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Public DMPs

Public DMPs are plans created using the DMPonline service and shared publicly by their owners. They are not vetted for quality, completeness, or adherence to funder guidelines.

Project title  Template  Organisation Owner Download
Creating synthetic data for social science projects: Machine-learning and Agent-based modelling go head to head University of Manchester Generic Template University of Manchester Jools Kasmire (new window) Opens in new window
Factors Influencing Usability and Perceived Satisfaction with a Continuous Monitoring and Deterioration Alerting System in a Non-ICU Setting: A Mixed-Methods Study University of Manchester Generic Template University of Manchester Jo-Fan Pan (new window) Opens in new window
Telecom-wavelength single-photon source based on organic erbium molecules University of Manchester Generic Template University of Manchester Huanqing Ye (new window) Opens in new window
Characterisation of the Tumour Microenvironment in Vestibular Schwannoma University of Manchester Generic Template University of Manchester Cathal Hannan (new window) Opens in new window
Study on neural engagement factors in crowdfunding decision making University of Manchester Generic Template University of Manchester Siobhan Caughey (new window) Opens in new window
Guilt and shame amongst mentally unwell offenders - Interviews with Legal and Clinical Professionals University of Manchester Generic Template University of Manchester Piyush Pushkar (new window) Opens in new window
Understanding barriers and facilitators to hearing aid use in teenagers University of Manchester Generic Template University of Manchester Sumeya Abdi (new window) Opens in new window
Competitive and soil nutrient controls of tree seedling establishment and survival in old field succession University of Manchester Generic Template University of Manchester Joshua Lynn (new window) Opens in new window
PhD University of Manchester Generic Template University of Manchester Wei Bin Timothy Siew (new window) Opens in new window
Image Segmentation Using Deep Learning Techniques University of Manchester Generic Template University of Manchester Zeinab Tirandaz (new window) Opens in new window