This is the data and code repository for the project Museums in the Pandemic (MIP): Risk, Closure, and Resilience (Birkbeck, University of London and King's College London).
Project home page: https://www.bbk.ac.uk/research/projects/museums-in-the-pandemic
This repository contains:
- The Python source code of an application designed to scrape large-scale online information about UK museums, from websites, Facebook, and Twitter (folder
mip/). - Jupyter Notebooks used for data analytics (folder
mip/notebooks_py). - Lists of museum websites, Twitter, and Facebook accounts (folder
data/museums). - Final datasets produced by the project (folder
data/analysis). - The input datasets (scraped websites, Twitter and Facebook messages) cannot be republished and are therefore not included.
This project is a continuation of the Mapping Museums project (see also the GitHub repository).
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.
- 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)
- PDRA: Val Katerinchuk (Birkbeck, UoL)
The code and data in this repository were used to produce the following publications:
- Larkin, J., Ballatore, A. and Mityurova, E. (2023) Museums, Covid-19 and the Pivot to Social Media. Curator: The Museum Journal
- Ballatore, Andrea, Valeri Katerinchuk, Alexandra Poulovassilis, and Peter T. Wood (2023) ‘Tracking Museums’ Online Responses to the Covid-19 Pandemic: A Study in Museum Analytics’. ACM Journal on Computing and Cultural Heritage
See also related publications at https://museweb.dcs.bbk.ac.uk/publications
Awarded £190,000 by the UKRI-AHRC Rapid Recovery Scheme (2021-2022)
Conda environment in conda_env with all Python packages.
Andrea Ballatore (King's College London) aballatore.space