Applications for this year’s Cultural Heritage Data School are now closed. Please sign up to our Data School mailing list for future events

The Cambridge Cultural Heritage Data School, led by Cambridge Digital Humanities, is an online intensive teaching programme which aims to bring together participants from the wider Galleries, Libraries, Archives and Museums (GLAM) sector and academia to explore the methods used to create, visualise and analyse digital archives and collections.

The curriculum will be structured around the digital collections and archives pipeline, covering the general principles and applied practices involved in the generation, exploration, visualisation, analysis and preservation of digital collections and archives. The programme will include the following modules:

  • The digital project lifecycle
  • Digital text mark-up and TEI
  • Text-mining and Named Entity Recognition with Python
  • Geodata, controlled vocabularies and principles of semantic data modelling
  • Using machine learning to work with large-scale image collections

The 2021 Cultural Heritage Data School teaching team includes:

  • Dr Anne Alexander (Director of Learning, CDH)
  • Huw Jones (Library Digital Humanities Coordinator, CDH Labs)
  • Chiara Capulli (Methods Fellow, CDH Learning)
  • Dr Mary Chester-Kadwell (Senior Software Developer, CDH Labs and Cambridge University Library)

Sessions will include live-taught instruction, demonstrations and discussions on Zoom, with access to self-paced study materials and support via email-based discussion groups between sessions. Participants will need a laptop or desktop computer and internet access to participate in the sessions. Some sessions will require software installation – full instructions will be provided but please ensure you have access rights to install software on the device you will be using.


No previous experience of programming is required to participate in the Data School. We welcome applications from outside the UK.


Q&A session with the Data School teaching staff

Please note that because of the fast moving nature of Coronavirus, the Data School timetable and content may be subject to change because of staff availability. We also recognise that the current circumstances are putting extra pressure on many people, especially those with caring responsibilities, and we understand that not everyone will necessarily be able to make every session. This will not be a barrier to participation in the Data School.​


  • Review full online programme here
  • Review Frequently Asked Questions here
  • Application deadline: Monday, 8 February 2021

Cambridge Digital Humanities is committed to democratising access to digital methods and tools and is offering the following subsidised participation fees to encourage applications from those who do not normally have access to this type of training. The fees include all teaching costs.

  • Standard Rate: £245
  • Small Organisations / University Staff: £145
  • Students / Unemployed / Community Projects / Unfunded Projects : £45

In addition, a small number of bursaries are available to those who can demonstrate financial need.

While we encourage applications from everyone, we particularly welcome applications from women and black and minority ethnic candidates as they have historically been under-represented in the technology and data science sector.

Questions related to the application procedure or course content: Karen Herbane (Digital Humanities Learning and Events Coordinator):


“Taking part in exchanges with both the CDH team and participants from diverse disciplines and backgrounds was exceptionally interesting, alongside having the opportunity to engage with cutting-edge technologies and tools in digital humanities research.

Thank you for such a transformational experience that surely will impact my teachings and academic research orientation.”

Dr. Abeer Naser Eddine, Assistant Professor, Lebanese University, Beirut, Lebanon – Attendee Cultural Heritage Data School 2020


Cambridge Digital Humanities

Tel: +44 1223 766886