This dataset comprises 2 collections of maps. The facsmile collection contains all the marginalia information from the original map as well as the map itself, while the georectified collection contains just the map with an associated index for locating them. Each collection comprises approximately 101 000 monochrome images at 6-inch (1:10560) scale. Each image is supplied in .tiff format with appropriate ArcView and MapInfo world files, and shows the topography for all areas of England, Wales and Scotland as either quarter or, in some cases, full sheets. The images will cover the approximate epochs 1880's, 1900's, 1910's, 1920's and 1930's, but note that coverage is not countrywide for each epoch. The data was purchased by BGS from Sitescope, who obtained it from three sources - Royal Geographical Society, Trinity College Dublin and the Ordnance Survey. The data is for internal use by BGS staff on projects, and is available via a customised application created for the network GDI enabling users to search for and load the maps of their choice. The dataset will have many uses across all the geoscientific disciplines across which BGS operates, and should be viewed as a valuable addition to the BGS archive. There has been a considerable amount of work done during 2005, 2006 and 2007 to improve the accuracy of the OS Historic Map Collection. All maps should now be located to +- 50m or better. This is the best that can be achieved cost effectively. There are a number of reasons why the maps are inaccurate. Firstly, the original maps are paper and many are over 100 years old. They have not been stored in perfect condition. The paper has become distorted to varying degrees over time. The maps were therefore not accurate before scanning. Secondly, different generations of maps will have used different surveying methods and different spatial referencing systems. The same geographical object will not necessarily be in the same spatial location on subsequent editions. Thirdly, we are discussing maps, not plans. There will be cartographic generalisations which will affect the spatial representation and location of geographic objects. Finally, the georectification was not done in BGS but by the company from whom we purchased the maps. The company no longer exists. We do not know the methodology used for georectification.

Lolium perenne (perennial ryegrass) is the most widely cultivated forage and amenity grass species in temperate areas worldwide and there is a need to understand the genetic architectures of key agricultural traits and crop characteristics that deliver wider environmental services. Our aim was to identify genomic regions associated with agriculturally important traits by integrating a bacterial artificial chromosome (BAC)-based physical map with a genome-wide association study (GWAS). BAC-based physical maps for L. perenne were constructed from ~212 000 high-information-content fingerprints using Fingerprint Contig and Linear Topology Contig software. BAC clones were associated with both BAC-end sequences and a partial minimum tiling path sequence. A panel of 716 L. perenne diploid genotypes from 90 European accessions was assessed in the field over 2 years, and genotyped using a Lolium Infinium SNP array. The GWAS was carried out using a linear mixed model implemented in TASSEL, and extended genomic regions associated with significant markers were identified through integration with the physical map. Between ~3600 and 7500 physical map contigs were derived, depending on the software and probability thresholds used, and integrated with ~35 k sequenced BAC clones to develop a resource predicted to span the majority of the L. perenne genome. From the GWAS, eight different loci were significantly associated with heading date, plant width, plant biomass and water-soluble carbohydrate accumulation, seven of which could be associated with physical map contigs. This allowed the identification of a number of candidate genes. Combining the physical mapping resource with the GWAS has allowed us to extend the search for candidate genes across larger regions of the L. perenne genome and identified a number of interesting gene model annotations. These physical maps will aid in validating future sequence-based assemblies of the L. perenne genome.

The UKSeaMap Predictive Habitats Map 2025 (version 1) is a broad-scale prediction that uses physical models of depth, light, sediment and energy to predict the physical seabed habitats for the whole UK seabed. This map covers the UK extended Continental Shelf as defined by the Continental Shelf (Designation of Areas) Order 2013, but excluding the intertidal zone, Dee Estuary and Morecambe Bay.

Two habitat classification systems are present in the final output:

EUNIS habitat classification system version 2007-11 The Marine Habitat Classification for Britain and Ireland (https://mhc.jncc.gov.uk/) version 22.04 The attribute table includes a column for each of level 2, level 3 and level 4 of each of these two classification schemes. In some cases, there were 2-3 options of habitat type, which were both included and separated by the word “OR“. There is also a column containing the most detailed unique habitat type for each of the two classification systems.

