In 2023, the natural growth rate of the population across China varied between 7.96 people per 1,000 inhabitants (per mille) in Tibet and -6.92 per mille in Heilongjiang province. The national total population growth rate turned negative in 2022 and ranged at -1.48 per mille in 2023. Regional disparities in population growth The natural growth rate is the difference between the birth rate and the death rate of a certain region. In China, natural population growth reached the highest values in the western regions of the country. These areas have a younger population and higher fertility rates. Although the natural growth rate does not include the direct effects of migration, migrants are often young people in their reproductive years, and their movement may therefore indirectly affect the birth rates of their home and host region. This is one of the reasons why Guangdong province, which received a lot of immigrants over the last decades, has a comparatively high population growth rate. At the same time, Jilin, Liaoning, and Heilongjiang province, all located in northeast China, suffer not only from low fertility, but also from emigration of young people searching for better jobs elsewhere. The impact of uneven population growth The current distribution of natural population growth rates across China is most likely to remain in the near future, while overall population decline is expected to accelerate. Regions with less favorable economic opportunities will lose their inhabitants faster. The western regions with their high fertility rates, however, have only small total populations, which limits their effect on China’s overall population size.

Corylus mandshurica, also known as pilose hazelnut, is an economically and ecologically important species in China. In this study, ten polymorphic simple sequence repeat (SSR) markers were applied to evaluate the genetic diversity and population structure of 348 C. mandshurica individuals among 12 populations in China. The SSR markers expressed a relatively high level of genetic diversity (Na = 15.3, Ne = 5.6604, I = 1.8853, Ho = 0.6668, and He = 0.7777). According to the coefficient of genetic differentiation (Fst = 0.1215), genetic variation within the populations (87.85%) were remarkably higher than among populations (12.15%). The average gene flow (Nm = 1.8080) significantly impacts the genetic structure of C. mandshurica populations. The relatively high gene flow (Nm = 1.8080) among wild C. mandshurica may be caused by wind-pollinated flowers, highly nutritious seeds and self-incompatible mating system. The UPGMA (unweighted pair group method of arithmetic averages) dendrogram was divided into two main clusters. Moreover, the results of STRUCTURE analysis suggested that C. mandshurica populations fell into two main clusters. Comparison of the UPGMA dendrogram and the Bayesian STRUCTURE analysis showed general agreement between the population subdivisions and the genetic relationships among populations of C. mandshurica. Group I accessions were located in Northeast China, while Group II accessions were in North China. It is worth noting that a number of genetically similar populations were located in the same geographic region. The results further showed that there was obvious genetic differentiation among populations from Northeast China to North China. Results from the Mantel test showed a weak but still significant positive correlation between Nei’s genetic distance and geographic distance (km) among populations (r = 0.419, P = 0.005), suggesting that genetic differentiation in the 12 C. mandshurica populations might be related to geographic distance. These data provide comprehensive information for the development of conservation strategies of these valuable hazelnut resources.

