I work as a teacher at the University of Alicante,
where I recently obtained my PhD on data libraries and linked open data.
And I'm also a software developer
at the Biblioteca Virtual Miguel de Cervantes.
And today, I'm going to talk about data quality.
Well, those are my colleagues at the university.
And as you may know, many organizations are publishing their data
or linked open data--
for example, the National Library of France,
the National Library of Spain, us, which is Cervantes Virtual,
the British National Bibliography,
the Library of Congress and Europeana.
All of them provide a SPARQL endpoint,
which is useful in order to retrieve the data.
And if I'm not wrong,
the Library of Congress only provide the data as a dump that you can't use.
When we publish our repository as linked open data,
my idea was to be reused by other institutions.
But what about if I'm an institution who wants to enrich their data
with any data from other data libraries.
Which data set should I use?
Which data set is better in terms of quality?
The benefits of the evaluation of data quality in libraries are many.
For example, methodologies can be improved in order to include new criteria,
in order to assess the quality.
And also, organizations can benefit from best practices and guidelines
in order to publish their data as linked open data.
What do we need in order to assess the quality?
Well, obviously, a set of candidates and a set of features.
For example, do they have a SPARQL endpoint,
do they have a web interface, how many publications do they have,
how many vocabularies do they use, how many Wikidata properties do they have,
and where can I get those candidates?
I use LOD Cloud--
but when I was doing this slide, I thought about using Wikidata
in order to retrieve those candidates.
For example, getting entities of type data library,
which has a SPARQL endpoint.
You have here the link.
And I come up with those data libraries.
The first one uses bibliographic ontology as main vocabulary,
and the others are based, more or less, on FRBR,
which is a vocabulary published by IFLA.
And this is just an example of how we could compare
data libraries using bubble charts on Wikidata.
And this is just an example comparing how many Wikidata properties
are per data library.
Well, how can we measure quality?
There are different methodologies,
for example, FRBR 1,
which provides a set of criteria grouped by dimensions,
and those in green are the ones that I found--
that I could assess by means of Wikidata.
And we also find that we could define new criteria,
for example, a new one to evaluate the number of duplications in Wikidata.
We use those properties.
And this is an example of SPARQL,
in order to count the number of duplicates property.
And about the results, while at the moment of doing this study,
not the slides, there was no property for the British National Bibliography.
They don't provide provenance information,
which could be useful for metadata enrichment.
And they don't allow to edit the information.
So, we've been talking about Wikibase the whole weekend,
and maybe we should try to adopt Wikibase as an interface.
And they are focused on their own content,
and this is just the SPARQL query based on Wikidata
in order to assess the population.
And the BnF provides labels in multiple languages,
and they all use self-describing URIs,
which is that in the URI, they have the type of entity,
which allows the human reader to understand what they are using.
And more results, they provide different output format,
they use external vocabularies.
Only the British National Bibliography
provides machine-readable licensing information.
And up to one-third of the instances are connected to external repositories,
which is really nice.
And while this study, this work has been done in our Labs team,
a lab in a GLAM is a group of people
who want to explore new ways
of reusing data collections.
And there's a community led by the British Library,
and in particular, Mahendra Mahey,
and we had a first event in London,
and another one in Copenhagen,
and we're going to have a new one in May
at the Library of Congress in Washington.
And we are now 250 people.
And I'm so glad that I found somebody here at the WikidataCon
who has just joined us--
Sylvia from [inaudible], Mexico.
And I'd like to invite you to our community,
since you may be part of a GLAM institution.
So, we can talk later if you want to know about this.
And this--it's all about people.
This is me, people from the British Library,
Library of Congress, Universities, and National Libraries in Europe
And there's a link here in case you want to know more.
And, well, last month, we decided to meet in Doha
in order to write a book about how to create a lab in our GLAM.
And they choose 15 people, and I was so lucky to be there.
And the book follows the Booksprint methodology,
which means that nothing is prepared beforehand.
All is done there in a week.
And believe me, it was really hard work
to have their whole book done in this week.
