Rik blahah

aka proto-awesome-nanocomponents

collections of components

Dendrosenecio quick chloroplast phylogeny

Method

1. obtain sequences

NCBI nuccore search term:

note: this is being very slowly compiled from other lists I have in less organised places.

General resources / introductions

A community-sourced library of college-level books addressing topics relevant to diversity https://diversityreadinglist.org
"You're Gonna Screw Up" by @AprilHathcock

Feminism

We should all be feminists by Chimamanda Ngozi Adichie (TED talk | UK Amazon | US Amazon)

Installing INK

The quickest and most maintainable way to get a production INK instance up and running is using Docker. INK is provided as a dockerised service, and can be run along with all dependency services using docker compose. This guide shows you how to do that, with commands designed for a fresh ubuntu LTS (16.04) installation.

Pre-requisites

Node.JS

You'll need Node.JS, preferably via a Node version manager.

implement SLEEP-backed metadata sources

take a dir of json files and store them in hypercore
- take the key from each JSON object, optionally specified (with jsonpath?) by user
- JSON objects conform to schema https://github.com/sciencefair-land/schemas/blob/master/datasources/article/metadata.json
- this hypercore will form the 'metadata' part of a sciencefair datasource - the other part is a regular hyperdrive of a directory containing article files
client integrated into sciencefair's datasource class, replacing the existing metadata feed
make a CLI for creating a datasource
add a way for the feed to update itself by pointing to a new feed key
feed creator should be able to specify new key

dat is an incredibly powerful technology for peer to peer sharing of versioned, secure, integrity-guaranteed data.

One thing it excels at is populating a live feed of data points from one source, and allowing any number of peers to subscribe to that feed. The data can only originate from the original source (this is guaranteed using public-key encryption), but the peers in the network can still sync the new data with one another. To subscribe to a given source you only need to know an alphanumeric key that uniquely identifies the source, and is automatically generated by dat.

There are many ways that this simple system can be used to build a new infrastructure for science. This is the first in a series of posts in which I'll explain how.

Here I briefly describe some ways dat can be used to automate some aspects of scientific discovery, increase resource and information reuse efficiency, and help keep our information resources up to date with science (a topic I will expand on signif

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	{ "one": "hello", "two": "venus", "three": "help", "four": "I'm stuck here" }
	{ "one": "hello", "two": "mercury", "three": "help", "four": "I'm stuck here" }
	{ "one": "hello", "two": "earth", "three": "help", "four": "I'm stuck here" }
	{ "one": "hello", "two": "mars", "three": "help", "four": "I'm stuck here" }

	# example of loading busco sample data, preparing gene set data, and making plots

	# if necessary, uncomment to install dependencies
	# install.packages("readr")
	# install.packages("UpSetR")

	library(readr)

	# function to load BUSCO results and label the first two columns
	load_busco <- function(path) {