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NERC Data Catalogue Service (Natural Environment Research Council)
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Viable bioaerosol and particulate data from four sites representing different levels of endotoxin exposure in south east England, 2016-2018
The dataset was collected between 2016-2018. A range of different environments were sampled including urban and rural sites in the South East of England, a compost facility in the East of England and a chicken farm in the South East of England. At each site viable bioaerosols (total bacteria, gram negative bacteria and fungi), endotoxin and particulates were measured. Full details about this dataset can be found at https://doi.org/10.5285/acae99ba-b088-4544-9c48-71d70498c16f
If you reuse this data, you should cite: Gladding, T.L., Rolph, C.A. (2022). Viable bioaerosol and particulate data from four sites representing different levels of endotoxin exposure in south east England, 2016-2018. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/acae99ba-b088-4544-9c48-71d70498c16f
This resource is available under the terms of the Open Government Licence
The dataset was generated according to accepted protocols as detailed in the supporting documentation. Pumps for the Andersen samplers were tested before each sampling day using inline flow meters. The Osiris particulate counter is serviced and recalibrated yearly as advised by the manufacturer. All equipment and filters for endotoxin analysis were depyrogenated before use. Controls included analysing unexposed filters and filters that were placed on the Andersen without the pumps running to check for contamination.
Countryside Survey 1998 estimates of linear feature lengths in Great Britain
This dataset consists of stock (length) data for linear features across Great Britain in 1998. The data are national estimates generated by analysing the sample data from 569 1km squares surveyed for the Countryside Survey long term monitoring project, then scaling up to a national level. The data are presented as mean lengths for 7 different feature categories within 45 different land class types, based on the ITE Land Classification. The Countryside Survey is a unique study or 'audit' of the natural resources of the UK's countryside, carried out by the Centre for Ecology & Hydrology. The sample sites are chosen from a stratified random sample, based on a 15 by 15 km grid of GB. Surveys have been carried out in 1978, 1984, 1990, 1998 and 2007 with repeated visits to the majority of squares. The countryside is sampled and surveyed using rigorous scientific methods, allowing us to compare new results with those from previous surveys. In this way we can detect the gradual and subtle changes that occur in the UK's countryside over time. In addition to linear features, habitat areas, species plot, soil plot, freshwater habitat and satellite map data are also produced by Countryside Survey. Full details about this dataset can be found at https://doi.org/10.5285/59422890-c8d3-49b8-b17e-4f5062475140
© Database Right/Copyright UK Centre for Ecology & Hydrology. All rights reserved.
The following acknowledgement shall be used on all copies of the data, publications and reports, including use in presentations to any audience. 'Countryside Survey data owned by NERC Centre for Ecology & Hydrology'
If you reuse this data, you should cite: Barr, C.J., Bunce, R.G.H., Clarke, R.T., Gillespie, M.K., Howard, D.C., Maskell, L.C., Norton, L.R., Scott, R.J., Scott, W.A., Smart, S.M., Stuart, R.C., Watkins, J.W., Wood, C.M., Wright, S.M. (2014). Countryside Survey 1998 estimates of linear feature lengths in Great Britain. NERC Environmental Information Data Centre https://doi.org/10.5285/59422890-c8d3-49b8-b17e-4f5062475140
This resource is made available under the terms of the Open Government Licence
The data were recorded onto paper field sheets in the summers of 1998 and 1999 by surveyors. Data were recorded from 569 1km squares sited across Britain, selected by random stratified sample. The boundary lines were digitized in 1998-2000. The digitized data were used to generate national estimates of the stock of linear features for Great Britain using the standard Countryside Survey methodology (refer to the online documentation). Estimates are presented by ITE Land Class (2007 version), as mean length per Land Class. Data are managed by the Centre for Ecology & Hydrology, Lancaster.
