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).

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.

Content

Metadata on user uploaded maps for Heroes of Might and Magic from http://heroesportal.net/.

Data was scraped on 29.01.2020.

Dataset language: Russian.

Comprehensive market data and analytics for X 1 DUNGEON MAP NM ADVENTURES IN THE FORGOTTEN REALMS AFR 242 MTG MAGIC including pricing distribution, seller metrics, and market trends.

Maps published for tourism promotion and information constitute a particular sub-group of tourist maps made using Geographic Information Systems (GIS). This paper proposes a methodological protocol for the systematic elaboration of tourism mapping. This procedure is applied to the design of the tourist map of the province of Huesca. The cartographic tool was designed based on the needs of tourism promotion by the Public Administration, seeking the halfway point between persuasion and precision when representing elements on the map. Given that data would have to be updated and modified in the future, open-source software was used so that the administration can then run, modify and update it. In addition, the project was validated through surveys to two different audiences (general and expert).

Additional file 4. Distribution of markers per chromosome obtained from WGS of the eight founder lines. Markers from GBS of the whole population and the resulting thinned markers used to construct the genetic map for QTL analysis are listed.

The genetic map of the MM population. (XLSX 2159 kb)

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.

Author: B Kirkland, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 1, grade 2Resource type: lessonSubject topic(s): geographic thinking, literature, mapsRegion: worldStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 9. The environment influences human actions; and humans both adapt to, and change, the environment.Objectives: Students will be able to:

1. Locate Minnesota and Pennsylvania on a U.S. map.
2. Compare the locations of Minnesota and Pennsylvania on a U.S. map. 3 Identify travel between the two states using positional terms. 4, Identify human and physical characteristics of a place.
3. Locate the Cretaceous period on a time line and compare it to the times of the dinosaurs.
4. Differentiate the world’s major habitats.
5. Describe how people adapt to the environment.
6. Describe how people adapt to a changing environment.
7. Draw the physical features of the story’s setting to convey spatial understanding.
8. Respond accurately to questions regarding plot, characters and setting.Summary: Step into a world of adventure—go back in time or to distant lands with Jack and Annie. From France in the Middle Ages to the prairies of America to the Moon, Jack and Annie make history and geography fun by taking you right there! In this first book, Dinosaurs Before Dark, Jack and Annie go back 65 million years ago to the Cretaceous period. Through this adventure students will learn about the physical features of this period and how the physical features give structure to habitats of living things.

Representation of the geo-referenced map only relating to the sheet in question, for the following three levels descritti:Livello 2: Morphological units (UM)Represent large units within which morphological traits (EM of Level 3) are grouped even different but whose predominance is characteristic and indicative of certain processes or geological phenomena.Level 3: Morphobatimetric elements (EM)Represent individual, physically distinct morphological elements, specifically associated with a precise geological process or, in certain cases, to indeterminable processes on an exclusively morphobatimetric basis. In this case, the genesis of the EM remains indefinite.Level 4: Criticality PointsRepresent one or more EMs of Level 3 which, in the opinion of the interpreter, indicate the existence of a risk, understood as a concrete possibility that, if a given event occurs, it could harm people and/or infrastructure (even if it is impossible to specify the probability and how long such an event may occur).

Stripe rust caused by the biotrophic fungus Puccinia striiformis Westend. is one of the most important diseases of wheat worldwide, causing high yield and quality losses. Growing resistant cultivars is the most efficient way to control stripe rust, both economically and ecologically. Known resistance genes are already present in numerous cultivars worldwide. However, their effectiveness is limited to certain races within a rust population and the emergence of stripe rust races being virulent against common resistance genes forces the demand for new sources of resistance. Multiparent advanced generation intercross (MAGIC) populations have proven to be a powerful tool to carry out genetic studies on economically important traits. In this study, interval mapping was performed to map quantitative trait loci (QTL) for stripe rust resistance in the Bavarian MAGIC wheat population, comprising 394 F6 : 8 recombinant inbred lines (RILs). Phenotypic evaluation of the RILs was carried out for adult plant resistance in field trials at three locations across three years and for seedling resistance in a growth chamber. In total, 21 QTL for stripe rust resistance corresponding to 13 distinct chromosomal regions were detected, of which two may represent putatively new QTL located on wheat chromosomes 3D and 7D.

