British Library Labs - CityLIS

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@BL_Labs #citylis @BL_DigiSchol labs@bl.uk http://labs.bl.uk
http://www.bl.uk/projects/british-library-labs
Funded by the Andrew W. Mellon Foundation
Mahendra Mahey
Experiment with our
Digital Collections
Mahendra Mahey
Manager of BL Labs
Working with the British Library’s Digital Collections & Data:
Insights from British Library Labs & a new role for Libraries
10:00 to 12:45, 26 March 2018
BL Labs Roadshow 2018
CityLIS, London
UK.
Running since March 2013
Core Team
• Adam Farquhar (PI)
• Mahendra Mahey
• Ben O’Steen
• Eleanor Cooper (0.5)

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The British Library
Inside the British Library
Space for 1200 readers, around 500,000 visitors per year
Building 37 uses low oxygen and robots
Reading room and delivery to London
Many items stored at Document Supply and Storage centre 48 hours away
Stockton-on-Tees
Author right to payment each time their books
are borrowed from public libraries.
St Pancras, London, UK
Many books are stored 4 stories below the building
UK Legal Deposit Library – Reference only
Founded in 1973 though origins stem back to British Museum Library 1753
Boston-Spa

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Collections – not just books!
> 180*million items
> 0.8* m serial titles
> 8* m stamps
> 14* m books
> 6* m sound recordings
> 4* m maps
> 1.6* m musical scores
> 0.3* m manuscripts
> 60* m patents
King’s Library *Estimates

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Living Knowledge Vision (2015 – 2023)
Custodianship Research Business
Culture Learning International
To make our intellectual heritage accessible to everyone,
for research, inspiration and enjoyment and be the most open, creative
and innovative institution of its kind by 2023 (50 year anniversary).
Document:http://goo.gl/h41wW7 Speech:https://goo.gl/Py9uHK
Roly Keating (Chief Executive Officer of the British Library)
To make our intellectual heritage accessible to everyone,
for research, inspiration and enjoyment and be the most open, creative
and innovative institution of its kind by 2023 (50 year anniversary).

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Wider…not just Digital Humanities
Researchers
Researchers
https://goo.gl/WutNyi
Artists
http://goo.gl/nNKhQ2
Librarians
Curators
https://goo.gl/9NWZUW
Software Developers
https://goo.gl/7QQ5Tf
Archivists
https://goo.gl/x7b4tg
Educators
https://goo.gl/qh01Mi

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• An area of scholarly activity, born from humanities computing, at the
intersection of computing/digital technologies and the
humanities.
• The field both employs technology in the pursuit of humanities
research, and subjects technology to humanistic questioning and
interrogation.
• DH is collaborative, crossdisciplinary, and computationally
engaged research, teaching, and publishing.
https://en.wikipedia.org/wiki/Digital_humanities
Defining digital humanities (DH)

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Digital research methods
Digital Scholarship
Visualisations
Application Programming Interfaces (APIs)
for datasets e.g. Metadata, Images, etc
Transcribing
Annotation
Location based searching & Geo-tagging
Corpus analysis, Text Mining &
Natural Language Processing
Crowdsourcing
Human Computation
In 20 years time?

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What about Digital?
Born Digital Digitised

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/
Knowledge Quarter London
80 knowledge organisations (as of 26/03/18) within 1 mile radius of
Kings Cross, http://www.knowledgequarter.london
http://www.turing.ac.uk (Headquartered at the British Library)
UK Web Archive and e-legal deposit (2013)
http://www.webarchive.org.uk/ukwa/
Born digital
Data all around us at
Kings Cross!
Born digital
Kings Cross!
Born digital
Kings Cross!

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All our physical
items are digitised
right?