And I'd like to introduce you to the book, which will be published--
it was supposed to be published this week,
but it will be next week.
And it will be published open, so you can have it,
and I can show you a little bit later if you want.
And those are the authors.
I'm here-- I'm so happy, too.
And those are the institutions--
Library of Congress, British Library-- and this is the title.
And now, I'd like to show you--
a map that I'm doing.
We are launching a website for our community,
and I'm in charge of creating a map with our institutions there.
This is not finished.
But this is just SPARQL, and below,
we see the map.
And we see here the new people that I found, here,
at the WikidataCon-- I'm so happy for this.
And we have here my data library of my university,
and many other institutions.
Also, from Australia--
if I can do it.
Well, here, we have some links.
There you go.
Okay, this is not finished.
We are still working on this, and that's all.
Thank you very much for your attention.
(applause)
[inaudible]
Good morning, everybody.
I'm Olaf Janssen.
I'm the Wikimedia coordinator
at the National Library of the Netherlands.
And I would like to share my work,
which I'm doing about creating Linked Open Data
for Dutch Public Libraries using Wikidata.
And my story starts roughly a year ago
when I was at the GLAM Wiki conference in Tel Aviv, in Israel.
And there are two men with very similar shirts,
and equally similar hairdos, [Matt]...
(laughter)
And on the left, that's me.
And a year ago, I didn't have any practical knowledge and skills
about Wikidata.
I looked at Wikidata, and I looked at the items,
and I played with it.
But I wasn't able to make a SPARQL query
or to do data modeling with the right shape expression.
That's a year ago.
And on the lefthand side, that's Simon Cobb, user: Sic19.
And I was talking to him, because, just before,
he had given a presentation
about improving the coverage of public libraries in Wikidata.
And I was very inspired by his talk.
And basically, he was talking about adding basic data
about public libraries.
So, the name of the library, if available, the photo of the building,
the address data of the library,
the geo-coordinates latitude and longitude,
and some other things,
including with all source references.
And what I was very impressed about a year ago was this map.
This is a map about public libraries in the U.K.
with all the colors.
And you can see that all the libraries are layered by library organizations.
And when he showed this, I was really, "Wow, that's cool."
So, then, one minute later, I thought,
"Well, let's do it for the country for that one."
(laughter)
And something about public libraries in the Netherlands--
there are about 1,300 library branches in our country,
grouped into 160 library organizations.
And you might wonder why do I want to do this project?
Well, first of all, because for the common good, for society,
because I think using Wikidata,
and from there, creating Wikipedia articles,
and opening it up via the linked open data cloud--
it's improving visibility and reusability of public libraries in the Netherlands.
And my second goal was actually a more personal one,
because a year ago, I had this yearly evaluation with my manager,
and we decided it was a good idea that I got more practical skills
on linked open data, data modeling, and also on Wikidata.
And of course, I wanted to be able to make these kinds of maps myself.
(laughter)
Then you might wonder why do I want to do this?
Isn't there already enough basic library data out there in the Netherlands
to have a good coverage?
So, let me show you some of the websites
that are available to discover address and location information
about Dutch public libraries.
And the first one is this one-- Gidsvoornederland.nl--
and that's the official public library inventory
maintained by my library, the National Library.
And you can look up addresses and geo-coordinates on that website.
Then there is this site, Bibliotheekinzicht--
this is also an official website maintained by my National Library.
And this is about public library statistics.
Then there is another one, debibliotheken.nl--
as you can see there is also address information
about library organizations, not about individual branches.
And there's even this one, which also has address information.
And of course, there's something like Google Maps,
which also has all the names and the locations and the addresses.
And this one, the International Library of Technology,
which has a worldwide inventory of libraries,
including the Netherlands.
And I even discovered there is a data set
you can buy for 50 euros or so to download it.
And there is also--seems to be I didn't download it,
but there seems to be address information available.
You might wonder is this kind of data good enough for the purposes I had?
So, this is my birthday list for my ideal public library data list.
And what's on my list?
First of all, the data I want to have must be up-to-date-ish--
it must be fairly up-to-date.
