This dataset is available for use for non-commercial purposes only on request as AfA248 dataset Groundwater Vulnerability Maps (2017). For commercial use please contact the British Geological Survey.

The Groundwater Vulnerability Maps show the vulnerability of groundwater to a pollutant discharged at ground level based on the hydrological, geological, hydrogeological and soil properties within a single square kilometre. The 2017 publication has updated the groundwater vulnerability maps to reflect improvements in data mapping, modelling capability and understanding of the factors affecting vulnerability Two map products are available: • The combined groundwater vulnerability map. This product is designed for technical specialists due to the complex nature of the legend which displays groundwater vulnerability (High, Medium, Low), the type of aquifer (bedrock and/or superficial) and aquifer designation status (Principal, Secondary, Unproductive). These maps require that the user is able to understand the vulnerability assessment and interpret the individual components of the legend.

• The simplified groundwater vulnerability map. This was developed for non-specialists who need to know the overall risk to groundwater but do not have extensive hydrogeological knowledge or the time to interpret the underlying data. The map has five risk categories (High, Medium-High, Medium, Medium-Low and Low) based on the likelihood of a pollutant reaching the groundwater (i.e. the vulnerability), the types of aquifer present and the potential impact (i.e. the aquifer designation status). The two maps also identify areas where solution features that enable rapid movement of a pollutant may be present (identified as stippled areas) and areas where additional local information affecting vulnerability is held by the Environment Agency (identified as dashed areas). Attribution statement: © Environment Agency copyright and/or database right 2017. All rights reserved.Derived from 1:50k scale BGS Digital Data under Licence 2011/057 British Geological Survey. © NERC.

This dataset is about book series. It has 1 row and is filtered where the books is Magic map. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Data for the 8-parent MAGIC map, including the genetic map, genetic data for the founding lines and founder population, and the imputed underlying genotypes.

I-MAGIC (Interactive Map-Assisted Generation of ICD Codes) is an interactive tool to demonstrate how the SNOMED CT to ICD-10-CM map can be used to generate ICD-10-CM codes from clinical problems coded in SNOMED CT.

These datasets contain phenotypic and genotypic data of a MAGIC (Multiparent Advanced Generation Inter-Crosses) population of common bean (Phaseolus vulgaris L.), developed by inter-crossing of eight Mesoamerican elite breeding lines. The main goal for this population is to be used for applications in breeding and breeding tool development, which will support efforts to develop climate resilient germplasm, as well as information for basic research questions aiming to uncover the genetic basis of important agronomic traits. The raw phenotypic data come from three different trials carried out in Palmira (Colombia). Two replicated trials were laid out in the field with an alpha-lattice experimental design in 2013 and 2014, and an additional non-replicated trial in 2016. Several agronomic traits were assessed, including Days to Flowering (DF), Days to Physiological Maturity (DPM), 100 seed weight (100SdW), Yield (Yd), Pod Harvest Index (PHI), Iron and Zinc content (SdFe and SdZn). The agronomic performance of the population was modeled using linear mixed models with spatial correction. From these models, best linear unbiased estimators / predictors were obtained (BLUEs/BLUPs). The genotypic datasets include a variant call format (VCF) file of 20,615 GBS variants genotyped for 629 RILs (recombinant inbred lines) and 8 founder. From this matrix, a large and dense genetic map was obtained. This map accounts for multiple recombination events from multiple founder lines using SNP data, conferring higher accuracy due to the large population size. It makes it suitable for analyzing the linkage and segregation patterns for genetic mapping in the species Phaseolus vulgaris.

The evolution of galaxies is influenced by many physical processes, which may vary depending on their environment. We combine Hubble Space Telescope (HST) and Multi-Unit Spectroscopic Explorer (MUSE) data of galaxies at 0.25<~z<~1.5 to probe the impact of environment on the size-mass relation, the main sequence (MS) relation, and the Tully-Fisher relation (TFR). We perform a morpho-kinematics modelling of 593 [OII] emitters in various environments in the COSMOS area from the MUSE-gAlaxy Groups In Cosmos survey. The HST F814W images are modelled with a bulge-disk decomposition to estimate their bulge-disk ratio, effective radius, and disk inclination. We use the [OII]{lambda}{lambda}3727,3729 doublet to extract the galaxies' ionised gas kinematics maps from the MUSE cubes, and we model those maps for a sample of 146 [OII] emitters, including bulge and disk components constrained from morphology and a dark matter halo. We find an offset of 0.03dex (1{sigma} significant) on the size-mass relation zero point between the field and the large structure sub-samples, with a richness threshold of N=10 to separate between small and large structures, and of 0.06dex (2{sigma}) with N=20. Similarly, we find a 0.1dex (2{sigma}) difference on the MS relation with N=10 and 0.15dex (3{sigma}) with N=20. These results suggest that galaxies in massive structures are smaller by 14% and have star formation rates reduced by a factor of 1.3-1.5 with respect to field galaxies at z=~0.7. Finally, we do not find any impact of the environment on the TFR, except when using N=20 with an offset of 0.04dex (1{sigma}). We discard the effect of quenching for the largest structures, which would lead to an offset in the opposite direction. We find that, at z=~0.7, if quenching impacts the mass budget of galaxies in structures, these galaxies would have been affected quite recently and for roughly 0.7-1.5Gyr. This result holds when including the gas mass but vanishes once we include the asymmetric drift correction.

