What are the classics of the "Science of Science" or "Meta Science"? If you were teaching a class on the subject, what would go in the syllabus?
psychological science" from the Open Science Collaboration of @BrianNosek et al. https://t.co/yjGczLZ6Je
(Look at that abstract, wow!)
https://t.co/xduj2A8c8q
A few observations from "The Science of Science", a useful review article appearing in this week's Science: https://t.co/D6DENKF4rS
— Michael Nielsen (@michael_nielsen) March 9, 2018
The book has been in my queue for some time, and is almost certainly a good overview of a huge chunk of economic thinking about science.
Great thread. I\u2019d add Paula Stephan\u2019s book: https://t.co/QdIPBchdym
— David Lang (@davidtlang) January 14, 2021
Twitter threading makes it a little hard to skim the thread. Expandable tree version here, thanks to @paulgb's great treeverse Chrome extension: https://t.co/vF4JF0Gf4J
More from Science
@mugecevik is an excellent scientist and a responsible professional. She likely read the paper more carefully than most. She grasped some of its strengths and weaknesses that are not apparent from a cursory glance. Below, I will mention a few points some may have missed.
1/
The paper does NOT evaluate the effect of school closures. Instead it conflates all ‘educational settings' into a single category, which includes universities.
2/
The paper primarily evaluates data from March and April 2020. The article is not particularly clear about this limitation, but the information can be found in the hefty supplementary material.
3/
The authors applied four different regression methods (some fancier than others) to the same data. The outcomes of the different regression models are correlated (enough to reach statistical significance), but they vary a lot. (heat map on the right below).
4/
The effect of individual interventions is extremely difficult to disentangle as the authors stress themselves. There is a very large number of interventions considered and the model was run on 49 countries and 26 US States (and not >200 countries).
5/
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I've recently come across a disinformation around evidence relating to school closures and community transmission that's been platformed prominently. This arises from flawed understanding of the data that underlies this evidence, and the methodologies used in these studies. pic.twitter.com/VM7cVKghgj
— Deepti Gurdasani (@dgurdasani1) February 1, 2021
The paper does NOT evaluate the effect of school closures. Instead it conflates all ‘educational settings' into a single category, which includes universities.
2/
The paper primarily evaluates data from March and April 2020. The article is not particularly clear about this limitation, but the information can be found in the hefty supplementary material.
3/
The authors applied four different regression methods (some fancier than others) to the same data. The outcomes of the different regression models are correlated (enough to reach statistical significance), but they vary a lot. (heat map on the right below).
4/
The effect of individual interventions is extremely difficult to disentangle as the authors stress themselves. There is a very large number of interventions considered and the model was run on 49 countries and 26 US States (and not >200 countries).
5/
It was great to talk about reproducible workflows for @riotscienceclub @riotscience_wlv. You can watch the recording below, but if you don't want to listen to me talk for 40 minutes, I thought I would summarise my talk in a thread:
My inspiration was making open science accessible. I wanted to outline the mistakes I've made along the way so people would feel empowered to give it a go. Increased accountability is seen as a barrier to adopting open science practices as an ECR
It also comes across as all or nothing. You are either fully open science or your research won't get anywhere. However, that can be quite intimidating, so I wanted to emphasise this incremental approach to adapting your workflow
There are two sides to why you should work towards reproducibility. The first is communal. It's going to help the field if you or someone else can reproduce your whole pipeline.
There is also the selfish element of it's just going to help you do your work. If you can't remember what your work means after a lunch break, you're not going to remember months or years down the line
Thank you again @JamesEBartlett for a fantastic talk (with a really nice personal touch) on reproducible workflows!
— RIOT Science Club Wolverhampton (@riotscience_wlv) February 16, 2021
Thanks especially for the co-leads @IMLahart for co-hosting and @DrManiBhogal for nabbing James!
Slides: https://t.co/CNqxzOhch1
Video: https://t.co/YjHEHuRJlz
My inspiration was making open science accessible. I wanted to outline the mistakes I've made along the way so people would feel empowered to give it a go. Increased accountability is seen as a barrier to adopting open science practices as an ECR
It also comes across as all or nothing. You are either fully open science or your research won't get anywhere. However, that can be quite intimidating, so I wanted to emphasise this incremental approach to adapting your workflow
There are two sides to why you should work towards reproducibility. The first is communal. It's going to help the field if you or someone else can reproduce your whole pipeline.
There is also the selfish element of it's just going to help you do your work. If you can't remember what your work means after a lunch break, you're not going to remember months or years down the line
Why are lunch breaks important for #code?
— Dr Rebecca Hirst (@HirstRj) February 11, 2021
If you can't remember what your variable names refer to after lunch, you sure as hell won't remember in 3 months.
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I'm going to do two history threads on Ethiopia, one on its ancient history, one on its modern story (1800 to today). 🇪🇹
I'll begin with the ancient history ... and it goes way back. Because modern humans - and before that, the ancestors of humans - almost certainly originated in Ethiopia. 🇪🇹 (sub-thread):
The first likely historical reference to Ethiopia is ancient Egyptian records of trade expeditions to the "Land of Punt" in search of gold, ebony, ivory, incense, and wild animals, starting in c 2500 BC 🇪🇹
Ethiopians themselves believe that the Queen of Sheba, who visited Israel's King Solomon in the Bible (c 950 BC), came from Ethiopia (not Yemen, as others believe). Here she is meeting Solomon in a stain-glassed window in Addis Ababa's Holy Trinity Church. 🇪🇹
References to the Queen of Sheba are everywhere in Ethiopia. The national airline's frequent flier miles are even called "ShebaMiles". 🇪🇹
I'll begin with the ancient history ... and it goes way back. Because modern humans - and before that, the ancestors of humans - almost certainly originated in Ethiopia. 🇪🇹 (sub-thread):
The famous \u201cLucy\u201d, an early ancestor of modern humans (Australopithecus) that lived 3.2 million years ago, and was discovered in 1974 in Ethiopia, displayed in the national museum in Addis Ababa \U0001f1ea\U0001f1f9 pic.twitter.com/N3oWqk1SW2
— Patrick Chovanec (@prchovanec) November 9, 2018
The first likely historical reference to Ethiopia is ancient Egyptian records of trade expeditions to the "Land of Punt" in search of gold, ebony, ivory, incense, and wild animals, starting in c 2500 BC 🇪🇹
Ethiopians themselves believe that the Queen of Sheba, who visited Israel's King Solomon in the Bible (c 950 BC), came from Ethiopia (not Yemen, as others believe). Here she is meeting Solomon in a stain-glassed window in Addis Ababa's Holy Trinity Church. 🇪🇹
References to the Queen of Sheba are everywhere in Ethiopia. The national airline's frequent flier miles are even called "ShebaMiles". 🇪🇹