So, doesn't have to be real time,
but let's say, a couple of months, or half a year,
delayed with official publication, that's okay for my purposes.
And I want to have it both library branches
and the library organizations.
Then I want my data to be structured, because it has to be machine-readable.
It has to be in open file format, such as CSV or JSON or RDF.
It has to be linked to other resources preferably.
And the uses--the license on the data needs to be manifest public domain or CC0.
Then, I would like my data to have an API,
which must be public, free, and preferably also anonymous
so you don't have to use an API key, or you have to register an account.
And I also want to have a SPARQL interface.
So, now, these are all the sites I just showed you.
And I'm going to make a big grid.
And then, this is about the evaluation I did.
I'm not going into it, but there is no single column
which has all green check marks.
That's the important thing to take away.
And so, in summary, there was no linked public free linked open data
for Dutch public libraries available before I started my project.
So, this was the ideal motivation to actually work on it.
So, that's what I've been doing for a year now.
And I've been adding libraries bit by bit, organization by organization to Wikidata.
I created also a project website on it.
It's still rather messy, but it has all the information,
and I try to keep it as up-to-date as possible.
And also all the SPARQL queries you can see are linked from here.
And I'm just adding really basic information.
You see the instances, images if available,
addresses, locations, et cetera, municipalities.
And where possible, I also try to link the libraries to external identifiers.
And then, you can really easily-- we all know,
generating some Listeria lists with public libraries grouped
by organizations, for instance.
Or using SPARQL queries, you can also do aggregation on data--
let's say, give me all the municipalities in the Netherlands
and the number of library branches in all the municipalities.
With one click, you can make these kinds of photo galleries.
And what I set out to do first,
you can really create these kinds of maps.
And you might wonder, "Are there any libraries here or there?"
There are--they are not yet in Wikidata.
We're still working on that.
And actually, last week, I spoke with a volunteer,
who's helping now with entering the libraries.
You can really make cool--in Wikidata,
and also with using the Cartographer extension,
you can use these kinds of maps.
And I even took it one step further.
I also have some Python skills, and some Leaflet things skills--
so, I created, and I'm quite proud of it, actually.
I created this library heat map, which is fully interactive.
You can zoom in to it, and you can see all the libraries,
and you can also run it off Wiki.
So, you can just embed it in your own website,
and it fully runs interactively.
So, now going back to my big scary table.
There is one column on the right, which is blank.
And no surprise, it will be Wikidata.
Let's see how it scores there.
(cheering)
So, I actually think of printing this on a T-shirt.
(laughter)
So, just to summarize this in words,
thanks to my project, now,
there is public free linked open data available for Dutch public libraries.
And who can benefit from my effort?
Well, all kinds of parties--
you see Wikipedia, because you can generate lists
and overviews and articles,
for instance, using this and be able to from Wikidata
for our National Library for--
IFLA also has an inventory of worldwide libraries,
they can also reuse the data.
And especially for Sandra,
it's also important for the Ministry-- Dutch Ministry of Culture--
because Sandra is going to have a talk about Wikidata
with the Ministry this Monday, next Monday.
And also, on the righthand side, for instance,
Amazon with Alexa, the assistant,
they're also using Wikidata,
so you can imagine that they also use,
if you're looking for public library information,
they can also use Wikidata for that.
Because one year ago, Simon Cobb inspired me
to do this project, I would like to call upon you,
if you have time available,
and if you have data from your own country about public libraries,
make the coverage better, add more red dots,
and of course, I'm willing to help you with that.
And Simon is also willing to help with this.
And so, I hope next year, somebody else
will be at this conference or another conference
and there will be more red dots on the map.
Thank you very much.
(applause)
Thank you, Olaf.
Next we have Ursula Oberst and Heleen Smits
presenting how can a small research library benefit from Wikidata:
enhancing library products using Wikidata.
Okay. Good morning. My name is Heleen Smits.
And my colleague, Ursula Oberst--where are you?
(laughter)
And I work at the Library of the African Studies Center
in Leiden, in the Netherlands.
