#rC3 - Wikidata for (Data) Journalists

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Herald: Wikidata for (Data) Journalists
by Elizabeth Giesemann.

Elisabeth Giesemann: So our agenda for
today is that we will have a look on key

points of data journalism. We will quickly
explain what Wikidata is, what tools you

can use inside of Wikidata for data
visualization, what other third party

tools are there for your research? Then we
have a look at critical research done with

Wikidata. And finally, we have a critical
look on the data of Wikidata itself.

Key points of data journalism are that you
want to interview a dataset, so you want

to find connections, correlations and
causalities behind the data. Also, you

want to visualize the data in a compelling
way and you want to write your own story.

You want to find a new spin
and a new look on- at the facts

and all of these things
you can do with Wikidata.

At Wikimedia Deutschland, we want
to support evidence-based reporting

that's why we want to support you
in using Wikidata.

Also data journalism helps you to tailor
your story to the users or your readers.

Data journalism helps you to create visual
storytelling instead of walls of text.

And this, again, helps you to convey facts
faster and way more easy

and that makes your story
way more inclusive.

So how do you get to a story
with Wikidata?

You want to find and recognize patterns
in a dataset, you can search for geographical

data, you can search for similarities and
differences in the data, and you can also

search for missing data, because that also
exists in Wikidata. You can visualize your

findings with the tools that you find in
the Wikidata Query Service. And what's

most important is you can connect to the
Wikidata community and find people who are

working on a similar subject or have a
similar research- research question to the

one that you have. So I included this
visualization to show you that data is

only the beginning of your story and the
path that you will take. We want you to

use the data in Wikidata for- to create a
compelling story and therefore contribute

value and your idea about what's in the
data. Because data is a lot, but it's not

everything, as we've seen in the last
month, many people aren't convinced by

facts. Also, there is a lack of time and
there is a lack of data- data literacy in

our society. It's not always easy to
understand the complexity of historical

events and developments, to understand the
complexity of medical data or demographic

changes. So it is important to have a
storytelling aspect to your data, have

good visualizations and an easy to
understand approach to convey the

significance of your data and your story.
And finally, it is important to remain

transparent and clear about the use and
analysis of the data. So what is Wikidata?

Wikidata is a free linked database that
can be read and edited by both humans and

machines, so it is a database of linked
open data. It- that means that the data

doesn't just sit there in tables. It can
be connected and combined with other data,

found on Wikidata. As such, it is a
realization of the semantic web as dreamt

by Tim Berners-Lee and also Wikidata won a
prize for its realization of the semantic

web. We just celebrated Wikidata- data's
8th birthday. It currently holds 90

million items and has 44,000 active users
and contributors, which makes it the most

edited Wikimedia project. It was initially
used to or thought of to support the

projects of the other projects of the
Wikimedia ecosystem and seen as a central

storage for the structured data of the
sister of projects like Wikivoyage,

Wikisource and the most famous Wikimedia
project, Wikipedia. But it also has

another function, which means- which is to
provide free and open data to the

Internet, and that became really huge. As
already said, we now have more than 80- 90

million data items on Wikidata. A
colleague of mine created this map and you

can see here the geolocation data that is
in Wikidata and we are very proud that

it's distributed all over the world but
it's also- we also take it with a grain of

salt, because as you can see, it's very
bright in Europe and on the east and west

coasts of the US, but there are very dark
spots where we can't record the knowledge

in the same way as we do in our Western
societies and that brings us to the

question of what is knowledge equity and
how can we actually best serve everybody

in our global society? So how does it
work? Wikidata items, which are real

things or concepts in the real world, like
Berlin, Barack Obama, helium, and these

items are identified with an ID, the QID.
So Q76 or Q... I don't, I can't read the

number now, so these items have labels,
descriptions, aliases and sitelinks.

Labels, that means it's described in all
of the languages that Wikidata holds

currently, those are around 300.
Descriptions are forms to describe what

the item holds and aliases, sometimes one
item has several names, etc, etc. An item

also has properties, those are used to
label to data like a person is born

somewhere, its date of birth or death or
the location of a specific building.

