NERC Data Catalogue Service
NERC Data Catalogue Service (Natural Environment Research Council)
Continents, countries, sea regions of the world.
Snow Survey of Great Britain: transcribed data for Scotland, 1945 to 2007
This dataset comprises observations of snowline from the Snow Survey of Great Britain (SSGB) at 140 sites across Scotland . Daily observations were made between 1945 and 2007. Observations were made by a ground observer who looked out from a given location at 0900 GMT each day and noted the elevation at which snow cover was greater than 50%. The initial aim was to 'secure representative data relating to the occurrence of snow cover at different altitudes in the various upland districts over the period October to June'. The data were collated by the British Glaciological Society until 1954 and thereafter by the Met Office. It has been transcribed from paper records held in the Met Office archives in Edinburgh. Full details about this dataset can be found at https://doi.org/10.5285/caf989a5-82d7-4db7-b6ff-c0475fdae07e
If you reuse this data, you should cite: Spencer, M. (2016). Snow Survey of Great Britain: transcribed data for Scotland, 1945 to 2007. NERC Environmental Information Data Centre https://doi.org/10.5285/caf989a5-82d7-4db7-b6ff-c0475fdae07e
This resource is made available under the terms of the Open Government Licence
The SSGB is a voluntary observer collected dataset. The SSGB observers looked out on the hills that surrounded their location and noted at what level snow was lying. Elevations were grouped into 150 m bands from 0 to 1200 m ASL or 500 feet increments earlier in the record, with most stations supplying metric returns by the early 1980s. The observers were asked (taken from January 1992 instructions) to record at 09:00 GMT or thereabouts when snow or sleet was falling at station level and if snow was lying at station level, with depth. Finally they were asked to record when fog or cloud obscured observation. Paper copy Scottish data, between Autumn 1945 and Summer 2007, are stored in the Met Office archives in Edinburgh. These paper copies were transcribed to an electronic format and comprise this dataset. Some data are missing as not all stations operated in all years. Some data are also missing due to observers being away or weather obscuring the view.
Data for lead (Pb) in terrestrial wildlife and resultant taxonomic models
Data comprise concentration ratios for lead and 210Pb in terrestrial wildlife and plants, and residual maximum likelihood (REML) model values for taxons (order, family and genus). Pb was used as an example of the derivation and application of a REML model for terrestrial wildlife species. The model outputs consists of a mean value for each taxa on a common scale after REML adjustment (referred to as the ‘REML mean’) taking account of the random factor (i.e. site). The REML mean value represents a relative scaling value. Funding for preparing this data set was provided by the TREE project (http://www.ceh.ac.uk/tree) funded by the NERC, the Environment Agency and Radioactive Waste Management Ltd. under the RATE programme. Full details about this dataset can be found at https://doi.org/10.5285/2bd9bd0e-2c52-4767-ae2e-9c70ba8c358d
If you reuse this data, you should cite: Beresford, N.A., Barnett, C.L. (2018). Data for lead (Pb) in terrestrial wildlife and resultant taxonomic models. NERC Environmental Information Data Centre https://doi.org/10.5285/2bd9bd0e-2c52-4767-ae2e-9c70ba8c358d
REML fitting was performed using the IBM SPSS statistical package (version 24) using ‘Linear Mixed Model’ analysis within which REML fitting is a standard option. Because of the data requirements of the REML analyses, as described above, then different amounts of data were available for the runs at genus, family and order levels. The Akaike information criterion (AIC) was used as an estimator of the relative quality of statistical models (the lower the AIC the better the model). To check that including site as a random factor resulted in a better model, all runs were repeated with site removed (ensuring that an intercept was included for the fixed parameter (i.e. taxon)).
