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  1. AMPLE: A Material Point Learning Environment [dataset] [Download]

    Title: AMPLE: A Material Point Learning Environment [dataset]
    Contributors: Creator: Augarde, Charles (Durham University, UK)
    Creator: Coombs, William (Durham University, UK)
    Description:
    Keywords: Material Point Method, MATLAB
    Date Uploaded: 17 May 2019
  2. Splitting and recombination of bright-solitary-matter waves [dataset] [Download]

    Title: Splitting and recombination of bright-solitary-matter waves [dataset]
    Contributors: Creator: Wales, Oliver J. (Joint Quantum Centre (JQC) Durham - Newcastle, Department of Physics, Durham University, Durham DH1 3LE, United Kingdom)
    Creator: Rakonjac, Ana (Joint Quantum Centre (JQC) Durham - Newcastle, Department of Physics, Durham University, Durham DH1 3LE, United Kingdom)
    Contact person: Rakonjac, Ana (Joint Quantum Centre (JQC) Durham - Newcastle, Department of Physics, Durham University, Durham DH1 3LE, United Kingdom)
    Creator: Billam, Thomas P. (Joint Quantum Centre (JQC) Durham–Newcastle, School of Mathematics and Statistics, Newcastle University, Newcastle upon Tyne NE1 7RU, UK)
    Creator: Helm, John L. (Dodd-Walls Centre for Photonic and Quantum Technologies, Department of Physics, University of Otago, 730 Cumberland Street, Dunedin 9016, New Zealand)
    Creator: Gardiner, Simon A. (Joint Quantum Centre (JQC) Durham - Newcastle, Department of Physics, Durham University, Durham DH1 3LE, United Kingdom)
    Creator: Cornish, Simon L. (Joint Quantum Centre (JQC) Durham - Newcastle, Department of Physics, Durham University, Durham DH1 3LE, United Kingdom)
    Description:
    Keywords: Physics, Solitons, Matter waves, Ultracold atoms, Atom interferometry
    Date Uploaded: 9 May 2019
  3. Generalised classification of fluvial scenes with deep learning methods [dataset] [Download]

    Title: Generalised classification of fluvial scenes with deep learning methods [dataset]
    Contributors: Creator: Carbonneau, Patrice (Durham University)
    Data collector: Dugdale, Steve (University of Nottingham)
    Data collector: Miyamoto, Hitoshi (Shibaura Institute of Tech., Japan)
    Data collector: Woodget, Amy (Loughborough University)
    Data collector: Fonstad, Mark (University of Oregon)
    Data collector: Dietrich, James (University of Northern Iowa)
    Data collector: Breckon, Toby (Durham University)
    Description: This data is linked to code in the following GitHub repository:  https://github.com/geojames/Self-Supervised-Classification
    Keywords: Deep learning, Fluvial remote sensing, UAV imagery
    Date Uploaded: 2 May 2019
  4. Multifaceted design optimisation for superomniphobic surfaces [dataset] [Download]

    Title: Multifaceted design optimisation for superomniphobic surfaces [dataset]
    Contributors: Creator: Panter, Jack (University of Durham, UK)
    Data collector: Panter, Jack (University of Durham, UK)
    Creator: Kusumaatmaja, Halim (University of Durham, UK)
    Contact person: Kusumaatmaja, Halim (University of Durham, UK)
    Editor: Gizaw, Yonas (P&G, USA)
    Description:
    Keywords: superomniphobic, contact angle hysteresis, critical pressure, wetting barrier, simultaneous optimisation
    Date Uploaded: 23 April 2019
  5. Kondo screening in a charge-insulating spinon metal [dataset] [Download]

