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Hey Carolin, we already have a task here for all countries simultaneously. The dataset has already been identified! Do you think adding this task for individual countries contributes to something more?

Oh, my bad. I had forgotten about that. I don't know how detailed the UN data is, so can't say if it contributes more. I just thought to have a number of bite-size tasks would be good, but can also appreciate to do it in one go.
I'd say depending on the UN data, we can leave those. Perhaps the "single-country-source" tasks are more specific on age groups, male-female, income, employment etc. - or is that also in the UN data?

I think a bit like shapefiles and in general "big & important" datasets I would keep it to one source, which also would help to maintain in the long run.
I've added the link to the dataset, you can get quite granular stuff if you want BUT I would say that is relevant for later on. First have population for all countries (so we can test downscaling) and then we can have it by age groups or male-female (at this stage I don't think they are relevant).
Income data should be a separate task and Employment data as well. For employment I have created this task (dataset located from the ILO but very very messy).

Before we go too wild with population data... do we need data on sex breakdown? Are age groups needed? I can see the use for it for specific studies, but I think collecting this info by default bloats our db and no one ends up using it.
Happy to be proven wrong.
Lastly data on a slightly lower level would be very handy for the different countries... so e.g. provincial level data for South Africa, State level for the US, etc. There is often lots of data on that level and downscaling might be useful from that level.

You are both right that we don't need this granular classification (for now, ever?), at least for current purposes. I had been thinking that it does exist, but atm I can't think of added value through it. Except in the case of income, where I think it could be either under demographic or economic data, we could use that, as seen in a "British Food Flow" study, where people would be interested in the "consumer profile".
I suggest that I then change the tasks to the slightly lower level, so US pop --> US states pop etc.

Agreed. Income and employment data is indeed quite relevant (economic) information and would be good to capture if available.

👍 for the lower level! I would still separate the task of income and employment into two separate ones and would "tag" them as economic.

Although small asterisk. For England and Switzerland we already have the info through the EUROSTAT dataset (to double check) 🎉!

Task was assigned to Gerardo Ezequiel

Status change: Open → In Progress

Status change: In Progress → Completed

Statistical portraits of all of Switzerland's cantons