Land Cover Map 1990 (1km percentage aggregate class, N. Ireland)
This dataset consists of the 1km raster, percentage aggregate class version of the Land Cover Map 1990 (LCM1990) for Northern Ireland. The 1km percentage product provides the percentage cover for each of 10 aggregated land cover classes for 1km x 1km pixels. This product contains one band per aggregated habitat class (producing a 10 band image). The 10 aggregate classes are groupings of the 21 target classes, which are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. The aggregate classes group some of the more specialised classes into more general categories. For example, the five coastal classes in the target class are grouped into a single aggregate coastal class. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM1990 is a land cover map of the UK which was produced at the UK Centre for Ecology & Hydrology by classifying satellite images (mainly from 1989 and 1990) into 21 Broad Habitat-based classes. It is the first in a series of land cover maps for the UK, which also includes maps for 2000, 2007, 2015, 2017, 2018 and 2019. LCM1990 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the UK CEH web site and the LCM1990 Dataset documentation) to select the product most suited to their needs. 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/10.5285/1278d7b5-da47-46b5-b1a6-049e726425a7
If you reuse this data, you should cite: Rowland, C.S., Marston, C.G., Morton, R.D., O'Neil, A.W. (2020). Land Cover Map 1990 (1km percentage aggregate class, N. Ireland). NERC Environmental Information Data Centre https://doi.org/10.5285/1278d7b5-da47-46b5-b1a6-049e726425a7
Registration is required to access this data
The percentage aggregate class is produced from the 25m raster product (which is derived from the vector product) by: 1. Using gdal_calc to aggregate the 23 target classes into the 10 aggregate classes saving them as 10 individual images (one for each class) where the presence of the class in question is recorded as 100 and absence is assigned 0. 2. Gdal_warp is then applied to each of the 10 presence/absence images to resample to 1km, using the 'average' option under resample. This produces images of percentage cover for each class. 3. Gdal_merge is then used to create a single file with 10 bands, where each band provides the percentage cover for the class i.e. band 1 provides percentage cover for class 1 etc.
Local tree names, uses and species identification in Mozambique
The dataset includes lists of local tree names, tree species identification and local uses of trees in seventeen different villages across three Districts in Mozambique, Africa. We collated species lists from seven villages in Mabalane District, Gaza Province, ten villages in Marrupa District, Niassa Province, and ten villages in Gurue District Zambezia Province. Data were collected in Mabalane between May-Sep 2014, Marrupa between May-Aug 2015, and Gurue between Sep-Dec 2015. Lists of local tree names were collated from several forest plots and agricultural field surveys occurring within the sampled villages, and their species identified in the field by the authors and/or from dried and pressed samples by botanists at the Universidade Eduardo Mondlane in Maputo. Tree species uses by local populations were recorded through a mixture of key informant interviews, focus group discussions, village surveys and ad-hoc observations. This dataset was collected as part of the Ecosystem Services for Poverty Alleviation (ESPA) funded ACES project , which aims to understand how changing land use impacts on ecosystem services and human wellbeing of the rural poor in Mozambique. Full details about this dataset can be found at https://doi.org/10.5285/52371ef0-855f-40c8-8567-f8965f9cbf03
If you reuse this data, you should cite: Woollen, E., Grundy, I., Bechel, A., Ryan, C.M., Nhaduco, O., Daude, I.A., Manguiao, A.A., Da Costa, A.A., Lisboa, S.N., Junior, J.F., Mutemba, L., Pais, A., Zavale, M., Cumbula, S., Xerinda, H., Buque, H., Meneses, D., Rodrigues, C., Mário, A.M., Ribeiro, N., Domingos, L.P., Mbandze, A., Armando, F., Jennings, P. (2017). Local tree names, uses and species identification in Mozambique. NERC Environmental Information Data Centre https://doi.org/10.5285/52371ef0-855f-40c8-8567-f8965f9cbf03
All data were quality assured by the first and second author. When in the field, all teams were trained in data collection methods to ensure consistent robust records. All field teams had a translator to translate from the local language to Portuguese or English. All observations recorded in Portuguese were translated to English by the lead author. If several different species were identified for the same local name a preference for the identification made in the field, rather than that of the dried sample, was made, as correct identification of whole trees are more likely to be correct. If identification was very uncertain, species were labelled as 'unknown'. Grouping all recorded local tree names and synonyms into similar sounding/spelled groups to assign a standardized local name was done by the lead for consistency.