Abstract (en): The China Multi-Generational Panel Dataset - Liaoning (CMGPD-LN) is drawn from the population registers compiled by the Imperial Household Agency (neiwufu) in Shengjing, currently the northeast Chinese province of Liaoning, between 1749 and 1909. It provides 1.5 million triennial observations of more than 260,000 residents from 698 communities. The population mainly consists of immigrants from North China who settled in rural Liaoning during the early eighteenth century, and their descendants. The data provide socioeconomic, demographic, and other characteristics for individuals, households, and communities, and record demographic outcomes such as marriage, fertility, and mortality. The data also record specific disabilities for a subset of adult males. Additionally, the collection includes monthly and annual grain price data, custom records for the city of Yingkou, as well as information regarding natural disasters, such as floods, droughts, and earthquakes. This dataset is unique among publicly available population databases because of its time span, volume, detail, and completeness of recording, and because it provides longitudinal data not just on individuals, but on their households, descent groups, and communities. Possible applications of the dataset include the study of relationships between demographic behavior, family organization, and socioeconomic status across the life course and across generations, the influence of region and community on demographic outcomes, and development and assessment of quantitative methods for the analysis of complex longitudinal datasets. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Standardized missing values.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Smallest Geographic Unit: Chinese banners (8) The data are from 725 surviving triennial registers from 29 distinct populations. Each of the 29 register series corresponded to a specific rural population concentrated in a small number of neighboring villages. These populations were affiliated with the Eight Banner civil and military administration that the Qing state used to govern northeast China as well as some other parts of the country. 16 of the 29 populations are regular bannermen. In these populations adult males had generous allocations of land from the state, and in return paid an annual fixed tax to the Imperial Household Agency, and provided to the Imperial Household Agency such home products as homespun fabric and preserved meat, and/or such forest products as mushrooms. In addition, as regular bannermen they were liable for military service as artisans and soldiers which, while in theory an obligation, was actually an important source of personal revenue and therefore a political privilege. 8 of the 29 populations are special duty banner populations. As in the regular banner population, the adult males in the special duty banner populations also enjoyed state allocated land free of rent. These adult males were also assigned to provide special services, including collecting honey, raising bees, fishing, picking cotton, and tanning and dyeing. The remaining populations were a diverse mixture of estate banner and servile populations. The populations covered by the registers, like much of the population of rural Liaoning in the eighteenth and nineteenth centuries, were mostly descendants of Han Chinese settlers who came from Shandong and other nearby provinces in the late seventeenth and early eighteenth centuries in response to an effort by the Chinese state to repopulate the region. 2016-09-06 2016-09-06 The Training Guide has been updated to version 3.60. Additionally, the Principal Investigator affiliation has been corrected, and cover sheets for all PDF documents have been revised.2014-07-10 Releasing new study level documentation that contains the tables found in the appendix of the Analytic dataset codebook.2014-06-10 The data and documentation have been updated following re-evaluation.2014-01-29 Fixing variable format issues. Some variables that were supposed to be s...

In 2022, the total permanent resident population of Liaoning province in China amounted to around ***** million inhabitants. Liaoning province is located in Northeast China.

Northeast China is a major soybean production region in China. A representative sample of the Northeast China soybean germplasm population (NECSGP) composed of 361 accessions was evaluated for their seed protein content (SPC) in Tieling, Northeast China. This SPC varied greatly, with a mean SPC of 40.77%, ranging from 36.60 to 46.07%, but it was lower than that of the Chinese soybean landrace population (43.10%, ranging from 37.51 to 50.46%). The SPC increased slightly from 40.32–40.97% in the old maturity groups (MG, MGIII + II + I) to 40.93–41.58% in the new MGs (MG0 + 00 + 000). The restricted two-stage multi-locus genome-wide association study (RTM-GWAS) with 15,501 SNP linkage-disequilibrium block (SNPLDB) markers identified 73 SPC quantitative trait loci (QTLs) with 273 alleles, explaining 71.70% of the phenotypic variation, wherein 28 QTLs were new ones. The evolutionary changes of QTL-allele structures from old MGs to new MGs were analyzed, and 97.79% of the alleles in new MGs were inherited from the old MGs and 2.21% were new. The small amount of new positive allele emergence and possible recombination between alleles might explain the slight SPC increase in the new MGs. The prediction of recombination potentials in the SPC of all the possible crosses indicated that the mean of SPC overall crosses was 43.29% (+2.52%) and the maximum was 50.00% (+9.23%) in the SPC, and the maximum transgressive potential was 3.93%, suggesting that SPC breeding potentials do exist in the NECSGP. A total of 120 candidate genes were annotated and functionally classified into 13 categories, indicating that SPC is a complex trait conferred by a gene network.

