About this dataset

Context

This list ranks the 40 cities in the Blue Earth County, MN by Hispanic Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic Asian Population: This column displays the rank of cities in the Blue Earth County, MN by their Hispanic Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Asian Population: The Hispanic Asian population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Hispanic Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Blue Earth County Hispanic Asian Population: This tells us how much of the entire Blue Earth County, MN Hispanic Asian population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

Pew Research Center conducted random, probability-based surveys among 13,122 adults (ages 18 and older) across six South and Southeast Asian countries: Cambodia, Indonesia, Malaysia, Singapore, Sri Lanka and Thailand. Interviewing was carried out under the direction of Langer Research Associates. In Malaysia and Singapore, interviews were conducted via computer-assisted telephone interviewing (CATI) using mobile phones. In Cambodia, Indonesia, Sri Lanka and Thailand, interviews were administered face-to-face using tablet devices, also known as computer-assisted personal interviewing (CAPI). All surveys were conducted between June 1 and Sept. 4, 2022.

This project was produced by Pew Research Center as part of the Pew-Templeton Global Religious Futures project, which analyzes religious change and its impact on societies around the world. Funding for the Global Religious Futures project comes from The Pew Charitable Trusts and the John Templeton Foundation (grant 61640). This publication does not necessarily reflect the views of the John Templeton Foundation.

As of July 2024, one report has been published that focuses on the findings from this data: Buddhism, Islam and Religious Pluralism in South and Southeast Asia: https://www.pewresearch.org/religion/2023/09/12/buddhism-islam-and-religious-pluralism-in-south-and-southeast-asia/

Corn (Zea mays) is one of the most widely grown crops throughout the world. However, many corn fields develop pest problems such as corn borers every year that seriously affect its yield and quality. Corn's response to initial insect damage involves a variety of changes to the levels of defensive enzymes, toxins, and communicative volatiles. Such a dramatic change secondary metabolism necessitates the regulation of gene expression at the transcript level. This Data In Brief paper summarizes the datasets of the transcriptome of corn plants in response to corn stalk borers (Ostrinia furnacalis) and/or methyl jasmonate (MeJA). Altogether, 39, 636 genes were found to be differentially expressed. The sequencing data are available in the NCBI SRA database under accession number SRS965087. This dataset will provide more scientific and valuable information for future work such as the study of the functions of important genes or proteins and develop new insect-resistant maize varieties. Includes supplementary tables and data in fasta and GTF format. Resources in this dataset:Resource Title: Datasets for transcriptomic analyses of maize leaves in response to Asian corn borer feeding and/or jasmonic acid. File Name: Web Page, url: https://www.sciencedirect.com/science/article/pii/S2352340916301792 Data in Brief Article including supplemental data in fasta and GTF format.

About this dataset

Context

The dataset tabulates the population of Globe by race. It includes the population of Globe across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Globe across relevant racial categories.

Key observations

The percent distribution of Globe population by race (across all racial categories recognized by the U.S. Census Bureau): 58.09% are white, 2.70% are Black or African American, 5.26% are American Indian and Alaska Native, 2.92% are Asian, 0.12% are Native Hawaiian and other Pacific Islander, 11.37% are some other race and 19.54% are multiracial.

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: This column displays the racial categories (excluding ethnicity) for the Globe
• Population: The population of the racial category (excluding ethnicity) in the Globe is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Globe total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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

This dataset is a part of the main dataset for Globe Population by Race & Ethnicity. You can refer the same here

Central Asian Food Scenes Dataset

In this work, we propose the first Central Asia Food Scenes Dataset that contains 21,306 images with 69,856 instances across 239 food classes. To make sure that the dataset contains various food items, we took as a benchmark the ontology of Global Individual Food Tool developed by Food and Agriculture Organization (FAO) together with the World Health Organization (WHO) [1]. The dataset contains food items across 18 coarse classes: 🍅 Vegetables •… See the full description on the dataset page: https://huggingface.co/datasets/issai/Central_Asian_Food_Scenes_Dataset.

