Digital scholarship blog

Enabling innovative research with British Library digital collections

268 posts categorized "Data"

23 December 2024

AI (and machine learning, etc) with British Library collections

Machine learning (ML) is a hot topic, especially when it’s hyped as ‘AI’. How might libraries use machine learning / AI to enrich collections, making them more findable and usable in computational research? Digital Curator Mia Ridge lists some examples of external collaborations, internal experiments and staff training with AI / ML and digitised and born-digital collections.

Background

The trust that the public places in libraries is hugely important to us - all our 'AI' should be 'responsible' and ethical AI. The British Library was a partner in Sheffield University's FRAIM: Framing Responsible AI Implementation & Management project (2024). We've also used lessons from the projects described here to draft our AI Strategy and Ethical Guide.

Many of the projects below have contributed to our Digital Scholarship Training Programme and our Reading Group has been discussing deep learning, big data and AI for many years. It's important that libraries are part of conversations about AI, supporting AI and data literacy and helping users understand how ML models and datasets were created.

If you're interested in AI and machine learning in libraries, museums and archives, keep an eye out for news about the AI4LAM community's Fantastic Futures 2025 conference at the British Library, 3-5 December 2025. If you can't wait that long, join us for the 'AI Debates' at the British Library.

Using ML / AI tools to enrich collections

Generative AI tends to get the headlines, but at the time of writing, tools that use non-generative machine learning to automate specific parts of a workflow have more practical applications for cultural heritage collections. That is, 'AI' is currently more process than product.

Text transcription is a foundational task that makes digitised books and manuscripts more accessible to search, analysis and other computational methods. For example, oral history staff have experimented with speech transcription tools, raising important questions, and theoretical and practical issues for automatic speech recognition (ASR) tools and chatbots.

We've used Transkribus and eScriptorium to transcribe handwritten and printed text in a range of scripts and alphabets. For example:

Creating tools and demonstrators through external collaborations

Mining the UK Web Archive for Semantic Change Detection (2021)

This project used word vectors with web archives to track words whose meanings changed over time. Resources: DUKweb (Diachronic UK web) and blog post ‘Clouds and blackberries: how web archives can help us to track the changing meaning of words’.

Graphs showing how words associated with the words blackberry, cloud, eta and follow changed over time.
From blackberries to clouds... word associations change over time

Living with Machines (2018-2023)

Our Living With Machines project with The Alan Turing Institute pioneered new AI, data science and ML methods to analyse masses of newspapers, books and maps to understand the impact of the industrial revolution on ordinary people. Resources: short video case studies, our project website, final report and over 100 outputs in the British Library's Research Repository.

Outputs that used AI / machine learning / data science methods such as lexicon expansion, computer vision, classification and word embeddings included:

Tools and demonstrators created via internal pilots and experiments

Many of these examples were enabled by on-staff Research Software Engineers and the Living with Machines (LwM) team at the British Library's skills and enthusiasm for ML experiments in combination with long-term Library’s staff knowledge of collections records and processes:

British Library resources for re-use in ML / AI

Our Research Repository includes datasets suitable for ground truth training, including 'Ground truth transcriptions of 18th &19th century English language documents relating to botany from the India Office Records'. 

Our ‘1 million images’ on Flickr Commons have inspired many ML experiments, including:

The Library has also shared models and datasets for re-use on the machine learning platform Hugging Face.

17 December 2024

Open cultural data - an open GLAM perspective at the British Library

Drawing on work at and prior to the British Library, Digital Curator Mia Ridge shares a personal perspective on open cultural data for galleries, libraries, archives and museums (GLAMs) based on a recent lecture for students in Archives and Records Management…

Cultural heritage institutions face both exciting opportunities and complex challenges when sharing their collections online. This post gives common reasons why GLAMs share collections as open cultural data, and explores some strategic considerations behind making collections accessible.

What is Open Cultural Data?

Open cultural data includes a wide range of digital materials, from individual digitised or born-digital items – images, text, audiovisual records, 3D objects, etc. – to datasets of catalogue metadata, images or text, machine learning models and data derived from collections.

Open data must be clearly licensed for reuse, available for commercial and non-commercial use, and ideally provided in non-proprietary formats and standards (e.g. CSV, XML, JSON, RDF, IIIF).

Why Share Open Data?

The British Library shares open data for multiple compelling reasons.

