We investigate the empirical relationship between ethnicity and culture, defined as a vector of traits reflecting norms, values, and attitudes. Using survey data for 76 countries, we find that ethnic identity is a significant predictor of cultural values, yet that within-group variation in culture trumps between-group variation. Thus, in contrast to a commonly held view, ethnic and cultural diversity are unrelated. Although only a small portion of a country's overall cultural heterogeneity occurs between groups, we find that various political economy outcomes (such as civil conflict and public goods provision) worsen when there is greater overlap between ethnicity and culture.

Diversity, Equity, Inclusion, and Accessibility (DEIA): In FY 2022, DHS approved the FY 2022-FY 2026 ICE DEIA Strategic Plan and Directorate Implementation Plans, which illustrates why creating a diverse and inclusive workforce is fundamental to ICE’s continuing ability to perform its critical mission efficiently and effectively. During this time, DEIA policies issued to the ICE workforce included the ICE Anti-Harassment Policy, the Diversity Policy, and the Civil Rights and Civil Liberties Policy. Additionally, ODCR, in collaboration with OHC, drafted new requirements for ICE to mandate diverse interview panels when filling supervisory positions via the competitive procedures in support of the DHS Secretary’s priority to advance DEIA.

The GIST Impact DEI data offers a glimpse into the gender pay gap trends at top European companies and delves deeper into how these pay disparities materialize at different levels of the hierarchy.

By analysing labour force participation and pay gap data, we provide a picture of how well these businesses are performing in terms of Diversity, Equity, and Inclusion (DEI). The analysis also serves as a benchmark to help gauge corporate progress on DEI commitments, particularly related to gender diversity.

GIST Impact’s analysis delivers meaningful quantitative data insights concerning women's workforce participation and career progression, drawing upon publicly available and secondary data sources. This method provides a more nuanced depiction of the impact of gender-inclusive policies and practices than simply presenting gender equality scores based on qualitative data.

Our workplace diversity Data analysis also gives context to theoretical frameworks such as the "glass ceiling" effect that underscores the discrimination faced by women in the workplace. The glass ceiling effect can have a significant impact on an individual's professional development, and addressing it requires proactive efforts to promote diversity, equity, and inclusion in the workplace.

GIST Impact's DEI data can be used to: - Measure diversity and gender pay gap of companies and portfolios - Benchmark companies within their sector - Benchmark a portfolio against indices - Screen companies for risk and opportunity - Integrate sustainability into portfolio decision-making

Jaehun/data-diversity-nli-ood-test dataset hosted on Hugging Face and contributed by the HF Datasets community

Demographic Data - Diversity Efforts

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

Explore demographic data on the Massachusetts executive branch workforce. Track our progress toward our goals to reflect the diversity of the people we serve, and to stand out as an employer of choice.

Analysis of ‘2015-2016 Demographic Data - Diversity Efforts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ade5460e-c0fd-48ce-8245-913bfb2a5583 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Demographic Data - Diversity Efforts

--- Original source retains full ownership of the source dataset ---

Users can obtain descriptions, maps, profiles, and ranks of U.S. metropolitan areas pertaining to quality of life, diversity, and opportunities for racial and ethnic groups in the U.S. BackgroundThe Diversity Data project operates a website for users to explore how U.S. metropolitan areas perform on evidence-based social measures affecting quality of life, diversity and opportunity for racial and ethnic groups in the United States. These indicators capture a broad definition of quality of life and health, including opportunities for good schools, housing, jobs, wages, health and social services, and safe neighborhoods. This is a useful resource for people inter ested in advocating for policy and social change regarding neighborhood integration, residential mobility, anti-discrimination in housing, urban renewal, school quality and economic opportunities. The Diversity Data project is an ongoing project of the Harvard School of Public Health (Department of Society, Human Development and Health). User FunctionalityUsers can obtain a description, profile and rank of U.S. metropolitan areas and compare ranks across metropolitan areas. Users can also generate maps which demonstrate the distribution of these measures across the United States. Demographic information is available by race/ethnicity. Data NotesData are derived from multiple sources including: the U.S. Census Bureau; National Center for Health Statistics' Vital Statistics Natality Birth Data; Natio nal Center for Education Statistics; Union CPS Utilities Data CD; National Low Income Housing Coalition; Freddie Mac Conventional Mortgage Home Price Index; Neighborhood Change Database; Joint Center for Housing Studies of Harvard University; Federal Financial Institutions Examination Council Home Mortgage Disclosure Act (HMD); Dr. Russ Lopez, Boston University School of Public Health, Department of Environmental Health; HUD State of the Cities Data Systems; Agency for Healthcare Research and Quality; and Texas Transportation Institute. Years in which the data were collected are indicated with the measure. Information is available for metropolitan areas. The website does not indicate when the data are updated.

