Data Science @ GitcoinDAO

Totally missed this in March - what an interesting insight into representation

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Great work @Fred and @ivanmolto - It is always important to look towards data, especially in a highly charged dynamic like a market downturn. It’s easy to make assumptions yet we must always keep ourselves in check to ensure governance decisions are not made emotionally but rather objectively and from a fully informed place of intention.

When we were discussing this and even though I had an inkling the contributor sell could not affect things as much, I really did not grasp just how much hodling was actually going on. The same with the pattern of other gov tokens which is why I made the suggestion to add that chart in order to give place this analysis against the broader market background.

I am keen to see these analyses progress and become a tool in the gov process. Perhaps, something we look to include in the improved versions of workstream accountability flow or in any voter matrix we may choose to adopt - provided they achieve balance between objectivity and personal takes.

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The inverse is also true! This analysis does not show that selling tokens to market DOES not create downward price.

How can you draw that conclusion from that graph? You just showed a bunch of data that has a bunch of outflows and a large decline in price. There’s no rigor to this analysis method. There’s no control group, nor is there any analysis of the liquidity of the market in this analysis.

GTC is down 50% vs ETH over the last 30 days, so I dont think you can say the outflows are in line with the rest of the market decline.

This chart is from coingecko:

I think doing an analysis about this could be important, but more rigor is needed to be able to form conclusions backed by data in my opinion.

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Hey! I really enjoy the discussion here. I would love to get an hour on the calendar to pull together various people with data backgrounds or interest within the DAO. We could do some introductions and share projects we’re working on.

Please fill out this lettuce meet if you’re interested: LettuceMeet - Easy Group Scheduling

All are welcome!

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Maybe this way:

the practise only deal with data anonymisation, and data infrastructure, and model performance evaluation.

With anonymised data, all the rest can be put as bounty so be publicly worked on by any contributors