0:00:07.138,0:00:08.288
Thanks folks.
0:00:09.627,0:00:11.991
As I mentioned before,[br]you can load up the slides here
0:00:11.991,0:00:16.661
by either the QR code or the short URL,[br]which is wikidatacon..., this is bit.ly,
0:00:16.661,0:00:19.920
wikidatacon19glamstrategies.
0:00:19.980,0:00:22.040
And the slides are also[br]on the program page
0:00:22.040,0:00:24.520
on the WikidataCon site.
0:00:24.549,0:00:27.269
And then, there's also an Etherpad here[br]that you can click on.
0:00:27.269,0:00:28.959
So, I'll be talking about a lot of things.
0:00:28.959,0:00:31.629
that you might have heard about it[br]at Wikimania, if you were there,
0:00:31.629,0:00:34.089
but we are going to go[br]into a lot more implementation details.
0:00:34.089,0:00:36.209
Because we're at WikidataCon,[br]we can dive deeper
0:00:36.209,0:00:38.430
into the Wikidata and technical aspects.
0:00:38.430,0:00:41.821
But Richard and myself, we are working[br]at the Met Museum right now
0:00:41.821,0:00:43.200
and their Open Access.
0:00:43.200,0:00:45.320
If you didn't know,[br]about two plus years ago,
0:00:45.320,0:00:46.920
entering to the third year,
0:00:46.920,0:00:49.320
there's been an Open Access[br]strategy at the Met,
0:00:49.320,0:00:52.763
where they're releasing their images[br]under CC0 license and their metadata.
0:00:52.763,0:00:54.639
And one of the things[br]they brought us on to do
0:00:54.639,0:00:58.409
is what things could we imagine doing[br]with this Open Access content.
0:00:58.409,0:01:00.469
So, we're going to talk[br]a little bit about that
0:01:00.469,0:01:02.598
in terms of the experiments[br]that we've been running,
0:01:02.598,0:01:04.044
and we'd love to hear your feedback.
0:01:04.044,0:01:07.028
So, I hope to talk about 20 minutes,[br]and then hope to get some conversation
0:01:07.028,0:01:09.853
with you folks, since we have[br]a lot of knowledge in this room.
0:01:09.923,0:01:12.472
This is the announcement,[br]and actually the one-year anniversary,
0:01:12.472,0:01:16.452
where Katherine Maher was actually there,[br]at the Met to talk about that anniversary.
0:01:16.452,0:01:19.172
So, one of the things that's challenging[br]I think for a lot of folks
0:01:19.172,0:01:21.097
is how do you explain Wikidata,
0:01:21.097,0:01:23.911
and this GLAM[br]contribution strategy to Wikidata
0:01:23.911,0:01:27.102
to C-level folks at an organization.
0:01:27.102,0:01:31.392
We can talk about it with data scientists,[br]Wikimedians, librarians, maybe curators,
0:01:31.392,0:01:34.452
but when it comes to talking about this[br]with a director of a museum,
0:01:34.452,0:01:36.862
or a director of a library,[br]what does it actually--
0:01:36.862,0:01:38.482
how does it resonate with them?
0:01:38.482,0:01:41.352
So, one way that we actually talked[br]about that I think makes sense,
0:01:41.352,0:01:43.978
is everyone knows about Wikipedia,
0:01:43.978,0:01:47.799
and for the English language edition,
0:01:47.799,0:01:49.733
at least, we're talking[br]about 6 million articles.
0:01:49.733,0:01:51.792
And it sounds like a lot,[br]but if you think about it,
0:01:51.792,0:01:54.361
Wikipedia is not really the sum[br]of all human knowledge,
0:01:54.361,0:01:59.512
it's the sum of all reliably sourced,[br]mostly western knowledge.
0:02:00.281,0:02:02.211
And there's a lot of stuff out there.
0:02:02.211,0:02:04.141
We have a lot of stuff[br]in Commons already--
0:02:04.141,0:02:07.382
56 million media files going up[br]every single day--
0:02:07.382,0:02:11.484
but these are very...[br]a different type of standard
0:02:11.484,0:02:13.011
to what goes into Wikimedia Commons.
0:02:13.011,0:02:16.431
And the way that we have described[br]Wikidata to GLAM professionals,
0:02:16.431,0:02:18.231
and especially the C levels,
0:02:18.231,0:02:22.061
is that what if we could have a repository[br]that has a notability bar
0:02:22.061,0:02:24.381
that is not as high as Wikipedia.
0:02:24.381,0:02:26.001
So, we want all these paintings,
0:02:26.001,0:02:28.161
but not every painting[br]necessarily needs an article.
0:02:28.581,0:02:30.241
Wikipedia is held back by the fact
0:02:30.241,0:02:33.082
that you need to have[br]language editions of Wikipedia.
0:02:33.171,0:02:36.681
So, can we store the famous thing--[br]things, not strings.
0:02:36.681,0:02:40.570
Can we be object oriented[br]and not really lexical oriented?
0:02:40.570,0:02:42.181
And can we store this in a database
0:02:42.181,0:02:44.540
that stores facts, figures,[br]and relationships?
0:02:44.540,0:02:46.291
And that's pretty much[br]what Wikidata does.
0:02:46.711,0:02:50.736
And Wikidata is also a universal[br]kind of crosswalk database to links
0:02:50.736,0:02:52.321
to other collections out there.
0:02:52.321,0:02:55.119
So, we think this really resonates[br]with folks when you're talking about
0:02:55.119,0:02:58.596
what is the value of Wikidata compared[br]to what they're normally familiar with,
0:02:58.596,0:03:00.326
which is just Wikipedia.
0:03:01.346,0:03:02.876
Alright, so what are the benefits?
0:03:02.876,0:03:05.086
You're interlinking[br]your collections with others.
0:03:05.086,0:03:07.676
So, unfortunately, I apologize[br]to librarians here,
0:03:07.676,0:03:09.337
I'll be talking mostly about museums,
0:03:09.337,0:03:11.816
but a lot of this also is valid[br]also for libraries.
0:03:11.816,0:03:15.867
But you're basically connecting[br]your collection with the global collection
0:03:15.867,0:03:18.166
of linked open data collections.
0:03:18.846,0:03:22.276
You can also receive enriched[br]and improved metadata back
0:03:22.276,0:03:25.656
after contributing and linking[br]your collections to the world.
0:03:25.656,0:03:28.436
And there are some pretty neat[br]interactive multimedia applications
0:03:28.436,0:03:30.596
that you get-- I don't want[br]to say for free,
0:03:30.596,0:03:33.596
but your collection in Wikidata[br]allows you to visualize things
0:03:33.596,0:03:35.276
that you've never seen before.
0:03:35.276,0:03:36.776
We'll show you some examples.
0:03:36.776,0:03:39.737
And so, how do you convey this[br]to GLAM professionals effectively?
0:03:39.737,0:03:41.746
Well, I usually like to start[br]with storytelling,
0:03:41.746,0:03:43.536
and not technical explanations.
0:03:43.536,0:03:46.368
Okay, so if everyone here[br]has a cell phone,
0:03:46.368,0:03:49.574
especially if you have an iPhone,[br]I want you to scan this QR code
0:03:49.574,0:03:51.645
and bring up the URL[br]that it comes up with.
0:03:51.645,0:03:53.393
Or if you don't have a QR scanner,
0:03:53.393,0:03:58.963
just type in w.wiki/Aij in a web browser.
0:04:00.036,0:04:01.942
So go ahead and scan that.
0:04:03.280,0:04:04.864
And what comes up?
0:04:06.778,0:04:09.458
Does anyone see a knowledge graph[br]pop up on your screen?
0:04:09.516,0:04:11.156
So, for folks here in WikidataCon,
0:04:11.156,0:04:13.266
this is probably not[br]revolutionary for you.
0:04:13.266,0:04:16.386
But what it does, it does a SPARQL query[br]with these objects,
0:04:16.386,0:04:18.836
and it shows the linkages between them.
0:04:18.836,0:04:20.897
And you can actually drag them[br]around the screen.
0:04:20.897,0:04:22.204
You can actually click on nodes.
0:04:22.204,0:04:24.458
If you're [inaudible] in a mobile,[br]it will expand that--
0:04:24.458,0:04:27.554
you can actually start to surf[br]through Wikidata this way.
0:04:27.554,0:04:29.741
So, for Wikidata veterans[br]this is pretty cool.
0:04:29.741,0:04:31.206
One shot, you get this.
0:04:31.206,0:04:33.313
For a lot folks who have never seen[br]Wikidata before,
0:04:33.313,0:04:35.574
this is a revolutionary moment for them.
0:04:36.176,0:04:39.236
To actually hand-manipulate[br]a knowledge graph,
0:04:39.236,0:04:42.186
and to start surfing through Wikidata[br]without having to know SPARQL,
0:04:42.186,0:04:43.823
without having to know what a Q item is,
0:04:43.823,0:04:45.860
without having to know[br]what a property proposal is,
0:04:45.860,0:04:48.623
they can suddenly start seeing[br]connections in a way that is magical.
0:04:48.623,0:04:50.264
Hey, I see [Jacob's] here.
0:04:50.264,0:04:52.143
Jacob's been using[br]some of this code, as well.
0:04:52.143,0:04:54.443
So, this is some code[br]that we'll talk about later on
0:04:54.443,0:04:57.254
that allows you to create[br]these visualizations in Wikidata.
0:04:57.254,0:04:59.283
And we've really seen this[br]turn a lot of heads
0:04:59.283,0:05:01.408
who have really[br]never gotten Wikidata before.
0:05:01.408,0:05:04.653
But after seeing these interactive[br]knowledge graphs, they get it.
0:05:04.653,0:05:06.233
They understand the power of this.
0:05:06.233,0:05:08.293
And especially this example here,
0:05:08.293,0:05:11.304
this was a really big eye-opener[br]for the folks at the Met,
0:05:11.304,0:05:14.545
because this is the artifact[br]that is the center of this graph,
0:05:14.545,0:05:17.823
right there, the Portrait of Madame X,[br]a very famous portrait.
0:05:17.823,0:05:20.982
And they did not even know[br]that this was the inspiration
0:05:20.982,0:05:24.693
for the black dress that Rita Hayworth[br]wore in the movie Gilda.
