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294 results for “Britain”
Figure 10 in YELLOW WATER LILIES (NUPHAR, NYMPHAEACEAE) IN GREAT BRITAIN: A NEW HYBRID, A REAPPRAISAL OF RECORDS, AND A REVISED STATUS OF N. ADVENA
Figure 10. Nuphar lutea leaves: A, in July 2021 with a high proportion of emergent leaves, from Pop5 population, Carlingwark Loch, England; B, in August 2020 with a mixture of emergent and floating leaves, from Pop1 population, Ashtead Park, England; C, in July 2021 with submerged and floating leaves, from (no population code) Elterwater, England. Photographs: R. V. Lansdown.
Figure 1 in YELLOW WATER LILIES (NUPHAR, NYMPHAEACEAE) IN GREAT BRITAIN: A NEW HYBRID, A REAPPRAISAL OF RECORDS, AND A REVISED STATUS OF N. ADVENA
Figure 1. Location of the 26 Nuphar advena records in the Botanical Society of Britain & Ireland database. Populations sampled for genetic analysis are marked with a triangle.
Figure 9 in YELLOW WATER LILIES (NUPHAR, NYMPHAEACEAE) IN GREAT BRITAIN: A NEW HYBRID, A REAPPRAISAL OF RECORDS, AND A REVISED STATUS OF N. ADVENA
Figure 9. Habit of Nuphar advena, showing leaves in July 2021, from NA1 population, Spottiswoode Loch. Photograph: R. V. Lansdown.
Linked collectors and determiners for: Caddisfly (Trichoptera) records from Britain (excluding Northern Ireland and Channel Islands) up to August 2022 from the National Trichoptera (Caddisfly) Recording Scheme.
Natural history specimen data linked to collectors and determiners held within, "Caddisfly (Trichoptera) records from Britain (excluding Northern Ireland and Channel Islands) up to August 2022 from the National Trichoptera (Caddisfly) Recording Scheme". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/bba60198-3eb5-4101-954d-3e2c87df4b73">https://bionomia.net/dataset/bba60198-3eb5-4101-954d-3e2c87df4b73</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/bba60198-3eb5-4101-954d-3e2c87df4b73">https://gbif.org/dataset/bba60198-3eb5-4101-954d-3e2c87df4b73</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Muscidae (Diptera) records from Britain and Ireland to 1985.
Natural history specimen data linked to collectors and determiners held within, "Muscidae (Diptera) records from Britain and Ireland to 1985". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/15f7a6a7-a684-42b1-a05f-4de243e389c4">https://bionomia.net/dataset/15f7a6a7-a684-42b1-a05f-4de243e389c4</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/15f7a6a7-a684-42b1-a05f-4de243e389c4">https://gbif.org/dataset/15f7a6a7-a684-42b1-a05f-4de243e389c4</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Ciidae (Coleoptera) records from Britain and Ireland to 2004.
Natural history specimen data linked to collectors and determiners held within, "Ciidae (Coleoptera) records from Britain and Ireland to 2004". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9703e992-4050-462f-80f3-18f766af5cf3">https://bionomia.net/dataset/9703e992-4050-462f-80f3-18f766af5cf3</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9703e992-4050-462f-80f3-18f766af5cf3">https://gbif.org/dataset/9703e992-4050-462f-80f3-18f766af5cf3</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Conchological Society of Great Britain & Ireland: non-marine molluscs (fossil & subfossil records).
Natural history specimen data linked to collectors and determiners held within, "Conchological Society of Great Britain & Ireland: non-marine molluscs (fossil & subfossil records)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/5850a253-4e7c-44ec-a784-87a52fd0556a">https://bionomia.net/dataset/5850a253-4e7c-44ec-a784-87a52fd0556a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5850a253-4e7c-44ec-a784-87a52fd0556a">https://gbif.org/dataset/5850a253-4e7c-44ec-a784-87a52fd0556a</a>. Formatted as a Frictionless Data package.
