This dataset contains details of the phenotypes (height, bud set and budburst) and genotypes (via SNP array) of trees from a common garden multi-species pine (Pinus sylvestris, Pinus mugo and Pinus uncinata) glasshouse trial between 2010 and 2013. Full details about this dataset can be found at https://doi.org/10.5285/55118e26-cf5c-41d6-9157-738fce6bdddf

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

This dataset contains ~50,000 single nucleotide polymorphisms (SNPs, DNA mutations) for Scots pine (Pinus sylvestris) and closely related members of the Pinus mugo complex, which were selected for inclusion on a 50K SNP Axiom array Full details about this dataset can be found at https://doi.org/10.5285/cbaa464a-ac18-42bf-8518-c746d8d97270

This dataset contains data pertaining to the phenotypes (height and budburst) and genotypes (via SNP array) for a subset of trees from a long term multi-site Scots pine experimental trial. Full details about this dataset can be found at https://doi.org/10.5285/52248442-a50f-4fc0-a73e-31c61cd27df9

Microgravity data collected at Uturuncu Volcano located in the Altiplano-Puna Volcanic Complex, central Andes, in November 2022. Raw data collected along a survey line spanning from Laguna Colorada to Laguna Verde using a field gravimeter. All data have been preprocessed and corrected for tidal and drift effects. Data are reported with respect to reference station UBAS located to the west of Uturuncu near the Laguna Colorada.

Data from the purpose-built dense raingauge network was established in Somerset as part of the HYREX project. HYREX (Hydrological Radar Experiment) was a NERC (Natural Environment Research Council) special topic running from May 1993 to April 1997. Field experiments with an emphasis on radar, plus related interpretation and modelling, were carried out to investigate the short term forecasting and hydrological implications of precipitation.

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

Computed air parcel trajectory used for campaign support during the Atmospheric Chemistry Studies in the Oceanic Environment (ACSOE) programme.

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