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 https://t.co/bNK6poNpzx
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 https://t.co/HS3dx5qJL3
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 https://t.co/iRSXISI5NE
Pre-registration is super helpful, but it takes time to learn how to do it effectively. I had no support for my first attempt and it was awful. https://t.co/o0QgrtBFfJ
But I learnt from my mistakes and eventually got to a point where I'm happy with my approach.
Some aspects of open science can't be added at the end. If you want to share your data, it has to be planned from the ethics stage and your participants have to consent to it. Just remember not all data can be shared openly for ethical reasons.
Sharing materials is one of the easiest things you can do, but very few researchers do it. You can share your stimuli (providing there's no copyright issues) and your task scripts. If you use @GorillaPsyc, there is an open materials function making it super easy.
Double check your analysis pipeline, especially when the results support your hypotheses. I caught an error in my first PhD study before it was too late, but it could have been super embarrassing.
You don't have to go the full R Markdown manuscript route, but I recommend at least scripting any data processing. If you do make a mistake, at least that error is reproducible. You can also use intermediaries like @tidystats to embed results from R into your Word file.
Finally, if you can't read the article in the first place, you can't reproduce it. Most journals allow you to post pre/post-prints and you can upload your dissertation/thesis to Thesis commons. Just double check it's permitted by your target journal: https://t.co/iIIw1VDHdC
Hopefully, this seminar shows people fuck up from time to time. It's about learning from those mistakes and doing what you can to minimise them in future. Try making your workflow more reproducible one step at a time. It'll be worth it in the long-run for you and the community.

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/
https://t.co/hXlo8qgkD0
Look like that they got a classical case of PCR Cross-Contamination.
They had 2 fabricated samples (SRX9714436 and SRX9714921) on the same PCR run. Alongside with Lung07. They did not perform metagenomic sequencing on the “feces” and they did not get


A positive oral or anal swab from anywhere in their sampling. Feces came from anus and if these were positive the anal swabs must also be positive. Clearly it got there after the NA have been extracted and were from the very low-level degraded RNA which were mutagenized from

The Taq.
https://t.co/yKXCgiT29w to see SRX9714921 and SRX9714436.
Human+Mouse in the positive SRA, human in both of them. Seeing human+mouse in identical proportions across 3 different sequencers (PRJNA573298, A22, SEX9714436) are pretty straight indication that the originals

Were already contaminated with Human and mouse from the very beginning, and that this contamination is due to dishonesty in the sample handling process which prescribe a spiking of samples in ACE2-HEK293T/A549, VERO E6 and Human lung xenograft mouse.

The “lineages” they claimed to have found aren’t mutational lineages at all—all the mutations they see on these sequences were unique to that specific sequence, and are the result of RNA degradation and from the Taq polymerase errors accumulated from the nested PCR process
What is going on at SWANSEA UNIVERSITY in Wales?
1.
https://t.co/d5NKtNlxxa
Sounds as if its connected to Bill Gates "LUCIFERASE" Vaccine?

2.
https://t.co/k0w1mjaPg0

"HILLARY RODHAM CLINTON SCHOOL OF LAW"
- AT SWANSEA UNIVERSITY!!!


3.
https://t.co/jifuWq6cGq
Remember all those FIRES, over the Summer!!!

4.
https://t.co/H3lstFyYx8
WUHAN PARTNERSHIP

"Links between Swansea & Wuhan date back to 1855 when Swansea missionary Griffith John founded the Wuhan Union Hospital.
This relationship was strengthened when representatives of the two cities signed an agreement".


5.
https://t.co/5b1JqiEQzi
Swansea University Strengthens Links with China
1/ Automobiles and Intake Fraction. Since cars are back in the news I thought I would retweet this model result I offered in early April 2020. I focused only on 1 micron particles & accounted for windows completely closed & cracked slightly open.


2/ Related air exchange rates were based on experimental results in literature for mid-sized sedans. Particle deposition to indoor surfaces were accounted for, as the surface to volume ratio in a 3 m3 cab is large. An important outcome was the intake fraction (IF)

3/ Here, IF is the number of particles (or virions in collective particles) inhaled by a receptor DIVIDED BY the number or particles (or virions in collective particles) emitted by an infector.

4/ Integrated over the two hour drive (in this example) the IF for all windows closed & a receptor at rest is 0.08 (8% of what comes out of the infectors respiratory system ends up in the respiratory system of the receptor). 8%! That is a very high intake factor.

5/ With additional ventilation from cracking a window open drops the IF to 0.012 (1.2%) still relatively high. Can get lower by opening more windows.

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Still wondering about this 🤔


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