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 | For those of you planning to study for a masters in Library or Information Science, LIS, here are some suggestions for background reading in preparation for your course. This is a personal selection of printed books, (although some may be availabl (More) |
 | We are excited to introduce Graeme who has joined the Modernising Administration for Students team from SASS for a period of 6 months, where he was working as a Cour (More) |
 | Originally published on The Occasional Informationist blog, 27 April 2020 In More) |
 | ***CityLIS alumna Anna Gialdini compares the classification scheme at the Warburg Institute with other Knowledge Classification Schemes to adumbrate how they affect people's perception of the world.It is reproduced he (More) |
 | ***CityLIS alumna Catherine Jenkins critically evaluates the impact of subject headings with reference to the Medical Subject Headings (MeSH) controlled vocabulary. It is reproduced here with the author’s permission as part of our CityLIS Writes init (More) |
| Check out our new website for the latest research on humanitarian communications: http://www.hnrn.co.uk/ (More) |
 | 2020 promises to be an exciting year for everyone involved in MAfS projects. Building on from the huge progress made in 2019, work is already continuing on the team’s various projects: SEAM and Personalised Timetables More) |
| Our renamed and relaunched Masters module Information and Data, Law and Ethics (IDLE) begins next week, and runs through the spring term. The legal and ethical dimensions of library and information science have always been an important feature of (More) |
 | Student Perspectives is our series of posts written by current CityLIS students. This post is written by Ellena Moyse looks at the american innovation of the Makerspace in public libraries. Ellena is on Twitter:@ElleMoyse Makerspace (definitio (More) |
| Research Centre for Machine Learning meeting on Explainable AI When: Fri, 29 November 2019, 4:00pm Where: AG01, College Building SHAP is an increasingly popular method for providing local explanations of AI system predictions. SHAP is based on t (More) |
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