Atmospheric gas and vegetation survey data from Parsonage Down, UK, in 2014
This dataset contains greenhouse gas flux data and vegetation survey data from an experiment based at Parsonage Down, UK. The vegetation survey comprises total species percentage cover and species richness data from four 50 cm by 50 cm quadrats. The greenhouse gas flux data comprises net ecosystem carbon dioxide exchange, photosynthesis and respiration data measured with an Infra-red Gas Analyser (IRGA); methane, carbon dioxide and nitrous oxide data measured using gas chromatography; and nitrate and ammonium from soil samples extracted with potassium chloride. The experiment investigated the effect of different plant groups on soil carbon stores and nutrient cycling, by using a mixture of hand weeding and herbicide spot spraying to create different plant communities on the species rich grassland at Parsonage Down. The resulting carbon and nutrient cycling rates were compared to the characteristics of the plant groups. The experiment ran from 2013 to 2015 and this dataset contains data from 2014 only. This experiment was part of the Wessex BESS project, a six-year (2011-2017) project aimed at understanding how biodiversity underpins the ecosystem functions and services that landscapes provide. Full details about this dataset can be found at https://doi.org/10.5285/e05b350f-3cf4-4f8d-aa3c-24d562ca756b
If you reuse this data, you should cite: Fry, E.L., Hall, A.L., Savage, J., Bardgett, R.D., Ostle, N., Pywell, R.F., Bullock, J.M., Oakley, S. (2017). Atmospheric gas and vegetation survey data from Parsonage Down, UK, in 2014. NERC Environmental Information Data Centre https://doi.org/10.5285/e05b350f-3cf4-4f8d-aa3c-24d562ca756b
For greenhouse gas exchange flux, a time series correlation was calculated and low correlations removed (<70%). Outliers were also removed at the beginning and end of sample where appropriate. Calibrations at two levels were done for methane gas flux (at 14, and 33 ppm), carbon dioxide gas flux (at 500 and 1000 ppm) and nitrous oxide gas flux (at 200 and 370 ppm). Standardised protocols were followed in all cases, details of which can be found in the supporting documentation.
Grid-to-Grid model estimates of river flow for Great Britain driven by UK Climate Projections 2018 (UKCP18) Regional (12km) data (1980 to 2080)
[THIS DATASET HAS BEEN WITHDRAWN]. Gridded hydrological model river flow estimates on a 1km grid over Great Britain for the period Dec 1980 - Nov 2080. The dataset includes monthly mean river flow, annual maxima of daily mean river flow (water years Oct - Sept), along with the date of occurrence, and annual minima of 7-day mean river flow (years spanning Dec-Nov), along with the date of occurrence (units: m3/s). The data are provided in gridded netCDF files. There is one file for each variable and ensemble member. To aid interpretation, two additional spatial datasets are provided: a) digitally-derived catchment areas and b) estimated locations of flow gauging stations both on the 1km x 1km grid. The data were produced as part of UK-SCAPE (UK Status, Change And Projections of the Environment; www.ceh.ac.uk/ukscape, Work Package 2: Case Study – Water) programme, a NERC-funded National Capability Science Single Centre award number NE/R016429/1. Full details about this dataset can be found at https://doi.org/10.5285/b7a98440-8742-40d5-a518-46dc6420416e
If you reuse this data, you should cite: Kay, A.L., Rudd, A.C., Davies, H.N., Lane, R.A., Bell, V.A. (2021). Grid-to-Grid model estimates of river flow for Great Britain driven by UK Climate Projections 2018 (UKCP18) Regional (12km) data (1980 to 2080). NERC EDS Environmental Information Data Centre https://doi.org/10.5285/b7a98440-8742-40d5-a518-46dc6420416e
This resource is available under the terms of the Open Government Licence
This dataset is a model output from the Grid-to-Grid hydrological model (Bell et al. 2009; https://doi.org10.1016/j.jhydrol.2009.08.031) driven by meteorological data derived from the United Kingdom Climate Projections 2018 (UKCP18) Regional (12km) projections (Met Office Hadley Centre 2018, catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604). The projections comprise a 12-member perturbed parameter ensemble (PPE) of the Met Office Hadley Centre Regional Climate Model (RCM) under representative concentration pathway (RCP) scenario RCP8.5. The meteorological variables used were daily precipitation, daily min and max temperature, and daily PE derived from other meteorological variables (using the Penman-Monteith scheme). See Supporting Information for more details.
