About this dataset

Context

This list ranks the 40 cities in the Blue Earth County, MN by 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 Asian Population: This column displays the rank of cities in the Blue Earth County, MN by their Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Asian Population: The 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 Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Blue Earth County Asian Population: This tells us how much of the entire Blue Earth County, MN 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/.

This uniquely granular dataset captures 13,427 development projects worth $843 billion financed by more than 300 Chinese government institutions and state-owned entities across 165 countries in every major region of the world from 2000-2017.

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/

About this dataset

Context

This list ranks the 50 states in the United States by Chinese population, as estimated by the United States Census Bureau. It also highlights population changes in each state 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
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Chinese Population: This column displays the rank of state in the United States by their Chinese population, using the most recent ACS data available.
• State: The State for which the rank is shown in the previous column.
• Chinese Population: The Chinese population of the state is shown in this column.
• % of Total State Population: This shows what percentage of the total state population identifies as Chinese. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total United States Chinese Population: This tells us how much of the entire United States Chinese population lives in that state. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This 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/.

Central Asia, site of the historic Silk Road trade network, has long been a conduit for the movement of people, energy, and mineral resources between Europe and Asia. Once part of the former Soviet Union, this region was and continues to be an important producer of base and precious metals, rare metals (RM), including niobium, tantalum, and beryllium, and a past producer of rare earth elements (REE). The Tien Shan and Pamir Mountains regions, encompassing parts of Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan, are of significant interest for mineral exploration as these regions are thought to host substantial undeveloped and undiscovered resources of REE and RM. Based on this legacy, and as an emerging REE and RM producing region, the Central Asian countries are implementing mining sector reforms to create a more attractive investment environment for domestic and foreign mining interests. During the most recent increase in REE prices, beginning in 2009 and culminating in a dramatic price spike in 2011, much mineral exploration activity for REE was undertaken in Kazakhstan, Kyrgyzstan, and Tajikistan. In order to assess the mineral potential for REE in Central Asia, the U.S. Geological Survey began in 2012 compiling an inventory of REE-RM occurrences in that region. These occurrences range in development status from mineral showings to previously developed deposits. Completed in 2016, the inventory consists of 384 REE-RM occurrences, including 160 in Kazakhstan, 75 in Kyrgyzstan, 60 in Tajikistan, 2 in Turkmenistan, and 87 in Uzbekistan. The inventory dataset includes detailed information on location, mineral deposit type, geology, production, resources, and development status. Four important groups of REE-RM mineral deposit types were recognized: (1) carbonatite and alkaline igneous rock-related deposits; (2) pegmatite and skarn/greisen deposits; (3) weathered-crust deposits, including laterite, derived from weathering of other REE-RM mineral deposits; and (4) sediment-hosted uranium deposits. This inventory is released as a database in two formats, a Microsoft Excel workbook and an ESRI ArcGIS 10.5 point feature class dataset built from the Excel workbook. The Excel workbook also includes data field definitions, explanations of the terminology and abbreviations, and references.

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

Success.ai’s Fashion & Apparel Data for Apparel, Fashion & Luxury Goods Professionals in Asia provides a robust dataset tailored for businesses seeking to connect with key players in Asia’s thriving fashion and luxury goods industries. Covering roles such as brand managers, designers, retail executives, and supply chain leaders, this dataset includes verified contact details, professional insights, and actionable business data.

With access to over 700 million verified global profiles and 130 million profiles focused on Asia, Success.ai ensures your outreach, marketing, and business development strategies are supported by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution positions you to succeed in Asia’s competitive and ever-growing fashion markets.

Why Choose Success.ai’s Fashion & Apparel Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of professionals in apparel, fashion, and luxury goods industries across Asia.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and enhancing communication efficiency.

2. Comprehensive Coverage of Asian Fashion Professionals

   • Includes profiles from major fashion hubs such as China, India, Japan, South Korea, and Southeast Asia.
   • Gain insights into regional consumer trends, emerging fashion markets, and luxury goods opportunities.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, market expansions, and product launches.
   • Stay aligned with evolving industry trends and capitalize on new opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with professionals across the global fashion and apparel industries, with a focus on Asia.
• 130M+ Profiles in Asia: Gain detailed insights into professionals shaping the region’s fashion and luxury goods markets.
• Verified Contact Details: Access work emails, phone numbers, and business locations for precise targeting.
• Leadership Insights: Engage with designers, brand managers, and retail leaders driving Asia’s fashion trends.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with decision-makers in apparel design, luxury goods branding, retail operations, and supply chain management.
   • Target individuals leading innovation in sustainable fashion, fast fashion, and digital transformation.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (luxury goods, ready-to-wear, footwear), geographic location, or job function.
   • Tailor campaigns to align with specific market needs, such as emerging e-commerce platforms or regional fashion preferences.

