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Golf coaches’ perceptions on the role and use of player errors in motor learning: A quantitative survey
No full textThis dataset provides the results of an online quantitative survey of 78 professional golf coaches exploring their perceptions towards the role and use of errors during motor learning and practice. Responses were recorded using a 5-point Likert scale. The dataset is related to the upcoming publication Ferguson, C., Collins, D. & Carson, H. J. (in preparation for submission), ‘Golf coaches’ perceptions of the role and use of player errors in motor learning: A quantitative survey’.
This dataset provides the results of an online quantitative survey of 78 professional golf coaches exploring their perceptions towards the role and use of errors during motor learning and practice. Responses were recorded using a 5-point Likert scale. The dataset is related to the upcoming publication Ferguson, C., Collins, D. & Carson, H. J. (in preparation for submission), ‘Golf coaches’ perceptions of the role and use of player errors in motor learning: A quantitative survey’.
The Professional Judgement and Decision Making approach is well-established within the sports coaching and psychology literature. While recently extended to consider the holistic skill development opportunities (i.e., psycho-motor and psycho-behavioural) afforded by players’ errors in practice, little research has explored how this approach can be effectively operationalised within the sports coaching process. Focusing on golf as an exemplar sport, this study aimed to develop existing applied knowledge on this topic by exploring 78 professional golf coaches’ perceptions on the role and use of player errors in learning, testing coaches’ perceptions when comparing errors for two distinct player populations, and assessing the perceived relevance and utility of the Professional Judgement and Decision Making approach to error utilisation. Findings indicate that coaches recognise the potential benefits and limitations of errors on players’ development but may lack a comprehensive understanding of how to incorporate errors effectively into practice design for holistic skill development. This study highlights the need for further exploration of errors in applied sports coaching environments.Dataset Explanation - this document explains the construction of the survey, highlighting the purpose of each section, as well as how to interpret the participants' responses (i.e., the Likert scale numbered responses
Neural network training/testing dataset
No full textFluorescence images of individual cells divided into tiles. Dataset contains images from 3 different strains and 5 positions in the microfluidic device per strain. Strain ID are 1352:Msn2, 762:Dot6, 742:Mig1
Casablanca - 2025 images
No full text### DOCOMOMO INTERNATIONAL MASS HOUSING ARCHIVE ### The provision of healthy modern housing for all was one of the foremost ideals of the Modern Movement, and inspired a vast wave of planning and building across the world during the 20th century. In the last quarter of the century, even as the foundational programmes of Europe and America lost their impetus, the baton was passed on to other countries, especially in eastern Asia, where the narrative of Modern mass housing was reinvigorated for the next century - a unique example of a key Modernist project that actually continues and thrives today, and which thus forms a principal focus of interest for DOCOMOMO – the leading international organisation promoting the documentation and conservation of buildings, sites and neighbourhoods of the Modern Movement. As heritage, the built legacies of this diverse and multi-generational adventure are almost always too controversial to qualify for conservation strategies. Instead, therefore, recording and inventorisation must dominate the heritage interest in this field. In the recognition of that fact, DOCOMOMO’s International Specialist Committee on Urbanism and Landscape, in partnership with the Scottish Centre for Conservation Studies at the University of Edinburgh, has launched the International Mass Housing Archive, whose aim is to provide an open-access library of images of significant housing projects in each working-group territory, free of copyright restrictions. These files may be copied, edited and shared on condition the appropriate citation is used, as per the terms of the attached Creative Commons Attribution licence. ### Structure ### The International Mass Housing Archive is subdivided under geographical headings corresponding to the constituent working groups of DOCOMOMO, and the individual housing projects are searchable under city and project name. Initially, the Image Archive will be managed and augmented centrally by DOCOMOMO and the SCCS, in partnership with University of Edinburgh Information Services, commencing with pilot city surveys sourced from our own photographic records in the first instance. The archive is related to several existing mass housing documentation initiatives. These include one concerning Britain, namely the online version of the 1994 book, Tower Block: http://towerblock.org/TowerBlock.pd
