Open letter to journal editors: dynamite plots must die. Dynamite plots, also known as bar and line graphs, hide important information. Editors should require authors to show readers the data and avoid these plots. https://t.co/0GNKEIUCJL pic.twitter.com/OS9ytEFRZN
— Rafael Irizarry (@rafalab) February 22, 2019
"The new answer to a 77-year-old problem"
😭
The new answer to a 77-year-old problem in data analysis, published today in @naturemethods. Instead of significance tests, use estimation graphics. Our software suite DABEST makes it easy for everyone to visualize effect sizes.https://t.co/UzwXJ7EUC5 pic.twitter.com/VtxyY0xaRM
— Adam Claridge-Chang (@adamcchang) June 19, 2019
Couldn't find D3 code for grouped horisontal box plots that show data points so I made this @mbostock @thisisalfie https://t.co/cQjDPhyZdw pic.twitter.com/y6RNmDB2p3
— Ulrik Lyngs (@ulyngs) June 28, 2017
made a pkg for pirate plots in ggplot: add any of points/means/bars/CIs/violins \u2013 better than ye olde bar/box plotshttps://t.co/Z2m2kW3hsl pic.twitter.com/npAirPQexM
— Mika Braginsky (@mbraginsky) September 28, 2017
See the new #PowerBI visual awesomeness for data points & sources, box-&-whisker plots! https://t.co/dOmgoxWfDE pic.twitter.com/HAUOAMJEJW
— Microsoft Power BI (@MSPowerBI) February 1, 2016
On bar/line graphs, & data presentation. Need for strict data treatment https://t.co/8aqICdCX9K @PLOSBiology pic.twitter.com/KQkYKdmWq7
— Crisanto Gutierrez (@CG_ath) November 30, 2015
\U0001f62e pretty stoked on this PR in the pipeline\u2026
— Mara Averick (@dataandme) May 10, 2018
"Nonstandard aesthetics" by @ClausWilke {ggplot2} https://t.co/X9hkxX44jo #rstats #dataviz pic.twitter.com/gKlEU8Gxa3
Perceptions of probability https://t.co/9xsr6FUR2e shown by @amandacox #openvisconf pic.twitter.com/hJsYY7ggB3
— Rob Simmon (@rsimmon) April 25, 2017
Ok. https://t.co/ifP2RNpLIn
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/
1/
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/
"NO LONGER BEST IN THE WORLD"
UNEP's new Human Development Index includes a new (separate) index: Planetary pressures-adjusted HDI (PHDI). News in Norway is that its position drops from #1 to #16 because of this, while Ireland rises from #2 to #1.
Why?
https://t.co/aVraIEzRfh
Check out Norway's 'Domestic Material Consumption'. Fossil fuels are no different here to Ireland's. What's different is this huge 'non-metallic minerals' category.
(Note also the jump in 1998, suggesting data problems.)
https://t.co/5QvzONbqmN
In Norway's case, it looks like the apparent consumption equation (production+imports-exports) for non-metal minerals is dominated by production: extraction of material in Norway.
https://t.co/5QvzONbqmN
And here we see that this production of non-metallic minerals is sand, gravel and crushed rock for construction. So it's about Norway's geology.
https://t.co/y6rqWmFVWc
Norway drops 15 places on the PHDI list not because of its CO₂ emissions (fairly high at 41st highest in the world per capita), but because of its geology, because it shifts a lot of rock whenever it builds anything.
UNEP's new Human Development Index includes a new (separate) index: Planetary pressures-adjusted HDI (PHDI). News in Norway is that its position drops from #1 to #16 because of this, while Ireland rises from #2 to #1.
Why?
https://t.co/aVraIEzRfh
Check out Norway's 'Domestic Material Consumption'. Fossil fuels are no different here to Ireland's. What's different is this huge 'non-metallic minerals' category.
(Note also the jump in 1998, suggesting data problems.)
https://t.co/5QvzONbqmN
In Norway's case, it looks like the apparent consumption equation (production+imports-exports) for non-metal minerals is dominated by production: extraction of material in Norway.
https://t.co/5QvzONbqmN
And here we see that this production of non-metallic minerals is sand, gravel and crushed rock for construction. So it's about Norway's geology.
https://t.co/y6rqWmFVWc
Norway drops 15 places on the PHDI list not because of its CO₂ emissions (fairly high at 41st highest in the world per capita), but because of its geology, because it shifts a lot of rock whenever it builds anything.