A compendium of earthworm data sources and associated information from the UK and Ireland, 1891-2021
This dataset presents a compendium of field-based earthworm data sources and associated meta-data from across the United Kingdom and Ireland (‘Worm source’). These were compiled up to 2021 and include 257 data sources, the earliest dating back to 1891. Source meta-data covers the type of quantitative earthworm data (i.e. incidence, abundance, biomass, taxa), methodological details (e.g. sampling method/s, location/s, whether sampled plots were natural or experimental, sampling year/s), and environmental information (e.g. habitat/land-use, inclusion of climate data and basic soil properties). Data sources were collected through literature searches on Web of Science and Google Scholar, as well as directly from original authors/data holders where possible. The data sources were compiled with the aim of gathering quantitative data on earthworm species and populations to develop earthworm abundance and niche models, and toward a modelling framework for earthworm impacts on soil processes. This work is part of the Soil Organic Carbon Dynamics (SOC-D) project funded by the NERC UK-SCAPE programme (Grant reference NE/R016429/1). Full details about this dataset can be found at https://doi.org/10.5285/1a1000a8-4e7e-4851-8784-94c7ba3e164f
If you reuse this data, you should cite: Mason, K.E., Ashwood, F., Schmidt, O., Cosby, J., Keith, A.M. (2022). A compendium of earthworm data sources and associated information from the UK and Ireland, 1891-2021. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/1a1000a8-4e7e-4851-8784-94c7ba3e164f
A systematic literature search was conducted to find data sources using Web of Science and Google Scholar (in June/July 2020). This used the following search term: (Earthworm* OR Oligochaeta OR Annelid* OR Lumbric* OR Clitellat*) AND (Diversity OR “Species richness” OR “OTU” OR Abundance OR individual* OR Density OR “tax* richness” OR “Number” OR Richness OR Biomass) AND (UK OR “United Kingdom” OR England OR Wales OR Scotland OR “NI” OR “Northern Ireland” OR “Ireland” OR “ROI”) Publications were selected and meta-data recorded if they contained location information and the presence of at least one species of earthworm or abundance/biomass data obtained from the field. Further publications were found through screening of review papers and contact with selected authors for additional information and resources, including published and unpublished datasets. In total, 257 data sources were compiled. Original sources (i.e. published papers, book chapters) could not be obtained for 17 studies at the time of compiling meta-data and are thus lacking any additional information.
Eddy covariance measurements of carbon dioxide, energy and water fluxes at a cropland and a grassland on lowland peat soils, East Anglia, UK, 2016-2019
This dataset contains time series observations of surface-atmosphere exchanges of net ecosystem carbon dioxide exchange (NEE), sensible heat (H) and latent heat (LE), and momentum (τ) measured at two managed lowland peatland environments in the East Anglian Fens, England, UK. One site is managed for the production of horticultural salad crops, the other is an area of managed grassland. Turbulent flux densities were monitored using the micrometeorological eddy covariance (EC) technique between 10th November 2016 and 25th September 2018 at the cropland site, and between 27th April 2017 and 31st March 2019 at the grassland. The dataset includes ancillary weather and soil physics observations, as well as variables describing atmospheric turbulence and the quality of the turbulent flux observations. Full details about this dataset can be found at https://doi.org/10.5285/2fe84b80-117a-4b19-a1f5-71bbd1dba9c9
If you reuse this data, you should cite: Morrison, R., Cooper, H., Cumming, A.M.J., Evans, C., Thornton, J., Winterbourn, J.B., Rylett, D., Jones, D.L. (2020). Eddy covariance measurements of carbon dioxide, energy and water fluxes at a cropland and a grassland on lowland peat soils, East Anglia, UK, 2016-2019. NERC Environmental Information Data Centre https://doi.org/10.5285/2fe84b80-117a-4b19-a1f5-71bbd1dba9c9
Eddy covariance (EC) and micrometeorological observations were made using open-path eddy covariance systems. Ancillary meteorological and soil physics observations were co-located with the eddy covariance stations. Turbulent flux densities were calculated from the raw EC data using the EddyPRO® Flux Calculation Software Version 6.1. Quality Control of EC data involved removal of statistical outliers and tests that theoretical requirements for the successful application of the EC technique were not violated significantly. Data gaps in EC data (NEE, LE, H) and the partitioning of NEE into gross ecosystem production and total ecosystem production were performed using the REddyProc Package for R (Reichstein et al., 2019). Further details are available in the supporting documentation accompanying the data.
