metric
2. Metrics have some use in triage, but only for mid-career people onwards. Never use biblometrics for early stage careers - all they show is how well funded the persons "parent" lab was. Beware between field/sub-field differences
3. On your CV, quote metrics (fine to use the one with the best result, ie, Google scholar :)) - saves the review fiddling around - but mre importantly highlight your best 5/10 papers that bring out the science you are most proud of.
4. Beware the use of metrics in committee setting - I think we should ban them in the final ranking in particular at the "top" because I think they might have some bottom to top discrimination but hopeless in ranking the top 20% IMHO
Even having the metrics prominently available and in tables ("for easy comparison") makes the committee have in my experience an anchoring effect that the implied ranking is a good starting point. Very human thing.
What's better? - have everyone rank independently, sum ranks is my view (ranking rather than scoring implicitly normalises). Or score independently. Don't get freaked out by variance in scoring/ranking - it's healthy.
Frankly it should be a warning sign if the committee is too tightly correlated - I'd say this is some implicit anchoring effect, though on one-off examples hard to know.

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More from @ewanbirney

Apr 2
A short, personal thread on what is odd about other cultures when interacting with Brits, and then also what I think is odd about Brits when interacting with other cultures - highly, highly personal, but from >30 years working internationally.
German+Dutch do not have to preface a challenge with "I think you might have missed something..." or some other British-style softening up. It is entirely fine - indeed polite/shows respect - just come out "you are wrong because X,Y" - this directness is surprising for a Brit.
Northern (Protestant/river/Prussian) Germans are very different from Southern (Catholic, Mountain+Forest) Germans. Don't confuse them. External stereotypes of Germans (in particular in Britain) is a weird mixture of both and you have to untangle this.
Read 20 tweets
Feb 20
The publication of the whole genomes from the US @AllofUsResearch cohort is great to see, but the choice of how to represent an overview of the genetic relationships has (rightly) drawn controversy, in particular how the concepts of ethnicity and race are mapped to it.
This is not in bad faith - the AllofUs cohort should be applauded in its diversity push and much of the but it is an illustration of the messiness of genetics and the inability to represent our complex relationships in any 2D space. Longer thread below>>
A reminder that genetics (the variation in DNA sequence passed down from your parents, +their parents etc) and race or ethnicity (a box people tick on surveys or on census) are quite different concepts, strongly linked only by visible features which are genetic, eg, skin colour
Read 28 tweets
Jan 19
Next monday is 4 hours of preliminary grant reviews - a necessary but intense part of being a scientist who goes through peer review is being the reviewer. As ever, I am rather amazed by scientists who make simple mistakes in their proposals. My thoughts for a good proposal:
For me as a reviewer you need to convince me of 4 major things. >>
1. Is the problem important/interesting? 2. What has changed in the last ~five years that means an important/interesting problem can now be tackled? 3. Can you actually perform the science? Is it likely to fail? 4. Why are you one of the best people in the world to do this?
Read 18 tweets
Dec 10, 2023
It is a dark, drizzly december sunday in London and I've just read yet another depressing thread of someone reaching for genetics to justify racism and superiority to themselves. It is deeply wrong, but such a recurrent thread worth both dismantling+ understanding the attraction
Let's dismantle first; although a feature of ethnicity/race is skin colour and other visible features, and although these have strong genetic components, counter-intutively for most people, ethnicity is *not* a good predictor for genetics
(certain manipulations of genetic information are reasonable predictors of ethnicity in a single country setting but the reverse is not true; the genetic space is far more high dimensional than these crude ethnicity labels, and it all breaks down when you go global)
Read 25 tweets
May 21, 2023
Here is the slightly cheesy montage for the great #nanoporeconf for 2023 - and, with a reminder of my conflict of interest - I am a longestablished paid consultant for Oxford Nanopore and a shareholder - here are my thoughts on the conference.
For long time nanopore scientists -and I am definitely one of those- one can definitely both plot progress London Calling conference (on the Thames in London) both in terms of what the company presents as near and long horizon+how the plenary speakers use and talk about nanopore
From the company side, much of this was giving a roadmap of key software and flow cells; the R10 flow cells (which is a distinct step up in quality) are now routine; what is not yet is high yield duplex which has being moving from Oxford to Alpha to broader Beta testers.
Read 34 tweets
May 21, 2023
My friend and economics/ markets guru @felixmwmartin commenting on super human AI and all too human market behaviour - on the money that AI will transform many things (science included - it has started in earnest) but also more broadly in the economy
Economics and biology are closer in data science than you might think - in particular micro economics and observational human biology aka epidemiology. Plenty of differences but lots of overlap as well, eg biased sampling, many hidden confounders, clearly correlated variables
A deeper issue is the need to understand causality / intervention- if I enacted this policy or provided this drug what would happen next. Finding the golden causal threads in the tangled Gordian knot (hairball?) of correlation is a common challenge shared by biology+economics
Read 6 tweets

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