The habitats were determined by combining 4 categorical input layers called 'habitat descriptors', which are the basis for describing physical habitats in the Marine Habitat Classification for Britain and Ireland. These are also present in the geodatabase.

Habitat descriptor data layers:

Seabed substrate type - created using the British Geological Survey's national broad-scale predictive sediment map - Marchant et al. (2025) and the JNCC-BGS-Cefas national broad-scale predictive rock map (JNCC, 2019) Biological zone (also known as biozone) - created using the depth to seabed, wave disturbance at the seabed and amount of light reaching the seabed. Kinetic energy at the seabed - created using energy from tidal currents and energy from waves Salinity regime - created using the Annex I Habitats Regulations datasets for coastal lagoons and estuaries features A methods report will be published in due course.

The UKSeaMap Predictive Map forms part of the UK Atlas of Seabed Habitats (UKASH), a suite of mapping products, offering the most complete characterisation of seabed habitats in the UK in the Marine Habitat Classification for Britain and Ireland and the European standard classification system, EUNIS. UKASH is composed of:

UKASH Library of Localised Maps: A standardised collection of individual, ground-truthed habitat maps from various sources. UKASH Mosaic of Localised Maps: A unified, non-overlapping map product that prioritises the most reliable maps from the UKASH Library of Localised Maps. UKSeaMap Predictive Map: A seamless, full-coverage predictive map of physical seabed habitats in the UK. UKASH Combined Map: The UKASH Mosaic of Localised Maps, with gaps filled by the UKSeaMap Predictive Map.

Further info: https://jncc.gov.uk/our-work/uk-atlas-of-seabed-habitats-ukash/#ukseamap

Supplementary information files for article: 'The future scope of large-scale solar in the UK: site suitability and target analysis'.Abstract:This paper uses site suitability analysis to identify locations for solar farms in the UK to help meet climate change targets. A set of maps, each representing a given suitability criterion, is created with geographical information systems (GIS) software. These are combined to give a Boolean map of areas which are appropriate for large-scale solar farm installation. Several scenarios are investigated by varying the criteria, which include geographical (land use) factors, solar energy resource and electrical distribution network constraints. Some are dictated by the physical and technical requirements of large-scale solar construction, and some by government or distribution network operator (DNO) policy. It is found that any suitability map which does not heed planning permission and grid constraints will overstate potential solar farm area by up to 97%. This research finds sufficient suitable land to meet Future Energy Scenarios (UK National Grid outlines for the coming energy landscape).

Land Cover Map 2021 (LCM2021) is a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2021. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2021. Land cover maps describe the physical material on the surface of the country. For example grassland, woodland, rivers & lakes or man-made structures such as roads and buildingsThis is a 10 m Classified Pixel dataset, classified to create a single mosaic of national cover. Provenance and quality:UKCEH’s automated land cover classification algorithms generated the 10m classified pixels. Training data were automatically selected from stable land covers over the interval of 2017 to 2019. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra.Land cover was validated by organising the pixel classification into a land parcel framework (the LCM2021 Classified Land Parcels product). The classified land parcels were compared to known land cover producing confusion matrix to determine overall and per class accuracy.View full metadata information and download the data at catalogue.ceh.ac.uk

This is the land parcels (polygon) dataset for the UKCEH Land Cover Map of 2018(LCM2018) representing Northern Ireland. It describes Northern Ireland's land cover in 2018 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived from the corresponding LCM2018 20m classified pixels dataset. All further LCM2018 datasets for Northern Ireland are derived from this land parcel product. A range of land parcel attributes are provided. These include the dominant UKCEH Land Cover Class given as an integer value, and a range of per-parcel pixel statistics to help to assess classification confidence and accuracy; for a full explanation please refer to the dataset documentation. LCM2018 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2018. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2018. LCM2018 was simultaneously released with LCM2017 and LCM2019. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/35f15502-d340-4ab5-a586-abd42f238b6e