Wind and Precipitation Extremes in Great Britain (1979-2019) to apply the methodology for Spatiotemporal Identification of Compound Hazards
<p>The data used in this study is extracted from ERA5. ERA5 is a climate reanalysis product which was released in 2019 by ECMWF and benefits from the latest improvements in the field (Hersbach et al., 2020). ERA5 data (ECMWF, 2020) is available 1979 to present (we use up to September 2019), with a spatial resolution of 0.25deg x 0.25deg and an hourly temporal resolution. The data resolves the atmosphere using 137 levels from the surface up to a height of 80 km (ECMWF, 2020). ERA5 data are generated with a short forecast of 18 h twice a day (06:00 and 18:00 UTC) and assimilated with observed data (ECMWF, 2020). more information about ERA5 can be found <a href="https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation">here</a>.</p> <p>The two following variables are extracted from the product:</p> <ul> <li> <p>Extreme precipitation (p): accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth’s in one hour (mm). This value is averaged over a grid cell.</p> </li> <li> <p>Extreme wind (w): hourly maximum wind gust at a height of 10 m above the surface of the Earth (m s-1). The WMO (2021) defines a wind gust as the maximum of the wind averaged over 3 s intervals. As this duration is shorter than a model time step, this value is deduced from other parameters such as surface stress, surface friction, wind shear and stability. This value is averaged over a grid cell.</p> </li> </ul> <p>Importation of the raw data</p> <p>Input data is divided into 4 files for each variables representing 4 periods:</p> <ol> <li>1979-1986</li> <li>1987-1997</li> <li>1998-2008</li> <li>2009-2019</li> </ol> <pre>library(ncdf4) filer=c(paste0(getwd(),"/data/in/raindat_7986.nc"), paste0(getwd(),"/data/in/raindat_8797.nc"), paste0(getwd(),"/data/in/raindat_9808.nc"), paste0(getwd(),"/data/in/raindat_0919.nc")) filew=c(paste0(getwd(),"/data/in/windat_7986.nc"), paste0(getwd(),"/data/in/windat_8797.nc"), paste0(getwd(),"/data/in/windat_9808.nc"), paste0(getwd(),"/data/in/windat_0919.nc")) Startdate=as.POSIXct("1979-01-01 10:00:00") Enddate=as.POSIXct("1986-12-31 23:00:00") # ncr = nc_open(filer) # ncw = nc_open(filew)</pre> <p>Intermediary data</p> <p>Intermediary data are stored in the “data/interdat” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "allraininclusters1.Rdata" "allraininclusters2.Rdata" ## [3] "allraininclusters3.Rdata" "allraininclusters4.Rdata" ## [5] "extremEventsWind.Rdata" "interclustRain.Rdata" ## [7] "interclustWind.Rdata" "metaclustRain.Rdata" ## [9] "metaclustWind.Rdata" "Rain_99_AllP.Rdata" ## [11] "rainP1.Rdata" "rainP2.Rdata" ## [13] "rainP3.Rdata" "rainP4.Rdata" ## [15] "rawclustRain.Rdata" "rawclustWind.Rdata" ## [17] "timeP1.Rdata" "timeP2.Rdata" ## [19] "timeP3.Rdata" "timeP4.Rdata" ## [21] "windP1.Rdata" "windP2.Rdata" ## [23] "windP3.Rdata" "windP4.Rdata" ## [25] "Wnd_99_AllP.Rdata" </code></pre> <ul> <li> <p>allraininclustersX: [data.frame] files are used to assess more accurately the accumulated precipitation during events by collecting precipitations from timesteps in which precipitation is above and below the threshold for every grid cell and the whole duration of the cluster.</p> </li> <li> <p>99_allp: [matrix] value of extreme precipitation and extreme wind gust threshold over the whole domain (one value per grid cell)</p> </li> <li> <p>interclust: [list] files contain a list of data from wind and precipitation clusters divided in the 4 periods aggregated over space and clusters (1 value per grid cell per cluster). These files are uses to create the files “RainEv_ldat” and “Windev_ldat”.</p> </li> <li> <p>metaclust: [list] files contain a list of metadata from wind and precipitation clusters divided in the 4 periods . These files are uses to create the files “RainEv_meta” and “Windev_meta”.</p> </li> <li> <p>rainPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing precipitation data for the period X.</p> </li> <li> <p>rawclust: [list] files contain a list of data.frame from wind and precipitation clusters divided in the 4 periods. These files are uses to create the files “RainEv_hdat” and “Windev_hdat”.</p> </li> <li> <p>timePX: [vector] contain vectors of time for the 4 periods.