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).

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.

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.

In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can potentially elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding.

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.

Methods_Supplemental1. docx File S1 contains a detailed description of the crossing scheme used to develop the MAGIC population.Methods_Supplemental2.pdf File S2 shows Manhattan plots similar to Figure 3 for individual environments.Methods_Supplemental3.pdf File S3 shows founder effect size plots for vgt1 similar to Figure 4 for individual environments.Methods_Supplemental4.xlsx File S4 is contains tables of all QTL identified in the study, their locations, and their effect sizes estimates from the bi-allelic, ancestral haplotype, and founder models.Biogemma_600K_Genotypes_AGPv4_final_norm.vcf.gz contains Affymetrix 600K SNP array genotype data for 344 MAGIC DH lines, 16 founders, and the tester, MBS847.

Details of the MAGIC maize founder lines. For each line, developer, breeding group, and pedigree are given. Web links to further information and seed availability are provided. (XLSX 9 kb)

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.

Additional file 7. Haplotype composition of the MAGIC lines. The parental source, the length and genomic construction of each haplotype in each MAGIC line is shown, using the method proposed by Kover et al. [10].

Additional file 3. Phenotypic variability, least significant difference (LSD) and broad-sense heritability (H2) for best linear unbiased predictors (BLUPs) of the evaluated traits in the trials of 2013, 2014 and 2016 of the MAGIC population.

Summary of power simulation results with varying sample sizes (100, 200, 300, 400, 500). For each simulated QTL, the effect and the variance explained are given. The power to detect each QTL is averaged on 400 independent runs (100 for each of MAF 0.125, 0.25, 0.375, and 0.5). Results are given for h 2 = 0.4 and 0.7. (XLSX 17 kb)

Additional file 13. Phenotypic performance of top 10 RILs of the common bean MAGIC population and their genotypes at 12 major QTL. Phenotypic values for each of the top ten MAGIC RIL lines are shown for each trial, color coded from desirable (green) to undesirable (red) values. For each RIL line founder haplotypes for the top 12 QTL are shown together with the haplotype effects color coded from desirable (blue) to undesirable (red) QTL effects. The data mean phenotype for each parental haplotype in the main QTL and maximum and minimum trait values or haplotype effects are shown below. Letters indicate significant differences using Tukey test (α = 0.05).

Additional file 11. Details of QTL identified by interval mapping, genetic and physical position, LOD, phenotypic variation explained, and founders’ allelic effects mapped for 7 traits in the MAGIC population evaluated in 2013, 2014 and 2016.

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.

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 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.

Multiparent Advanced Generation Inter-Cross (MAGIC) population is an ideal genetic and breeding material for quantitative trait locus (QTL) mapping and molecular breeding. In this study, a MAGIC population derived from eight tobacco parents was developed. Eight parents and 560 homozygous lines were genotyped by a 430K single-nucleotide polymorphism (SNP) chip assay and phenotyped for nicotine content under different conditions. Four QTLs associated with nicotine content were detected by genome-wide association mapping (GWAS), and one major QTL, named qNIC7-1, was mapped repeatedly under different conditions. Furthermore, by combining forward mapping, bioinformatics analysis and gene editing, we identified an ethylene response factor (ERF) transcription factor as a candidate gene underlying the major QTL qNIC7-1 for nicotine content in tobacco. A presence/absence variation (PAV) at qNIC7-1 confers changes in nicotine content. Overall, the large size of this MAGIC population, diverse genetic composition, balanced parental contributions and high levels of recombination all contribute to its value as a genetic and breeding resource. The application of the tobacco MAGIC population for QTL mapping and detecting rare allelic variation was demonstrated using nicotine content as a proof of principle.

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 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).

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.

Additional file 10. Significant markers identified in genome wide association studies, genetic and physical position, p value, allele frequency, phenotypic effect and founder genotypes associated with 9 traits in the MAGIC population evaluated in 2013, 2014 and 2016. Favorable alleles are colored in green.