Types of data processing Claude's Code Interpreter can handle

Introduction

We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file Short_Description_of_Data_Analysis.pdf and Statistical_formulas.pdf

The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.

The dataset was expanded on 23rd September 2024 to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.

Short Description of Data Analysis and Attached Files (datasets):

Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.

Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: https://www.ksh.hu/stadat_files/nep/hu/nep0035.html, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the Data_of_demography.xlsx file.

In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.

The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): https://www.nnk.gov.hu/index.php/efi (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: https://semmelweis.hu/egeszsegfejlesztes/en/ (n=1). (accessed 05 Dec. 2023.)

Geocoordinates were determined using Google Maps (N=108): https://www.google.com/maps. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file.

The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:

https://qgis.org/en/site/forusers/download.html. (accessed 04 Jan. 2024.)

The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the

Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file,

imported via .csv file.

The OpenStreetMap tileset is directly accessible from www.openstreetmap.org in QGIS. (accessed 04 Jan. 2024.)

The Hungarian county administrative boundaries were downloaded from the following website: https://data2.openstreetmap.hu/hatarok/index.php?admin=6 (accessed 04 Jan. 2024.)

HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.

Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).

A brief description of the statistical formulas applied is included in the Statistical_formulas.pdf.

Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.

• Aggregated number of HDOs by county: Number_of_HDOs.xlsx
• Standardised data (Number of HDOs per 100,000 residents): Standardized_data.xlsx
• Calculation of the Lorenz curve: Lorenz_curve.xlsx
• Calculation of the Gini index: Gini_Index.xlsx
• Calculation of the LQ index: LQ_Index.xlsx
• Calculation of the Herfindahl-Hirschman Index: Herfindahl_Hirschman_Index.xlsx
• Calculation of the Entropy index: Entropy_Index.xlsx
• Regression and correlation analysis calculation: Regression_correlation.xlsx

Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:

• Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.
• Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.
• Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv
• Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv
• Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv
• Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv
• Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv

For easier readability, the files have been provided in both SPV and PDF formats.

The translation of these supplementary files into English was completed on 23rd Sept. 2024.

If you have any further questions regarding the dataset, please contact the corresponding author: domjan.peter@phd.semmelweis.hu