And the African Studies Center is a center devoted--
is an academic institution devoted entirely to the study of Africa,
focusing on Humanities and Social Studies.
We used to be an independent research organization,
but in 2016, we became part of Leiden University,
and our catalog was integrated into the larger university catalog.
Though it remained possible to do a search in the part of the Leiden--
of the African Studies Catalog, alone,
we remained independent in some respects.
For example, with respect to our thesaurus.
And also with respect to the products we make for our users,
such as acquisition lists and work dossiers.
And it is in the field of the web dossiers
that we have been looking
for possible ways to apply Wikidata,
and that's the part where Ursula will in the second part of this talk
show you a bit what we've been doing there.
The web dossiers are our collections
of titles from our catalog that we compile
around a theme usually connected to, for example, a conference,
or to a special event, and actually, the most recent web dossier we made
was connected to the year of indigenous languages,
and that was around proverbs in African languages.
Our first steps--
next slide--our first steps on the Wiki path as a library,
were in 2013, when we were one of 12 GLAM institutions
in the Netherlands,
part of the project of Wikipedians in Residence,
and we had for two months, a Wikipedian in the house,
and he gave us trainings for adding articles to Wikipedia,
and also, we made a start with uploading photo collections to Commons,
which always remained a little bit dependent on funding, as well,
whether we would be able to digitize them,
and to mostly have a student assistant to do this.
But it was actually a great adding to what we could offer
as an academic library.
In May 2018, so is that my Ursula, my colleague Ursula--
she started to really explore-- dive into Wikidata
and see what we as a small and not very much experienced library
in these fields could do with that.
So, I mentioned, we have our own thesaurus.
And this is where we started.
This is a thesaurus of 13,000 terms,
all in the field of African studies.
It contains a lot of African languages,
names of ethnic groups in Africa,
and other proper names,
which are perhaps especially interesting for Wikidata.
So, it is a real authority control
to vocabulary with 5,000 preferred terms.
So, we submitted the request to Wikidata,
and that was actually very quickly met with a positive response,
which was very encouraging for us.
Our thesaurus was loaded into Mix-n-Match,
and by now, 75% of the terms
have been manually matched with Wikidata.
So, it means, well, that we are now--
we are added as an identifier--
for example, if you click on Swahili language,
what happens then in Wikidata on the number that--
that connects our term-- is the Wikidata term--
we enter into our thesaurus,
and from there, you can do a search directly in the catalog
by clicking the button again.
It means, also, that Wikidata has not really integrated
into our catalog.
But that's also more difficult.
Okay, we have to give the floor
to Ursula for the next part.
(Ursula) Thank you very much, Heleen.
So, I will talk about our experiences
with incorporating Wikidata elements
to our web dossier.
A web dossier is--oh, sorry, yeah, sorry.
A web dossier, or a classical web dossier, consists of three parts:
an introduction to the subject,
mostly written by one of our researchers;
a selection of titles, both books and articles from our collection;
and the third part, an annotated list
with links to electronic resources.
And this year, we added a fourth part to our web dossiers,
which is the Wikidata elements.
And it all started last year,
and my story is similar to the story of Olaf, actually.
Last year, when I had no clue about Wikidata,
and I discovered this wonderful article by Alex Stinson
on how to write a query in Wikidata.
And he chose a subject-- a very appealing subject to me.
Namely, "Discovering Women Writers from North Africa."
I can really recommend this article,
because it's very instructive.
And I thought I will be-- I'm going to work on this query,
and try to change it to: "Southern African Women Writers,"
and try to add a link to their work in our catalog.
And on the right-hand side, you see the SPARQL query
which searches for "Southern African Women Writers."
If you click on the button, on the blue button on the lefthand side,
the search result will appear beneath.
The search result can have different formats.
In my case, the search result is a map.
And the nice thing about Wikidata
is that you can embed to this search result
into your own webpage,
and that's what we are now doing with our work dossiers.
So, this was the very first one on Southern African women writers,
listed classical three elements,
plus this map on the lefthand side,
which gives extra information--
a link to the Southern African women writer--
a link to her works in our catalog,
and a link to the Wikidata record of her birth place, and her name,
her personal record, plus a photo, if it's available on Wikidata.
