Data on worker behaviour, experimental treatment and sampling, queen longevity, queen and colony fecundity and queen morphometrics collected for an experiment manipulating costs of reproduction in bumble bee queens (Bombus terrestris). Full details about this dataset can be found at https://doi.org/10.5285/8efcd65a-afd7-4857-a107-9820c732b62a

Data on developmental time, fecundity, morphometrics, experimental treatment, mating and sampling from an experiment manipulating larval nutrition in female fruit flies (Drosophila melanogaster). Full details about this dataset can be found at https://doi.org/10.5285/90dee4ce-187c-4094-8895-b079d29922f5

The data consists of names, types, voltages, constraint status and national grid references for 56,865 electricity substations (33 kV or larger) in Great Britain in 2018. It was compiled from information on individual transmission or distribution network operator websites and interpreted to produce a classification of constraint status (where applicable). The data set was compiled from information on individual transmission or distribution network operator websites. The work was funded by the Natural Environment Research Council Award NE/M019713/1. Full details about this dataset can be found at https://doi.org/10.5285/0eed5c99-f409-4329-a98e-47f496bb88a2

The data is from four three-component broadband seismometers deployed along the lower east rift zone during the 2018 Kilauea eruption for four months. The instruments were deployed towards the end of July before the eruption ceased, and were placed in locations that would complement the existing USGS seismic network.

GIS-based computer generated real-time landscape models, and other computer generated static images were produced and used alongside photographs in more in-depth interviews and focus groups. (Some elements of this dataset are not part of this data submission due to copyright restrictions, though images may be included in the report). The study is part of the NERC Rural Economy and Land Use (RELU) programme. Future policies are likely to encourage more land use under energy crops: principally willow, grown as short rotation coppice, and a tall exotic grass Miscanthus. These crops will contribute to the UK's commitment to reduce CO2 emissions. However, it is not clear how decisions about appropriate areas for growing the crops, based on climate, soil and water, should be balanced against impacts on the landscape, social acceptance, biodiversity and the rural economy. This project integrated social, economic, hydrological and biodiversity studies in an interdisciplinary approach to assessing the impact of converting land to Miscanthus grass and short-rotation coppice (SRC) willows. Two contrasting farming systems were focused on: the arable-dominated East Midlands; and grassland-dominated South West England. The public attitudes questionnaire data from this study are available at the UK Data Archive under study number 6615 (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 is the fruit set and marketable fruit set (percentage and success: failure) of commercial raspberry plants under four different pollination treatments. The data also includes fruit measurements (weight in grams and length and width in mms) of these fruit and the number of seeds per fruit for a subset of the collected fruits. Full details about this dataset can be found at https://doi.org/10.5285/de5b4f33-f679-4798-8daf-51a314e78204

This dataset contains information about the locations and local environmental conditions of 123 Malaise trap samples collected in November-December 2021 in the 908 km2 forested ‘leakage belt’ buffer zone of the Gola Rainforest National Park (GRNP) in eastern Sierra Leone, where cocoa, a driver of deforestation, is the main cash crop. Each trap was set out for 5 days with >99% ethanol. The samples were transported from Sierra Leone to the UK, where they have been sent for metabarcoding for arthropods (using Leray2 PCR primers). The work was supported by the Natural Environment Research Council (Grant NE/S014063/1). Full details about this dataset can be found at https://doi.org/10.5285/161315e4-71c1-481d-906c-149ab2e9705c

This dataset contains riverine hydrochemical data generated at monthly intervals between 2010 and 2016 from 20 sites across the River Wensum catchment, UK. Data were obtained via manual grab sampling of river water from each of the 20 locations across the catchment, followed by subsequent laboratory analysis to determine concentrations of nutrients, carbon, major ions and suspended solids. Full details about this dataset can be found at https://doi.org/10.5285/71ddb087-59e6-432a-8d3e-72cbce251ee9

This is a dataset generated from information extracted from previously published studies, for the purpose of a meta-analysis investigating fitness benefits of different migratory strategies in partially migratory populations. Each line of data includes a mean and associated variance for a given fitness metric for both migrants and residents extracted from a study, in addition to information concerning population location, study species, type of fitness metric, year data were collected, and details on the publication from which the data were obtained. Data were collected as part of a NERC-funded PhD project, grant number NE/L002582/1. Full details about this dataset can be found at https://doi.org/10.5285/1a4e8d59-e112-4de6-a06b-9ea47ff15815

Data presented here include imagery with ground-sampling distances of 3 cm and 7 cm for March 2019, May 2019 and July 2019. Also included are the corresponding ground-truth training and verification data presented as shapefiles, as well as the classification output and other data relevant to the project such as the width of floral units. The imagery was acquired by Spectrum Aviation using A6D-100c (50mm) Hasselblad cameras with bayer filters, mounted on a Sky Arrow 650 manned aircraft. Ground-truth data for training maximum likelihood classifications and for verifying the accuracy of classifications were gathered within eight days of imagery acquisition. Ground-truth data were acquired from sown field margins and hedgerow surrounding one study field. This dataset was acquired from March to July 2019 at a farm in Northamptonshire, UK. Data were acquired as part of a NERC funded iCASE PhD studentship (NERC grant NE/N014472/1) based at the University of East Anglia and in collaboration with Hutchinsons Ltd. The aim of the research was to map the floral units of five nectar-rich flowering plant species using very high resolution multispectral imagery. Each species constitutes an important food resource for pollinators. The plant species in question were Prunus spinosa, Crataegus monogyna, Silene dioica, Centaurea nigra and Rubus fruticosus. Full details about this dataset can be found at https://doi.org/10.5285/cf68be0c-e969-4190-8ec6-abeedb51b42c