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16 record(s)

 

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  • This dataset identified bacteria able to grow in the presence of several Antibiotics in a British agricultural soil, by DNA stable isotope probing (SIP). The dataset was created with samples of the 'heavy' and 'light' fractions of the treatments and also from control soils. The 16S rRNA genes from these samples were amplified and sequenced by barcoded Illumina sequencing. Polymerase chain reaction (PCR) primers 515FB (GTGYCAGCMGCCGCGGTAA) and 806RB (GGACTACNVGGGTWTCTAAT) from the Earth Microbiome project targeting the V4 region of the 16S rRNA gene (approximately 250 nucleotides) were used. Library preparation and sequencing was performed at the National Oceanographic Centre (NOC) of the University of Southampton, UK, following methodologies described by Caporaso et al. (2012). Samples were pooled in an equimolar concentration and sequenced on separate runs for MiSeq using a 2 bp x 300 bp paired end protocol.

  • LCM2007 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) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors. It also covers Northern Ireland and incorporates information derived from other ancillary datasets. LCM2007 was classified using a nomenclature corresponding to Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it records further detail where possible, incorporating land cover classes sought by other users. LCM2007 is produced in both vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Product consists of the dominant Target Class per 1km square, raster dataset (Northern Ireland only).

  • LCM2007 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) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors. It also covers Northern Ireland and incorporates information derived from other ancillary datasets. LCM2007 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, incorporating land cover classes sought by other users. LCM2007 is produced in both vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. This dataset consists of 1km percentages per Target Class. Northern Ireland only.

  • LCM2007 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) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors. It also covers Northern Ireland and incorporates information derived from other ancillary datasets. LCM2007 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, incorporating land cover classes sought by other users. LCM2007 is produced in both vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. This dataset consists of the 1km raster, percentage Aggregate Class. Northern Ireland only.

  • This data is NERC-funded but not held by the EIDC. This data is archived in the National Center for Biotechnology Information (NCBI). This dataset consists of paired-end Illumina MiSeq reads (with primers removed) generated from a 16S rRNA gene metabarcoding study. The majority of the samples processed were faecal samples from a British commercial pig unit (n = 372). Sequences derived from a synthetic mock bacterial community were also included as controls (n = 6)

  • Data includes impacts on root nodule biomass, stomatal conductance, injury rates, and N-fixation in the white clover cultivar (T. repens cv. crusader). An ozone-exposure experiment was conducted in solardomes during the spring and summer of year 2012 on modern clover (Trifolium spp.) cultivars. The effects of ozone pollution (30, 35, 40, 45, 52, 67 parts per billion (ppb) treatment means) on the growth and functioning of the clover cultivars was investigated. Both cultivars had positive increases in ozone-injury rates, although stomatal conductance was unaffected by ozone exposure. Reductions in root nodule biomass and nodule number occurred in white clover, and red clover displayed an increase in nodule density. Nitrogen fixation rates were suppressed in white clover, which could have important implications for the sustainability of managed pasture. The work was carried out as part of a NERC funded PhD. Project number NEC04456. Full details about this dataset can be found at https://doi.org/10.5285/b63fbb6c-5030-43a1-b0ea-160bb5a83078

  • Data includes raw shoot biomass and yield, production and gas exchange, nodulation and N-fixation and forage quality data, including relative and consumable food values. The impacts of ozone on the growth and functioning of high-sugar ryegrass pasture mesocosms was assessed in year 2013. Pasture mesocosms, containing perennial ryegrass (Lolium perenne L) and white clover (Trifolium repens L), were grown in the early spring and exposed to ozone in solardomes from late April 2013 to the end of September 2013. Ozone (30, 35, 40, 45, 52, 67 parts per billion (ppb) treatment means) had a large effect on the pasture mesocosms. The work was carried out as part of a NERC funded PhD. Project number NEC04456. Full details about this dataset can be found at https://doi.org/10.5285/e0bcdc39-ab79-413c-bf76-d6ffbc510f15

  • Illumina short-read sequence data from targeted capture of 434 genes, including 150 chemosensory genes, in 104 individuals distributed across eight host races of the pea aphid. This data is NERC-funded but not held by the EIDC. This data is archived in the European Bioinformatics Institute SRA (Sequence Read Archive) with project accession reference PRJEB6325

  • These data are NERC-funded but not held by the EIDC. These data are archived in the SAFE repository, hosted by Zenodo. This dataset contains calculated greenhouse gas fluxes and associated parameters from 56 static chambers that were installed within the Stability of Altered Forest Ecosystems (SAFE) landscape in Malaysian Borneo. Of the chosen ‘fragment’ in the SAFE design, all chambers were put in the 10 ha fragments. Four chambers each were placed in each of the two 10 ha plots in Logged Fragmented Forest (LFE), Fragment B, and Fragment E resulting in 8 per site. Additionally 12 chambers were installed in a 7-year old oil palm plantation, 8 in a young (2-year) old oil palm and 8 in a 12 – year old oil palm plantations. All 56 chambers were sampled for greenhouse gases (methane, nitrous oxide and carbon dioxide soil respiration) every two months over a two-year period from January 2015 to November 2016, resulting in 12 measurement occasions for each of the chambers. Other environmental parameters were measured during the time of chamber enclosure as possible explanatory variables for correlation with recorded greenhouse gas fluxes including soil and air temperature, soil moisture, soil mineral N (nitrate and ammonium).

  • This dataset comprises three gridded drought indicators based on remote sensing data for Europe. The data has a spatial resolution of 0.05 degree and a temporal resolution of 1 month for the period going from 2000 to 2015. The three drought indicators are: the Vegetation Condition Index (VCI) based on satellite product NDVI (Normalised Difference Vegetation Index); the Temperature Condition Index (TCI) based on remotely sensed LST (Land Surface Temperature); the Vegetation Health Index (VHI) which is a combination of VCI and TCI, calculated using MODIS products. Full details about this dataset can be found at https://doi.org/10.5285/4e0d0e50-2f9c-4647-864d-5c3b30bb5f4b