It is still unknown which factors of physical activity behaviour (PAB) may be effective and how they may influence PAB in UK children. The objective of the current study was to generate a conceptual analysis of the factors of PAB in UK children (5-12 years) using the input of researchers in the field of physical activity (PA experts; PAE) and researchers in other fields (non-PA experts; non-PAE). The concept mapping approach was used to identify potential (new) factors of PAB in children, assess their importance based on rating of potential modifiability and effect, and generate a concept map depicting the associations between them. In the first (brainstorming) stage (n=32 experts) yielded 93 factors, including 14 (new) not identified in previous reviews. In the second (rating and sorting) stage (n=26 experts), 32 factors were rated as important and four-cluster concept map was generated including themes related to Society/community, Home/social setting, Personal/social setting, and Psychological/emotional factors. Two additional concept maps were generated for PAE and non-PAE. From expert opinion, we identified new factors of PAB that warrant further research and we highlight the need to consider the interaction between intrapersonal and external factors when designing interventions to promote PA in UK children.The data has been downloaded from Ariadne (minds21.org) and includes the raw data and the analysed data (clustering and rating data). Participant information has been removed from the data files and replaced with participant numbers.

A collection of 1:250 000 scale geophysical maps in the Universal Transverse Mercator (UTM) projection, covering the United Kingdom and continental shelf areas between 1975 – 1990. Mapping is divided into squares which cover 1 degree by 1 degree of latitude / longitude. A geophysical map is a graphical representation of data collected through various geophysical methods to investigate the subsurface characteristics of the Earth. Geophysics is the study of the physical properties and processes of the Earth using measurements of physical quantities such as gravity, magnetic fields, seismic waves, electrical resistivity, and others. The collection includes aeromagnetic anomaly maps (1975 – 1990), Bouguer gravity anomaly maps (1975 – 1989) and a small number of free air anomaly maps (1981 – 1989). These maps are hard-copy paper records stored in the National Geoscience Data Centre (NGDC) and are delivered as digital scans through the BGS website.

This service is a representation of the Land Classification of Great Britain. The Land Classification is a classification of sets of environmental strata (land classes) to be used as a basis for ecological survey. The classification was originally developed by the Institute of Terrestrial Ecology (ITE) in the late 1970s. The strata were created from the multivariate analysis of 75 environmental variables, including climatic data, topographic data, human geographical features and geology data. The Land Classification has provided a stratification for successive ecological surveys (the Countryside Survey of Great Britain), the results of which have characterised the classes in terms of botanical, zoological and landscape features. Additionally, the Land Classification can be used to stratify a wide range of ecological and biogeographical surveys to improve the efficiency of collection, analysis and presentation of information derived from a sample. There are three layers in this WMS (1) the 1990 version of the Land Classification which contains 32 classes - classifying all 240,000km squares in Great Britain (2) the 1998 version in which the Land Classification was adjusted to 40 classes as a consequence of the need to provide National Estimates for habitats in Scotland in addition to GB (3) the 2007 version in which the Land Classification was adjusted once again, to 45 classes, as a consequence of the need to provide Wales-only estimates in addition to those for Scotland and GB.

"One of the two datasets that make up the Priority River Habitat Map. Consists of rivers and streams that exhibit a high degree of naturalness. The naturalness classification used to map priority river habitat is based on recent work to review the river SSSI series. It evaluates four main components of habitat integrity: hydrological, physical, physico-chemical (water quality) and biological. An additional classification of the naturalness of headwaters (defined as streams with a catchment area of <10km2 to coincide with WFD typology boundaries) uses land cover data as a surrogate for direct information on river habitat condition (information which is generally lacking on headwaters). Streams and rivers operating under natural processes, free from anthropogenic impact and with a characteristic and dynamic mosaic of small-scale habitats that supports characteristic species assemblages (including priority species), are the best and most sustainable expression of river ecosystems. Key elements are: a natural flow regime; natural nutrient and sediment delivery regimes; minimal physical modifications to the channel, banks and riparian zone; natural longitudinal and lateral hydrological and biological connectivity; an absence of non-native species; low intensity fishery activities. These conditions provide the best defence against climate change, maximising the ability of riverine ecosystems to adapt to changing conditions. They also provide the most valuable and effective transitional links with other priority habitats, including lakes, mires and coastal habitats. In English rivers and streams, high levels of naturalness are rare. "

