University of Stirling
University of Stirling: DataSTORRE (Stirling Online Repository for Research Data)Not a member yet
118 research outputs found
Sort by
Transitions to more harmful forms of gambling during Covid-19 pandemic: behaviours and targeted marketing in young people and bettors on sport
No full textThis longitudinal qualitative dataset comes from an ESRC funded study exploring gambling behaviour among sports bettors during the COVID-19 pandemic. 29 interviews with regular sports bettors were conducted during the study. Sixteen sports bettors took part in the first interview between July-November 2020, and 13 in the follow-up interview between March-September 2021. Permissions are in place to potentially share 25 out of 29 interviews.Files in the format serial-SB-year are all interview transcripts. The other files have the description, name and year of the document in the file name, e.g. information sheet, topic guide, consent form.Special conditions
De-identified qualitative interview data from this study will be suitable for sharing provided that
appropriate safeguards are in place:
1. De-identified transcripts will only be shared where participant permission has been explicitly
obtained and, where it is considered that additional de-identification or redaction of the transcripts
may be required, costs for this would need to be covered by the party making the for data sharing
request. While de-identification/redaction measures will be done before transcripts are shared, we
acknowledge there is a very small chance that someone could be identifiable from their answers
due to the rich nature of qualitative data. Consequently, we plan to inform participants that ‘strong
measures are in place to make it difficult for anyone to be identified from their typed up interview’
rather than make a guarantee that a person’s answers could never be traced back to them.
2. Once all planned articles are accepted for publication, other interested parties may request access
to de-identified transcripts. Any requests for access will be reviewed by the PIs and assessed
according to factors such as whether a strong case can be made for further analysis of the data. If
granted, appropriate legal/data-sharing agreements will be drafted and signed. This agreement will
include, as a minimum, the need for the requestor to: declare links with gambling companies;
specify how data will be used; the scientific case justifying the need for further analysis; how
participant privacy and confidentiality will be fully protected; and relevant expertise of those
performing analysis
PhD Studentship 1 +3
No full textThis data was extracted from referrals made to two different CAMHS teams in Scotland for children who presented to the referrer as suicidal.SPSS file with descriptive data of referrals made to two CAMHS for suicidal children in Scotland Jan-June 201
Sustainable Plastic Attitudes to benefit Communities and their Environment: SNREC-GCRF SPACES project
No full textSewage-associated plastic wastes, such as wet wipes and cotton bud sticks, commonly wash up on beaches; however, it is unclear whether this represents a public health risk. In this study, sewage-associated plastic waste, and naturally occurring substrates (seaweed and sand), were collected from ten beaches along the Firth of Forth estuary (Scotland, UK) and analysed using selective media for the faecal indicator organisms (FIOs) E. coli and intestinal enterococci (IE), and potential human pathogens (Vibrio spp.). Minimum inhibitory concentration (MIC) analysis was used to determine antibiotic resistance in selected strains. FIOs and Vibrio were more often associated with wet wipes and cotton bud sticks than with seaweed, and there was evidence of resistance to several antibiotics. This work demonstrates that plastics associated with sewage pollution can facilitate the survival and dissemination of FIOs and Vibrio and thus, could present an as yet unquantified potential risk to human health at the beach.Sewage_Forth_Pathogens_RawData.xlsx - • This dataset contains the raw data collected during this study. It includes quantity and weight measurements of the sand, seaweed and sewage-associated plastic waste samples collected at the different sites. Samples were analysed using selective media for Vibrio spp., E. coli and intestinal enterococci. CFU counts for the water and sand samples, as well as presence and absence data for the plastic samples are included. There is also data on the minimum inhibitory concentration to five selected antibiotics for the different samples
The Influence of Information Search on Preference Formation and Choice (INSPiRE)
No full textIn this stated choice experiment respondents choose among bottles of wine described using seven attributes: 1) Country of origin, 2) colour of the wine, 3) alcohol by volume, 4) grape variety, 5) characteristic of the wine, 6) whether the wine was organic, and 7) price. The attribute levels were established after scrapping the websites of three large supermarket chains in the UK. Using the web-scraped wine data, we calculated a set of probability weights to establish the likelihood of each experimentally designed wine bottle being available in the supermarket. We used these weights to sample individual random profiles from the restricted factorial each time an individual entered the survey. The survey was programmed in Shiny, which is an R package, and the data was gathered during January 2020. In total, 4,121 respondents were randomly allocated to one of 10 treatments, each designed to test a specific aspect of search and preference learning. Example tasks and the instruction videos can be found at https://choice-tasks.inspire-project.info/. Respondents were randomly recruited from the UK population aged 18 and over. We did not use any quota sampling to ensure that our samples were representative of the target population.data-inspire-project-choice.csv
stated preference choice data in "wide" format with one row per choice situation
variable-description.pdf
a description of the variables (pdf file)0.5Individual data not currently deposited. It will be made available in the very near future. In the meantime contact Danny Campbell for access ([email protected])
Everyday Care: what makes it therapeutic for children?
