This blog post was written during a presentation at the British Library Labs Symposium in November 2014. It is likely full of errors and omissions having been written real-time.

Adam Farquhar, Principal Investigator of the British Library Labs project

In summer 2014 BL ran a survey to improve understanding of digital research behaviour. Around 1600 particpants, 57% femail, 50% academic inc. 32% postgraduates. Nearly 75% were registered readers at the BL. 58% from Arts & Humanities, 21.5% Social Sciences, 13.1% STM. 42.4% from London and a further 35% from other parts of the UK.

92% would recommend the library and 82% said the Library plays an important role in digital research – which was 3 times more than the result for the same question in 2011.

63% of users are satisfied with BL digital services – remote access to more BL electronic resource, the option to view BL digital content on personal devices could improve this.

Some things not changing – perhaps against expectations. Most readers work alone still but using social media more than previously.

1 in 6 respondents were using programming in their research.

Digital collection at BL has been growing rapidly – now around 9million items (huge jump in 2012 from under 2 million to almost 7 million). But remember a book counts as one item – even if many images and pages made available separately, and an ‘item’ in the web archive is a WARC file that can contain many thousands of websites. Looking at size of content in gigabytes the growth is more linear.

The Digital Collections are extremely varied – datasets, images, manuscripts, maps, sounds, newspapers, multimedia, books and text, web archive, journal articles, e-theses, music, playbills.

Lessons from work so far

• Lesson 1: More is more
  • it’s about digital content – without this you can’t do digital scholarship. Getting the digital content is “bloody hard work”
  • digital deposit coming and will be the basis for the national digital collection in years to come – but not a panacea
  • partnerships – e.g. DC Thomson for further Newspaper digitisation
  • partnership with Google to digitise around 250k works
• Lesson 2: Less is more
  • Delivering a single ‘perfect’ system won’t be perfect for everyone
  • Deliver people more systems that give more access to more content
• Lesson 3: Bring your own tools
  • People want to bring their own tools with them – need to enable this to happen
• Lesson 4: Be creative
  • Let people be creative with the content
• Lesson 5: Start small – finish big
  • Easy to start with small things – 5 books, 50 books – do this before trying to work with larger collections

Conclusions
* Researchers are embracing digital technology and methods
* Digital collections with unique content are large enought to support research – with some caveats
* Library staff need training to keep pace with change
* Open engagement fits ermeging practice
* Radical re-tooling is needed to support researcher demands…
* … but existing technology provides what we need

This blog post was written during a presentation at the British Library Labs Symposium in November 2014. It is likely full of errors and omissions having been written real-time.

Peter Balman, software developer

“Visibility” is a project, funded by money from the ‘IC Tomorrow’ (BL and TSB initiative) v important to institutions like the BL who are releasing data publicly and want to understand the value and impact of doing this

The challenge:
“This challenge is to encourage and establish the necessary feedback to measure the use and impact o f public-domain content”

Looking at the BL release of images under CC0 licence on Flickr. What is the value? what is the ROI?

What can we look at?
* How often is an image used
* What are the demographics of those using the images
* What do people talk about when they use images or refer to images from the collection

Where to start?
BL know anecdotally of re-use, but no knowledge about which images being used, and what proportion of collection being used?
The ‘journey’ of an image in the collection isn’t a linear narrative – it is a tree branching off in different directions.

Approaches:
* Take small section of collection and examine in depth
* Look at all million images and crunch the data

Peter aiming to build an application where you can look at an image, and look at information about how it is being used, mentioned etc., and finally promote images in terms of how they’ve been used.

For each image:
* Search web for the image (e.g. with Tineye, Image Raider)
* Natural language processing on the related page looking for context
* Once you have data what do you do? Organisation of data into categories as per the LATCH theory (time, category, place)

Product ready and starting to crunch data, looking for more institutions to test the tool.

This blog post was written during a presentation at the British Library Labs Symposium in November 2014. It is likely full of errors and omissions having been written real-time.

Adam Tovell, Digital Music Curator, British Library & Daniel Wolff, City University

Goal is to develop research methods and s/w infrastructure for exploring and analysing large-scale music collection & provide researchers and users with datasets and computational tool to analyse music audio, scores and metadata.

