The England species map was funded by DEFRA’s Natural Capital and Ecosystem Assessment (NCEA) programme. The map was created using satellite remote sensing data (Sentinel-2) and machine learning to classify common tree species in England. The model was trained to distinguish 35 common tree species, with minority species grouped into “Other broadleaf” or “Other conifer” classes for better classification performance. The final product comprises a species classification and confidence raster output.

The species map represents a predicted distribution of common tree species in England, produced using a time series of multispectral satellite remote sensing data (Sentinel-2) and machine learning. A classifier based on the XGBoost algorithm was trained to distinguish tree species, utilising a time-series of surface reflectance data and labelled training samples from the sub-compartment database (SCDB). To enhance classification performance, minority species with fewer than 1,000 training samples were grouped into broader categories, resulting in a total of 35 classes. Given the significant class imbalances, a sample weighting strategy was employed to guard against significant underfitting of the minority classes. Model evaluation demonstrated strong classification performance, with an overall accuracy of 89% and balanced class accuracy of 90%. Predictions were made at the pixel level and used to generate a species classification and confidence raster output. Field validation for Norway spruce within the Ips typographus demarcated area, confirmed a precision of 69%, aligning with test data results for this class. Additional validation using National Forest Inventory (NFI) data further reinforced model reliability, though accuracy was observed to be worse for underrepresented species.

Attribution statement: © Forestry Commission copyright and/or database right 2024. All rights reserved.

This layer is deprecated.The generalised BGS geology data is now available in the ArcGIS Living Atlas hereGeneralised digital geological map data based on BGS's published poster maps of the UK (North and South). Bedrock related themes created by generalisation of 1:50 000 data to make the 2007 fifth edition Bedrock Geology map.

Superficial related themes digitised from 1977 first edition Quaternary map (North and South).

Many BGS geology maps are now available digitally. The Digital Geological Map of Great Britain project (DiGMapGB) has prepared 1:625 000, 1:250 000, 1:50 000 and 1:10 000 scale datasets for England, Wales and Scotland. Work continues to upgrade these.

The geological areas (or polygons) are labelled or attributed with a name (based on their lithostratographical, chronostratographical or lithodemic nomenclature) and their composition (rock type or lithology). This information is arranged in two themes: bedrock geology and superficial deposits. Faults and other linear features are available in a separate theme.

Geology maps are the foundation for many other types of earth science related maps and are of potential use to a wide range of customers.The original dataset can be found here.

The species map represents a predicted distribution of common tree species in England, produced using a time series of multispectral satellite remote sensing data (Sentinel-2) and machine learning. A classifier based on the XGBoost algorithm was trained to distinguish tree species, utilising a time-series of surface reflectance data and labelled training samples from the sub-compartment database (SCDB). To enhance classification performance, minority species with fewer than 1,000 training samples were grouped into broader categories, resulting in a total of 35 classes. Given the significant class imbalances, a sample weighting strategy was employed to guard against significant underfitting of the minority classes.

Model evaluation demonstrated strong classification performance, with an overall accuracy of 89% and balanced class accuracy of 90%. Predictions were made at the pixel level and used to generate a species classification and confidence raster output. Field validation for Norway spruce within the Ips typographus demarcated area, confirmed a precision of 69%, aligning with test data results for this class. Additional validation using National Forest Inventory (NFI) data further reinforced model reliability, though accuracy was observed to be worse for underrepresented species.

Map Index Sheets from Block and Lot Grid of Property Assessment and based on aerial photography, showing 1983 datum with solid line and NAD 27 with 5 second grid tics and italicized grid coordinate markers and outlines of map sheet boundaries. Each grid square is 3500 x 4500 feet. Each Index Sheet contains 16 lot/block sheets, labeled from left to right, top to bottom (4 across, 4 down): A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S. The first (4) numeric characters in a parcelID indicate the Index sheet in which the parcel can be found, the alpha character identifies the block in which most (or all) of the property lies.

