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295 results for “ethnicity”
Gender and ethnic diversity of members of US university natural resource program external advisory board members, 2017-2022
This dataset contains deidentified demographics information for the members of external advisory boards that serve university natural resource programs. Data collected in 2017 and 2022 represents a sample of land-grant, National Association of University Forestry Program-affiliated, TIMES-ranked universities and colleges. Each row represents a member of an advisory board. Data collection was completed in two years: 2017 and 2022. Data was collected from department webpages and lists of advisory board members provided by department personnel. As needed, information was augmented through internet searches for public LinkedIn pages, organizational pages, local news stories, etc. Data include a unique respondent ID, a code for the university they are from, their employer affiliation (e.g., NGO, federal government, NR business, etc.) and their gender and ethnicity measured as binary variables.
Ethnic and Migrant Minorities (EMM) Survey Registry: All metadata records
<p>The <a href="https://ethmigsurveydatahub.eu/emmregistry/">Ethnic and Migrant Minorities (EMM) Survey Registry</a> is a free online tool that allows users to search for and learn about existing quantitative surveys undertaken with EMM (sub)populations conducted in 34 European countries, from 2000 onwards, through compiled survey-level metadata.</p> <p>The first version was produced by a team led by CEE (Sciences Po, CNRS) and jointly funded through the COST Action 16111 – ETHMIGSURVEYDATA (a network of more than 200 European researchers active in the ethnic and migration studies field), the Horizon 2020 infrastructure project SSHOC (within Task 9.2 on Ethnic and Migration Studies, within Work Package 9 on Data Communities) and the project FAIRETHMIGQUANT (an Open Science project funded by the French Agence Nationale de la Recherche, ANR).</p> <p>This specific record includes the metadata for 2,120 survey records as .dta, .sav and .csv files published on the Registry, as of 31.07.2025.</p>
Ethnic Discrimination in the Swiss Labour Market
<p>Discrimination of ethnic minorities in hiring decisions in the labour market has become a common phenomenon. Across OECD countries ethnic minority applicants have to write approximately 50% more applications to be invited for a job interview compared to an equally qualified majority applicant. Little is known about the extent of discrimination in the Swiss Labour Market, since the last correspondence test conducted in Switzerland is more than 15 years old and targets only a very specific sector of the labour market - the transition from school to apprenticeships.<br> To fill this gap in knowledge on discrimination in the Swiss labour market, a new correspondence test was conducted. It measures discrimination in various occupations, against different ethnic minority groups, including candidates with German or French backgrounds, and looks at the differences and similarities between the German and French speaking regions in Switzerland.</p>
Inter-Chemical Correlation results for the study: HHEARx2018-2537 (Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort.)
Title: Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort. <br>Species: Homo sapiens <br>Number of samples: 630 <br>Number of named analytes: 16 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=66 <br>
Data from: Breaking Barriers? Ethnicity and socioeconomic background impact on early career progression in the fields of ecology and evolution
<p>The academic disciplines of Science, Technology, Engineering and Mathematics (STEM) have long suffered from a lack of diversity. While in recent years there has been some progress in addressing the underrepresentation of women in STEM subjects, other characteristics that have the potential to impact on equality of opportunity have received less attention. In this study, we surveyed 188 early career scientists (ECRs), defined as within ten years of completing their PhD, in the fields of ecology, evolutionary biology, behaviour, and related disciplines. We examined associations between ethnicity, age, sexual orientation, sex, socioeconomic background, and disability, with measures of career progression, namely publication record, number of applications made before obtaining a postdoc, type of contract, and number of grant applications made. We also queried respondents on perceived barriers to progression, and potential ways of overcoming them. Our key finding was that socioeconomic background and ethnicity were associated with measures of career progression. While there was no difference in the number of reported first-authored papers on PhD completion, ethnic minority respondents reported fewer other-authored papers. In addition, ECRs from a lower socioeconomic background were more likely to report being in teaching and research positions, rather than research only positions, the latter being perceived as more prestigious by some institutions. We discuss our findings in the context of possible inequality of opportunity. We hope that this study will stimulate wider discussion, and help to inform strategies to address the underrepresentation of minority groups in the fields of ecology and evolution, and STEM subjects more widely.</p>
SSHOC Training Material Video 1 - What is the Ethnic and Migrant Minorities Survey Registry? - training video in English, French and Spanish
<p>A generic video, like the one produced about the SSH Marketplace, clearly and succinctly introducing and presenting the EMM Survey Registry. Would serve as an introductory video to train any new user on what the EMM Survey Registry is and who this tool is for. Would also present all available resources about the Registry (so introducing the full set of training materials available about the Registry)</p> <p>This has been produced in English, French, Spanish</p>
SSHOC Training Material Video 3 - Contributing metadata to the COVID-19 collection of the Ethnic and Migrant Minorities (EMM) Survey Registry as a data producer - training video in English, French and Spanish
<p>Contributing metadata to the COVID-19 collection of the Ethnic and Migrant Minorities (EMM) Survey Registry as a data producer</p> <p>A training video targeting COVID-19 survey producers to entice contributions to the COVID-19 collection of the EMM Survey Registry<br> <strong>Target Audience for the video</strong>: Survey producers (academic and non-academic) of COVID-19 surveys with EMM respondents</p>