Description of the data The data described here were produced from the ANR projects ADAPTOM (ANR-13-ADAP-0013) and TomEpiSet (ANR-16-CE20-0014). An 8-way tomato MAGIC population was phenotyped over 12 environments including three geographical location (France, Israel and Morocco) and four conditions (control, and water-deficit, high-temperature and salinity stress). A set of 397 MAGIC lines were genotyped for 1345 markers, used together with the phenotypic traits for linkage mapping analysis. Genotype-by-environment interaction (GxE) was evaluated and phenotypic plasticity computed through different statistical models. Each file in the dataset has its own description below. • Phenotype files The Phenotypes files contain the 10 phenotypic traits that were evaluated. Phenotypic data averaged per genotype and environment are in the file “Phenotype_per_Environment”. The input phenotypes for the linkage mapping analysis are in the file “Pheno_Input_QTL_detection”. They represent for each trait the estimated average performance, slope and variance from the Finlay & Wilkinson regression model and sensitivity to environmental covariates from the factorial regression model, respectively. • MAGIC Genotyping information This file presents the genetic map with 1345 SNP markers used in linkage mapping analyses. The genotypic information of the eight founders and 397 MAGIC lines are also presented • Daily recorded climactic parameters This file presents the daily climatic parameters recorded within the greenhouses. The different parameters were computed over 24 hours. • Custom R script for the two-stage analysis of GxE The file “Two-stage-analysis_magicMET.txt” contains the custom R script used for analysis of factorial regression and Finlay-Wilkinson regression models. Average performance and plasticity parameters were derived from these analyses. Example have been given for fruit weight phenotype averaged per genotype and environment. The input file “Var_environment_P2P3” presents the average climatic parameters used particularly for the factorial regression model. • Custom R script for QEI modelling The files “QEI_Glbal_marker_effect_model5.txt” and “QEI_main_plus_interactive_effect_model6.txt” describe the custom R script used for the detection of interactive QTLs (QEI). Example of fruit weight phenotype have been developed. The input files for the script are “FW_pheno_GxE.csv”, the average phenotypic data per genotype and environment for fruit weight example and the parental haplotype probabilities “Proba_parents.txt” that were computed from R/qtl2 package with the function calc_genoprob. The “Geno_ID.csv” file gives the correspondence between genotype name and ID.

Multiparent advanced generation intercross (MAGIC) populations were recently developed to allow the high-resolution mapping of quantitative traits. We present a genetic linkage map of an elite but highly diverse eight-founder MAGIC population in common wheat (Triticum aestivum L.). Our MAGIC population is composed of 394 F6:8 recombinant inbred lines lacking significant signatures of population structure. The linkage map included 5435 SNP markers distributed over 2804 loci and spanning 5230 cM. The analysis of population parameters, including genetic structure, kinship, founder probabilities, and linkage disequilibrium and congruency to other maps indicated appropriate construction of both the population and the genetic map. It was shown that eight-founder MAGIC populations exhibit a greater number of loci and higher recombination rates, especially in the pericentromeric regions, compared to four-founder MAGIC, and biparental populations. In addition, our greatly simplified eight-parental MAGIC mating design with an additional eight-way intercross step was found to be equivalent to a MAGIC design with all 210 possible four-way crosses regarding the levels of missing founder assignments and the number of recombination events. Furthermore, the MAGIC population captured 71.7% of the allelic diversity available in the German wheat breeding gene pool. As a proof of principle, we demonstrated the application of the resource for quantitative trait loci mapping analyzing seedling resistance to powdery mildew. As wheat is a crop with many breeding objectives, this resource will allow scientists and breeders to carry out genetic studies for a wide range of breeder-relevant parameters in a single genetic background and reveal possible interactions between traits of economic importance.

Representation of the geo-referenced map only relating to the sheet in question, for the following three levels descritti:Livello 2: Morphological units (UM)Represent large units within which morphological traits (EM of Level 3) are grouped even different but whose predominance is characteristic and indicative of certain processes or geological phenomena.Level 3: Morphobatimetric elements (EM)Represent individual, physically distinct morphological elements, specifically associated with a precise geological process or, in certain cases, to indeterminable processes on an exclusively morphobatimetric basis. In this case, the genesis of the EM remains indefinite.Level 4: Criticality PointsRepresent one or more EMs of Level 3 which, in the opinion of the interpreter, indicate the existence of a risk, understood as a concrete possibility that, if a given event occurs, it could harm people and/or infrastructure (even if it is impossible to specify the probability and how long such an event may occur).