Knowledge of the genetic structure and evolutionary history of tree species across their ranges is essential for the development of effective conservation and forest management strategies. Acer mono var. mono, an economically and ecologically important maple species, is extensively distributed in Northeast China (NE), whereas it has a scattered and patchy distribution in South China (SC). In this study, the genetic structure and demographic history of 56 natural populations of A. mono var. mono were evaluated using seven nuclear microsatellite markers. Neighbor-joining tree and STRUCTURE analysis clearly separated populations into NE and SC groups with two admixed-like populations. Allelic richness significantly decreased with increasing latitude within the NE group while both allelic richness and expected heterozygosity showed significant positive correlation with latitude within the SC group. Especially in the NE region, previous studies in Quercus mongolica and Fraxinus mandshurica have also detected reductions in genetic diversity with increases in latitude, suggesting this pattern may be common for tree species in this region, probably due to expansion from single refugium following the last glacial maximum (LGM). Approximate Bayesian Computation-based analysis revealed two major features of hierarchical population divergence in the species’ evolutionary history. Recent divergence between the NE group and the admixed-like group corresponded to the LGM period and ancient divergence of SC groups took place during mid-late Pleistocene period. The level of genetic differentiation was moderate (FST = 0.073; G′ST = 0.278) among all populations, but significantly higher in the SC group than the NE group, mirroring the species’ more scattered distribution in SC. Conservation measures for this species are proposed, taking into account the genetic structure and past demographic history identified in this study.

About this dataset

Context

This list ranks the 3 cities in the Washington County, NE by Chinese population, as estimated by the United States Census Bureau. It also highlights population changes in each city 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 city in the Washington County, NE by their Chinese population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Chinese Population: The Chinese population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Chinese. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Washington County Chinese Population: This tells us how much of the entire Washington County, NE Chinese population lives in that city. 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/.

About this dataset

Context

This list ranks the 1 cities in the Valley County, NE by Chinese population, as estimated by the United States Census Bureau. It also highlights population changes in each city 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 city in the Valley County, NE by their Chinese population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Chinese Population: The Chinese population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Chinese. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Valley County Chinese Population: This tells us how much of the entire Valley County, NE Chinese population lives in that city. 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/.

About this dataset

Context

This list ranks the 5 cities in the Madison County, NE by Chinese population, as estimated by the United States Census Bureau. It also highlights population changes in each city 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 city in the Madison County, NE by their Chinese population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Chinese Population: The Chinese population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Chinese. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Madison County Chinese Population: This tells us how much of the entire Madison County, NE Chinese population lives in that city. 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/.

Abstract

China Living Standards Survey (CLSS) consists of one household survey and one community (village) survey, conducted in Hebei and Liaoning Provinces (northern and northeast China) in July 1995 and July 1997 respectively. Five villages from each three sample counties of each province were selected (six were selected in Liaoyang County of Liaoning Province because of administrative area change). About 880 farm households were selected from total thirty-one sample villages for the household survey. The same thirty-one villages formed the samples of community survey. This document provides information on the content of different questionnaires, the survey design and implementation, data processing activities, and the different available data sets.

Geographic coverage

The China Living Standards Survey (CLSS) was conducted only in Hebei and Liaoning Provinces (northern and northeast China).

Kind of data

Sample survey data [ssd]

Sampling procedure

The CLSS sample is not a rigorous random sample drawn from a well-defined population. Instead it is only a rough approximation of the rural population in Hebei and Liaoning provinces in Northeastern China. The reason for this is that part of the motivation for the survey was to compare the current conditions with conditions that existed in Hebei and Liaoning in the 1930’s. Because of this, three counties in Hebei and three counties in Liaoning were selected as "primary sampling units" because data had been collected from those six counties by the Japanese occupation government in the 1930’s. Within each of these six counties (xian) five villages (cun) were selected, for an overall total of 30 villages (in fact, an administrative change in one village led to 31 villages being selected). In each county a "main village" was selected that was in fact a village that had been surveyed in the 1930s. Because of the interest in these villages 50 households were selected from each of these six villages (one for each of the six counties). In addition, four other villages were selected in each county. These other villages were not drawn randomly but were selected so as to "represent" variation within the county. Within each of these villages 20 households were selected for interviews. Thus the intended sample size was 780 households, 130 from each county.

Unlike county and village selection, the selection of households within each village was done according to standard sample selection procedures. In each village, a list of all households in the village was obtained from village leaders. An "interval" was calculated as the number of the households in the village divided by the number of households desired for the sample (50 for main villages and 20 for other villages). For the list of households, a random number was drawn between 1 and the interval number. This was used as a starting point. The interval was then added to this number to get a second number, then the interval was added to this second number to get a third number, and so on. The set of numbers produced were the numbers used to select the households, in terms of their order on the list.