Hourly water temperature of 28 experimental mesocosms and air temperature from July to September 2022
The dataset comprises hourly water temperature data of an experimental mesocosm facility as well as air temperatures from beginning of July to end of September 2022. The experiment aimed to investigate the effect of different nutrient regimes on Taste and Odour issues. Mesocosms were filled with reservoir water and water temperature was measured by platinum resistance thermometers (PRT) shaded by a plastic cover, sited around mid-depth, radially offset by 30 cm from the mesocosm's side wall and logged by a Campbell Scientific Data Logger. Air temperature was measured at a weather station within the mesocosm compound (Vaisala weather transmitter WXT520) at around 2.7m height. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/2858a0e9-9b20-4c2c-892c-4a99fe49e315
If you reuse this data, you should cite: Feuchtmayr, H., Rankin, G., McShane, G. (2023). Hourly water temperature of 28 experimental mesocosms and air temperature from July to September 2022. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/2858a0e9-9b20-4c2c-892c-4a99fe49e315
The hourly average presented here is calculated from 5-minutely data over the following hour, e.g. the value for 1 p.m. is the average of the data between 1 and 1:55 p.m. All data is given in GMT (Greenwich Mean Time) and dates from 6th July to 30th September 2022. Whenever the difference between the hourly averaged two water temperature sensor data was less than 0.2 °C, the average between both temperatures was calculated. If the difference was larger than 0.2 °C, data was checked against the overall average of the temperature sensors recorded by each data logger. Whenever the difference was larger than 0.2 °C, one of the two temperature sensors had drifted either higher than the average or below the average temperature. In those cases, this sensor was ignored and only data from one temperature sensor was used during those occasions (see table in the supporting documentation).
Channel topography and grain-scale sediment structure, measured in Bury Green Brook (UK) between 2014 and 2018
Dataset contains Terrestrial laser scanner (TLS) and CT scan data collected during fieldwork on a small gravel-bed river. TLS data show the river bed surface topography collected at five intervals between September 2014 and October 2018. CT scan data show the 3D structure of sections of the river bed. CT data has been processed to segment the images into gravel grains and fine-grained matrix. Full details about this dataset can be found at https://doi.org/10.5285/b30b4d56-f0a9-43e8-aacc-09d9b5b1f9fc
If you reuse this data, you should cite: Hodge, R.A., Voepel, H.E., Leyland, J., Sear, D.A., Ahmed, S. (2022). Channel topography and grain-scale sediment structure, measured in Bury Green Brook (UK) between 2014 and 2018. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/b30b4d56-f0a9-43e8-aacc-09d9b5b1f9fc
TLS data were collected from the channel under conditions when there was little to no flow in the channel. On some field visits, wire baskets were buried in the channel bed. On the following visit, the baskets were extracted, fixed in place by dipping in molten wax, and CT scanned. CT data were processed to remove any scan artefacts, and to segment the gravel grains from the surrounding matrix and other materials.
Distribution of individual ash trees in Great Britain
The dataset consists of a map of individual ash trees (Fraxinus excelsior) across Great Britain. The data is derived from Countryside Survey 2007 and includes individual trees in the landscape, clumps of trees and veteran trees. Trees were mapped in 569 1km sample squares across Britain, and this national estimate dataset was derived from the sample data using ITE Land Classes. Full details about this dataset can be found at https://doi.org/10.5285/0c3567a8-3700-4d52-a21f-de1bd709141a
If you reuse this data, you should cite: Maskell, L.C., Henrys, P.A., Norton, L.R., Smart, S.M., Wood, C.M. (2013). Distribution of individual ash trees in Great Britain. NERC Environmental Information Data Centre https://doi.org/10.5285/0c3567a8-3700-4d52-a21f-de1bd709141a
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 for further information.
Electrical resistivity tomography surveys from the Makutapora Basin, Tanzania, June-July 2019
This datasets contains Electrical Resistivity Tomography surveys taken in the Makutapora Basin, Central Tanzania, using an AGI SuperSting R8 (STING) resistivity meter. Survey geometry, parameters and coordinates are also included. Full details about this dataset can be found at https://doi.org/10.5285/1998da32-a978-41a4-8a66-81df1e625cca
If you reuse this data, you should cite: Zarate, E., Cuthbert, M.O. (2021). Electrical resistivity tomography surveys from the Makutapora Basin, Tanzania, June-July 2019. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/1998da32-a978-41a4-8a66-81df1e625cca
The data were generated using .stg outputs from the STING meter and converted to .dat files using a bespoke Python code while also removing anomalous values.
