This dataset consists of an ecology-focused survey of stillwaters along the rivers Yure and Swale and sediment flux measurements recorded at sites along the river Esk. The dataset results from a study which was part of the Rural Economy and Land Use (RELU) programme. The project analysed the complex network of natural and socio-economic relationships around angling in the river environment, including institutions of governance and land use practices at a range of interconnected scales. The sustainability, integrity and ecological value of river catchments are currently major issues for science. The management of freshwaters and their ecologies requires addressing processes that work across the boundaries between the natural environment, economy and society. This research focused upon these cross-cutting processes in an interdisciplinary, holistic assessment of river environments through the case of angling. Angling benefits from and influences river quality, design and management. It also links urban and rural environments and is an economic driver for the rural economy, involving about 4 million people in England and Wales and contributing 6 billion pounds to the economy through freshwater angling alone. This research aimed to provide insights into how environmental and socio-economic drivers for rural change work. This project therefore aimed to identify and analyse the complex network of influences and feedbacks around angling in the rural environment. These include natural and socio-economic influences, interdisciplinary research from both natural and social science disciplines (aquatic ecology, geomorphology, anthropology, sociology, human geography), as well as stakeholders from government, NGOs and the local community. This project focused upon three rivers in northern England - the Esk, Ure and Swale - in the course of an integrated and fine-grained study. The postal survey and business interviews from this study are available at the UK Data Archive under study number 6580 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

This data contains the time series flow discharge results of hydrological simulation of the River Trent at Colwick using UKCP09 Weather Generator inputs for a variety of time slices and emissions scenarios. The Weather Generator (WG) inputs were run on a hydrological model (Leathard et al., unpublished), calibrated using the observed record 1961-2002. Each simulation is derived from one-hundred, 30-year time series of weather at the WG location 4400355 for Control, Low, Medium and High emissions scenarios for the 2020s, 2030s, 2040s, 2050s and 2080s time slices. The datasets include the relevant accompanying input WG data. Full details about this dataset can be found at https://doi.org/10.5285/986d3df3-d9bf-42eb-8e18-850b8d54f37b

Hydrological monitoring data for 55 years from 1967 to 2022 for the Coalburn catchment (1.5 km2). The catchment is located in Northern England within Kielder forest, Northumberland, and is the longest running forest research catchment in the UK. In 1972/73 the upland grassland was ploughed and planted with a conifer forest. The trees are now mature and around 30% of the catchment has been felled. From 1967 to 1993 a mixture of hourly and daily data is available and from 1993 onwards all the data is hourly. The data consist of precipitation, discharge, potential evapotranspiration, other meteorological data and snow depths. The data has been extensively quality controlled and can be used for hydrological modelling or data analysis to understand the effects of forests on river flows. Full details about this dataset can be found at https://doi.org/10.5285/88d72918-324e-42a8-a4f2-bbbc322814ff

Hourly precipitation (mm) recorded at distributed points around Kampala between April 2019 and March 2020. Only timestamps where data were available from all sensors have been included. There are 8094 records in total and no missing values. Timestamps are recorded as “YYYY-MM-DD hh:mm:ss”. The geographic coordinates of the sensors are provided in GeoJSON format. The column names in the CSV file correspond to the “id” field in the GeoJSON file. Full details about this dataset can be found at https://doi.org/10.5285/3df031ad-34ec-4abc-8528-f8f20bad12b8

Data comprise modelled flood extents for the Kampala district produced by simulating rainfall events over a 5m Digital Elevation Model (DEM) using a 2D finite-volume hydrodynamic model. The DEM was obtained from Makerere University and rainfall events were sampled across a range of depths and durations (for 20, 40, 60, 80 and 100 mm of rainfall over 1, 3 and 6 hours using flood depth thresholds of 0.1, 0.2 and 0.3 mm). The effects of infiltration were included within green areas based on spatial data obtained from Makerere University. Maximum depths were converted into extents using various thresholds. Full details about this dataset can be found at https://doi.org/10.5285/e53dea2e-cb25-4f0f-b5f9-937eecf15aff

The dataset contains model output from the CityCAT hydrodynamic model showing maximum water depths in Jakarta, Indonesia, during the January/February 2007 flood. The hourly rainfall and hourly lateral inflow boundary conditions from rivers used to obtain the flooding depths are also included. Full details about this dataset can be found at https://doi.org/10.5285/8e58f0bb-3ff1-41e8-b8f4-380983ec68bc

The datasets originate from a field experiment designed to assess the mobility and longevity of DNA in a soil environment. We applied radish DNA - in the form of leaves (solid treatment) and PCR product (liquid treatment) to experimental plots in southern Iceland. We sampled the soil from different depths under the plots over a period of 15 months, looking for traces of radish DNA. The dataset encompasses 1) DNA metabarcoding data (plants) derived from soil samples; 2) a survey of extant plant communities; 3) a record of near-surface soil temperatures for the duration of the experiment; and 4) data quantifying the decomposition of radish leaves applied to the plots. The radish DNA was applied to six experimental plots in June 2023; soil samples were collected from below the plots in May 2024 (M24) and October 2024 (O24). DNA extraction and PCR was conducted by Dr Doreen Huang (University of Southampton). DNA from the soil samples was sequenced by a commercial third party (Novogene). Full details about this dataset can be found at https://doi.org/10.5285/7c6cbb74-96ad-4269-bad7-51025963ef23

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

The dataset collates the relative concentration of nearly 300 antimicrobial resistance (AMR) genes, and concentrations of polycyclic aromatic hydrocarbons (PAH) and potentially toxic elements (PTE; e.g., “metals”) found in soils across northeastern England during a sampling expedition in June 2016 by researchers at Newcastle University. Top soils (15 cm depths; “A” horizon) were obtained from 24 rural and urban locations around Newcastle upon Tyne, representing a spectrum of landscape conditions relative to anticipated PTE contamination. There are three files related to different types of data collected: antimicrobial resistance genes, metal concentrations and PAH concentrations. The high-throughput analysis of nearly 300 AMR genes include many resistance traits representing major antibiotic classes: aminoglycosides, beta lactams, FCA (fluoroquinolone, quinolone, chloramphenicol, florfenicol and amphenicol resistance genes), MLSB (macrolide, lincosamide, streptogramin B), tetracycline, vancomycin, sulphonamide, and efflux pumps. PAH data represent the US Environmental Protection Agency priority polycyclic aromatic hydrocarbons as one of the measures of pollution impact. The other measure of impact is based on levels of twelve PTE represented by “total” and “two bio-available” concentrations, based on three extraction methods. Elements included aluminium, arsenic, beryllium, cadmium, chromium, copper, iron, lead, mercury, nickel, phosphorus, and zinc. Full details about this dataset can be found at https://doi.org/10.5285/35b49db6-8522-4c6b-a779-820268292603

3D digital elevation models of Tsho Rolpa glacier lake, Nepal, generated from unmanned aerial vehicle (UAV) imagery, with a spatial resolution of 10 centimetres. It is combined with bathymetry data so that both the lakebed elevation (DTM) and the lake surface elevation (DSM) are obtained. Full details about this dataset can be found at https://doi.org/10.5285/8e483692-3b65-41d2-a7fd-5a3cd589a71c