No full textThis dataset includes 29 field notes covering 161 hours of participant observations, 12 online, semi-structured interview transcripts (6 x one-to-one and 6 x small groups) and transcripts from 10 in person one-to-one semi-structured interviews. These were collected between February and November 2021 as part of an ethnographic research project entitled 'Everyday Care: what makes it therapeutic for children?' The project explored the everyday experiences of children and staff in a residential childcare setting in Scotland that includes both a care and an education campus. The purpose of the project was to investigate the everyday experience of living and working in a 'therapeutic environment'.There are 29 field notes relating to 161 hours of participant observation undertaken in a Scottish residential childcare setting between February and November 2021. There are 22 transcripts of semi structured interviews with staff working in the residential setting.Confidentiality has been protected using a combination of pseudonymisation and redaction. Typically the latter has been used where consent could not be established
Data from 'Sensory plasticity in a socially plastic bee'
No full textThe social Hymenoptera have contributed much to our understanding of the evolution of sensory systems. Attention has focussed chiefly on how sociality and sensory systems have evolved together. In the Hymenoptera, the antennal sensilla are important for optimising the perception of olfactory social information. Social species have denser antennal sensilla than solitary species, which is thought to enhance social cohesion through nest-mate recognition. In the current study, we test whether sensilla numbers vary between populations of the socially plastic sweat bee Halictus rubicundus from regions that vary in climate and the degree to which sociality is expressed. We found region level differences in both olfactory and hygro/thermoreceptive sensilla numbers. We also found evidence that olfactory sensilla density is developmentally plastic: when we transplanted bees from Scotland to the south-east of England, their offspring (which developed in the south) had more olfactory hairs than the transplanted individuals themselves (which developed in Scotland). The transplanted bees displayed a mix of social (a queen plus workers) and solitary nesting, but neither individual nor nest phenotype was related to sensilla density. We suggest that this general, rather than caste-specific sensory plasticity provides a flexible means to optimise sensory perception according to the most pressing demands of the environment. Sensory plasticity may support social plasticity in H. rubicundus but does not appear to be causally related to it.Bee.antenna read.me This read me provides details about the headers in each of the .txt files in this repository:
Files:I0R - all files for calculating intra-observer reliability. All values in the same row represent repeated counts of sensillae from the same antenna.
File name: IOR-Q-Sp-2.txt Sp1: Number of type i sensilla counted across 3 quadrats, first measurement; Sp2: Number of type i sensilla counted across 3 quadrats, second measurement File name: IOR-Q-Stb-2.txt Stb1: Number of type ii sensilla counted across 3 quadrats, first measurement; Stb2: Number of type i sensilla counted across 3 quadrats, second measurement File name: IOR-Q-Sacc-2.txt Sacc1: Number of type iii sensilla counted across 3 quadrats, first measurement; Sacc2: Number of type iii sensilla counted across 3 quadrats, second measurement File name: IOR-full-2.txt Sacc-all-1: Number of type iii sensilla counted across full antennal segment, first measurement; Sacc-all-2: Number of type iii sensilla counted across full antennal segment, second measurement
File name: IOR-Q-Sp-3.txt Sp1: Number of type i sensilla counted across 3 quadrats, first measurement; Sp2: Number of type i sensilla counted across 3 quadrats, second measurement; Sp3: Number of type i sensilla counted across 3 quadrats, third measurement File name: IOR-Q-Stb-3.txt Stb1: Number of type ii sensilla counted across 3 quadrats, first measurement; Stb2: Number of type i sensilla counted across 3 quadrats, second measurement; Stb3: Number of type i sensilla counted across 3 quadrats, third measurement File name: IOR-Q-Sacc-3.txt Sacc1: Number of type iii sensilla counted across 3 quadrats, first measurement; Sacc-all-2: Number of type iii sensilla counted across 3 quadrats, second measurement; Sacc3: Number of type iii sensilla counted across 3 quadrats, third measurement File name: IOR-full-3.txt Scac1: Number of type iii sensilla counted across full antennal segment, first measurement; Scac2: Number of type iii sensilla counted across full antennal segment, second measurement; Scac3: Number of type iii sensilla counted across full antennal segment, third measurement;
File name: Full-antennae-all.txt This file is used for analyses of regional differences in sensilla counts (REgion models) Imaged: Date the bee antenna was imaged on Individual: The unique code given to each indiviudal bee Seg.ID: The unique code given to each antennal segment for each bee (x 2 per bee). Suffix .9 refers to segment 11 and .10 to segment 12. Seg: Segment number, 9 = segment 11 and 10 = segment 12 (tip) Scac: Total type iii sensilla counted across each antennal segment Sp.sum: Sum of type i sensilla counted across 3 quadrants (50x50uM) (x 2 segments per bee) Stb.sum: Sum of type ii sensilla counted across 3 quadrants (50x50uM) (x 2 segments per bee) Region: North (Belfast), South-West, Scotland. Refers to where bees were collected. Used as indpendent variable in 'region' model. Population: More detailed data about where in each region bees were collected (Bodmin vs Boscastle for the South-West bees). Latitude: Latitude of site where bees were sampled Longitude: Longitude of site where bees were sampled Year: Year that bees were sampled Month: Month of year (numerical; Jan = 1) when bees were sampled.