3. Industry and Regional Insights

   • Leverage data on consumer behaviors, market growth, and regional trends in Asia’s fashion and luxury goods sectors.
   • Refine marketing strategies, product development, and partnership outreach based on actionable insights.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Brand Expansion

   • Design targeted campaigns to promote apparel, luxury goods, or retail solutions to fashion professionals in Asia.
   • Leverage multi-channel outreach, including email, phone, and social media, to maximize engagement.

2. Product Development and Consumer Insights

   • Utilize data on regional trends and consumer preferences to guide product development and marketing strategies.
   • Collaborate with brand managers and designers to tailor collections or launch new offerings aligned with market demands.

3. Partnership Development and Retail Collaboration

   • Build relationships with retail chains, luxury brands, and supply chain leaders seeking strategic alliances.
   • Foster partnerships that expand distribution channels, enhance brand visibility, or improve operational efficiencies.

4. Market Research and Competitive Analysis

   • Analyze trends in Asia’s fashion industry to refine business strategies, identify market gaps, and anticipate consumer demands.
   • Benchmark against competitors to stay ahead in the fast-paced fashion landscape.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality fashion and apparel data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, streamlining workfl...

Currently, in the field of chart datasets, most existing resources are mainly in English, and there are almost no open-source Chinese chart datasets, which brings certain limitations to research and applications related to Chinese charts. This dataset draws on the construction method of the DVQA dataset to create a chart dataset focused on the Chinese environment. To ensure the authenticity and practicality of the dataset, we first referred to the authoritative website of the National Bureau of Statistics and selected 24 widely used data label categories in practical applications, totaling 262 specific labels. These tag categories cover multiple important areas such as socio-economic, demographic, and industrial development. In addition, in order to further enhance the diversity and practicality of the dataset, this paper sets 10 different numerical dimensions. These numerical dimensions not only provide a rich range of values, but also include multiple types of values, which can simulate various data distributions and changes that may be encountered in real application scenarios. This dataset has carefully designed various types of Chinese bar charts to cover various situations that may be encountered in practical applications. Specifically, the dataset not only includes conventional vertical and horizontal bar charts, but also introduces more challenging stacked bar charts to test the performance of the method on charts of different complexities. In addition, to further increase the diversity and practicality of the dataset, the text sets diverse attribute labels for each chart type. These attribute labels include but are not limited to whether they have data labels, whether the text is rotated 45 °, 90 °, etc. The addition of these details makes the dataset more realistic for real-world application scenarios, while also placing higher demands on data extraction methods. In addition to the charts themselves, the dataset also provides corresponding data tables and title text for each chart, which is crucial for understanding the content of the chart and verifying the accuracy of the extracted results. This dataset selects Matplotlib, the most popular and widely used data visualization library in the Python programming language, to be responsible for generating chart images required for research. Matplotlib has become the preferred tool for data scientists and researchers in data visualization tasks due to its rich features, flexible configuration options, and excellent compatibility. By utilizing the Matplotlib library, every detail of the chart can be precisely controlled, from the drawing of data points to the annotation of coordinate axes, from the addition of legends to the setting of titles, ensuring that the generated chart images not only meet the research needs, but also have high readability and attractiveness visually. The dataset consists of 58712 pairs of Chinese bar charts and corresponding data tables, divided into training, validation, and testing sets in a 7:2:1 ratio.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

CBFdataset is a dataset of Chinese bamboo flute (CBF) performances, created for ecologically valid analysis of music playing techniques in context.