Broadening Access and Engagement: by releasing over a million images on platforms like Flickr Commons, the Library has achieved an incredible 1.5 billion views. Open data allows people worldwide to experience wonder and delight with collections they might never physically access in the UK.

Deepening Access and Engagement: crowdsourcing and online volunteering provide opportunities for enthusiasts to spend time with individual items while helping enrich collections information. For instance, volunteers have helped transcribe complex materials like Victorian playbills, adding valuable contextual information.

Supporting Research and Scholarship: in addition to ‘traditional’ research, open collections support the development of reproducible computational methods including text and data mining, computer vision and image analysis. Institutions also learn more about their collections through formal and informal collaborations.

Creative Reuse: open data encourages artists to use collections, leading to remarkable creative projects including:

Animation featuring an octopus holding letters and parcels on a seabed with seaweed
Screenshot from Hey There Young Sailor (Official Video) - The Impatient Sisters

 

16 illustrations of girls in sad postures
'16 Very Sad Girls' by Mario Klingemann

 

A building with large-scale projection
The BookBinder, by Illuminos, with British Library collections

 

Some lessons for Effective Data Sharing

Make it as easy as possible for people to find and use your open collections:

  • Tell people about your open data
  • Celebrate and highlight creative reuses
  • Use existing licences for usage rights where possible
  • Provide data in accessible, sustainable formats
  • Offer multiple access methods (e.g. individual items, datasets, APIs)
  • Invest effort in meeting the FAIR, and where appropriate, CARE principles

Navigating Challenges

Open data isn't without tensions. Institutions must balance potential revenue, copyright restrictions, custodianship and ethical considerations with the benefits of publishing specific collections.

Managing expectations can also be a challenge. The number of digitised or born-digital items available may be tiny in comparison to the overall size of collections. The quality of digitised records – especially items digitised from microfiche and/or decades ago – might be less than ideal. Automatic text transcription and layout detection errors can limit the re-usability of some collections.

Some collections might not be available for re-use because they are still in copyright (or are orphan works, where the creator is not known), were digitised by a commercial partner, or are culturally sensitive.

The increase in the number of AI companies scraping collections site to train machine learning models has also given some institutions cause to re-consider their open data policies. Historical collections are more likely to be out of copyright and published for re-use, but they also contain structural prejudices and inequalities that could be embedded into machine learning models and generative AI outputs.

Conclusion

Open cultural data is more than just making collections available—it's about creating dynamic, collaborative spaces of knowledge exchange. By thoughtfully sharing our shared intellectual heritage, we enable new forms of research, inspiration and enjoyment.

 

AI use transparency statement: I recorded my recent lecture on my phone, then generated a loooong transcription on my phone. I then supplied the transcription and my key points to Claude, with a request to turn it into a blog post, then manually edited the results.

13 December 2024

Looking back on the Data Science Accelerator

From April to July this year an Assistant Statistician at the Cabinet Office and a Research Software Engineer at the British Library teamed up as mentee (Catherine Macfarlane, CO) and mentor (Harry Lloyd, BL) for the Data Science Accelerator. In this blog post we reflect on the experience and what it meant for us and our work.

Introduction to the Accelerator

Harry: The Accelerator has been around since 2015, set up as a platform to ‘accelerate’ civil servants at the start of their data science journey who have a business need project and a real willingness to learn. Successful applicants are paired with mentors from across the Civil Service who have experience in techniques applicable to the problem, working together one protected day a week for 12 weeks. I was lucky enough to be a mentee in 2020, working on statistical methods to combine different types of water quality data, and my mentor Charlie taught me a lot of what I know. The programme played a huge role in the development of my career, so it was a rewarding moment to come back as a mentor for the April cohort. 

Catherine: On joining the Civil Service in 2023, I had the pleasure of becoming part of a talented data team that has motivated me to continually develop my skills. My academic background in Mathematics with Finance provides me with a strong theoretical foundation, but I am striving to improve my practical abilities. I am particularly interested in Artificial Intelligence, which is gaining increasing recognition across government, sparking discussions on its potential to improve efficiency.

I saw the Data Science Accelerator as an opportunity to deepen my knowledge, address a specific business need, and share insights with my team. The prospect of working with a mentor and immersing myself in an environment where diverse projects are undertaken was particularly appealing. A significant advantage was the protected time this project offered - a rare benefit! I was grateful to be accepted and paired with Harry, an experienced mentor who had already completed the programme. Following our first meeting, I felt ready to tackle the upcoming 12 weeks to see what we could achieve!