Nitrous oxide emissions from Whim peatbog after thirteen years of experimental nitrogen deposition
The data set contains fluxes of nitrous oxide (N2O) from Whim peatbog in central Scotland, measured using chamber methods. A nitrogen deposition experiment was carried out at the site, where nitrogen was applied in different forms (ammonium, nitrate, and ammonia) at doses from zero to ~100 kg N per ha per year. Flux measurements were made over a six month period starting at the end of March, 2012. The experiment was begun in 2002 under the NERC Global Nitrogen Enrichment (GaNE) programme, and continued under various funding sources. 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/10.5285/7c1dd26a-5df5-4b90-854b-70d0d97eb78a
If you reuse this data, you should cite: Levy, P.E., Leeson, S.R. (2017). Nitrous oxide emissions from Whim peatbog after thirteen years of experimental nitrogen deposition. NERC Environmental Information Data Centre https://doi.org/10.5285/7c1dd26a-5df5-4b90-854b-70d0d97eb78a
The data were collected using the static chamber method. Gas samples were taken from chambers in the field via syringe and injected into vials for storage and later analysis. Samples were analysed on a gas chromatagraph (GC) in the lab. Concentrations were calculated from GC peak areas, calibrated against standard gases of known concentration. Fluxes were calculated from the concentration change in each chamber by linear and non-linear regression using the R package RCflux. Fluxes are expressed as nmoles of nitrous oxide per square metre per second. Each flux has an associated 95 % confidence interval, based on the regression fit, which gives a guide to the reliability and uncertainty of each measurement.
Fine root production in human-modified forests of Eastern Amazonia
This dataset includes measurements of soil respiration in 20 plots (250 x 10 m each) in the Brazilian Amazon. Study plots were distributed across a gradient of forest disturbance, including: undisturbed primary forests , logged primary forests, logged-and-burned primary forests, and secondary forests. Data were collected from October 2014 until May 2018. In December 2015, during the El Niño-mediated drought, eight of our study plots were affected by understory fires. Full details about this dataset can be found at https://doi.org/10.5285/def51d3d-d653-40ca-8231-a238b0c66975
If you reuse this data, you should cite: Berenguer, E., Rossi, L.C., Seixas, M.M.M., Barlow, J. (2019). Fine root production in human-modified forests of Eastern Amazonia . NERC Environmental Information Data Centre https://doi.org/10.5285/def51d3d-d653-40ca-8231-a238b0c66975
We assessed roots production in 20 study plots (0.25ha) distributed along a gradient of forest disturbance in Eastern Amazonia: undisturbed primary forests (n = 5), logged primary forests (n = 5), logged-and-burned primary forests (n = 5), and secondary forests (n = 5). Forest disturbance classes were based on both an analysis of canopy disturbance in a chronosequence of satellite images (1988 to 2010) and on field assessments of fire scars, charcoal, and logging debris. In each plot we installed four in-growth soil cores (12 cm Diameter x 40 cm deep) using a plastic mesh (mesh size = 1.5cm). Each in-growth core was placed 50m apart. Cores were set 30cm deep into the soil, with 10cm aboveground. Before each sampling, both the soil temperature were measured inside and outside the in-growth core. In the field, each soil core was placed on a plastic sheet and broken in smaller parts until was homogenically spread. For the first measurement, called root stock, roots were collected in 24 intervals of five minutes each (three minutes break between intervals). The soil was homogenized before every new interval. After the sampling, the soil was placed back in the hole with the in-growth core inside. The sampled roots were taken to the laboratory and washed in a plastic sieve (2 mm mesh size), oven-dried at 60oC for 72 h and weighed. The second measure is taken 3 months after the first one, which gives us the first stock of fine root production. We collect the soil in the same hole and use the same method to collect the roots, but with 12 intervals of five minutes each (three minutes break between intervals). The laboratory procedure was the same. After three months, we take another measure in the same hole, using the same method as the second measure. After one year, the soil core is taken around 30 cm next to the first one, as the soil becomes physically disturbed and compact, which influences root production. For this new in-growth core, we start the sampling again with 24 intervals.