In fact, the number of households in the sample is 785, as opposed to 780. Most of this difference is due to a village in which 24 households were interviewed, as opposed to the goal of 20 households

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire

The household questionnaire contains sections that collect data on household demographic structure, education, housing conditions, land, agricultural management, household non-agricultural business, household expenditures, gifts, remittances and other income sources, and saving and loans. For some sections (general household information, schooling, housing, gift-exchange, remittance, other income, and credit and savings) the individual designated by the household members as the household head provided responses. For some other sections (farm land, agricultural management, family-run non-farm business, and household consumption expenditure) a member identified as the most knowledgeable provided responses. Identification codes for respondents of different sections indicate who provided the information. In sections where the information collected pertains to individuals (employment), whenever possible, each member of the household was asked to respond for himself or herself, except that parents were allowed to respond for younger children. Therefore, in the case of the employment section it is possible that the information was not provided by the relevant person; variables in this section indicate when this is true.

The household questionnaire was completed in a one-time interview in the summer of 1995. The survey was designed so that more sensitive issues such as credit and savings were discussed near the end. The content of each section is briefly described below.

Section 0 SURVEY INFORMATION

This section mainly summarizes the results of the survey visits. The following information was entered into the computer: whether the survey and the data entry were completed, codes of supervisor’s brief comments on interviewer, data entry operator, and related revising suggestion (e.g., 1. good, 2. revise at office, and 3. re-interview needed). Information about the date of interview, the names of interviewer, supervisor, data enterer, and detail notes of interviewer and supervisor were not entered into the computer.

Section 1 GENERAL HOUSEHOLD INFORMATION

1A HOUSEHOLD STRUCTURE 1B INFORMATION ABOUT THE HOUSEHOLD MEMBERS’ PARENTS 1C INFORMATION ABOUT THE CHILDREN WHO ARE NOT LIVING IN HOME

Section 1A lists the personal id code, sex, relationship to the household head, ethnic group, type of resident permit (agricultural [nongye], non-agricultural [fei nongye], or no resident permit), date of birth, marital status of all people who spent the previous night in that household and for household members who are temporarily away from home. The household head is listed first and receives the personal id code 1. Household members were defined to include “all the people who normally live and eat their meals together in this dwelling.” Those who were absent more than nine of the last twelve months were excluded, except for the head of household. For individuals who are married and whose spouse resides in the household, the personal id number of the spouse is noted. By doing so, information on the spouse can be collected by appropriately merging information from the section 1A and other parts of the survey.

Section 1B collects information on the parents of all household members. For individuals whose parents reside in the household, parents’ personal id numbers are noted, and information can be obtained by appropriately merging information from other parts of the survey. For individuals whose parents do not reside in the household, information is recorded on whether each parent is alive, as well as their schooling and occupation.

Section 1C collects information for children of household members who are not living in home. Children who have died are not included. The information on the name, sex, types of resident permit, age, education level, education cost, reasons not living in home, current living place, and type of job of each such child is recorded.

Section 2 SCHOOLING

In Section 2, information about literacy and numeracy, school attendance, completion, and current enrollment for all household members of preschool age and older. The interpretation of pre-school age appears to have varied, with the result that while education information is available for some children of pre-school age, not all pre-school children were included in this section. But for ages 6 and above information is available for nearly all individuals, so in essence the data on schooling can be said to apply all persons 6 age and above. For those who were enrolled in school at the time of the survey, information was also collected on school attendance, expenses, and scholarships. If applicable, information on serving as an apprentice, technical or professional training was also collected.