File name: Knepp-transplant-full.txt This file is used for analysing whether there is an effect of where bees developed on sensilla numbers (Transplant models). Imaged: Date the bee antenna was imaged on Individual: The unique code given to each indiviudal bee Seg.ID: The unique code given to each antennal segment for each bee (x 2 per bee). Suffix .9 refers to segment 11 and .10 to segment 12. Seg: Segment number, 9 = segment 11 and 10 = segment 12 (tip) Scac: Total type iii sensilla counted across each antennal segment Sp.sum: Sum of type i sensilla counted across 3 quadrants (50x50uM) (x 2 segments per bee) Stb.sum: Sum of type ii sensilla counted across 3 quadrants (50x50uM) (x 2 segments per bee) Origin: Where the bees were collected (Natal site = Migdale, transplant site = Knepp) Latitude: Latitude of site where bees were sampled Longitude: Longitude of site where bees were sampled Year: Year that bees were sampled Month: Month of year (numerical; Jan = 1) when bees were sampled.
File name: Bee.phen.txt This file is used for testing whether bees from solitary or social nests, bees of different phenotypes (workers, solitary foundresses), or bees of different ages have different sensilla numbers. Imaged: Date the bee antenna was imaged on Individual: The unique code given to each indiviudal bee Seg.ID: The unique code given to each antennal segment for each bee (x 2 per bee). Suffix .9 refers to segment 11 and .10 to segment 12. Seg: Segment number, 9 = segment 11 and 10 = segment 12 (tip) Scac: Total type iii sensilla counted across each antennal segment Sp.sum: Sum of type i sensilla counted across 3 quadrants (50x50uM) (x 2 segments per bee) Stb.sum: Sum of type ii sensilla counted across 3 quadrants (50x50uM) (x 2 segments per bee) Age: Whether the bee was fresh (newly emerged and had not provisioned a nest) or old (had provisioned a nest for several weeks) Phenotype.2: The phenotype of the individual bee. Future-rep = future reproductive, a B1 individual that emerged in 2020 and did not provision that year; Worker = a B1 individual that provisioned a nest containing a queen; Sol-F = a foundress that emerged in SCO in 2019 and laid eggs/provisioned a nest alone without workers in the south-east; Sol-B1 = a female that emerged in the B1 generation in 2020 in the SE and provisioned a nest alone Nest.phenotype = phenotype of the nest, solitary (only one female provisioning alone) or social (multiple bees provisioning the same nest). Latitude: Latitude of site where bees were sampled Longitude: Longitude of site where bees were sampled Year: Year that bees were sampled Month: Month of year (numerical; Jan = 1) when bees were sampled.
Antenna-Supp-20.5.21: Table S1 Intraclass correlation coefficients for intra-rater reliability of all antenna types. Table S2: Shown are monthly maximum, minimum and mean temperatures (means ± standard deviations across all months) in years that bees that were collected from each site developed. Data from two years are provided for Migdale (Scotland) as bees were collected in two years (2018 and 2020 after developing in 2017 and 2019 (www.wunderground.com/history). Also included are soil temperatures recorded at depths of 1cm and 15cm at Migdale and Knepp sites in 2019 and 2020 respectively (using TinyTag data loggers; Gemini data loggers). At Knepp 1 logger was deployed and recovered at 1cm and 1 at 15cm. At Migdale 4 loggers were deployed and recovered at 1cm and 2 at 15cm.