Photo of the mentee and mentor on a video call
With one of us based in England and the other in Scotland virtual meetings were the norm. Collaborative tools like screen sharing and Github allowed us to work effectively together.

The Project

Catherine: Our team is interested in the annual reports and accounts of Arm’s Length Bodies (ALBs), a category of public bodies funded to deliver a public or government service.  The project addressed the challenge my team faces in extracting the highly unstructured information stored in annual reports and accounts. With this information we would be able to enhance the data validation process and reduce the burden of commissioning data from ALBs on other teams. We proposed using Natural Language Processing to retrieve this information, analysing and querying it using a Large Language Model (LLM).

Initially, I concentrated on extracting five features, such as full-time equivalent staff in the organisation, from a sample of ALBs across 13 departments for the financial year 22/23. After discussions with Harry, we decided to use Retrieval-Augmented Generation (RAG), to develop a question-answering system. RAG is a technique that combines LLMs with relevant external documents to improve the accuracy and reliability of the output. This is done by retrieving documents that are relevant to the questions asked and then asking the LLM to generate an answer based on the retrieved material. We carefully selected a pre-trained LLM while considering ethical factors like model openness.

RAG
How a retrieval augmented generation system works. A document in this context is a segmented chunk of a larger text that can be parsed by an LLM.

The first four weeks focused on exploratory analysis, data processing, and labelling, all completed in R, which was essential for preparing the data for input into the language model. The subsequent stages involved model building and evaluation in Python, which required the most time and focus. This was my first time using Python, and Harry’s guidance was extremely beneficial during our pair coding sessions. A definite highlight for me was seeing the pipeline start to generate answers!

To bring all our results together, I created a dashboard in Shiny, ensuring it was accessible to both technical and non-technical audiences. The final stage involved summarising all our hard work from the past 12 weeks in a 10 minute presentation and delivering it to the Data Science Accelerator cohort.

Harry: Catherine’s was the best planned project of the ones I reviewed, and I suspected she’d be well placed to make best use of the 12 weeks. I wasn’t wrong! We covered a lot of the steps involved in good reproducible analysis. The exploratory work gave us a great sense of the variance in the data, setting up quantitative benchmarks for the language model results drove our development of the RAG system, and I was so impressed that Catherine managed to fit in building a dashboard on top of all of that.

Our Reflections

Catherine: Overall this experience was fantastic. In a short amount of time, we managed to achieve a considerable amount. It was amazing to develop my skills and grow in confidence. Harry was an excellent mentor; he encouraged discussion and asked insightful questions, which made our sessions both productive and enjoyable. A notable highlight was visiting the British Library! It was brilliant to have an in-person session with Harry and meet the Digital Research team.

A key success of the project was meeting the objectives we set out to achieve. Patience was crucial, especially when investigating errors and identifying the root problem. The main challenge was managing such a large project that could be taken in multiple directions. It can be natural to spend a long time on one area, such as exploratory analysis, but we ensured that we completed the key elements that allowed us to move on to the next stage. This balance was essential for the project's overall success.

Harry: We divided our days between time for Catherine to work solo and pair programming. Catherine is a really keen learner, and I think this approach helped her drive the project forward while giving us space to cover foundational programming topics and a new programming language. My other role was keeping an eye on the project timeline. Giving the occasional steer on when to stick with something and when to move on helped (I hope!) Catherine to achieve a huge amount in three months. 

Dashboard
A page from the dashboard Catherine created in the last third of the project.

Ongoing Work

Catherine: Our team recognises the importance of continuing this work. I have developed an updated project roadmap, which includes utilising Amazon Web Services to enhance the speed and memory capacity of our pipeline. Additionally, I have planned to compare various large language models, considering ethical factors, and I will collaborate with other government analysts involved in similar projects. I am committed to advancing this project, further upskilling the team, and keeping Harry updated on our progress.

Harry: RAG, and the semantic rather than key word search that underlies it, represents a maturation of LLM technology that has the potential to change the way users search our collections. Anticipating that this will be a feature of future library services platforms, we have a responsibility to understand more about how these technologies will work with our collections at scale. We’re currently carrying out experiments with RAG and the linked data of the British National Bibliography to understand how searching like this will change the way users interact with our data.

