1529 research outputs found
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iMCD Lifelogging Data
No full textOverview:
The Integrated Multimedia City Household Survey dataset was produced as a result of a collaborative Economic and Social Research Council, ESRC-funded project across the University of Glasgow, Newcastle and Sheffield.
The purpose of the iMCD datasets was to provide a 360° overview of a life in the city, combining various datasets and methods of collection.
IMCD Household Survey and Travel Diary:
Part of the overall collection is the iMCD Household Survey, which is available in anonymised form. The iMCD Household Survey interviewed a representative sample of adults (2095 people from 1508 household) in the Glasgow and Clyde Valley Planning area between April and November 2015. The Survey has a unique combination of questions covering detailed information about each household and their attitudes and behaviours to transport, education, computer and mobile phone usage, and sustainability. These data would be of interest to planners, researchers or individuals wanting to gain a better understanding of these topics and their interactions.
Access and restrictions:
Licences are available for non-commercial academic research use only. The data is available to request as Safeguarded data under UBDC's End User Licence. 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:
All the iMCD datasets are available at https://data.ubdc.ac.uk/datasets
including iMCD GPS data at https://data.ubdc.ac.uk/datasets/4636e682-bff9-4209-886d-f72c745d7aa0
and iMCD Lifelogging data at https://data.ubdc.ac.uk/datasets/c70b23b2-317b-4f5f-a90e-456633c6ee97
Other related outputs: https://www.ubdc.ac.uk/news/data-for-insights-into-mobilit
Mid infrared high-resolution photon-counting LiDAR dataset
No full textSupporting dataset for Mid infrared high-resolution photon-counting LiDAR manuscript. This includes wavelength-dependent characterisation data of a tungsten silicide superconducting nanowire single photon detector from a wavelength of 1550 – 5438 nm alongside all data associated with the single-pixel scanning LiDAR at 3500 nm wavelength
The effect of AMPKα1 deletion on anticontractile activity and FGF-21 release by mouse abdominal aortic perivascular adipose tissue (PVAT).
Full text linkThis data is from a study designed to investigate the role of AMPK in endothelium-dependent anticontractile effects of PVAT and which PVAT-derived factors could be involved. It contains data on vascular function using myography to measure contraction and relaxation of mouse aortic rings from wild-type (WT) and global AMPKα1 knockout (KO) aortic rings. It also has secretory function of aortic PVAT data from an immunoblotting array and ELISA, and gene expression data using qPCR. Human Umbilical Vein Endothelial Cells (HUVECs) and 3T3-L1 adipocytes how adding conditioned media (CM) derived from WT and KO PVAT affects eNOS expression, and nitric oxide (NO) release as well as AMPK-dependant pathways
glasgow-ipl/pam2026-blackholes-prisoners: PAM2026 Code
No full textCode to reproduce the paper "Black Holes and Prisoners: Understanding AS112 Deployment Characteristics", by Elizabeth Boswell, Xinyan Xian, Mingshu Wang, Stephen McQuistin and Colin Perkins, which will appear in PAM 2026 https://pam2026.at/
She grows louder
Full text linkSpace Junk (2023), 34-track album/core artefact (03:00:02) [16-bit/44.1kHz .wav] and orbiting works
“Space Junk Digital Liner Notes” (2023) (25:39) [audio/.m4a] and 54-page lyric booklet [video/.mp4/PDF]
“The Land of Nod” (2023), track 2, [music video] (03:25) [.mp4]
“Do You Wish to Continue?” (2023), track 19, [music video] (05:44) [.mp4]
“Dream Catcher” (2023), track 3, [durational performance footage from the Space Junk album launch] (13:15) [.mov]
Saloon (2023), audiovisual show [highlights video] (07:20) [.mov]
Blackout Baby (2023), 4-track EP (20:20) [24-bit/44.1kHz .wav]
Track lists are included
Visualising strongly focused 3D light fields in an atomic vapour
No full textIntensity measurements visualising the spectrally and spatially dependent absorption of the longitudinal polarisation component by atoms in the hyperfine Paschen-Back regime. Beams with varying degrees and distributions of initially radial polarisation components are strongly focussed into a vapour cell of Rb-87 under a longitudinal magnetic field of 1.5 T. For certain wavelengths, the atoms were predicted and are observed to exclusively absorb the longitudinal polarisation component generated by the focusing of radially polarised light. The transmitted light is collimated and measured with a photodiode at two cell temperatures, and an additional spatial measurement was taken with a camera
Travel mode detection Glasgow City 2019-2023
No full textOverview
The Travel Mode Detection (TMD) data product provides a travel mode matrix that quantifies the number of trips made by each transport mode between every origin–destination (OD) pair. Leveraging mobile phone GPS traces alongside annotated travel diary records, TMD enables detailed insights into modal splits at the urban scale for the period 2019–2023.
Data Sources
1. Mobile GPS Data
Anonymised, timestamped location points from the HUQ mobile app, capturing trajectories of consenting users across the UK.
2. Travel Diary Data
Secondary labelled survey data from TravelAI, detailing true travel modes and trip attributes used to train and validate the classification model.