    Title: Kondo screening in a charge-insulating spinon metal [dataset]
    Contributors: Creator: Gomilšek, Matjaž (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia & Durham University, South Rd, Durham DH1 3LE, United Kingdom)
    Data curator: Gomilšek, Matjaž (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia & Durham University, South Rd, Durham DH1 3LE, United Kingdom)
    Creator: Žitko, Rok (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia & Faculty of Mathematics and Physics, University of Ljubljana, Jadranska c. 19, SI-1000 Ljubljana, Slovenia)
    Creator: Klanjšek, Martin (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia)
    Creator: Pregelj, Martin (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia)
    Creator: Baines, Christopher (Laboratory for Muon Spin Spectroscopy, Paul Scherrer Institute, CH-5232 Villigen PSI, Switzerland)
    Creator: Li, Yuesheng (Experimental Physics VI, Center for Electronic Correlations and Magnetism, University of Augsburg, 86159 Augsburg, Germany)
    Creator: Zhang, Qingming (National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China & School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China)
    Creator: Zorko, Andrej (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia)
    Contact person: Zorko, Andrej (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia)
    Description:
    Keywords: Kondo effect, spin liquid, non-Fermi liquid, gauge field, geometric frustration, magnetism, impurity, percolation, muon-spin relaxation, nuclear magnetic resonance, numerical renormalization group
    Date Uploaded: 17 April 2019
  6. Optimisation of the coherence transfer delay in PSYCOSY experiments [dataset] [Download]

    Title: Optimisation of the coherence transfer delay in PSYCOSY experiments [dataset]
    Contributors: Creator: Kenwright, Alan (Durham University, UK)
    Description:
    Keywords: NMR Data
    Date Uploaded: 4 April 2019
  7. A configurational force driven cracking particle method for modelling crack propagation in 2D [dataset] [Download]

    Title: A configurational force driven cracking particle method for modelling crack propagation in 2D [dataset]
    Contributors: Creator: Weilong, Ai (Durham University, UK)
    Creator: Bird, Robert (Durham University, UK)
    Creator: Coombs, William M. (Durham University, UK)
    Creator: Augarde, Charles E. (Durham University, UK)
    Description:
    Keywords: configurational force, cracking particle method, meshless, crack propagation
    Date Uploaded: 3 April 2019
  8. Data management plans (DMPs and maDMPs) [other] [Download]

    Title: Data management plans (DMPs and maDMPs) [other]
    Contributors: Creator: Syrotiuk, Nicholas (Durham University, UK)
    Description: Slides available on Prezi.com until we pay to download them in a proprietary file format:  https://prezi.com/p/udmgch_km-mb
    Keywords: Data management plans, Machine-actionable data management plans, DMPs, maDMPs
    Date Uploaded: 2 April 2019
  9. Local magnetism, magnetic order and spin freezing in the 'nonmetallic metal' FeCrAs [dataset] [Download]

    Title: Local magnetism, magnetic order and spin freezing in the 'nonmetallic metal' FeCrAs [dataset]
    Contributors: Creator: Lancaster, T (Durham University, UK)
    Creator: Huddart, B M (Durham University, UK)
    Creator: Birch, M T (Durham University, UK)
    Creator: Pratt, F L (STFC-ISIS Facility, UK)
    Creator: Porter, D G (Diamond Light Source, UK)
    Creator: Clark, S J (Durham University, UK)
    Creator: Julian, S R (University of Toronto, Canada)
    Creator: Hatton, P D (Durham University, UK)
    Creator: Blundell, S J (University of Oxford, UK)
    Creator: Wu, W (University of Toronto, Canada)
    Description:
    Keywords: muon-spin relaxation, non-Fermi liquid, density functional theory, non-collinear magnetic order, magnetic x-ray scattering
    Date Uploaded: 27 March 2019
  10. Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach [Neural Network Weights] [Download]

    Title: Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach [Neural Network Weights]
    Contributors: Creator: Atapour-Abarghouei, Amir (Durham University, UK)
    Contact person: Atapour-Abarghouei, Amir (Durham University, UK)
    Editor: Atapour-Abarghouei, Amir (Durham University, UK)
    Contact person: Breckon, Toby P. (Durham University, UK)
    Editor: Breckon, Toby P. (Durham University, UK)
    Description: The data file has been created using PyTorch ( https://pytorch.org
    ) and contains the weights of a generative model trained to perform monocular depth estimation and semantic segmentation. The file is needed as part of the pipeline for the project at  https://github.com/atapour/temporal-depth-segmentation
    Instructions on how the file can be used and how the code is run can also be found at  https://github.com/atapour/temporal-depth-segmentation
    Keywords: Monocular Depth Estimation, Semantic Segmentation, Temporal Consistency, Computer Vision
    Date Uploaded: 15 March 2019