0:05:24.693,0:05:26.783
So, just by seeing this graph, they said,
0:05:26.783,0:05:29.353
"Wait a minute. This is one[br]of our most visited portraits.
0:05:29.353,0:05:31.683
I didn't know that this was true."
0:05:31.683,0:05:35.214
And there's actually two other books[br]published about that painting.
0:05:35.214,0:05:38.983
You can see all these things,[br]not just within the realm of GLAM,
0:05:38.983,0:05:41.441
but it extends to fashion,[br]it extends to literature.
0:05:41.441,0:05:43.381
You're starting to see[br]the global connections
0:05:43.381,0:05:47.481
that your artworks have,[br]or your collections have via Wikidata.
0:05:48.722,0:05:50.342
So, how do we do this?
0:05:50.842,0:05:53.098
If you can remember nothing else[br]from this presentation,
0:05:53.098,0:05:56.432
this one page is your one-stop shopping.
0:05:56.432,0:05:58.592
Now, fortunately, you don't have[br]to memorize all this.
0:05:58.592,0:06:03.292
It's actually right here at[br]Wikidata:Linked_open_data_workflow.
0:06:03.560,0:06:06.170
So, we'll be talking about some[br]of these different phases
0:06:06.170,0:06:10.670
of how you first prepare,[br]reconcile, and examine
0:06:11.160,0:06:14.190
what the GLAM organization might have[br]and what does Wikidata have.
0:06:14.190,0:06:15.374
And then, what are the tools
0:06:15.374,0:06:18.664
to actually ingest[br]and correct or enrich that
0:06:18.664,0:06:20.241
once it's in Wikidata.
0:06:20.241,0:06:22.691
And then, what are some of ways[br]to reuse that content,
0:06:22.691,0:06:25.161
or to report and create[br]new things out of it.
0:06:25.161,0:06:31.191
So, this is the simpler version of a chart[br]that Sandra and the GLAM folks
0:06:31.191,0:06:33.111
at the foundation have created.
0:06:33.111,0:06:35.534
But this is trying[br]to sum up, in one shot--
0:06:35.534,0:06:38.133
because we know how hard things[br]are to find in Wikidata--
0:06:38.133,0:06:41.733
to find in one shot all the different[br]tools you should pay attention to
0:06:41.733,0:06:43.475
as a GLAM organization.
0:06:44.969,0:06:50.606
So, just using the Met as an example,[br]we started with what is the ideal object
0:06:50.606,0:06:53.398
that we have in Wikidata[br]that comes from the Met?
0:06:53.398,0:06:55.882
This is a typical shot of a Wikidata item,
0:06:55.882,0:06:57.385
in the mobile mode there.
0:06:57.385,0:06:59.244
And this is one[br]of the more famous paintings
0:06:59.244,0:07:00.729
we used as a model, here.
0:07:00.729,0:07:03.315
We have the label,[br]description, and aliases.
0:07:03.915,0:07:05.225
And then, we found out,
0:07:05.225,0:07:07.035
"What are the core statements[br]that we wanted?"
0:07:07.035,0:07:10.035
We wanted instance of, image,[br]inception, collection.
0:07:10.035,0:07:13.239
And what are some other properties[br]we would like if we had it?
0:07:13.239,0:07:15.960
Depiction information,[br]material used, things like that.
0:07:16.879,0:07:19.369
We actually do have an identifier.
0:07:19.369,0:07:22.199
The Met object ID is P3634.
0:07:22.199,0:07:24.629
So, for some organizations,[br]you might want to propose
0:07:24.629,0:07:28.529
a property just to track your items[br]using an object ID.
0:07:29.369,0:07:31.899
And then, for the Met,[br]just trying to circumscribe
0:07:31.899,0:07:35.519
what objects do we want to upload[br]and keep in Wikidata--
0:07:35.519,0:07:38.927
the thing that we first identified[br]were collection highlights.
0:07:38.927,0:07:43.649
These are like a hand-selected set[br]of 1,500 to 1,000 items
0:07:43.678,0:07:48.878
that were going to be given priority[br]to upload to Wikidata.
0:07:48.939,0:07:51.709
So, Richard and the crew[br]out of Wikimedia in New York
0:07:51.709,0:07:53.105
did a lot of this early work.
0:07:53.105,0:07:55.571
And then, now, we're systematically[br]going through to make sure
0:07:55.571,0:07:56.689
they're all complete.
0:07:56.689,0:07:58.221
And there's a secondary set
0:07:58.221,0:08:01.390
called the Heilbrunn Timeline[br]of Art History-- about 8,000 items
0:08:01.390,0:08:07.149
that are seminal pieces of work,[br]artists' works throughout history.
0:08:07.149,0:08:09.499
And there are about 8,000[br]that the Met has identified,
0:08:09.499,0:08:11.812
and we're also putting that[br]on Wikidata, as well,
0:08:11.812,0:08:13.143
using a different destination.
0:08:13.143,0:08:16.271
Here, described by source--[br]Heilbrunn Timeline of Art History.
0:08:16.271,0:08:19.841
So, the collection highlight[br]is denoted here as collection--
0:08:19.841,0:08:21.265
Metropolitan Museum of Art,
0:08:21.265,0:08:22.976
subject has role collection highlight.
0:08:22.976,0:08:26.872
And then, these 8,000[br]or so are like that in Wikidata.
0:08:29.741,0:08:33.816
I couldn't show this chart at Wikimania,[br]because it's too complicated.
0:08:33.816,0:08:35.389
But WikidataCon, we can.
0:08:35.389,0:08:38.845
So, this is something that is really hard[br]to answer sometimes.
0:08:39.490,0:08:42.169
What makes something[br]in Wikidata from the Met,
0:08:42.169,0:08:44.658
or from the New York Public Library,[br]or from your organization?
0:08:44.658,0:08:47.609
And the answer is not easy.[br]It's: depends.
0:08:47.644,0:08:49.684
It's complicated, it can be multi-factor.
0:08:49.684,0:08:53.254
So, you could say, "Well, if I had[br]an object ID in Wikidata,
0:08:53.254,0:08:54.804
that is an embed object."
0:08:54.804,0:08:56.674
But maybe someone didn't enter that.
0:08:56.674,0:08:59.924
Maybe they only put in[br]Collection: Met which is P195,
0:08:59.924,0:09:02.684
or they put in the accession number,
0:09:02.684,0:09:06.984
and they put collection as the qualifier[br]to that accession number.
0:09:06.984,0:09:11.454
So, there's actually, one, two, three[br]different ways to try to find Met objects.
0:09:11.454,0:09:14.214
And probably the best way to do it[br]is through a union like this.
0:09:14.214,0:09:16.173
So, you combine all three,[br]and you come back,
0:09:16.173,0:09:18.064
and you make a list out of it.
0:09:18.064,0:09:20.813
So unfortunately, there is[br]no one clean query
0:09:20.813,0:09:23.684
that'll guarantee you all the Met objects.
0:09:23.684,0:09:27.873
This is probably[br]the best approach for this.
0:09:27.873,0:09:29.384
And for some institutions,
0:09:29.384,0:09:32.505
they're probably doing[br]something similar to that right now.
0:09:32.505,0:09:35.824
Alright, so example here,[br]is that what you see here
0:09:35.824,0:09:39.684
manifests itself differently--[br]not differently, but as this in a query,
0:09:39.684,0:09:40.904
which can get pretty complex.
0:09:40.904,0:09:43.063
So, if we're looking[br]for all the collection highlights,
0:09:43.063,0:09:47.713
we'd break this out into the statement[br]and then the qualifier as this:
0:09:47.782,0:09:49.712
subject has role collection highlight.
0:09:49.712,0:09:51.450
So, that's one way that we sort out
0:09:51.450,0:09:54.124
some of these special[br]designations in Wikidata.
0:09:55.166,0:09:58.716
So, the summary is,[br]representing "The Met" is multifaceted,
0:09:58.716,0:10:01.536
and needs to balance simplicity[br]and findability.
0:10:01.536,0:10:04.896
How many people here have heard[br]of Sum of All Paintings as a project?
0:10:04.995,0:10:07.088
Ooh, God, good, a lot of you!
0:10:07.088,0:10:09.105
So, it's probably one[br]of the most active ones
0:10:09.105,0:10:10.525
that deals with these issues.
0:10:10.525,0:10:17.057
So, we always debate whether we should[br]model things super-accurately,
0:10:17.057,0:10:19.815
or should you model things[br]so that they're findable.
0:10:19.815,0:10:21.997
These are kind of at odds with each other.
0:10:21.997,0:10:24.232
So, we usually prefer findability.
0:10:24.232,0:10:27.001
It's no good if it's perfectly modeled,[br]but no one can ever find it,
0:10:27.001,0:10:30.013
because it's so strict[br]in terms of how it's defined at Wikidata.
0:10:30.013,0:10:31.882
And then, we have some challenges.
0:10:31.882,0:10:35.367
Multiple artifacts might be tied[br]to one object ID,
0:10:35.367,0:10:37.396
which might be different in Wikidata.
0:10:37.396,0:10:42.097
And then, mapping the Met classification[br]to instances has some complex cases.
0:10:42.097,0:10:44.282
So, the way that the Met classifies things
0:10:44.282,0:10:46.775
doesn't always fit[br]with how Wikidata classifies things.
0:10:46.775,0:10:49.982
So, we show you some examples here[br]of how this works.
0:10:49.982,0:10:53.602
So, this is a great example[br]of using a Python library
0:10:53.602,0:10:56.487
to actually ingest[br]what we know from the Met,
0:10:56.487,0:10:58.313
and then try to sort out what they have.
0:10:58.313,0:10:59.887
So, this is just for textiles.
0:10:59.887,0:11:02.076
You can see that they got[br]a lot of detail here
0:11:02.076,0:11:05.399
in terms of woven textiles, laces,[br]printed, trimmings, velvets.
0:11:05.399,0:11:07.907
We first looked into this in Wikidata.
0:11:07.907,0:11:10.175
We did not have[br]this level of detail in Wikidata.
0:11:10.175,0:11:12.207
We still don't have all this resolved.
0:11:12.207,0:11:14.764
You can see that this[br]is really complex here.
