Nottingham Research Data Management Repository
Welcome to the online digital research data repository of multi-disciplinary research datasets produced at the University of Nottingham, hosted by Digital and Technology Services and managed and curated by University of Nottingham Libraries.
University of Nottingham researchers who have produced research data associated with an existing or forthcoming publication, or which has potential use for other researchers, are invited to upload their dataset. For each published dataset, a Datacite DOI is issued by this service. For detailed instructions as to how to make a deposit, please see our Data Sharing Guide
Items may only be deposited by accredited members of University of Nottingham UK and Malaysia, or their delegated agents.
Recently Added
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Raw Data for Magnetic order in a metal thiocyanate perovskite-analogue
(The University of Nottingham, 2022-06-28)Data include: - neutron diffraction data - neutron diffraction refinement - infrared spectrum - bulk magnetic measurements -
NHS COVID-19 App trust and attitudes questionnaire data
(The University of Nottingham, 2022-06-24)Questionnaire data from a study carried out as part of the Trustworthy Autonomous Systems hub looking at how attitudes towards use of the NHS COVID-19 app and issues of trust and trustworthiness. 1,001 participants, carried ... -
Dataset for Training Generative Adversarial Networks for Optical Property Mapping using Synthetic Image Data
(The University of Nottingham, 2022-06-30)Data and code used to generate deep learning model to generate results in the above paper -
Aquatic invertebrate communities and Pb body burdens at abandoned Pb mine sites
(The University of Nottingham, 2022-06-13)This dataset contains the raw data for the aquatic invertebrate communities and Pb body burdens, as found during this study. The invertebrates were sampled from streams in abandoned Pb mine sites and in nearby control ... -
Catalogue of radial velocity predictions for Gaia DR3
(University of Nottingham, 2022-06-10)This catalogue accompanies our article (Naik & Widmark, 2022), in which we demonstrate the use of Bayesian neural networks for predicting the missing radial (line-of-sight) velocities of stars observed by the Gaia ...
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