Gridded (1km) physical river characteristics for the UK v2
The dataset includes six files of UK physical river characteristics including five files of gridded data at 1 km x 1 km resolution and one comma separated table. The data includes: • Drainage directions (D8 flow method), ESRI coding • Drainage directions (D8 flow method), unifhy (python hydrology framework) coding • Catchment areas (km2) • Widths of bankfull rivers (m) • Depths of bankfull rivers (m) • NRFA gauging station locations (easting (m), northing (m)) Two versions of drainage directions are provided, both have the same drainage directions but different numbering systems. The comma separated NRFA (National River Flow Archive) gauging station locations table provides the best locations of 1499 river flow gauging stations on the 1km grids, together with the approximate error in the 1km × 1km gridded delineation of the upstream catchment area. All datasets are provided on the British National Grid. Full details about this dataset can be found at https://doi.org/10.5285/8df65124-68e9-4c68-8659-1c6b82c735e9
If you reuse this data, you should cite: Davies, H.N., Rameshwaran, P., Bell, V.A. (2022). Gridded (1km) physical river characteristics for the UK v2. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/8df65124-68e9-4c68-8659-1c6b82c735e9
The 1 km x 1 km outflow drainage direction and catchment areas datasets are generated from a 50 m x 50 m resolution digital terrain: IHDTM (Integrated Hydrological Digital Terrain Model). Bankfull river widths on a 1 km x 1 km grid are generated using the OS MasterMap Water Network Layer width data in a regression relationship with catchment area and rainfall characteristics. The 1km × 1km gridded bankfull river depth dataset is generated using observed measurements from a range of sources in a regression relationship with catchment area and rainfall characteristics. Finally, the NRFA (National River Flow Archive) gauging station location dataset on a 1 km x 1 km grid is generated by identifying the location (easting, northing) on the catchment areas dataset that best represents the true gauging station location. An indication of the quality of these data is provided by the percentage error between the 1km-derived and the IHDTM catchment areas. See supporting information for more details.
Conservation and development investments in the Corridor Ankeniheny-Zahamena (CAZ), Madagascar, 2007-2014
Conservation and development investments in the Ankeniheny-Zahamena Corridor (CAZ) includes information on investment type, implementing agency, and timing and location of investments. Data were collected as part of a project funded under the Ecosystem Services for Poverty Alleviation (ESPA) programme. Work package 4, 'Can Paying 4 Global Ecosystem Services values reduce poverty?' (P4GES) project Full details about this dataset can be found at https://doi.org/10.5285/f969f92f-baa6-4621-bf27-ea29409f16d0
If you reuse this data, you should cite: Tabor, K., Rasolohery, A., Ramanahadray, S., Rambeloson, A. (2017). Conservation and development investments in the Corridor Ankeniheny-Zahamena (CAZ), Madagascar, 2007-2014. NERC Environmental Information Data Centre https://doi.org/10.5285/f969f92f-baa6-4621-bf27-ea29409f16d0
Lineage statement We collected details on all investments in the CAZ from 2007-2104 through a document view of reports and financial records from Conservation International Madagascar, national NGOs, and international agencies. Inconsistencies in documentation in both the types of investments as well as the implementing agencies proved a major challenge to collecting accurate data, particularly of spatial data on the location of communities that received investments. The finest spatial resolution of the investments was recorded at the fokontany-scale. While we did document a total of 629 investments, only 553 of those investments could be mapped to the containing fokontany. The remaining 76 where mapped to the containing commune. We used expert local knowledge from Conservation International Madagascar staff working in CAZ to fill in gaps in the spatial and temporal information of partner organization investments data as well as missing spatial information in the financial and project reports.