0:11:14.764,0:11:18.012
Anonymous is just not anonymous[br]for a lot of databases.
0:11:18.012,0:11:20.126
There's a lot of qualifications--
0:11:20.126,0:11:23.045
whether the nationality, or the century.
0:11:23.045,0:11:26.282
So, trying to map all this to Wikidata[br]can be complex, as well.
0:11:26.282,0:11:30.450
And then, this shows you[br]that of all the works in the Met,
0:11:30.450,0:11:33.976
about 46% are open access right now.
0:11:33.976,0:11:38.694
So, we still have about just over 50%[br]that are not CC0 yet.
0:11:40.134,0:11:43.444
(man) All the objects in the Met,[br]or all objects on display?
0:11:43.444,0:11:45.957
(Andrew) It's weird. It's not on display.
0:11:45.957,0:11:47.866
But it's not all objects either.
0:11:47.866,0:11:52.176
It's about 400 to 500 thousand objects[br]in their database at this point.
0:11:52.176,0:11:53.840
So, somewhere in between.
0:11:55.380,0:11:57.609
So, starting points.[br]This is always a hard one.
0:11:57.609,0:12:03.514
We just had this discussion[br]on the Facebook group recently
0:12:03.514,0:12:04.923
about where do people go
0:12:04.923,0:12:07.887
to find out where the modeling[br]should look like for a certain thing.
0:12:07.887,0:12:09.271
It's not easy.
0:12:09.271,0:12:12.115
So, normally, what we have to do[br]is just point people to,
0:12:12.115,0:12:15.281
I don't know, some project[br]that does it well now?
0:12:15.281,0:12:17.230
So, it's not a satisfying answer,
0:12:17.230,0:12:19.910
but we usually tell folks[br]to start at things like visual arts,
0:12:19.910,0:12:22.308
or Sum of All Paintings[br]does it pretty well,
0:12:22.308,0:12:25.569
or just go to the project chat to find out[br]where some of these things are.
0:12:25.569,0:12:27.444
We need better solutions for this.
0:12:27.444,0:12:30.939
This is just a basic flow[br]of what we're doing with the Met here.
0:12:30.939,0:12:33.119
We're basically taking[br]their CSV, and their API,
0:12:33.119,0:12:35.979
and we're consuming it[br]into a Python data frame.
0:12:35.979,0:12:38.159
We're taking the SPARQL code--
0:12:38.159,0:12:40.499
the one that you saw[br]before, this super union--
0:12:40.499,0:12:43.779
bring that in, and we're doing[br]a bi-directional diff,
0:12:43.779,0:12:45.999
and then seeing what new things[br]have been added here,
0:12:45.999,0:12:47.729
what things have been subtracted there,
0:12:47.729,0:12:51.529
and we're actually making those changes[br]either through QuickStatements,
0:12:51.529,0:12:53.439
or we're doing it through Pywikibot.
0:12:53.439,0:12:55.512
So, directly editing Wikidata.
0:12:56.204,0:12:59.405
So, this is the big slide[br]I also couldn't show at Wikimania,
0:12:59.405,0:13:01.485
because it would have flummoxed everyone.
0:13:01.485,0:13:04.924
So, this is a great example[br]of how we start with the Met database,
0:13:04.924,0:13:06.824
we have this crosswalk database,
0:13:06.824,0:13:09.209
and then we generate[br]the changes in Wikidata.
0:13:09.209,0:13:12.644
The way this works is this is an example[br]of one record from the Met.
0:13:12.644,0:13:15.744
This is an evening dress-- we're working[br]with the Costume Institute recently,
0:13:15.744,0:13:17.518
the one that puts on the Met Gala.
0:13:17.518,0:13:20.442
So, we have one evening dress[br]here, by Valentina.
0:13:20.442,0:13:22.100
Here's a date, accession number.
0:13:22.100,0:13:25.105
So, these things can be put[br]into Wikidata directly.
0:13:25.105,0:13:27.744
A field equals the date, accession number.
0:13:27.744,0:13:29.404
But what do we do with things like this?
0:13:29.404,0:13:33.868
This is an object name, which is basically[br]like a classification of what it is,
0:13:33.868,0:13:35.648
like an instance of for the Met.
0:13:35.648,0:13:37.396
And the designer's Valentina.
0:13:37.396,0:13:41.571
So, what we do is we take these[br]and we run all the unique object names
0:13:41.571,0:13:43.801
and all the unique designers[br]through OpenRefine.
0:13:43.801,0:13:46.720
So, we get maybe 60% matches[br]if we're lucky.
0:13:46.720,0:13:48.418
We put that into a spreadsheet.
0:13:48.418,0:13:53.178
Then we ask volunteers[br]or the curators at the Met
0:13:53.178,0:13:55.333
to help fill in this crosswalk database.
0:13:55.333,0:13:57.312
This is just simply Google Sheets.
0:13:57.312,0:13:59.911
So, we say, here are all the object names,[br]the unique object names
0:13:59.911,0:14:02.731
that match lexically exactly[br]with what's in the Met database,
0:14:02.731,0:14:05.912
and then you say this maps to this Q ID.
0:14:05.912,0:14:08.556
So, we first started[br]this maybe like only about--
0:14:08.556,0:14:11.233
well, 60% were failed,[br]some of these were blank.
0:14:11.233,0:14:13.751
So, we tap folks in specific groups.
0:14:13.751,0:14:17.316
So there's like a Wiki Loves Fashion[br]little chat group that we have.
0:14:17.316,0:14:20.304
And folks like user PKM[br]were super useful in this area.
0:14:20.304,0:14:22.794
So she spent a lot of time[br]looking through this, and saying,
0:14:22.794,0:14:24.764
"Okay, Evening suit is this,[br]Ewer is that."
0:14:24.764,0:14:27.759
So, we looked through[br]and made all this mappings here.
0:14:27.759,0:14:30.719
And then, what happens is now,[br]when we see this in the Met database,
0:14:30.719,0:14:33.201
we look it up in the crosswalk database,[br]and we say, "Oh, yeah.
0:14:33.201,0:14:36.169
These are the two Q numbers[br]we need to put into Wikidata."
0:14:36.169,0:14:39.089
And then, it generates[br]the QuickStatement right there.
0:14:39.089,0:14:41.328
Same thing here with Designer: Valentina.
0:14:41.328,0:14:44.138
If Valentina matches here,[br]then it gets generated
0:14:44.138,0:14:45.838
with that QuickStatement right there.
0:14:45.838,0:14:48.069
If Valentina does not exist,[br]then we'll create it.
0:14:48.069,0:14:51.288
You can see here, Weeks--[br]look at that high Q ID right there.
0:14:51.288,0:14:53.918
We just created that recently,[br]because there was no entry before.
0:14:53.918,0:14:55.358
Does that makes sense to everyone?
0:14:55.358,0:14:57.727
- (man 2) What's the extra statement?[br]- (Andrew) I'm sorry?
0:14:57.727,0:15:00.610
- (man 2) What's the extra statement?[br]- (Andrew) Oh, the extra statement.
0:15:00.610,0:15:03.131
So, believe it or not, we have[br]an Evening blouse, Evening dress,
0:15:03.131,0:15:05.010
Evening pants,[br]Evening ensemble, Evening hat--
0:15:05.010,0:15:08.650
do we want to make a new Wikidata item[br]for Evening pants,Evening everything?
0:15:08.650,0:15:10.444
So, we said, "No."[br]We probably don't want to.
0:15:10.444,0:15:13.859
We'll just say, "It's a dress,[br]but it's also evening wear",
0:15:13.859,0:15:15.117
which is what that is.
0:15:15.117,0:15:17.301
So, we're saying an instance[br]of both things.
0:15:17.931,0:15:21.398
I'm not sure it's the perfect solution,[br]but it's a solution at this point.
0:15:21.744,0:15:22.944
So, does everyone get that?
0:15:22.944,0:15:25.564
So, this is kind of a crosswalk database[br]that we maintain here.
0:15:25.564,0:15:28.025
And the nice thing about it,[br]it's just Google Sheets.
0:15:28.025,0:15:29.264
So, we can get people to help
0:15:29.264,0:15:31.375
that don't need to know[br]anything about this database,
0:15:31.375,0:15:34.384
don't need to know about QuickStatements,[br]don't need to know about queries.
0:15:34.384,0:15:36.226
They just go in and fill in the Q number.
0:15:36.226,0:15:37.244
Yeah.
0:15:37.244,0:15:40.902
(woman) So, when you copy[br]object name and you find the Q ID,
0:15:40.902,0:15:43.145
the initial 60%[br]that you mentioned as an example,
0:15:43.145,0:15:45.223
is that by exact match?
0:15:46.483,0:15:48.103
(Andrew) Well, it's through OpenRefine.
0:15:48.103,0:15:52.014
So, it does its best guess,[br]and then we verify to make sure
0:15:52.014,0:15:54.444
that the OpenRefine match makes sense.
0:15:54.444,0:15:56.114
Yeah.
0:15:56.203,0:15:57.794
Does that make sense to everyone?
0:15:57.794,0:16:00.304
So, some folks might be doing[br]some variation on this,
0:16:00.304,0:16:03.403
but I think the nice thing about this[br]is that, by using Google Sheets,
0:16:03.403,0:16:08.234
we remove a lot of the complexities[br]of these two areas from this.
0:16:08.234,0:16:11.193
And we'll show you some code[br]that does this later on.
0:16:11.813,0:16:15.273
- (man 3) How do you generate [inaudible]?[br]- (Andrew) How do you generate this?
0:16:15.273,0:16:17.272
- (man 3) Yes.[br]- (Andrew) Python code.
0:16:17.272,0:16:19.134
I'll show you a line that does this.
0:16:19.134,0:16:21.136
But you can also go up here.
0:16:21.136,0:16:25.096
This is the whole Python program[br]that does this, this, and that,
0:16:25.096,0:16:27.296
if you want to take a look at that.
0:16:28.026,0:16:29.026
Yes.
0:16:29.026,0:16:31.207
(man 4) Did you really use[br]your own vocabulary,
0:16:31.207,0:16:35.426
or is there something [inaudible].
0:16:35.426,0:16:37.246
- (Andrew) This right here?[br]- (man 4) Yeah.