Here are six files that provide details for all 44,120 identified single nucleotide polymorphisms (SNPs) or the 215 outlier SNPs associated with the evolution of rapid character displacement among replicate islands with (2Spp) and without competition (1Spp) between two Anolis species. On 2Spp islands, A. carolinensis occurs higher in trees and have evolved larger toe pads. Among 1Spp and 2Spp island populations, we identify 44,120 SNPs, with 215-outlier SNPs with improbably large FST values, low nucleotide variation, greater linkage than expected, and these SNPs are enriched for animal walking behavior. Thus, we conclude that these 215-outliers are evolving by natural selection in response to the phenotypic convergent evolution of character displacement. There are two, non-mutually exclusive perspective of these nucleotide variants. One is character displacement is convergent: all 215 outlier SNPs are shared among 3 out of 5 2Spp island and 24% of outlier SNPS are shared among all five out of five 2Spp island. Second, character displacement is genetically redundant because the allele frequencies in one or more 2Spp are similar to 1Spp islands: among one or more 2Spp islands 33% of outlier SNPS are within the range of 1Spp MiAF and 76% of outliers are more similar to 1Spp island than mean MiAF of 2Spp islands. Focusing on convergence SNP is scientifically more robust, yet it distracts from the perspective of multiple genetic solutions that enhances the rate and stability of adaptive change. The six files include: a description of eight islands, details of 94 individuals, and four files on SNPs. The four SNP files include the VCF files for 94 individuals with 44KSNPs and two files (Excel sheet/tab-delimited file) with FST, p-values and outlier status for all 44,120 identified single nucleotide polymorphisms (SNPs) associated with the evolution of rapid character displacement. The sixth file is a detailed file on the 215 outlier SNPs. Complete sequence data is available at Bioproject PRJNA833453, which including samples not included in this study. The 94 individuals used in this study are described in “Supplemental_Sample_description.txt” Methods Anoles and genomic DNA: Tissue or DNA for 160 Anolis carolinensis and 20 A. sagrei samples were provided by the Museum of Comparative Zoology at Harvard University (Table S2). Samples were previously used to examine evolution of character displacement in native A. carolinensis following invasion by A. sagrei onto man-made spoil islands in Mosquito Lagoon Florida (Stuart et al. 2014). One hundred samples were genomic DNAs, and 80 samples were tissues (terminal tail clip, Table S2). Genomic DNA was isolated from 80 of 160 A. carolinensis individuals (MCZ, Table S2) using a custom SPRI magnetic bead protocol (Psifidi et al. 2015). Briefly, after removing ethanol, tissues were placed in 200 ul of GH buffer (25 mM Tris- HCl pH 7.5, 25 mM EDTA, , 2M GuHCl Guanidine hydrochloride, G3272 SIGMA, 5 mM CaCl2, 0.5% v/v Triton X-100, 1% N-Lauroyl-Sarcosine) with 5% per volume of 20 mg/ml proteinase K (10 ul/200 ul GH) and digested at 55º C for at least 2 hours. After proteinase K digestion, 100 ul of 0.1% carboxyl-modified Sera-Mag Magnetic beads (Fisher Scientific) resuspended in 2.5 M NaCl, 20% PEG were added and allowed to bind the DNA. Beads were subsequently magnetized and washed twice with 200 ul 70% EtOH, and then DNA was eluted in 100 ul 0.1x TE (10 mM Tris, 0.1 mM EDTA). All DNA samples were gel electrophoresed to ensure high molecular mass and quantified by spectrophotometry and fluorescence using Biotium AccuBlueTM High Sensitivity dsDNA Quantitative Solution according to manufacturer’s instructions. Genotyping-by-sequencing (GBS) libraries were prepared using a modified protocol after Elshire et al. (Elshire et al. 2011). Briefly, high-molecular-weight genomic DNA was aliquoted and digested using ApeKI restriction enzyme. Digests from each individual sample were uniquely barcoded, pooled, and size selected to yield insert sizes between 300-700 bp (Borgstrom et al. 2011). Pooled libraries were PCR amplified (15 cycles) using custom primers that extend into the genomic DNA insert by 3 bases (CTG). Adding 3 extra base pairs systematically reduces the number of sequenced GBS tags, ensuring sufficient sequencing depth. The final library had a mean size of 424 bp ranging from 188 to 700 bp . Anolis SNPs: Pooled libraries were sequenced on one lane on the Illumina HiSeq 4000 in 2x150 bp paired-end configuration, yielding approximately 459 million paired-end reads ( ~138 Gb). The medium Q-Score was 42 with the lower 10% Q-Scores exceeding 32 for all 150 bp. The initial library contained 180 individuals with 8,561,493 polymorphic sites. Twenty individuals were Anolis sagrei, and two individuals (Yan 