Ash tree distribution in Great Britain 2007 View Service
This service displays a series of datasets consisting of mean estimate distribution maps of ash trees (Fraxinus excelsior) across Great Britain. It includes ash trees in areas less than half a hectare, ash trees in woody linear features and individual ash trees. The data are derived from Countryside Survey 2007. Trees were mapped in 569 1km sample squares across Britain using a stratified random sampling system based on the ITE Land Classification. Mean national estimates were produced by scaling up from the sample data.
Data were collected as part of the Countryside Survey monitoring project in 2007. Data were collected from 591 1km sample squares and scaled up to national estimates using the ITE Land Classification. See supporting documentation linked to dataset records for further information.
Flower-Insect Timed Count data from the UK Pollinator Monitoring Scheme, 2017-2022
This dataset contains information on the location of the count, some environmental variables, and the number of insects of each type counted for the Flower-Insect Timed Count survey as part of the UK Pollinator Monitoring Scheme (PoMS). It covers the years 2017 to 2022 (note that 2017 was a pilot year and has less data than subsequent years). The “public” FIT Count asks volunteer citizen scientists to count the number of insects, identified into broad taxon groups, seen landing on the flowers of a particular target plant within a 50 cm × 50 cm quadrat during a period of ten minutes. The “1 km square” FIT Count uses the same methodology but is carried out by PoMS volunteers and staff as part of the PoMS 1 km square survey, which takes place within a randomly allocated set of 1 km squares across the UK, and also gathers data on pan-trapped insects (see separate dataset). In 2017-2020 the 1 km square survey took place in England, Scotland and Wales only; from 2021 the survey has full UK coverage, but with limited data from Northern Ireland in 2021 which was a pilot year for that country. UK PoMS is co-ordinated by UKCEH, with the following delivery partners up to 2022: the Bumblebee Conservation Trust, Butterfly Conservation, British Trust for Ornithology, Hymettus, Natural History Museum, the University of Reading and University of Leeds. PoMS Steering Group members in 2022 were JNCC, DAERA, Defra, NatureScot, Welsh Government, All-Ireland Pollinators Plan, Natural England, Science and Advice for Scottish Agriculture, Buglife and National Biodiversity Network. Full details about this dataset can be found at https://doi.org/10.5285/699f2172-88c1-44b7-a8f5-a22296a9e2cb
The following acknowledgement is required for use of this dataset: The UK Pollinator Monitoring Scheme (UK PoMS) is a partnership funded jointly by the UK Centre for Ecology & Hydrology (UKCEH) and Joint Nature Conservation Committee (JNCC) (through funding from the Department for Environment, Food & Rural Affairs, Scottish Government, Welsh Government and Department of Agriculture, Environment and Rural Affairs for Northern Ireland). UKCEH's contribution is part-funded by the Natural Environment Research Council formerly as part of the UK-SCAPE programme (award NE/R016429/1) and now as part of the NC-UK programme (award NE/Y006208/1) delivering National Capability. Between 2017 and 2021, PoMS was funded by UKCEH and Defra (England), Welsh Government, Scottish Government, DAERA (Northern Ireland), and JNCC. PoMS is indebted to the many volunteers who carry out surveys and contribute data to the scheme.