NERC EDS Environmental Information Data Centre
UK Environmental Change Network (ECN) Fine Grain Vegetation data: 1994-2012
[THIS DATASET HAS BEEN WITHDRAWN]. Fine Grain Vegetation data from the UK Environmental Change Network (ECN) terrestrial sites. These data are collected at all of ECN's terrestrial sites using a standard protocol (see supporting documentation). In this protocol, 10m x 10m plots are randomly selected within each vegetation type on the site - species presence is recorded in 40cm x 40 cm cells randomly selected within these plots. They represent continuous records every three years (some sites record data annually) from 1992 to 2012. ECN is the UK's long-term environmental monitoring programme. It is a multi-agency programme sponsored by a consortium of fourteen government departments and agencies. These organisations contribute to the programme through funding either site monitoring and/or network co-ordination activities. These organisations are: Agri-Food and Biosciences Institute, Biotechnology and Biological Sciences Research Council, Cyfoeth Naturiol Cymru - Natural Resources Wales, Defence Science & Technology Laboratory, Department for Environment, Food and Rural Affairs, Environment Agency, Forestry Commission, Llywodraeth Cymru - Welsh Government, Natural England, Natural Environment Research Council, Northern Ireland Environment Agency, Scottish Environment Protection Agency, Scottish Government and Scottish Natural Heritage. Full details about this dataset can be found at https://doi.org/10.5285/5a1efdfc-8f51-4ee9-9ed4-677bddd3f135
If you reuse this data, you should cite: Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N., Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R., Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N., Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Eaton, E., Flexen, M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M., Kahl, J., Lane, M., Langan, S., Lloyd, D., McElarney, Y., McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M., Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G., Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R., Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson, K., Watson, H., Whittaker, M. (2016). UK Environmental Change Network (ECN) Fine Grain Vegetation data: 1994-2012 . NERC Environmental Information Data Centre https://doi.org/10.5285/5a1efdfc-8f51-4ee9-9ed4-677bddd3f135
Data are collected at all of ECN's terrestrial sites using a standard protocol (http://www.ecn.ac.uk/measurements/terrestrial/v). In this protocol 10m x 10m plots are randomly selected within each vegetation type on the site - species presence is recorded in 40cm x 40 cm cells randomly selected within these plots. Prior to deposit of the data in the EIDC, data are managed by the ECN Data Centre at CEH Lancaster according to defined protocols. Verification steps include numeric range checks (i.e. checking if a value falls within a specified range), categorical checks (e.g. checking that a species code appears on the standard code list), formatting (i.e. that the dataset conforms to the specified data format) and logical integrity checks (i.e. checking the data make sense, e.g. that the dates in one dataset match those in a related dataset). Appropriate range settings for ECN variables have been selected following discussion with specialists in each field. Where data fall outside these ranges, a cautious approach has been adopted towards discarding data on the principle that apparent errors may be valid outliers. Such values are discarded only if there is a clear explanation (e.g. an instrumentation error) and corrections are made where possible. If the reason is unclear, the values are stored, but are qualified using pre-defined quality codes or free-text descriptions. Data providers also use these codes or free text to describe factors affecting sampling outside their control, instrument damage or site management effects.
Trait responses of aquatic organisms from microfibre exposure studies published 2015-2024 v2
The resource includes data extracted from published laboratory studies investigating the impacts of microfibres (fibres < 5 mm in size) on various traits within aquatic organisms. Data were accessible on 19/06/2024 and represent studies from 2015 to 2024. The dataset includes measured responses in a range of aquatic organisms after microfibre exposure. Measures correspond to the following defined traits: 1. Development-Healthy molting and growth of organs and structures. 2. Fecundity-Number of offspring and quality of gametes. 3. Feeding-Intake, handling and egestion of resources. 4. Growth-Change in organism weight, length or derived measures. 5. Hatching -Number, emergence time and morphology of offspring. 6. Non-apical endpoints-Indices and measures of general organism wellbeing. 7. Survival-Organisms alive at a particular time point. This dataset was created to collate microfibre study findings for meta-analyses and also to identify gaps within the literature. This work was supported by NERC (NE/Y003985/1) as part of the Future Fibres Networking Grant. This dataset is a corrected version of https://doi.org/10.5285/e4d566bf-b571-460e-816e-0efa1ab3335a Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/8b7fcc6c-ac73-4f28-b82f-ea818b064915
If you reuse this data, you should cite: Parker, B., Lewis, C., Galloway, T. (2025). Trait responses of aquatic organisms from microfibre exposure studies published 2015-2024 v2. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/8b7fcc6c-ac73-4f28-b82f-ea818b064915
The dataset was generated from an initial Web of Science database search of studies investigating the exposure impacts of microfibres (small fibres < 5 mm in size) on the responses of aquatic organisms. Web of Science results were evaluated and then the citing and cited studies from these results also screened. Finally, the compiled results were then assessed for eligibility as detailed in the supporting information and only eligible responses extracted. QA steps were used to exclude a number of studies and responses as outlined in the resource supporting information. This dataset is a corrected version of https://doi.org/10.5285/e4d566bf-b571-460e-816e-0efa1ab3335a which had several incorrectly calculated standard exposures and consequently incorrect assignment to standard exposure categories. This new version includes details on how these standard exposures were calculated as well as including more details on the assignment of several categories. Finally, "Health" endpoints are now referred to as "non-apical endpoints".