Conclusions

Disappointingly the Data Science Accelerator was wound down by the Office for National Statistics at the end of the latest cohort, citing budget pressures. That has made us one of the last mentor/mentee pairings to benefit from the scheme, which we’re both incredibly grateful for and deeply saddened by. The experience has been a great one, and we’ve each learned a lot from it. We’ll continue to develop RAG at the Cabinet Office and the British Library, and hope to advocate for and support schemes like the Accelerator in the future!

11 November 2024

British National Bibliography resumes publication

The British National Bibliography (BNB) has resumed publication, following a period of unavailability due to a cyber-attack in 2023.

Having started in 1950, the BNB predates the founding of the British Library, but despite many changes over the years its purpose remains the same: to record the publishing output of the United Kingdom and the Republic of Ireland. The BNB includes books and periodicals, covering both physical and electronic material. It describes forthcoming items up to sixteen weeks ahead of their publication, so it is essential as a current awareness tool. To date, the BNB contains almost 5.5 million records.

As our ongoing recovery from the cyber-attack continues, our Collection Metadata department have developed a process by which the BNB can be published in formats familiar to its many users. Bibliographic records and summaries will be shared in several ways:

  • The database is searchable on the Share Family initiative's BNB Beta platform at https://bl.natbib-lod.org/ (see example record in the image below)
  • Regular updates in PDF format will be made freely available to all users. Initially this will be on request
  • MARC21 bibliographic records will be supplied directly to commercial customers across the world on a weekly basis
Image comprised of five photographs: a shelf of British National Bibliography volumes, the cover of a printed copy of BNB and examples of BNB records
This image includes photographs of the very first BNB entry from 1950 (“Male and female”) and the first one we produced in this new process (“Song of the mysteries”)

Other services, such as Z39.50 access and outputs in other formats, are currently unavailable. We are working towards restoring these, and will provide further information in due course.

The BNB is the first national bibliography to be made available on the Share Family initiative's platform. It is published as linked data, and forms part of an international collaboration of libraries to link and enhance discovery across multiple catalogues and bibliographies.

The resumption of the BNB is the result of adaptations built around long-established collaborative working partnerships, with Bibliographic Data Services (who provide our CIP records) and UK Legal Deposit libraries, who contribute to the Shared Cataloguing Programme.

The International Federation of Library Associations describes bibliographies like the BNB as "a permanent record of the cultural and intellectual output of a nation or country, which is witnessed by its publishing output". We are delighted to be able to resume publication of the BNB, especially as we prepare to celebrate its 75th anniversary in 2025.

For further information about the BNB, please contact [email protected].

Mark Ellison, Collection Metadata Services Manager

06 November 2024

Recovered Pages: Crowdsourcing at the British Library

Digital Curator Mia Ridge writes...

While the British Library works to recover from the October 2023 cyber-attack, we're putting some information from our currently inaccessible website into an easily readable and shareable format. This blog post is based on a page captured by the Wayback Machine in September 2023.

Crowdsourcing at the British Library

Screenshot of the Zooniverse interface for annotating a historical newspaper article
Example of a crowdsourcing task

For the British Library, crowdsourcing is an engaging form of online volunteering supported by digital tools that manage tasks such as transcription, classification and geolocation that make our collections more discoverable.

The British Library has run several popular crowdsourcing projects in the past, including the Georeferencer, for geolocating historical maps, and In the Spotlight, for transcribing important information about historical playbills. We also integrated crowdsourcing activities into our flagship AI / data science project, Living with Machines.

  • Agents of Enslavement uses 18th/19th century newspapers to research slavery in Barbados and create a database of enslaved people.
  • Living with Machines, which is mostly based on research questions around nineteenth century newspapers

Crowdsourcing Projects at the British Library

  • Living with Machines (2019-2023) created innovative crowdsourced tasks, including tasks that asked the public to closely read historical newspaper articles to determine how specific words were used.
  • Agents of Enslavement (2021-2022) used 18th/19th century newspapers to research slavery in Barbados and create a database of enslaved people.
  • In the Spotlight (2017-2021) was a crowdsourcing project from the British Library that aimed to make digitised historical playbills more discoverable, while also encouraging people to closely engage with this otherwise less accessible collection of ephemera.
  • Canadian wildlife: notes from the field (2021), a project where volunteers transcribed handwritten field notes that accompany recordings of a wildlife collection within the sound archive.
  • Convert a Card (2015) was a series of crowdsourcing projects aimed to convert scanned catalogue cards in Asian and African languages into electronic records. The project template can be found and used on GitHub.
  • Georeferencer (2012 - present) enabled volunteers to create geospatial data from digitised versions of print maps by adding control points to the old and modern maps.
  • Pin-a-Tale (2012) asked people to map literary texts to British places.

