After a great workshop in Paris (and hopefully, testing being ok, I will be returning next week) I've been thinking about my travel in the new normal, thinking about green (lowering carbon)
This has been informed by conversations with colleagues such as @embl's green officer, @BrenRouseHD, faculty colleagues such as @Alexbateman1, @PaulFlicek and Deltev Arendt (many thanks); these are currently my thoughts on this (insight from colleagues; missteps from me!)
First off pre-pandmic science travel was useful but often mad; flying for single meetings (sometimes in windowless rooms) with fast turn arounds. Not only was it carbon expensive but it was also bad for family life and just plain health.
Contrasted to that physical meeting and un-agenda'd conversations (side chats over coffee, open-ended discussions during dinner) are hard to impossible to recreate virtually, in particular for people who don't know each other well.
Virtual meetings do work well for agenda'd items, for exchange of ideas. @embl was avid user of video conferencing for some things pre-pandemic (eg, student TACs) but we've taken this to a whole new level and it is an important tool for intra and inter institutional communication
So - Goals for travel: travel for the right reasons; good. Travel for stupid reasons; bad. When travel make sure you do the things you can't do online (means: unagenda conversations are good, trust building). Minimise carbon released during travel, get a good work-life balance.
What does this map to for me? Here is my starting point.
1. note travel and review each 6 months to a year how well this plan is working. Ideally make an explicit note of the number of trips, time away from home and carbon released due to travel.
2. Plan and consolidate travel better, going to see multiple people / institutes in a single trip, chaining together visits (fewer, longer trips). This means thinking about a rough list of places to visit over a year and thinking about how one coordinates this.
3. The travel should always aim to do the things you can't do virtually; book coffee meetings, dinners, be varied.
4. Wherever possible take trains. For all journeys <4 hours (for me; UK, Belgium, much of France + Netherlands) go by train. For >4 to <7hour train, seriously consider this, either using night trains (even longer, but your are sleeping), or breaks, or just the complex trip.
4a A little side note. Frustratingly there are not direct Frankfurt to London to trains (my most common travel is London where I live to Heidelberg, near Mannheim/Frankfurt). Wherever possible I will do the "night in Paris by Gare D'Est" approach, or the full day's travel >>
... I will continue to pray for direct trains London <=> Frankfurt and/or some smooth change overs (eg, Lille <=> Mannheim obviously would be brilliant for me. A bit of a niche route; there is a Lille <=> Strasbourg train but it is often not at the right time).
5. I will use trains where possible on internal movements to lengthen my journey and will aim to "zoom back" to work to make chained trips work for work. Where sensible I will dovetail seeing family friends with my family in with these trips.
6. I do expect to fly but for there to be less flights due to this plan, and the (eventually, once the pandemic is new normal everywhere) intercontinental flights I expect to be 3 or 4 institions in one trip (I already did this Japan and West Coast; I will now do this East Coast)
7. Use responsible offsetting on carbon released (advice from @BrenRouseHD), but don't pretend this is a solution to the carbon released problem by travel. Have this mindset be part of @embl's green plan.
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In general the response I think to the announcement of a polygenic-risk-score informed embryo selection has been right - one where the science is wrong, the clinical harm/benefit therefore also wrong, and one where ethical/societal considerations have to be folded in. However...
There are some people who say "but even if this is wrong now, it might not in the future" (true) and also "if genetics works, then this should work" often with some handwaving towards farm animal genetics/breeding/selection. In this twitter thread I'd like to tackle this.
(Context: I am a geneticist/genomicist. My two favourite organisms to study humans and Japanese rice paddy fish. I'm on the experiments/practical data science side, but have a pretty good understanding of the theory/stats side, partly because I've coded it myself/in my group)
A reminder; in the UK this process would clearly fall under HFEA, and applications to do this would almost certainly be rejected on ethical / societal grounds, on clinical harm to benefit and underlying scientific validity
I’m very positive about the use of genomics in healthcare - many diverse uses and its growing - but I am firmly against this use on ethics, clinical (I’m not an expert) and science (I am an expert). Blogged on this in 2019 ewanbirney.com/2019/05/why-em…
I think the imperial weights thing in the UK is silly (deeply silly) but I do think there was more method in "12" units (and for that matter, 60). 12 is a nice number for division (halves, thirds, quarters, sixths) and then the next nice number for division is 60 (fifths).
Of course the pounds to stone (14!) and then madness of Guineas (I still don't really understand) doesn't fit this. On historical numerology, I was reminded of the arcane voting system for the Dodge in Venice that involves 11, 13s and 17s as supposed "hard to game" prime numbers
As well as the measuring unit changing depending on what you were measuring (this is another moment of deep madness) I think this use of effectively base 12 might be more about early medieaval maths and plenty of mental arithmetic.
As we enter yet another period of COVID uncertainity over outcomes (due mainly to human behaviour - what does "baseline/new normal" contacts look like in an European Autumn/Winter) a reminder about models. There are at least 3 different types; explanatory, forecast and scenario.
Explanatory - usually retrospective data to fit an understanding of the world (say infection->hospitalistion/not->death/discharge) for time series. Examples: excess deaths attributable to COVID, vaccine efficacy models and biological properties of variants.
Forecast - fit an up to date time series to understand outcomes in the near future, sometimes just to understand "now" (hence "nowcasting"). Examples: R rate and near time extrapolation; hospitalisation capacity near term management (often not public).
My annual reminder; if you propose doing large scale data gathering and analysis, just a *one sentence* power calculation, or "we have confidence this approach can provide robust results due to similar work of XXX in system YYY with similar sample sizes".
Why is this important in a grant? As a reviewer wont be able to fully check your power calculation (usually) but I do want to see that you are honest with *yourself* about whether the stats are going to work out. Too few samples, expected weak effects => it's never going to work
If your power calculation (which will always be pulling numbers out of the air for effect size etc; such is science) says its very unlikely you will find a credible result then... you need to reset your goals.
COVID thoughts from London, after a yesterday's day of slow cricket; TL;DR - the pandemic is reasonably predictable except for human behaviour on contacts; Europe will be likely navigating a complex winter; US (still) needs to vaccinate; The world needs more vaccines.
Context: I am an expert in genetics and computational biology. I know experts in viral genomics, infectious epidemiology, immunology and clinical trials. I have some COIs - I am a longestablished consultant to Oxford Nanopore and was on the Ox/Az vaccine trial.
Reminder: SARS-CoV-2 is a respiratory human virus where a subset of people infected get a horrible disease, COVID, often leading to death. Left unchecked many people would die and healthcare systems would have overflowed.