One of the two datasets that make up the Priority River Habitat Map. Consists of rivers and streams that exhibit a high degree of naturalness. The naturalness classification used to map priority river habitat is based on recent work to review the river SSSI series. It evaluates four main components of habitat integrity: hydrological, physical, physico-chemical (water quality) and biological. An additional classification of the naturalness of headwaters (defined as streams with a catchment area of <10km2 to coincide with WFD typology boundaries) uses land cover data as a surrogate for direct information on river habitat condition (information which is generally lacking on headwaters). Streams and rivers operating under natural processes, free from anthropogenic impact and with a characteristic and dynamic mosaic of small-scale habitats that supports characteristic species assemblages (including priority species), are the best and most sustainable expression of river ecosystems. Key elements are: a natural flow regime; natural nutrient and sediment delivery regimes; minimal physical modifications to the channel, banks and riparian zone; natural longitudinal and lateral hydrological and biological connectivity; an absence of non-native species; low intensity fishery activities. These conditions provide the best defence against climate change, maximising the ability of riverine ecosystems to adapt to changing conditions. They also provide the most valuable and effective transitional links with other priority habitats, including lakes, mires and coastal habitats. In English rivers and streams, high levels of naturalness are rare.Full metadata can be viewed on data.gov.uk.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

This is a digital map containing polygons representing areas of vegetation within Roudsea Wood National Nature Reserve (NNR), Cumbria. Vegetation was mapped in the field on a basemap as parcels according to tree cover type, tree stocking rates and ground flora communities. The map covers the western side of the reserve (the woodland). The field map was originally created by staff at the Nature Conservancy’s Merlewood Research Station, Grange-over-Sands, Cumbria in 1962 and digitized by the Centre for Ecology & Hydrology from the original field map in 2019. Full details about this dataset can be found at https://doi.org/10.5285/a8d710fb-177d-467c-b2c1-2b215f582d2c