Methodology
1. Pre-processing of GPS Trajectories:
- Filter out low-accuracy GPS points.
- Segment continuous traces into candidate trips using stay‐point detection (≥ 5 min within 500 m radius).
2.Feature Extraction:
- Mobility Features: trip distance, average speed, acceleration patterns, heading changes.
- Spatial Context: land‐use classification around trajectory, proximity to transport infrastructure (bus stops, train stations, green spaces).
3. Model Training:
- Split TravelAI diary data into 80% train / 20% test sets.
- Train a supervised machine-learning classifier (e.g. XGBoost) to predict modes (car, bus, bicycle, train, walk).
- Hyperparameter tuning via cross-validation to optimise precision and recall.
4. Performance on Test Set:
- Accuracy: 95.6%
- Precision: 86.2%
- Recall: 87.2%
5.Application to HUQ Trips
- Apply trained model to all HUQ-detected trips with complete trajectory data.
- Aggregate counts of trips by predicted mode for each OD pair, producing the TMD matrix.
Manual Validation
Because HUQ lacks ground-truth labels, a stratified random sample of detected trips was manually checked for plausibility. “Feasibility”, whether the predicted mode matches realistic expectations given trip characteristics, was used as the metric:
| Mode | Sample Feasibility |
| ------- | ------------------ |
| Bicycle | 52 % |
| Bus | 64 % |
| Car | 92 % |
| Train | 14 % |
| Walk | 4 % |
Lower feasibility for train and walk largely reflects sparse trajectory points and the small sample sizes for these modes.
Access and Restrictions
The TMD product is available for non-commercial academic research under UBDC’s End User Licence. Data requests are subject to safeguarding protocols to protect user privacy.
More Information
- User Guide for Travel Mode Detection Matrix: Comprehensive documentation on data schema, CSV formats, and example queries
Influence of Boundary Conditions and Heating Modes on Convective Onset in Rotating Spherical Shells
No full textCritical values found at the onset of convection for a range of parameters and velocity data collected to form figures and plots in the paper
Effects of an Aspergillus niger fermentation product and yeast mannan-rich fraction on dairy calf performance and rumen development
Full text linkThis dataset is the primary data collected from an in vivo experiment to measure the effects of the dietary inclusion of a fermentation product of Aspergillus niger, alone, or in combination with the mannan-rich fraction of Saccharomyces cerevisiae cell walls, on health, growth, feed intake and rumen variables in thirty Holstein-Friesian bull calves over a 70 d trial
Airbnb raw scraped data collection
No full textOverview:
Airbnb lists worldwide a selection of homes and boutique hotels available for booking holidays.
The Urban Big Data Centre (UBDC) conducts a daily web scraping exercise for the Airbnb website, with the code for this exercise openly available at https://github.com/urbanbigdatacentre/ubdc-airbnb. This exercise has been collecting daily information since 2020 on property characteristics, daily booking calendar updates, booking policies, host information, and guest reviews. Data are collected to support academic research by UBDC on the short-term rental sector (Wang 2023, 2024).
These aggregated tables are published as a by-product of that work. This dataset generated from the scraped web content estimates available and occupied listings, visits, occupancy rates and income by tracking individual listings' daily booking calendars, following the open method published by Wang et al. (2024). These metrics are key performance and stock indicators.
From June 2021, it has covered thirty Travel to Work Areas (TTWAs) in Scotland and ten in other parts of the UK. The first version of the dataset covers the Scottish TTWAs only.
The dataset provides monthly estimates, covering a total of 30 months from June 2021 to December 2023. It is published at the Middle Super Output Area (MSOA)/Intermediate Zone (IZ) level for each TTWA, provided there are more than five observed Airbnb listings in that MSOA/IZ. MSOA is the term used in England and Wales, IZ is the term used in Scotland. For simplicity, we refer to both as MSOAs. MSOAs without sufficient observations are omitted.
UBDC extracts/organises Airbnb scraped data into structuralized interconnected parts including booking activities, customer reviews, spatial location, host status and structural and service amenities. This dataset is the Airbnb daily listings/calendar dataset.
. Airbnb daily listings/calendars
. Airbnb small area summaries
Airbnb raw scraped data collection:
This file contains project information and it was not shared with researchers or available for research. It is archived for integrity purposes only. The data is archived so that UBDC has a back-up copy, if and when they choose to restore the AirBnb project.
Access and restrictions:
Access is only available to internal UBDC staff for non-commercial academic research use only. However the code for Airbnb webscraping is openly available at https://github.com/urbanbigdatacentre/ubdc-airbnb
More information:
Wang, Y., Livingston, M., McArthur, D.P. and Bailey, N., 2023. The challenges of measuring the short-term rental market: an analysis of open data on Airbnb activity. Housing Studies, pp.1-20. https://doi.org/10.1080/02673037.2023.2176829
Wang, Y., Livingston, M., McArthur, D. P. and Bailey, N. (2024) Enhancing our understanding of short-term rental activity: A daily scrape-based approach for airbnb listings. Plos one, 19(2), pp. e0298131