Description
This repository contains Python code and data used in the Museums in the Pandemic (MIP) project, including aggregated social media datasets and analysis results. The input data cannot be disseminated for copyright reasons. Project description: Museums have an important role in our economy, education and cultural life. They add to the texture and richness of villages, towns and cities, and can help build and maintain communities. During the pandemic, their continuing existence has been under threat, and while many museums have benefitted from emergency funding or government schemes, their position remains precarious. In order to better support the UK museum sector, the museum services need to identify which types of museums are at risk of closure, which remain resilient, and which close on a permanent basis. Doing so presents a considerable challenge. Data collection is selective and tends not to cover unaccredited museums, it is dispersed across multiple platforms, there are no mechanisms for documenting closure, and establishing risk of closure entirely relies on individual organisations self-reporting. The Museums in the Pandemic project investigates how ‘big data techniques’ can inform research into the UK museum sector. It combines qualitative and quantitative research, and has three inter-related strands: Developing new ways to collect data on museums. We will use web analytics, natural language processing, and sentiment analysis to digitally track trends as they emerge. The data will be analysed with respect to museum characteristics – such as governance, location and size – to provide a nuanced understanding of the sector at a given moment. Manually checking and validating the information generated by big data collection. Using interview-based research to better understand what constitutes risk during a pandemic, the triggers for permanent closure, and how museums have and continue to remain resilient. URL: https://www.bbk.ac.uk/research/projects/museums-in-the-pandemic PI: Fiona Candlin (Birkbeck, UoL) Co-I: Andrea Ballatore (King's College London) Co-I: Alex Poulovassilis (Birkbeck, UoL) Co-I: Peter Wood (Birkbeck, UoL)
Date made available | 6 Jun 2023 |
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Publisher | King's College London |