1610 & Yin 1411) clustered with A. sagrei and were not used to define A. carolinesis’ SNPs. Anolis carolinesis reads were aligned to the Anolis carolinensis genome (NCBI RefSeq accession number:/GCF_000090745.1_AnoCar2.0). Single nucleotide polymorphisms (SNPs) for A. carolinensis were called using the GBeaSy analysis pipeline (Wickland et al. 2017) with the following filter settings: minimum read length of 100 bp after barcode and adapter trimming, minimum phred-scaled variant quality of 30 and minimum read depth of 5. SNPs were further filtered by requiring SNPs to occur in > 50% of individuals, and 66 individuals were removed because they had less than 70% of called SNPs. These filtering steps resulted in 51,155 SNPs among 94 individuals. Final filtering among 94 individuals required all sites to be polymorphic (with fewer individuals, some sites were no longer polymorphic) with a maximum of 2 alleles (all are bi-allelic), minimal allele frequency 0.05, and He that does not exceed HWE (FDR <0.01). SNPs with large He were removed (2,280 SNPs). These SNPs with large significant heterozygosity may result from aligning paralogues (different loci), and thus may not represent polymorphisms. No SNPs were removed with low He (due to possible demography or other exceptions to HWE). After filtering, 94 individual yielded 44,120 SNPs. Thus, the final filtered SNP data set was 44K SNPs from 94 indiviuals. Statistical Analyses: Eight A. carolinensis populations were analyzed: three populations from islands with native species only (1Spp islands) and 5 populations from islands where A. carolinesis co-exist with A. sagrei (2Spp islands, Table 1, Table S1). Most analyses pooled the three 1Spp islands and contrasted these with the pooled five 2Spp islands. Two approaches were used to define SNPs with unusually large allele frequency differences between 1Spp and 2Spp islands: 1) comparison of FST values to random permutations and 2) a modified FDIST approach to identify outlier SNPs with large and statistically unlikely FST values. Random Permutations: FST values were calculated in VCFTools (version 4.2, (Danecek et al. 2011)) where the p-value per SNP were defined by comparing FST values to 1,000 random permutations using a custom script (below). Basically, individuals and all their SNPs were randomly assigned to one of eight islands or to 1Spp versus 2Spp groups. The sample sizes (55 for 2Spp and 39 for 1Spp islands) were maintained. FST values were re-calculated for each 1,000 randomizations using VCFTools. Modified FDIST: To identify outlier SNPs with statistically large FST values, a modified FDIST (Beaumont and Nichols 1996) was implemented in Arlequin (Excoffier et al. 2005). This modified approach applies 50,000 coalescent simulations using hierarchical population structure, in which demes are arranged into k groups of d demes and in which migration rates between demes are different within and between groups. Unlike the finite island models, which have led to large frequencies of false positive because populations share different histories (Lotterhos and Whitlock 2014), the hierarchical island model avoids these false positives by avoiding the assumption of similar ancestry (Excoffier et al. 2009). References Beaumont, M. A. and R. A. Nichols. 1996. Evaluating loci for use in the genetic analysis of population structure. P Roy Soc B-Biol Sci 263:1619-1626. Borgstrom, E., S. Lundin, and J. Lundeberg. 2011. Large scale library generation for high throughput sequencing. PLoS One 6:e19119. Bradbury, P. J., Z. Zhang, D. E. Kroon, T. M. Casstevens, Y. Ramdoss, and E. S. Buckler. 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633-2635. Cingolani, P., A. Platts, L. Wang le, M. Coon, T. Nguyen, L. Wang, S. J. Land, X. Lu, and D. M. Ruden. 2012. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6:80-92. Danecek, P., A. Auton, G. Abecasis, C. A. Albers, E. Banks, M. A. DePristo, R. E. Handsaker, G. Lunter, G. T. Marth, S. T. Sherry, G. McVean, R. Durbin, and G. Genomes Project Analysis. 2011. The variant call format and VCFtools. Bioinformatics 27:2156-2158. Earl, D. A. and B. M. vonHoldt. 2011. Structure Harvester: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genet Resour 4:359-361. Elshire, R. J., J. C. Glaubitz, Q. Sun, J. A. Poland, K. Kawamoto, E. S. Buckler, and S. E. Mitchell. 2011. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6:e19379. Evanno, G., S. Regnaut, and J. Goudet. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611-2620. Excoffier, L., T. Hofer, and M. Foll. 2009. Detecting loci under selection in a hierarchically structured population. Heredity 103:285-298. Excoffier, L., G. Laval, and S. Schneider. 2005. Arlequin (version 3.0): An integrated software package for population genetics data analysis.