Structural characterization of small molecule binding site hotspots within the global proteome is uniquely enabled by photo-affinity labeling (PAL) coupled with chemical enrichment and unbiased analysis by mass spectrometry (MS). MS-based binding site hotspot maps provide structural resolution of interaction sites in conjunction with identification of target proteins. However, binding site hotspot mapping has been confined to relatively simple small molecules to date; extension to more complex compounds would enable the structural definition of new binding modes in the proteome. Here, we extend PAL and MS methods to derive a binding site hotspot map for the immunosuppressant rapamycin, a complex macrocyclic natural product that forms a ternary complex with the proteins FKBP12 and FRB. Photo-rapamycin was developed as a diazirine-based PAL probe for rapamycin, and the FKBP12–photo-rapamycin–FRB ternary complex formed readily in vitro. Photo-irradiation, digestion, and MS analysis of the ternary complex revealed a McLafferty rearrangement product of photo-rapamycin conjugated to specific surfaces on FKBP12 and FRB. Molecular modeling of the ternary complex based on the binding site map revealed a 5.0 Å minimum distance constraint between the conjugated residues and the diazirine carbon. Molecular dynamics further predicted a 9.0 Å labeling radius for the diazirine upon photo-activation that may be useful in the interpretation of binding site measurements from PAL more broadly. Thus, in characterizing the ternary complex of photo-rapamycin by MS, we applied binding site hotspot mapping to a macrocyclic natural product and extracted a precise structural measurement for interpretation of PAL products that may enable the discovery of new ligand space in the “undruggable” proteome.

This dataset consists of a 1km resolution raster version of the Land Cover Map 2000 for Northern Ireland. The raster consists of 27 bands. Within each band, each 1km pixel represents a percentage cover value for one of 27 target (or 'sub') classes, broadly representing Broad Habitats (see below). The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2000. LCM2000 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. LCM2000 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2000 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2000 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Note that the Band numberings in the dataset run from 1-27 rather than 0-26 and therefore each band relates to the one below it in the subclass code list (i.e. 1 = Unclassified, labelled as 0 in the list). Full details about this dataset can be found at https://doi.org/10.5285/8eed6d77-714a-438a-aa65-887b1ef62378

A series of approximately 3250 navigational charts covering the world. The series is maintained by Admiralty Notices to Mariners issued every week. New editions or new charts are published as required. Two thirds of the series are now available in metric units.

In areas where the United Kingdom is, or until recently has been, the responsible hydrographic authority - i.e. Home Waters, some Commonwealth countries, British colonies, and certain areas like the Gulf, Red Sea and parts of the eastern Mediterranean - the Admiralty charts afford detailed cover of all waters, ports and harbours. These make up about 30 per cent of the total series. Modern charts in these areas usually have a source data diagram showing the sources from which the chart was compiled. The quantity and quality of the sources vary due to age and the part of the world the chart depicts. The other 70 per cent are derived from information on foreign charts, and the Admiralty versions are designed to provide charts for ocean passage and landfall, and approach and entry to the major ports.

The series contains charts on many different scales, but can be divided very broadly as follows:

Route planning 1:10 million Ocean planning 1:3.5 million Coast approach or landfall identification 1:1 million Coasting 1:300,000 to 1:200,000 Intricate or congested coastal waters 1:150,000 to 1:75,000 Port approach 1:50,000 or larger Terminal installation 1:12,500 or larger

Charts on scales smaller than 1:50,000, except in polar regions, are on Mercator projection. Since 1978 all charts on 1:50,000 and larger have been produced on Transverse Mercator projection. Prior to 1978 larger scale charts were on a modified polyconic projection referred to as 'gnomonic', not to be confused with the true Gnomonic projection.

Most of the detail shown on a chart consists of hydrographic information - soundings (selected spot depths) in metres (on older charts in fathoms or feet) reduced to a stated vertical datum; depth contours; dredged channels; and the nature of the seabed and foreshore. Features which present hazards to navigation, fishing and other marine operations are also shown. These include underwater rocks and reefs; wrecks and obstructions; submarine cables and pipelines and offshore installations. Shallow water areas are usually highlighted with pale blue tint(s). Also shown are aids established to assist the navigator - buoys, beacons, lights, fog signals and radio position finding and reporting services; and information about traffic separation schemes, anchorages, tides, tidal streams and magnetic variation. Outline coastal topography is shown especially objects of use as fixing marks. As a base for navigation the chart carries compass roses, scales, horizontal datum information, graduation (and sometimes land map grids), conversion tables and tables of tidal and tidal stream rates.