Aquatic carbon and greenhouse gas concentrations in the Auchencorth Moss catchment following drain blocking
This dataset contains concentrations of dissolved organic carbon, inorganic carbon, CO2, CH4 and N2O in the Black Burn stream which drains Auchencorth Moss peatland in South East Scotland. Concentrations and fluxes have been measured within the Black Burn on an approximately weekly to fortnightly basis from approximately 2006 to present (see https://doi.org/10.5285/3f0820a7-a8c8-4dd7-a058-8db79ba9c7fe). Concentrations in this dataset are from a series of new sites, upstream of the long-term sampling record, adjacent to an area of drains blocked by Scottish Natural Heritage. Measurements began during the drain blocking. Data was collected initially as part of a masters project for University of Edinburgh through Scotland's Rural College and continued by the Centre for Ecology & Hydrology. Full details about this dataset can be found at https://doi.org/10.5285/88ffbf44-0ec0-41d6-9814-04bc3535cd84
© Database Right/Copyright Scottish Natural Heritage
If you reuse this data, you should cite: Dinsmore, K.J., Murphey, O., Leith, F., Carfrae, J. (2016). Aquatic carbon and greenhouse gas concentrations in the Auchencorth Moss catchment following drain blocking. NERC Environmental Information Data Centre https://doi.org/10.5285/88ffbf44-0ec0-41d6-9814-04bc3535cd84
The study was set up by Dr Kerry Dinsmore. Water and headspace (for gas analysis) samples were collected primarily by Orla Murphey and Dr Fraser Leith. Analysis was carried out at CEH Edinburgh, again primarily by Orla Murphey and Dr Fraser Leith. Field equipment was installed by Orla Murphey and Dr Kerry Dinsmore and maintained by Orla Murphey and Dr Fraser Leith. Further detail is provided in the supporting documentation.
Annual Forest Loss at 15m resolution in the Corridor Ankeniheny-Zahamena (CAZ), Madagascar, 2006-2014
The dataset delineates annual forest loss in the CAZ digitized from annual 15 metre resolution LANDSAT imagery. This dataset creates a baseline for possible long-term, near real-time monitoring of encroachment and illegal logging within the Ankeniheny-Zahamena Corridor (CAZ), Madagascar. This analysis includes a summary of suspected encroachment and illegal logging from June 2006 to December 2010 within the "protected area," which consists of zones such as priority for conservation, sustainable use, and controlled settlements. Full details about this dataset can be found at https://doi.org/10.5285/c63c543f-3e95-4c1c-8c69-12f942271813
If you reuse this data, you should cite: Cano, C.A. (2017). Annual Forest Loss at 15m resolution in the Corridor Ankeniheny-Zahamena (CAZ), Madagascar, 2006-2014. NERC Environmental Information Data Centre https://doi.org/10.5285/c63c543f-3e95-4c1c-8c69-12f942271813
Landsat 7 SLC-off imagery at a resolution of 15 m was compared to Landsat 2005 imagery and the Change in Natural Forest Cover from 1990 to 2005 developed by the United States Agency for International Development (USAID), Conservation International and the Madagascar Ministry of Environment, Water, Forests and Tourism. The imagery was acquired from the U.S. Geological Survey (USGS) Global Visualization Viewer (Glovis), available at http://glovis.usgs.gov/. ENVI 4.7 was used for processing and gap filling of the imagery. Visual interpretation based on shape, color, proximity to features and texture was used to digitize suspected forest encroachment using ArcGIS 9.3.1. The data have not been validated; therefore some of the clearings are likely due to natural disturbances such as landslides, flooding, natural fires or tree falls.
