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Call for Data School Methods Fellowship applications 2021/22

Methods Fellow information session announced:

Monday, 6 September 2021: 2.00 – 2.45pm

This information session is open to staff and PhD students at the University of Cambridge interested in applying for a Data School Methods Fellowship - it will be an informal opportunity to ask questions about the scheme.

Register in advance for the session here


CDH Learning invites applications for new Methods Fellows for the CDH Data Schools during the academic year 2021/22. Methods Fellows will assist in the development and delivery of the Data Schools which are intensive online teaching programmes. The goals of the Data Schools are to: 
  • Democratise access to tools and methods for digital data collection, analysis and reporting;
  • Foster the development of ethical practices in digital research; and
  • Encourage dialogue between academia, news media, civil society, the public sector and industry about the social, legal, ethical and policy implications of digital research methods
Our Cultural Heritage Data Schools bring together participants from the broader Galleries, Libraries, Archives and Museums (GLAM) sector and academia, to explore the methods used to create, visualise and analyse digital archives and collections. 
Participants in the Social Data Schools are drawn from the media, civil society (including trade unions, community groups and advocacy organisations), and academia. We focus on enabling participation from the Global South and from groups and communities who face structural obstacles to accessing this kind of training, whether due to lack of resources or discrimination and prejudice. 
For more information on the content of our recent programmes, click on these links: 
Successful candidates will develop and lead the delivery of around 6 hours of live teaching during the Data Schools, in addition to providing asynchronous support for participants via an email forum and preparing material for self-paced learning for the Data School virtual learning environment. 
They will join a wider cohort of Methods Fellows who are also developing teaching on our Learning Programme, which offers digital methods training to around 400 students and staff at the University of Cambridge each year. 
The Methods Fellowships will be of particular interest to early-career researchers interested in translating their knowledge and expertise of digital methods into teaching formats. CDH also welcomes applications from professional staff and PhD students.
Methods Fellows benefit from mentoring by CDH Learning staff and are an integral part of the wider CDH community, accessing a vibrant network of researchers and spanning a wide range of disciplines and departments. 

Provisional dates for 2022 Data Schools

  • Cultural Heritage Data School: first three weeks in March 2022
  • Social Data School: last three weeks in June 2022
Data School sessions generally take place between 13:30–16:00 UK time 

What Methods Fellows bring to the CDH Data Schools

  • Methods Fellows use their expertise in methods or practices relevant to the objectives of the Data Schools to deliver online teaching to Data School participants.
  • Methods Fellows are expected to be active in the growing CDH community. 

What CDH offers Methods Fellows

  • An honorarium of £1200 for content design, development and delivery of online teaching sessions. This will usually involve around four hours of remote live teaching during the Data School teaching period, the preparation of self-paced preparatory materials covering the content of the teaching hours, and up to two hours of asynchronous teaching answering participant questions via email through Moodle between sessions (or through online “office hours”.) 
  • Opportunities to experiment with novel pedagogical approaches and course content
  • Mentoring and support from Learning Programme staff
  • Opportunities to apply for CDH funding for DH conference attendance
  • Assistance and advice with DH grant proposals
  • Networking and peer-learning as part of a supportive community of researchers

What you need to become a Data School Methods Fellow: 

  • Expertise in methods or practices relevant to the objectives of the Data Schools
  • Passion for communicating complex ideas to diverse learners in an international context
  • Commitment to developing accessible and inclusive teaching which aims to reduce barriers to participation experienced by learners in marginalised communities or low-resource settings
  • Experience in delivering teaching or training (although not necessarily in a formal classroom setting)
  • A desire to expand your knowledge of the DH community in Cambridge.

What we are looking for in Data School Methods Fellowship proposals: 

The curriculum of the Data Schools is structured around the lifecycle of a digital project, covering ethical research design, and the capture, transformation, analysis, presentation, sustainability (or destruction) of data. We aim to incorporate participatory and project-based learning within the curriculum and therefore are keen to receive proposals from prospective Methods Fellows which offer Data School participants hands-on experience in trying out digital methods across key elements of the research project lifecycle. Data Schools in previous years have introduced participants to the following methods and tools – however we welcome proposals for new areas. Please see the FAQ section below for more guidance on free vs paid-for software and open-source vs proprietary software. 

Cultural Heritage Data School 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

Social Data School modules

  • Ethical digital research design and the digital project lifecycle
  • Data protection and surveillance in a networked world 
  • How Machine Learning shapes the media  
  • Data exploration, structuration and preparation
  • Social Network visualisation and analysis
  • Machine Learning and computer vision: a critical and experimental introduction

Software tools and programming languages previously covered

  • Openrefine + extensions (such as Named Entity Recognition): data cleaning, filtering and aggregation
  • Regex: more efficient filtering
  • Voyant tools : text visualisation without programming
  • Gephi: social network visualisation and analysis
  • Jupyter notebooks
  • Introduction to Python
  • QGIS
  • Oxygen XML editor
  • Doccano 
  • Google Colab notebooks for automated text generation and image processing
  • Google Teachable Machine

How to apply:

Submit a two-page statement, which should give equal weight to the following sections, to by 14 September 2021. 
We will contact all applicants to let them know the outcome of the selection process by 6 October 2021