And you have to retrieve a nice map
with a lot of red dots on the African continent.
You need nice data in Wikidata, complete, sufficient data.
So, with our second web dossier on public art in Africa,
we also started to enhance the data in Wikidata.
In this case, for a public art-- we edited geo-locations--
geo-locations to Wikidata.
And we also searched for works of public art in commons,
and if they don't have a record on Wikidata yet,
we edited the record to Wikidata.
And the third thing we do,
because when we prepare a web dossier,
we download the titles from our catalog,
and the tiles are in MARC 21,
so we have to convert them to a format that is presentable on the website,
and it takes not much time and effort to convert the same set of titles
to Wikidata QuickStatements,
and then, we also upload a title set to Wikidata,
and you can see the titles we uploaded
from our latest web dossier
on African proverbs in Scholia.
A really nice tool that visualizes Scholia publications
being present in Wikidata.
And, one second--when it is possible, we add a Scholia template
to our web dossier's topic.
Thank you very much.
(applause)
Thank you, Heleen and Ursula.
Next we have Adrian Pohl presenting using Wikidata
to improve spatial subject indexing and regional bibliography.
Okay, hello everybody.
I'm going right into the topic.
I only have ten minutes to present a three-year project.
It wasn't full time. (laughs)
Okay, what's the NWBib?
It's an acronym for North-Rhine Westphalian Bibliography.
It's a regional bibliography that records literature
about people and places in North Rhine-Westphalia.
And the monograph's in it--
there are a lot of articles in it, and most of them are quite unique,
so, that's the interesting thing about this bibliography--
because it's often less quite obscure stuff--
local people writing about that tradition,
and something like this.
And there's over 400,000 entries in there.
And the bibliography started in 1983,
and so we only have titles from this publication year onwards.
If you want to take a look at it, it's at nwbib.de,
that's the web application.
It's based on our service, lobid.org, the API.
Because it's cataloged as part of the hbz union catalog,
which comprises around 20 million records,
it's an [inaudible] Aleph system we get the data out of there,
and make RDF out of it,
and provide it as via JSON or the HTTP API.
So, the initial status in 2017
was we had nearly 9,000 distinct strings
about places--referring to places, in North Rhine-Westphalia.
Mostly, those were administrative areas, like towns and districts,
but also monasteries, principalities, or natural regions.
And we already used Wikidata in 2017,
and matched those strings with Wikidata API to Wikidata entries
quite naively to get the geo-coordinates from there,
and do some geo-based discovery stuff with it.
But this had some drawbacks.
And so, the matching was really poor,
and there were a lot of false positives,
and we still had no hierarchy in those places,
and we still had a lot of non-unique names.
So, this is an example here.
Does this work?
Yeah, as you can see, for one place, Brauweiler,
there are four different strings in there.
So, we all know how this happens.
If there's no authority file, you end up with this data.
But we want to improve on that.
And as you can also see, that while the matching didn't work--
so you have this name of the place
and there's often the name of the superior administrative area,
and even on the second level, a superior administrative area
often in the name
to identify the place successfully.
So, the goal was to build a full-fledged spatial classification based on this data,
with a hierarchical view of places,
with one entry or ID for each place.
And we got this mock-up by NWBib editors in 2016, made in Excel,
to get a feeling of what they would like to have.
There you have the-- Regierungsbezirk--
that's the most superior administrative area--
we have in there some towns or districts--rural districts--
and then, it's going down to the parts of towns,
even to this level.
And we chose Wikidata for this task.
We also looked at the GND, the Integrated Authority File,
and GeoNames--but Wikidata had the best coverage,
and the best infrastructure.
The coverage for the places and the geo-coordinates we need,
and the hierarchical information, for example.
There were a lot of places, also, in the GND,
but there was no hierarchical information in there.
And also, Wikidata provides the infrastructure
for editing and versioning.
And there's also a community that helps maintaining the data,
which was quite good.