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#bldigital
1-2 %* digitised
* estimate
Digitisation
Partnerships
Commercial & Other Organisations
Amount
increasing rapidly
Bias in digitisation
http://goo.gl/bR9UJL
Sample Generator

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Playbills, Books, Newspapers
(includes Optical Character Recognition (OCR))
Digital collections and Datasets
British National
Bibliography
http://bnb.data.bl.uk
http://sounds.bl.ukhttp://dml.city.ac.uk/
Music (Recordings & Sheet) & Sounds
http://goo.gl/frSMJt
Broadcast News (TV and Radio)
http://goo.gl/cwThHw
http://goo.gl/pBkisZhttp://goo.gl/E8aRyQ
Usage data
EtHOS
Web ArchiveImages, Manuscripts & Maps
http://www.qdl.qa/
Qatar Digital Library
http://idp.bl.uk/
International
Dunhuang
Project
Maps
http://www.bl.uk/maps/
Hebrew Manuscripts
http://goo.gl/4sbCp9
Flickr &
Wikimedia Commons
https://goo.gl/LZRmaZ

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Finding Open Cultural Heritage Datasets
Collection Guides (199 as of 26/03/2018)
https://www.bl.uk/collection-guides/
Datasets about our collections
Bibliographic datasets relating to our published and
archival holdings
Datasets for content mining
Content suitable for use in text and data mining
research
Datasets for image analysis
Image collections suitable for large-scale image-
analysis-based research
Datasets from UK Web Archive
Data and API services available for accessing UK Web
Archive
Digital mapping
Geospatial data, cartographic applications, digital aerial
photography and scanned historic map materials
https://data.bl.uk
Download collections as zips, no API
Each dataset has a Digital Object Identifier (DOI)
can be referenced for research
Not all discoverable via
search engines!

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How are we
doing this?

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Competition
Awards
Projects
Tell us your ideas of what to do with our digital content
Show us what you have already done with our digital
content in research, artistic, commercial and learning and
teaching categories
Talk to us about working on collaborative projects

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• The Library has to go out to meet researchers, regularly and
cyclically to tell them what we have and learn what they
want to do
• Debunk ‘myths’ about the Library
• Show researchers the reality of our data
• Their ideas always change once they see the data!
https://goo.gl/esqpRb
Lots of two-way communication!
BL Labs runs annual ‘Roadshows’
around the UK

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Have you got X?
https://upload.wikimedia.org/wikipedia/commons/5/50/Real_wuerzburg.jpg
Looking for Physical Content in the British Library

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Have you got X digitised / in digital form?
http://www.yorkmix.com/wp-content/uploads/2014/04/mr-simms-sweet-shoppe-york.jpg
Looking for Digitised / Digital Content in the BL

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•Digitisation costs time, resources & access
can depend on restrictions imposed by
funders, legal, ethical, practical etc. …
•Still…700 Digitisation projects
(as of 26/03/2018)
But not all found through search
engines or even online!
So little digitised…why?
© £ 

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https://goo.gl/qpCLlk
https://goo.gl/wMTS3Z
• Dialogue typically:
– you are ‘lucky’ & we have the digital content
/ data relevant to your research
– we don’t have exactly what your looking for,
but is there anything of interest? Let’s talk…
– engagement is hard work and it’s constantly
required to maintain interest in our digital
collections!
• Artists find this dialogue easier…
• We also tend to attract researchers with ‘fuzzier’
research boundaries and possibly open to more
interdisciplinary / collaborative research
What engagement does the BL have with
researchers wanting use our digital content?

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Overview of interactions with the
“researcher”