This boundary dataset complements 13 other datasets as part of a study that compared ancient settlement patterns with modern environmental conditions in the Jazira region of Syria. This study examined settlement distribution and density patterns over the past five millennia using archaeological survey reports and French 1930s 1:200,000 scale maps to locate and map archaeological sites. An archaeological site dataset was created and compared to and modelled with soil, geology, terrain (contour), surface and subsurface hydrology and normal and dry year precipitation pattern datasets; there are also three spreadsheet datasets providing 1963 precipitation and temperature readings collected at three locations in the region. The environmental datasets were created to account for ancient and modern population subsistence activities, which comprise barley and wheat farming and livestock grazing. These environmental datasets were subsequently modelled with the archaeological site dataset, as well as, land use and population density datasets for the Jazira region. Ancient trade routes were also mapped and factored into the model, and a comparison was made to ascertain if there was a correlation between ancient and modern settlement patterns and environmental conditions; the latter influencing subsistence activities. This boundary dataset was generated to define the extent of the study area, which comprises the border between Syria and Turkey, Syria and Iraq, the River Tigris and the River Euphrates. All related data collected was confined within this boundary dataset with the exception of the archaeological dataset. Archaeological sites were identified and mapped along both banks of the River Euphrates. Also, the town of Dayr az-Zawr, where the 1963 precipitation and temperature monthly values were collected for one of the datasets, falls outside the Jazira Region. Derived from 1:200,000 French Levant Map Series (Further Information element in this metadata record provides list of sheets).The boundary line dataset was captured from 11 map sheets, which were based on the French Levant surveys conducted in Syria during the 1930s and mapped at a scale of 1:200,000. The size of each map measures 69 x 59 cm. The boundary line on each sheet was traced to mylar. Subsequently, each mylar sheet was photocopied and reduced in size to an 11 x 17 inch sheet. These sheets were merged to form the contiguous area comprising the full extent of the boundary for the study area. This was then traced again to another mylar sheet and subsequently scanned and cleaned for further processing and use in a GIS as a polygon coverage. Thesis M 2001 MATH, Ohio University Mathys, Antone J 'A GIS comparative analysis of bronze age settlement patterns and the contemporary physical landscape in the Jazira Region of Syria'., French Levant Map Series (1:200,000) for Syrie (Syria). Projected to Lambert grid. These are colour maps measuring to 69 x 59 cm in size. The dataset was created from the following sheet numbers and titles: 1) NI-37 XVII, Abou Kemal 2) NI-37 XVIII, Ana 3) NI-37 XXI, Ressafe 4) NI-37 XXII, Raqqa 5) NI-37 XXIII, Deir ez Zoir 6) NI-37 XXIV, Bouara 7) NI-37-III, Djerablous 8) NJ-37 IV, Toual Aaba 9) NJ-37 V, Hassetche 10) NJ-37 VI, Qamishliye-Sinjar 11) (No sheet number), Qaratchok-Darh Dressepar la Service Geographique des F.F.L. en 1945 Reimprime par l'Institut Geographique National en 1950 (Originally produced by this Geographic Service of the F.F.L. (Forces Francaises Libres) in 1945 and reprinted by the National Geographic Institute in 1950). Paris: France. Institut Geographique National, 1945-1950. Original map series might be traced to Beirut: Bureau Topographique des Troupes francaises du Levant, 1933-1938. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-06-09 and migrated to Edinburgh DataShare on 2017-02-21.

UKSeaMap 2018V2 is a broad-scale physical habitat map for UK waters. It is a by-product of the 2013-2016 activities of the EMODnet Seabed Habitats 2013–2016 consortium. This dataset contains two products: a roughly 100 m* resolution broad-scale predictive seabed habitat map in geodatabase format, and a set of confidence maps in GeoTIFF format. The data has been clipped to cover the current extent of the UK continental shelf.

*3 arc second = 93 m latitudinally by between 44 m (north) and 53 m (south) longitudinally

Classification system:

• EUNIS habitat classification system, with additional deep sea zones
• MSFD Benthic Broad Habitat Types
• The Marine Habitat Classification for Britain and Ireland (https://mhc.jncc.gov.uk/)

Input data layers:

• Seabed substrate type
• Depth to the seabed
• Amount of light reaching the seabed
• Wave disturbance at the seabed
• Kinetic energy at the seabed caused by tidal currents and waves

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This dataset shows the potential and high confidence mapped extents of Annex I habitat 'Sandbank' within the boundaries of the UK continental shelf. 'Sandbank' here refers to the habitat (1110) listed under Annex I of EC Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora ÔÇô commonly known as the Habitats Directive.

This dataset is presented in its most detailed form so you can fully interrogate the dataset and all its underlying attributes representing the underlying logic and data sources resulting in areas of 'Potential' or 'High' confidence Annex I Sandbanks.

Where areas of Sandbank contain differing underlying attributes, descriptor values or data sources, a single Annex I feature may be represented by multiple polygons. These subdivisions are irrelevant in terms of the overall extent of the feature.

Additional information source: Pinder, J. (2020). Method for Creating version 3 of the UK Composite Map of Annex I Sandbanks slightly covered by seawater all of the time.

