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I'm a PhD candidate in Medical Biophysics at the University of Toronto studying epigenetics and cancer. Math and physics background taking a step into bioinformatics and evolution

This is a good article on and from Nature

nature.com/articles/d41586-019

I recommend looking at the list at the end on papers you can read, discussions you can have, and long term goals you can set over 10 weeks for transitioning to more transparent and collaborative scientific practices

Off to the Fields Medal Public Symposium! It's always nice to see how mathematicians present their work to the public, especially brilliant ones

Often, papers Gelman discusses have some type of fallacy in them, so here's his handy guide of things to look out for:

statmodeling.stat.columbia.edu

Here's an important quote that really highlights something present in my own field of and too:

> And that’s why the authors’ claim that fixing the errors “does not change the conclusion of the paper” is both ridiculous and all too true. It’s ridiculous because one of the key claims is entirely based on a statistically significant p-value that is no longer there. But the claim is true because the real “conclusion of the paper” doesn’t depend on any of its details

It contains an extensive list of a history of articles on reproducibility in psychology as well as a discussion of how these ideas play out in everyday scientific publishing, and what it means more psychology specifically and science more generally

Excellent article by Andrew Gelman

statmodeling.stat.columbia.edu

I like that he's not going against Fiske, herself, but about the ideas that she has used in articles as used in judging articles as an editor

A professor in the chem department has been running a simulation on all the login nodes of one of the super computers.

It's been two days now.

The US Census Bureau is already making use of these algorithms and has them available for people to look at on GitHub

github.com/uscensusbureau/cens

I love this kind of research that puts individuals first, while maximizing the utility of their information for the public good

Her paper can be found here arxiv.org/abs/1909.12237

I look forward to reading this and seeing these algorithms in action

I attended a talk by Ruobin Gong (Rutgers University) about differential privacy and the 2020 US census bureau. This is super cool stuff that provides the foundation for a paradigm shift in how data is stored and analyzed in public.

By perturbing individual-level data with known distributions that maintain privacy of each individual against certain kinds of queries, privacy and transparency don't have to be at odds with one another

My goodness. 2500 lines of my code just went in to astropy.

> From now on, all our research content will also be published in the ‘Article’ format; the shorter, ‘Letter’ format has been retired.

I couldn't be happier with this decision. Nature finally realized that their Letter format sucked, and authors were treating them like articles just without headers or any hope of being readable.

Making them all articles is definitely the way to go, I greatly appreciate this change

nature.com/articles/d41586-019

#introduction
Hi #scicomm fellows, just discovered this instance so I migrated here from nasqueron.

I'm a postdoc in #ComplexSystems, currently working in #ComputationalNeuroscience but in the process of reconversion towards #environmental issues.

Interested in #sustainability and #foss, #sf and #illustration, #japan and some more...

よろしくお願いします!

In principle I really like what some libre OSes are doing (looking at you @elementary); building services and reusable components for common things like email etc, much like Apple does with iOS. I think having one language, one SDK, and components for all the cool stuff has enabled rapid development of apps on mobile and to some lesser extent on the Mac.

There are some really interesting ideas in this paper about genetic databases and privacy. It's sort of like finding out what email addresses are in a database by spamming the log in and seeing what responses you get.

biorxiv.org/content/10.1101/79

Followed by a brief discussion in this Twitter thread
twitter.com/DocEdge85/status/1

All Things #OrgMode: #PIM, Scientific Writing, Presentation, Programming
karl-voit.at/2019/10/26/all-th

If you're into research and not using #emacs, please do at least watch the part from John Kitchin. You´ll be amazed.

#publicvoit #reveal #babel #orgref #scimax

Did the same graphic designer do work for both JetBrains and the Francis Crick Institute? Logos are remarkably similar in style

Their final figure sums up their results quite nicely! Please share with your colleagues, if relevant

Here's a really good paper from @keegankorthauer@twitter.com on methods controlling false discovery rates.

doi.org/10.1186/s13059-019-171

While they focus on , they give a good overview on what and controlling methods do and what they're for that's applicable to anyone in any field performing multiple simultaneous hypothesis tests

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