www.bl.uk
Phase 1: Exploration
• Exploration phase allows a researcher to:
• understand the data in an open-ended fashion,
• discover potential tools to work with the data,
• gain awareness of their capabilities and limitations,
• develop a firmer research query and
• gauge the costs, risks and time needed.
• Outputs of the exploration are not intended to be shareable,
beyond personal experience and key features (data size, formats, tool
successes, etc).
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www.bl.uk
Phase 2: Query-Focussed
• “Query-Focussed”, the familiar project but due to phase 1:
• A firmer and more informed query by the researcher
• Suitable datasets already lined up
• A good idea of the initial toolset and capabilities (human and computer)
required
• Project output is outlined, and relevant reuse applications are begun.
• Clear agreements on what happens at the end of the project – data
deletion, virtual machine deletion/archiving/etc.
• Project may iterate on initial ideas, depending on researcher’s cost/risk
appetite
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www.bl.uk
Phase 2: Query-Focussed
• Wrap-up
• Work (code, notes) exported and given to researcher
• All derivative data is licenced or retained based on reuse agreements
(Access & Reuse board, etc)
• Provisions made for the project are wound-down, as agreed (derivative
data deleted after a grace period, etc)
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Openly Licensed Digital Content?
15% Openly
Licensed
Around 80%*
available online
Working through to make more open…
Though some collections will always only be available onsite due to
various reasons including legal, ethical etc
Breakdown by collection*
Manuscripts 59%
Books 9%
Maps and Views 7%
Newspapers 3%
Archives and Records 3%
Paintings, Prints and Drawings 2%
*Based on number of digitisation projects (700 as of 25/13/18)
Largest proportion of funding
Public / Private Partnership
15 %* Openly Licensed – most online
85 %* Available onsite only at the moment
*Estimates

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The Story of the Digital Collection…
Digital
Collection
Curator
Who paid for the digitisation?
Who did the digitisation?
Technology used
Born digital?
Published
Unpublished
Where is it?
Can it still be accessed?
Generates income
Reputational risk in using?
Legalities
Politics when digitised
Personalities involved
Surprises (e.g. gaps)
Descriptive information
Old format not supported
What media was the
digitisation done from?
Is there any background documentation?
No Descriptive information
Inconsistent descriptive information
Still there?
Good to know the background ‘Story’ of a Digital Collection’
if you want to use it for research and make conclusions…

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Open Content vs Onsite Only Access
• Access easier for openly licensed content
• More challenging for on-site, in-copyright, non-print legal
deposit, data protected, old content media & contemporary
material (post 1877)
https://goo.gl/Y5zCXg
©

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only in
Reading
Rooms due
to ©
only on
site due to
© or
ethical etc
not online /
available –
various storage
devices,
personal data
online
and open
British Library
online
behind
paywall
Challenges of access to Digital Collections

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How do we give access to
onsite-only
Digital Collections
(85% of our Digital Collections)?

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READING
ROOM
ON
SITE
NOT
ONLINE
OPEN
British Library
£
Labs Residency Model
Challenges of access to Digital Collections

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Accessing digital collections onsite
OPEN
£
• Have to be ‘onsite’ (interpretations vary)
• Need to be ‘security cleared’ ‘trusted’ for some collections
– Hence ‘Researcher in Residence Model’
• Permission required (depending on ‘story’ of collection)
• Content could be on various media formats
(not always online)
• 5 - 20 % re-use of material for non commercial research for
some collections, depends on agreements in place
• We are learning ‘pathways’ so that this becomes ‘everyday’ to
provide onsite access to some digital collections in the future

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Why are we doing this?

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Why are doing this? (1)
Working closely with and listening
to those who want use our digital
collections and data for their work

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We can learn how we are and should be supporting them and
this therefore shapes the problems we work on, such as:
• Access to digital collections / data?
• Advice, guidance, technical
support, training
• Services, Tools and Processes?
• Many more reasons…

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Where are the gaps between what users want & what we can
give?
How do we build the bridges to overcome the gaps?
https://goo.gl/6CwCeE

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How do we help users ‘navigate’ their way through the
‘maze’ of the Library to what they want to do?
Sometimes requires understanding the culture of the organisation
https://goo.gl/62JnQT

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What did people
actually do?
Examples from Text and Images
Over 100 examples (including sound, video):
http://labs.bl.uk/Ideas+for+Labs
http://labs.bl.uk/Other+Uses+of+Collections

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Example Pattern of Research
1, 2, 3
1. Find / identify new things in messy stuff
2. Unlock hidden history / data
3. Celebrate new discoveries

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Finding / identifying invisible / well hidden
things in ‘messy’ historical data
https://goo.gl/mcpa8B
Not the British Library!
Example Pattern of Research 1

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Messiness in historical data
• 'Begun in Kiryu, Japan, finished in France'
• 'Bali? Java? Mexico?'
• Variations on USA:
– U.S.
– U.S.A
– U.S.A.
– USA
– United States of America
– USA ?
– United States (case)
• Inconsistency in uncertainty
– U.S.A. or England
– U.S.A./England ?
– England & U.S.A.

