The event will focus on how Germany, and NFDI in particular, is moving forward with the definition and setting up of #FAIRDataSpaces as common, cloud-based data spaces for industry and research in compliance with the #FAIRPrinciples.
Ich war letztes Jahr auf der @FORGE23 in Tübingen und habe mit meinen Kolleg:innen von @Textplus einen Workshop zu #FDM für #FAIR#Editionen gehalten. Darüber könnt ihr jetzt im Blog lesen
🆕 blog! “Reductive Thinking and the Unfairness of Spotify Payments”
In "Theory Of Games And Economic Behavior" by John von Neumann and Oskar Morgenstern, the authors discuss the card game of poker. There are dozens of variations of poker, each with their own intricacies. But they all boil down to the same pattern - is my hand stronger than your hand? …
In "Theory Of Games And Economic Behavior" by John von Neumann and Oskar Morgenstern, the authors discuss the card game of poker. There are dozens of variations of poker, each with their own intricacies. But they all boil down to the same pattern - is my hand stronger than your hand?
Here's how the authors frame it:
Since a “square deal” amounts to assuming that all possible hands are dealt with the same probability, we must interpret the drawing of the above number s as a chance move, each one of the possible values s = 1, • • • , S having the same probability 1/S. Thus the game begins with two chance moves: The drawing of the number s for player 1 and for player 2, which we denote by s1 and s2. 19.1.2
Essentially, in two player poker, you could distribute cards labelled 1 - 100 and have people bet / bluff on whether their number is higher or lower than their opponents. That might not be a fun game - but it is a useful toy example for thinking about formal rules for a game.
It is sometimes helpful for us to reduce the complexities of the real world into simple examples. It allows us to examine our base assumptions about reality without getting bogged down in messy practicalities.
Let's take Spotify as an example. I often hear that artists complain that they get paid micro-cents per listen and that streaming is destroying their livelihood. I've no idea how much a recording artist gets every time their song is played on the radio, and I've no idea if Spotify is better or worse than the record deals generated by corrupt studio bosses.
So let's reduce Spotify to a toy example. Imagine a streaming service where people pay a fixed monthly subscription to get unlimited access to media.
This streaming service has only two users. They each pay £10 for the service. The service has no operating expenses and takes no profit. That money needs to be fairly split between the artists. We do not care about record companies, publishers, contracts, fees, taxes etc. We'll ignore copyright lengths as well. Some media is more expensive to produce than others, again ignored. We're assuming all things are equal.
So, what should happen in this scenario:
User 1 listens once to a 3 minute song by Ariana Grande.
User 2 listens once to a 3 minute song by Billie Eilish.
That's all they do for that month.
I think most reasonable people would say that artists A & B would split the money evenly. All things being equal, they each get £10.
Now let's take a different scenario.
User 1 listens to 90 songs by Ariana Grande.
User 2 listens to 10 songs by Billie Eilish.
How should the money be fairly split? 50:50? 90:10? Something else?
What I find interesting is that there isn't an obviously fair split. Some people think the service should pay out proportional to total consumption across all users. But a significant minority think that the money should be split per individual customer. Both positions are reasonable and I can see the arguments for each.
Is it fair for some users to subsidise others? Is it fair if artist A gets paid less per stream than artist B? Should there be a maximum or minimum amount an artist can earn? Would people accept a logarithmic formula which decreases the profitability of an artist the more times they are streamed?
When artists complain about fairness in streaming, they're probably right; it is unfair.
But when pundits start saying there is an obviously fairer solution, they're probably wrong.
And that's the purpose of this exercise. Even at the most reduced example, there isn't an obvious way to pay artists fairly.
Once you scale up to millions of users, in different countries, interacting with complex licencing regimes, exclusive deals, songs of varying lengths and of varying copyright, etc then it becomes unsolvable without radically reconfiguring how we approach consumerism.
I've written before about the Feynman Algorithm which is a universal method for solving any problem. It goes:
Write down the problem.
Think real hard.
Write down the solution.
I think step 0 needs to be a von Neumann reduction:
Reduce the problem to its very simplest use case.
Write down the problem.
Think real hard.
Write down the solution.
Return to step (0) and increase the complexity.
I suppose what I'm trying to say is if you can't handle me at my worst, you don't deserve me at my best if you can't solve a problem at its simplest level, you can't solve it at its most complex.
I’m on a journey to curb my #carbon footprint and I’m taking aim at my #wardrobe. An estimated 84% of the #emissions related to #fashion are upstream (production, distribution, retail) so the best thing we can do is buy less. About 25-30% of items in the typical G20 closet never get worn, so my mission is to audit my #closet and see what I do and don’t wear to work out how to live in my #fair consumption window.
This was the last module in this Open Science 101 curriculum which means I earned my badge 🥳 !
More importantly, I learned a lot.
I knew a few things from before, like #FAIR data, Zenodo DOIs, notebooks, github, et... But I feel the curriculum can really help me add purpose and structure to what I hope will be my #OpenScience journey onward.
I do recommend taking this #NASA#TOPS 101 curriculum online! If you have questions about it, just ask.
🧠INCF’s Principles of FAIR data management for neuroscience is
for students, researchers, data professionals (stewards, curators, librarians, etc), funders, & research administrators interested in maintaining scientific rigor & reproducibility.
#NISO just announced a new #standard "enabling the combination of arbitrary portions of content, data, semantics, & other resources from separate sources [e.g. articles, books, data sets, metadata schemes] into a single, standards-based format optimized for interchange, search, & display." https://www.niso.org/publications/z39105-2023-cpld
I wrote for @Nature about how archivists and librarians have laid the groundwork for a smooth transition to open, #FAIR, and reproducible research data. Bring us in early on your next project and get involved in digital library communities to build longlasting relationships✨ https://www.nature.com/articles/d41586-023-03935-1
Useful and practical OA textbook and guide on research data management and archival.
It situates in the Canadian context, but I think it's useful for anyone who wishes to archive and preserve data on repositories and using FAIR principles of open data and open science.
We did this for our COVID-19 School Dashboard project code and data archived on @borealisdata
Digitalisering heeft grote impact. We moeten anders met data omgaan. De data waarover de overheid beschikt, moet beter vindbaar, koppelbaar en bruikbaar zijn. De legitimiteit van de overheid moet sterker. Daarom zijn we vandaag samen om tot die overheidsbrede standaard te komen.