Gooding, Paul

English

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The GDD Network: Towards a 
Global Dataset of Digitised 
Texts
Dr. Paul Gooding (Lecturer in Information Studies, University of Glasgow)
DCDC2019
paul.gooding@glasgow.ac.uk
Presentation Overview
1. Introduction to the Global Digitised Dataset 
Network (GDDNetwork);
2. Contexts for the network;
3. Work to date:
• Developing potential use cases;
• Data matching (HathiTrust);
4. What might a sustainable, scalable dataset –
and related services – look like?
The GDDNEtwork Core Partners
• GDDNetwork – Network to investigate the 
development of a global dataset of 
digitised texts.
• AHRC-funded Research Network (Feb 
2019– Jan 2020).
• Investigating the feasibility of a global 
registry/dataset of digitised texts.
• More on this later, but first…
The Research and Funding Context
Arts and Humanities Research Council:
Digital Transformations in the Arts & Humanities;
Focus on the implications of the digital shift
Particularly interested in the emergence of ”digital 
scholarship”:
• Data Science;
• Digital Humanities;
• Implications of born-digital archiving;
• Open Publishing and Open Data.
This project responds to a specific call:
Research Networking Scheme for “UK-US 
Collaborations in Digital Scholarship in Cultural 
Institutions” – announced in October 2018. 
Network Overview
Set out to address a key 
problem:
• Libraries, archives, and many other 
organisations are digitising collections, 
but much of it is uncoordinated:
• Hard for researchers to make the 
best use of growing collections;
• Organisations wishing to target their 
digitisation efforts are unable to 
easily collaborate;
• Other forms of collaboration could 
emerge (co-ordinated digital 
preservation?).
Identified several 
potential beneficiaries:
• Digital scholars seeking 
large corpora of texts, or 
metadata pertaining to 
digitised collections.
• Readers wishing to find a 
digitised text.
• Libraries undertaking 
digitisation programmes.
Aim to verify the utility 
of such a resource, 
and to investigate the 
feasibility of 
developing a 
continuously updated 
international registry 
of digitised texts.
Network Objectives
and Deliverables 
Undertake a trial matching of data from UK Libraries with the existing HathiTrust dataset of 
digitised texts.
Hold workshops to explore the range of benefits a global dataset of digitised texts could bring 
to different groups.
Deliver a dataset that combines HathiTrust and UK Library metadata on digitised texts.
Develop options for an ongoing and sustainable collaborative network of relevant parties that is 
able to deliver on the ultimate goal of creating a global dataset of digitised texts, along with 
appropriate services to the scholarly community.
Contexts for the 
Network
1.) “The Collective Collection”: “One important trend is that 
libraries and the organizations that provide services to them will 
devote more attention to system-wide organization of collections 
- whether the ”system” is consortium, a region, or a country” 
(Dempsey, 2013). 
2.) Cross-border challenges to collaborative global efforts: 
Copyright & Intellectual Property; “Ownership” of library 
collections.
3.) Mass digitisation as a driver of change – for researchers and 
for libraries. 
4.) The growth of data-driven research - Data Science, Digital 
Humanities – that relies upon digital collections from libraries.
5.) What does it mean for us to call a resource global? 
Linguistically, culturally, technologically, practically?
Work to 
Date
1.) Developing Use Cases 
for a global dataset of 
digitised texts.
2.) Holdings Analysis. 
3.) Community 
engagement and 
workshops.
Use Cases for a Global Dataset 
of Digitised Texts
• Team meeting in Chicago:
• Brainstorming agile user stores:
• “As a *…* I want to *…* so that I can *…*”
• London Workshop (June 2019):
• Further brainstorming to identify additional user 
stories;
• “Investment” exercise: voting for preferred use cases in 
order to suggest priority investment areas;
• Group discussions around feasibility, key stakeholders, 
ways forward.
Use Cases: 
Preliminary 
Analysis
• Five themes emerged:
• Efficiency, Cost, Impact, Value:
• “As a collections manager I want to know what has already 
been digitised so that I can avoid duplication of effort”.
• Discovery & Access:
• “As a reader I want to easily, remotely access a digital 
resource so that I can find the information I’m after.”
• Provenance:
• “As a digital scholar I want to understand the provenance 
of the dataset so that I can put the digitised materials in 
context and apply my own relative score to the source (e.g. 
how much I trust it).”
• Research:
• “As a digital scholar I want to download a list of links to 
digitised texts from different libraries so thatI can create a 
corpus specific to my needs.”
• Product/Service Development:
• “As a vendor I want to know what libraries have digitised 
so that I can include a new discovery channel in my 
product.”
Use cases: 
Expert 
Workshop 
Most Popular Use Cases: June 2019 Expert Workshop (878 votes)
discovery & access efficiency, cost,  impact, value product / service development provenance research
Use Cases: 
Learning 
Outcomes
• Bias towards “Research” due to presence of 
several involved in digital scholarship.
• Library service providers underrepresented in 
network to date, reflected in lack of use cases 
for vendors.
• Need to identify and reach out to new 
stakeholder groups in remaining project time.
• BIG ONE: the scope and extent of the dataset 
needs careful definition:
• Many assumed case studies were built 
upon the idea that it would provide direct 
access to digitised full text.
• Focus to date has been primarily on 
unifying metadata, NOT aggregating full 
text.
Use cases: 
Survey
• Lightweight survey designed to 
ensure stakeholder communities 
were targeted (right)