SSHOC Training Material Video 2 - How to exploit the Ethnic and Migrant Minorities Survey Registry as an academic and non-academic user - training video in English, French and Spanish
<p>A training video about how to search for and learn about surveys using the EMM Survey Registry, both as an academic and non-academic user.</p> <p>This video has been produced in English, French, Spanish</p>
Figure 9. Classification accuracy regardless the ethnic group (Total accuracy 75%)-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Our experiments show that, the impact of ethnic group on the accuracy of emotions<br> recognition is a positive where the accuracy of emotion recognition considering ethnic group is<br> 83.3% as shown in Figure 8, and we got 75% of accuracy regardless ethnic group as shown in<br> Figure 9.</p>
Figure 8. Classification accuracy of emotions considering the ethnic group-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>To study the accuracy of emotion recognition for our approach regardless the ethnic group<br> we used 108 images for the training representing six emotions of six persons. For testing, we used<br> 36 images representing six emotions of six persons.<br> On the other hand, to study the accuracy of emotion recognition for our approach<br> considering the ethnic group we used 36 images for training for each ethnic group representing six<br> emotions of six persons, and test the classifier by using 12 images representing six emotions of six<br> persons.</p>
Association of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model
<p>this dataset are “ssociation of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model” Supplementary Material.</p>
Figure 6. Interface of FFE program-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Points, Features Extraction and save all input information for the classifier (Features, Ethnic group,<br> Gender and emotion). Figure 6 shows the interface of FFE program.</p>
Figure 7. Samples of MSFDE dataset-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>We performed groups of experiments to study the impact of ethnic group (race) in the<br> accuracy of emotion recognition with three kinds of ethnic groups (Asian, Caucasian as African).<br> So we have three experiments, each experiment has a neural network as a classifier, and each neural<br> network has three layers where there are 16 neurons in the hidden layer except Asian network has<br> 17 neurons (the best result with 17 neurons for Asians).</p>
Figure 3. 46 points are selected on face elements to describe the emotions.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>The number of points and the position of points are not standardized, but it is depending on<br> the features that will be extracted, and used for the classifier. Many researches use various number<br> of points and positions based on their view about the feature to be considered [13] [18] [19]. Figure<br> 3 shows the points we used.</p>
Figure 1. A proposed approach-of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Our proposed approach uses the face expression to detect the emotions through five steps<br> that shows in Figure 1.</p>
Figure. 2. Examples of Angry from different races. (A,B) African. (C,D) Asian. (E,F) Caucasian.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>We chose 46 points which are distributed over human face image and use these points for<br> features extraction. The choice of these points is to determine the shape of each element of the face<br> (eyes, eyebrows and mouth), because the shape of these elements is changeable for each emotion,<br> but these changes are different for each race as shown in Figure 2.</p>
Figure 4. Distance between eyebrow and eye.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Based on what we stated above, we need to extract 28 features, which describe the distances<br> between certain points explained in the previous stage, these features are classified into six groups,<br> and each group describes the features of one face element. All features are a vertical distances<br> between two points. Group one contains seven features for mouth, groups two and three contains 14<br> features for eyes, groups four and five contain six features for eyebrows, and the last group has one<br> feature only which is the distance between the beginning of the eyebrow and the beginning of the<br> eye in same side, this is significant (from point 23 to 15) because it is used to measure the distance<br> of eyebrow from the eye. This feature is shown in Figure 4 by a line.</p>
Figure 5. ANN Structure 4.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>For classification purpose of the emotions, we use ANN of supervised learning based on<br> backpropagation algorithm. Backpropagation neural network architecture is used with its standards<br> learning function with 28 inputs representing the extracted features and 6 outputs representing 6<br> emotions, happy, sad, angry, fear, shame and disgust. the emotions. We have also a hidden layer<br> with 16 nodes selected after various trails to obtain the best results. The used ANN is depicted in<br> Figure 5.</p>
hg19KIndel: Ethnicity normalized human reference genome
<p>The above zip files (hg19KIndel_Resource.zip) contains the following folders. Specific Details about how each file within the below mentioned folders were derived are present in individual README files for each folder</p> <p>1) hg19Kindel</p> <p> -hg19Kindel.fa - fasta file representing the modified assembly</p> <p>2) Gene Annotations</p> <p> -hg19_refGene.txt - RefSeq gene annotation downloaded from UCSC table browser (for hg19)<br> -hg19Kindel_refGene.txt - RefSeq gene annotation corresponding to hg19Kindel Genome</p> <p>3) SnpEff_Database</p> <p> -snpEffectPredictor.bin - Binary file used by SnpEff to annotate variants</p> <p>4) LiftOver_and_Chain_File</p> <p> -hg19Kindeltohg19.over.chain - UCSC chain file for lifting coordinates from hg19Kindel to hg19<br> -convert_cordinates_vcf.py - Python script to liftover variants(vcf) (only point coordinates) called on hg19Kindel to hg19 coordinate frame</p> <p> </p>
Figuras que compõem a Carta Executiva "Paridade Étnica e Competitividade das Companhias Brasileiras", publicadas na edição inaugural do Journal of Racial and Ethnic Social Equality
<p>As figuras aqui disponibilizadas são as que compõem a Carta Executiva, que pode ser acessada livremente no site oficial da revista: https://jrese.org/</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.