Uniform Coding UnitsPrior to 1982, Alaska Department of Fish and Game - Division of Wildlife Conservation (ADFG-DWC) had a variety of coding schemes (18) relating harvest and management information to geographical areas. This made it difficult when comparing statewide wildlife information gathered across the state. In 1982, a new standardized statewide, geographically-based, hierarchy system of coding was created called the Uniform Coding Unit or UCU system. Game management units (GMUs), Subunits, and uniform coding units (UCUs) are the underlying geographic foundation of the wildlife and habitat management and regulations for ADFG-DWC. The GMU/UCU system consists of five Regions which are divided into twenty-six (26) Game Management Units (GMUs). Many of the GMUs are divided into Subunits (e.g. GMU 15 has three (3) Subunits, 15A, 15B, and 15C). GMUs that are not divided into subunits have a "Z" designation for the subunit. GMUs and Subunits are further divided into Major Drainages, Minor Drainages and Specific Areas. The smallest of these areas (down to the "specific area") is referred to as a Uniform Coding Unit (UCU) and has a unique 10 character code associated with it. (NOTE: UCU layer is for internal and official use only, not for public use or distribution). The UCU code is used for geographically classifying harvest and management information. Data that cannot be tied to a specific code can be generalized to the next higher level of the hierarchy. For example:a location description that is within multiple "specific areas" within a "minor drainage" can be coded to the minor code with a "00" for the specific area. Unknown "minor drainages" can be coded to the "major drainage" level, etc. If the subunit is unknown or the area covers multiple subunits within a unit, the subunit can be specified as a "Z" code (e.g. an area within subunits 15A and 15B could be recorded as 15Z). If a geographic location covers multiple units or the unit is unknown, the most general code (statewide code) is recorded as 27Z-Z00. The original hardcopy master maps were drawn to portray the UCUs fairly accurately geographically, but were not necessarily precisely drawn (i.e. left vs. right bank of a river, or exact ridge line). Each UCU was represented by drawing boundaries on USGS 1:250,000 scale quadrangle maps with a thick magic marker. A list (database) of place-names and their corresponding UCU codes was created and is still used today to assign permit, harvest, and sealing information to one of these geographic areas. In 1988, the UCU boundaries were digitized (traced) from the original maps into a computerized Geographic Information System (ArcInfo). Minor changes were made in 1989. Effective July 1, 2006 - GMU 24 is now divided up into four subunit 24A, 24B, 24C, 24D. - GMU 21A and 21B - - boundary has been modified. Phase I2006-2008 - initial clean-up of boundaries for GMU 6, 9, 10, 12, 16, 19, 20, 25. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Jan 2009 - Priority has shifted to getting the bulk of the updates into the master. Verification and modifications based on the UCU list and the AB corrections will come at a later date. This shift is to attempt to get the master into a permanent SDE GDB, set it up with the GDB topology, make additional clean-up/edits using the GDB tools, set up versioning, make it easier to replicate to area offices, and to take advantage of the tools/features available thru ArcGIS Server with versioned GDBs. June 2009 - initial clean-up of boundaries for Southeast (GMU 1-5), GMU 17, and GMU 18. These have NOT been verified against the UCU master list or by area biologists. -ras July 1 2009 - initial clean-up of boundaries for GMU 7 and 8. Also some adjustments for 25D based on the NHD 2008 version and ArcHydro Tools "raindrop" feature. These have NOT been verified against the UCU master list or by area biologists. -ras Sept 17, 2009 - initial clean-up of boundaries for GMU 13. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Oct 21, 2009 - initial clean-up of boundaries for GMU 14 These modification have NOT been verified against the UCU master list or by area biologists. -rasNov 19, 2009 - initial clean-up of boundaries for GMU 15. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Dec 7, 2009 - initial clean-up of boundaries for GMU 22. These modification have NOT been verified against the UCU master list or by area biologists. -ras March 3, 2010 - initial clean-up of boundaries for GMU 23. These modification have NOT been verified against the UCU master list or by area biologists. -rasApril 10, 2010 - initial clean-up of boundaries for GMU 26. These modification have NOT been verified against the UCU master list or by area biologists. -ras May 2010 - This completes Phase I of refining the UCUs - bulk heads-up re-digitizing of all arcs. Phase II - Converting and establishing procedures for maintaining the master in an Enterprise GDB is underway. Effective July 1, 2010, Region II was split into Region 2 (GMU's 6, 7, 8, 14C, 15) and Region 4 (GMU's 9, 10, 11, 13, 14AB, 16, 17. This version was updated to reflect the change. An archive of the previous version (with Regions I, II, III, and V) is available on request as GMUMaster_063010. -ras2012-present - minor updates continue as needed and time allows, and as newer base maps are used.2014 minor updates continue as needed, including updates to domain listings (not affecting GIS geometry)Effective July 1, 2014- revision to GMU 18/19/21 boundary to clarify/correct previous insufficient boundary description. Passed during Spring 2014 Board of Game.