• Survey allowed people to type their 
own answers, which were then 
coded in line with categories used at 
expert workshop.
• Focus on this stage on “benefits” –
some responses queried this, but we 
wanted to know what use cases 
would benefit users the most, should 
a global digitised dataset exist.
• 86 usable responses gathered 
between  28th July 2019 and 11th
October 2019.
0
10
20
30
40
50
60
Total
Job title of survey participants
Information Professional
Researcher
Other (Curator)
Other (please specify)
Student
Teacher
Family historian
(blank)
Q.1) How
would it 
help you 
improve 
your current 
work 
practices 
(n=86)?
Q.1) How would it help you improve your current work practices 
(n=86)?
discovery & access efficiency, cost,  impact, value product / service development provenance research
Q.2) What 
would it 
enable you 
to do that 
you can't do 
now (n=81)?
Q.2) What would it enable you to do that you can't do now (n=81)?
discovery & access efficiency, cost,  impact, value product / service development provenance research
Q.3) What 
would be 
the broader 
benefits for 
others in 
your role 
(n=79)?
Q.3) What would be the broader benefits for others in 
your role (n=79)?
discovery & access efficiency, cost,  impact, value product / service development provenance research
Survey learning 
outcomes
• Some respondents were confused by the concept:
• Need to scope and, ultimately, explain the service 
as it is developed.
• Addiitonal categories of interest emerged:
• Teaching;
• Positioning of the dataset in relation to other 
services;
• Support for library users, and collaboration in 
research, digitisation and infrastructure;
• Clear interest in the project among stakeholders.
• But caution advised:
• Small, self-selecting sample - not necessarily 
representative of broader stakeholder community
• Further work needed on areas of concern:
• Language and geographical reach;
• Balance between larger and smaller 
organisations;
• Data quality and holdings analysis.
Holdings Analysis 
(Led by HathiTrust)
• With thanks to HathiTrust for the work and 
slides – Natalie Fulkerson, Josh Steverman, 
Martin Warin, and Heather Christenson.
• Partner libraries effectively went through a 
trial “onboarding” process similar to that 
undertaken by new HathiTrust members.
• Key goals:
• To identify the extent of overlap between 
Partner Libraries and HathiTrust;
• To identify an effective methodology for 
matching data across the library 
catalogues – essential to allow accurate 
deduplication. 
HathiTrust 
Datasets -
overview
Bibliographic records 
stored in Zephir
• MARC format
• Contributed by 
HathiTrust member 
libraries as part of 
ingest
• Clustered on OCLC 
number
HathiFile
• Tab-delimited text 
file representing 
every item in the 
collection
• Derived from Zephir 
bibliographic 
records, plus rights 
and access codes, 
various HathiTrust-
generated 
generated 
administrative 
identifiers
Library 
Records 
received
• MARC records for:
• Digitised monographs;
• Print holdings.
• Varied according to format 
and availability of records:
Organisation No. of 
digitised 
records
No. of 
print 
records
British Library 516,212 -
National Library of 
Scotland
10,919 9,640,360*
National Library of 
Wales
2,290 3,224,243
Approaches to Data Matching
1. Standard HathiTrust overlap analysis:
• Attempt to match library holdings records 
to the HathiFile using OCLC number 
(OCN)
• OCNs present in library record (in the 
MARC 035 field):
• Digitised items only.
2. Look for other usable identifiers:
• Locate other possible identifiers in library 
records to match agains corresponding 
fields in the HathiFile (or, later, in Zephir)
• For example – ISBN
• Lower than expected matching rates, and 
inconsistent coverage of ISBNs:
# 
digitized 
records
# print 
records
# ISBNs 
- digital
# ISBNs -
print
% ISBNs -
print
British 
Library 516,212 - 34 - -
National 
Library of 
Scotland 10,919 9,640,360 55 2,709,837 28*
National 
Library of 
Wales 2,290 3,224,243 17 3,128,171 97**
Exploratory 
Method #4
• Continues the work of Michael Morris-Pearce, a former 
HathiTrust colleague at CDL
• Query: Can you train a support vector machine (SVM) 
classifier to distinguish between title matches and non-
matches?
• Machine Learning process:
• Setup
• Training/Iteration phase
• Implementation phase
Results (Take these with a BIG pinch of salt)
Holdings 
analysis: 
Learning 
outcomes
Duplicate detection is hard…
• Short titles, long titles, common titles;
• Different manifestations of the same 
work.
…Involves tradeoffs:
• Resource-intensive methods yield better 
results.
Implications for aggregation:
• Duplicate detection (overlap) vs. 
Clustering – how to express relationships 
to registry users?
Conclusions and future work
• Engagement work – sense that there is a need for a resource that specifically 
addresses digitised texts:
• Follow-on work required to define scope of: materials / participants / functionality / 
geographic reach / language coverage.
• What might a sustainable project look like?
• Scalability
• Requirement for effective business model / planning to ensure regular updating.
• Limited value if this is a one-off ingest of materials – must be on an ongoing service.
• Must extend beyond the US/UK context of the original funding call to be considered truly 
“global”.
• Future work:
• Identify possible paths and business models;
• Identify overlap with existing resources, and potential routes to collaboration;
• Address key research challenges around provenance, data quality and version control.
Thank you for listening!
Any questions?
Contact:
paul.gooding@glasgow.ac.uk
@pmgooding
Project website:
https://gddnetwork.arts.gla.ac.uk/


The GDD Network: Towards a Global Dataset of Digitised Texts

http://eprints.gla.ac.uk/203254/1/203254.pdf

The GDD Network: Towards a Global Dataset of Digitised Texts

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