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Open Refine

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• Cultural heritage records contain uncertainty and fuzziness (e.g. date ranges, multiple
values, uncertain or unavailable information)—Curators and staff at institutions often
have unique expertise in deciphering these anomalies-ask them! ( [1960] vs.1960 can
have a big impact depending on what you’re doing)
• Optical Character Recognition in particular is an imperfect art-need to consider how
bad it is, how this might effect your findings, and what needs doing to mitigate it.
• Keeping data clean, organised, open and described well will not only make your life
easier, but enable its widespread re-use beyond and increase future impact. (Datasets
you’ve created in the course of your research projects could even be used to enhance
national collections!)
• Decisions always need to be made while normalising information for visualisation.
Documenting them is important for your research but also future re-use!
• Is your aim enquiry or presentation? All of this will have an impact on the tools and
data cleaning choices you make.
Things to consider: Data + Tools

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#digitalhumanities
dancohen/lists/digitalhumanities
@ProfHacker
@Dhnow
@BL_DigiSchol
And more links to resources here: http://scottbot.net/teaching-yourself-to-code-in-dh/

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Unearthing / unlocking
hidden histories & data
to stimulate new research
https://goo.gl/vJ291F
It’s an
18th Century Poem!

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Celebrating hidden histories / data
creatively through events, art &
performance
https://goo.gl/Ql0Bwz
Re-enacting, re-discovering history

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Experiments with Text

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https://goo.gl/oUNj5N
https://goo.gl/ImAUv4
Finding things in ‘messy’
Optical Character Recognised (OCR) text
Mrs Folly
• Clean up some manually
• Get human ‘ground truth’
• Write computer code (sometimes
it’s machine learning) to find
things reliably in it ‘automatically’
• Try code on messy content
• Tweak if necessary
• Digital ‘lasso’ around content
• Human sift through
Mrs Folly
An example pattern of research

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Looking through a rubbish bin?
https://goo.gl/UeEvqs
Good stuff! Some Rubbish

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Let’s use Text and Data Mining
Machine Learning / Reading!

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Smell of soup & Machine Learning
Thanks to Memo Akten (@memotv on twitter) for the inspiration!
https://goo.gl/toq4Bo
Nasreddin, 13th Century Turkish Sufi
http://web2.uvcs.uvic.ca/elc/studyzone/330/reading/smell1.htm

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Machine Learning / Reading
Analogies to how humans read / learn
Machines acquire ‘knowledge’ / data, use that
knowledge / data to make sense / identify patterns
https://goo.gl/k68fTf
https://goo.gl/gXmVQL Can you see the bird?

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Need to stress still requires computational
& human effort…
Smart data needs smart people!
https://goo.gl/gDQEAz
Labs doing this on a case by case basis
so methods can vary
Machine Learning / Reading still
requires ‘Human Effort’!

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Legalities of Machine Learning /
Text and Data mining
https://goo.gl/toq4Bo
Legalities of Machine Learning / Text and Data
mining still up for discussion…Often misunderstood
Is it the same as humans reading and looking for
patterns…just a bit quicker?

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http://victorianhumour.tubmblr.com
Victorian Meme Machine (2014)
https://goo.gl/HMqDt3
Bob Nicholson
http://victorianhumour.tumblr.com/
Bob Nicholson interviewed on
BBC Radio 4 Making History Programme:
http://goo.gl/fmV9ep
And telling jokes to the public:
http://goo.gl/xIDRhz
Bob obtained further funding from his university
Looking for more collaborations
https://www.youtube.com/watch?v=-GRgj7Q5OM0
Rob Walker, Victorian Mother-in-law Jokes
Victorian Comedy Night, 7 Nov 2016
Learnt about access paths
to digital collections

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Play from 1m 5 seconds t o 1m 31 seconds

