About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Eugene. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Eugene, the median household income for the households where the householder is White increased by $8,181(14.35%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $57,027 in 2013 and $65,208 in 2023.
• Black or African American: In Eugene, the median household income for the households where the householder is Black or African American increased by $10,067(26.34%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $38,226 in 2013 and $48,293 in 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Eugene.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

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About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Eugene. The dataset can be utilized to gain insights into gender-based income distribution within the Eugene population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Eugene, among individuals aged 15 years and older with income, there were 68,192 men and 71,087 women in the workforce. Among them, 29,599 men were engaged in full-time, year-round employment, while 22,779 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.86% fell within the income range of under $24,999, while 8.41% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 25.10% of men in full-time roles earned incomes exceeding $100,000, while 17.40% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

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Reconstruction of historical relationships between geographic regions within a species' range can indicate dispersal patterns and help predict future responses to shifts in climate. Ascaphus truei (coastal tailed frog) is an indicator species of the health of forests and perennial streams in the Coastal and Cascade Mountains of the Pacific Northwest of North America. We used two genetic techniques — microsatellite and genotype-by-sequencing (GBS) — to compare the within region genetic diversity of populations near the northern extent of the species’ range (British Columbia, Canada) to two geographic regions in British Columbia and two in Washington, USA, moving towards the core of the range. Allelic richness and heterozygosity declined substantially as latitude increased. The northernmost region had the lowest mean expected heterozygosities for both techniques (microsatellite, M = 0.20, SE = 0.080; GBS, M = 0.025, SE = 0.0010) and the southernmost region had the highest (microsatellite, M = 0.88, SE = 0.054; GBS, M = 0.20, SE = 0.0029). The northernmost regions (NC and MC) clustered together in population structure models for both genetic techniques. Our discovery of reduced diversity may have important conservation and management implications for population connectivity and the response of A. truei to climate change. Methods We employed an opportunistic non-random sampling scheme for tissue collection, and streams were included based on accessibility. Larvae were caught using a dipnet while flipping over rocks. Tissue samples consisted of skin clipped from the posterior of the tail, and were preserved in 95% ethanol and stored at -80 °C. Sampling in British Columbia, Canada, was in 2014 and 2015. We recieved purified DNA from a colleague from two geographic regions in Washington, USA (see Spear & Storfer, 2008; Spear et al., 2012). DNA was extracted from tail clips using the DNeasy Blood and Tissue kit (Qiagen, Inc., Toronto, ON) following the manufacturer's instructions. This paragraph is in reference to the microsatellite dataset. We used ten polymorphic microsatellite DNA markers (Spear et al., 2008). PCR thermal cycling included an initial denaturation at 95 °C for 15 minutes, followed by 35 cycles at a locus specific annealing temperature for 30 seconds, an extension at 72 °C for 30 seconds, and a further denaturation at 95 °C for 30 seconds. The cycles were followed by an additional elongation at the annealing temperature for 60 seconds. One primer per pair was labeled with a fluorescent tag (FAM, PET, or VIC). Four amplicon pools were created based on size and generated multilocus genotypes using fragment analysis with the Applied Biosystems 3130xL (Burlington, ON). We scored microsatellite genotypes with GeneMapper (Applied Biosystems). The following 6 paragraphs are in reference to the genotype-by-sequencing dataset and the mtDNA dataset. We sent purified DNA for nextRAD library preparation, sequencing, and initial filtering to SNPsaurus, LLC (Eugene, OR). Fifteen samples were sent for sequencing in duplicate and triplicate to determine the efficacy of the genotyping method. SNPsaurus converted genomic DNA into nextRAD genotyping-by-sequencing libraries. Genomic DNA was randomly fragmented with Nextera reagent (Illumina, Inc), which also ligates short adapter sequences to the ends of the fragments. The Nextera reaction was scaled for fragmenting 25 ng of genomic DNA, although 50 ng of genomic DNA was used for input to compensate for any degraded DNA in the samples and to increase fragment sizes. Fragmented DNA was then amplified for 27 cycles with 74 °C extension, with one of the primers matching the adapter and extending 10 nucleotides into the genomic DNA with the selective sequence GTGTAGAGCC. Thus, only fragments starting with a sequence that could be hybridized by the selective sequence of the primer were efficiently amplified. SNPsaurus sequenced the nextRAD libraries on a HiSeq 4000 with two lanes of 150 base pair (bp), single reads (University of Oregon) and 20x depth of coverage. SNPsaurus LLC conducted the bioinformatic analysis of the raw reads to produce a vcf genotype file for population genetic analysis. Custom scripts that trimmed the sequence reads based on bbduk (BBMAP TOOLS): bash bbmap/bbduk.sh in=$file out=$outfile ktrim=r k=17 hdist=1 mink=8 ref=bbmap/resources/nextera.fa.gz minlen=100 ow=t qtrim=r trimq=10. A reference adapter was matched to the reads and all bases to the right were trimmed (as these were single-end reads), allowing for one mismatch. Reads were trimmed at bases with a quality score of 10 or less and reads shorter than 100 base pairs were removed. After trimming, an analysis of the reads (bbduk) shows 83.4% of bases had the highest-possible quality score and 1.4% had a quality score lower than Q20. Average quality declined slightly along the read length to a minimum of Q37.7 at nucleotide 150. A de novo reference was created by collecting 10 million reads in total, evenly from the samples, and excluding clusters that had counts fewer than 20 or more than 1000. The remaining clusters were then aligned to each other to identify allelic loci and collapse allelic haplotypes to a single representative. For each sample, all reads were mapped to the reference with an alignment identity threshold of 95% using bbmap (BBMAP TOOLS). Genotype calling was done using SAMTOOLS v1.8 and BCFTOOLS v1.8 (samtools mpileup -gu -Q 10 -t DP, DPR -f ref.fasta -b samples.txt | bcftools call -cv - > genotypes.vcf), generating a vcf file. The vcf file was filtered to remove alleles with a population frequency of less than 3% (referred to as minor allele frequency). Loci were removed that were heterozygous in all samples or had more than 2 alleles in a sample (suggesting collapsed paralogs). The presence of artifacts was checked by counting SNPs at each read nucleotide position and determining that SNP number did not increase with reduced base quality at the end of the read. From the vcf file provided, we removed loci that were variable due to base insertions or deletions using VCFTOOLS v0.1.14. We removed loci with greater than or equal to 40 % missing data in at least one geographic region and loci with an excessive heterozygosity p-value of less than or equal to 0.005 per region to further reduce the potential impact of paralogs (GENALEX). We used the previously published Ascaphus mitochondrial genome from GenBank (Gissi et al., 2006) with the nextRAD sequencing data to extract mtDNA reads. The mtDNA genome was indexed, each genotype was separated into individual files, and each file was aligned to the mtDNA genome with the Burrows-Wheeler Aligner algorithm BWA-MEM (BWA). Using SAMTOOLS v1.8, alignments were removed if they had a MAPQ score (−10 log10Pr {mapping position is wrong}) of ≤ 30. We removed duplicate sequences and merged files using PICARD, retaining a single sample from those sent in triplicate or duplicates for sequencing. Files were sorted and indexed using SAMTOOLS. SNPs were called using FREEBAYES v0.9.10. The ploidy was set to 1, and the defaults were used for all other settings. Loci that had more than 5% missing calls across haplotypes were not included in the analysis, and haplotypes with greater than 40% missing calls were removed.

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