Dagmar Fraser

Happy birthday to one of my favourite haters, Charles Darwin

R4E is hosting a webinar for all, no cost. Learn how to design an effective poster webinar
www.repro4everyone.org/blog/r4e-web...
Thank-you to @addgene.bsky.social for sponsorship! #AcademicSky #EduSky #neuroskyence

Illustration of the Dutch Reach. You should reach across with your non-door-side hand to open a car door, because it forces you to look behind you.

Last night, I got properly "doored" for the first time. A car in the road to my right paused, and the rear left passenger threw open his door without looking, right into my path. I ended up on the floor, badly winded but ok.

If you don't know what the "Dutch reach" is, NOW is the time to learn.

"Dooring" is a hazard that cyclists face all the time, normally when passing parked cars.

The "Dutch reach" is a way of opening a car door that forces you to look behind - you reach with your non-door hand, turning your shoulders. You should *never* open a car door without checking behind you.

Poster with the text - New to MATLAB? Join us for a gentle intro! On-Ramp with experts, pizza, and free merch! February 26th, 1500-1700, 52 Pritchatts Road G16 Open to all levels of ability… just bring a laptop. Places limited – email d.s.fraser@bham.ac.uk with pizza topping preference (e.g. gluten free / vegan). follow @dagmarfraser.bsky.social MATLAB Student Ambassador & MATLAB Special Interest Group Chair

What next? blogs.mathworks.com/matlab/2025/...

#MATLAB #MATLABambassador

Stuck? Ask me, or the MATLAB tuned ai playground uk.mathworks.com/matlabcentra...

YouTube video by MATLAB How to Get Started Learning MATLAB: 10 Tips for Students

10 essential MATLAB tips for beginners to help learn MATLAB faster & with more confidence - youtu.be/KGn5m-LD9Ng?...

Self-Paced Online Courses - MATLAB & Simulink Learn MATLAB for free with MATLAB Onramp and access interactive self-paced online courses and tutorials on Deep Learning, Machine Learning and more.

Ready to learn MATLAB and can’t wait till the in-person on ramp on the 26th?

Start here with MathWorks free online learning just click MATLAB Onramp matlabacademy.mathworks.com - login with you University of Birmingham email address

New preprint 🎉 Psych constructs are complex. Symptoms overlap, people rarely fit neat categories, and patterns are non-linear. Most methods compromise this richness. Self-Organising Maps don't. We provide a step-by-step tutorial with annotated R code to make them accessible.

doi.org/10.31234/osf...

Bluesky Map Interactive map of 3.4 million Bluesky users, visualised by their follower pattern.

I made a map of 3.4 million Bluesky users - see if you can find yourself!

bluesky-map.theo.io

I've seen some similar projects, but IMO this seems to better capture some of the fine-grained detail

Original post: universeodon.com/@Edmonds_Sca...

Diagram showing four phases of methodological research (Theory, Exploration, Systematic Comparison, Evidence Synthesis) with an arrow indicating that preregistration usefulness increases from early to late phases. Each phase lists its aim, elements, outcome, and an example from factor retention research.

Does it make sense to preregister simulation studies?
This question has sparked a lot of debate.

▶️We* work through the why, when, and how
▶️We discuss different phases of methodological research to clarify where preregistration might (or might not) add value

📝 Preprint: doi.org/10.31234/osf...

What’s a multiverse good for anyway? Julia M. Rohrer, Jessica Hullman, and Andrew Gelman Multiverse analysis has become a fairly popular approach, as indicated by the present special issue on the matter. Here, we take one step back and ask why one would conduct a multiverse analysis in the first place. We discuss various ways in which a multiverse may be employed – as a tool for reflection and critique, as a persuasive tool, as a serious inferential tool – as well as potential problems that arise depending on the specific purpose. For example, it fails as a persuasive tool when researchers disagree about which variations should be included in the analysis, and it fails as a serious inferential tool when the included analyses do not target a coherent estimand. Then, we take yet another step back and ask what the multiverse discourse has been good for and whether any broader lessons can be drawn. Ultimately, we conclude that the multiverse does remain a valuable tool; however, we urge against taking it too seriously.

