Alex Weber @weberam2@qoto.org

On a roll here . . .

R for Data Manipulation and Visualization online tutorial.
Includes working with dplyr, janitor, ggplot2, leaflet, plotly & gganimate. Plus the{librarian} #rstats 📦 for package management.
Lessons by Uku-Kaspar Uustalu, senior data scientist at Tufts University, and Kyle Monahan, senior data science specialist at Tufts.
Repo: https://github.com/tuftsdatalab/r-data-viz
Web page: https://tuftsdatalab.github.io/intro-r/
@rstats
@uustalu

Some good tips in there:

From: @sls
https://neuromatch.social/@sls/111225766630331376

I'm one of the (probably many) researchers who uses #sciHub instead of my university's access portals. Not in small part because it's faster and easier. Gatekeeping knowledge (or anything really) increases barriers to access for everyone, even people "granted" access by organizations and systems that rely on enclosure to maintain their profits or class position

@fenneladon

Is Sci-Hub just a handy tool for accessing papers? Sure. But there’s more to it. @leonido and I argue that this illegal yet legitimate service played a crucial role in the broader #OpenAccess transformation: https://journals.sagepub.com/doi/10.1177/01914537231203558 (English translation of a paper previously published in German)

@albertcardona @adredish @NicoleCRust @jpeelle @ct_bergstrom So far I have not heard a reason why a merit-triaged lottery system is bad for science funding.

There have been a lot of good points about barriers to making it happen (big change in general psychology of the US funding, loss of agency for funding officials, other I might have missed).

I’ll stop commenting here and just listen to what others have to say but it’s an honest question that stands: What is bad about a merit-triaged lottery system for funding science (public entities)?

In case you missed it, you can make your entire posts searchable without using hashtags *IF* you opt into the new full text search system. Here's how to do that:

➡️ https://fedi.tips/how-do-i-opt-into-or-out-of-full-text-search-on-mastodon/

However, it's still worth using hashtags because:

-Many people follow hashtags, using relevant tags in a post reaches them

-Many people aren't opting into full text search, so you can still find their posts with hashtag searches

-Hashtags are useful for indicating the topics you actively want to discuss

here’s the table of contents for the Pocket Guide to Debugging again since a bunch of people have mentioned that they find the table of contents to be really useful on its own

https://wizardzines.com/zines/debugging-guide/

I'm happy to announce the start of a new free and open online course on neuroscience for people with a machine learning or similar background, co-developed by @marcusghosh. YouTube videos and Jupyter-based exercises will be released weekly. There is a Discord for discussions.

For more details about the structure of the course, and to watch the first video "Why neuroscience?" go straight to the course website:

https://neuro4ml.github.io

Currently available are videos for "week 0" and exercises for "week 1", but more coming soon.

Why did I create this course? Well, I think both neuroscience and ML can be enriched by knowing about each other and my feeling is that a general purpose intro to neuro or comp-neuro isn't the right way to inspire people in ML to be interested in neuro.

I hear a lot about neuroscience inspiring AI, but I think there's understandable scepticism about that from ML people. I don't want people to take neuro ideas and apply directly to ML, I just think we get a richer picture of what both fields are doing if we think more widely.

In other words, we should be thinking that we are somehow studying the same problem in different ways. You see that in the early history of the field, and it's very inspiring. (Yes, this is pretty much just saying that cognitive science is cool, but my scope is a bit narrower.)

The focus then is not on how neuroscientists think the brain works, but on the mechanisms the brain uses. These are strange, inspiring, and often their contribution to intelligent behaviour is still deeply mysterious.

The first video of the main part, on the structure of neurons, finishes with recent research (from @ilennaj and @kordinglab among others) on what the function of dendritic structure might be. No answers, just ideas.

And that's going to be another key part of this course. Research level problems are not hard to find in neuroscience, and the aim of this course is to empower students with the tools to start finding and working on them straight away.

Most of the exercises in the course won't have correct answers. They're starting points for further investigation. We'll be downloading and exploring open neuroscience datasets using methods from computational neuroscience and ML.

The course is not supposed to be comprehensive. It's a short course and the aim is more to get inspired and start on a longer road. I'd expect everyone to get something different out of it, and I'm happy if for some people their take home is "neuroscience is not for me"!

In some ways, it's the course I would have liked to get me into neuroscience and for my incoming PhD students from non-neuro backgrounds to be able to take. It's personal, and full of the sort of stuff that inspires me to be interested in neuroscience.

Well, I hope that some of you might be interested to follow along in the next few weeks, and since it's the first time I'm giving this course please do give feedback by email, Discord or however you like. Also, please feel free to re-use materials however you like.

#neuroscience #compneuro #machinelearning #ML

Since #SciHub isn't updating during its lawsuit, those of us with access to paywalled research should view it as an obligation to make that research available to everyone. Can we reboot the #ICanHazPDF hashtag? How about we follow it and the @icanhazpdf group.

Re: @ml https://ecoevo.social/@ml/111191070326071349

Another #rstats post - what are your favourite examples when teaching students to think critically about data/figures and interpretations?
For example, Simpson's Paradox and the importance of considering important subpopulations - is brilliantly displayed in the Palmer Penguins dataset.