Representation of only the georeferenced map relating to the sheet considered, for the three levels described below:Level 2: Morphological Units (MU)They represent large units within which morphological traits (Level 3 EM) are grouped, even if different but whose predominance it is characterizing and indicative of certain geological processes or phenomena. Level 3: Morphobathymetric Elements (EM) They represent individual, physically distinct morphological elements that can be specifically associated with a precise geological process or, in some cases, with processes that cannot be determined on an exclusively morphobathymetric basis. In this case the genesis of the EM remains undefined. Level 4: Critical Points They represent one or more Level 3 EMs which, in the interpreter's opinion, indicate the existence of a risk, understood as a concrete possibility that, if a specific event should occur, it could harm people and/or infrastructures (even if it is impossible to specify the probability and in what times such an event could occur).

Representation of the geo-referenced map only relating to the sheet in question, for the following three levels descritti:Livello 2: Morphological units (UM)Represent large units within which morphological traits (EM of Level 3) are grouped even different but whose predominance is characteristic and indicative of certain processes or geological phenomena.Level 3: Morphobatimetric elements (EM)Represent individual, physically distinct morphological elements, specifically associated with a precise geological process or, in certain cases, to indeterminable processes on an exclusively morphobatimetric basis. In this case, the genesis of the EM remains indefinite.Level 4: Criticality PointsRepresent one or more EMs of Level 3 which, in the opinion of the interpreter, indicate the existence of a risk, understood as a concrete possibility that, if a given event occurs, it could harm people and/or infrastructure (even if it is impossible to specify the probability and how long such an event may occur).