The dataset comprises monophonic recordings of classic CBF pieces and isolated playing techniques, recorded by 10 professional CBF performers; and expert annotations of seven playing techniques: vibrato, tremolo, trill, flutter-tongue (FT), acciaccatura, portamento, and glissando. The recorded pieces include Busy Delivering Harvest (BH) 扬鞭催马运粮忙, Jolly Meeting (JM) 喜相逢, Morning (Mo) 早晨, and Flying Partridge (FP) 鹧鸪飞. All data was recorded in a professional recording studio using a Zoom H6 recorder at 44.1kHz/24-bits. The difference between different Versions 1.2, 1.1, and 1.0:

V1.2 is the complete CBFdataset with a total duration of 2.6 hours.

V1.1 splits the CBFdataset into two subsets according to playing technique types: CBF-periDB and CBF-petsDB. The former contains all the full-length pieces, isolated playing techniques, and annotations of four periodic modulations: vibrato, tremolo, trill, and flutter-tongue. The latter comprises the same full-length recordings, isolated playing techniques, and annotations of three pitch evolution-based techniques: acciaccatura, portamento, and glissando.

V1.0 includes only the CBF-periDB.

Related updates, demos, and code for reproducibility are available at http://c4dm.eecs.qmul.ac.uk/CBFdataset.html. Any queries, please feel free to contact Changhong at changhong.wang@telecom-paris.fr. Please cite the following paper when using this dataset:

Changhong Wang, Emmanouil Benetos, Vincent Lostanlen, and Elaine Chew, "Adaptive Scattering Transforms for Playing Technique Recognition," IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP), 30 (2022): 1407-1421.

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 series of 11 datasets is drawn from Rhoads, Edward J. M. Stepping Forth into the World: The Chinese Educational Mission to the United States, 1872-81. Hong Kong University Press, 2011. They document the 120 young Chinese who participated in the pioneering Chinese Educational Mission (CEM) in the United States (1872-1881). The first 8 files are drawn directly from the tables in Rhoads: Table 2.1 CEM students, by detachment (p.14-17) Table 5.1. Initial host family assignments (p.51-54) Table 7.1. CEM students in middle schools (by state and locality) (p. 90-94) Table 7.2 CEM students in public high schools (by state and locality) (p.96-99) Table 7.3 CEM students in private academies (by state and locality) (p.99-100) Table 8.1 CEM students in colleges (by academic year of enrollment) (p.116-118) Table 9.1 Deaths, dismissals, and withdrawals from the CEM (by date) (p.136) Table 9.2 CEM students in the June 1880 census (p.138-142) Based on these tables, I created three synthetic datasets which can be used for statistical and network analyses: cem_attributes: students' vital attributes, including their multiple names and transliteration, date and place of birth, and other attribute data (one row for each individual). cem_host: students' host families in the United States cem_education: students' educational curricula Each file contains two tabs, one for the data (data), one for the description of variables (key). Grey columns refer to the unstructured information given in the original source.

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

Almost 50 years have passed since Sartori introduced to the world one of the most famous innovations in the history of political science: a new party systems typology. Despite many criticisms and refinements since then, Sartori’s typology still constitutes, as stated by Peter Mair in 1990, “the most effective and exhaustive framework within which to contrast the properties of different party systems”. In the current research note, and taking into consideration that previous typologies have not yet been that successful, we propose a new classification of party systems – which not only embeds the notion of polarization into the typology, but also allows us to populate the “polarized pluralist” type beyond Sartori’s “centre-based” (Italian) model – in Asia, a continent almost completely ignored by Sartori in his seminal work. Using an original dataset that includes the most important characteristics of party systems in the region and building on Sartori’s original conceptualization, we examine to what extent party systems in Asian democracies, both contemporary (Bhutan, East Timor, India, Indonesia, Japan, Malaysia, Mongolia, Nepal, Pakistan, Philippines, South Korea, Sri Lanka and Taiwan) and historical (Bangladesh 1991–2006, Kyrgyzstan 2010–2020, Myanmar 2015–2020 and Thailand 1992–2013), have changed. Our discussion of a new party system typology is particularly relevant and important to Asia, as its many new democracies still need to shift from plurality electoral rules adopted during the early post-independence periods to more mature, power-dispersing political institutions that accommodate their rich ethnic and religious diversity, as it happened in Europe after the World Wars.

Capture Protocol & File Structure: - Participants photographed the right palm three times with the rear camera and three times with the front camera, each on a different background / lighting condition.

