This dataset is in vitro validation of a potassium sensor that will be inserted into a plant stem. The dataset shows the sensitivity and selectivity of the fabricated potassium sensor. The data was obtained by measuring changes in electrical current with an increase in concentrations of the primary ion (K+) and interfering ion (Na+) to extract the sensitivity and selectivity, respectively using a semiconductor parameter analyser. K+ ion sensing data, measured directly inside a plant stem, are absent as the in vivo experiment should be optimised further. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/89998967-a974-4136-b650-b9af9f9d6969

This dataset contains urine frequency and volume data measured from tri-axial accelerometers on Welsh mountain ewes free-grazing two contrasting upland field sites (semi-improved and unimproved pasture) in North Wales, across two seasons each (spring and autumn). The data, were collected using tri-axial accelerometers glued to the hind of Welsh Mountain ewes to study the urination behaviour of free-grazing sheep. Using a Boolean algorithm, the characteristic squatting position that ewes exhibit upon urination was detected in the accelerometer data. Initially the performance of the accelerometers with sheep in urine collection pens was assessed. Data were collected on the volume of each urination event and recorded the time of each observed urination event. This initial data was used to assess whether the accelerometers and Boolean algorithm were successful in identifying urination events, but also to ascertain whether the time spent in the squatting position would correlate with the volume of urine produced (thus allowing the technique to be able to estimate urine volume from squatting time only in subsequent field deployments). Information on when, where and how often livestock urinate are key data to be able to assess the scale and nature of nitrogen pollution arising from grazed agroecosystems. Urine patches deposited by grazing livestock are large sources of emissions of the greenhouse gas, nitrous oxide, due to high concentrations of nitrogen deposited over relatively small areas. These data were collected in the NERC funded Uplands-N2O project (grant award: NE/M015351/1). Full details about this dataset can be found at https://doi.org/10.5285/127afd24-d2cd-457f-b837-2dd5d328f101

Half-hourly data from eight eddy covariance towers deployed in the Sevilleta Refuge (New Mexico, USA). The main sensors deployed were sonic anemometer, relative humidity sensor and carbon dioxide concentration sensor . They were deployed and maintained by Fabio Boschetti and Andrew Cunliffe (University of Exeter). The data were collected to test the new design of eddy covariance towers and investigate the spatial variability of fluxes. Data were collected from 2018-11-01 to 2019-11-01. The data contains very few small gaps due to maintenance. Half-hourly data were gap-filled using code published on GitHub. The research was funded through NERC grant reference NE/R00062X/1 - "Do dryland ecosystems control variability and recent trends in the land CO2 sink?" Full details about this dataset can be found at https://doi.org/10.5285/e96466c3-5b67-41b0-9252-8f8f393807d7

This dataset contains high-frequency water quality measurements taken at multiple sites in the River Frome catchment area, Dorset, UK. Water quality data consists of a mixture of 30-min sensor readings and frequent grab samples. Also included are high-frequency discharge readings at Environment Agency (EA) managed flow gauging stations and at sewage treatment work (STW) effluents. These measurements were taken between 12/08/2022 and 14/09/2022 inclusive. Measurements were recorded at multiple river sites, boreholes, and STW effluents. All sites are contained within the catchment area between the Environment Agency gauging stations located at Dorchester and East Stoke i.e., the lower part of the River Frome. In total, 24 monitoring sites exist. The data were collected for PhD project “Supporting river water-quality management by high-resolution modelling: a case study in a lowland permeable chalk catchment” awarded to Thomas Homan and funded under GW4 FRESH CDT, supported by the Natural Environment Research Council (Grant NE/R0115241). Water quality data were collected by Thomas Homan (PhD candidate at the University of Bath). Wessex Water Ltd. provided high frequency measurements of ammonium and discharge at their STW sites, borehole water quality measurements, as well as high-frequency nitrate measurements at sites: Louds Mill and East Stoke. The Environment Agency discharge data were provided under the terms of the Open Government Licence. Full details about this dataset can be found at https://doi.org/10.5285/ccaf2871-1a1a-4cb4-aadf-883fb984a90f

This dataset contains the raw data from GNSS/INS (Global Navigation Satellite System/Inertial Navigation System) buoys deployed during the 2019-2020 MOSAiC expedition. These buoys recorded the data from GNSS and Sensors. The raw GNSS data contain time, latitude, longitude, velocity, and fix type. The raw Sensors data contain time, acceleration, gyroscope, magnetometer, and temperature. These data were sampled at 10 Hz. The original data was in ANPP format (see advancednavigation.com), which have been converted to structured ASCII formats (such as RINEX, CSV) using Spatial Manager software. The buoys were assembled by the University of Huddersfield team and the deployment was done by the MOSAiC ice team throughout the expedition. This work was funded by NERC MOSAiC program NE/S002545/1.

These datasets show how lake water-pressure fluctuated through time over several months in seasonally-frozen catchments in winter. These catchments were in three settings: the lowland Finnish Arctic, an alpine valley and a high cirque in Switzerland. The water-pressure data are accompanied by water temperature and (except for Orajarvi), ground temperature for the same periods. Together, they were used to detect and quantify the water content of snow falling on the lake surfaces. The locations, method of data collection and analysis and the results are described in detail in Pritchard, H. D., Farinotti, D., & Colwell, S. (2021). This work was funded by Natural Environment Research Council (UK) core funding to the British Antarctic Survey, and a fellowship from the Swiss Federal Institute for Forest, Snow and Landscape Research WSL, CH-8903 Birmensdorf, Switzerland.