</p> </li> <li> <p>windPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing wind gust data for the period X.</p> </li> </ul> <p>Output data</p> <p>Output data contains metadata and raw data of single and compound hazard clusters are stored in the “data/out” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "compoundclusters.csv" "CompoundRW_79-19.v3x.Rdata" ## [3] "extremEvents_Rain.Rdata" "extremEvents_Wind.Rdata" ## [5] "Rain_stfprint.Rdata" "rainclusters.csv" ## [7] "RainEv_hdat_1979-2019.Rdata" "RainEv_ldat_1979-2019.Rdata" ## [9] "Rainev_ldatp_1979-2019.Rdata" "RainEv_meta_1979-2019.Rdata" ## [11] "RainEv_metap_1979-2019.Rdata" "Wind_stfprint.Rdata" ## [13] "windcluster.csv" "WindEv_hdat_1979-2019.Rdata" ## [15] "WindEv_ldat_1979-2019.Rdata" "WindEv_meta_1979-2019.Rdata" </code></pre> <ul> <li> <p>CompoundRW: [data.frame] contains metadata for the compound hazard clusters identified</p> </li> <li> <p>_hdat: [data.frame] hourly data of precipitation and wind gust clusters.</p> </li> <li> <p>_ldat: [data.frame] aggregated data over space and clusters (1 value per grid cell per cluster) for wind gust and precipitation clusters.Rain_ldatp contains aggregated values including non-extreme timesteps. Created from allraininclustersX.</p> </li> <li> <p>_meta:[data.frame] metadata for wind gust and precipitation clusters</p> </li> <li> <p>stfprint: [data.frame] files containing duration*footprint of each hazard clusters during all clusters</p> </li> <li> <p>sptdf: [data.frame] data.frame containing spatial, temporal, cluster and intensity information</p> </li> </ul> <p>Codes assiciated to the method are availaible here: https://github.com/Alowis/SI-CH</p>
Britain Breathing 2016-2019 Air Quality and Meteorological Dataset
<p>This data set is a collection of daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for the UK for the years 2016-2019, inclusive. The dataset contains Temperature, Relative Humidity, and Pressure data, downloaded from the Met Office MIDAS archives via the MEDMI server (https://www.data-mashup.org.uk/). Also downloaded from the MEDMI server are daily pollen measurements for the UK. PM10, PM2.5, NO2, NOx (as NO2), O3, and SO2 measurements from the DEFRA AURN network, and also model forecasts of the same made using the EMEP model.</p> <p>The paper describing this dataset is available here: <a href="https://www.nature.com/articles/s41597-022-01135-6">https://www.nature.com/articles/s41597-022-01135-6</a></p> <p>The tools used to download and process these measurement datasets are available here: <a href="https://zenodo.org/record/4545257">https://zenodo.org/record/4545257</a></p> <p>The dataset is designed for use with the region estimator toolset, available in this repository: <a href="https://github.com/UoMResearchIT/region_estimators">https://github.com/UoMResearchIT/region_estimators</a></p> <p>Emissions over the UK for the EMEP model runs were generated using the NAEI 2016 UK emission dataset, available in netcdf form here: <a href="https://zenodo.org/record/3997165#.X9KUBF6nzUI">https://zenodo.org/record/3997165#.X9KUBF6nzUI</a>. The running scripts, and operation inputs for EMEP, are available here: <a href="https://zenodo.org/record/3997301#.X9KUAF6nzUI">https://zenodo.org/record/3997301#.X9KUAF6nzUI</a> and <a href="https://zenodo.org/record/3997271#.X9KV1F6nzUI">https://zenodo.org/record/3997271#.X9KV1F6nzUI</a>.</p> <p>The dataset is presented in CSV format, as three files:</p> <ol> <li>turing_aq_daily_met_pollen_pollution_original_data.csv: original data (described below)</li> <li>turing_aq_daily_met_pollen_pollution_with_imputation_data.csv: original plus imputed data (described below)</li> <li>site_location_data.csv: location metadata (site_id, latitude, longitude, postcode area)</li> </ol> <p> </p> <p>The columns intended to be used as indexes are:</p> <ul> <li>timestamp, <ul> <li>date of measurements on that row</li> </ul> </li> <li>site_id, <ul> <li>measurement site ID, corresponding to sites in the three networks: <ul> <li>AURN [indicated by AQ],</li> <li>MIDAS [indicated by WEATHER],</li> <li>or pollen [indicated by POLLEN].