 

Research Projects

The Living with Machines project included a large component of crowdsourcing research through practice, led by Digital Curator Mia Ridge.

Mia was also the Principle Investigator on the AHRC-funded Collective Wisdom project, which worked with a large group of co-authors to produce a book, The Collective Wisdom Handbook: perspectives on crowdsourcing in cultural heritage, through two 'book sprints' in 2021:

This book is written for crowdsourcing practitioners who work in cultural institutions, as well as those who wish to gain experience with crowdsourcing. It provides both practical tips, grounded in lessons often learned the hard way, and inspiration from research across a range of disciplines. Case studies and perspectives based on our experience are woven throughout the book, complemented by information drawn from research literature and practice within the field.

More Information

Our crowdsourcing projects were designed to produce data that can be used in discovery systems (such as online catalogues and our item viewer) through enjoyable tasks that give volunteers an opportunity to explore digitised collections.

Each project involves teams across the Library to supply digitised images for crowdsourcing and ensure that the results are processed and ingested into various systems. Enhancing metadata through crowdsourcing is considered in the British Library's Collection Metadata Strategy.

We previously posted on twitter @LibCrowds and currently post occasionally on Mastodon https://glammr.us/@libcrowds and via our newsletter.

Past editions of our newsletter are available online.

31 October 2024

Welcome to the British Library’s new Digital Curator OCR/HTR!

Blog pictureHello everyone! I am Dr Valentina Vavassori, the new Digital Curator for Optical Character Recognition/Handwritten Text Recognition at the British Library.

I am part of the Heritage Made Digital Team, which is responsible for developing and overseeing the digitisation workflow at the Library. I am also an unofficial member of the Digital Research Team, where I promote the reuse and access to the Library’s collections.

My role has both an operational component (integrating and developing OCR and HTR in the digitisation workflow) and a research and engagement component (supporting OCR/HTR projects in the Library). I really enjoy these two sides of my role, as I have a background as a researcher and as a cultural heritage professional.

I joined the British Library from The National Archives, London, where I worked as a Digital Scholarship Researcher in the Digital Research Team. I worked on projects involving data visualisation, OCR/HTR, data modelling, and user experience.

Before that, I completed a PhD in Digital Humanities at King’s College London, focusing on chatbots and augmented reality in museums and their impact on users and museum narratives. Part of my thesis explored how to use these narratives using spatial humanities methods such as GIS. During my PhD, I also collaborated on various digital research projects with institutions like The National Gallery, London, and the Museum of London.

However, I originally trained as an art historian. I studied art history in Italy and worked for a few years in museums. During my job, I realised the potential of developing digital experiences for visitors and the significant impact digitisation can have on research and enjoyment in cultural heritage. I was so interested in the opportunities, that I co-founded a start-up which developed a heritage geolocation app for tourists.

Joining the Library has been an amazing opportunity. I am really looking forward to learning from my colleagues and exploring all the potential collaborations within and outside the Library.

29 October 2024

Happy Twelfth Birthday Wikidata!

Today the global Wikidata community is celebrating its 12th birthday! Wikidata originally went live on the 29th October 2012, back when Andrew Gray was the British Library’s first Wikipedian in Residence and since then it has massively expanded.  

Wikidata is a free and open knowledge base that can be read and edited by both humans and machines, which acts as central storage for the structured data of its Wikimedia sister projects including Wikipedia and Wikisource. Wikidata content is available under a free license (CC0), exported using standard formats (JSON & RDF), and can be interlinked to other open data sets on the linked data web.

Drawing of four people around a birthday cake

Over the past year Wikidata passed the incredible milestone of 2 Billion edits, making it the most edited Wikimedia project of all time. However, this growth has created Wikidata Query Service stability challenges and scaling issues. To address these, the development team have been working on several projects including splitting the data in the Query Service and releasing the multiple languages code to be able to handle the current size of Wikidata better.