0:16:37.246,0:16:39.721
(Andrew) Yeah. So, this[br]is the Met's own vocabulary.
0:16:39.721,0:16:43.031
So, most museums use[br]a system called TMS.
0:16:43.031,0:16:44.891
It's like their own management system.
0:16:44.891,0:16:47.654
So, they'll usually--[br]this is the museum world--
0:16:47.654,0:16:50.771
they'll usually roll[br]their own vocabulary for their own needs.
0:16:50.771,0:16:54.022
Museums are very late[br]to interoperable metadata.
0:16:54.022,0:16:57.282
Librarians and archivists have this[br]kind of as baked into them.
0:16:57.282,0:16:58.664
Museums are like, "Meh..."
0:16:58.664,0:17:01.471
Our primary goal[br]is to put objects on display,
0:17:01.471,0:17:04.141
and if it plays well with other people,[br]that's a side benefit.
0:17:04.141,0:17:05.931
But it's not a primary thing that they do.
0:17:05.931,0:17:08.031
So, that's why it's complicated[br]to work with museums.
0:17:08.031,0:17:11.161
You need to map their vocabulary,[br]which might be a mish-mash
0:17:11.161,0:17:14.576
of famous vocabularies,[br]like Getty AAT, and other things.
0:17:14.576,0:17:17.911
But usually, it's to serve[br]their exact needs at their museum.
0:17:17.911,0:17:19.591
And that's what's challenging.
0:17:19.591,0:17:21.091
And I see a lot of heads nodding,
0:17:21.091,0:17:23.161
so you've probably seen this a lot[br]at these museums.
0:17:23.161,0:17:25.429
So, I'll move on to show you[br]how this actually is done.
0:17:25.429,0:17:26.749
Oh, go ahead.
0:17:26.749,0:17:28.711
(man 5) How do you[br]bring people, to collaborate,
0:17:28.711,0:17:31.595
and put some Q codes into your database?
0:17:31.595,0:17:32.971
(Andrew) How do you-- I'm sorry?
0:17:32.971,0:17:35.038
(man 5) How do you bring... [br]collaborate people?
0:17:35.038,0:17:38.290
(Andrew) Ah, so for this,[br]these are projects we just go to,
0:17:38.780,0:17:41.750
for better or for worse,[br]like Facebook chat groups that we know,
0:17:41.750,0:17:43.007
are active in these areas.
0:17:43.007,0:17:45.685
Like Sum of All Paintings,[br]Wiki Loves Fashion--
0:17:45.685,0:17:47.918
which is a group[br]of maybe five or seven folks.
0:17:48.548,0:17:50.759
But we need a better way[br]to get this out to folks
0:17:50.759,0:17:52.339
so we get more collaborators on this.
0:17:52.339,0:17:53.879
This doesn't scale well, right now.
0:17:53.879,0:17:56.089
But for small groups,[br]it works pretty well.
0:17:56.108,0:17:57.568
I'm open to ideas.
0:17:57.568,0:17:59.619
(man 5) [inaudible]
0:17:59.619,0:18:01.669
(Andrew) Oh yeah. Please come on up.
0:18:01.669,0:18:02.948
If folks want to come up here,
0:18:02.948,0:18:05.357
there's a little more room[br]in the aisle right here.
0:18:06.057,0:18:09.629
So, we are utilizing Python[br]for this mostly.
0:18:09.774,0:18:13.354
If you don't know, there is[br]a Python notebook system
0:18:13.354,0:18:14.884
that WMFLabs has.
0:18:14.884,0:18:17.345
So, you can actually go on[br]and start playing with this.
0:18:17.345,0:18:19.624
So, it's pretty easy[br]to generate a lot of stuff
0:18:19.624,0:18:21.401
if you know some of the code that's there.
0:18:21.401,0:18:22.455
[inaudible], yeah.
0:18:22.485,0:18:23.922
(woman 2) Why do you put everything
0:18:23.922,0:18:27.821
into Wikidata,[br]and not into your own Wikibase?
0:18:29.401,0:18:31.127
(Andrew) If you're using[br]your own Wikibase?
0:18:31.127,0:18:33.741
(woman 2) Yeah. Why don't you[br]use your own Wikibase?
0:18:33.741,0:18:35.990
and then go to [inaudible]
0:18:35.990,0:18:38.390
(Andrew) That's its own ball of--
0:18:38.390,0:18:41.630
I don't want to maintain[br]my own Wikibase at this point. (laughs)
0:18:42.190,0:18:44.400
If I can avoid doing[br]the Wikibase maintenance,
0:18:44.400,0:18:45.760
I would not do it.
0:18:46.530,0:18:48.080
(man 6) Would you like a Wikibase?
0:18:48.080,0:18:50.050
(Andrew) We could. It's possible.
0:18:50.050,0:18:54.154
(man 7) But again,[br]what they use [inaudible]
0:18:54.154,0:18:59.868
about 2,000, 8,000, 10,000,[br]of 400,000 digital [inaudible].
0:18:59.868,0:19:04.300
So that's only 2.5%,
0:19:04.300,0:19:08.782
[inaudible]
0:19:08.782,0:19:12.601
(Andrew) So, I'd say, solve it for 1,500,[br]then scale up to 150 thousand.
0:19:12.601,0:19:14.428
So, we're trying to solve it
0:19:14.428,0:19:16.876
for the best[br]well-known objects, and then--
0:19:16.876,0:19:19.875
(man 7) When do you think[br]that will happen?
0:19:20.855,0:19:25.788
I understand that those are people[br]that shouldn't go onto Wikidata.
0:19:25.788,0:19:29.856
So you go to Commons[br]or your own Wikibase solution,
0:19:29.856,0:19:31.695
not to be a [inaudible]--
0:19:31.695,0:19:34.588
(Andrew) Right. That's why we're going[br]with the 2,000 and 8,000.
0:19:34.588,0:19:37.460
We're pretty confident[br]these are highly notable objects
0:19:37.460,0:19:39.085
that deserve to be in Wikidata.
0:19:39.085,0:19:40.465
Beyond that, it's debatable.
0:19:40.465,0:19:44.265
So, that's why we're not[br]vacuuming 400-thousand things at one shot.
0:19:44.265,0:19:48.936
We're starting with notable 2,000,[br]notable 8,000, then we'll talk after that.
0:19:49.515,0:19:52.775
So, these are the two lines of code[br]that do the most stuff here.
0:19:52.775,0:19:54.217
So, even if you don't know Python,
0:19:54.217,0:19:56.146
it's actually not that bad[br]if you look at this.
0:19:56.146,0:19:58.105
There's a read_csv function.
0:19:58.105,0:20:00.015
You're taking the crosswalk URL,
0:20:00.015,0:20:02.336
basically, the URL[br]of that Google Spreadsheet.
0:20:02.336,0:20:04.875
You're grabbing the spreadsheet[br]that's called "Object Name",
0:20:04.875,0:20:06.685
and you're basically creating[br]a data structure
0:20:06.685,0:20:08.165
that has the Object Name and the QID.
0:20:08.165,0:20:09.645
That's it. That's all you're doing.
0:20:09.645,0:20:11.655
Just pulling that in to the Python code.
0:20:11.655,0:20:15.914
Then, you're actually matching[br]whatever the entity's name is,
0:20:15.914,0:20:17.754
and then looking up the QID.
0:20:17.754,0:20:21.689
Okay, so, this is just to tell you[br]that's not super hard.
0:20:21.689,0:20:24.234
The code is available right there,[br]if you want to look at it.
0:20:24.234,0:20:26.474
But these two lines of code,[br]which takes a little while
0:20:26.474,0:20:29.524
when you're writing it from scratch[br]to create these two lines of code,
0:20:29.524,0:20:30.904
but once you have an example,
0:20:30.904,0:20:34.484
it's pretty darn easy to plug in[br]your own data set, your own crosswalk,
0:20:34.484,0:20:36.844
to generate the QuickStatements.
0:20:36.844,0:20:38.525
So, I've done a lot of the work already,
0:20:38.525,0:20:41.385
and I invite you[br]to steal the code and try it.
0:20:42.365,0:20:44.936
So, when it comes to images,[br]it's a little more challenging.
0:20:44.936,0:20:48.215
So, at this point, Pattypan[br]is probably your best bet.
0:20:48.215,0:20:51.385
Pattypan is a tool that is[br]a spreadsheet-oriented tool.
0:20:51.385,0:20:54.855
You fill in the metadata, you point[br]to the local file on your computer,
0:20:54.855,0:20:57.435
and it uploads it to Commons[br]with all that information,
0:20:57.435,0:21:02.125
or another alternative[br]is if you set P4765 to a URL--
0:21:03.105,0:21:06.195
because this is the Commons-compatible[br]image available at URL,
0:21:06.195,0:21:08.544
Martin Dahhmers has a bot,[br]at least for paintings,
0:21:08.544,0:21:12.020
that will just swoop through and say,[br]"Oh, we don't have this image.
0:21:12.020,0:21:15.113
Here's a Commons compatible one.
0:21:15.113,0:21:17.709
Why don't I slip it from that site[br]and put it into Commons?"
0:21:17.709,0:21:18.995
And that's what his bot does.
0:21:18.995,0:21:20.733
So, you can actually take[br]a look at his bot
0:21:20.733,0:21:24.102
and modify it for your own purposes,[br]but that is also another alternative
0:21:24.102,0:21:28.061
that doesn't require you[br]to do some spreadsheet work there.
0:21:28.061,0:21:30.452
If you might have heard[br]of GLAM Wiki Toolset,
0:21:30.452,0:21:32.552
it's effectively end[br]of life at this point.
0:21:33.322,0:21:37.362
It hasn't been updated, and even the folks[br]who have been working with it in the past
0:21:37.362,0:21:39.332
have said Pattypan[br]is probably your best bet.
0:21:39.332,0:21:41.722
Has anyone used GWT these days?
0:21:41.741,0:21:43.591
A few of you, a little bit.
0:21:43.591,0:21:45.161
It's just not being further developed,
0:21:45.161,0:21:47.852
and it's not compatible with a lot[br]of our authentication protocols
0:21:47.852,0:21:49.280
that we have now.