This dataset exceeds the size and feature limits of the Shapefile format, so is unavailable on the Natural England Open Data Geoportal in that format. Please select ESRI File Geodatabase or another format to download. The Priority Habitat Inventory is a spatial dataset that maps priority habitats identified in the UK Biodiversity Action Plan and listed as being of principal importance for the purpose of conserving or enhancing biodiversity, under Section 41 of the Natural Environment and Rural Communities Act (2006).Habitats mapped in the PHIThe PHI currently maps 27 terrestrial and freshwater priority habitats across England. Priority Habitat NameHabCodeBlanket bogBLBOGCalaminarian grasslandCALAMCoastal & floodplain grazing marshCFPGMCoastal saltmarshSALTMCoastal sand dunesCSDUNCoastal vegetated shingleCVSHIDeciduous woodlandDWOODLimestone pavementsLPAVELowland calcareous grasslandLCGRALowland dry acid grasslandLDAGRLowland fensLFENSLowland heathlandLHEATLowland meadowsLMEADLowland raised bogLRBOGMaritime cliff & slopeMCSLPMountain heath & willow scrubMHWSCMudflatsMUDFLPurple moor grass & rush pasturesPMGRPReedbedsRBEDSSaline lagoonsSLAGOTraditional orchardsTORCHUpland calcareous grasslandUCGRAUpland hay meadowsUHMEAUpland heathlandUHEATUpland flushes, fens & swampsUFFSWLakesLAKESPondsPONDSNon Priority Habitats mapped in the PHIThe PHI also includes four habitat classes which are not priority habitats, but which hold potential importance for conservation of biodiversity in England. These can indicate a mosaic of habitat which may contain priority habitats, have restoration potential and/or contribute to ecological networks. Where evidence indicates the presence of unmapped or fragmented priority habitats within such polygons, these are attributed as additional habitats. Non-Priority Habitat NameHabCodeDescriptionFragmented heathFHEATThis refers to areas of degraded and relict upland heathland, typically in a mosaic with acid grassland that fails to meet the Upland Heathland priority habitat definition.Grass moorlandGMOORThis includes large areas of upland grassland, which may contain mosaics of priority habitat, but tends to be species-poor, grass dominated acid grassland above the moorland line.Good quality semi-improved grasslandGQSIGThis includes grasslands with biodiversity value that do not meet priority grassland habitat definitions.No main habitatNMHABIn some cases, a priority habitat may be present within a polygon, but its extent may be less than the minimum mapping unit, or it may not be accurately mappable. Feature Descriptions and CodesFor some polygons the PHI contains additional information about the main habitats in the form of feature descriptions and corresponding feature codes. These are new fields to the PHI and currently only sparsely populated. We expect the use of these fields to expand over coming updates with new features and codes. Feature DescriptionFeature CodePriority ponds and lakesOligotrophic lakesOLIGODystrophic lakesDYSTRMesotrophic lakesMESOTEutrophic standing watersEUTROIce age pondICEAGPond with floating matsPWFLMDeciduous woodlandUpland oakwoodUPOWDLowland beech and yew woodlandLBYWDUpland mixed ashwoodsUMAWDWet woodlandWETWDLowland mixed deciduous woodlandLMDWDUpland birchwoodsUPBWDAncient semi natural woodlandASNWDPlantations on ancient woodlandPAWDSGrasslandCountryside Stewardship OptionCSOPTWaxcap grasslandWAXCPHeathlandDry heathlandDRYHLWet heathlandWETHLCoastal sand dunesDunes under coniferous woodlandCWDUNDunes under deciduous woodlandDWDUNGeneralDegradedDEGRDSpatial framework: Wherever possible habitats are mapped to polygons in OS Mastermap. These polygons are merged or split where necessary to create resulting habitat patches.Coverage: EnglandUpdate Frequency: The PHI is updated twice a year.Metadata: Full metadata can be viewed on data.gov.uk.Uses include: National planning and targeting for nature recovery; agri-environment scheme targeting; local development planning; Local Nature Recovery Strategies.Contact: If you have any questions or feedback regarding the Priority Habitats’ Inventory, please contact the Habitats’ Inventory Project Team at the following email address.HabitatInventories@naturalengland.org.uk Attributes AliasField nameExample ValueDescriptionMain habitatsMainHabsLowland dry acid grassland, Lowland heathlandName(s) of habitat(s) present in the polygon.Habitat codesHabCodesLDAGR, LHEATList of codes(s) representing main habitat(s) present in the polygon.Habitat feature descriptionsFeatDescDry heathlandAdditional information about the nature of the habitat or features present.Habitat feature codesFeatCodesDRYHLList of code(s) corresponding to the habitat feature descriptions.Other habitat classificationsOtherClassPhase1(D5)Additional habitat classification information relating to main habitats.Additional habitats presentAddHabsGQSIG, LFENSList of code(s) for additional habitats that may be present within the polygon.Primary data sourcesPrimSourceNatural England's SSSI database ENSIS (LDAGR), Northumberland County Council Phase 1 Survey 2003 (LHEAT)List of primary sources for the main habitats present in the polygon, with corresponding HabCode in brackets.Area in hectaresAreaHa0.14Polygon area in hectares rounded to one decimal place.Publication versionVersionJuly_24Date of publication for the current PHI update: Month_Year.Unique IDUIDPHIDXXXXXXXXXX _YYYYYYYYYYYUnique ID for the polygon based on XY location coordinates.

Representation of only the georeferenced map relating to the sheet considered, for the three levels described below:Level 2: Morphological Units (MU)They represent large units within which morphological traits (Level 3 EM) are grouped, even if different but whose predominance it is characterizing and indicative of certain geological processes or phenomena. Level 3: Morphobathymetric Elements (EM) They represent individual, physically distinct morphological elements that can be specifically associated with a precise geological process or, in some cases, with processes that cannot be determined on an exclusively morphobathymetric basis. In this case the genesis of the EM remains undefined. Level 4: Critical Points They represent one or more Level 3 EMs which, in the interpreter's opinion, indicate the existence of a risk, understood as a concrete possibility that, if a specific event should occur, it could harm people and/or infrastructures (even if it is impossible to specify the probability and in what times such an event could occur).

Representation of the geo-referenced map only relating to the sheet in question, for the following three levels descritti:Livello 2: Morphological units (UM)Represent large units within which morphological traits (EM of Level 3) are grouped even different but whose predominance is characteristic and indicative of certain processes or geological phenomena.Level 3: Morphobatimetric elements (EM)Represent individual, physically distinct morphological elements, specifically associated with a precise geological process or, in certain cases, to indeterminable processes on an exclusively morphobatimetric basis. In this case, the genesis of the EM remains indefinite.Level 4: Criticality PointsRepresent one or more EMs of Level 3 which, in the opinion of the interpreter, indicate the existence of a risk, understood as a concrete possibility that, if a given event occurs, it could harm people and/or infrastructure (even if it is impossible to specify the probability and how long such an event may occur).