This dataset contains Hospital Supplier Diversity Plans.

As outlined in Health and Safety Code Section 1339.85-1339.87, licensed hospitals with operating expenses of fifty million dollars ($50,000,000) or more, and each licensed hospital with operating expenses of twenty-five million dollars ($25,000,000) or more that is part of a hospital system, shall submit an annual report to the department on its minority, women, LGBT, and disabled veteran business enterprise procurement efforts during the previous year.

Details on reporting requirements can be found in Section 1339.87.

For more on Hospital Supplier Diversity Plans.

Data notes: The information contained in a hospital’s plan on minority, women, LGBT, and disabled veteran business enterprises is provided for informational purposes only.

Suppliers are not required to disclose the above information to hospitals, and therefore not all diverse spending will be accurately identified.

Diversity statistics for selection exercises showing

FCO(S) - Staff Diversity information & statistics

Replication Data and Stata Do-File for: "The Diverse Effects of Diversity on Democracy"

No description was included in this Dataset collected from the OSF

Included in this dataset are SNP and fasta data for the Pea Single Plant Plus Collection (PSPPC) and the PSPPC augmented with 25 P. fulvum accessions. These 6 datasets can be roughly divided into two groups. Group 1 consists of three datasets labeled PSPPC which refer to SNP data pertaining to the USDA Pea Single Plant Plus Collection. Group 2 consists of three datasets labeled PSPPC + P. fulvum which refer to SNP data pertaining to the USDA PSPPC with 25 accessions of Pisum fulvum added. SNPs for each of these groups were called independently; therefore SNP names that are shared between the PSPPC and PSPPC + P. fulvum groups should NOT be assumed to refer to the same locus. For analysis, SNP data is available in two widely used formats: hapmap and vcf. These formats can be successfully loaded into TASSEL v. 5.2.25 (http://www.maizegenetics.net/tassel). Explanations of fields (columns) in the VCF files are contained within commented (##) rows at the top of the file. Descriptions of the first 11 columns in the hapmap file are as follows: rs#- Name of locus (i.e. SNP name) alleles- Indicates the SNPs for each allele at the locus chrom- Irrelevant for these datasets, since markers are unordered. pos- Irrelevant for these datasets, since markers are unordered. strand- Irrelevant for these datasets, since markers are unordered assembly#- required field for hapmap format. NA for these datasets center- required field for hapmap format. NA for these datasets protLSID- required field for hapmap format. NA for these datasets assayLSID- required field for hapmap format. NA for these datasets panel- required field for hapmap format. NA for these datasets QCcode- required field for hapmap format. NA for these datasets The fasta sequences containing the SNPs are also available for such downstream applications as development of primers for platform-specific markers. For more information about this dataset, contact Clarice Coyne at Clarice.Coyne@usda.gov or coynec@wsu.edu. Resources in this dataset:Resource Title: PSPPC SNPs in hapmap format. File Name: PSPPC.hmp.txtResource Description: 66591 unanchored SNPs for the PSPPC collection in hapmap formatResource Software Recommended: TASSEL,url: http://www.maizegenetics.net/tassel Resource Title: PSPPC SNP FASTA Sequences. File Name: PSPPC.fa.txtResource Description: FASTA sequences for each allele of the PSPPC SNP datasetResource Title: PPSPPC + P. fulvum SNPs in hapmap format. File Name: PSPPC+fulvums.hmp.txtResource Description: 67400 SNPs from the PSPPC augmented with 25 P. fulvum accessions in hapmap format. SNP names are independent and unrelated to plain PSPPC SNP files.Resource Software Recommended: TASSEL,url: http://www.maizegenetics.net/tassel Resource Title: PSPPC + P. fulvum SNP FASTA Sequences. File Name: PSPPC+fulvums.fa.txtResource Description: FASTA sequences for each allele of the PSPPC + P. fulvum SNP dataset. SNP names are independent and unrelated to plain PSPPC SNP files.Resource Title: PSPPC + P. fulvum SNPs in vcf format. File Name: PSPPC+fulvums.vcf.txtResource Description: 67400 SNPs from the PSPPC augmented with 25 P. fulvum accessions in vcf format. SNP names are independent and unrelated to plain PSPPC SNP files.Resource Software Recommended: TASSEL,url: http://www.maizegenetics.net/tassel Resource Title: PSPPC SNPs in vcf format. File Name: PSPPC.vcf.txtResource Description: 66591 SNPs from the PSPPC in vcf formatResource Software Recommended: TASSEL,url: http://www.maizegenetics.net/tassel Resource Title: README. File Name: Data Dictionary.docxResource Description: These data are for the Pea Single Plant Plus Collection (PSPPC) and the PSPPC augmented with 25 P. fulvum accessions. The 6 datasets can be divided into two groups. Group 1 consists of 3 datasets labeled “PSPPC” which refer to SNP data pertaining to the USDA Pea Single Plant Plus Collection. Group 2 consists of 3 datasets labeled “PSPPC + P. fulvum” which refer to SNP data pertaining to the PSPPC with 25 accessions of Pisum fulvum added. SNPs for each of these groups were called independently; therefore any SNP name that is shared between the PSPPC and PSPPC + P. fulvum groups should NOT be assumed to refer to the same locus. For analysis, SNP data is available in two widely used formats: hapmap and vcf. These files were successfully loaded into the standalone version of TASSEL v. 5.2.25 (http://www.maizegenetics.net/tassel). Explanations of fields (columns) in the VCF files are contained within commented (##) rows at the top of the file. The first 11 columns required for the hapmap format are as follows: rs#- Name of locus (i.e. SNP name) alleles- Indicates the SNPs for each allele at the locus chrom- N/A, since markers are unordered. pos- N/A, since markers are unordered. strand- N/A, since markers are unordered assembly#- N/A center- N/A protLSID- N/A assayLSID- N/A panel- N/A QCcode- N/A The fasta sequences containing the SNPs are also available here for such downstream applications as development of primers for platform-specific markers.