Statements hold informations in
properties, so P47 shares the border with

another, like, country or the population.
Statements also have qualifiers to expand

the information and then also they have
references which is very important because

for scientific research, you want to have
those references. So here we see again our

item, Berlin, Q64. The property is the
population of 3.7 million. So what's new

about research with Wikidata is that you
can ask your own questions. Before, you

would go to a library and some- the
librarians - librarians are awesome, but

they would give you books with specific
facts in them and you would consume them

and try to use them for your research. At
Wikidata you can ask very specific

questions that nobody else came up with
before. So for your research, you want to

do your own Wikidata queries, that's what
we have the Wikidata Query Service for.

The good news is that you don't have to
learn Python or R or become a data

scientist, but you want to learn a bit of
SPARQL. We included a few resources here

in this presentation and there's also
going to be a talk given by my colleague

Lucas on the 29th on how to query Wikidata
with SPARQL. We also have a guided tour on

Wikidata on our website which I can
recommend. OK, so, um, as said, once you

queried your data, you can visualize your
results for more compelling storytelling

and there are several ways of doing this
and I'm going to show you some of this

just to give you an idea. You could, for
instance, ask the query service to show

you airports that are named after a person
and color code them according to their

gender. Gender of the person, not the
airport, obviously. You can ask the query

service, show me everything connected to
the item Berlin. You can ask it to show

you the population of the countries that
are bordering Germany and how it

developed. You can also ask the query
service to show you the most common cause

of death among noble people. Or here it
shows you an- an historical overview of

space probes. Or all of the children and
grandchildren of Genghis Khan. So we had a

look on the visualizations inside of
Wikidata's Query Service, but there are

also tools that use Wikidata's data for
their own visualizations. And I'm going to

show you some of them now. So here is
Histropedia, which makes time beams of

historical events using data from
Wikidata. This is Inventaire. Basically,

it lets you create your own private
library and then uses the data from

Wikidata to describe the publications.
Here is "Ask me anything". That's done by

different researchers in Europe, and it
lets you pose questions in natural

language to Wikidata so you don't have to
use the query service. That's a way that

to use Wikidata that's also used by a lot
of voice assistants like Siri and Alexa.

And here you have Scholia, which is
basically a platform for scientific

publications that are published under open
access and collected, and it can answer

your questions like who published what
paper, with whom, who and when or who

wrote the first paper on COVID, when was
it published, etc. And here we have "Sum

of All Paintings". Basically, it's a
database that creates all of the paintings

in the world and lists their metadata so
you can combine it in your own specific

way. So I showed you a couple of examples,
what you could do, and I want to hint at

other researchers who did great stuff with
Wikidata and used it for very cool

storytelling. If my slides work, OK, here
we go. So, um, "Women's representation and

voice in media coverage of the coronavirus
crisis", that's the- that's a study done

by a researcher called Laura Jones
regarding the representation of female

experts within the coverage of
coronavirus. It uses evaluations of

Wikipedia and Wikidata to show- to show
how much representation was there, of

female experts. And, as we see, it's not a
lot. Finally, there is another great

example I want to tell you about, it's a
project called Enslaved.org. It's a linked

open data platform based on Wikibase,
which is the software behind Wikidata and

it basically shows or it collects and
connects data related to the transatlantic

slave trade. So, people who suffered under
the slave trade and the records that were

done by the people active in this slave
trade, those data is collected. It has

been collected in several databases and
Enslaved build one large database to

connect them and rebuild the stories,
which I think is a really great idea to or

really great way to humanize people who
have been dehumanized with data. Like you

can see here, they collect- they collect
data from newspapers and from the

slaveholders to recount a story of
individuals. So finally, I also want to

talk to you about one thing in Wikidata
that is always on our minds, which is that