Supporting Health Ageing at Work qualitative interviews
No full textDataset comprises qualitative lifecourse interviews (anonymised) from participants aged 50+ in three case study settings: finance, manufacturing/engineering and social care. Interviews focused on how health and work interact and on organisational supports for health and wellbeing as employees age.Text files (Engliah
ASZ010 - As - Basic vocabulary (animals, cultural items, qualities, numerals, humans)
No full textThis dataset contains elicited basic vocabulary from As with Nurma Kapitanlaut (year of birth unknown, possibly in the 1920s or 1930s), a native speaker of As. The words cover the semantic domains of animals, cultural items, qualities, numerals, and humans (corresponding to items in green ink on pages 22-28 and 37-42 of the Fieldnotes). As far as possible, the words were recorded in isolation, utterance-final, and utterance-medial context; however, the speaker was in poor health at the time, so this was not always possible. The session was recorded on 30 March 2023, in Suprau village (Southwest Papua province, Indonesia). The recording of the session is split over two audio files, ASZ010-01 and ASZ010-02. This dataset relates to the British Academy Postdoctoral Fellowship 'Synchronic and diachronic investigations in Raja Ampat-South Halmahera, a little-known subbranch of Austronesian' (PF19\100004); additional fieldwork funds were provided by a British Academy Small Grant (SG1920\100342). Further deposits are available in the related DataShare sub-community (https://datashare.ed.ac.uk/handle/10283/8573)
Dataset for "A Micropore Nanoband Electrode Array for Enhanced Electrochemical Generation/Analysis in Flow Systems"
No full textThis is an accompanying data set to a paper which will contain all the required information a reader would need. The abstract for the paper is:
Our previous work has established that micron resolution photolithography can be employed to make microsquare nanoband edge electrode (MNEE) arrays. The MNEE configuration enables systematic control of the parameters (electrode number, cavity array spacing, and nanoelectrode dimensions and placement) which control geometry, conferring consistent high-fidelity electrode response across the array (e.g. high signal, high signal-to-noise, low limits of detection and fast, steady-state, reproducible and quantitative response) and allowing the tuning of individual and combined electrode interactions. Building on this, in this paper we now produce and characterise a Micropore Nanoband Electrode (MNE) Array designed for flow-through detection, where an MNEE edge electrode configuration is used to form a nanotube electrode embedded in the wall of each micropore, formed as an array of pores of controlled pore size and placement through an insulating membrane of sub-micrometer thickness. The success of this approach is established by the close correspondence between experiment and simulation and the enhanced and quantitative detection of redox species flowing through the micropores over the very wide range of flow rates relevant e.g. to (bio)sensing and chromatography. Quantitative electrochemical reaction with low conversion, suitable for analysis, is demonstrated at high flow, whilst quantitative electrochemical reaction with high conversion, suitable for electrochemical product generation, is enabled at lower flow. The fundamental array response is analysed in terms of established flow theories, demonstrating the additive contributions of within pore enhanced diffusional (nanoband edge) and advective (Levich-type) currents, the control of the degree of diffusional overlap between pores through pore spacing and flow rate, the control by design across length scales ranging from nanometer through micrometer to a centimetre array and the ready determination of physicochemical parameters, enabling discussion of the potential of this breakthrough technology to addresses unmet needs in generation and analysis
Windows on the Past: Digital Analysis of Window Design in Later Medieval England (1200-1300)
No full textThe dataset consists of a group of point cloud models of the church at Binham Priory in Norfolk, UK. These were generated from a laser scanning (LIDAR) survey conducted on 1st July 2024. The survey was undertaken as part of the Windows on the Past: Digital Analysis of Window Design in Later Medieval England (1200-1300) project, funded by the Paul Mellon Centre for Studies in British Art. Its Principal Investigator is James Hillson, who at the time was a Lecturer in Architectural History at the University of Edinburgh.