</li> </ul> </li> </ul> </li> </ul> <p>The data columns are:</p> <ul> <li>O3, PM10, PM2.5, NO2, NOXasNO2, SO2, <ul> <li>daily mean and maximum values in ug/m3 (all with "_max", "_mean", and "_flag" tags)</li> <li>AURN measurement data</li> </ul> </li> <li>O3_EMEP, NO2_EMEP, SO2_EMEP, NOXasNO2_EMEP, PM2.5_EMEP, PM10_EMEP, <ul> <li>daily mean and maximum values in ug/m3 (all with "_max", and "_mean" tags)</li> <li>EMEP model forecasts</li> </ul> </li> <li>alnus, ambrosia, artemisia, betula, corylus, fraxinus, platanus, poaceae, quercus, salix, ulmus, urtica, <ul> <li>daily pollen grain counts</li> </ul> </li> <li>temperature, relativehumidity, pressure, <ul> <li>daily mean and maximum values in degC, %, and hPa (all with "_max", "_mean", and "_flag" tags).</li> <li>Met Office measurement data</li> </ul> </li> </ul> <p>The "_flag" columns indicate data points which have been partially, or fully, imputed. The values for these will be in the range 0-1, and indicate the fraction of the hourly values within that day that are imputed (0 = none, 1 = all 24 hourly datapoints are imputed). No imputation is done in the original dataset, so the "_flag" data in this dataset will always be zero (the number of hourly data points used to calculate the daily mean and maximum are not recorded in this dataset).</p> <p>The station location metadata includes longitude, latitude, and UK postcode area data. Where sites lie outside of the UK the postcode is replaced with regional indicator (here: Republic of Ireland (ROI)).</p> <p> </p> <p>Please cite the following paper if you use this dataset: Reani, M., Lowe, D., Gledson, A., Topping, D., & Jay, C. (2022). UK daily meteorology, air quality, and pollen measurements for 2016–2019, with estimates for missing data. <em>Scientific Data</em>, <em>9</em>(1), 43. https://doi.org/10.1038/s41597-022-01135-6</p>
Figure 5 in The Milliped order Glomeridesmida (Diplopoda: Pentazonia: Limacomorpha) in Oceania, the East Indies, and southeastern Asia; first records from Palau, the Philippines, Vanuatu, New Britain, the Island of New Guinea, Cambodia, Thailand, and Borneo and Sulawesi, Indonesia
Figure 5. Known (isolated symbols and solid lines) and projected (dotted line) global distributions of Limacomorpha/ Glomeridesmida/idea/inae. New records of G. sumatranus and G. javanicus lie within the outlined area around Sumatra and Java; other new records are denoted by triangles, those in Borneo and Vanuatu, and the eastern two in New Guinea, representing two or more closely proximate localities. The left question mark signifies potential occurrence in Madagascar; the right one denotes the general, unsubstantiated record from New Ireland (Hoffman 1980, Jeekel 2003).
Figures 1-4 in The Milliped order Glomeridesmida (Diplopoda: Pentazonia: Limacomorpha) in Oceania, the East Indies, and southeastern Asia; first records from Palau, the Philippines, Vanuatu, New Britain, the Island of New Guinea, Cambodia, Thailand, and Borneo and Sulawesi, Indonesia
Figures 1-4. Female glomeridesmid/idan from Palau. 1) Dorsal view. 2) Sublateral view. 3) Caudal end, lateral view. 4) Drawing of the epiproct and 19th and 20th terga, dorsal view.
Figures 19–22 in The species of four genera of Metopiinae (Hymenoptera: Ichneumonidae) in Britain, with new host records and descriptions of four new species
Figures 19–22. Synosis caesiellae. (19) ♀, Port Appin, dorsal view of head showing ocelli. (20) ♀, Port Appin, propodeum. (21) ♀, Berrie Dale, ex?Carpatolechia, propodeum. (22) ", Burghfield, ex Swammerdamia, propodeum.
Figures 13–15 in The species of four genera of Metopiinae (Hymenoptera: Ichneumonidae) in Britain, with new host records and descriptions of four new species
Figures 13–15. Synosis parenthesellae. (13) ♀, dorsal view of head showing ocelli. (14) ", dorsal view of head showing ocelli. (15) ♀, propodeum.
Figures 2–5 in The species of four genera of Metopiinae (Hymenoptera: Ichneumonidae) in Britain, with new host records and descriptions of four new species
Figures 2–5. Apolophus borealis ♀. (2) Head, lateral view, showing narrow mandible. (3) Head and fore leg, showing long malar space and foreshortened tarsal segments. (4) Metasomal apex, showing large hypopygium. (5) Whole insect, lateral view.
Figures 16–18 in The species of four genera of Metopiinae (Hymenoptera: Ichneumonidae) in Britain, with new host records and descriptions of four new species
Figures 16–18. Synosis fieldi. (16) ", face in lateral view (a), compared to " S. caesiellae (b). (17) ", dorsal view of head showing ocelli. (18) ", propodeum.