Wikidata is not perfect. I highly
recommend the talk by Os Keyes

"Questioning Wikidata" in which it is
explained that all classification systems

are inherently dangerous and Wikidata is a
large encyclopedic wiki classification

system which makes choices, ethical and
political choices, about what is notable,

about how to categorize information. And
these choices, they reduce complexity and

reduce also specific forms of- of history,
like oral history. This reduction has

consequences. As you know, Wikidata is
used by many programs, apps, voice

assistance and what- what and how we store
information in Wikidata really matters. So

we ask ourselves, what is encyclopedic
knowledge? And how can we organize it in a

more inclusive way? Encyclopedic knowledge
is a Western concept, and we can and must

do better than just use our own Western
view to organize the world. But then also

the wiki principle applies, we have a huge
community behind Wikidata that helps us to

make these decisions, and you can also
become a part of this by researching

Wikidata, using it for your work and also
contributing your research. So once again,

I want to tell you, you can use Wikidata
as a tool for your storytelling. Wikidata

can help you find connections between
data. Wikidata can help you find- can help

you build visualization in its query
service. You can ask questions about

historical data correlations more
critically than you could- than you could

before. And- but there are also downsides
to- downsides to Wikidata because it is an

encyclopedic way of organizing Western
knowledge. So this was only a start. I'm

looking forward to our Q&A session now and
if you have further questions, concerns or

have ideas, you can contact me and my
colleagues and you can also contact me

individually. Thank you.

Herald: Hello and welcome to Elizabeth.
Thank you very much for your interesting

talk. That was a very great introduction.
Elisabeth: Hi. Yeah, thanks for having me.

I'm happy that I was able to talk a bit
about Wikidata and how you could do

storytelling with it. I wanted to add
that, obviously, you can ask me questions

now, but also I want to hint at the great
introduction of Wikidata that one of my

colleagues gave. Yesterday, two of my
colleagues, which is already online, and

tomorrow there will be a query service
workshops where you can learn a bit more

in-depth how to query Wikidata.
Herald: Yeah, that's a very good hint.

There's actually there's two questions in
the chat right now. The first one is, are

your slides going to be published because
people are interested in your links to the

tutorials, obviously.
Elisabeth: Yes, that was, uh, I asked

before, I think the talk will be published
and the slides. Is there a Wikipaka board

where I can put it? Otherwise, I can also
put a link on our Twitter account,

Wikimedia Deutschland. And yeah...
Herald: I think Twitter for now would

probably be the best idea, I actually have
to check on the Wikipaka board, but we

will let you know where you can find
everything.

Elisabeth: I put it on the Wikimedia
Deutschland Twitter. It's @wmde I think

Herald: we will also retweet it
obviously. You will find it, I promise.

Elisabeth: OK.
Herald: There's another question. What

resources would you recommend for self-
studying the writing of queries for

query.wikidata.org?
Elisabeth: Mhm. Um, I put some links in

the- in the slides. There is... yeah, we
have, like, a few tutorials on Wikidata.

There was also a couple of months ago, a
very nice and very easy tutorial published

by Wikimedia Israel. And I- so we didn't
do it, but I can recommend it, it's a very

low key introduction to your first
queries.

Herald: OK. We will also publish that
somehow. I have a question for you as

well. You mentioned that Wikidata is like
a great way for meeting other people that

are working on similar topics. So is there
some kind of like greater community of

journalists using Wikidata?
Elisabeth: So far, the community is mostly

research based. That's also why we wanted
to reach out here. So I would recommend

getting in touch with the community on
there regarding the research topics that

you have. And you can also get in touch
with us and we connect you. I have a noise

in my ear, but I hope it's only me.
Herald: Well, I don't have it, so it might

just be you, but I feel like there might
be also an echo on the stream, that's what

people on the chat are saying.
Elisabeth: Oh, OK.

Herald: So I don't have any other questions
in the chat and since there seems to be an

echo on the stream, I don't want to annoy
people any further. So I would suggest for

everyone who has further questions to you
that you can meet in our Big Blue Button

meetup room that I will be posting in the
chat right now and we will continue our

program here at 2:20 with another talk
about Flutter by "The one with the braid",

so I'm saying bye for now.
Elisabeth: Thanks, bye.

Herald: Bye.

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