The Ancient Woodland Inventory identifies over 52,000 ancient woodland sites in England. Ancient woodland is identified using presence or absence of woods from old maps, information about the wood's name, shape, internal boundaries, location relative to other features, ground survey, and aerial photography. The information recorded about each wood and stored on the Inventory Database includes its grid reference, its area in hectares and how much is semi-natural or replanted. Guidance document can be found on our Amazon Cloud Service Prior to the digitisation of the boundaries, only paper maps depicting each ancient wood at 1:50 000 scale were available.Full metadata can be viewed on data.gov.uk.

Accelerating crop improvement is critical to meeting food security demands in a changing climate. Long-read sequencing offers advantages over short reads in resolving structural variations (SVs) and aligning complex genomes, but its high cost has limited adoption in breeding programs. This study explores long-read low-pass (LRLP) sequencing as a cost-effective alternative for genomic analysis in a peanut multi-parent advanced generation intercross. Here we analyze LRLP using a variety of methodologies including Khufu on a linear graph, pangraph, and dynamic pangraph, as well as open source tools to analyze SVs and coverage. Consistently we find increased variants called for LRLP data compared to short read data. With a 1.63x average depth, LRLP sequencing covered 55% of the genome and 58% of gene space, outperforming short-read sequencing, which achieved only 17% and 11%, respectively even at a depth of 1.68x. Enhanced alignment accuracy and data retention further demonstrated LRLP’s efficacy.Our results highlight LRLP sequencing as a scalable, cost-effective tool for high-resolution trait mapping, with transformative potential for plant breeding and broader genomic applications.************* Files******************Short_linear_09.hapmap- A haplotype map of 125 individual peanut samples derived from a MAGIC breeding population. The file represents SNPs and was produced by Khufu, a proprietary genotyping pipeline. The input data for this analysis was low-depth short-read sequences.Long_linear_09.hapmap-A haplotype map of 125 individual peanut samples derived from a MAGIC breeding population. The file represents SNPs and was produced by Khufu, a proprietary genotyping pipeline. The input data for this analysis was low-depth long-read sequences.Short_linear_09_imputation_eval.txt- A file produced by Khufu that scores the accuracy of imputation.Long_linear_impuation_eval.txt- A file produced by Khufu that scores the accuracy of imputation.Long.panmap- A variant map of 125 individual peanut samples derived from a MAGIC breeding population produced by KhufuPan, a proprietary genotyping pipeline using a pangenome graph as a reference. SNPs, indels, and SVs are represented in this panmap. The input data for this analysis was low-depth long-read sequences.Long.panmap.fa- A file produced by KhufuPan, a proprietary genotyping pipeline using a pangenome graph as a reference. This file corresponds to Long.panmap and shows the sequences of the variants. The input data for this analysis was low-depth long-read sequences.Short.panmap- A variant map of 125 individual peanut samples derived from a MAGIC breeding population produced by KhufuPan, a proprietary genotyping pipeline using a pangenome graph as a reference. SNPs, indels, and SVs are represented in this panmap. The input data for this analysis was low-depth short-read sequences.Long.panmap.fa- A file produced by KhufuPan, a proprietary genotyping pipeline using a pangenome graph as a reference. This file corresponds to Short.panmap and shows the sequences of the variants. The input data for this analysis was low-depth short-read sequences.LRLP_Sequencing_Statistics.xlsx- Raw sequencing statistics from PacBio's Revio Sequencer of all peanut samples.LRLP_PBSV_info.xlsx- Data on structural variants called in all LRLP data using PacBio's tool PBMM2 and PBSV.LRLP_snpeff_summary.html- link to snpEff results for LRLP peanut samples.CostCalculationInfo.xlsx- Cost breakdowns used to calculate cost per value.