Key rules enforced: - One hand per frame, no gloves, palm roughly parallel to sensor, palm area ≥50 % of image - Any background except direct sunlight or harsh spotlight - Rings and other jewellery allowed

Dataset Statistics: Hand in frame: - Right 96.37 % - Left 3.63 %

Dominant hand (self‑declared): - Right 91.28 % - Left 8.70 %

Gender: - Male 84.34 % - Female 15.22 %

Age: <18 - 10.19 % 18‑25 - 42.62 % 25‑30 - 24.43 % 30‑40 - 14.42 % 40‑50 - 6.37 % 60+ - 1.89 %

Ethnicity: - African 61.14 % - South Asian 11.60 % - East Asian 7.72 % - Caucasian 5.31 % - other groups <1 % each

Occupation (top): - Student 31.74 % - Teacher 2.55 % - Housewife 2.51 % - Entrepreneur 2.35 % - Farmer 0.75 % - Driver 0.70 % - Nurse 0.56% - Tailor 0.3 % (200+ categories total)

Jewellery visible: 57.90 % of images

Top phone makes: - Apple 21.63 % - Samsung 14.80 % - Xiaomi 12.68 %

Amount of photos per ID -> amount of IDs: 1 -> 4360 2 -> 2528 3 -> 446 4 -> 81 5 -> 25 6 -> 99 7 -> 6 8 -> 582 9 --> 305 10 -> 749 11+ -> 1862

The dataset is production‑ready: neatly organised, fully annotated and ethically sourced with explicit participant consent, ready for direct ingestion into computer‑vision workflows.

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

World Countries provides a detailed basemap layer for the countries of the world. This layer has been designed to be used as a basemap and includes fields for official names and country codes, along with fields for continent and display. Particularly useful are the fields LAND_TYPE and LAND_RANK that separate polygons based on their size. These fields are helpful for rendering at different scales by providing the ability to turn off small islands that may clutter small-scale (zoomed out) views. The sources of this dataset are Esri, Garmin, U.S. Central Intelligence Agency (The World Factbook), and International Organization for Standardization (ISO). This layer was published in October 2024. It is updated every 12-18 months or as significant changes occur.

This dataset comprises the data and the code for the manuscript "An Open Dataset of Chinese Duration Expressions".Duration information is essential for understanding and analyzing our world. In textual contexts, duration information is typically conveyed in two formats: numeric (e.g., 1 hour) and verbal (e.g., shortly). To analyze duration information in text, it is crucial to understand how people map duration expressions to corresponding numerical duration. However, the literature has yet to provide lexicons supporting such conversion. Furthermore, existing databases of time-related expressions often lack information about word frequency – a robust predictor of information processing. Here, we report an open dataset of 2,101 Chinese duration expressions, each annotated with its corresponding numerical duration. To obtain high-quality data for word frequency, we obtained the frequency of each duration expression from a large-scale corpus of 10 billion Chinese characters (BLCU Corpus Center (BCC) Corpus) and computed an adjusted frequency for each expression. This dataset provides a valuable resource for research on temporal information in Chinese, facilitating studies in natural language processing, psychology, and linguistics.

Genetic studies of human local adaptation have been facilitated greatly by recent advances in high-throughput genotyping and sequencing technologies. However, few studies have investigated local adaptation in Asian populations on a genome-wide scale and with a high geographic resolution. In this study, taking advantage of the dense population coverage in Southeast Asia, which is the part of the world least studied in term of natural selection, we depicted genome-wide landscapes of local adaptations in 63 Asian populations representing the majority of linguistic and ethnic groups in Asia. Using genome-wide data analysis, we discovered many genes showing signs of local adaptation or natural selection. Notable examples, such as FOXQ1, MAST2, and CDH4, were found to play a role in hair follicle development and human cancer, signal transduction, and tumor repression, respectively. These showed strong indications of natural selection in Philippine Negritos, a group of aboriginal hunter-gatherers living in the Philippines. MTTP, which has associations with metabolic syndrome, body mass index, and insulin regulation, showed a strong signature of selection in Southeast Asians, including Indonesians. Functional annotation analysis revealed that genes and genetic variants underlying natural selections were generally enriched in the functional category of alternative splicing. Specifically, many genes showing significant difference with respect to allele frequency between northern and southern Asian populations were found to be associated with human height and growth and various immune pathways. In summary, this study contributes to the overall understanding of human local adaptation in Asia and has identified both known and novel signatures of natural selection in the human genome.

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.