0:21:49.280,0:21:52.928
Okay. So, right now, we have basic[br]metadata added to Wikidata,
0:21:52.928,0:21:54.997
with pretty good results from the Met,
0:21:54.997,0:21:58.117
and we have a Python script here[br]to also analyze that.
0:21:58.117,0:22:00.307
You're welcome to steal[br]some of that code, as well.
0:22:00.307,0:22:02.817
So, this is what we are showing[br]to the Met folks, now.
0:22:02.817,0:22:06.087
We actually have Listeria lists[br]that are running
0:22:06.087,0:22:07.627
to show all the inventory
0:22:07.627,0:22:10.967
and all the information[br]that we have in Wikidata.
0:22:10.967,0:22:15.612
And I'll show you very quickly[br]about a project that we ran to show folks.
0:22:15.612,0:22:18.547
So, what are the benefits of adding[br]your collections to Wikidata?
0:22:18.547,0:22:21.917
One is to use AI in the image classifier
0:22:21.917,0:22:24.787
to actually help train[br]a machine learning model
0:22:24.787,0:22:29.447
with all the Met's images and keywords,[br]and let that be an engine for other folks
0:22:29.447,0:22:32.047
to recognize content.
0:22:32.047,0:22:36.408
So, this is a hack-a-thon that we had[br]with MIT and Microsoft last year.
0:22:36.408,0:22:39.238
The way this works, is we have[br]the paintings from the Met,
0:22:39.238,0:22:40.277
and we have the keywords
0:22:40.277,0:22:43.157
that they actually paid a crew[br]for six months to work on
0:22:43.157,0:22:46.937
to add hand keyword tags[br]to all the artworks.
0:22:47.567,0:22:50.077
We ingested that[br]into an AI system right here,
0:22:50.077,0:22:51.367
and then, what we did was say,
0:22:51.367,0:22:55.428
"Let's feed in new images that[br]this AI ML system had never seen before,
0:22:55.428,0:22:56.747
and see what comes out."
0:22:56.747,0:23:00.037
And the problem is that it comes out[br]with pretty good results,
0:23:00.037,0:23:02.267
but it's maybe only 60% accurate.
0:23:02.267,0:23:04.797
And for most folks,[br]60% accurate is garbage.
0:23:04.797,0:23:08.627
How do I get the 60% good[br]out of this pile of stuff?
0:23:08.627,0:23:11.127
The good news is that our community[br]knows how to do that.
0:23:11.127,0:23:13.157
We can actually feed this[br]into a Wikidata game
0:23:13.157,0:23:14.997
and get the good stuff out of that.
0:23:14.997,0:23:16.228
That's basically what we did.
0:23:16.228,0:23:17.647
So, this is the Wikidata game--
0:23:17.647,0:23:19.757
you'll notice this is[br]Magnus' interface right there--
0:23:19.757,0:23:21.182
being played at the Met Museum,
0:23:21.182,0:23:22.207
in the lobby.
0:23:22.207,0:23:25.437
We actually had folks at a cocktail party[br]drinking champagne
0:23:25.437,0:23:27.427
and hitting buttons on the screen.
0:23:27.427,0:23:31.048
Hopefully, accurately. (chuckles)
0:23:31.048,0:23:33.444
(applause)
0:23:33.444,0:23:35.116
We had journalists, curators,
0:23:35.116,0:23:37.506
we had some board members[br]from the Met there as well.
0:23:37.506,0:23:38.810
And this was great.
0:23:38.810,0:23:40.061
No log in, whatever.
0:23:40.061,0:23:42.106
(lowers voice) We created[br]an account just for this.
0:23:42.106,0:23:44.117
So, they just hit yes-no-yes-no.
0:23:44.117,0:23:45.256
This is great.
0:23:45.256,0:23:47.526
You saw this, it said,[br]"Is there a tree in this picture?"
0:23:47.526,0:23:49.148
You don't have to train anyone on this.
0:23:49.148,0:23:52.213
You just hit yes--[br]depicts a tree, not depicted.
0:23:52.213,0:23:55.910
I even had my eight-year-old boys[br]play this game with a finger tap.
0:23:56.540,0:24:00.047
And we also created a little tool[br]that showed all the depictions going by
0:24:00.047,0:24:01.505
so people could see them.
0:24:03.189,0:24:06.453
It basically is like--[br]how do you sift good from bad?
0:24:06.453,0:24:08.350
This is where the Wikimedia[br]community comes in,
0:24:08.350,0:24:11.034
that no other entity could ever do.
0:24:12.084,0:24:15.052
So, in that first few months[br]that we had this,
0:24:15.052,0:24:19.017
over 7,000 judgments,[br]resulting in about 5,000 edits.
0:24:19.912,0:24:22.227
We did really well on tree,[br]boat, flower, horse,
0:24:22.227,0:24:24.907
things that are in landscape paintings.
0:24:25.146,0:24:27.466
But when you go to things[br]like gender discrimination,
0:24:27.466,0:24:29.901
and cats and dogs, not so good, I know.
0:24:29.901,0:24:32.159
Because there's so many different[br]types of cats and dogs
0:24:32.159,0:24:33.456
in different positions.
0:24:33.456,0:24:36.105
But horses, a lot easier[br]than cats and dogs.
0:24:36.735,0:24:38.742
But also, I should note[br]that Wikimedia Foundation
0:24:38.742,0:24:42.697
is now looking into doing[br]image recognition on Commons uploads
0:24:42.697,0:24:46.368
to do these suggestions as well,[br]which is an awesome development.
0:24:46.667,0:24:49.627
Okay, so, dashboards.
0:24:50.750,0:24:53.358
Let's just show you[br]some of these dashboards.
0:24:53.418,0:24:55.097
Folks you work with love dashboards.
0:24:55.097,0:24:56.817
They just want to see stats.
0:24:56.817,0:24:58.797
So, we have them, like BaGLAMa.
0:24:58.797,0:25:00.787
We have InteGraality.
0:25:00.787,0:25:02.767
Is JeanFred here?
0:25:03.447,0:25:06.247
I think this is a very new thing[br]relative to last WikidataCon.
0:25:06.247,0:25:08.327
We actually have a tool[br]which will create
0:25:08.327,0:25:10.967
this property completeness[br]chart right here.
0:25:10.967,0:25:12.987
So, it's called InteGraality,[br]with two A's.
0:25:13.206,0:25:15.526
It's on that big chart[br]that I showed you before.
0:25:15.526,0:25:19.086
And it can just autogenerate[br]how complete your items are
0:25:19.086,0:25:21.036
in any set, which is really cool.
0:25:21.566,0:25:23.771
So, we can see that paintings[br]are by far the highest,
0:25:23.771,0:25:26.057
we have sculptures, drawings, photographs.
0:25:26.121,0:25:29.322
And then, they also like to see[br]what are the most popular artworks
0:25:29.322,0:25:31.148
in the Wikisphere?
0:25:31.148,0:25:33.417
So, just looking at the site links[br]in Wikidata--
0:25:33.417,0:25:37.781
you can see and rank[br]all these different artworks there.
0:25:39.568,0:25:41.926
Also another thing they'd like to see
0:25:41.926,0:25:46.879
is what are the most frequent creators[br]of content or Met artworks--
0:25:46.879,0:25:49.193
what are the most commonly[br]depicted things.
0:25:49.193,0:25:51.982
So, these are very easy[br]to generate in SPARQL,
0:25:51.982,0:25:54.622
you could look at it right there,[br]using bubble graphs.
0:25:54.673,0:25:56.991
Then place of birth[br]of the most prominent artists,
0:25:56.991,0:25:58.814
we have a chart there, as well.
0:25:58.814,0:26:01.142
So, structured data on Commons.
0:26:01.142,0:26:04.301
I just want to show you very briefly[br]in case you can't get to Sandra's session,
0:26:04.301,0:26:06.226
but you definitely should go[br]to Sandra's session.
0:26:06.226,0:26:10.693
You actually can search in Commons[br]for a specific Wikibase statement.
0:26:11.353,0:26:15.333
I don't always remember the syntax,[br]but you have burn in your brain
0:26:15.333,0:26:19.893
and say, it's haswbstatement:P1343=
0:26:19.893,0:26:22.695
whatever-- basically, your last[br]two parts of the triple.
0:26:22.695,0:26:26.162
I always get haswb and wbhas mixed up.
0:26:26.162,0:26:28.183
I always get the colon[br]and the equals mixed up.
0:26:28.183,0:26:32.022
So just do it once, remember it,[br]and you'll get the hang of it.
0:26:32.022,0:26:34.772
But simple searches are must faster[br]than SPARQL queries.
0:26:34.772,0:26:36.478
So, if you can just look[br]for one statement,
0:26:36.478,0:26:38.392
boom, you'll get the results.
0:26:39.181,0:26:43.711
So, things like this, you can look[br]for symbolically or semantically,
0:26:43.711,0:26:47.511
things that depict[br]the Met museum, for example.
0:26:48.051,0:26:50.051
So, finally, community campaigns.
0:26:50.051,0:26:51.681
Richard has been a pioneer in this area.
0:26:51.681,0:26:54.071
So, once you have the Wikidata items,
0:26:54.071,0:26:57.050
they can actually assist[br]in creating Wikipedia articles.
0:26:57.050,0:26:59.785
So, Richard, why don't you tell us[br]a little bit about the Mbabel tool
0:26:59.785,0:27:01.009
that you created for this.
0:27:01.009,0:27:03.192
(Richard) Hi, can I get this on?
0:27:04.649,0:27:06.109
(Andrew) Oh, use [Joisey's].
0:27:06.109,0:27:08.319
(Richard) It's on, now. I'm good.
0:27:08.949,0:27:10.769
So, we had all this information[br]on Wikidata.
0:27:10.769,0:27:13.729
[inaudible] browsing data[br]on our evenings and weekends
0:27:13.729,0:27:15.649
to learn about art-- not everyone does.
0:27:15.649,0:27:19.319
We have quite a bit more people[br][inaudible] Wikipedia,
0:27:19.319,0:27:22.260
so how do we get this information[br]from Wikidata to Wikipedia?
0:27:22.260,0:27:25.289
One of the ways of doing this[br]is this so-called Mbabel,
0:27:25.289,0:27:28.069
which developed with the help[br]of a lot of people in [inaudible].
0:27:28.069,0:27:30.639
People like Martin and others.