Varieties of Risk
No full textThe data comprises survey responses from over 1000 participants. The survey was designed by researcher from the University of Stirling in collaboration with the Scottish Avalanche Information Service (SAIS) who publish daily avalanche risk bulletins for outdoor sports participants throughout the winter. An important aspect of risk communication is to understand the audience and so the core aim of the survey was to understand how people use the avalanche information service and identify the role it plays in their decision-making during planning of outdoor activities and when pursuing them. To that end, the survey covers three main aspects: 1) the survey helps to identify different user profiles 2) it identifies and probes survey respondents familiarity with the avalanche bulletin. (3) it probes the survey respondents understanding of various risk communication tools used by the SAIS. The data underwrites the publication of the SAIS User Survey Summary Report 2021.The data contains one .csv-file which contains all the data which underwrites the SAIS Survey Report -- Summary Report 2021. The data is fuily anonymised and only contains generic data about individual. The data covers. A. SAIS survey respondents’ profile and avalanche knowledge. B. SAIS survey respondents’ familiarity with SAIS reports. C SAIS survey respondents’ understanding of the hazard scale and danger rose.This is a complete data for the summary report. The complete survey will be published once academic work is accepted and associated with these outputs
The Beacon Project: Using Biodiversity and Energy justice to resolve Conflicts between Sustainable Development Goals
No full textThe dataset relates to socio-ecological surveys undertaken in September-October 2017 in Kazakhstan. The socio-ecological surveys were conducted in order to understand peoples' motivations for hunting waterfowl across the Kostanay and North Kazakhstan regions, as part of a UN-AEWA project focused on the conservation of the Lesser White-fronted Goose (Anser erythropus). The dataset contains fully anonymised data on participants' responses to socio-ecological questionnaires (please see related publication for full details).The dataset comprises a MS Excel spreadsheet with data for each anonymised socio-ecological survey participant. Basic socio-economic information is included for each participant who gave informed consent to be included in the study. Socio-economic data are separated across spreadsheet columns, including information on e.g. gender, age, employment, hunting licence ownership. The spreadsheet also includes information on participants' knowledge of different waterfowl species' protection status, as well as values relating to participants' responses to the 'Unmatched Count Technique', a method used to anonymously explore the potential for illegal hunting activity (please see the associated publication for full details on methods)
Ecology and evolution of buzz pollination
No full textIn buzz-pollinated plants, bees apply thoracic vibrations to the flower, causing pollen release from anthers, often through apical pores. Bees usually grasp one or more anthers with their mandibles, and vibrations are transmitted to the focal anther(s), adjacent anthers, and the whole flower. Pollen release depends on anther vibration, and thus it should be affected by vibration transmission through flowers with distinct morphologies, as found among buzz-pollinated taxa. We compare vibration transmission between focal and non-focal anthers in four species with contrasting anther arrangements: Cyclamen persicum, Exacum affine, Solanum dulcamara and S. houstonii. We used a mechanical transducer to apply bee-like vibrations to focal anthers, measuring the vibration frequency and displacement amplitude at focal and non-focal anther tips simultaneously using high-speed video analysis (6,000 frames per second). In flowers in which anthers are tightly arranged (C. persicum and S. dulcamara), vibrations in focal and non-focal anthers are indistinguishable in both frequency and displacement amplitude. In contrast, flowers with loosely arranged anthers (E. affine) including those with differentiated stamens (heterantherous S. houstonii), show the same frequency but higher displacement amplitude in non-focal anthers compared to focal anthers. We suggest that stamen arrangement modulates vibration transmission, potentially affecting pollen release and bee behaviour.The dataset contains a csv file corresponding to stamen vibration data obtained from analysis of high-speed video footage of artificially applied vibrations. "Distal" treatment refers to the non-focal anther
Will more productive Arctic ecosystems sequester less soil carbon? A key role for priming in the rhizosphere ("PRIME-TIME")
No full textThis dataset was used in the manuscript "Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem". The goal of the project was to ascertain if we could measure soil respiration remotely (i.e. using remote sensing, for example using an UAV- Unmanned Aerial Vehicle). Measurements of soil temperature, NDVI and CO2 efflux were conducted in July 2018 in Abisko, Northern Sweden as part of an undergraduate project. Soil cores were collected and analysed for moisture, organic matter/carbon and root biomass.The dataset consists of the average values for soil temperature (‘Temperature’), normalized difference vegetation index (‘NDVI’), leaf area index (‘LAI’), soil respiration (‘Rs’), soil moisture (‘Moisture’), organic matter in the organic horizon (‘OM.org’), carbon in the organic horizon (‘C.Org’) and fine root biomass (‘Roots’). Furthermore, the data indicate where these measurements were made (Blocks 1-4), ‘Community’ i.e. type of plant functional type (B= B. nana, E= E. nigrum, L= Lichen, W= Willow) and level of greenness as ascertain visually from low (‘L’), medium (‘M’) and high (‘H’). ‘Collar’ refers to the location of where the infra-red gas analyser was placed for the repeated measurements of CO2 efflux