Occurrence records of tropical Asia's butterflies collated from online databases and published literature. This dataset consists of 730,190 occurrence records for 3,752 species.Methods:We collected occurrence records for tropical Asian Papilionoidea (Lepidoptera: Nymphalidae, Papilionidae, Lycaenidae, Pieridae, Hesperiidae, Riodinidae)(-11.426 – 35.64 N, 67.588 – 174.990 E) for the years 1970-present. Records were extracted from the Global Biodiversity Information Facility on 15 April 2024. We included only presence records derived from human observation, preserved specimens, material samples, or literature, provided they had associated coordinates. We omitted all records with >100,000 m coordinate uncertainty, so-called “fuzzy” taxon matches, and records for which the scientific name was missing or incomplete, unless nomenclature could be extracted using a BOLD identifier (boldsystems.org/). This resulted in a final number of GBIF records = 651,285. Note to commercial users: 432,340 GBIF records included within this dataset have a CC BY-NC 4.0 license. See column Z for data license types and/or visit the GBIF-derived dataset (https://doi.org/10.15468/dl.9wyfb6) to rerun the query and filter data according to their licenses.We also extracted data from the B2D2 Database of Butterflies for Borneo provided by JKH/the Darwin Initiative (n = 19,417) and a dataset for Bangladesh provided by SC (Chowdhury et al. 2021) (n = 18,278), and unpublished datasets from coauthors AN, DJL, LVV, TK, and YB (n = 13,993). To fill geographic gaps when all of these records were plotted, we conducted targeted searches of the published literature on Google Scholar (details below), resulting in an additional 27,217 records. Pre-1970 data were omitted where possible from all sources. Final binomial synonym harmonization, validation, and authority assignment were conducted by DJL using a taxonomic reference prepared by Gerardo Lamas (Lamas, 2015. Catalogue of the butterflies (Papilionidae). Available from the author.).For geographic regions with relatively few GBIF records (e.g., China, Myanmar, Thailand) and for species with < 10 records, we conducted targeted literature searches using Google Scholar in English, simplified Chinese, and traditional Chinese (genus OR genus + species + country name). For all species records in published sources, we extracted coordinates, locality name, locality type (e.g., exact coordinates, city, national park, island, or province), country, and year of record (where available). If exact coordinates were not provided by the source, we used Google Earth Pro (v7.3.6.9345) to estimate the locality centroid for any record provided at the province level or below (e.g., national park or city). For records from islands ≤ 100 km in length or diameter (e.g, localities within the Philippines and Indonesia), we estimated the island or archipelago centroid. If a range of coordinates was provided (e.g., records from The Butterflies of Vietnam), coordinates within the range were chosen haphazardly by selecting a point within the range provided on Google Earth Pro. Data sources for all records are provided in the reference column (C) in Occurrence Records of Tropical Asian Butterflies: 1970-2024 and alphabetically in Data Sources for: Occurrence Records of Tropical Asian Butterflies: 1970-2024.

Introduction

Welcome to the East Asian Human Face with Occlusion Dataset, carefully curated to support the development of robust facial recognition systems, occlusion detection models, biometric identification technologies, and KYC verification tools. This dataset provides real-world variability by including facial images with common occlusions, helping AI models perform reliably under challenging conditions.

Facial Image Data

The dataset comprises over 5,000 high-quality facial images, organized into participant-wise sets. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure robustness and real-world utility, images were captured under diverse conditions:

Metadata

Each image is paired with detailed metadata to enable advanced filtering, model tuning, and analysis:

This rich metadata helps train models that can recognize faces even when partially obscured.

Use Cases & Applications

This dataset is ideal for a wide range of real-world and research-focused applications, including:

Secure & Ethical Collection

Abstract

The third wave of the Asian Barometer survey (ABS) conducted in 2010 and the database contains nine countries and regions in East Asia - the Philippines, Taiwan, Thailand, Mongolia, Singapore, Vietnam, Indonesia, Malaysia and South Korea. The ABS is an applied research program on public opinion on political values, democracy, and governance around the region. The regional network encompasses research teams from 13 East Asian political systems and 5 South Asian countries. Together, this regional survey network covers virtually all major political systems in the region, systems that have experienced different trajectories of regime evolution and are currently at different stages of political transition.