Okay, but there was a requirement by the NWBib editors.
They did not want to directly rely on Wikidata,
which was understandable.
We don't have those servers under our control,
and we won't know what's going on there.
There might be some unwelcome edits that destroy the classification,
or parts of it, or vandalism.
So, we decide to put an intermediate SKOS file in between,
on which the application would-- which should be generated from Wikidata.
And SKOS is the Simple Knowledge Organization System--
it's the standard way to model
a classification in the linked data world.
So, how we did it? Five steps.
I will come to each of the steps in more detail.
We match the strings to Wikidata with a better approach than before.
Created classification based on Wikidata, edit,
then back the links from Wikidata to NWBib
with a custom property.
And now, we are in the process of establishing a good process
for updating the classification in Wikidata.
Seeing--having a DIF of the changes,
and then publishing it to the SKOS file.
I will come to the details.
So, the matching approach--
as the API wasn't very sufficient,
and because we have those different levels in the strings,
we build a custom Elasticsearch index for our task.
I think by now, you could probably, as well, use OpenRefine for doing this,
but at that point in time, it wasn't available for Wikidata.
And we build this index base on SPARQL query,
and for entities in NRW, and with a specific type.
And the query evolved over time a lot.
And we have a few entries that you can see the history on GitHub.
So, where we put in the matching index,
in the *spatial* object, is what we need in our data.
It's the *label* and the *ID* or the link to Wikidata,
the geo-coordinates, and the type from Wikidata [inaudible], as well.
But also for the matching, very important that *aliases* and the broader thing--
and this is also an example where the name of the broader entity
and the district itself are very similar.
So, it's important to have some *type* information, as well,
for the matching.
So, the nationwide results were very good.
We could automatically match more than 99% of records
with this approach.
These were only 92% of the strings.
So, obviously, the results--
those strings that only occurred one or two times
often didn't appear in Wikidata.
And so, we had to do a lot of work with those with the [long tail].
And for around 1,000 strings, the matching was incorrect.
But the catalogers did a lot of work in the Aleph catalog,
but also in Wikidata, they made more than 6,000 manual edits to Wikidata
to reach 100% coverage by adding aliases-type information,
creating new entries.
Okay, so, I have to speed up.
We created classification based on this, on the hierarchical statements.
P131 is the main property there.
We added the information to our data.
So, we now have this in our data spatial object--
and we *focus* this--the link to Wikidata, and the *types* are there,
and here's the *ID* from the SKOS classification
we built based on Wikidata.
And you can see there are Q identifiers in there.
Now, you can basically query our API
with such a query using Wikidata URIs,
and get literature, in this example, about Cologne back.
Then we created a Wikidata property for NWBib and edit those links
from Wikidata to the classification-- batch load them with QuickStatements.
And there's also a nice--
also a move to using a qualifier on this property
to add the broader information there.
So, I think people won't mess around that work with this,
and as with the P131 statement.
So, this is what it looks like.
This will go to the classification where you can then start a query.
Now, we have to build this update and review process,
and we will add those data like this,
with a zero sub-field to Aleph,
and the catalogers will start using those Wikidata based IDs,
URIs, for cataloging for spatial indexing.
So, by now, there are more than 400,000 NWBib entries with links to Wikidata,
and more than 4,400 Wikidata entries with links to NWBib.
Thank you.
(applause)
Thank you, Adrian.
I got it. Thank you.
So, as you've seen me before, I'm Hilary Thorsen.
I'm Wikimedian in residence
with the Linked Data for Production Project.
I am based at Stanford,
and I'm here today with my colleague, Lena Denis,
who is Cartographic Assistant at Harvard Library.
And Christine Fernsebner Eslao is here in spirit.
She is currently back in Boston, but supporting us from afar.
So, we'll be talking about Wikidata and Libraries
as partners in data production, organization, and project inspiration.
And our work is part of the Linked Data for Production Project.
So, Linked Data for Production is in its second phase,
called Pathway for Implementation.
