1529 research outputs found
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Phenotypic plasticity as a route to population shifts via tipping points
No full textCode used for generation of the figures in the paper with title listed above
Glasgow CCTV Object Detection Data
No full textOverview:
The Urban Big Data Centre runs the Glasgow CCTV Object Detection project in partnership with the Glasgow City Council and the Glasgow Centre for Population Health. Glasgow City Council is responsible for the capture of half-hourly images by all the cameras in the project whereas the Data Science Team at the Urban Big Data Centre is responsible for processing the objects of interest localized on the images and for making this data available to users through their CCTV API. This project aims to develop methods to use CCTV images to produce regular statistics on activity levels in urban areas.
Glasgow CCTV Object Detection data:
The Glasgow CCTV Object Detection platform provides counts of street traffic and pedestrians recorded at 85 different locations across Glasgow. Our model, developed by the team at UBDC, uses object detection technology to record the number of people and vehicles passing by CCTV cameras.
Logging new data at regular intervals each day, this project aims to improve public understanding of mobility and traffic patterns across Glasgow.
This platform provides access to the project's latest data through an API and .csv file download so that individuals and researchers can use it to promote better understanding of our city.
Access and restrictions:
The CCTV dataset is open data. This dataset is available in the CCTV API website by one of two ways:
1.CSV download: Manually press the button on the website to download all data in csv format (only Yolo data is available through this way). An alternative way to explore the Yolo data is by using the API Dashboard, thus having a first sense of the counts of the objects in different locations, over time.
2.API request. Live data is accessible as open data directly via API on the CCTV website at https://glasgow-cctv.ubdc.ac.uk/. Ex.: https://glasgow-cctv.ubdc.ac.uk/api/yolo/records/?date_after=20230224&date_before=20230226
Yearly data for archival is sent to the University of Glasgow's Enlighten repository.
More information:
Further information about the Glasgow CCTV Object Detection project is available on the UBDC website at https://www.ubdc.ac.uk/news/the-glasgow-cctv-object-detection-projec
Right size, right place: scale-dependency of managed realignment to mitigate flood hazards in urban estuaries
Full text linkThe data is organized into six main folders: Flood_extent, Intervention_extent, Longitudinal_extent, MHWS_extent, Tidal_prism and R_Code. Flood_extent and Longitudinal_extent folders refer to flood water elevation extent in raster files and longitudinal cross section along the Clyde estuary of this flood water elevation in tabulated form produced from the TUFLOW flood numerical model. On the other hand, Intervention_extent folder contains shapefiles needed to accommodate managed realignments in the TUFLOW model. MHWS_extent folder contains shapefile of mean high-water spring (MHWS) extent in the Clyde estuary. Tidal_prism folder comprises of geomorphic change detection (GCD) results produced using GCD 7 Standalone software. GCD is used to quantify the volume of water between high and low tide (i.e. subtraction of water elevation between high and low tide). R_Code folder contains R language code to produce figure 2-4 used in the paper and to analyse datasets available here. Raster and shapefiles could be opened using standard GIS software such as ArcGIS Pro or QGIS, while tabulated data could be opened using Microsoft Excel. On the other hand, GCD files could be opened using GCD 7 Standalone software available to be downloaded for free from https://gcd.riverscapes.net/Download/ while the R Code could be open using text editor and run in the RStudio available here https://cran.rstudio.com/
Phosgene synthesis catalysis: reaction kinetics and adsorption characteristics over the Norit RX3 Extra activated carbon formulation
No full textThe purpose of this data is to underpin a publication in RSC Advances, the dataset includes spectroscopic information and processed data to produce the figures presented in this publicatio
Optical, contact-free assessment of brain tissue stiffness and neurodegeneration
No full textThe dataset includes both data and code that can be used to recreate the Figures included in the paper
Dataset from Opti-track, SDR, and Radar for Indoor Localisation
Full text linkThis dataset includes synchronized data collected individually from three modalities—OptiTrack motion capture, Software-Defined Radios (SDR), and radar sensors. The data was generated to evaluate and compare their performance in enhancing indoor localisation accuracy for unmanned ground vehicles (UGVs). The dataset includes raw data from each modality, temporal alignment files, and metadata for synchronisation
Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and Manipulation
No full textFlat'n'Fold is a large-scale dataset designed for garment manipulation, consisting of 1,212 human and 887 robot demonstrations of flattening and folding 44 unique garments across 8 categories. It provides comprehensive data, including synchronized multi-view RGB-D images, point clouds, and human and robot action data, capturing the entire manipulation process from crumpled to folded states.