In recent years, proximity labelling has established itself as an unbiased and powerful approach to map the interactome of specific proteins. While physiological expression of the labelling enzyme is beneficial for the mapping of interactors, generation of the desired cell lines remains time-consuming and challenging. Using our established pipeline for the rapid generation of C- and N-terminal CRISPR-Cas9 knock-ins (KIs) based on antibiotic selection, we were able to compare the performance of commonly used labelling enzymes when endogenously expressed. Endogenous tagging of the μ subunit of the AP-1 complex with TurboID allowed identification of known interactors and cargo proteins that simple overexpression of a labelling enzyme fusion protein could not reveal. We used the KI-strategy to compare the interactome of the different adaptor protein (AP) complexes and clathrin and were able to assemble lists of potential interactors and cargo proteins that are specific for each sorting pathway. Our approach greatly simplifies the execution of proximity labelling experiments for proteins in their native cellular environment and allows going from CRISPR transfection to mass spectrometry analysis and interactome data in just over a month.

Tagged AKT2 was expressed in HEK293T cells. For quantification SILAC labeling was performed. MBP-TAP: Cell lysates were mixed at a 1:1 ratio with unlabeled wild type cells before Tandem Affinity Purification. MAP-TAP: Same amount of labeled and unlabeled cell lysates were purified via Tandem Affinity Purification and afterwards eluates were mixed at a 1:1 ratio. AKT2 and co-purified proteins were digested with trypsin, fractionated via SCX and analyzed via LC-MS. Sequence database-search of the MS data was performed against the uniprot human taxonomy-9606 database containing 83659 entries using the SEQUEST algorithm with the following parameters: trypsin specificity, two missed cleavage sites, precurser ion mass accuracy tolerance of 10–30 ppm, cysteine carbamidomethylation, methionine oxidation, pSTY, N-terminal protein acetylation and, when performed, SILAC labels Lys-6, Arg-6 specified as modifications. The minimal cross-correlation score (XCorr) was set to 2.0, 2.5 and 3.0 for charge states +2, +3 and +4 respectively. The Delta Cn had to be >0.1 and the minimal peptide probability allowed was 0.05. The minimum number of peptides necessary for protein identification was three.

A PDF map of the London boroughs as at December 2017. The map shows the London boroughs split into inner London and outer London. (File Size - 227 KB).

The Risk-behaviour map is a document intended to aid access to and transfer of key data between research groups in the European project Safeconsume. The map covers only steps from retail to consumption for the case studies in Safeconsume where the consumer can reduce risk for foodborne infection (CCHs, Critical Consumer Handling).

The map contains information about observed/reported behaviours that can affect risk for foodborne infection divided by country, consumer group, pathogen, food and step in the journey from retail to consumption.

Details about data collection is given in: Skuland, S.E., Borda, D., Didier, P., Dumitras¸cu, L., Ferreira, V., Foden, M., Langsrud, S., Maître, I., Martens, L., Møretrø, T., Nguyen-The, C., Nicolau, A. I., Nunes, C., Rosenberg, T. G., Teigen, H. M., Teixeira, P., Truninger, M., 2020. European Food Safety: Mapping Critical Food Practices and Cultural Differences in France, Norway, Portugal, Romania and the UK, in: Skuland, S.E. (Ed.). SIFO report, Oslo. ODA Open Digital Archive: European food safety: Mapping critical food practices and cultural differences in France, Norway, Portugal, Romania and the UK (oslomet.no)

Questions about the RM-map can be raised to the SafeConsume project coordinator: Solveig.langsrud@nofima.no

Variable list:

Classification

    <p> </p>
    </td>
  </tr>
  <tr>
    <td>
    <p>Causes/sources </p>
    </td>
    <td>
    <p>Free text </p>
    </td>
    <td>
    <p> </p>
    </td>
  </tr>
  <tr>
    <td>
    <p>Consumer ID </p>
    </td>
    <td>
    <p>Free text </p>
    </td>
    <td>
    <p> </p>
    </td>
  </tr>
  <tr>
    <td>
    <p>Pathogen </p>
    </td>
    <td>
    <p>Predefined, multiple choice </p>
    </td>
    <td>
    <ul>
      <li>
      <p>Salmonella: S. Enterica </p>
      </li>
      <li>
      <p>Campylobacter: C. jejuni </p>
      </li>
      <li>
      <p>Listeria: Listeria monocytogenes </p>
      </li>
      <li>
      <p>Norovirus </p>
      </li>
      <li>
      <p>Toxoplasma: Toxoplasma gondii </p>
      </li>
    </ul>
    </td>
  </tr>
  <tr>
    <td>
    <p>Expert opinion: Effect on pathogen </p>
    </td>
    <td>
    <p>Predefined </p>
    </td>
    <td>
    <ul>
      <li>
      <p>High reduction: This behaviour will have a high reduction on the level of pathogens on food/surfaces/hands </p>
      </li>
      <li>
      <p>Median reduction: This behaviour will reduce the level of pathogens on food/surfaces/hands </p>
      </li>
      <li>
      <p>No effect: This behaviour will most likely not have a significant effect on the level of viable pathogens (< 1 log10 reduction/increase or less than 10 cells/particles transfer) For food choice: Random choice is rated as no effect </p>
      </li>
      <li>
      <p>Median increase: This behaviour will lead to a higher number of pathogens </p>
      </li>
      <li>
      <p>High increase: This behaviour will significantly increase the number of pathogens on food/surfaces/hands </p>
      </li>
    </ul>
    </td>
  </tr>
  <tr>
    <td>
    <p>Effect on pathogen comment </p>
    </td>
    <td>
    <p>Free text </p>
    </td>
    <td>
    <p> </p>
    </td>
  </tr>
  <tr>
    <td>
    <p>Consumer group </p>
    </td>
    <td>
    <p>Predefined </p>

    <p> </p>
    </td>
    <td>
    <ul>
      <li>
      <p>Elderly; >70 years, men and women </p>
      </li>
      <li>
      <p>Pregnant women; immunocompromized </p>
      </li>
      <li>
      <p>Young family: Couples (married or cohabitant) where the women is pregnant or living with their own child(ren) (including stepchildren and adopted children) aged less than 12 months </p>
      </li>
      <li>
      <p>Young, single man: Men age 20-29, Living alone or

Topographic Satellite Image Map Dronning Maud Land (Landsat MSS and NOAA AVHRR); Satellite imagery mosaic compiled from Landsat-2, -4 and -5 data and from NOAA AVHRR digital mosaic of National Remote Centre, Farnborough (UK, 1988) where Landsat data was not available; Coast line, Ice front and Grounding line taken from SCAR Antarctic Digital Database, Version 1.0 (1993); Digital Elevation Model (DEM) from ERS-1 waveform data; Positions of automatic weather stations provided by Van de Wal, Institute for Marine and Atmospheric Esearch, Utrecht (NL); Positions of EPICA survey sites 1995-1998 provided by Alfred-Wegener-Institut, Bremerhaven (DE), British Antarctic Survey, Cambridge (UK), Norsk Polarinstitutt, Oslo (NO), Stockholm University (SE); ; Inset maps: Location diagram, List of English equivalents of German, Norwegian and Russian generic terms; Geographical names on the map are used in their original language according to guidelines relating to historical priority; Scale: 1:2000000;Projection: Polar Stereographic; Bounding Box: POLYGON ((-19 -69, 21 -69, 39 -78, 0 -80.5, -36 -78.5, -19 -69));Datum: OSU-91A geoid

This GIS shapefile provides boundary and attribute data for the parishes and places enumerated in the 1851 census for England and Wales. These data derive from the 173 digital maps of the boundaries of English and Welsh parishes and their subdivisions produced to a very high standard by Roger Kain and Richard Oliver in 2001, which was expertly converted into a single GIS of some 28000 polygons by Burton et al in 2004. However, what they produced was not yet ready for the mapping of census data due to a modest number (<10%) of administrative units which either lacked boundaries, were unlocated, had labelling errors, or incorrect census numbers. The Occupational Structure of Britain c.1379-1911 research programme undertook the task of enhancing the Burton et al. GIS to provide a comprehensive shapefile of parish and places as listed in the 1851 and 1831 censuses for the mapping of demographic and occupational data with tolerable accuracy for the whole of England and Wales. To this end it was also decided to add additional attributes concerning counties, hundreds and boroughs in 1831, counties in 1851 and registration sub-districts, districts and counties in 1851 from which shapefiles of these different larger scale administrative units could be assembled.These data were created as part of a research program directed by Leigh Shaw-Taylor and Tony Wrigley, which aims ultimately to reconstruct the evolution of the occupational structure of Britain from the late medieval period down to the early twentieth century. This GIS shapefile derives from 173 digital maps of the boundaries of English and Welsh parishes and their subdivisions produced by Kain and Oliver (2001), converted into a single GIS of some 28000 polygons by Burton et al (2004). The GIS attribute data were checked, edited and enhanced with extra data from the census by Max Satchell, Tony Wrigley and a number of research assistants, with technical support from Peter Kitson and Gill Newton. Max Satchell checked and in some cases edited the GIS polygon data using a variety of cartographic and documentary sources. The work involved changing one or more elements of information about place, parish, county, or three figure census number for 2,461 (10.8 per cent) of 22,729 lines of data in the Burton et al. GIS. Each polygon had the name of the ancient hundred, wapentake, borough or equivalent unit added, as given in the 1831 census. In situations where a polygon from the Burton et al. GIS encompassed two or more hundreds it was subdivided, if cartographic sources of boundary data were available. The registration subdistricts, districts and counties were also added from the 1851 census. A fuller account can be found in the associated documentation.