If you reuse this data, you should cite: UK Pollinator Monitoring Scheme (2025). Flower-Insect Timed Count data from the UK Pollinator Monitoring Scheme, 2017-2022. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/699f2172-88c1-44b7-a8f5-a22296a9e2cb
Public FIT Counts are carried out at locations and dates chosen by the participants, at any time when weather conditions are suitable between 1 April and 30 September each year. A set of fourteen target flower types is provided, but participants are free to choose a different flower if a target species is not available. Guidance is provided to participants on how to select a suitable location and position a quadrat, and on how to identify the insect group categories when counting visiting insects. One km square FIT Counts follow the same methodology for the ten-minute count, but take place in up to 95 1 km squares, during four visits each summer, aiming for one visit in each month from May to August. There will be gaps in the data where a square cannot be visited during a particular year or month. Data is entered using online forms that promote consistency in data formats. QA checks are carried out on any photos provided of the target flowers, and these are corrected where the original identification is not correct. Participants can optionally add photos to show examples of the insect groups that they have seen and counted, although these photos should not be taken during the count itself (so as to avoid disturbing visiting insects) so cannot be corrected in the same way. The data is then passed through a QA process in R (code available in the metadata) to detect and resolve any errors in the dataset, or highlight where the methodology was not followed. Martin Harvey is the PoMS co-ordinator at UKCEH and the first point of contact for queries via the poms@ceh.ac.uk email. Claire Carvell is the project manager for PoMS, also based at UKCEH Wallingford and responsible for strategic direction, overseeing delivery of the surveys, data management and reporting, and liaising with JNCC and other partners. Nadine Mitschunas leads the field team with Chris Andrews and Angus Garbutt, and Francesca Mancini leads on statistical analysis of PoMS data, with Robin Hutchinson working on data management and communications. Other UKCEH team members are Nick Isaac, Lucy Ridding, Marc Botham and Helen Roy, and our partners are represented by Richard Comont (BCT), Richard Fox and Megan Lowe (BC), Dawn Balmer and Rob Jaques (BC), Paul Lee and Rowan Edwards (Hymettus), Mike Garratt and Simon Potts (Reading University), Bill Kunin (Leeds University) and Alfried Vogler (Natural History Museum).
NERC EDS Environmental Information Data Centre
Soil nutrients, bulk density and pyrogenic carbon in permanent monitoring plots along altitudinal and forest perturbation gradients in the Colombian Andes, 2019-2022
The dataset contains information on soil nutrients including pH, available phosphorus, total phosphorus, texture analysis in sand, clay and silt, and exchangeable Ca, Mg, K, Na. Soil pyrogenic carbon, including Total Organic Carbon (TOC) (%C_(Bulk)), pyrogenic carbon D13C (lower, median, and upper), pyrogenic carbon (Cpyc), Cpyc%/CBulk, organic carbon and soil bulk density and dry weight of roots. The datasets include information about the plot location and parameters of the locality. The data were collected between 2019 and 2022 from 27 forest monitoring plots (0.5 ha each) in five locations along an altitudinal (lowland, mid-elevation, and highland forests) and forest perturbation (low, medium, and high perturbation levels) gradient in Andean ecosystems in Colombia. The objective of this data collection was to determine the change in soil characteristics between the disturbance gradients. This information is important for understanding the drivers of variation in forest resilience and the impacts of disturbance on ecosystem functioning. This data set was obtained within the framework of the BioResilience project, a transdisciplinary investigation that seeks to understand the Resilience of forest ecosystems after the post-conflict period in Colombia. This research was supported by the UK Natural Environment Research Council Fund (NE/R017980/1). Full details about this dataset can be found at https://doi.org/10.5285/fbcfc877-a38e-4c26-8d8b-7226392493db
If you reuse this data, you should cite: Montes-Pulido, C., Feldpausch, T.R., Pennington, T., Bird, M., Quesada, C. (2023). Soil nutrients, bulk density and pyrogenic carbon in permanent monitoring plots along altitudinal and forest perturbation gradients in the Colombian Andes, 2019-2022. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/fbcfc877-a38e-4c26-8d8b-7226392493db
