This dataset contains data from a soil survey at Moor House, Cumbria, UK undertaken by Mike Hornung in 1965 as part of his PhD. It contains information about the soil horizons, chemical analyses and soil mineralogy. Full details about this dataset can be found at https://doi.org/10.5285/e4cf73b0-eef6-4118-a018-40eb374f5de7

This is a spatial dataset containing polygons representing different geology types in the Moor House National Nature Reserve, northern Pennines, England. The survey was undertaken by G.A.L. Johnson under a grant by The Nature Conservancy in the 1950s and 1960s. Full details about this dataset can be found at https://doi.org/10.5285/0e3aefb2-ce86-4d09-8ff0-6d165dfd48db

Climatic data from four sites (Newton Rigg, Widdybank Fell, Moor House and Great Dun Fell) along an altitudinal gradient in Cumbria 1952-1990. Standard Meteorological Office procedures were used. Full details about this dataset can be found at https://doi.org/10.5285/ab42fb7d-809f-4492-9ba5-c8aeebdee838

This is a digital soil map of the Moor House - Upper Teesdale National Nature Reserve. Mapped polygons represent a range of soil types. The site lies in the North Pennine uplands of England and has an area of 74 km2. It is England's highest and largest terrestrial National Nature Reserve (NNR), a UNESCO Biosphere Reserve and a European Special Protection Area. Habitats include exposed summits, extensive blanket peatlands, upland grasslands, pastures, hay meadows and deciduous woodland. Altitude ranges from 290 to 850 m. Moor House - Upper Teesdale is part of the Environmental Change Network (ECN) whcih is the UK's long-term environmental monitoring programme. Full details about this dataset can be found at https://doi.org/10.5285/b36357bd-988c-41fa-a3a8-3b21cef5f0b6

The data contain solar radiation, air temperature, temperature depression, wind velocity, wind direction and rainfall from Automatic Weather Stations (AWS) located at Moor House and Helbeck, Cumbria, UK. This data contain a mixture of hourly and daily readings. The data were collected between July 1974 and February 1987 at Moor House and between May 1974 and October 1983 at Helbeck. Full details about this dataset can be found at https://doi.org/10.5285/77df4b96-c5e4-419a-b7cf-fc1d9a05c61c

This dataset contains daily water discharge data from Trout Beck in the Moor House-Upper Teesdale National Nature Reserve in the northern Pennines, Cumbria, UK. Data were automatically recorded at a gauging station located on a weir on the Trout Beck and collated by a variety of staff at the Moor House Research Station on behalf of the Wear and Tees River Board and the National Rivers Authority. Full details about this dataset can be found at https://doi.org/10.5285/9e044339-dd06-4f69-b93c-40a79562931c

The dataset comprises of plant species recorded from plots located within the Moor House National Nature Reserve, with associated plot information such as slope and aspect, also peat depth. The sampling strategy was based on a grid, using 2 x 2 metre square plots. The majority of the plots were recorded in the summers of 2008 and 2009 by surveyors employed by the Centre for Ecology & Hydrology. Full details about this dataset can be found at https://doi.org/10.5285/7a7d08e3-48e2-4aad-855b-9d6767b9ae9b

The Airborne Research & Survey Facility (ARSF, formerly Airborne Remote Sensing Facility) is managed by NERC Scientific Services and Programme Management. It provides the UK environmental science community, and other potential users, with the means to obtain remotely-sensed data in support of research, survey and monitoring programmes. The ARSF is a unique service providing environmental researchers, engineers and surveyors with synoptic analogue and digital imagery of high spatial and spectral resolution.The NEODC holds the entire archive of Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) data acquired by the NERC ARSF. High-resolution scanned digital versions of the entire collection of analogue photographs are now also available as well as selected LiDAR-derived elevation and terrain models for selected sites flown using the sensor.

Map service of soil types, geology and vegetation in the Moor House region of the Moor House - Upper Teesdale National Nature Reserve. The site lies in the North Pennine uplands of England and has an area of 74 km2. It is England's highest and largest terrestrial National Nature Reserve (NNR), a UNESCO Biosphere Reserve and a European Special Protection Area. Habitats include exposed summits, extensive blanket peatlands, upland grasslands, pastures, hay meadows and deciduous woodland. Altitude ranges from 290 to 850 m. Moor House - Upper Teesdale is part of the Environmental Change Network (ECN) which is the UK's long-term environmental monitoring programme.

73 ecosystem services variables for 11 Environmental Change Network (ECN) sites throughout the UK mainland. The variables cover provisioning (food, fibre, fuel, genetic resources, biochemical and pharmaceuticals, ornamental), regulating (air quality regulation, climate regulation, water regulation, erosion, human diseases, biological control, pollination, natural hazard, other hazards) and cultural (cultural diversity) service types. The list of variables was agreed at an ECN site manager's workshop as representative of the high level categories defined by the Millennium Ecosystem Assessment. The variables were calculated from data from three sources (i) data collected for the ECN to standard ECN protocols, (ii) data obtained by site managers from a variety of other sources for their site and (iii) expert knowledge of site managers. The data were from a single year (usually 2009) or were averages of annual measurements. Established in 1992, the ECN is the UK's long-term environmental monitoring and research programme and makes regular measurements of air, soil, water and a range of animals and plants across a network of sites to determine how and why the natural environment is changing. The ECN is a multi-agency programme sponsored by a consortium of UK government departments and agencies. Full details about this dataset can be found at https://doi.org/10.5285/2c5f823d-0dca-4e66-b021-de3e81131979