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This data is NERC-funded but not held by the EIDC. This data is archived in the UK Data Service ReShare repository. This dataset contains material from work package 4.1 from the IWUN project: ‘A new green paradigm for wellbeing: an integrated approach to GBI (green and blue infrastructure) planning, health and social care’. The qualitative research for this work package took place between late 2017 and mid 2018. The central element of this research was a process of identifying greenspace interventions to improve wellbeing, and engaging stakeholders through a survey, interviews and focus groups to shortlist those interventions considered most practicable in Sheffield. The interviews and focus groups also discussed how such interventions could be implemented, what benefits were associated with them, and the processes involved in deciding whether or not to invest in the chosen actions. The dataset contains (a) anonymised interview and focus group transcripts to identify stakeholder preferences for greenspace interventions to improve wellbeing; (b) anonymised notes from associated public events; (c) notes from a thematic analysis of the interview and focus group material; (d) records of voting preferences from stakeholder groups in selecting possible greenspace interventions. This work was supported by the Natural Environment Research Council award number NE/N013565/1 as part of the Valuing Nature programme.
This data contains the strain and wind data collected for 21 trees in Wytham Woods, a mature temperate woodland in southern England, from September 2015 to June 2016. This data was collected in order to (a) extract the resonant frequencies of trees, (b) to estimate the critical wind speeds at which the trees would break and (c) to test a finite element model of tree-wind dynamics. The strain data was collected at 4Hz using two strain gauges per tree attached at 1.3metres on the trunk and approximately perpendicular to each other. The wind data provided were collected from the canopy walkway in Wytham Woods using a cup anemometer (Vector Instruments A100LK/5M) in winter and a Gill Sonic-1 in summer, the time resolution varies between these instruments. Local climate data, including long term wind data, are available from the Environmental Change Network (https://doi.org/10.5285/fc9bcd1c-e3fc-4c5a-b569-2fe62d40f2f5 or data.ecn.ac.uk). Full details about this dataset can be found at https://doi.org/10.5285/533d87d3-48c1-4c6e-9f2f-fda273ab45bc
This data collection results from abundance surveys of 7 species of weeds in ca. 500 lowland arable fields in 49 farms over three years. Each field was divided into large grids of 20x20 metre cells, and the density of seven species was estimated three times a year. The study is part of the NERC Rural Economy and Land Use (RELU) programme. In the context of changing external and internal pressures on UK agriculture, particularly those associated with the ongoing reform of the EU Common Agricultural Policy, it is imperative to determine whether all of the various dimensions of sustainability - including the relevant economic and environmental objectives as well as social and cultural values - can be integrated successfully at the farm and landscape levels. Although the ways in which economic, technological, and regulatory changes are likely to affect the profitability and management of farms of varying size are reasonably well understood, there is not the knowledge or understanding to predict the resulting effects on biodiversity. For example, the effect of changes in arable farming practices on field weeds and, in turn, on habitats and food supply required to sustain farm birds is a case in point. This knowledge is critical, however, if we are to understand the ecological consequences of changes in agricultural policy. Furthermore, it is also important if we are to design and justify changes in farming methods that can not only enhance nature conservation, but do this is ways that are practical and appealing from a farmer's point of view. This understanding is essential if we are to achieve an agriculture that is sustainable in both economic and environmental terms and is widely perceived to have social and cultural value. A consistent theme in all components of this research project is to understand the behaviour (of farmers, weeds or birds) and then use this information to produce predictive models. Whilst there have been a number of models of economic behaviour, weed populations and bird populations - including many by the research team here - the really novel component of this research is to integrate these within one framework. Farmer interviews on economic attitudes and preferences associated with and importance of different land-use objectives to lowland arable farmers are available at the UK Data Archive under study number 6728 (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).
The data comprise summary statistics for performance of a genotyping microarray for a test set of 87 samples for four pine species. The summary statistics comprise state (polymorphic, monomorphic), mean allele frequency and conversion rate, estimated for each locus as a mean across 87 sample genotypes. The array comprised 49,829 SNPs (single nucleotide polymorphisms) from several sources. The majority (N = 49,052) were obtained from transcriptome sequencing of four pine species: Pinus sylvestris, Pinus mugo, Pinus uncinata and Pinus uliginosa. The SNP set was filtered by the array manufacturer (Thermo Fisher) based on p-convert values signifying the SNP array quality, and a list of recommended and non-recommended SNP probes (avoiding SNPs with polymorphisms within 35 bp) was provided to the authors. These included SNPs that were common to all species and also SNPs fixed in one species and polymorphic within and among others. A further set of SNPs (N = 578) were included from candidate genes (N = 279), which had been resequenced in previous population genetic studies of the pine species. Variation in mitochondrial DNA (mtDNA) was targeted by inclusion of a set of mtDNA- specific SNPs (N = 14). Finally, a set of SNPs putatively associated with susceptibility to Dothistroma needle blight (discovered in Pinus radiata, European Nucleotide Archive accession numbers ERS1034542-53) were also included (N = 185). Full details about this dataset can be found at https://doi.org/10.5285/0ba33e96-67cb-4650-b2bd-6ee13fa7de97
The data set contains the qualitative results from fieldwork from the ‘sense of place’ and 'contemporary social representations' workpackage components of the WetlandLIFE project. Fieldwork included two discussant focus groups and thirty semi-structured interviews with specialist users of wetlands. The University of Brighton's social science qualitative fieldwork seeks to capture the different perspectives of people whose lives are intimately connected to particular English wetlands, in order to understand the range and diversity of wellbeing practices in these spaces. The target cohort are those groups of people, or organisations, that are particularly drawn to wetlands, or who could be expected to make regular use of these spaces, particularly for their health and wellbeing. Such Specialist Interest Groups (SIGs) would include birders, walkers, wildlife photographers, artists and anglers alongside educators, naturalists, spiritual practitioners and ecologists. They may not live close to the wetland sites but their field of interest, or sense of place, would be expected to include them. These interviews and focus groups took place at the case study sites in the Somerset Levels (Westhay Moor and Shapwick Heath), Bedfordshire (Priory Country Park and Millennium Country Park) and North Lincolnshire (Alkborough Flats) between January 2018 and September 2018. This data is NERC-funded but not held by the EIDC. This data is archived in the UK Data Service ReShare repository.
