Hi Richard, 

This is a great idea, and is something I would like us to push towards in Structured Commons: easy crowd-focused ways for us to refine understanding of what is involved in a multimedia file. However, all of the precedents we have use either machine prediction, or human curated prediction (categories/infoboxes,etc) of whether or not the content is in there. Also, the further we spread these games, the more we need to think about confirmation of the "new" data that we are generating: for example, most of the crowd-sourcing strategies on Zooniverse, require a certain number of folks to affirm a decision, before they are added to a likely positive queue (https://www.zooniverse.org/). We may want to start with items that have "depicts" compatible categories on Commons that could be sorted into more specific categories -- or where the institutional descriptions have string matches.

As Magnus describes also: I don't think the structure or data model right now is very crowd-sourcing friendly at the moment: asking folks to identify what is "depicted" in an image, may solicit a lot of less than-accurate descriptions (for example, if you aren't familiar with farm equipment in 19th century france, you are likely to not know what the right language is for describing those pastoral settings, etc). The more we push this beyond folks who are wiki-savvy, the more we loose the self-filtering of folks interested in understanding our data and community model before contributing (I am also not familiar with any documentation of institution-friendly ways for monitoring batches of data that get worked on by these institutions per #2 at http://blog.hatnote.com/post/161358369742/wikicite-2017-and-the-7-features-wikidata-needs).

Thats not to say we shouldn't work on this: I just think some on-wiki consultation and design (per Magnus) would be rather important.We should also take a look at the interface/strategy used by GeneWiki: https://github.com/SuLab/genewiki . They have the best other-environment, open ecosystem for contributing to a particular domain on Wikidata.

Cheers,

Alex 

On Wed, Jun 7, 2017 at 6:31 AM, Magnus Manske <magnusmanske@googlemail.com> wrote:
Can do, but this can get quite complicated. Example:
https://www.wikidata.org/wiki/Q2917717#P180

There have to be "target search" (e.g. "bull"), zero to many qualifiers (some qualifier properties may be used several times in a single statement, like "applies to part"), some of these should be offered by default because they occur often ("applies to part"), with optional default values ("foreground", "right"), while allowing arbitrary values ("sky")...

Any development should also take the upcoming Commons Wikibase into account, so the code can be re-used to annotate any Commons image.

This all calls for a separate tool, rather than shoehorning it into e.g. the Distributed Game. Some planning, on-wiki, with "community buy-in" might not be too outrageous a suggestion, surely?

On Tue, Jun 6, 2017 at 8:22 PM Pharos <pharosofalexandria@gmail.com> wrote:
Hi Wikidata-niks,

As part of the Metropolitan Museum of Art project, I am interested in facilitating more public editing of Wikidata items for artwork through external tools, including that by relative newbies who might have an interest in art history.

One basic property for artworks that is particularly suited for this field is Depicts (P180), for example saying that a particular painting depicts a particular person (or building, or mountain, or divinity, or type of clothing).

We can do this to some extent now with Listeria and its 'wdedit' option, but this requires js customization and significant wiki background on the user''s part.

I was thinking something like the Wikidata Distributed Game might be interesting and broadly accessible to the public, but that tool currently only allows multiple-choice edits, and doesn't have a text entry box option.

Would it be possible to have some WiDaR-sh tool that could fill this niche for artworks?

I think it could be of very broad usefulness and interest to art communities.

Thanks,
Pharos
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Alex Stinson 
GLAM-Wiki Strategist
Wikimedia Foundation
Twitter:@glamwiki/@sadads

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