And it's an Andrew W. Mellon Foundation grant,
involving the partnership of several universities,
with the goal of constructing a pathway for shifting the catalog community
to begin describing library resources with linked data.
And it builds upon a previous grant,
but this iteration is focused on the practical aspects
of the transition.
One of these pathways of investigation
has been integrating library metadata with Wikidata.
We have a lot of questions,
but some of the ones we're most interested in
are how we can integrate library metadata with Wikidata,
and make contribution a part of our cataloging workflows,
how Wikidata can help us improve our library discovery environment,
how it can help us reveal more relationships
and connections within our data and with external data sets,
and if we have connections in our own data that can be added to Wikidata,
how libraries can help fill in gaps in Wikidata,
and how libraries can work with local communities
to describe library and archival resources.
Finding answers to these questions has focused on the mutual benefit
for the library and Wikidata communities.
We've learned through starting to work on our different Wikidata projects,
that many of the issues libraries grapple with,
like data modeling, identity management, data maintenance, documentation,
and instruction on linked data,
are ones the Wikidata community works on too.
I'm going to turn things over to Lena
to talk about what she's been working on now.
Hi, so, as Hilary briefly mentioned, I work as a map librarian at Harvard,
where I process maps, atlases, and archives for our online catalog.
And while processing two-dimensional cartographic works
is relatively straighforward, cataloging archival collections
so that their cartographic resources can be made discoverable,
has always been more difficult.
So, my use case for Wikidata is visually modeling relationships
between archival collections and the individual items within them,
as well as between archival drafts in published works.
So, I used Wikidata to highlight the work of our cartographer named Erwin Raisz,
who worked at Harvard in the early 20th-century.
He was known for his vividly detailed and artistic land forms,
like this one on the screen--
but also for inventing the armadillo projection,
writing the first cartography textbook in English
and other various important contributions
to the field of geography.
And at the Harvard Map Collection,
we have a 66-item collection of Raisz's field notebooks,
which begin when he was a student and end just before his death.
So, this is the collection-level record that I made for them,
which merely gives an overview,
but his notebooks are full of information
that he used in later atlases, maps, and textbooks.
But researchers don't know how to find that trajectory information,
and the system is not designed to show them.
So, I felt that with Wikidata, and other Wikimedia platforms,
I'd be able to take advantage
of information that already exists about him on the open web,
along with library records and a notebook inventory
that I had made in an Excel spreadsheet
to show relationships and influences between his works.
So here, you can see how I edited and reconciled library data
in OpenRefine.
And then, I used QuickStatements to batch import my results.
So, now, I was ready to create knowledge graphs
with SPARQL queries to show patterns of influence.
The examples here show how I leveraged Wikimedia Commons images
that I connected to him.
And the hierarchy of some of his works
that were contributing factors to other works.
So, modeling Raisz's works on Wikidata allowed me to encompass in a single image,
or in this case, in two images, the connections that require many pages
of bibliographic data to reveal.
So, this video is going to load.
Yes! Alright.
This video is a minute and a half long screencast I made,
that I'm going to narrate as you watch.
It shows the process of inputting and then running a SPARQL query,
showing hierarchical relationships between notebooks, an atlas, and a map
that Raisz created about Cuba.
He worked there before the revolution,
so he had the unique position of having support
from both the American and the Cuban governments.
So, I made this query as an example to show people who work on Raisz,
and who are interested in narrowing down what materials they'd like to request
when they come to us for research.
To make the approach replicable for other archival collections,
I hope that Harvard and other institutions will prioritize Wikidata look-ups
as they move to linked data cataloging production,
which my co-presenters can speak to the progress on
better than I can.
But my work has brought me-- has brought to mind a particular issue
that I see as a future opportunity, which is that of archival modeling.
So, to an archivist, an item is a discrete archival material
within a larger collection of archival materials
that is not a physical location.
So an archivist from the American National Archives and Records Administration,
who is also a Wikidata enthusiast,
advised me when I was trying to determine how to express this
using an example item,
that I'm going to show as soon as this video is finally over.
Alright. Great.
Nope, that's not what I wanted.
Here we go.
It's doing that.