The aim of the project was to use laser scanning data to analyse window design processes, focusing on the critical period of the introduction of bar tracery into medieval England (c. 1200-1300). This resulted in both RAW scanning data and processed point clouds for two key sites in East Anglia: Binham Priory (Norfolk, UK) and the South Transept at Ely Cathedral (Cambridgeshire, UK). The dataset provided here consists of the RAW scanning data and the processed point cloud data for Binham Priory, made publicly available in .e57 format for any non-commercial purpose.
Further details regarding the dataset and the surveying process can be found in the accompanying .xls spreadsheet.The files are organised as follows:
Group 1 - Finding Aids and Metadata
• WotP_Binham_Priory_point_cloud_metadata.xls - Spreadsheet of metadata for scanning process and point clouds.
• WotP_Binham_Priory_Site_Plan_Bay_and_Window_Key.jpg - Plan identifying bay positions for point clouds.
Group 2 - Interior Point Cloud Models (.e57 format)
• WotP_Binham_Priory_point_cloud_interior_C7.e57 - Processed point cloud model for interior of Binham Priory, Bay C7.
• WotP_Binham_Priory_point_cloud_interior_C8.e57 - Processed point cloud model for interior of Binham Priory, Bay C8.
• WotP_Binham_Priory_point_cloud_interior_C9.e57 - Processed point cloud model for interior of Binham Priory, Bay C9.
• WotP_Binham_Priory_point_cloud_interior_C10.e57 - Processed point cloud model for interior of Binham Priory, Bay C10.
• WotP_Binham_Priory_point_cloud_interior_C11.e57 - Processed point cloud model for interior of Binham Priory, Bay C11.
• WotP_Binham_Priory_point_cloud_interior_C12.e57 - Processed point cloud model for interior of Binham Priory, Bay C12.
• WotP_Binham_Priory_point_cloud_interior_C13.e57 - Processed point cloud model for interior of Binham Priory, Bay C13.
• WotP_Binham_Priory_point_cloud_interior_N13.e57 - Processed point cloud model for interior of Binham Priory, Bay N13.
Group 3 - Exterior Point Cloud Models (.e57 format)
• WotP_Binham_Priory_point_cloud_exterior_C13.e57 - Processed point cloud model for exterior of Binham Priory, Bay C13.
Group 4 - Interior RAW Point Clouds (.e57 format)
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-8.e57 - RAW point cloud data for interior of Binham Priory (1 of 12 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-9.e57 - RAW point cloud data for interior of Binham Priory (2 of 12 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-10.e57 - RAW point cloud data for interior of Binham Priory (3 of 12 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-11.e57 - RAW point cloud data for interior of Binham Priory (4 of 12 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-12.e57 - RAW point cloud data for interior of Binham Priory (5 of 12 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-13.e57 - RAW point cloud data for interior of Binham Priory (6 of 12 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-14.e57 - RAW point cloud data for interior of Binham Priory (7 of 12 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-15.e57 - RAW point cloud data for interior of Binham Priory (8 of 12 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-17.e57 - RAW point cloud data for interior of Binham Priory (9 of 12 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-18.e57 - RAW point cloud data for interior of Binham Priory (10 of 12 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-19.e57 - RAW point cloud data for interior of Binham Priory (11 of 12 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-20.e57 - RAW point cloud data for interior of Binham Priory (12 of 12 setups).
Group 5 - Exterior RAW Point Clouds (.e57 format)
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-1.e57 - RAW point cloud data for exterior of Binham Priory (1 of 6 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-2.e57 - RAW point cloud data for exterior of Binham Priory (2 of 6 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-3.e57 - RAW point cloud data for exterior of Binham Priory (3 of 6 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-4.e57 - RAW point cloud data for exterior of Binham Priory (4 of 6 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-5.e57 - RAW point cloud data for exterior of Binham Priory (5 of 6 setups).
• WotP_Binham_Priory_RAW_point_cloud_WR3-18-6.e57 - RAW point cloud data for exterior of Binham Priory (6 of 6 setups).