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Katrina Navickas (2015)
Political Meetings Mapper
http://politicalmeetingsmapper.co.uk
https://goo.gl/Qq78Oa
Labs Symposium 2015
https://goo.gl/BSA3be
Interview 2015
The Chartist Newspaper
http://goo.gl/vOLSnH
Chartist Monster Meeting
Chartists Walking Tour and
Re-enactment London
Learnt that domain knowledge
reduces noise

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Play for 36 seconds

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Black Abolitionist Performances & their
Presence in Britain (2016) – Hannah-Rose Murray
Frederick
Douglass
Ellen
Craft
Josiah
Henson
Ida B
Wells
A Performance by
Joe Williams &
Martelle Edinborough
http://frederickdouglassinbritain.com/
Started to implement
Machine Learning Techniques

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Data-mining verse in 18th Century newspapers
BL Labs Project 16-17, Jennifer Batt
https://goo.gl/5Akthd
Slides courtesy Jennifer Batt

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What thoj' among ourrelves, with too much Heat, or t
W: fweutimes.wongle, wvhen we Ihould debate, W –
(A confequential Ill which Freedom drawvs, fl t
A bad Efficf, but from a noble Caufe) t
We can with univeifal Zcal advance, to
To cutb the faithlefs Arrogancccof V rance. hi
Dublin Journal, 10-14 September, 1745 Slides courtesy Jennifer Batt

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Verse: 81% lines begin with
initial capital
Prose: 52% lines begin with
initial capital
Westminster Journal 3 March 1745
Slides courtesy Jennifer Batt
Started to refine
Machine Learning Techniques
Jennifer Batt @ the BL on World Poetry Day
‘40,000’ things found…
Possibly using Gale Primary
Sources interface to see if we
can sift this data

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Use of Overproof
OCR Correction?
Re-OCR with
ABBY FineReader?
https://www.abbyy.com/en-gb/
http://overproof.projectcomputing.com/
RE-OCR
Cleaning up OCR Text – significant improvement
up (depending on original image quality)

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Virtual Infrastructure for OCR text
OCR text ‘scraped’ from
digitised newspapers
and put in internal cloud
Jupyter notebook
Write python code and results
in web browser
http://jupyter.org
Access available for researchers ‘in residence’
https://www.docker.com/
http://dhbox.org/

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Experiments with Images

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65,000 digitised 19th Century books
Image: Artwork by Alicia Martin 2007 / 2008
Paid for by:
For a full list:
https://goo.gl/HqPQMS
Subjects include:
Philosophy
Poetry
History
Literature
1789 - 1876

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30 August 2012

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002819694
Unique number

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OCR XML Generated by ABBY Fine Reader
Optical Character Recognition

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Images from books captured too!

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We did some of our own
experiments…do as we tell others!
Experiment with our
Digital Collections@BL_Labs

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Ben O’Steen of @BL_Labs after Hack Event, August 2013

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1,020,418
images
needed identifying!

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One major problem!
•We know about the books these images come
from but we know nothing about the actual
images!
•How will we identify them?
•How will we find them later?
•How can we do that with 1 million images?
•Try a few experiments!

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Running face recognition
on the images
Face Recognition Algorithm
Trained on Photographs

Late August 2013

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Face Recognition
Algorithms worked
better for female
faces than men’s

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The Mechanical Curator
Snipped image posted
almost randomly
every hour…
on a Tumblr blog
One of our early followers was…
Ben O’Steen, 30 September 2013
Has a slight ‘mood’…
once image published,
tries to find 8 similar images
e.g. ‘slanty’, ‘circular’ etc.
& then gets ‘bored’
follow…
@MechCuratorBot

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Where to put the 1 Million images?
• Internal platform? Would take too long!
• BL Labs is a time limited project, needed evidence of impact
and good example to show the Library what we need, fast!
• External platform? Put our content where the ‘light’ is!
• Wikipedia / Wikimedia –no! Not enough metadata for the
images.
• Another platform that meets our needs…
• Flickr Commons

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Flickr Commons (100 + GLAMs as of 08/12/17)