Samples were analyzed in the laboratory of the National Open and Distance University (UNAD), Instituto Nacional de Pesquisas da Amazônia, and James Cook University. Total Organic Carbon (TOC) was calculated by elemental analysis using a Costech ECS-4010 with a zero blank autosampler. Pyrogenic carbon (Cpyc) was analyzed via hydrogen pyrolysis (HyPy). The ratio of pyrogenic carbon to total organic carbon (Cpyc%/CBulk) was determined by elemental analysis following HyPy, which quantifies the Stable Polycyclic Aromatic Carbon component of total PyC in a sample. Soil pH was determined by 10 grams of fine earth shaken with 25 ml of water for 1 hour on a circular shaker, followed by a 1-hour rest period. The pH of the supernatant was then measured using an automatic probe. Available phosphorus (p_available) were analyzed for a sequential extraction with reagents of increasing strength. The first step being resin extractable P, followed by 0.5 M NaHCO3,0.1MNaOH and 1M HCl. Total phosphorus (tot_p) was calculated by acid digestion using concentrate sulfuric acid followed by H2O2. Particle size analysis was undertaken using the Boyoucos method. 50 g of fine earth was first treated with physical and chemical dispersants and then separated into sand (particle sizes between 0.05 and 2 mm),silt (particle sizes between 0.002 mm and 0.05mm) and clay contents (particle sizes<0.002 mm) by the means of a series of hygrometer readings in a settling soil solution. Exchangeable Ca, Mg, K and Na were determined by the silver thiourea method (Ag-TU). This method allows an appropriate one-step centrifuge extraction using 0.01 M Ag-TU solution, which achieves complete cation exchange. Samples for bulk density determinations were oven dried until constant weight, cooled to room temperature in a sealed desiccant before final weight determinations were made.
Vector maps of the 1960 Second Land Utilisation Survey in England and Wales, from 110 published maps
This is a vector dataset representing land use in the 1960s across a selection of areas in England and Wales. The land use data is digitised from 110 published maps of the 1960s Second Land Utilisation Survey (2LUS) which was directed by Professor Alice Coleman. The published maps were digitised using a semi-automated supervised classification, followed by extensive post-processing to clean the vector dataset. The resulting dataset maps 11 land-use categories including “Arable”, “Broadleaved and mixed woodland”, “Coastal”, “Coniferous woodland”, “Grassland”, “Heath, moorland and rough land”, “Market gardening and orchards”, “Open space”, “Settlement”, “Unvegetated land”, “Water and Marsh”. Full details about this dataset can be found at https://doi.org/10.5285/03940756-afe9-428b-8c69-275e6b889251
If you reuse this data, you should cite: Ridding, L.E., Wickenden, A.R., Orsler, Z., Rowland, C.S., Hampton, J.M., Mitchell, B., Edwardes, A., Mullin, K., Haughton, G., Thurston, N., Pola, I., Sinclair, G., Sinclair, M.R., Shaw, J., Pywell, R.F. (2025). Vector maps of the 1960 Second Land Utilisation Survey in England and Wales, from 110 published maps. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/03940756-afe9-428b-8c69-275e6b889251
All 110 published Second Land Utilisation Survey (2LUS) maps were digitised using a semi-automated supervised classification method. Firstly, maps were scanned and saved as Tiff images at 300 dots per inch at a colour depth of 8 bits per channel (24-bit depth). These were georeferenced in ArcGIS v10.7 using the Ordnance Survey (OS) 1km National Grid. For the classification, georeferenced 3-band images were converted to 16-bit single band images (256 colours). A manual look-up table was created for each map image identifying the dominant unique pixel values of the different land use classes. Any “noise” present on the images including text, contour lines and symbols were assigned a single blank value of 0. Similarly, pixel values which were common to multiple land use classes, such as the white colour were also assigned to 0. For these reasons it was not possible to classify either the “unvegetated land” or the “derelict land” land use categories. The model reclassified the 256 colours within a range of 0 to 10, representing the 10 land use categories and the noise category (pixel value = 0), with the latter being removed and replaced. Classified images were vectorised and manually cleaned using eliminate functions in ArcGIS. All “unvegetated land” was manually digitised as this could not be classified, along with a new category for “coastal” areas which were not classified. Railway lines, roads and very small rivers were inconsistently classified and were therefore removed, while “industry” and “transport” classes were grouped with “settlement” to ensure uniformity. “Derelict land” could not be classified but was often mapped in the classification as “settlement”, so this was later grouped with this category. Woodland was manually split into broadleaved and coniferous using the woodland symbology provided on the OS background, this usually aligned with the manual over-printed symbols but the latter was not present for all woodlands, thus the OS backdrop was used. The date of each OS basemap varied, however these were largely published or reprinted in the 1960s and were largely consistent with the surveyed land cover data, though there were exceptions e.g. Bristol East. To validate the classified 2LUS maps, 3,000 random points were generated across the digitised maps. For each point, a manual assessment was undertaken assigning the most representative land cover class to the point based on the raw georeferenced 3-band images. A confusion matrix between the manually assigned points and the classified 2LUS maps revealed an overall accuracy of 95.8%. Please see supporting documentation for more information.