File structure and descriptions can be found in the following attached spreadsheet (.xls format):
• WotP_Binham_Priory_point_cloud_metadata.xls
The locations of individual models within the building can be identified using the following planimetric map:
• WotP_Binham_Priory_Site_Plan_Bay_and_Window_Key.jp
Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition
No full textTraining a team to complete a complex task via multi-agent reinforcement learning can be difficult due to challenges such as policy search in a large joint policy space, and non-stationarity caused by mutually adapting agents.
To facilitate efficient learning of complex multi-agent tasks, we propose an approach which uses an expert-provided decomposition of a task into simpler multi-agent sub-tasks.
In each sub-task, a subset of the entire team is trained to acquire sub-task-specific policies. The sub-teams are then merged and transferred to the target task, where their policies are collectively fine-tuned to solve the more complex target task.
We show empirically that such approaches can greatly reduce the number of timesteps required to solve a complex target task relative to training from-scratch.
However, we also identify and investigate two problems with naive implementations of approaches based on sub-task decomposition, and propose a simple and scalable method to address these problems which augments existing actor-critic algorithms.
We demonstrate the empirical benefits of our proposed method, enabling sub-task decomposition approaches to be deployed in diverse multi-agent tasks.See individual README files for more information.
Structure:
- results: target task training results, including ablations and forgetting results.
- chainball_explore: files for demonstrating miscoordinated exploration in chainball. Includes the chainball forward probability matrix used in experiments.
- chainball_zoo
- cooking_zoo
- vmas_zo
Georeferenced KH-9 Imagery - Tri-border area
No full textThis dataset contains georeferenced KH-9 images used in the manuscript "Detecting Vietnam War bomb craters in declassified KH-9 satellite imagery" (in submission). It contains the following images, provided as .tif files at a resolution of one meter per pixel (1mpp), as used in the analysis, and eight meters per pixel (8mpp): D3C1204-200292A077, D3C1204-200292A078, D3C1204-200292A079, D3C1204-200292A080, D3C1204-200292A081, D3C1204-200292A082.
The KH-9 images were acquired on the 4. November 1972 by a HEXAGON KH-9 reconnaissance satellite and cover parts of Cambodia, Lao PDR and Viet Nam. The original images (not georeferenced) were provided as part of the Declassified Satellite Imagery - 3 collection (https://doi.org/10.5066/F7WD3Z10) courtesy of the U.S. Geological Survey
SUPERSEDED - Voices of Experience Conversations 2016-2019
No full text## This item has been replaced by the one which can be found at https://doi.org/10.7488/ds/8010 ## Full verbatim transcriptions of audio-recorded conversations. Conversations take place between an older person from the built environment profession paired with an individual at the outset of their career. The pairings have a common work theme or project and VoE Conversations take place in a setting which has a connection with the work of each of the participants. The emphasis is on female experience. Voices of Experience is a collaborative project led by Suzanne Ewing (University of Edinburgh), Jude Barber (Collective Architecture) and Nicola McLachlan (Collective Architecture).Voices of Experience – Conversation 1 [2016] - Margaret Richards and Nicola McLachlan
Voices of Experience - Conversation 2 [2016] - Fiona Sinclair and Mairi Laverty
Voices of Experience – Conversation 3 [2016] – Dorothy Bell and Emma Fairhurst
Voices of Experience – Conversation 4 [2016] – Anne Duff and Cathy Houston
Voices of Experience - Conversation 5 [2017] – Kirsteen Borland and Heather Claridge
Voices of Experience – Conversation 6 [2017] – Jocelyn Cunliffe and Melanie Hay
Voices of Experience – Conversation 7 [2017] - Denise Bennetts and Grace Marks
Voices of Experience – Conversation 8 [2017] – Kate Macintosh and Elaine Keenan
Voices of Experience – Conversation 9 [2019] - Joyce Deans and Ruta Turcinaviciute
Voices of Experience - Conversation 10 [2019] – Adele Patrick and Akiko Kobayash