• Develop and evaluate music research methods for big data
• Develop and infrastructure (technical, insitutional, legal) for large-scale music analysis
• Develop tools for larg-scale computational musicology
• Use and produce Big Music Data sets

It is possible to use software to analyse aspects of a musical recording. For example looking at:
* Visualisation
* Timings
* Intonation
* Dynamics
* Chord progressions
* Melody

Derived data from s/w analysis can be used to inform research questions.

So far these approaches have been applied to small amounts of music

Field of Music Information Retrieval apply the same techniques to larger bodies of music. These kinds of approaches are behind things like some music recommendation services.

To bring together MIR techniques with musicology academic research approaches need a large body of recorded music – which is where the BL music collection comes in – enabling Large-scale Musicology. BL has over 400 different recording of Chopin’s Nocturne in E-flat major op.9, no.2 – you can ask questions like:
* how has performance changed over time?
* do performers influence each other?
* does place affect performance?
* etc.

BL music collections have over 3 million unique recordings covering a very wide range of genres – popular, traditional, classical, with detailed metadata and a legal framework for making them available to people – sometimes online, and sometimes on-site.

Musicological Questions
* Automatic analysis of scores
* structural analysis from audio
* analysing styles & trends over time
* new similarity metrics (e.g. performance based)
* …

Data sets currently being used:
* British Library – currently curating available music data collections from BL sound archive (currently done around 40k recordings)
* CHARM – 5000 copyright-free recordings + metadata
* ILikeMusic – commercial music library of 1.2M tracks

Analysis results so far:
* ILikeMusic – chord detection
* CHARM – instrumentation analysis
* MIDI-scal transcription
* High-res transcription (create scores from recording)
* BL – key detection, + more

Visualisations – available at http://dml.city.ac.uk

Automatic Tagging – e.g. genre, style, period. To expensive to tag large datasets, automated classification challenging especially without ‘ground truth’.

This blog post was written during a presentation at the British Library Labs Symposium in November 2014. It is likely full of errors and omissions having been written real-time.

Dr Beatrice Alex, University of Edinburgh

Looking for mentions of places in Edinburgh using data sources including:
* HathiTrust
* British Library Nineteenth Century Books Collection (main source)
* Project Gutenberg
* Oxford Text Archive data

Interested in using EEBO/ECCO

Workflow:
* Digitised documents from collections above
* Document retrieveal and filtering -> to get ranked lists of Edinburgh specific candidates
* Manual curation – curation of Edinburgh specific literature – need a human in the loop to get the level of detail they desired
* Text minimg – fine-grained location extraction and geo-referencing using the Edinburgh Geoparser
* All data stored in database that then powers the visualisations etc.

Big data IN -> Small data OUT

All input documents must first be:
* Converted to a common format
* Identified as written English text
* Post-corrected automatically if necesssary
* Linguistic pre-processing

• Document retrieval. The goal is to find all Edinburgh loco-specific items which fit our remit (fiction, autobio, travel)
• Get ranked dcouments
• Assisted Curation is done with Palimpsest Annotation Tool (developed at St Andrew’s). Human makes decisions about whether items are ‘in or out’ (e.g. poetry marked as such and then excluded for the moment – may come back to this later)

Gazetteer Creation
* Text minign tools use the Edinburgh Geoparser to mark-up place names and resolve them to coordinates with a choice of gazetteer as the reference source – e.g. Geonames

Not all place matches in the gazetteer are interesting to the project – e.g. ‘Spring’. Clean these out. Have built the gazetteer and now building on this – e.g. want to do further linguistic analysis, building a mobile app so you can explore the literature based on your location

Final outputs will be web-based visualisations and a mobile app – the aim is to create interfaces for both literary scholars and the general public.

This blog post was written during a presentation at the British Library Labs Symposium in November 2014. It is likely full of errors and omissions having been written real-time

Bob Nicholson from Edge Hill.

Victorian’s not associated with humour – “We are not amused”. But jokes were everywhere in Victorian culture – perhaps forgotten or downplayed – you can quote from the great Victorian literature, but what is your favourite Victorian joke?

Jokes reveal lost of things – slang etc. Were an area of existing research for Bob.