Coastal Biodiversity and Ecosystem Service Sustainability (CBESS) the erosion rate of sediment cores from salt marsh sites at Morecambe Bay and Essex
The dataset comprises the erosion rate (percent mass loss per hour) observed in sediment cores (16 centimetre (cm) diameter, 30cm height) subjected to flume tank flow for three 'waterfall' flows (Low, medium, high). Sampling was conducted at six salt marsh sites at four spatial scales: 1 m (the minimal sampling unit) nested within a hierarchy of increasing scales of 1-10 metre (m), 10-100 m and 100-1000 m. Three of the sites were in Morecambe Bay, North West England and three of the sites were in Essex, South East England. The Morecambe Bay samples were taken during the winter and summer of 2013. The Essex samples were taken during the winter, early spring and summer of 2013. This data was collected as part of Coastal Biodiversity and Ecosystem Service Sustainability (CBESS): NE/J015644/1. The project was funded with support from the Biodiversity and Ecosystem Service Sustainability (BESS) programme. BESS is a six-year programme (2011-2017) funded by the UK Natural Environment Research Council (NERC) and the Biotechnology and Biological Sciences Research Council (BBSRC) as part of the UK's Living with Environmental Change (LWEC) programme. Full details about this dataset can be found at https://doi.org/10.5285/7e3b5549-881f-47ea-b003-08542bf6193e
If you reuse this data, you should cite: Ford, H., Garbutt, A., Skov, M. (2016). Coastal Biodiversity and Ecosystem Service Sustainability (CBESS) the erosion rate of sediment cores from salt marsh sites at Morecambe Bay and Essex. NERC Environmental Information Data Centre https://doi.org/10.5285/7e3b5549-881f-47ea-b003-08542bf6193e
Sediment cores (16 centimetre (cm) diameter, 30 cm height) including above ground vegetation were collected in each 1 x 1 m quadrat. Each core was placed horizontally in the 'waterfall force' of the re-circulation flume for 1.5 hours (0.5 hour at low force, 0.5 hour at medium force, 0.5 at high force). Sediment erosion rate was calculated from the amount of mass lost over half an hour with measurements taken time = 0 minutes (mins), 5 mins, 10mins, 15mins and 30mins. Results were recorded onto field sheets. These data were transferred into an Excel file and calculations carried out. Results were exported as comma separated value files for ingestion into the EIDC. The location of the sample sites was determined by randomly allocated quadrats. Each site consisted of a rectangular area of saltmarsh between 400 x 500 m to 1000 x 1000 m in size, dependent upon saltmarsh length (parallel to shore) and width (perpendicular to shore), including part of the low, mid and high marsh zones. Twenty-two 1 x 1 m quadrats were randomly allocated to each site rectangle using R (R Development Core Team, 2014) to specify four different spatial scales (A = 1 quadrat only, B = 3 quadrats at 1 m to 10 m apart, C = 6 quadrats at 10 m to 100 m apart, D = 12 quadrats at 100 m to 1000 m or site maximum).
Instituto Nacional de Pesquisas da Amazônia