#DDDD $LBPS $LOAC
A thread on the potential near term catalysts behind why I have increased my position in 4d Pharma @4dpharmaplc (LON: #DDDD):
1) NASDAQ listing. This is the most obvious.
The idea behind this is that the huge pool of capital and institutional interest in the NASDAQ will enable a higher per-share valuation for #DDDD than was achievable in the UK.
Comparators to @4dpharmaplc #DDDD (market capitalisation £150m) on the NASDAQ and their market capitalisation:
Seres Therapeutics: $2.33bn = £1.72bn (has had a successful phase 3 C. difficile trial); from my previous research (below) the chance of #DDDD achieving this at least once is at least
Kaleido Biosciences: $347m = £256m. 4 products under consideration, compared to #DDDD's potential 16. When you view @4dpharmaplc's 1000+ patents and AI-driven MicroRx platform (not to mention their end-to-end manufacturing capability), 4d's undervaluation is clear.
A thread on the potential near term catalysts behind why I have increased my position in 4d Pharma @4dpharmaplc (LON: #DDDD):
1) NASDAQ listing. This is the most obvious.
The idea behind this is that the huge pool of capital and institutional interest in the NASDAQ will enable a higher per-share valuation for #DDDD than was achievable in the UK.
Comparators to @4dpharmaplc #DDDD (market capitalisation £150m) on the NASDAQ and their market capitalisation:
Seres Therapeutics: $2.33bn = £1.72bn (has had a successful phase 3 C. difficile trial); from my previous research (below) the chance of #DDDD achieving this at least once is at least
While looking at speculative pharmaceutical stocks I am reminded of why I am averse to these risky picks.#DDDD was compelling enough, though, to break this rule. The 10+ treatments under trial, industry-leading IP portfolio, and comparable undervaluation are inescapable.
— Shrey Srivastava (@BlogShrey) December 16, 2020
Kaleido Biosciences: $347m = £256m. 4 products under consideration, compared to #DDDD's potential 16. When you view @4dpharmaplc's 1000+ patents and AI-driven MicroRx platform (not to mention their end-to-end manufacturing capability), 4d's undervaluation is clear.
I took a look at Shell's first ever 1.5C scenario and found that it is... remarkably similar to its “well-below 2C” scenario.
Oil, gas, coal, solar.... all basically unchanged.
The key difference: A new forest the size of Brazil to suck up the extra CO2.
Including "nature-based solutions" in the outlook brings forward the date for net-zero emissions to 2058.
Without them their pathway for CO2 emissions is the same as the previous one.
(It's also towards the higher end of 1.5C emissions pathways.)
The "Brazil-sized" forest idea isn't actually new, it has been kicking around for a couple of years.
It was referenced in the "well-below 2C" scenario although not formally included in it, and Shell's CEO has been framing it as the only viable way of getting to 1.5C.
Fine, but who is going to plant all those trees? Well... Shell says it will plant some of them.
Only yesterday Shell said forests were a key part of its net-zero strategy.
Not everyone is convinced though
https://t.co/RaJm7tOHxb
Given that Shell's 1.5C scenario also sees a big scaling up of bioenergy, the question remains: where are all those trees and bioenergy crops going to go?
Oil, gas, coal, solar.... all basically unchanged.
The key difference: A new forest the size of Brazil to suck up the extra CO2.
Including "nature-based solutions" in the outlook brings forward the date for net-zero emissions to 2058.
Without them their pathway for CO2 emissions is the same as the previous one.
(It's also towards the higher end of 1.5C emissions pathways.)
The "Brazil-sized" forest idea isn't actually new, it has been kicking around for a couple of years.
It was referenced in the "well-below 2C" scenario although not formally included in it, and Shell's CEO has been framing it as the only viable way of getting to 1.5C.
Fine, but who is going to plant all those trees? Well... Shell says it will plant some of them.
Only yesterday Shell said forests were a key part of its net-zero strategy.
Not everyone is convinced though
https://t.co/RaJm7tOHxb
Shell plans to use forests to remove 120 Mt/yr of CO2 by 2030.
— Greg Muttitt (@FuelOnTheFire) February 12, 2021
Appropriate land for forestation is finite, and risks competition with food production and human rights of current land owners/users, esp Indigenous
Given that Shell's 1.5C scenario also sees a big scaling up of bioenergy, the question remains: where are all those trees and bioenergy crops going to go?