Initial Idea:
* Find way of extracting jokes from newspapers
* Start marking up jokes with metadata/semantic tagging
* Try to find suitable image from the BLs image collection
* Overlay text on a suitable image to push out to social media

Issues:
* Where to look?
* Books – e.g. “Book of Humour, Wit and Wisdom” – a joke book. Manually extracted these
* Newspapers – many had weekly joke columns – e.g. 20 jokes per week over many years – thousands of jokes
* Existing markup breaks newspapers down to columns
* But difficult to get access to the source data in appropriate format
* Have manually downloaded and extracted for now
* OCR/Transcription
* Poor OCR not good enough for re-publishing the jokes
* Need to use manual transcription
* Using Omeka to provide transcription platform (using ‘Scripto’)
* Quicker to type up text than markup broken OCR
* Simple xml markup j = joke, t = title, a= attribution
* Want to go further – mark up names, dialogue
* Publishing Jokes
* Original idea of putting speech bubbles on pictures extremely challenging
* Instead putting jokes next to image of person – as if they are telling the joke
* Looking for images that can be used in this way
* Would also like to find images that would work for dialogue style jokes
* Ideally would like to be able to use images which somehow add to the narrative of the joke

What Next?

Coming soon “The Mechanical Comedian” – will tweet a joke each day
http://www.victorianhumour.tumblr.com
@victorianhumour

Eventually will publish database of jokes at http://victorianhumour.com

Will start inviting users to re-interpret jokes – trying to make terrible jokes funny again

All tools used in the project have been free and open source. Allows you to get started cheaply.
Next:
* Seek external funding & new partnerships
* Expand and automate joke extraction
* Implement a new transcription platform
* Develop an accessible online database of jokes

Big picture

Repurposing – difficult to use the digitised versions of newspapers
Remixing – bringing together disparate elements
Gamification – new ways of engaging people with the material
Labs – has allowed Bob to bring an idea and to start experimenting

To follow
www.digitalvictorianist.com
@digivictorian

This blog post was written during a presentation at the British Library Labs Symposium in November 2014. It is likely full of errors and omissions having been written real-time.

Why this tool?

Libraries contain thousands of literary & documentary artefacts up to 4000 years old. How to bring these effectively to a modern audience.

“Images of their own are dull” – browse interfaces tend not to give the user much information. Even at the page image level, it can be difficult to make sense of what you are seeing.

One approach is to put the text on top of an image:
* Correltates words in image/text
* can be searched but…
* only works with OCR
* if text has errors, hard to fix
* text can’t be formattted or annotated

Another approach is to put the text next to the image:
* format text for different devices
* can annotate test for stufy
* easy to verify and edit
* BUT
* must keep image and text in sync
* increases mental effort to find corresponding words in the text/image

If you are going to link text to the image of the text, what level should you do this at?
* ilink at page-level – useful but too coarse. Doesn’t reduce mental effort much
* link at line level
* link at word level

Word level probably most desirable, but how to achiev it?
Manual approach:
* Manually draw shapes around words
* link them to the text by adding markup to the transcription
* BUT
* tedious & expensive
* markup gets complex
* end up needing multiple transcriptions

TILT approach:
* find word in an image without reconginsing their content
* Use an exsiting transcripto f the page content
* Link these two component mostly automatically

Design:

TILT Service
^ ^ ^
Image Text or HTML GeoJSON
^ ^ v
TILT web-based GUI

First you have to prepare image in GUI – identify different parts of the text
Stages:
* Colour to greyscale
* Greyscale to Black and White
* Find lines
* Find word shapes
* link word-shapes to text

Recognising words is a challenge:
* (in most languages) Words are blocks of connected pixels with small gaps between them
* But if there are 300 words on a page are the 299 largest gaps always between words

How to represent word shapes? Simple polygons do the trick

Measure width of words in text, and then tries to match against lengths in transcription – so if the word shapes have not been recognised correctly, the matching algorithm just selects more or less text in the transcription.

Now looking at using of ‘anchor points’ in text that allows the user to identify the start and end of ‘clean’ text in a larger manuscript which might have messy sections that can’t be done automatically. This allows you do what you can automatically, and only deal with the messy bits in a manual way.

Still working on a GUI to work with

Code on GitHub