The data file associated with this dataset is too large for standard download (~2TB). Please use the request data button to arrange alternative access. This dataset is licensed with a Creative Commons Attribution CC-BY 4.0 license
CGSTL Nightlight Data
No full textOverview:
The high spatial resolution multi-spectral night-time light remote sensing imagery is collected by the Chinese commercial satellite - Jilin1-03 (JL1-3), which integrates the advantages of space-borne and air-borne night-time light sensors. JL1-3 enables to capture night-time light images in spatial resolution less than 1 m with three spectral bands including 430–512 nm (blue), 489–585 nm (green), and 580–720 nm (red) in 8-bit depth. JL1-3 data have been used in studies such as land use mapping, light source identification, socioeconomic activities, etc. UBDC obtained Nightlight data from Chang Guang Satellite Technology Co., Ltd (CGSTL).
CGSTL Nightlight data:
UBDC's night-time images of Glasgow were obtained in December 2021 and Edinburgh images were obtained in January 2022. Data can be used for various research topics such as the nighttime activities, urbanization, light pollution, etc.
Access and restrictions:
UBDC's licence agreement with CGSTL limits access to academics conducting non-commercial, academic research. To use the data, researchers need to apply to UBDC setting out a summary of the work they plan to undertake so that the usage can be assessed against these criteria. Please apply to UBDC. If the intended use falls within the terms of the licence, researchers will be asked to sign an End User Licence agreement. Datasets will be shared with eligible applicants on receipt of completed license agreements.
More information:
Further information is available on the CGSTL website at https://www.jl1.cn/EWeb/index.asp
Strava Tyne and Wear
No full textOverview:
UBDC researchers have worked extensively with data from Strava, a fitness app that allows users to track a variety of sporting activities. The app is perhaps most popular among cyclists. Data on cycling has traditionally been difficult to gather or has lacked detail but apps like Strava let users log and share their journeys. This enables planners to understand which routes people use, and which origins/destination pairs are busiest. This can help us track how popular cycling is over time, evaluate impacts of infrastructure, and plan new interventions to encourage active travel.
UBDC has the following Strava datasets:
o Strava Scotland data available for 2015 – March 2020
o Strava Glasgow 2013 – March 2020
o Strava Manchester 2015 – 2018
o Strava Tyne and Wear 2015 – 2018
o Strava Sheffield 2017
Strava data:
Strava is a social network, recording data from its users who upload their cycle rides or running activity via smartphone or GPS device. This dataset comprises anonymised data from Strava users in Manchester. The aggregate information provides GIS compatible data that offer an hourly count of users at street level and wait times at intersections based on the Open Street Map roads network. The origin and destination of trips is also available, resolved to census output areas.
Data from Strava, widely used social networking platform tailored for enthusiasts who enjoy activities such as cycling and running, for Scotland and the Northeast of England (Glasgow, Scotland, Manchester, Tyne & Wear, Sheffield). The time period of the data coverage varies for different cities. To ensure user privacy, all the data has been anonymized and also binned.
The GIS compatible data offers:
1.Hourly User Count at Street Level: This gives insights into how many users are active on a specific street on an hourly basis. (Binning: 0 if this number is less than or equal to 3, otherwise rounded up in multiples of 5)
2.Wait Times at Intersections: This information can provide how long Strava users typically wait at various intersections. Such data can be useful for urban planning and traffic management, highlighting potential areas for infrastructure improvement.