The mission and task of each national research team are to administer survey instruments to compile the required micro-level data under a common research framework and research methodology to ensure that the data is reliable and comparable on the issues of citizens' attitudes and values toward politics, power, reform, and democracy in Asia.

The Asian Barometer Survey is headquartered in Taipei and co-hosted by the Institute of Political Science, Academia Sinica and The Institute for the Advanced Studies of Humanities and Social Sciences, National Taiwan University.

Geographic coverage

13 East Asian political systems: Japan, Mongolia, South Koreas, Taiwan, Hong Kong, China, the Philippines, Thailand, Vietnam, Cambodia, Singapore, Indonesia, and Malaysia; 5 South Asian countries: India, Pakistan, Bangladesh, Sri Lanka, and Nepal

Analysis unit

-Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Compared with surveys carried out within a single nation, cross-nation survey involves an extra layer of difficulty and complexity in terms of survey management, research design, and database modeling for the purpose of data preservation and easy analysis. To facilitate the progress of the Asian Barometer Surveys, the survey methodology and database subproject is formed as an important protocol specifically aiming at overseeing and coordinating survey research designs, database modeling, and data release.

As a network of Global Barometer Surveys, Asian Barometer Survey requires all country teams to comply with the research protocols which Global Barometer network has developed, tested, and proved practical methods for conducting comparative survey research on public attitudes.

Research Protocols:

• National probability samples that give every citizen in each country an equal chance of being selected for interview. Whether using census household lists or a multistage area approach, the method for selecting sampling units is always randomized. The samples may be stratified, or weights applied, to ensure coverage of rural areas and minority populations in their correct proportions. As such, Asian Barometer samples represent the adult, voting-age population in each country surveyed.

A model Asian Barometer Survey has a sample size of 1,200 respondents, which allows a minimum confidence interval of plus or minus 3 percent at 95 percent probability.

Mode of data collection

Face-to-face [f2f]

Research instrument

A standard questionnaire instrument containing a core module of identical or functionally equivalent questions. Wherever possible, theoretical concepts are measured with multiple items in order to enable testing for construct validity. The wording of items is determined by balancing various criteria, including: the research themes emphasized in the survey, the comprehensibility of the item to lay respondents, and the proven effectiveness of the item when tested in previous surveys.

Survey Topics: 1.Economic Evaluations: What is the economic condition of the nation and your family: now, over the last five years, and in the next five years? 2.Trust in institutions: How trustworthy are public institutions, including government branches, the media, the military, and NGOs. 3.Social Capital: Membership in private and public groups, the frequency and degree of group participation, trust in others, and influence of guanxi. 4.Political Participatio: Voting in elections, national and local, country-specific voting patterns, and active participation in the political process as well as demonstrations and strikes. Contact with government and elected officials, political organizations, NGOs and media. 5.Electoral Mobilization: Personal connections with officials, candidates, and political parties; influence on voter choice. 6.Psychological Involvement and Partisanship: Interest in political news coverage, impact of government policies on daily life, and party allegiance. 7.Traditionalism: Importance of consensus and family, role of the elderly, face, and woman in theworkplace. 8.Democratic Legitimacy and Preference for Democracy: Democratic ranking of present and previous regime, and expected ranking in the next five years; satisfaction with how democracy works, suitability of democracy; comparisons between current and previous regimes, especially corruption; democracy and economic development, political competition, national unity, social problems, military government, and technocracy. 9.Efficacy, Citizen Empowerment, System Responsiveness: Accessibility of political system: does a political elite prevent access and reduce the ability of people to influence the government. 10.Democratic vs. Authoritarian Values: Level of education and political equality, government leadership and superiority, separation of executive and judiciary. 11.Cleavage: Ownership of state-owned enterprises, national authority over local decisions, cultural insulation, community and the individual. 12.Belief in Procedural Norms of Democracy: Respect of procedures by political leaders: compromise, tolerance of opposing and minority views. 13.Social-Economic Background Variables: Gender, age, marital status, education level, years of formal education, religion and religiosity, household, income, language and ethnicity. 14.Interview Record: Gender, age, class, and language of the interviewer, people present at the interview; did the respondent: refuse, display impatience, and cooperate; the language or dialect spoken in interview, and was an interpreter present.