0:27:31.689,0:27:34.659
So, basically to take[br]some basic art information,
0:27:34.659,0:27:37.688
and use it to populate[br]a Wikipedia article.
0:27:37.688,0:27:40.241
So, by who created this work,[br]who was the artist,
0:27:40.241,0:27:42.313
when it was created, et cetera.
0:27:42.313,0:27:44.626
The nice thing about this[br]is it can generate works.
0:27:44.626,0:27:46.210
We started with English Wikipedia,
0:27:46.210,0:27:48.608
but it's been developed[br]in other languages.
0:27:48.608,0:27:50.938
So, Portuguese Wikipedia,[br]our Brazilian friends
0:27:50.938,0:27:53.508
who've done a lot of work and taking it[br]to realms beyond art,
0:27:53.508,0:27:57.283
to stuff like elections[br]and political work as well.
0:27:57.283,0:28:01.128
And the nice thing about this[br]is we can query on Wikidata--
0:28:01.758,0:28:06.928
so different artists-- so for example,[br]we've done projects with Women in Red,
0:28:06.928,0:28:08.472
looking at women artists.
0:28:08.472,0:28:12.753
Projects related to Wiki Loves Pride,[br]looking at LGBT-identified artists,
0:28:12.753,0:28:14.073
African Diaspora Artists,
0:28:14.073,0:28:16.493
and a lot of different groups[br]and things of time periods,
0:28:16.493,0:28:19.293
different collections,[br]and also looking at articles
0:28:19.293,0:28:22.213
that have been and haven't been[br]translated to different languages.
0:28:22.213,0:28:24.923
So all of the articles that haven't[br]been translated to Arabic yet.
0:28:24.923,0:28:28.329
You need to find some interesting articles[br]maybe that are relevant to a culture
0:28:28.329,0:28:30.459
that haven't been translated[br]into that language yet.
0:28:30.459,0:28:32.659
We actually have a number of works[br]in the Met collection
0:28:32.659,0:28:35.199
that are in Wikipedias[br]that aren't in English yet,
0:28:35.199,0:28:37.259
because it's a global collection.
0:28:37.769,0:28:40.449
So, there are a lot of ways,[br]and hopefully, we can spread it around
0:28:40.449,0:28:44.709
of creating Wikipedia content, as well,[br]that is driven by these Wikidata items,
0:28:44.709,0:28:47.549
and that also maybe[br]can help spread the improvement
0:28:47.549,0:28:49.529
to Wikidata items, as well, in the future.
0:28:49.529,0:28:52.403
(Andrew) And there's a number of folks[br]here using Mbable already, right?
0:28:52.403,0:28:54.124
Who's using Mbable[br]in the room? Brazilians?
0:28:54.124,0:28:58.690
And also, if [Armin] is here,[br]we have our winner
0:28:59.165,0:29:03.146
of the Wikipedia Asia Month,[br]and Wiki Loves Pride contest.
0:29:03.146,0:29:05.720
So, thank you for joining,[br]and congratulations.
0:29:06.493,0:29:09.993
We'll have another Wiki Asia Month[br]campaign in November.
0:29:10.173,0:29:13.383
The way I like to describe it[br][inaudible]
0:29:13.383,0:29:15.443
It doesn't give you a blank page.
0:29:15.443,0:29:16.863
It gives you the skeleton,
0:29:16.863,0:29:18.962
which is really a much better[br]user experience
0:29:18.962,0:29:21.472
for edit-a-thons and beginners.
0:29:21.472,0:29:23.526
So, it's a lot of great work[br]that Richard has done,
0:29:23.526,0:29:25.841
and people are building on it,[br]which is awesome.
0:29:25.906,0:29:29.066
(woman 3) [inaudible] for some of them,[br]which is really nice.
0:29:29.066,0:29:30.376
Yeah, exactly.
0:29:30.376,0:29:32.956
(woman 3) [inaudible]
0:29:32.956,0:29:35.815
Right. We should have put a URL here.
0:29:35.815,0:29:38.196
(man 8) [inaudible]
0:29:38.196,0:29:40.055
Oh, that's right.[br]We have the link right here.
0:29:40.055,0:29:43.725
So if you click-- this is a Listeria list,[br]it's autogenerating all that for you.
0:29:43.725,0:29:46.205
And then, you click on the red link,[br]it'll create the skeleton,
0:29:46.205,0:29:47.491
which is pretty cool.
0:29:47.491,0:29:49.172
Alright, we're on the final stretch here.
0:29:49.172,0:29:51.990
The tool that we're going[br]to be announcing--
0:29:51.990,0:29:55.047
well, we announced a few weeks ago,[br]but only to a small set of folks,
0:29:55.047,0:29:57.038
but we're making a big splash here,
0:29:57.038,0:29:59.345
is the depiction tool[br]that we just created.
0:29:59.345,0:30:05.298
Wikipedia has shown that volunteer[br]contributors can add a lot of these things
0:30:05.298,0:30:06.681
that museums can't.
0:30:06.681,0:30:10.263
So, what if we created a tool[br]that could let you enrich
0:30:10.263,0:30:15.907
the metadata about artworks[br]in terms of the depiction information?
0:30:15.907,0:30:19.477
And what we did was we applied[br]for a grant from the Knight Foundation,
0:30:19.477,0:30:22.684
and we created this tool--[br]and is Edward here?
0:30:22.760,0:30:26.590
Edward is our wonderful developer[br]who in like a month, said,
0:30:26.590,0:30:28.050
"Okay, here's a prototype."
0:30:28.050,0:30:33.103
After we gave him a specification,[br]and it's pretty cool.
0:30:33.900,0:30:35.849
- So what we can do--[br]- (applause)
0:30:35.849,0:30:37.169
Thanks, Edward.
0:30:37.569,0:30:39.269
We're working within collections of items.
0:30:39.269,0:30:41.629
So, what we do, is we can[br]bring up a page like this.
0:30:41.629,0:30:44.789
It's no longer looking[br]at a Wikidata item with a tiny picture.
0:30:44.789,0:30:48.484
If we're working with what's depicted[br]in the image, we want the picture big.
0:30:48.484,0:30:51.201
And we don't really have tools[br]that work with big images.
0:30:51.201,0:30:53.348
We have tools that deal[br]with lexical and typing.
0:30:53.348,0:30:56.715
So one of the big things that Edward did[br]was made a big version of the picture,
0:30:56.715,0:30:58.739
scrape whatever you can[br]from the object page
0:30:58.739,0:31:00.633
from a GLAM organization,[br]give you context.
0:31:00.633,0:31:02.773
I can see dogs, children, wigwam.
0:31:02.773,0:31:05.782
These are things that direct the user[br]to add meaningful information.
0:31:05.782,0:31:09.024
You have some metadata[br]that's scraped from the site, too.
0:31:09.024,0:31:11.868
Teepee, Comanche--[br]oh, it's Comanche, not Navajo,
0:31:11.868,0:31:13.556
because I know the object page said that.
0:31:13.556,0:31:15.702
And you can actually start typing[br]in the field, there.
0:31:15.702,0:31:17.628
And the cool thing is that[br]it gives you context,
0:31:17.628,0:31:19.566
It doesn't just match anything[br]to Wikidata,
0:31:19.566,0:31:23.107
it first matches things that have already[br]been used in other depiction statements.
0:31:23.107,0:31:25.456
Very simple thing,[br]but what a godsend it is
0:31:25.456,0:31:27.166
for folks who have tried this in the past.
0:31:27.166,0:31:29.116
Don't give me everything[br]that matches teepee.
0:31:29.116,0:31:33.321
Show me what other paintings[br]have used teepee in the past.
0:31:33.355,0:31:36.175
So, it's interactive, context-driven,[br]statistics-driven,
0:31:36.175,0:31:37.936
by showing you what is matched before.
0:31:37.936,0:31:40.336
And the cool thing is once you're done[br]with that painting,
0:31:40.336,0:31:42.196
you can start to work in other areas.
0:31:42.196,0:31:44.936
You want to work within the same artist,[br]the collection, location,
0:31:45.876,0:31:47.295
other criteria here.
0:31:47.295,0:31:49.146
And you can even browse[br]through the collections
0:31:49.146,0:31:51.582
of different organizations,[br]just work on their paintings.
0:31:51.582,0:31:53.670
So, we wanted people[br]to not live in Wikidata--
0:31:53.670,0:31:56.307
kind of onesy-twosies with items,[br]but live in a space
0:31:56.307,0:31:59.232
where you're looking at artworks[br]in collections that make sense.
0:31:59.683,0:32:01.792
And then, you can actually[br]look through it visually.
0:32:01.792,0:32:04.237
It kind of looks like Krotos[br]or these other tools,
0:32:04.237,0:32:07.726
but you can actually live edit[br]on Wikidata at the same time.
0:32:07.726,0:32:09.104
So, go ahead and try it out.
0:32:09.104,0:32:10.609
We've only have 14 users,
0:32:10.609,0:32:14.667
but we've had 2,100 paintings worked on,[br]with 5,000 plus depict statements.
0:32:14.667,0:32:16.126
That's pretty good for 14.
0:32:16.126,0:32:18.119
So, multiply that by 10--
0:32:18.119,0:32:20.515
imagine how many more things[br]we could do with that.
0:32:20.515,0:32:23.797
So, you can go ahead and go[br]to art.wikidata.link and try out the tool.
0:32:23.797,0:32:26.594
It uses OLAF authentication,[br]and you're off to the races.
0:32:26.594,0:32:29.187
And it should be very natural[br]without any kind of training
0:32:29.187,0:32:31.782
to add depiction statements to artworks.
0:32:31.837,0:32:35.170
But you can put any object.[br]We don't restrict the object right now.
0:32:35.170,0:32:37.278
So, you could put any Q number
0:32:38.468,0:32:41.208
to edit this content if you want.
0:32:41.275,0:32:44.645
But we primarily stick with paintings[br]and 2D artworks, right now.
0:32:46.184,0:32:49.405
Okay. You can actually look[br]at the recent changes
0:32:49.405,0:32:52.175
and see who's made edits recently to that.
0:32:52.815,0:32:54.855
Okay? Okay, so we're going[br]to wind it down.