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British Library Flickr Commons
Why Flickr Commons?
• Free!
• Each image has it’s own unique web address, easy to share
• Can Tag images
• Has Application Programming Interface (API)
Late August 2013

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Opportunities
– increasing traffic to Library services
You can purchase
a ‘High Res’ Copy
View in the
Library Item Viewer
Download .pdf
All illustrations
in book
Other illustrations in books
Published in same year
View the item in
the Library Catalogue Tags auto generated
User generated
Tag
Grouping for image

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Worked better for female faces than men’s
Press
http://mechanicalcurator.tumblr.com
Posts image every 30 minutes
http://www.flickr.com/photos/britishlibrary/
1,020,418 images
need tagging!
Creative uses of images
Face recognition
Algorithms based on photos
Mechanical Curator
with an algorithmic brain
(Circles, Squares and Slanty etc)
http://goo.gl/qPPgxX
Wikimedia
Flickr Commons
Individual URL & API
Snipping out images
from 65,000 Digitised Books*
>800,000,000* views
>17,000,000* tags
https://goo.gl/FgZ4HM
Work @ BL by Ben O’Steen, Labs
and Digital Research Team*Matt Prior - http://goo.gl/j29Tnx
Since Dec 2013
Tumblr
*Estimates
>More demand to see
physical items

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Tagging, Tagging, Tagging…

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Tagging a million images
Iterative Crowdsourcing
http://goo.gl/j6fxac
Cardiff University’s
Lost Visions Project
http://www.metadatagames.org/
Metadata Games
James Heald
Mario Klingemann
Chico 45
Use computational methods
Human Tagger
Top British Library Flickr Commons Taggers
18 hard core taggers
How to reward and keep motivated this ‘small group?
Average for ‘crowd’ is 1 tag per person
What kind of ‘task’ can this ‘crowd’ do?
Mobile games for ‘Ships’, ‘Covers’ and ‘Portraits’ Interface for tagging

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Adam Crymble (2015)
Crowdsource Arcade
http://goo.gl/LBfJ4W
http://goo.gl/OH9pOZ
https://goo.gl/7z0j8p
30 mins talk
Labs Symposium (2015)
https://goo.gl/SSRsdd
5 min interview (2015)
http://goo.gl/0APpE8
Game Jam
Using Arcade Games
to help Tag images
‘Art Treachery’ and ‘Tag Attack’

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Play for 42 seconds

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Special Jury’s Prize (2015)
James Heald – Wikimedia and Map work
https://goo.gl/WYZCB2
http://goo.gl/HNQq5e
https://goo.gl/VPgffL
https://commons.wikimedia.org/
https://goo.gl/djtm1b
Labs Symposium (2015)Geotagging maps
50,000 Maps
Found in Flickr 1 million
Human & Computational Tagging
& Community engagement
Geo-referencing work
https://www.bl.uk/georeferencer

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SherlockNet: Competition Winner 2016
Karen Wang, Luda Zhao and Brian Do
Using Convolutional Neural Networks to Automatically Tag and Caption
the British Library Flickr Commons 1 million Image Collection
12 categories
>15.5 million tags added
>100,000 captions
bit.ly/sherlocknet
Pooled surrounding
OCR text on page
from similar images
Used Microsoft COCO (photographs) &
British Museum Prints and Drawings
collections as training sets.
Tags Captions

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Artistic / Creative Works
http://goo.gl/dM8ieA
Mario Klingeman (2015)
Code Artist / Curator
https://www.youtube.com/watch?v=Q3SBxO34Zlc
David Normal 2014 and 2015
Collages/Paintings & Lightboxes
http://goo.gl/bNxGZZ
Kris Hoffman (2016)
Animation for Fashion Week 2016
https://goo.gl/QilqqT
Jiayi Chong 2016 - Animation tool
https://www.facebook.com/RealmlandStory/
Paul Rand Pierce 2016
Graphic Novel on Facebook
Tragic Looking Women
44 Men who Look 44
(Notice the direction faces)
A Hat on the Ground
Spells trouble