Cleaning operations

Quality checks are enforced at every stage of data conversion to ensure that information from paper returns is edited, coded, and entered correctly for purposes of computer analysis. Machine readable data are generated by trained data entry operators and a minimum of 20 percent of the data is entered twice by independent teams for purposes of cross-checking. Data cleaning involves checks for illegal and logically inconsistent values.

Title: Top 5000 Asian Dramas Dataset (1983-2023)

Description :

Explore the world of Asian television with this comprehensive dataset featuring the top 5000 dramas from five different languages - Korean, Japanese, Chinese, Taiwanese, and Thai. Spanning from 1983 to 2023, this dataset provides a unique insight into the evolution of dramatic storytelling across Asia.

Columns :

Name: The title of the drama.

Episodes: The number of episodes in the drama series.

Released Year: The year when the drama was originally released.

Language: The language in which the drama was produced.

Rating: The average viewer rating or score assigned to the drama.

Ranking: The position or ranking of the drama in the dataset, based on popularity or critical acclaim.

This dataset is a treasure trove for enthusiasts, researchers, and analysts interested in Asian television, providing a rich source of information on a wide array of dramas from different cultures and time periods.

Occurrence dataset: A relatively large (~1500) dataset of fossil mammal occurrence data for the Paleocene, Eocene and Oligocene (66 Ma - 23 Ma) of Mongolia and Northern China above 30 degrees North. Occurrence data comprises species or genus name, specimen information where possible, geological unit specimen was found in, age (range) of specimen and/or geological unit and any other relevant information. Data taken from multiple sources. The majority comes from the Palaeobiology Database (PBDB), an open-access community dataset of global fossil occurrences (and some trait data) for all time periods and taxonomic groups. Our dataset used only the mammal records from our study region and time period. A very small amount of data (10's of occurrences) was taken from the NOW (New and Old Worlds) Database of fossil mammals (NOW database), another open-access community dataset. This database contains only mammal occurrence and trait data for fossil mammals throughout geological history and across the world. Additional occurrence data (~100) was collected first hand from the literature by Dr Gemma Benevento.

Body Size dataset: Lower first molar (m1) length and width (which can be used to estimate mammal body size) was collected for approximately 60% of the individual species in the occurrence dataset (~430 species).

This dataset tracks annual asian student percentage from 1991 to 2023 for Rim Of The World Senior High School vs. California and Rim Of The World Unified School District

We assess the potential impact of international migration on population ageing in Asian countries by estimating replacement migration for the period 2022-2050.

This open data deposit contains the code (R-scripts) and the datasets (csv-files) for the replacement migration scenarios and a zero-migration scenario:

• Constant chronological old-age dependency ratio (Constant OADR scenario)
• Constant prospective old-age dependency ratio (Constant POADR scenario)
• Constant chronological working-age population (Constant WA scenario)
• Constant prospective working-age population (Constant PWA scenario)
• Zero-migration (ZM scenario)

Countries included in the analysis: Armenia, China, Georgia, Hong Kong, Japan, Macao, North Korea, Singapore, South Korea, Taiwan, Thailand.

Please note that for Armenia and Hong Kong (2023) and Georgia (2024) later baseline years are applied due to the UN country-specific assumptions on post-Covid-19 mortality.

For detailed information about the scenarios and parameters:

Dörflinger, M., Potancokova, M., Marois, G. (2024): The potential impact of international migration on prospective population ageing in Asian countries. Asian Population Studies. https://doi.org/10.1080/17441730.2024.2436201

All underlying data (UN World Population Prospects 2022) are openly available at:

https://population.un.org/wpp/Download/Archive

Code

1_Data.R:

• Load and merge data from UN World Population Prospects 2022
• Define sample
• Prepare data (prospective old-age thresholds, model sex and age pattern of migrants)

2_Scenarios.R:

• Replacement migration scenarios:
  • Constant chronological old-age dependency
  • Constant prospective old-age dependency
  • Constant chronological working-age population
  • Constant prospective working-age population
• Zero-migration scenario

3_Robustness_checks.R:

• Run replacement migrations scenarios with different model sex and age patterns for net migration

Program version used: RStudio "Chocolate Cosmos" (e4392fc9, 2024-06-05). Files may not be compatible with other versions.