0:32:54.855,0:32:58.386
Ooh, one minute, then we'll do some Q&A.
0:32:58.915,0:33:03.081
So, the final thing that I think[br]is useful for museum types especially,
0:33:03.081,0:33:07.307
is there's a very famous author[br]named Nina Simon in the museum world,
0:33:07.307,0:33:11.204
where she likes to talk about[br]how do we go from users,
0:33:11.204,0:33:14.968
or I guess your audience,[br]contributing stuff to your collections
0:33:14.968,0:33:18.004
to collaborating around content,[br]to actually being co-creative
0:33:18.004,0:33:19.714
and creating new things.
0:33:19.714,0:33:20.984
And that's always been tough.
0:33:20.984,0:33:24.154
And I'd like to argue that Wikidata[br]is this co-creative level.
0:33:24.154,0:33:26.914
So, it's not just uploading[br]a file to Commons,
0:33:26.914,0:33:28.234
which is contributing something.
0:33:28.234,0:33:31.194
It's not just editing an article[br]with someone else, which is collaborative.
0:33:31.194,0:33:34.833
But we are now seeing these tools[br]that let you make timelines,
0:33:34.833,0:33:36.133
and graphs, and bubble charts.
0:33:36.133,0:33:38.833
And this is actually the co-creative part[br]that's really interesting.
0:33:38.833,0:33:40.353
And that's what Wikidata provides you.
0:33:40.353,0:33:42.235
Because suddenly,[br]it's not language dependent--
0:33:42.235,0:33:45.146
we've got this database[br]that's got this rich information in it.
0:33:45.946,0:33:48.606
So, it's not just pictures, not just text,
0:33:48.606,0:33:50.522
but it's all this rich multimedia
0:33:50.522,0:33:52.607
that we have the opportunity to work on.
0:33:52.607,0:33:55.851
So, this is just another example[br]of this connected graph
0:33:55.851,0:33:57.389
that you can take a look at later on
0:33:57.389,0:33:59.860
to show another example[br]of The Death of Socrates,
0:33:59.860,0:34:02.312
and the different themes[br]around that painting.
0:34:03.252,0:34:05.653
And it's really easy[br]to make this graph yourself.
0:34:05.653,0:34:08.172
So again, another scary graphic[br]that only makes sense
0:34:08.172,0:34:09.822
for Wikidata folks, like you.
0:34:09.822,0:34:13.682
You just give it a list of Wikidata items,[br]and it'll do the rest, that's it.
0:34:14.102,0:34:15.662
You'll give the list.
0:34:15.705,0:34:17.664
Keep all this code the same.
0:34:17.664,0:34:21.364
So, fortunately, Martin and Lucas[br]helped do all this code here.
0:34:21.364,0:34:23.864
Just give it a list of items[br]and the magic will happen.
0:34:23.864,0:34:25.624
Hopefully, it won't blow up your computer,
0:34:25.624,0:34:28.755
because you're putting in[br]a reasonable number of items there.
0:34:28.755,0:34:31.593
But as long as you have the screen space,[br]it'll draw the graph,
0:34:31.593,0:34:33.283
which is pretty darn cool.
0:34:33.283,0:34:37.223
And then, finally, two tools--[br]I realized at 2 a.m. last night
0:34:37.223,0:34:39.744
a few people said,[br]"I didn't know about these tools."
0:34:39.744,0:34:41.343
And you should know about these tools.
0:34:41.343,0:34:44.613
So, one is Recoin, which shows you[br]the relative completeness of an item
0:34:44.613,0:34:46.773
compared to other items[br]of the same instance.
0:34:46.773,0:34:49.473
And then, Cradle, which is a way[br]to have a forms-based way
0:34:49.473,0:34:50.693
to create content.
0:34:50.693,0:34:52.453
So, these are very useful for edit-a-thons
0:34:52.453,0:34:54.753
where if you know that[br]you're working with just artworks,
0:34:54.753,0:34:57.553
don't just let people create items[br]with a blank screen.
0:34:57.553,0:35:00.275
Give them a form to fill out[br]to start entering in information
0:35:00.275,0:35:01.818
that's structured.
0:35:01.818,0:35:04.588
And then, finally, we've gone[br]through some of this, already.
0:35:06.268,0:35:09.539
This is my big chart that I love[br]to get people's feedback on.
0:35:09.539,0:35:14.296
How do we get people[br]across the chasm to be in this space?
0:35:14.328,0:35:16.839
We have a lot of folks who, now,[br]can do template coding,
0:35:16.839,0:35:20.040
spreadsheets, QuickStatements,[br]SPARQL queries, and then we got--
0:35:20.935,0:35:24.259
how do we get people to this side[br]where we have Python
0:35:24.259,0:35:26.694
and the things that can do more[br]sophisticated editing.
0:35:26.694,0:35:28.625
It's really hard[br]to get people across this.
0:35:28.625,0:35:30.785
But I would like to say[br]it's hard to get people across,
0:35:30.785,0:35:32.847
but the content and the technology[br]is not that hard.
0:35:32.847,0:35:35.380
We actually need more people[br]to learn about regular expressions.
0:35:35.380,0:35:38.307
And once you get some kind[br]of experience here,
0:35:38.307,0:35:41.830
you'll find that this is a wonderful world[br]that you can learn a lot in,
0:35:41.830,0:35:44.700
but it does take some time[br]to get across this chasm.
0:35:44.829,0:35:46.289
Yes, James.
0:35:46.289,0:35:52.148
(James) [inaudible]
0:35:53.127,0:35:57.192
No, what it means is that the graph[br]is not necessarily accurate
0:35:57.192,0:35:59.178
in terms of its data points.
0:35:59.308,0:36:03.427
But what it means-- I guess[br]it's more like this is a valley.
0:36:03.786,0:36:06.716
It's like we need to get people[br]across this valley here.
0:36:06.716,0:36:10.146
(woman 4) [inaudible]
0:36:10.146,0:36:11.546
I would say this is the key.
0:36:11.546,0:36:16.296
If we can get people who know this stuff,[br]but can grok this stuff,
0:36:16.296,0:36:17.918
it gets them to this stuff.
0:36:17.918,0:36:19.668
Does that make sense? Yeah.
0:36:19.668,0:36:24.155
So, my vision for the next few years,[br]we can get better training
0:36:24.155,0:36:27.516
in our community to get people[br]from batch processing,
0:36:27.516,0:36:29.847
which is pretty much what this is,[br]to kind of intelligent--
0:36:29.847,0:36:32.726
I wouldn't say intelligent,[br]but more sophisticated programming,
0:36:32.726,0:36:35.486
that would be a great thing,[br]because we're seeing this is a bottleneck
0:36:35.486,0:36:37.846
to a lot of the stuff[br]that I just showed you up there.
0:36:37.846,0:36:39.086
Yes.
0:36:39.135,0:36:42.105
(man 9) [inaudible]
0:36:42.105,0:36:45.984
Okay, wait, you want to show me something,[br]show me after the session, does that work?
0:36:45.984,0:36:47.584
Okay. Yes, Megan.
0:36:47.584,0:36:50.804
- (Megan) Can I have a microphone?[br]- Microphone, yes.
0:36:50.834,0:36:54.528
- (Megan) [inaudible][br]- Yeah.
0:36:55.316,0:36:56.636
And we have lunch after this,
0:36:56.636,0:36:59.006
so if you want to stay[br]a little bit later, that's fine, too.
0:36:59.006,0:37:01.009
- [inaudible][br]- We're already at lunch break? Okay.
0:37:01.009,0:37:03.094
(Megan) So, thank you so much[br]to both you and Richard
0:37:03.094,0:37:04.799
for all the work you're doing at the Met.
0:37:04.799,0:37:07.027
And I know that you're[br]very well supported in that.
0:37:07.027,0:37:09.100
(mic feedback)[br]I don't know what happened there.
0:37:09.100,0:37:15.071
For the average volunteer community,[br]how do you balance doing the work
0:37:15.071,0:37:19.124
for the cultural heritage organization[br]versus training the professionals
0:37:19.124,0:37:21.792
that are there to do that work?
0:37:21.792,0:37:24.412
Where do you find the balance[br]in terms of labor?
0:37:25.672,0:37:26.962
It's a good question.
0:37:27.397,0:37:30.467
(Megan) One that really comes up,[br]I think, with this as well.
0:37:30.467,0:37:33.158
- With this?[br]- (Megan) Yeah, and with building out...
0:37:33.187,0:37:36.277
where we put efforts in terms[br]of building out competencies.
0:37:36.333,0:37:39.398
Yeah. I don't have a great answer for you,[br]but it's a great question.
0:37:39.398,0:37:40.658
(Megan) Cool.
0:37:40.658,0:37:43.580
(Richard) There are a lot[br]of tech people at [inaudible]
0:37:43.580,0:37:46.158
who understand this side of the graph,[br]and don't understand it--
0:37:46.158,0:37:48.878
the people in [inaudible][br]who understand this part of the graph,
0:37:48.878,0:37:50.658
and don't understand[br]this part of the graph.
0:37:50.658,0:37:53.928
So, the more we can get Wikimedians[br]who understand some of this,
0:37:53.928,0:37:57.748
with some tech professionals at museums[br]who understand this,
0:37:57.748,0:37:59.408
then that makes it a little bit easier--
0:37:59.408,0:38:01.968
and hopefully, as well as[br]training up Wikimedians,
0:38:01.968,0:38:05.587
we can also provide some guidance[br]and let the museums [inaudible]
0:38:05.587,0:38:07.438
to take care of themselves[br]in the [inaudible].
0:38:07.496,0:38:09.285
Yeah, that's a good point.
0:38:09.285,0:38:11.961
How many people here know[br]what regular expressions are?
0:38:11.961,0:38:13.216
Raise your hand.
0:38:13.216,0:38:17.397
Okay, so how many people are comfortable[br]specifying a regular expression?
0:38:17.397,0:38:19.267
So, yeah, we need more work here.
0:38:19.267,0:38:20.771
(laughter)
0:38:20.771,0:38:23.199
(man 10) I want to suggest that--
0:38:24.648,0:38:28.575
maybe not getting[br]every Wikidata practitioner,
0:38:28.575,0:38:33.607
or institution practitioner[br]to embrace Python programming is the way.