The geochemical composition and stratigraphic position of 44 tephra layers in southern Iceland
The dataset comprises two elements: 1) logs of nine sedimentary profiles recorded in three locations in southern Iceland and 2) geochemical analyses of tephra samples taken from these profiles. The three locations were Heimaey (V), an island in the Vestmannaeyjar archipelago (four profiles); Seljaland (S) in southern Iceland (two profiles), and Húshólmi (H) on the Reykjanes peninsula (three profiles). All three locations are associated with the earliest phases of the settlement of Iceland by the Norse in the ninth century CE. The datasets were collected to establish a tephrochronological framework for the three sites. The logs are based on field observations made by Prof. Andrew Dugmore (University of Edinburgh) and Dr Richard Streeter (University of St Andrews) in June 2023. The geochemical analyses were carried out by Dr Streeter and conducted using an Electron Probe Micro Analyser (EPMA). Full details about this dataset can be found at https://doi.org/10.5285/dd570900-245c-4586-82b7-e548cbdc4ac5
If you reuse this data, you should cite: Cutler, N., Dugmore, A., Streeter, R. (2025). The geochemical composition and stratigraphic position of 44 tephra layers in southern Iceland. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/dd570900-245c-4586-82b7-e548cbdc4ac5
The sedimentary logs are based on field observations and encompass layer thickness and sediment characteristics (qualitative assessment of colour, texture, grain size, etc.) The geochemical analyses are unstandardised proportional abundances of major elements measured by an Electron Probe Micro Analyser (EPMA).
Radiocarbon measurements on river wood deposits collected in August 2019, Mackenzie River, Canada
The data comprises of dimensions of large wood pieces and the isotope composition (radiocarbon, stable carbon isotopes) of cellulose extracted from the wood samples. Large Wood (LW) samples were collected from the Mackenzie River delta region, Northwest Territories, Canada, in August 2019 and were analysed at the National Environmental Isotope Facility, UK. The scientific aims were to help constrain the source and age of wood carried by this large river system draining to the Arctic Ocean. Full details about this dataset can be found at https://doi.org/10.5285/19be1a45-c457-40af-a582-395257d7a3b0
If you reuse this data, you should cite: Hilton, R., Ascough, P., Sendrowski, A., Kramer, N., Wohl, E. (2023). Radiocarbon measurements on river wood deposits collected in August 2019, Mackenzie River, Canada. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/19be1a45-c457-40af-a582-395257d7a3b0
1) Large wood samples from 6 regions in the Mackenzie delta area (Aklavik, Inuvik, Coast, Mackenzie River, Arctic Red River, Peel River), with a range of different sites sampled in each region. 2) Samples of wood were collected from the outermost part of wood pieces. 3) Cellulose was extracted and the %C, stable carbon isotope composition (d13C, permil) and radiocarbon activity (Fraction Modern, F14C) were measured on the cellulose. Quality assurance on the geochemical measurements was via analytical standards and blank tests made during the sample analyses. 4) Radiocarbon measurements were converted to estimates of age - providing constraint on the age of the earliest growth rings - using OxCal 4.4 to provide a lower and upper bound age. 5) Wood age modelled using OxCal 4.4, reporting the 95% confidence range of lower and upper ages. For samples with F14C<1, IntCal20 was used. For samples with F14C>1, Bomb21NH1 calibration curve was used.
Land Cover Map 2022 (10m classified pixels, N. Ireland)
This is a 10m pixel data set representing the land surface of Northern Ireland, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. It is a two-band raster in GeoTiff format. The first band gives the most likely land cover type; the second band gives the per-parcel probability of the land cover. A full description of this and all UKCEH LCM2022 products are available from the LCM2022 product documentation. Full details about this dataset can be found at https://doi.org/10.5285/c2d1e87f-1fa8-4421-82a7-df960b4b5821
If you reuse this data, you should cite: Marston, C.G., Morton, R.D., O'Neil, A.W., Rowland, C.S. (2024). Land Cover Map 2022 (10m classified pixels, N. Ireland). NERC EDS Environmental Information Data Centre https://doi.org/10.5285/c2d1e87f-1fa8-4421-82a7-df960b4b5821
UKCEH's automated land cover algorithms classify 10 m pixels across the whole of UK. Training data were automatically selected from stable land covers over the interval of 2019 to 2021. 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 10 m pixel classification into a land parcel framework (the LCM2022 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy. Details are available from the product documentation. The 25 m rasterised land parcels product is created by pixelating the corresponding land parcel product.