DH Project Management

1
mahendra.mahey@bl.uk & 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
Project Management and DH projects
1500 - 1630, Monday 18 December, 2017
CHASE AHDA Winter School 2018
The Open University in London/Futurelearn,
1-11 Hawley Crescent, Camden Town,
London, NW1 8NP

2
Breakdown of session
• Introductions
• Background to BL Labs and DH Projects
• Developing project ideas as proposals, tips and challenges
• Feedback and suggestions

3
What is Project Management?
1) Can range from informal and small scale focusing largely
on common sense flexible approaches to management…
2) To large scale formal approaches using methodologies
such PRINCE 2 (PRojects IN Controlled Environments),
AGILE, SCRUM and tools such as MS Project
• BL Labs uses both
• Focus on the first one to help develop your own ideas
• Not a session on Project Management!

4
Introductions
• Name
• Affiliation
• Project idea (one sentence)

5
The British Library
Inside the British Library
Space for 1200 readers, around 500,000 visitors per year
Building 37 uses low oxygen and robots
Boston Spa also has a Reading room and provides delivery of items 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 1759
Boston-Spa

6
BL Labs supports…
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
Anyone
interested in our
digital collections
and data

7
Physical 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 George IV bequeathed Library *Estimates

8
Born Digital Digitised
Let’s talk Digital…

9
/
Knowledge Quarter London
80 knowledge organisations (as of 07/12/17) 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!

10
All our physical
items are digitised
right?

11
#bldigital
1-2 %* digitised
* estimate
Digitisation
Partnerships
Commercial & Other Organisations
Amount
increasing rapidly
Bias in digitisation
So learn the story behind
the digital collection
http://goo.gl/bR9UJL
Sample Generator

12
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

13
Finding Open Cultural Heritage Datasets
Collection Guides (183 as of 05/12/17)
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!

14
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

15
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 (693 as of 08/12/17)
Largest proportion of funding
Public / Private Partnership
15 %* Openly Licensed – most online
85 %* Available onsite only at the moment
*Estimates

16
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…

17
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

18
Example Pattern of Research
1, 2, 3
1. Find / identify new things in messy stuff
2. Unlock hidden history / data
3. Celebrate by telling new stories!

19
Finding / identifying invisible / well hidden
things in ‘messy’ historical data
https://goo.gl/mcpa8B
Not the British Library!
Example Pattern of Research 1
Some of the challenges we face at the Library

20
Unearthing / unlocking
hidden histories & data
to stimulate new research
https://goo.gl/vJ291F
It’s an
18th Century Poem!

21
Celebrating hidden histories / data
creatively through events, art, performance
and story telling
https://goo.gl/Ql0Bwz
Re-enacting, re-discovering history

22
Experiments with Text

23
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

24
Looking through a rubbish bin?
https://goo.gl/UeEvqs
Good stuff! Some Rubbish

25
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?

26
Need to stress still requires computational
& human effort…
https://goo.gl/gDQEAz
Labs doing this on a case by case basis
so methods can vary
Machine Learning / Reading still
requires ‘Human Effort’!

27
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?

28
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

29
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

30
Data-mining verse in 18th Century newspapers
BL Labs Project 16-17, Jennifer Batt
https://goo.gl/5Akthd
Slides courtesy Jennifer Batt

31
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

32
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…

33
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)

34
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/

35
BL Labs Competition Entry Process
• Think of a project which uses the British Library’s Digital
Collections or Data
• Examine our data and discuss idea
• Propose mini project
• Proposals assessed and successful ones worked on
• 3 examples from 2014, 2015, 2016 given

36
Elements of Proposal
(https://goo.gl/K85hTQ)
• Title and Summary
• Research Question(s)
• How it showcasing digital collections / data
• Methods (text mining, visualisations, statistical analysis)
• Evidence of how you have or will develop the skills, knowledge and
expertise to successfully carry out the project
• Evidence of idea is achievable on a technical, curatorial and legal basis
• Plan
• Risk assessment* (new suggestion)

37
Title and Summary
• Try to summarise the project as once sentence
• Abstract / Summary around 400 words

38
Research Question(s)
• This/these should be very clear

39
Showcasing digital collections / data
• How does your idea showcase digital collections / data
• Have you seen the digital collections and data?
• Do you know the ‘story’ of the collection?
• What state is it in?
• Have you done some initial experiments?
• Will it require cleaning, e.g. using tools like open refine?
• Reality check in terms of what you can actually achieve with the data
will determine idea and scope

40
Methods (text mining, visualisations,
statistical analysis)
• Think of what is going to be required to implement methods, e.g. skills,
time and other resources
• Plan accordingly
• Tools required / software / hardware

41
Evidence of how you have or will develop the
skills, knowledge and expertise to successfully
carry out the project
• List skills, presentations, publications etc.
• Are there gaps?
• How are they are going to be filled?

42
Evidence of idea is achievable on a
technical, curatorial and legal basis
• Technical factors
• Is the project technically feasible?
• Whether the technical skills required to complete the project and who
will be required to implement them have been clearly identified.
• Legal factors
• Whether the legal terms of use for the digital collections identified have
been checked and compliance demonstrated in the proposal.
• Whether the idea contains information that ensures the project does not
in any way infringe intellectual property rights or any other rights of any
third party.

43
Evidence of idea is achievable on a
technical, curatorial and legal basis
• Curatorial factors
• Can it be demonstrated that the digital content is available, accessible
and can be realistically used for the project?
• Background research for people connected to the collection / the story
of the collection
• Is any extra worked required to make the digital content usable for the
project has been clearly identified (where appropriate).

44
Plan
• Define period of time X and Y
• Activity described here (e.g. what, when and by who)
• Break down into manageable chunks / units
• Can run parallel
• Build in reasonable review points and lag.
• How will be it be monitored?
• It’s a plan, it can change!

45
Risk
• Have a view of assessing risks
• Risk / Mitigation / Likelihood / Impact
• Use Low, Medium and High for Likelihood and Impact
Risk Mitigation Likelihood
(after
mitigation)
Impact
Insufficient support from UK
research councils.
Build compelling case.
Carry out research to gauge
demand and commitment to
resourcing.
Adapt model according to our
findings.
M H

46
Labs mindset…
1. Start a conversation, generate positive energy
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

DH Project Management

More Related Content

Similar to DH Project Management

More from labsbl

Recently uploaded

DH Project Management

Editor's Notes