0:38:33.717,0:38:39.657
But as Richard just said, finding more[br]bridging people-- people like you--
0:38:39.657,0:38:41.137
who speak both--
0:38:41.137,0:38:44.042
who speak Python,[br]but also speak GLAM institution--
0:38:44.812,0:38:48.392
to help the GLAM's own[br]technical department, which may not--
0:38:49.233,0:38:51.951
they know Python,[br]they don't know this stuff.
0:38:52.640,0:38:54.186
That's, I think, what's needed.
0:38:54.235,0:38:59.034
People like you, people like me,[br]people who speak both of these jargons
0:38:59.034,0:39:01.835
to help make the connections,[br]to document the connections.
0:39:01.835,0:39:03.344
You're already doing this, of course.
0:39:03.344,0:39:05.534
You share your code, et cetera,[br]you're doing tutorials.
0:39:05.534,0:39:07.044
But we need more of this.
0:39:07.044,0:39:09.223
I'm not sure we need[br]to make everyone programmers.
0:39:09.223,0:39:10.612
We already have programmers.
0:39:10.612,0:39:12.332
We need to make them understand
0:39:12.332,0:39:14.612
the non-programming[br]material they need to--
0:39:14.612,0:39:15.782
I think that's a great point.
0:39:15.782,0:39:18.062
We don't need to make everyone[br]highly proficient in this,
0:39:18.062,0:39:20.312
but we do need people[br]knowledgeable to say that,
0:39:20.312,0:39:23.004
"Yeah, we can ingest 400 thousand rows[br]and do something with it."
0:39:23.004,0:39:25.284
Whereas, if you're stuck[br]on this side, you're like,
0:39:25.284,0:39:27.444
"400 thousand rows[br]sounds really big and scary."
0:39:27.444,0:39:30.364
But if you know that it's possible,[br]you're like, "No problem."
0:39:30.364,0:39:32.284
400 thousand is not a problem.
0:39:32.284,0:39:35.414
(woman 5) I would just like to chime in[br]a little bit in that
0:39:35.414,0:39:39.674
that there may be countries and areas[br]where you will not find a GLAM
0:39:39.674,0:39:44.404
with any skilled technologists.
0:39:44.434,0:39:47.834
So, you will have to invent[br]something there in the middle.
0:39:48.502,0:39:49.634
That's a good point.
0:39:49.778,0:39:51.378
Any questions? Sandra.
0:39:55.648,0:39:57.807
(Sandra) Yeah, I just wanted[br]to add to this discussion.
0:39:57.807,0:40:01.656
Actually, I've seen some very good cases[br]where it indeed has been successful
0:40:01.656,0:40:05.476
to train GLAM professionals to work[br]with this entire environment,
0:40:05.476,0:40:09.276
and where they've done fantastic jobs,[br]also at small institutions.
0:40:10.046,0:40:14.986
It also requires that you have chapters[br]or volunteers that can train the staff.
0:40:15.163,0:40:17.513
So, it's really like a bigger environment.
0:40:18.192,0:40:22.044
But I think that's a model[br]that if we can manage to make that grow,
0:40:22.044,0:40:24.263
it can scale very well, I think.
0:40:24.673,0:40:25.693
Good point.
0:40:25.693,0:40:30.896
(woman 5) [inaudible]
0:40:32.029,0:40:34.217
Sorry, just noting that we don't have
0:40:34.217,0:40:37.820
any structured trainings[br]right now for that.
0:40:38.209,0:40:42.498
We might want to develop those,[br]and that would be helpful.
0:40:42.608,0:40:44.408
We have been doing that for education
0:40:44.408,0:40:47.488
in terms of teaching people[br]Wikipedia and Wikidata.
0:40:47.488,0:40:50.008
It's just a matter of taking it[br]one step further.
0:40:50.528,0:40:52.168
Right. Stacy.
0:40:54.518,0:40:56.988
(Stacy) Well, I'd just like to say[br]that a lot of professionals
0:40:56.988,0:41:02.006
who work in this area of metadata[br]have all these skills already.
0:41:02.006,0:41:08.966
So, I think part of it is just proving[br]the value to these organizations,
0:41:08.966,0:41:13.126
but then it's also tapping[br]into professional associations who can--
0:41:13.195,0:41:16.745
or ways of collaborating within[br]those professional communities
0:41:16.745,0:41:21.374
to build this work, and the documentation[br]on how to do things
0:41:21.374,0:41:23.234
is really, really important,
0:41:23.234,0:41:27.454
because I'm not sure about the role[br]of depending on volunteers,
0:41:27.454,0:41:32.294
when some of this work is actually work[br]GLAM organizations do anyway.
0:41:32.395,0:41:35.355
We manage our collections[br]in a variety of ways through metadata,
0:41:35.355,0:41:37.126
and this is actually one more way.
0:41:37.126,0:41:40.495
So, should we also not be thinking[br]about ways to integrate this work
0:41:40.495,0:41:43.946
into a GLAM professional's regular job.
0:41:43.985,0:41:46.125
And then that way you're generating--
0:41:46.125,0:41:48.885
and when you think[br]about sustainability and scalability,
0:41:48.885,0:41:53.426
that's the real trick to making this[br]sustainable and both scalable,
0:41:53.745,0:41:58.695
is that once this is the regular[br]work of GLAM folks,
0:41:58.695,0:42:00.885
we're not worried as much about this part,
0:42:00.885,0:42:03.503
because it's just turning[br]that little switch to get this
0:42:03.503,0:42:05.763
to be a part of that work.
0:42:05.863,0:42:08.063
Right. Good point. [Shani]?.
0:42:11.603,0:42:13.229
(Shani) You're absolutely right.
0:42:13.229,0:42:16.122
But I want to echo what you said before.
0:42:16.152,0:42:21.566
And yes, Susana-- this might work[br]for more privileged countries
0:42:22.082,0:42:25.042
where they have money,[br]they have people doing it.
0:42:25.682,0:42:29.042
It doesn't work for places[br]that are still developing,
0:42:29.042,0:42:32.282
that don't have resources--[br]they don't have all of that.
0:42:32.592,0:42:36.832
And they can barely do[br]what they need to do.
0:42:36.886,0:42:41.066
So, it's difficult for them, and then,[br]the community is really helpful.
0:42:41.906,0:42:45.495
These are the cases where the community[br]can have a huge impact actually,
0:42:45.985,0:42:50.349
working with the GLAMS,[br]because they can't do it all
0:42:50.979,0:42:52.296
as part of their jobs.
0:42:52.834,0:42:55.034
So, we need to think about that as well.
0:42:55.053,0:42:58.223
And having these examples,[br]actually, is hugely important,
0:42:58.223,0:43:00.763
because it's helping[br]to still convince them,
0:43:00.763,0:43:05.842
that it's critical to invest in it[br]and to work with volunteers,
0:43:05.842,0:43:09.082
so, with non-professionals[br]of sorts, to get there.
0:43:10.003,0:43:12.650
I can imagine a future where[br]you don't have to know all this code.
0:43:12.650,0:43:14.379
These would just be[br]kind of like Lego bricks
0:43:14.379,0:43:15.801
you can slap together,
0:43:15.801,0:43:18.761
saying, "Here's my database.[br]Here's the crosswalk. Here's Wikidata,"
0:43:18.761,0:43:21.311
and just put it together,[br]and you don't have to even code,
0:43:21.311,0:43:23.835
you just have to make sure[br]the databases are in the right place.
0:43:23.835,0:43:25.375
Yep. Okay.
0:43:26.747,0:43:28.705
(man 11) Sorry. [inaudible]
0:43:28.705,0:43:34.025
I think if I would have done this project,[br]I'd probably have done it the same way.
0:43:34.025,0:43:36.146
So, I think that's maybe a good sign.
0:43:36.146,0:43:39.725
I was wondering how did[br]the whole financing work of this project?
0:43:39.725,0:43:40.840
How did the-- I'm sorry?
0:43:40.840,0:43:43.255
The financing of this project work.
0:43:43.795,0:43:45.755
- The financing?[br]- Yeah, the money.
0:43:46.425,0:43:47.505
That's a good question.
0:43:47.505,0:43:49.185
Well, so, there are different parts of it.
0:43:49.185,0:43:53.073
So, the Knight grant funded[br]the Wiki Art Depiction Explorer.
0:43:53.198,0:43:56.928
But I, for the last, maybe what--[br]nine months--
0:43:56.928,0:43:58.768
I've been their Wikimedia strategist.
0:43:58.768,0:44:01.618
So, I've been on[br]since February of this year.
0:44:01.618,0:44:04.818
So, that's pretty much they're paying[br]for my time to help with their--
0:44:04.818,0:44:07.968
not only the upload of their collections,[br]but developing these tools, as well.
0:44:07.968,0:44:11.659
- (Richard) So the Met's paying you?[br]- Yeah, that's right.
0:44:11.762,0:44:14.894
(Richard) The grant, at least part[br]of it has come from--
0:44:14.894,0:44:16.959
There was a grant for Open Access.
0:44:16.959,0:44:20.176
And this is under that campaign[br]and with the digital department.
0:44:20.176,0:44:24.297
So, working as contractors throughout[br]the Open Access campaign for the Met.
0:44:27.948,0:44:30.116
(man 12) I'm sorry.[br]I guess before you were hired,
0:44:30.116,0:44:31.313
and before there was a grant,
0:44:31.313,0:44:33.780
there was probably a lot[br]of volunteer work done to make sure--
0:44:33.780,0:44:35.303
Richard did a lot of work before that.
0:44:35.303,0:44:37.219
And then, Wikimedia New York[br]did a lot of work,
0:44:37.219,0:44:38.927
but it was kind of in bursts.
0:44:38.927,0:44:41.045
It wasn't as comprehensive[br]as we're talking about now
0:44:41.045,0:44:45.915
in terms of having-- making sure[br]those two layers are complete
0:44:45.915,0:44:47.310
in Wikidata.
0:44:48.640,0:44:50.543
Alright, yeah. I think that's it.
0:44:50.543,0:44:53.843
So, I'm happy to talk after lunch,[br]or after the break, if you want.
0:44:54.683,0:44:56.223
Okay. Thank you.
0:44:56.223,0:44:59.197
(applause)