Realistic afforestation scenarios in Great Britain at a 1 km scale to run with the land surface model JULES
<p>Afforestation scenarios created in the work of Buechel et al. (XXXX) which cover Great Britain at a 1 km spatial resolution and attempt to represent potential realistic broadleaf afforestation. The datasets represent a 50% and 100% afforestation scenario. The netCDF files are designed so that may be run with the Joint UK Land Environment Simulator (JULES), a community land surface model. The dataset is structured similar to the CHESS-land dataset (Martinez-De La Torre, 2018) where each grid contains information on the fractional coverage of eight different land cover types: Broadleaf woodland, needleleaf woodland, grassland, shrubland, crops, bare soil, urban areas and inland water. </p> <p> </p> <p>This dataset was created as part of the NERC doctoral training partnerships (grant number NE/L002612/1).</p> <p> </p> <p>Martinez-de la Torre, A.., Blyth, E.M.. M. and Robinson, E.L.. L. (2018) ‘Water, carbon and energy fluxes simulation for Great Britain using the JULES Land Surface Model and the Climate Hydrology and Ecology research Support System meteorology dataset (1961-2015) [CHESS-land]’. NERC Environmental Information Data Centre. doi:10.5285/c76096d6-45d4-4a69-a310-4c67f8dcf096.</p> <p> </p>
Great Britain coastline boundary (modified from 2011 Census boundary data) [GeoJSON]
<p><strong>Original purpose</strong></p> <p>This coastline boundary dataset was originally derived for research on population proximity to the UK coast. It required adaptation of boundary files in order to prevent areas close to major rivers from being counted as ‘coastal’. With no single definition of what ‘coastal’ is, we took a decision to cut off the coastline where major estuaries/rivers narrowed to approximately 1km. The original publication that used this approach and informed the development of the dataset is cited below (Wheeler et al, 2012).</p> <p>Please note therefore that this is a somewhat arbitrary definition of what is coastal, and you will need to make sure this definition is appropriate for your application for this to be useful.</p> <p><strong>Method & Data Format</strong></p> <ul> <li>Original source data: UK Census 2011 Lower-layer Super Output Areas / Data Zones – full resolution / Mean High Water version.</li> <li>LSOA/DZ boundaries were dissolved to create outline boundary at Mean High Water.</li> <li>Major estuaries/rivers were manually truncated where they narrowed to approximately 1km width.</li> <li>Data are provided as a GeoJSON file</li> <li>Co-ordinate system is British National Grid (EPSG 27700)</li> </ul> <p><strong>Original data source & copyright</strong></p> <p>This boundary dataset was derived from Ordnance Survey/Office for National Statistics/Scottish Government data, under Open Government Licence. Its use/re-use is dependent on appropriate citation and acknowledgement of the original source data.</p> <p>Licence: Adapted and redistributed under Open Government Licence: <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/</a></p> <p>Copyright statements to appear on any maps/publications containing these data:</p> <p><strong>Contains National Statistics data © Crown copyright and database right 2012</strong></p> <p><strong>Contains Ordnance Survey data © Crown copyright and database right 2012</strong></p> <p><strong>Copyright Scottish Government, contains Ordnance Survey data © Crown copyright and database right (2012).</strong></p> <p> </p> <p><strong>Citation and Attribution</strong></p> <p>The original source of the approach and methodology for this coastal definition should be cited as:</p> <p>Wheeler, B.W., White, M., Stahl-Timmins, W., Depledge, M.H., 2012. Does living by the coast improve health and wellbeing? Health and Place 18: 5, 1198-1201. doi: 10.1016/j.healthplace.2012.06.015</p> <p>The boundary dataset requires the copyright statements as above to be stated on any publication/redistribution.</p> <p>The adapted data are redistributed here under CC-BY Licence - <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Regional IUCN Redlist for Freshwater and Diadromous Fishes of Great Britain (England, Scotland and Wales)
<p>A regional IUCN redlist assessment of extinction risk for freshwater and diadromous fishes in Great Britain, including assessments for Engand, Scotland and Wales. The dataset comprises summary data for assessments under Criteria A-E and narratives to support the the assessment of all native freshwater fish species listed for Great Britain.</p>
Geographic distribution change and climatic niche change of Odonates in Great Britain
Open the record for dataset details and reuse information.
ECMWF/HRES - 2019 to 2021 - Great Britain
<p>Dataset of single-value Numerical Weather Predictions used as input for the probabilistic wind power forecasting tool developed for the paper: <strong>Seamless short- to mid-term probabilistic wind power forecasting</strong>.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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