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DBC & Data Science - Where to go and why?


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Om formålet og perspektivern i DBC's data science udvikling. Oplæg v/Bo Weymann på 'Data Science Day' den 14. januar 2016 på DBC.

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DBC & Data Science - Where to go and why?

  1. 1. Bo Weymann DBC & data science – where to go and why?
  2. 2. So why do we produce a knowledge system, recommenders, automagic metadata,….. Using Math…... As Christian told you about..........
  3. 3. A combined vision – BIG META DATA and replication on librarian skills based on Machine Learning, Datascience and librarians We could for a moment call it Librarian computing
  4. 4. librarian skills are valuable in many contexts - the problem is that there are so few of them Datascience as a strategic tool for libraries can compensate this and maybe even bring librarian skills in to situations and in ways that are innovative
  5. 5. library users want digital solutions and services in the same way as dominating media giant do through solutions with cognitive understanding - but libraries do not need to know and help the user from a commercial aim
  6. 6. In DBC we produce a lot of metadata - BUT To create and aggregate metadata in those amounts as library users need only through intellectual processes and librarians m/w – are a NO GO
  7. 7. So we felt a scence of necessity Academic networking, courses and experiments in: Machine learning, datascience Inspiration from commercial
  9. 9. Librarian Computing shall be used in production - in end user interfaces as well as production of metadata and metadata systems
  10. 10. Information specialist’s can Structure the knowledge & Navigating the large amounts of it the librarian can recommend it best in context & communicate and convey her commitment
  11. 11. The information specialist
  12. 12. Skills Create and aggregate metadata - Cognitive machine based on large amounts of data and BIG DATA Create a new taxonomi from a data set Can seek out new relevant data sets Can connect taxonomies …........
  13. 13. librarian intermediary
  14. 14. Skills empathize with the user's needs be critical tell why she recommends something to you Cognitive "search engine” and recommender system based on Machine Learning and datascience, existing web services, user feedback, user behavior, taxonomies, metadata, data sets from social media, etc. gives users the best possible content depending on context…..with transparancy