This dataset contains water flow velocity, discharge, and suspended sediment compositions of the Irrawaddy (Ayeyarwady) River at Pyay, Myanmar and the Salween (Thanlwin) River at Hpa-An, Myanmar. The suspended sediment samples and the hydrological data were collected both during peak monsoon conditions (August 2017 and August 2018) and peak dry season conditions (February 2018 and May 2019). Water velocity was measured using Acoustic Doppler Current Profiler (ADCP) while collecting suspended sediment samples at various depths in the river. Additional flow velocity data was collected while laterally crossing the river channel from bank to bank, and was used to calculate total river discharge at these sites. The dataset includes suspended sediment concentrations, particulate organic carbon concentrations, and particle size distributions of sediment samples collected at various depths and locations in the two river channels. Full details about this dataset can be found at https://doi.org/10.5285/86f17d61-141f-4500-9aa5-26a82aef0b33
This dataset includes fully anonymised participant information, fully anonymised interview transcripts from audio-recorded interviews with 55 urban residents aged 17 to 86 years living in a UK northern city, and participants' anonymised drawings of 'feel good nature places'. The data were collected in seeking to understand cultures and values of nature and mental wellbeing among urban residents, particularly in the context of cultural background, gender, age, urban deprivation and levels of mental health. The project population sample was weighted to include more people of Black, Asian and Ethnic Minority background and more people living in an area of urban deprivation.
The borehole information pack from borehole GGA03r, site 01 of the UK Geoenergy Observatories (UKGEOS) Glasgow facility. This release from the British Geological Survey (BGS) contains BGS and Drillers’ logs, a listing of archived rock chips and a descriptive report. The environmental baseline characterisation and monitoring borehole was drilled between 20th June and 15th August 2019 (start of drilling to casing installation date) to 41.72 m drilled depth. The cased borehole was hydrogeologically tested in January 2020. Rock chip samples were taken during the drilling process and have been archived at the National Geological Repository at BGS Keyworth. Further details can be found in the accompanying report http://nora.nerc.ac.uk/id/eprint/528077 DOI https://dx.doi.org/10.5285/7971dbc3-d4a3-4f74-90a9-89b46d39ad49
The borehole information pack from borehole GGA04, site 02 of the UK Geoenergy Observatories (UKGEOS) Glasgow facility. This release from the British Geological Survey (BGS) contains BGS and Drillers’ logs, cased hole and open hole wireline data, a listing of archived rock chips and a descriptive report. The mine water characterisation and monitoring borehole was drilled between 28th June and 22nd October 2019 (start of drilling to casing installation date) to 53.63 m drilled depth. The cased borehole was wireline logged and hydrogeologically tested in January 2020. Rock chip samples were taken during the drilling process and have been archived at the National Geological Repository at BGS Keyworth. Further details can be found in the accompanying report http://nora.nerc.ac.uk/id/eprint/528078 DOI https://dx.doi.org/10.5285/83ab3481-45d9-475d-8814-008edc9fb1cb
The borehole information pack from borehole GGA01, site 01 of the UK Geoenergy Observatories (UKGEOS) Glasgow facility. This release from the British Geological Survey (BGS) contains BGS and Drillers’ logs, cased hole wireline data, a listing of archived rock chips and a descriptive report. The mine water characterisation and monitoring borehole was drilled between 11th June and 21st August 2019 (start of drilling to casing installation date) to 52 m drilled depth. The cased borehole was wireline logged and hydrogeologically tested in January 2020. Rock chip samples were taken during the drilling process and have been archived at the National Geological Repository at BGS Keyworth. Further details can be found in the accompanying report http://nora.nerc.ac.uk/id/eprint/528075, DOI https://dx.doi.org/10.5285/0d496c68-f79b-4956-8cd2-4970d1e86145.