Accelerator Fund

We are providing funding to open research projects at The University of Manchester.

Interior of Alan Turing Building (offices)

The Open Research Accelerator Fund has been supporting the development of open research practice since 2022 by funding projects across The University of Manchester.

This year there were a record number of applications across all three faculties highlighting the diversity of the open research movement at the University. We are pleased to announce that over £130,000 has been awarded to open research projects. The successful applicants displayed a commitment to the University’s open research principles and demonstrated how they will contribute to a sustainable culture change.

Winning entries of the 2023/24 Accelerator Fund:

Neil Mitchell (Faculty of Science and Engineering)

  • Improving Transparency, Accessibility, and Reusability of Scientific Ocean Drilling Data

Lukas Hughes-Noehrer (Faculty of Science and Engineering)

  • Between Theory and Practice: Investigating Open-Source Software and Code at the University of Manchester (IOSSC)

Nuno Pinto (Faculty of Humanities)

  • DSS MS: Decision Support Systems for local community engagement in Moss Side

David Buil Gil (Faculty of Humanities)

  • CrimRxiv - The global open access hub for criminology

Cristina Temenos (Faculty of Humanities)

  • Data for the People: Exploring Zotero’s Capabilities as an Open Data Interface

Siobhan Caughey (Faculty of Humanities )

  • Psychophysiological Database of Emotion Elicitation

Patrick Parkinson (Faculty of Science and Engineering)

  • Understanding Open Research Practices in the Natural Sciences

Akinloluwa Babalola (Faculty of Science and Engineering)

  • Development of an Implementation Flowchart for Open Research Awareness and Understanding to Engineering Postgraduate Research Students

Francisco Espinoza (Faculty of Humanities)

  • Best Practices for open research in Political Science: sharing experiences

Paul Stott (Faculty of Biology, Medicine and Health)

  • Designing and conducting open and reproducible experiments in linguistics

You can also read two examples of successful applications from last year, Sharing Qualitative Methods and Machine Learning Framework for Clinical Text Mining.

We will be sharing further details of this year’s successful projects over the forthcoming months. For all enquiries relating to the Accelerator Fund, please email: