Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
116
datasets available to search
ShareScore release 0.9.0
Dataset results
116 results for “Great Britain”
A large ensemble of CMIP6-based transient climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments often require a large ensemble of local-scale transient climate scenarios. Each ensemble member represents plausible long weather series at a local scale. The climate projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. Moreover, very few projections are usually available for each GCM due to a high computational cost. An alternative approach involves employing a stochastic weather generator to produce a large number of transient scenarios based on the climate projections from GCMs. In a current dataset, transient climate scenarios were generated using the LARS-WG weather generator, based on climate projections from GCMs from the CMIP6 ensemble across 26 representative sites throughout the UK. Each transient scenario spans the period from 2020 to 2090. At each site, 100 transient scenarios were generated for two emission scenarios (SSP2-4.5 and SSP5-8.5) and five selected GCMs from CMIP6 (ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR, and MRI-ESM2-0). The choice of GCMs were based on their performance over northern Europe and their climate sensitivity. The use of a subset of GCMs substantially reduces computational time required for impact assessment, while allowing to quantify uncertainties in impacts related to uncertain future climate. The dataset can be used with impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare, when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</p>
CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments require local-scale climate scenarios. The climate change projections from <span>Global Climate Models (GCMs) </span>are difficult to use at local scale due to their <span>coarse spatial and temporal resolution. </span><span>It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of </span>realisations <span>to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A</span><span> <span>dataset of future climate change scenarios was therefore generated at </span></span><span>26 representative sites across the UK</span><span> based on the latest </span><span>CMIP6 multi-model ensemble </span><span>downscaled to local-scale by using a </span><span>stochastic weather generator LARS-WG 7.0. The data set provides </span><span>1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (</span><span>Shared Socioeconomic Pathways - SSPs <em>viz</em>. </span>SSP2-4.5 and <span>SSP5-8.5)</span><span>.</span><span> </span><span>A total of </span>15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. <span>LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. </span>Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, <em>viz</em>.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs.<span> <span>The 1000 years of </span></span>realisations <span>of daily weather for the baseline as well as future climate change scenarios are helpful for estimating </span>seasonality and<span> inter-annual variation, and for detecting short, </span>low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset <span>can be used as an input to climate change impact models in various fields, including, </span><span>land and water resources, agriculture and food production, </span>ecology and epidemiology, and <span>human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</span></p>
Predicted occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution
<p>The dataset contains predictions of occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution + all covariate layers used for modeling. Over seven million electronic health records (EHRs), among which 11,741 EHRs reported tick attachment, were used to evaluate climate, environmental and animal host factors affecting the risk of tick attachment in cats and dogs in Great Britain (GB). The tick presence/absence EHRs for dogs and cats were further overlaid with spatiotemporal time-series of climatic, vegetation, human influence, hydrological and terrain variables (slope, wetness index) to produce a spatiotemporal regression matrix; an Ensemble Machine Learning framework was used to fine-tune hyperparameters for Random Forest (classif.ranger), Gradient boosting (classif.xgboost) and GLM-net (classif.glmnet) algorithms, which were then used to produce a final ensemble meta-learner that predicts the probability of occurrence of ticks across GB with monthly intervals.</p> <ul> <li>gb1km_covariates.zip contains ALL covariate layers as GeoTIFFs (time-series) used for modeling ticks dynamics;</li> <li>data_1km_2014_M01.rds = contains all covariates for January 2014 prepared as SpatialGridDataFrame (R data object);</li> </ul> <p>Codes of files indicate e.g.:</p> <ul> <li>"monthly.tick.prob_savsnet.mar_p_1km_s_2014_2021" = monthly occurrence probability for January based on the training data from 2014 to 2021;</li> <li>"monthly.tick.prob_savsnet.oct_md_1km_s_20211001_20211031" = monthly prediction (model) error derived as the standard deviation from multiple base learners;</li> </ul> <p>The dataset is described in detail in the following publication:</p> <ul> <li>Arsevska, E., Hengl, T., Singelton, D. et al. (2023?) <strong>Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021</strong>. Submitted to Parasites & Vectors (in review).</li> </ul> <p>The model summary shows:</p> <pre><code>Call: stats::glm(formula = f, family = "binomial", data = getTaskData(.task, .subset), weights = .weights, model = FALSE) Deviance Residuals: Min 1Q Median 3Q Max -1.4749 -0.0557 -0.0471 -0.0430 3.7611 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.64495 0.02095 -364.957 < 2e-16 *** classif.ranger 4.95061 0.63615 7.782 7.13e-15 *** classif.xgboost 189.75543 5.53109 34.307 < 2e-16 *** classif.glmnet 140.24208 5.05375 27.750 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 170604 on 7303013 degrees of freedom Residual deviance: 162571 on 7303010 degrees of freedom AIC: 162579 Number of Fisher Scoring iterations: 9</code></pre> <p><em>Acknowledgements</em>: We are grateful to data providers in veterinary practice (VetSolutions, Teleos, CVS, and other practitioners). We are grateful to the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale Bioinformatics Facility, doi: <a href="https://entrepot.recherche.data.gouv.fr/dataverse/migale">10.15454/1.5572390655343293E12</a>) for providing computing resources. We are also grateful for<br> the help and support provided by <a href="https://www.liverpool.ac.uk/savsnet/">SAVSNET team members</a> Bethaney Brant, Susan Bolan and Steven Smyth.<br> This study was funded mainly by a grant from the <strong>Biotechnology and Biological Sciences Research Council</strong>,<br> BB/NO19547/1 and <strong>British Small Animal Veterinary Association</strong> (BSAVA). The research was partly funded by the National Institute for <strong>Health Research Health Protection Research Unit</strong> (NIHR HPRU) in Emerging and Zoonotic Infections at the <strong>University of Liverpool</strong> in partnership with <strong>Public Health England</strong> (PHE) and <strong>Liverpool School of Tropical Medicine</strong> (LSTM). This work has been partially funded by the <em>“Monitoring outbreak events for disease surveillance in a data science context"</em> (MOOD) project from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 874850 (<a href="https://mood-h2020.eu/">https://mood-h2020.eu/</a>). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England.</p>
Great Britain's hourly natural gas demand at a local level (distribution level) from 2017-01 to 2018-03
<p>An hourly local natural gas demand dataset created for the UK Energy Research Centre's Phase 3 FlexiNET project.</p> <p>A briefing note using part of the data can be found - http://www.ukerc.ac.uk/publications/local-gas-demand-vs-electricity-supply.html</p> <p>The dataset has been aggregated from the hourly operational demand data from Great Britain's four Gas Distribution Network companies (GDNs) and provides an empirical record rather than modelled data.</p> <p>Columns ['utc_index', 'utc_aware', 'utcdiff', 'localtime_aware', 'localtime_naive_text', 'localtimediff', 'gb_demand_kWh_cleaned']</p> <p>Column descriptions:</p> <p>utc_index: datetime in utc</p> <p>utc_aware: datetime in utc timezone aware</p> <p>utcdiff: difference between subsequent values for utc_aware column (as check step value - should only be 0 days 01:00:00.000000000)</p> <p>localtime_aware: London local time</p> <p>localtime_naive_text: London local time as text</p> <p>localtimediff: difference between subsequent values for localtime_aware column (as check step value - should have one value per year of 02:00 hours for clock forward in March to British Summer Time, 00:00 for clock backward in October, and 01:00 for all other values)</p> <p>gb_demand_kWh_cleaned: the aggregate demand for natural gas through the local gas networks in kWh over the hour</p>
Graph Data: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios
<p>Data used for creating the figures in the paper: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios.</p> <p>It contains the flow exceedances (as mm day<sup>-1</sup>), flow duration slope, median elasticity and runoff ratio for the different afforestation scenarios. Also included is the information on the changes of broadleaf afforestation. </p> <p>If you have any questions, please email marcus.buechel@ouce.ox.ac.uk.</p>
Great Britain coastal areas
<p>Geographic areas in Great Britain that can be considered to be coastal.</p> <p>Datasets include:</p> <ul> <li>England/Wales LSOA</li> <li>England/Wales MSOA</li> <li>England/Wales/Scotland Local Authorities</li> <li>England/Wales/Scotland counties</li> <li>England/Wales parishes</li> <li>Scottish Data Zones</li> </ul> <p>This dataset was created using <a href="https://zenodo.org/record/7985671">existing coastline data</a> and <a href="https://gitlab.com/then-try-this/climate-tool/-/blob/f78c6b21b2350758a78ed50a4ba9408ab044aaed/data/builder/coastal.py">script</a> to calculate which zones are within 50m of the coastline. The boundary datasets used can be found under the <a href="https://gitlab.com/then-try-this/climate-tool/-/blob/main/docs/sources.md">'boundaries' section here</a>.</p>
Great Britain's primary substation service areas and annual domestic energy statistics
<p>This geospatial data is a combination of Great Britain's 4436 primary substation service areas which have been parsed into a single shapefile for energy systems analysis. The original component datasets were provided by the six distribution network operator (DNO) companies in Great Britain (National Grid Electricity Distribution, Electricity North West Ltd, Scottish and Southern Electricity Networks, UK Power Networks, Scottish Power Energy Networks and Northern Power Grid). Attribution is given to the original data owners at each of these six DNOs and the resulting dataset from this work has been created and published under an open licence with each DNO's permission. </p><p>The data is available to download as two geojson files in the WGS84 coordinate system. One is a streamlined version which just contains the polygons along with a unique primary identifier (UPID), primary substation name, DNO licence area and local authority. The other contains the polygons along with richer energy data which was aggregated to the primary substation level from publicly available Department for Energy Security and Net Zero, Office for National Statistics and National Grid ESO datasets. This data is also available to download in tabular form as a csv file. The meter numbers and consumption values are the means of those reported from 2015-2020. The substation polygons were those as received or publicly available as of the time period of this study (2021-22).</p><p>The pre-print manuscript of the methodology used to create this dataset can be found on arXiv at:</p><p>https://doi.org/10.48550/arXiv.2311.03324</p><p>Funding to support this work was received from the Engineering and Physical Sciences Research Council (EP/W008726/1) under the Gas Net New project and the Alan Turing Institute's Science of Cities and Regions Programme. Thanks are also given to the contributors of QGIS and the Geopandas Python library, both of which were used in this analysis. </p>
Great Britain (GB) Domestic Electricity Usage by Low Carbon Technology by Season
<p><strong>Important</strong>: As an research not-for-profit organisation, if you found this dataset useful we would appreciate your time in filling out <a href="https://docs.google.com/forms/d/e/1FAIpQLSfqCAoQt4AzuGH8Th5tJjnkGP956Fgc6O8T6wJaM7Nhd_nRdg/viewform?usp=pp_url&entry.1276408097=10.5281/zenodo.6576108">this short survey</a>.</p> <p> </p> <p>This dataset contains 3 aggregate datasets from the electricity smart meter data of over 25,000 customers in Great Britain (GB) from March 2021 - March 2022.</p> <p>For each consumer, we know (via a survey) what low carbon technologies (LCTs) they own. The potential LCT options are: Solar PV, Heat Pump (Air Source, or Ground Source), Electric Vehicle, Battery, Electric Storage Heaters.</p> <p>For simplicity, this dataset contains only customers with one type of LCT (with the exception of Solar PV, where we include Solar PV + Battery customers as is common in GB). We do not include customers with multiple LCTs (for example home battery + EV)</p> <p>We include quantiles of usage for each half hour (the "profile") for each type of LCT ownership "archetype", both overall (when season=None) and by season. As is common in the literature, we normalise by the square meterage of the house using open EPC data in GB (https://epc.opendatacommunities.org/) to get the watt hours per square meter. You can also find the raw, unnormalised, kwh values by quantile in this release. These two datasets have the quantiles for each half hour period. In addition, we release the daily quantiles of electricity consumption, in kwh per square meterage, by LCT type.</p> <p>In summary the data we are releasing, aggregated over 25,000 customers over 1 year of usage from March 2021 - March 2020 is:</p> <ul> <li>daily_elec_consumption_quantiles_by_lct_ownership.csv - The daily quantiles of usage [kWh/m2] by LCT</li> <li>lct_elec_consumption_profiles.csv - The half hourly quantiles of usage [Wh/m2] by LCT by season</li> <li>lct_elec_consumption_profiles_kwh.csv - The half hourly quantiles of usage [kWh] by LCT by season</li> </ul> <p>We believe this data will be useful for modelling efforts, as customers with different types of LCTs use energy at different times of the day, and by different amounts daily. By releasing this data openly, we hope forecasting scenarios for the future energy system are more accurate. We have a supporting blog post on our website at https://www.centrefornetzero.org/res/lessons-from-early-adopters-electricity-consumption-profiles/.</p>
An epidemiological overview of the equine influenza epidemic in Great Britain during 2019: Dataset
<p>This repository contains datasets and code used for the manuscript as titled. All details regarding the data source and considerations that should be noted are discussed in the manuscript.</p> <p><strong>Referencing this dataset</strong></p> <p>Fleur Whitlock, John Grewar & J. Richard Newton (2022) An epidemiological overview of the equine influenza epidemic in Great Britain during 2019 [Dataset]. University of Cambridge. <a href="https://doi.org/10.5281/zenodo.5886153">https://doi.org/10.5281/zenodo.7010228</a></p> <p> </p>
Weekly plots of Great Britain's half-hourly electrical system weather dependent generation, net imports and overall demand from 2008-11-10
<p>Plots that show the electrical system transition of Great Britain, they were created to form the individual frames for a video of the transition.</p>
Daily opening stock gas storage data for Great Britain from 2010-10-01 in kWh
<p>version 1.0.3 has data to 2023-08-23, data values are in kWh</p> <p>Original data from:</p> <p>https://www.nationalgas.com/data-and-operations/transmission-operational-data</p> <p>under section Supplementary reports</p> <p>under link ‘Daily storage and LNG operator information (1)’</p> <p>Please check the licence conditions from National Gas - the data published here is merely combined from different files and parsed into a more useable format.</p>
Fig. 8. General Charipinae features. A in Revision of the types of species of Alloxysta described by Cameron and Fergusson (Hymenoptera: Figitidae: Charipinae) and deposited in the Natural History Museum (London), including a key to the fauna of Great Britain
Fig. 8. General Charipinae features. A. Closed radial cell (Alloxysta brevis). B. Partially open radial cell (A. macrophadna). C. Open radial cell (A. medinae). D. Pronotal carinae absent (A. brevis). E. Pronotal carinae present (A. citripes). F. Propodeal carinae absent (A. victrix). G. Propodeal carinae present (A. castanea).
Fig. 7. Alloxysta semiaperta Fergusson, 1986. A. Fore wing. B in Revision of the types of species of Alloxysta described by Cameron and Fergusson (Hymenoptera: Figitidae: Charipinae) and deposited in the Natural History Museum (London), including a key to the fauna of Great Britain
Fig. 7. Alloxysta semiaperta Fergusson, 1986. A. Fore wing. B. Fore wing radial cell (arrow indicates that the radial cell is partially open). C. Antenna, ♂. D. Antenna, ♀. E. Propodeum. F. Lateral habitus, ♀. G. Pronotum (arrow indicates presence of pronotal carinae).
Fig. 6 in Revision of the types of species of Alloxysta described by Cameron and Fergusson (Hymenoptera: Figitidae: Charipinae) and deposited in the Natural History Museum (London), including a key to the fauna of Great Britain
Fig. 6. Alloxysta pleuralis (Cameron, 1879). A. Fore wing. B. Pronotum (arrows indicate pronotal carinae). C. Antenna, ♂. D. Antenna, ♀. E. Propodeum.
Fig. 3 in Revision of the types of species of Alloxysta described by Cameron and Fergusson (Hymenoptera: Figitidae: Charipinae) and deposited in the Natural History Museum (London), including a key to the fauna of Great Britain
Fig. 3. Alloxysta crassa (Cameron, 1889). A. Fore wing. B. Antenna, ♀. C. Fore wing radial cell (arrow illustrates that the radial cell is completely open). D. Pronotum. E. Lateral habitus, ♀. F. Propodeum.
Fig. 4 in Revision of the types of species of Alloxysta described by Cameron and Fergusson (Hymenoptera: Figitidae: Charipinae) and deposited in the Natural History Museum (London), including a key to the fauna of Great Britain
Fig. 4. Alloxysta mullensis (Cameron, 1883). A. Fore wing. B. Fore wing radial cell. C. Pronotum. D. Propodeum. E. Antenna, ♀. F. Lateral habitus, ♀.
Fig. 5 in Revision of the types of species of Alloxysta described by Cameron and Fergusson (Hymenoptera: Figitidae: Charipinae) and deposited in the Natural History Museum (London), including a key to the fauna of Great Britain
Fig. 5. Alloxysta piceomaculata (Cameron, 1883). A. Fore wing. B. Fore wing radial cell. C. Propodeum. D. Antenna, ♀. E. Lateral habitus, ♀. F. Pronotum.
Fig. 1. Alloxysta abdera Fergusson, 1986. A. Fore wing. B in Revision of the types of species of Alloxysta described by Cameron and Fergusson (Hymenoptera: Figitidae: Charipinae) and deposited in the Natural History Museum (London), including a key to the fauna of Great Britain
Fig. 1. Alloxysta abdera Fergusson, 1986. A. Fore wing. B. Pronotum (arrows indicate pronotal carinae). C. Antenna, ♀. D. Antenna, ♂ (arrow shows curved F2). E. Propodeum. F. Fore wing radial cell (arrow indicates that the radial cell is completely open). G. Lateral habitus, ♀.
Fig. 2 in Revision of the types of species of Alloxysta described by Cameron and Fergusson (Hymenoptera: Figitidae: Charipinae) and deposited in the Natural History Museum (London), including a key to the fauna of Great Britain
Fig. 2. Alloxysta basimacula (Cameron, 1886). A. Fore wing. B. Fore wing radial cell (arrow indicates that the radial cell is completely open). C. Pronotum. D. Antenna, ♀. E. Propodeum. F. Lateral habitus, ♀.
Churches, Arks of Migratory Narratives: A Comparative Study of the Greek-Orthodox Religioscapes in Germany and Great Britain
<p><strong>GO Religioscapes</strong></p> <p><em>The present research project deals with the Greek and Greek-Cypriot migrant communities in Germany and Britain, with reference to the religiocultural evidence found in the public sphere, which illustrate the particularities of their establishment and integration in the receiving country. As regards the Greek Gastarbeiter, they identified their communities with their parishes as the church often functioned as head of community and a mediator between them and the state. The bulk of the Greek-Cypriot Commonwealth migrants on the other hand, found the Greek-Orthodox Archdiocese already established as well, and as they expanded and dispersed across the British Isles, so did their parishes, which, in both cases, have served as arks of culture and identity. Therefore, one observes the phenomenon of interwoven migrant and church narratives; in the lapse of time, community and church, being closely knit, jointly constructed their migrant narratives of de- and reterritorialisation, cultural adaptation and hybridisation, essentially their own distinct sense of being and belonging. The particularities of this constantly under construction identity are manifest in the iconographical themes, aesthetics and concepts of their churches, which, albeit within canonical specifications, deviate from the normative typology as it is graphically attested by the occurrences of the phenomenon thereof. It is typical, however, of the Byzantine iconographic tradition to include and demonstrate the socio-political conditions of its time and place; and, those visual manifestations are part of a sociocultural reality as such, given that they possess a contextual dimension with reference to their symbolic content, their thematic endorsement and the appropriation of extra-ecclesiastical identity elements, but they are also an act and a medium of communication in their own right. It is therefore feasible to decode their aforementioned content and articulate the narrative that they convey.</em></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.