State-space modelling of geolocation data reveals sex differences in the use of management areas by breeding northern fulmars

We thank Orkney Islands Council for access to Eynhallow and Talisman Energy (UK) Ltd and Marine Scotland for fieldwork and equipment support. Handling and tagging of fulmars was conducted under licences from the British Trust for Ornithology and the UK Home Office. EE was funded by a Marine Alliance for Science and Technology for Scotland/University of Aberdeen College of Life Sciences and Medicine studentship and LQ was supported by a NERC Studentship. Thanks also to the many colleagues who assisted with fieldwork during the project, and to Helen Bailey and Arliss Winship for advice on implementing the state-space model.

Modelling Provenance of Sensor Data for Food Safety Compliance Checking

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State-space modelling of geolocation data reveals sex differences in the use of management areas by breeding northern fulmars

We thank Orkney Islands Council for access to Eynhallow and Talisman Energy (UK) Ltd and Marine Scotland for fieldwork and equipment support. Handling and tagging of fulmars was conducted under licences from the British Trust for Ornithology and the UK Home Office. EE was funded by a Marine Alliance for Science and Technology for Scotland/University of Aberdeen College of Life Sciences and Medicine studentship and LQ was supported by a NERC Studentship. Thanks also to the many colleagues who assisted with fieldwork during the project, and to Helen Bailey and Arliss Winship for advice on implementing the state-space model.

Basement structure of the Hontomín CO2 storage site (Spain) determined by integration of microgravity and 3D seismic data

We dedicate this paper to the memory of Prof. Andres Perez Estaún, who was a great and committed scientist, wonderful colleague and even better friend. The datasets in this work have been funded by Fundación Ciudad de la Energía (Spanish Government, www.ciuden.es) and by the European Union through the “European Energy Programme 15 for Recovery” and the Compostilla OXYCFB300 project. Dr. Juan Alcalde is currently funded by NERC grant NE/M007251/1. Simon Campbell and Samuel Cheyney are acknowledged for thoughtful comments on gravity inversion

Conceptual modelling to assess how the interplay of hydrological connectivity, catchment storage and tracer dynamics controls nonstationary water age estimates

Acknowledgements We would like to gratefully acknowledge the data provided by SEPA, Iain Malcolm. Mark Speed, Susan Waldron and many MSS staff helped with sample collection and lab analysis. We thank the European Research Council (project GA 335910 VEWA) for funding and are grateful for the constructive comments provided by three anonymous reviewers.

Modelling storage-driven connectivity between landscapes and riverscapes : towards a simple framework for long-term ecohydrological assessment

Acknowledgements: We thank Iain Malcolm of Marine Scotland Science for access to data from the Girnock and the Scottish Environment Protection Agency for historical stage-discharge relationships. CS contributions on this paper were in part supported by the NERC/JPI SIWA project (NE/M019896/1).

Using high resolution tracer data to constrain water storage, flux and age estimates in a spatially distributed rainfall-runoff model

Acknowledgements The authors would like to thank Jonathan Dick, Josie Geris, Jason Lessels, and Claire Tunaley for data collection and Audrey Innes for lab sample preparation. We also thank Christian Birkel for discussions about the model structure and comments on an earlier draft of the paper. Climatic data were provided by Iain Malcolm and Marine Scotland Fisheries at the Freshwater Lab, Pitlochry. Additional precipitation data were provided by the UK Meteorological Office and the British Atmospheric Data Centre (BADC).We thank the European Research Council ERC (project GA 335910 VEWA) for funding the VeWa project.

Sentiment-Topic Modelling in Text Mining

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Bone morphology of the femur and tibia captured by statistical shape modelling predicts rapid bone loss in acute spinal cord injury patients

We would like to thank the study participants and the clinical and research staff at the Queen Elizabeth National Spinal Injury Unit, as without them this study would not have been possible. We are grateful for the funding received from Glasgow Research Partnership in Engineering for the employment of SC during data collection for this study. We would like to thank the Royal Society of Edinburgh's Scottish Crucible scheme for providing the opportunity for this collaboration to occur. We are also indebted to Maria Dumitrascuta for her time and effort in producing inter-repeatability results for the shape models.

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Bone morphology of the femur and tibia captured by statistical shape modelling predicts rapid bone loss in acute spinal cord injury patients

We would like to thank the study participants and the clinical and research staff at the Queen Elizabeth National Spinal Injury Unit, as without them this study would not have been possible. We are grateful for the funding received from Glasgow Research Partnership in Engineering for the employment of SC during data collection for this study. We would like to thank the Royal Society of Edinburgh's Scottish Crucible scheme for providing the opportunity for this collaboration to occur. We are also indebted to Maria Dumitrascuta for her time and effort in producing inter-repeatability results for the shape models.

Modelling the cost-effectiveness of adopting risk stratified approaches to extended screening intervals in the national diabetic retinopathy screening programme in Scotland

Funding sources: The study was funded by a research grant from the Chief Scientist’s Office of the Scottish Government Health and Social Care Directorates (CZH/4/971). The funder played no role in study design, data collection, data analysis, manuscript preparation and/or publication decisions. The views expressed herein are those of the authors and do not necessarily reflect those of the funder.

Using individual tracking data to validate the predictions of species distribution models

The authors would like to thank the College of Life Sciences of Aberdeen University and Marine Scotland Science which funded CP's PhD project. Skate tagging experiments were undertaken as part of Scottish Government project SP004. We thank Ian Burrett for help in catching the fish and the other fishermen and anglers who returned tags. We thank José Manuel Gonzalez-Irusta for extracting and making available the environmental layers used as environmental covariates in the environmental suitability modelling procedure. We also thank Jason Matthiopoulos for insightful suggestions on habitat utilization metrics as well as Stephen C.F. Palmer, and three anonymous reviewers for useful suggestions to improve the clarity and quality of the manuscript.

ELUM: a spatial modelling tool to predict soil greenhouse gas changes from land conversion to bioenergy in the UK

Acknowledgements This work is based on the Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial (ELUM) project, which was commissioned and funded by the Energy Technologies Institute (ETI). The authors are grateful to Niall McNamara (Centre for Ecology & Hydrology, Lancaster) for coordinating the project and to Dagmar Henner (University of Aberdeen) for project assistance. We are also grateful to staff at the ETI, particularly to Geraldine Newton-Cross, Geraint Evans and Hannah Evans for constructive advice and feedback, and to Jonathan Oxley for project support. The ELUM Software Package contains Ordnance Survey data © Crown copyright and database right 2012.

A framework for modelling mobile radio access networks for intelligent fault management

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