(humming)
Nope. Sorry. Sorry.
Alright, I don't know why it's not going full screen again.
I can't get it to do anything.
But this is the-- oh, my gosh.
Stop that. Alright.
So, this is the item that I mentioned.
So, this was what the archivist
from the National Archives and Records Administration
showed me as an example.
And he recommended this compromise, which is to use the *part of* property
to connect a lower level description to a higher level of description,
which allows the relationships between different hierarchical levels
to be asserted as statements and qualifiers.
So, in this example that's on screen,
the relationship between an item, a series, a collection, and a record group
are thus contained and described within a Wikidata item entity.
So, I followed this model in my work on Raisz.
And one of my images is missing.
No, it's not. It's right there. I'm sorry.
And so, I followed this model on my work on Raisz,
but I look forward to further standardization.
So, another archival project Harvard is working on
is the Arthur Freedman collection of more than 2,000 hours
of punk rock performances from the 1970s to early 2000s
in the Boston and Cambridge, Massachussets areas.
It includes many bands and venues that no longer exist.
So far, work has been done in OpenRefine on reconciliation of the bands and venues
to see which need an item created in Wikidata.
A basic item will be created via batch process next spring,
and then, an edit-a-thon will be held in conjunction
with the New England Music Library Association's meeting in Boston
to focus on adding more statements to the batch-created items,
by drawing on local music community knowledge.
We're interested in learning more about models for pairing librarians
and Wiki enthusiasts with new contributors who have domain knowledge.
Items will eventually be linked to digitized video
in Harvard's digital collection platform
once rights have been cleared with artists,
which will likely be a slow process.
There's also a great amount of interest
in moving away from manual cataloging and creation of authority data
towards identity management,
where descriptions can be created in batches.
An additional project that focused on
creating international standard name identifiers, or ISNIs,
for avant-garde and women filmmakers
can be adapted for creating Wikidata items for these filmmakers, as well.
Spreadsheets with the ISNIs, filmmaker names, and other details
can be reconciled in OpenRefine, and uploaded with QuickStatements.
Once people in organizations have been described,
we'll move toward describing the films in Wikidata,
which will likely present some additional modeling challenges.
A library presentation wouldn't be complete
without a MARC record.
Here, you can see the record for Karen Aqua's taxonomy film,
where her ISNI and Wikidata Q number
have been added to the 100 field.
The ISNIs and Wikidata Q numbers that have been created
can then be batch added back into MARC records via MarcEdit.
You might be asking why I'm showing you this ugly MARC record,
instead of some beautiful linked data statements.
And that's because our libraries will be working in a hybrid environment
for some time.
Our library catalogs still relies on MARC records,
so by adding in these URIs,
we can try to take advantage of linked data,
while our systems still use MARC.
Adding URIs into MARC records makes an additional aspect
of our project possible.
Work has been done at Stanford and Cornell to bring data
from Wikidata into our library catalog using URIs already in our MARC records.
You can see an example of a knowledge panel,
where all the data is sourced from Wikidata,
and links back to the item itself, along with an invitation to contribute.
This is currently in a test environment, not in production in our catalog.
Ideally, eventually, these will be generated
from linked data descriptions of library resources
created using Sinopia, our linked data editor
developed for cataloging.
We found that adding a look-up to Wikidata in Sinopia is difficult.
The scale and modeling of Wikidata makes it hard to partition the data
to be able to look up typed entities,
and we've run into the problem
of SPARQL not being good for keyword search,
but wanting our keyword APIs to return SPARQL-like RDF descriptions.
So, as you can see, we still have quite a bit of work to do.
This round of the grant runs until June 2020,
so, we'll be continuing our exploration.
And I just wanted to invite anyone
who's continued an interest in talking about Wikidata and libraries,
I lead a Wikidata Affinity Group that's open to anyone to join.
We meet every two weeks,
and our next call is Tuesday, November the 5th,
so if you're interested in continuing discussions,
I would love to talk with you further.
Thank you, everyone.
And thank you to the other presenters
for talking about all of their wonderful projects.
(applause)