In some cases, a full-coverage map displaying the best available data everywhere at the expense of consistency is required. The production of such a product showing EUNIS level 3 habitats requires integrating EUNIS level 3 seabed habitat data from fine and broad-scale habitat maps. The product aims to create a complete map that presents the best available information on the distribution of EUNIS level 3 habitats at any locations in UK waters. This data product is required for, among other things, assessments of progress towards networks of marine protected areas (MPAs) and marine spatial planning in the UK. The aim was to produce a single map layer displaying the best quality EUNIS level 3 data for any given location. Furthermore, the process for producing the layer needed to be: • Repeatable; • Transparent; • Easy to explain and understand; • Objective; • Fully documented; • Appropriate for EUNIS level 3 habitats; and, • Appropriate for the UK intertidal and subtidal areas Any location would show only a single value describing the habitat at EUNIS level 3, i.e. no overlapping polygons leading to multiple possible values at a location. The data at any location should be the best available to describe the EUNIS level 3 habitat type (i.e. the most likely to be correct). EUNIS level 3 habitats (Appendix 1) describe physical habitats classified using biologically meaningful parameters, substrate type, and additionally for rock: energy and biological zone. Therefore, a method was created to choose data based on their ability to describe these physical variables.

The DesHCA project aimed to identify supportive home designs that older people would find acceptable. To contribute to this, the team aimed to find out how older people currently live in their homes and what they find positive and negative about them. The home mapping data collection exercise in DesHCA focused on learning about older people’s experiences of living in their homes as they age. The goal was to gather insights from older people to create a clear picture of what people wanted, needed, and worried about in regards to adapting their home. A creative mapping method was used to explore how older people thought about, felt about, and used their homes. The Participants were re-contacted six months later in Wave 2 of data collection and asked about any changes to their home or health since the first interview.

Participants were asked to create a map of their home (which could include taking photographs, filming, or drawing) and we also interviewed them about their home. Most participants made their creative map during the interview, allowing researchers to ask questions about specific areas and items that might otherwise have gone unnoticed. This approach allowed the creative mapping interviews to capture a lot of data on the physical aspects of people’s homes, including what they liked and disliked about their home, what worked well for them, and what they would like to change in the future if they could. They also delved further, looking beyond the building itself to learn about how participants liked to use the different areas in their home, what kind of activities they liked to do there, and how their home had changed over time.

The data consist of: -16 home maps drawn by 19 participants, -46 Wave 1 interview transcripts (11 of which involve two people) -an overview table summarising changes reported since Wave 1 interviews, and -4 interview transcripts from full Wave 2 interviews.

A Natural England commissoined verification survey of both intertidal sediments and intertidal rocky reef within the Cumbria Coast rMCZ. Phase I Biotope mapping was carried out across the rMCZ for broad scale habitats. Phase II infaunal sediment sampling was carried out to provide information on the benthic infaunal assemblages and particle size distribution of the study sites by means of core sampling. Sediment surface scrapes were obtained for heavy metals and organic contaminants analysis. Phase II quantitative survey of intertidal rocky reefs comprised of a quadrat survey with percentage cover of species present within each quadrat being recorded. The data was used to produce a EUNIS Level 3 boradscale habitat map of the Cumbria Coast rMCZ.

Aerial imagery and OS mapping was digitised to produce baseline maps of biotope boundaries. The maps were annotated in the field to identify biotopes and boundaries as well as significant features. In addition, intertidal sediment cores were taken at 16 stations (0.01m2 cores 3 replicates at each station) distributed throughout the Cumbria Coast rMCZ at the low, mid and high shore, in order to assess the benthic species present, along with an additional sample for Particle Size Analysis. Samples were sieved over a 0.5mm sieve. Sediment scrape samples were also taken at 4 stations at the midshore for Tributyl tin, heavy metal and organic contaminant analysis. The Phase II quantitative survey of intertidal rocky reefs comprised of 9 quadrat sites per transect with 3 replicate quadrats at each low, mid and upper shore zone or the main biotope present. Percentage cover of species present within each 0.25m2 quadrat was recorded. The methods used for data collection and processing followed protocols and standards for biotope mapping and sampling. MEDIN Data Guideline for sediment sampling by grab or core for benthos.