New preprint! So, what's a multiverse analysis good for anyway?>

With @jessicahullman.bsky.social and @statmodeling.bsky.social

juliarohrer.com/wp-content/u...

❄️ Obituary — Journal of Glaciology (1962): www.cambridge.org/core/journal...

📚 Nakaya, U. & Terada, T. (1935). Simultaneous observations of the mass, falling velocity and form of individual snow crystals. J. Fac. Sci., Hokkaido Univ., Ser. II, 1(7), 191–200. eprints.lib.hokudai.ac.jp/dspace/bitst...

📖 Wikipedia — Ukichiro Nakaya: en.wikipedia.org/wiki/Ukichir...

There's a conference in Hokkaido — neurowintersummit.org — and I might just go to see the abundant snow, as well as the science bit!

#MATLAB #MATLABambassador

loss_dB = snowpl(r, f, rs); % ≈ 1 dB

One decibel. Doesn't sound like much. But autonomous systems, like self-driving cars, budget every fraction — and that budget rests partly on a physicist who studied snow because his lab couldn't afford anything else.

Nakaya's hand-measured data from a Hokkaido winter - three properties, one crystal at a time - feeds forward into models that help autonomous vehicles see through snow.

% Radar Toolbox: snowpl
r = 10e3; % 10 km
f = 77e9; % 77 GHz (automotive radar)
rs = 0.75; % light snow, mm/h liquid

Why does this 1935 paper appear in MATLAB's radar toolbox?

Path loss through snow depends on how much liquid water hangs suspended in the air between sensor and target. That depends on how fast crystals fall — which depends on their mass and drag. Which depends on their shape.

Nakaya wrote that snow crystals may be called letters or heiroglyphs, sent from heaven. ❄️ As a snowboarder I agree.

Their shape ❄️ encodes the temperature and humidity where they formed - a hexagonal plate tells a different story than a dendritic fern. Read the crystal, read the cloud.

By 1935, Nakaya and colleague Tôiti Terada had done something no one had attempted: simultaneous measurements of the mass, falling velocity, and crystalline form of individual snow crystals. Not averages across samples. Individual crystals; caught, weighed, timed, photographed. 3000 of them. ❄️

In 1930, Ukichiro Nakaya arrived at Hokkaido University's physics department. Minimal equipment. Almost no funding. But snow fell outside the window every winter, and that was free.

So he started photographing snowflakes. ❄️

The paper's title is almost absurdly literal: "Simultaneous Observations of the Mass, Falling Velocity and Form of Individual Snow Crystals."

I had to know more. ❄️

Still in Tignes ❄️, still trying to link snow and MATLAB!

The Radar Toolbox has a function called snowpl - it calculates how much signal a radar or lidar loses in a blizzard. Dig into the references and you'll find it cites a paper from 1935. Over ninety years old!

Free MATLAB webinar for Neuroscientists! Just register on SfN website.

#MATLAB #MATLABambassador

Analyzing Binary Judgments: A Comparison of ANOVA, Signal Detection Theory, and Generalized Linear Mixed Models in the Context of the Illusory Truth Effect: https://osf.io/xn397

Link: www.mathworks.com/help/images/...

#MATLAB #ImageProcessing #Snowboarding #Tignes #STEMlife

The linked example shows troughs at 5, 7, and 11 pixel radii - three distinct populations.

The technical term is 'morphological opening' - the concept is intuitive, just sift with increasingly large mesh sizes and see what falls through.

The clever bit 💡: take the derivative of this intensity curve - the rate of change of intensity as the sieve size increases. Troughs in that derivative (where intensity drops fastest) reveal where populations of snowflakes cluster 📊.