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Imaginary Cities – BL Labs Project 16-18
Michael Takeo Magruder
An artistic exploration seeking to create provocative fictional cityscapes for the Information Age
from the British Library’s digital collection of historic urban maps

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British Fashion Colleges Council and
Teatum Jones
Alanna Hilton

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Lessons Learned & Challenges…
It all starts from a conversation
• Start with a conversation, our data isn’t visible highly on search engines
(yet!) & not easy to find. Need to create and embrace serendipity &
opportunities for use by talking!
• Need to have several conversations with several stakeholders & tap into
their tacit knowledge that isn’t always written down sometimes to progress
ideas.
• Often misunderstandings because of jargon & different meaning of words.
https://goo.gl/XaHYT9
?
Audience
research &
Digital
interests
Digital
collections
we have
This is where Labs works

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Expectations change when researchers
actually see the data, systems &
experience the ‘culture’ of the organisation.
https://goo.gl/ytmWnu
Expectations change with researchers see the data

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Opening & using digital collections occasionally
requires a need to let go of the emotional &
psychological connection to them
Letting ‘go’ of the connection to collections…
https://goo.gl/OYAsmK

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Embrace dirty data, it may never be perfect!
Cleaning data is hard work & resource consuming!
Embrace ‘dirty data’…use it or clean it!
https://goo.gl/mcpa8B
Not the British Library!

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Careful of making conclusions based on
‘black box’ software & techniques (e.g.
sentiment analysis), learn the assumptions
behind them first!
Beware of ‘Black Box’ software…

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We tend to work with researchers who can be
‘flexible’ with their research questions & are
willing to embrace challenges, collaborate, the
pioneers!
Work with ‘flexible’ collaborative researchers…
https://goo.gl/wMTS3Z

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Many researchers have the domain knowledge
but lack technical / digital skills to use Digital
Research methods.
Should they be teamed up with those that want
to solve problems or get trained?
Digital skills training needed for Humanities researchers…
https://goo.gl/i5GVfI
https://goo.gl/kwcK8J

109
Huge appetite to use digital content & data
for anyone’s ideas!
(e.g. Flickr Commons stats).
Huge demand for open digital content…
https://goo.gl/yQ5s4U

110
Labs mindset…
1. Start a conversation, generate positive energy,
be kind, have fun and try to support ideas
2. Start with small experiments, but think big.
3. Fail faster (don’t be afraid) and persevere.
4. Reject perfectionism! Good enough is
sometimes…good enough!
5. Celebrate the uses of digital collections
https://goo.gl/noASfl

111
https://goo.gl/SUOO0J
The Magic of Openness!
• If digitised / digital collections are not
used, what is the point of digitising /
keeping them (i.e. apart from
preservation)?
• Opening up our digital collections offers
new ways for the Library’s content to be
re-discovered, remixed, re-imagined and
‘re-energised’
• Generates plenty of examples to inspire
use by others!

112
Hey there Young Sailor!
Ling Low 2016 – Hey there Young Sailor
https://www.youtube.com/watch?v=bcOP1E5bRE0VIMEO.COM/SWEETANDLOWFILMS
@SWEETNLOWFILMS ON INSTAGRAM
@SWEETNLOWLING ON TWITTER
The Impatient Sisters
Play to fade!

113
Explore or Imagine Our Data!
• CSV of Metadata
https://data.bl.uk/digbks/dig19cbooks-mdata-csv.csv
• 19th Century Books - Book Metadata - 01/09/2013.
https://data.bl.uk/digbks/db21.html
• Digitised Books - Flickr Tag History - Dec 2013 to March 2016.
TSV
https://data.bl.uk/digbks/db15.html
• Digitised Hebrew Manuscripts - Metadata
https://data.bl.uk/hebrewmanuscripts/heb1.html
• Digitised Hebrew Manuscripts: Or 2210 - Or 2364
https://data.bl.uk/hebrewmanuscripts/heb8.html
• Theatrical playbills from Britain and Ireland (OCR text only)
https://data.bl.uk/playbills/pb2.html
• Portraits of actors, views of theatres and playbills (covering
1750 - 1821 in a single volume)
https://data.bl.uk/singlesheet/por1.html
• Volumes of Lysons Collectanea (Amusements), comprising
broadsides, cuttings, advertisements on amusements.1660-
1840.
https://data.bl.uk/singlesheet/ad1.html
https://data.bl.uk
• Have a look at the data.
• Data Quality
• Issues
Or an idea you have thought of
what to do with the data!
http://labs.bl.uk/Ideas+for+Labs
Smaller datasets