Datasets

The datasets contain the key information on population size, the relevant indicators (OADR, POADR, WA, PWA) and replacement migration volumes and rates by country and year. Please see readme_datasets.txt for detailed information.

Acknowledgements

Part of the research was developed in the Young Scientists Summer Program at the International Institute for Applied Systems Analysis, Laxenburg (Austria) with financial support from the German National Member Organization.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of White Earth by race. It includes the distribution of the Non-Hispanic population of White Earth across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of White Earth across relevant racial categories.

Key observations

With a zero Hispanic population, White Earth is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 76 (100% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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: This column displays the racial categories (for Non-Hispanic) for the White Earth
• Population: The population of the racial category (for Non-Hispanic) in the White Earth is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of White Earth total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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

This dataset is a part of the main dataset for White Earth Population by Race & Ethnicity. You can refer the same here

BackgroundZika virus (ZIKV; Flaviviridae, Flavivirus) was declared a public health emergency of international concern by the World Health Organization (WHO) in February 2016, because of the evidence linking infection with ZIKV to neurological complications, such as Guillain-Barre Syndrome in adults and congenital birth defects including microcephaly in the developing fetus. Because development of a ZIKV vaccine is a top research priority and because the genetic and antigenic variability of many RNA viruses limits the effectiveness of vaccines, assessing whether immunity elicited against one ZIKV strain is sufficient to confer broad protection against all ZIKV strains is critical. Recently, in vitro studies demonstrated that ZIKV likely circulates as a single serotype. Here, we demonstrate that immunity elicited by African lineage ZIKV protects rhesus macaques against subsequent infection with Asian lineage ZIKV.Methodology/Principal FindingsUsing our recently developed rhesus macaque model of ZIKV infection, we report that the prototypical ZIKV strain MR766 productively infects macaques, and that immunity elicited by MR766 protects macaques against heterologous Asian ZIKV. Furthermore, using next generation deep sequencing, we found in vivo restoration of a putative N-linked glycosylation site upon replication in macaques that is absent in numerous MR766 strains that are widely being used by the research community. This reversion highlights the importance of carefully examining the sequence composition of all viral stocks as well as understanding how passage history may alter a virus from its original form.Conclusions/SignificanceAn effective ZIKV vaccine is needed to prevent infection-associated fetal abnormalities. Macaques whose immune responses were primed by infection with East African ZIKV were completely protected from detectable viremia when subsequently rechallenged with heterologous Asian ZIKV. Therefore, these data suggest that immunogen selection is unlikely to adversely affect the breadth of vaccine protection, i.e., any Asian ZIKV immunogen that protects against homologous challenge will likely confer protection against all other Asian ZIKV strains.

Introduction

Welcome to the Hispanic Human Face with Occlusion Dataset, carefully curated to support the development of robust facial recognition systems, occlusion detection models, biometric identification technologies, and KYC verification tools. This dataset provides real-world variability by including facial images with common occlusions, helping AI models perform reliably under challenging conditions.

Facial Image Data

The dataset comprises over 3,000 high-quality facial images, organized into participant-wise sets. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure robustness and real-world utility, images were captured under diverse conditions:

Metadata

Each image is paired with detailed metadata to enable advanced filtering, model tuning, and analysis:

This rich metadata helps train models that can recognize faces even when partially obscured.

Use Cases & Applications

This dataset is ideal for a wide range of real-world and research-focused applications, including:

Secure & Ethical Collection

Dataset

This dataset tracks annual asian student percentage from 1992 to 2023 for Lake Arrowhead Elementary School vs. California and Rim Of The World Unified School District

This dataset tracks annual asian student percentage from 1996 to 2023 for Brooks Global Elementary School vs. North Carolina and Guilford County Schools School District