3.Origin and Destination of Trips: The data reveals where users start (origin) and finish (destination) their activities.
However, to maintain user privacy, these origins and destinations are grouped by census output areas. This offers a balance between useful data granularity and user confidentiality. This dataset, based on the Open Street Map roads network, offers a rich source of information for a variety of professionals – from urban planners to transport researchers
Access and restrictions:
UBDC data is available for non-commercial, academic research by UK-based academics under an End User Licence. Data can be used for research in the social sciences, including transportation research. Usage of the data for teaching is also permitted.
Access to the Data Strava provides access to the Metroview platform for urban infrastructure planning organizations around the world to understand mobility patterns, identify opportunities for investment and evaluate the impact of infrastructure changes. Access to Metroview is free of charge and is available at Strava’s discretion based on the application requirements. Previous extracts of Strava Metro data for the above geographies and timeframes are available through the UBDC data catalogue. To apply for Strava Metro data in other geographies or for additional timeframes, please see the Strava website https://metro.strava.com/
More information:
Further information can be found at https://metro.strava.com
Strava Scotland
No full textOverview:
UBDC researchers have worked extensively with data from Strava, a fitness app that allows users to track a variety of sporting activities. The app is perhaps most popular among cyclists. Data on cycling has traditionally been difficult to gather or has lacked detail but apps like Strava let users log and share their journeys. This enables planners to understand which routes people use, and which origins/destination pairs are busiest. This can help us track how popular cycling is over time, evaluate impacts of infrastructure, and plan new interventions to encourage active travel.
UBDC has the following Strava datasets:
o Strava Scotland data available for 2015 – March 2020
o Strava Glasgow 2013 – March 2020
o Strava Manchester 2015 – 2018
o Strava Tyne and Wear 2015 – 2018
o Strava Sheffield 2017
Strava data:
Strava is a social network, recording data from its users who upload their cycle rides or running activity via smartphone or GPS device. This dataset comprises anonymised data from Strava users in Manchester. The aggregate information provides GIS compatible data that offer an hourly count of users at street level and wait times at intersections based on the Open Street Map roads network. The origin and destination of trips is also available, resolved to census output areas.
Data from Strava, widely used social networking platform tailored for enthusiasts who enjoy activities such as cycling and running, for Scotland and the Northeast of England (Glasgow, Scotland, Manchester, Tyne & Wear, Sheffield). The time period of the data coverage varies for different cities. To ensure user privacy, all the data has been anonymized and also binned.
The GIS compatible data offers:
1.Hourly User Count at Street Level: This gives insights into how many users are active on a specific street on an hourly basis. (Binning: 0 if this number is less than or equal to 3, otherwise rounded up in multiples of 5)
2.Wait Times at Intersections: This information can provide how long Strava users typically wait at various intersections. Such data can be useful for urban planning and traffic management, highlighting potential areas for infrastructure improvement.
3.Origin and Destination of Trips: The data reveals where users start (origin) and finish (destination) their activities.
However, to maintain user privacy, these origins and destinations are grouped by census output areas. This offers a balance between useful data granularity and user confidentiality. This dataset, based on the Open Street Map roads network, offers a rich source of information for a variety of professionals – from urban planners to transport researchers
Access and restrictions:
UBDC data is available for non-commercial, academic research by UK-based academics under an End User Licence. Data can be used for research in the social sciences, including transportation research. Usage of the data for teaching is also permitted.
Access to the Data Strava provides access to the Metroview platform for urban infrastructure planning organizations around the world to understand mobility patterns, identify opportunities for investment and evaluate the impact of infrastructure changes. Access to Metroview is free of charge and is available at Strava’s discretion based on the application requirements. Previous extracts of Strava Metro data for the above geographies and timeframes are available through the UBDC data catalogue. To apply for Strava Metro data in other geographies or for additional timeframes, please see the Strava website https://metro.strava.com/
More information:
Further information can be found at https://metro.strava.com