Heat Map of Wikidata’s geographic coverage as of October 2024
Map of Wikidata’s geographic coverage as of October 2024

Another major focus during the past year has been promoting Wikidata reuse. To make it easier to access Wikidata’s data there is a new REST API. Plus developers who build with Wikidata’s data now have access to a Wikidata developer portal, which holds important information and provides inspiration about what is possible with Wikidata’s data.

The international library community actively engages with Wikidata. In 2019 the IFLA Wikidata Working Group was formed to explore the integration of Wikidata and Wikibase with library systems, and alignment of the Wikidata ontology with library metadata formats such as BIBFRAME, RDA, and MARC. There is also the LD4 Wikidata Affinity Group, who hold Affinity Group Calls and Wikidata Working Hours throughout the year.

If you are new to Wikidata and want to learn more, there are many resources available, including this Zine about Wikidata, created by our recent Wikimedian in Residence Dr Lucy Hinnie, and these videos:

You may also want to check out the online Bibliography of Wikidata, which lists books, academic conference presentations and peer-reviewed papers, which focus on Wikidata as their subject.

This post is by Digital Curator Stella Wisdom.

28 August 2024

Open and Engaged 2024: Empowering Communities to Thrive in Open Scholarship

 British Library is delighted to host its annual Open and Engaged Conference on Monday 21 October, in-person and online, as part of the International Open Access Week. The Conference is supported by the Arts and Humanities Research Council (AHRC) and Research Libraries UK (RLUK).  

Save the Date flyer for Open & Engaged 2024 on 21 October, in person and online, and with logos for sponsors UKRI, Ars and Humanities Research Council and RLUK

Open and Engaged 2024: Empowering Communities to Thrive in Open Scholarship will centre leveraging the power of communities in the axis of open scholarship, open infrastructure, emerging technologies, collections as data, equity and integrity, skills development and sustainable models to elevate research of all kinds for the public good. We take a cross sectoral approach to the conference programme – unifying around shared-values for openness – by reflecting on practices within research libraries both in higher education and GLAM (Galleries, Libraries, Archives, Museums) sectors as well as the national and public libraries.  

Open and Engaged 2024 is supported by the Arts and Humanities Research Council (AHRC) and Research Libraries UK (RLUK). Everyone interested in the conference topics is welcome to join us on Monday, 21 October! 

This will be a hybrid event taking place at the British Library’s Knowledge Centre in St. Pancras, London, and streamed online for those unable to attend in-person. 

The event will be recorded and recordings made available in the British Library’s Research Repository.

Registration

Registration is closed for in-person and online attendance. Registrants have been contacted with details. Any questions, please contact [email protected].  

Programme 

Slides and recordings of the talks are available as a collection in the British Library’s Research Repository.

09:30  Registration

10:00  Welcome remarks

10:10  Opening keynote panel: Cross disciplinary approach to open scholarship

Chaired by Sally Chambers, Head of Research Infrastructure Services at the British Library.

10:50    Empowering communities through equity, inclusivity, and ethics

Chaired by Beth Montague-Hellen, Head of Library and Information Services at the Francis Crick Institute.

This session addresses the role of the communities in governance, explores the ethical implications of AI for citizens and highlights the value of public engagement, and discusses the central importance of equity, inclusivity, and integrity in scholarly communications.

11:40  Break

12:10    Deepening partnership in skills development through shared values

Chaired by Kirsty Wallis, Head of Research Liaison at UCL.

This session explores initiatives that foster skills development in libraries with a cross sectoral approach and dives into the role of libraries to support communities in building resilience.

13:00  Lunch

13:45   Open repositories for research of all kinds

This session addresses the role of infrastructure to carry out open scholarship practices, explores the practice as research in the axis of diverse outputs and infrastructure, discusses institutional resilience in digital strategies. 

Chaired by William J Nixon, Deputy Executive Director at Research Libraries UK (RLUK).

14:45  Break

15:15   Enabling collections as data: from policy to practice  

Chaired by Jez Cope, Data Services Lead at the British Library.

This session dives into the digital collections as data by exploring policies and practices across different sectors, public-private partnerships in making collections publicly available, dynamics in preservation versus access approach in national libraries whilst underlining the public good. 

16:15   Closing keynote: Stories Change Lives

Chaired by Liz White, Director of Library Partnerships at the British Library

16:45 Closing remarks

17:00 Networking session

19:00  End

The hashtag for the event is #OpenEngaged on social media platform of your choice. If you have any questions, please contactus at [email protected].  

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