In the name of transparency, let's have a look at these "transparency audits". Is it legit? Who's running it? Who's advising, and how? What are they doing?

tl-dr: it looks to be a rogue operation with an in-name only advisory board, and some questionable / shady tactics.

🧵
Who seems to run @curatescience, and on what authority? The leader is listed as Etienne P. Lebel (@eplebel). Has done work in open science before.

What authority does this entity have?
Self-professedly: none, in particular. They state they operate as a non-profit.
What about their advisory board? It looks quite respectable!

However - as far as I can tell, precious few of them actually advised or approved of this project. Evidence of such in the next couple tweets.
Wolf Vanpaemel - listed in the main team as "conceptual advisor" - who apparently is not involved....

Multiple tweets in picture - link to tweets:
Listed advisors: Alex Holcombe, Dorothy Bishop, and EJ Wagenmakers, and Julia Rohrer all appear to have been unaware of this particular program, and are not necessarily on board. One of them has even previously asked to be removed from the board.
There are a couple listed advisors who appear to have at least tacitly endorsed the idea (retweeted, for example), but their involvement is unclear.

This feels to me like *at least* an inflated advisory board, used to lend credibility and imply approval where it does not exist.
It looks to be fairly specifically Etienne Lepel running this.

Who's he? Obviously limited - but what about a quick look at a recent tweet. Oh.

Look, all possible debates about economic system's aside, this seems to me to be... not a very valid point.

And what are they actually doing?

In the middle of a pandemic, they are coercing academics to bend to their will to do extra work to be scored onto their leader board

MANY other criticisms of their approach (coerced audits, Goodhart's law, science-as-a-competition, cost & accessbility of open-access), are in lots of other tweets.

For me, suffice to say - I value transparency & open-science, and find this approach extremely lacking.
It feels relevant to me to note that by vast majority (even if one takes the advisory board as legit) - it is very white, and very European. The core team seems to be all male.

It's not very representative of the group of scientists they seem to claim audit-powers over.
The more I look into Curate Science the more it appears to be a rogue operation of a self-appointed individual or small group who are bulldozing through bad plans, with an in-name-only advisory board to give legitimacy, run by a questionable individual, and with no oversight.
Anyways, I feel down the tweet rabbit hole on this, and though I would share because, well, I quite value transparency.

/end.
Addendum - I'm not the only person to point out the representation of the Curate Science team. This rather sickening response from their account makes it pretty clear they don't get it, are not at all listening or acting in good faith, and should not be given any leeway here.

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More from @Tomdonoghue

2 Jul 20
New Educational Materials for Learning Data Science:

We've created a public online resource for our Python based course "Data Science in Practice".

Materials include tutorials, guided assignments, and project guidance. (Additional info below).

Webite:
datascienceinpractice.github.io
This is work with @bradleyvoytek, who created this course, and @Shannon_E_Ellis who teaches and develops it. (I have worked on course materials & the site).

Thousands of students have taken this course at UC San Diego, and now we're making the materials more openly available.
The premise of this course is to be a guide to the hands-on and practical elements of doing data science. It digs into the day-to-day of data practice, designed to be a complement to more technically in-depth courses in statistics and machine learning.
Read 8 tweets
14 Apr 20
I've spent my PhD learning what turns out to be work-from-home friendly science. I also incessantly keep notes & links.

So, here are lists of all the resources I know for learning data analysis, open-source development, open-access data and open-science practices, etc:
Quick note / disclaimer: this is somewhat tuned to cognitive neuroscience / electrophysiology - but a lot of it is also pretty general.

Also, the overview and links for everything included below is also here:
github.com/openlists/Over…
For programming, Python is a powerful open-source and general-purpose language with a vibrant user community.

Here is a list of resources to learn both standard library and scientific Python:
github.com/openlists/Pyth…
Read 12 tweets

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