These data comprise collection records of Heliconius butterfly samples collected in the Chocó-Darien ecoregion between the Andes and the Pacific in Ecuador and Colombia, and the Pacific coast of the Darien region of Panama. Samples were collected over five sampling trips between 2014 and 2016. Data were collected for a study of clinal variation across this region in Heliconius erato and Heliconius melpomene, so focus on these two species. However, in most cases all observed Heliconius species were collected. The dataset includes photographs of the wings of most of the specimens, which were used for an analysis of colour and pattern variation. Many of these individuals also have genomic information available for them on the European Nucleotide Archive (ENA) - the data includes ENA accession numbers. Data were collected as part of a NERC fellowship project (NE/K008498/1). Full details about this dataset can be found at https://doi.org/10.5285/cb23c552-caee-4221-bdd3-83b172139ae1

The data consists of potential activity of enzymes released by microorganisms in soils of old growth forests in the Central Amazon. Soils were sampled in January 2018, seven months after nutrient addition in a large-scale nutrient fertilisation experiment. Enzyme activities are given as a mean of the microbial community per plot, where five points inside each plot were sampled in the 0-5 cm and 5-10 cm soil layers. The dataset depicts the potential activities of the following enzymes: ß-glucosidase, N-acetyl ß-glucosaminidase and phosphomonoesterase at two different soil depths. Samples were collected at the AFEX project area in Manaus, Brazil at the Biological Dynamics of Forest Fragments Project (BDFFP/ INPA). The study was funded by NERC, BDFFP (logistical support) and Brazilian government (student scholarship). Full details about this dataset can be found at https://doi.org/10.5285/b9a2523e-09ba-43c7-acfa-a894a231b133

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 dataset contains groundwater levels from 10 boreholes located in the Gandak Basin, Bihar, North India. The data was collected using automatic level loggers recording at 15-minute intervals between April 2017 and February 2019. This data set quantifies the effects of groundwater abstraction on, and seasonal changes in groundwater levels. The data were collected as part of the NERC sponsored project Coupled Human and Natural Systems Environment (CHANSE), grant number NE/N01670X/1 Full details about this dataset can be found at https://doi.org/10.5285/21df678b-6eb6-4559-9005-8eb7953b48ef

This dataset consists of a 1km resolution raster version of the Land Cover Map 2000 for Great Britain. Each 1km pixel represents the dominant target class (or 'sub class') across the 1km area. The target classes broadly represent Broad Habitats (see below). The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2000. LCM2000 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB). Like the earlier 1990 products, LCM2000 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2000 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2000 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Full details about this dataset can be found at https://doi.org/10.5285/abff8409-0995-48d2-9303-468e1a9fe3df

1 km gridded estimates of daily and monthly rainfall for Great-Britain and Northern Ireland (together with approximately 3000 km2 of catchment in the Republic of Ireland) from 1890 to 2017. The rainfall estimates are derived from the Met Office national database of observed precipitation. To derive the estimates, monthly and daily (when complete month available) precipitation totals from the UK rain gauge network are used. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall, was used to generate the daily and monthly estimates. The estimated rainfall on a given day refers to the rainfall amount precipitated in 24 hours between 9am on that day until 9am on the following day. The CEH-GEAR dataset has been developed according to the guidance provided in BS 7843-4:2012. Full details about this dataset can be found at https://doi.org/10.5285/ee9ab43d-a4fe-4e73-afd5-cd4fc4c82556

The dataset contains model output from an agricultural land use model at kilometre scale resolution over Great Britain (GB) for four different climate and policy scenarios. Specifically, arable area is modelled for with or without a climate tipping point (standard (medium emissions scenario SRES-A1B) climate change vs Atlantic Meridional Overturning Circulation (AMOC) collapse) and with or without widespread irrigation use for farmers from 2000 to 2089. Full details about this dataset can be found at https://doi.org/10.5285/e1c1dbcf-2f37-429b-af19-a730f98600f6

This dataset consists of soil data for 64 field sites on paired farm sites, with 29 variables measured for soil texture and structural condition, aggregate stability, organic matter content, soil shear strength, fuel consumption, work rate, infiltration rate, water quality and hydrological condition (HOST) data. The study is part of the NERC Rural Economy and Land Use (RELU) programme. A move to organic farming can have significant effects on wildlife, soil and water quality, as well as changing the ways in which food is supplied, the economics of farm business and indeed the attitudes of farmers themselves. Two key questions were addressed in the SCALE project: what causes organic farms to be arranged in clusters at local, regional and national scales, rather than be spread more evenly throughout the landscape; and how do the ecological, hydrological, socio-economic and cultural impacts of organic farming vary due to neighbourhood effects at a variety of scales. The research was undertaken in 2006-2007 in two study sites: one in the English Midlands, and one in southern England. Both are sites in which organic farming has a 'strong' local presence, which we defined as 10 per cent or more organically managed land within a 10 km radius. Potential organic farms were identified through membership lists of organic farmers provided by two certification bodies (the Soil Association and the Organic Farmers and Growers). Most who were currently farming (i.e. their listing was not out of date) agreed to participate. Conventional farms were identified through telephone listings. Respondents' farms ranged in size from 40 to 3000 acres, with the majority farming between 100 and 1000 acres. Most were mixed crop-livestock farmers, with dairy most common in the southern site, and beef and/or sheep mixed with arable in the Midlands. In total, 48 farms were studied, of which 21 were organic farmers. No respondent had converted from organic to conventional production, whereas 17 had converted from conventional to organic farming. Twelve of the conventional farmers defined themselves as practicing low input agriculture. Farmer interview data from this study are available at the UK Data Archive under study number 6761 (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 dataset contains prey items of common guillemot Uria aalge and razorbill Alca torda observed during the 2018 breeding season at East Caithness Special Protection Area (SPA), Buchan Ness to Collieston Coast SPA and Isle of May National Nature Reserve, off the east coast of Scotland. Diet of these two species has been studied on the Isle of May since the 1980s (Harris & Wanless 1985, 1986; Wilson et al 2004; Daunt et al. 2008; Thaxter et al 2013). To our knowledge, only two previous studies of diet has been undertaken at Buchan Ness to Collieston Coast SPA (in 2006, 6km to the north of the site used in this study; Anderson et al. 2014; and in 2017, using a similar protocol as in 2018; Daunt et al. 2017), and one previous study of diet has been undertaken at East Caithness SPA (2017; Daunt et al. 2017). Full details about this dataset can be found at https://doi.org/10.5285/d7164910-17cb-44cd-bccd-6a9c31b6ed70

This dataset consists of the 1km raster, dominant aggregate class version of the Land Cover Map 2015 (LCM2015) for Great Britain. The 1km dominant coverage product is based on the 1km percentage product and reports the aggregated habitat class with the highest percentage cover for each 1km pixel. The 10 aggregate classes are groupings of 21 target classes, which are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. The aggregate classes group some of the more specialised classes into more general categories. For example, the five coastal classes in the target class are grouped into a single aggregate coastal class. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM2015 is a land cover map of the UK which was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. It is the latest in a series of land cover maps, which began with Land Cover Map of Great Britain (LCMGB) in 1990 (LCMGB is now often referred to as LCM1990), and was followed by Land Cover Maps for 2000 and 2007 (LCM2000 and LCM2007 respectively), both of which covered the entire UK. LCM2015 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the CEH web site and the LCM2015 Dataset documentation) to select the product most suited to their needs. Full details about this dataset can be found at https://doi.org/10.5285/711c8dc1-0f4e-42ad-a703-8b5d19c92247