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In search of the sweet spot: infrastructure at the intersection of cultural heritage and data science?

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A short paper for a panel on 'Data Science & Digital Humanities: new collaborations, new opportunities and new complexities' at Digital Humanities 2019, Utrecht.

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In search of the sweet spot: infrastructure at the intersection of cultural heritage and data science?

  1. 1. In search of the sweet spot: infrastructure at the intersection of cultural heritage and data science? Dr Mia Ridge, Digital Curator, British Library @mia_out @BL_DigiSchol @LivingWMachines Data Science & Digital Humanities: new collaborations, new opportunities and new complexities panel, DH2019
  2. 2. www.bl.uk Why should GLAMs and data scientists collaborate? GLAMs (galleries, libraries, archives and museums) have vast collections • The British Library has up to 200 million items, including: 14 million books; 8 million stamps; 310,000 manuscript volumes; 4 million maps; Pamphlets, magazines, newspapers, sheet music; Television and radio recordings; Websites, e-books, e-journals. Over 3 million new items are added every year 2
  3. 3. www.bl.uk Why should GLAMs and data scientists collaborate? GLAMs (galleries, libraries, archives and museums) have vast collections • The British Library has up to 200 million items, including: 14 million books; 8 million stamps; 310,000 manuscript volumes; 4 million maps; Pamphlets, magazines, newspapers, sheet music; Television and radio recordings; Websites, e-books, e-journals. Over 3 million new items are added every year But – that scale means cataloguing is often minimal / focused on particular uses, so they’re not easily findable 3
  4. 4. www.bl.uk WOMAT-AFR-BEA-227-2-2 Data science methods could invent and provide routes into collections
  5. 5. www.bl.uk 5Cook's Handbook for London, 1897 Data scientists need (challenging) sources
  6. 6. www.bl.uk 6Cook's Handbook for London, 1897 Data scientists need (challenging) sources (Are our collections too challenging?)
  7. 7. What stands in the way of collaboration? ‘to get the most out of machine learning at your organization, you need the right team and the right mindset. The latter requires a cultural shift that prioritizes and rewards experimentation, measurement, and testing throughout your organization’ - Google, ‘Everything a marketer needs to know about machine learning’ 7 Image from page 440 of "Bell telephone magazine" (1922)
  8. 8. ‘you need the right team and the right mindset. The latter requires a cultural shift that prioritizes and rewards experimentation, measurement, and testing throughout your organization’ - Google, ‘Everything a marketer needs to know about machine learning’ ‘And a lot of spare capacity across teams. Whose job changes when you bring in data science?’ - me 8 What stands in the way of collaboration?
  9. 9. • GLAM data can add up to terabytes of data – transfer, storage and processing become expensive • Copyright / licensing and data protection issues • Are GLAM and academic data science outcomes aligned? Novelty vs application, long-term, at scale? • How do we integrate AI-generated metadata at scale without flooding the catalogue with ‘mentions’? 9 What else stands in the way of collaboration?
  10. 10. www.bl.uk Opportunities to shift GLAM infrastructure from ‘catalogue’ to ‘lake’ and provide platforms for collaborative work? 10 https://www.flickr.com/photos/missouristatearchives/11653956994
  11. 11. www.bl.uk 11 Thank you! Questions? Dr Mia Ridge, Digital Curator, British Library @mia_out @BL_DigiSchol @LivingWMachines Data Science & Digital Humanities: new collaborations, new opportunities and new complexities panel, DH2019

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