As the #PinePhone ($150 Linux smartphone) development is moving fast, a series of articles might be redundant and confusing for most people. This article, to be dynamically updated as development proceeds, gathers what is known at this stage, and will hopefully be an answer to some of your questions.
You can read our latest article on TuxPhones.com: https://tuxphones.com/pinephone-pictures-specs-release-date-software-support/
RT @GarethSoye@twitter.com
Now I know what owl legs look like and life can never be the same again.
🐦🔗: https://twitter.com/GarethSoye/status/1162748129973821445
"A Request to Librarians: Please Ask OverDrive About Libby Accessibility – Kelly's Corner" http://blog.kellyford.org/a-request-to-librarians-please-ask-overdrive-about-libby-accessibility/
#Librarians #libraries #books #OverDrive #Libby #Accessibility
Trying a similar request again: can anyone recommend a good #OpenAccess journal that covers environmental justice?
How does it feel to use and manage packages in #Julia? Does it feel closer to the experience in #Rlang or in #Python? #Julialang
Academic journal web design fail
@bgcarlisle @hugh @bthall I hear you. If only we could convince journals to adopt an epub-like plain HTML format that would support responsive design and not rely on gobs of JavaScript...
Generating argumentation maps by writing the logical structure of an argument: https://argdown.org/
Interesting stuff. This could also be used for digging into arguments and assumptions about #futures.
I just learned about the National Coalition of Independent Scholars. Are any of you here members of it or know people that are members of it?
https://en.wikipedia.org/wiki/National_Coalition_of_Independent_Scholars
Open-mindedness... "How Intellectual Humility Can Make Us More Curious, Reflective & Able to Learn More: Read the Findings of a New Study" http://www.openculture.com/2019/07/how-intellectual-humility-can-make-us-more-curious-reflective-able-to-learn-more.html
The traditional method for forced rhubarb goes back nearly 200 years.
Rhubarb is planted in fields, fertilised well with manure, and left to grow pretty much wild for 2 years. During this time, it'll store a ton of energy in its roots as carbohydrates. Then at the start of Winter, the rhubarb is moved somewhere warm and completely dark.
In the darkness, the plants start to grow vivaciously, converting those carbs into sugars. As a result, it's sweeter and more tender than Summer rhubarb.
If you have reproducibility issues with Jupyter notebooks you should give a shot to Nextjournal (https://nextjournal.com/)
Here's a great presentation of the platform by the company CEO
Academic life, that means conferences. They are an important part of science: Many of my collaboration projects were thought up at a conference or brought into a public form for one. We may (and should) find other ways to present our work and network with potential new colleagues in the future, but for now it means I sit at the airport to fly half around the globe and back to meet other people doing the same, which is clearly not sustainable. What ideas for alternatives are out there?
@bthall if i'm not mistaken, this already exists. for example, if you're in a julia shell, you can type
?≥
and you get this:
```
help?> ≥
"≥" can be typed by \ge<tab>
search: ≥
>=(x, y)
≥(x,y)
Greater-than-or-equals comparison operator. Falls back to y <= x.
Examples
≡≡≡≡≡≡≡≡≡≡
julia> 'a' >= 'b'
false
julia> 7 ≥ 7 ≥ 3
true
julia> "abc" ≥ "abc"
true
julia> 5 >= 3
true
```
#Idea: Docstrings for math functions/operations. With #Julialang you can use Unicode, so you could even use general purpose #math symbols for function names.
@bthall I would try visualising the data points from both perspectives. I've found RapidMiner a good software (non free for productive purposes) for doing this. It allows you to apply most data science algorithms, visualize them and find connections or new data points. Once I find a good representation of my data, I can move the actual implementation to something more efficient (R, Numpy, scikit, etc)
For people here familiar with #textmining or #graphtheory, have you heard of methods that bring together the two approaches?
For analyzing my wiki's contents I could analyze the explicit linkages between articles with graph theory and the implicit linkages with text mining, but I wonder about how much better it'd be to analyze both graphs. I think that #LDA in textmining creates a graph representation anyway, so it may just require tools for integrating two related graphs.
I'm a Christian philosopher that's studying economics.
I aspire to do weird but illuminative stuff w/ economics.