This dataset contains content generated by six large language models, including GPT‐3.5, GPT‐4o, Mistral 7B, Mistral Large, Claude 3 Haiku, and Claude 3.5 Sonnet. Please refer to our paper for more details on the process of content generation.

Plant species cover-abundance and presence observed in multi-scale plots. Plant species and associated percent cover in 1m2 subplots and plant species presence in 10m2 and 100m2 subplots are reported from 400m2 plots. Archived plant vouchers and foliar tissue support the data and additional analyses.

This dataset covers all publically listed companies in the United States from 2000 to 2018, which are listed in the S&P index. The starting point of 2000 is due to the minimal data available in the BoardEX database before this time in relation to board directors' information. Compustat is the source of financial data. As previous research indicates, financial and utilities firms are excluded from the sample due to their distinct regulations, which expose their directors to liability risks that non-financial firms are not subject to (Adams and Mehran, 2012; Sila et al., 2016). The sample size of non-financial firms amounts to 17,220. Financial variable outliers are adjusted to the 98% level in accordance with Bharath and Shumway's (2008) study.

Many organizations are interested in increasing the diversity of their workforce and spend millions of dollars on diversity training. Yet there is little empirical evidence that such training increases diversity in organizations. We implemented a large-scale field experiment in a global telecommunications and engineering firm (n = 10,433) testing whether behaviorally designed training increases the diversity of who is hired. In particular, the diversity training was timely (delivered immediately before hiring managers shortlisted candidates), tailored to the hiring decision, delivered by senior members of the organization, and made diversity salient. Results show that behaviorally designed diversity training can positively influence the hiring of women and non-national applicants relative to business as usual. Our findings suggest that behaviorally designed diversity training can work to change the diversity of hires but that its success relies on carefully considered design choices and the decision context.

R script for diversity estimates and testing with DivNet.