114
The Future of BL Labs
• Continue to engage with researchers
• Learn what they want to do
• Collect evidence of demand
• Develop Business Model and Support
process to make ‘Business as Usual’ at
the British Library
• Help to create pathway to developing
a ‘Digital Research Suite’ at the
British Library by 2019
http://www.library.pitt.edu/digital-scholarship-services
https://goo.gl/W4TjGt
• Many are being ‘inspired’ by our
model…

115
Business Model Canvas
https://www.youtube.com/watch?v=QoAOzMTLP5s

116
Business Model Canvas – Digital Research Lab – British Library
Key Partners Key Activities Value Propositions Customer
Relationships
Customer Segments
Key Resources Channels
Cost Structure Revenue Streams
http://www.businessmodelgeneration.com

117
Alejandro: Master’s student in Comparative
Literature (Researcher Technical)
• Alejandro wants to speak to somebody about working on a project which will build and analyse a corpus from digitised
literary works and newspaper articles first published from 1950 to the present day. He is particularly interested in how
humorous prose of PG Wodehouse has permeated literary works.
• Alejandro is confident in using digital research methods and tools to help with his research as his undergraduate degree
was in Computer Science. He is a talented programmer, having contributed to a number of Open Source projects and
winning a number of software development competitions at university.
• Research Question/Enquiry: How has the humorous prose of PG Wodehouse permeated modern literary works?
• Services Required:
• Access to the relevant digital collections and data for his research.
• Advice on the potential legal restrictions on carrying out his work.
• Ability to create and store the corpus and use computational tools to analyse it.
• Share the results under an open access license
• Download the final results of his research, and cite the derived datasets on data.bl.uk.

118
Rosslyn: PhD student in Gender Studies
(Researcher Non-Technical)
• Rosslyn is a gender historian developing research questions around how gender-specific terms are used in writing and how they
may change over time. She thinks she would like to use the UK Web Archive as one of her corpora for this and has a vague idea
that she may need to employ digital research methods after reading an article about natural language processing.
• Rosslyn has never used these methods before and has basic digital skills (e.g. MS Office). Rosslyn would like to sit down with
someone to discuss her idea and get guidance about moving this project forward. She is happy to collaborate with other
researchers.
• Research question/Enquiry: How do gender-specific terms change over time in the UK Web Archive?
• Services Required:
• Access to expert information, advice and training on the appropriate digital research methods to use for this project.
• Case studies and connections / collaborations with other researchers who can help her understand their approaches and
experiences using BL digital collections and data for similar projects.
• Getting a clear understanding of the UK Web Archive, how it can be used, its limitations / challenges and whether it is the
appropriate corpus to carry out her research.
• Ability to create and store the corpus and use computational tools to analyse it.
• Share the results under an open access license.
• Download the final results of his research, and cite the derived datasets on data.bl.uk.

119
Discussion
• Have a look at the Personas
• Write your own
• We will discuss your needs
• Choose one for Value Proposition

120
Value Proposition Canvas
Customer SegmentValue Map

121
Customer Segment
• Customer Jobs
– What are customers trying to get done in their
work?(Important – Insignificant)
• Pains
– What are the bad outcomes, risks, obstacles related to
customer jobs? (Extreme – Moderate)
• Gains
– What outcomes do customers want to achieve or concrete
benefits they are seeking? (Essential – Nice to have)

122
Value Map
• Pain Relievers
– How products and services alleviate customer pains?
• Gain Creators
– How products and services create customer gains?
• Products and Services
– List all Products and Services value proposition is built
around

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