Additional file 15: Table S14. Data and analysis for the search for epigenetic signatures of outlier expression (Excel table).

Ovaj skup podataka uključuje finansijske izvještaje, račune i blokade, te nekretnine. Podaci uključuju prihode, rashode, dobit, imovinu, obaveze i informacije o nekretninama u vlasništvu kompanije. Finansijski podaci, finansijski sažetak, sažetak kompanije, preduzetnik, zanatlija, udruženje, poslovni subjekti.

Files used for full data (APRILEXCEL1), for full data without outliers (April Excel No Outliers 1), and for subgroup analyses. Includes R script (MetaBH).

Additional file 9: Table S9. Analysis details for the distribution of outlier genes among tissues and species (Excel file with ten tabs). Table S9A: lists of outlier genes occurring in more than one mouse organ in DOM. Table S9B: OO patterns of mouse genes that are expressed in more than one tissue. Table S9C: lists of brain outlier genes occurring in more than one mouse taxon. Table S9D: lists of heart outlier genes occurring in more than one mouse taxon. Table S9E: lists of kidney outlier genes occurring in more than one mouse taxon. Table S9F: lists of liver outlier genes occurring in more than one mouse taxon. Table S9G: lists of mammary outlier genes occurring in more than one mouse taxon. Table S9H: lists of outlier genes occurring in more than one human organ. Table S9I: OO patterns of human genes that are expressed in more than one tissue. Table S9J: lists of mouse and human orthologous genes that show OO patterns in the data.

This dataset provides comprehensive information on road intersection crashes involving public transport (Bus, Bus-train, Combi/minibusses, midibusses) recognised as "high-low" outliers within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in at least 10% of the total "high-low" outlier public transport road intersection crashes for the years 2017, 2018, 2019, and 2021.The dataset is meticulously organised according to support metric values, ranging from 0,10 to 0,17, with entries presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 65,9 KBNumber of Files: The dataset contains a total of 1280 association rulesDate Created: 23rd May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-low" outlier fishnet grid cells from a cluster and outlier analysis, all the road intersection crashes involving public transport that occurred within the "high-low" outlier fishnet grid cells were extracted to be processed by association analysis. The association analysis of these crashes was processed using Python software and involved the use of a 0,10 support metric value. Consequently, commonly occurring crash attributes among at least 10% of the "high-low" outlier road intersection public transport crashes were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2021 (2020 data omitted)

Feline T4 2014 till July 2015 by RegionFeline Total T4 by Breed Excel

This dataset provides comprehensive information on road intersection crashes involving motorcycles (Motor tricycle, Motorcycle: under 125cc, Motorcycle: Above 125cc, Quadru-cycle) that have resulted in injuries recognised as "high-low" outliers within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in at least 5% of the total "high-low" outlier motorcycle road intersection crashes resulting in injuries for the years 2017, 2018 and 2019.The dataset is meticulously organised according to support metric values, ranging from 0,10 to 0,202, with entries presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 38,8 KBNumber of Files: The dataset contains a total of 426 association rulesDate Created: 23rd May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-low" outlier fishnet grid cells from a cluster and outlier analysis, all the road intersection crashes involving a motorcycle resulting in injuries that occurred within the "high-low" outlier fishnet grid cells were extracted to be processed by association analysis. The association analysis of these crashes was processed using Python software and involved the use of a 0,10 support metric value. Consequently, commonly occurring crash attributes among at least 10% of the "high-low" outlier road intersection motorcycle crashes resulting in injuries were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2019

This dataset provides comprehensive information on road intersection crashes induced by road and environment factors (Animals in road, Aqualane, Blinded, Falling object, Object in road, Pothole, Roadworks, Severe weather conditions/poor visibility, Slippery road - gravel, Slippery road - oil, Slippery road - gravel, Wild animals in road) recognised as "high-low" clusters within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in at least 10% of the total "high-high" cluster road intersection crashes induced by road and environment factors for the years 2017, 2018, 2019 and 2021. The dataset is meticulously organised according to support metric values, ranging from 0,10 to 0,315, with entries presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 7,95 MBNumber of Files: The dataset contains a total of 187837 association rulesDate Created: 24th May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-low" outlier fishnet grid cells from a cluster and outlier analysis, all the road intersection crashes induced by road and environment factors that occurred within the "high-low" outlier fishnet grid cells were extracted to be processed by association analysis. The association analysis of these crashes was processed using Python software and involved the use of a 0,10 support metric value. Consequently, commonly occurring crash attributes among at least 10% of the "high-low" outlier road intersection crashes induced by road and environment factors were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2021 (2020 data omitted)

Additional file 10: Table S10. List of outlier genes with replication data from two sequencing experiments (Excel table).