Complete Antarctic contour dataset at 1000 m intervals, split and labelled according to whether the contour represents an ice or rock surface. Data have been prepared from various map and remotely sensed datasets. This dataset has been generalised from the high resolution contour dataset. Further information regarding source and source data can be found within the high resolution attribute table. Certain inconsistencies and errors are currently known and a comprehensive update is planned for version 7.3.

This dataset comprises estimates of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA’s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.

This release of the data is version 5. Compared to version 4, version 5 consists of an update of the three maps of AGB (aboveground biomass) for the years 2010, 2017, 2018, 2019, 2020 and new AGB maps for 2015, 2016 and 2021. New AGB change maps have been created for consecutive years (2015-2016, 2016-2017 and 2020-2021), alongside an update of change maps for years 2010-2020, 2017-2018, 2018-2019 and 2019-2020, and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.

The data products consist of two (2) global layers that include estimates of: 1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots 2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)

Additionally provided in this version release are new aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).

In addition, files describing the AGB change between two consecutive years (i.e., 2015-2016, 2016-2017, 2018-2017, 2019-2018, 2019-2020, 2020-2021) and over a decade (2020-2010) are provided (labelled as 2015_2016, 2016_2017, 2017_2018, 2018_2019, 2019_2020 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.

Data are provided in both netcdf and geotiff format.

Additional file 4. ROSES for systematic map protocol. This file follows the ROSES flow diagram and outlines the review documentation.

Conversion between phosphatidylinositol-3-phosphate and phosphatidylinositol-3,5-bisphosphate on endosomal membranes is critical for maturation of early endosomes to late endosomes/lysosomes, and is regulated by the PIKfyve-Vac14-Fig4 complex. In this study, we screened for the cellular interactome of Vac14 and Fig4 using proximity-dependent biotin labelling (BioID) in 293T cells.

This file contains the digital vector boundaries for Westminster Parliamentary Constituencies, in the United Kingdom, as at 4th July 2024.The boundaries available are: (BFC) Full resolution - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Westminster_Parliamentary_Constituencies_July_2024_Boundaries_UK_BFC/FeatureServerREST URL of WFS Server –https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Westminster_Parliamentary_Constituencies_July_2024_Boundaries_UK_BFC/WFSServer?service=wfs&request=getcapabilitiesREST URL of Map Server –https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Westminster_Parliamentary_Constituencies_July_2024_Boundaries_UK_BFC/MapServer

The highest average temperature recorded in 2024 until November was in August, at 16.8 degrees Celsius. Since 2015, the highest average daily temperature in the UK was registered in July 2018, at 18.7 degrees Celsius. The summer of 2018 was the joint hottest since institutions began recording temperatures in 1910. One noticeable anomaly during this period was in December 2015, when the average daily temperature reached 9.5 degrees Celsius. This month also experienced the highest monthly rainfall in the UK since before 2014, with England, Wales, and Scotland suffering widespread flooding. Daily hours of sunshine Unsurprisingly, the heat wave that spread across the British Isles in 2018 was the result of particularly sunny weather. July 2018 saw an average of 8.7 daily sun hours in the United Kingdom. This was more hours of sun than was recorded in July 2024, which only saw 5.8 hours of sun. Temperatures are on the rise Since the 1960s, there has been an increase in regional temperatures across the UK. Between 1961 and 1990, temperatures in England averaged nine degrees Celsius, and from 2013 to 2022, average temperatures in the country had increased to 10.3 degrees Celsius. Due to its relatively southern location, England continues to rank as the warmest country in the UK.