This dataset contains information on the location of the count, some environmental variables, and the number of insects of each type counted for the Flower-Insect Timed Count survey as part of the UK Pollinator Monitoring Scheme (PoMS). It covers the years 2017 to 2020 (note that 2017 was a pilot year and has less data than subsequent years). This is version 2 of the dataset; the previous version contained some duplicate data rows, which have been deduplicated in version 2. The “public” FIT Count asks volunteer citizen scientists to count the number of insects, identified into broad taxon groups, seen landing on the flowers of a particular target plant within a 50 cm × 50 cm quadrat during a period of ten minutes. The “1 km square” FIT Count uses the same methodology, but is carried out by PoMS volunteers and staff as part of the PoMS 1 km square survey, which takes place within a randomly allocated set of 1 km squares in England, Scotland and Wales, and also gathers data on pan-trapped insects (see separate dataset). The UK Pollinator Monitoring Scheme is a partnership between the UK Centre for Ecology & Hydrology, Bumblebee Conservation Trust, Butterfly Conservation, British Trust for Ornithology, Hymettus, Natural History Museum, University of Reading and University of Leeds, working with the Bees, Wasps and Ants Recording Society, wider stakeholders and volunteer networks. PoMS is jointly funded by Defra, the Welsh and Scottish Governments, JNCC and project partners. Full details about this dataset can be found at https://doi.org/10.5285/13aed7ac-334f-4bb7-b476-4f1c3da45a13

[THIS DATASET HAS BEEN WITHDRAWN]. This dataset contains information on the location of the count, some environmental variables, and the number of insects of each type counted for the Flower-Insect Timed (FIT) Count survey as part of the UK Pollinator Monitoring Scheme (PoMS). It covers the years 2017 to 2020 (note that 2017 was a pilot year and has less data than subsequent years). The “public” FIT Count asks volunteer citizen scientists to count the number of insects, identified into broad taxon groups, seen landing on the flowers of a particular target plant within a 50 cm × 50 cm quadrat during a period of ten minutes. The “1 km square” FIT Count uses the same methodology, but is carried out by PoMS volunteers and staff as part of the PoMS 1 km square survey, which takes place within a randomly allocated set of 1 km squares in England, Scotland and Wales, and also gathers data on pan-trapped insects. The UK Pollinator Monitoring Scheme is a partnership between the UK Centre for Ecology & Hydrology, Bumblebee Conservation Trust, Butterfly Conservation, British Trust for Ornithology, Hymettus, Natural History Museum, University of Reading and University of Leeds, working with the Bees, Wasps and Ants Recording Society, wider stakeholders and volunteer networks. PoMS is jointly funded by Defra, the Welsh and Scottish Governments, JNCC and project partners. Full details about this dataset can be found at https://doi.org/10.5285/61b7df6e-4e27-460a-84a5-c100f0dc919f

This dataset contains faecal bacteria data (mainly E. coli but also some Intestinal enterococci) for the River Wharfe and its tributaries in Yorkshire. They were collected and analysed as part of citizen science projects (termed the Ashlands and iWharfe projects) designed to assess the extent to which the river is contaminated by sewage effluent and capable of meeting national bathing water standards. In the Ashlands project, samples were taken upstream and downstream of the Sewage Treatment Works in Ilkley. The data were used in a successful application by the Ilkley Clean River Group for the river in Ilkley to be designated as a bathing water, the first running water site so designated in the UK. The iWharfe data are from samples collected on two occasions (2020 and 2021) at sites along the full length of the river and selected tributaries and show in particular the level of faecal bacteria contamination at recreational sites along the river. Overall, the data show not only the importance of sewage effluent in causing high concentrations of faecal bacteria in the river but also the role of agricultural sources presumed to be from farm livestock. Full details about this dataset can be found at https://doi.org/10.5285/a8f553c0-8fff-4b23-9461-fd59d0fd2ed3