Additional file 12: Table S12. Data for outlier genes occurring in modules in a semi-graphic depiction (Excel file with three tabs). Table S12A: Depiction of mouse outlier modules based on shared OO in at least three individuals for gene pairs and larger groups of genes. Table S12B: Depiction of human outlier modules based on shared OO in at least three individuals for gene pairs and larger groups of genes. Tale S12C: Depiction of Drosophila outlier modules based on shared OO in at least three individuals for gene pairs and larger groups of genes.

This dataset provides comprehensive information on unsignalled road intersection crashes recognised as "high-low" clusters within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in at least 10% of the total "high-high" cluster unsignalled road intersection crashes resulting for the years 2017, 2018 and 2019. The dataset is meticulously organised according to support metric values, ranging from 0,10 to 0,223, with entries presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 57,4 KB Number of Files: The dataset contains a total of 1050 association rulesDate Created: 24th May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-low" outlier fishnet grid cells from a cluster and outlier analysis, all the unsignalled road intersection crashes that occurred within the "high-low" outlier fishnet grid cells were extracted to be processed by association analysis. The association analysis of these crashes was processed using Python software and involved the use of a 0,05 support metric value. Consequently, commonly occurring crash attributes among at least 10% of the "high-low" outlier unsignalled road intersection crashes were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2019

Additional file 7: Table S7. Gene lists with transcriptome data (TPM) for four organs of the human GTEx data (Excel file with eight tabs). Organ names are provided in the Tab titles. For each organ, "all_TPM"includes data for all genes above the minimal expression cutoff value, "OO"is the corresponding sub list for all genes with at least one over-outlier expression.

Additional file 8: Table S8. Gene lists with transcriptome data (CPM) for Drosophila data (Excel file with twelve tabs). For Drosophila melanogaster (Dmel) there are two parts (head and body), for Drosophila simulans (Dsim) there are four populations, as indicated in the tabs. In each case, "all" includes data for all genes above the minimal expression cutoff value, "OO" is the corresponding sub list for all genes with at least one over-outlier expression

Additional file 5: Table S5. Gene lists with transcriptome data (TPM) for five organs of the mouse SPI populations (Excel file with ten tabs). Organ names are provided in the Tab titles. For each organ, "all_TPM"includes data for all genes above the minimal expression cutoff value, "OO"is the corresponding sub list for all genes with at least one over-outlier expression.

Additional file 1: Table S1. Sample lists for all data used in the study (Excel file with 7 tabs). Table S1A: mouse DOM population samples. Table S1B: mouse MUS, SPR and SPI population samples. Table S1C: human GTEX samples. Table S1D: Drosophila melanogaster and Drosophila simulans samples. Table S1E: mouse inbred strain C57BL/6 samples. Table S1F: human samples from sn-study. Table S1G: human samples from Rush AD study.

Additional file 6: Table S6. Gene lists with transcriptome data (TPM) for brain of the mouse inbred strain C57BL/6 (Excel file with two tabs). "BL6_brain_TPM_all"includes data for all genes above the minimal expression cutoff value, "BL6_brain_OO"is the corresponding sub list for all genes with at least one over-outlier expression

Additional file 13: Table S13. Data and analysis for the data from the single nuclei sequencing experiments in human brain (Excel table with two tabs). Table S13A: list of individuals and cell types analyzed. Table S13B: variance comparisons for the OO genes.