Section 3 EMPLOYMENT

3A GENERAL INFORMATION 3B MAJOR NON-FARM JOB IN 1994 3C THE SECOND NON-FARM JOB IN 1994 3D OTHER EMPLOYMENT ACTIVITIES IN 1994 3E SEARCHING FOR NON-FARM JOB 3F PROCESS FOR GETTING MAJOR NON-FARM JOB 3G CORVEE LABOR

All individuals age thirteen and above were asked to respond to the employment activity questions in Section 3. Section 3A collects general information on farm and non-farm employment, such as whether or not the household member worked on household own farm in 1994, when was the last year the member worked on own farm if he/she did not work in 1994, work days and hours during busy season, occupation and sector codes of the major, second, and third non-farm jobs, work days and total income of these non-farm jobs. There is a variable which indicates whether or not the individual responded for himself or herself.

Sections 3B and 3C collect detailed information on the major and the second non-farm job. Information includes number of months worked and which month in 1994 the member worked on these jobs, average works days (or hours) per month (per day), total number of years worked for these jobs by the end of 1994, different components of income, type of employment contracts. Information on employer’s ownership type and location was also collected.

Section 3D collects information on average hours spent doing chores and housework at home every day during non-busy and busy season. The chores refer to cooking, laundry, cleaning, shopping, cutting woods, as well as small-scale farm yard animals raising, for example, pigs or chickens. Large-scale animal

In 2022, the total permanent resident population of Heilongjiang province in China amounted to around ***** million inhabitants. Heilongjiang province is located in Northeast China.

Supplementary Material 1

About this dataset

Context

This list ranks the 3 cities in the Seward County, NE by Chinese population, as estimated by the United States Census Bureau. It also highlights population changes in each city 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 city in the Seward County, NE by their Chinese population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Chinese Population: The Chinese population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Chinese. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Seward County Chinese Population: This tells us how much of the entire Seward County, NE Chinese population lives in that city. 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/.

Essential information on the population dynamics and the health and welfare of Chinese donkeys is scarce. The objectives of this study were to describe the demographic characteristics, management and health care of a sample of donkeys under smallholder farm conditions of northeastern China. A cross-sectional survey of 731 randomly selected donkey owners on smallholder farms (1,658 donkeys) in 40 villages of northeastern China was conducted. Data on the composition and management of the donkeys and their routine health care were analyzed. The surveyed donkey population consisted of mostly (83.8%) jenny/filly donkeys with a mean age of 6.2 ± 5.0 years. Most (91.2%) of the farms kept 1–4 donkeys. The majority of donkeys were used for breeding and labor. Most (93.8%) of the farms did not have bedding, and their mean stable size was 17.7 ± 10.1 m2. All of the animals were turned out for at least part of the year. The mean size of the turnout areas on the farms was 17.8 m2. The condition of 12.5% of the donkeys was evaluated as “poor” with a body condition score of 1 on a scale of 5. More than one third (37.9%) of the donkeys had never been dewormed. Also, none of them were ever vaccinated or received dental care from a veterinarian. Their hoofs were trimmed once (45.9%) or twice (27.6%) a year. Forty percent of the donkeys were reported to suffer from at least one medical problem in the preceding year. The most common medical problems were colic, respiratory disorders and skin conditions. Owners seemed to underestimate some of the most prevalent diseases in donkeys, suggesting that their knowledge of the management of donkeys, including routine healthcare practices should be improved to ensure the health and welfare of donkeys in northeastern China.

For each locus: Fis, coefficient of inbreeding; Fst, Genetic differentiation coefficient; Nm, Gene flowSummary of F statistics and gene flow for the 10 loci.

Short tandem repeats (STRs) are consecutive repetition of a repeat motif and widely used in forensic medicine and human genetics because of their high polymorphism. In the current study, 23 autosomal STR loci were genotyped from 1263 unrelated healthy individuals living in Panjin City, Liaoning Province, Northeastern China using the VeriFilerTM Express PCR Amplification Kit. The population comparison was performed between the Panjin Han population and the other relevant groups to further explore the structure of Panjin Han and its relationship with the other groups. The results found 316 alleles across the 23 STRs and the corresponding allelic frequencies ranged from 0.5198 to 0.0004. Except for D3S1358, TPOX, TH01, and D3S1358, all STR loci were highly polymorphic (PIC > 0.7), with the Penta E locus having the highest degree of polymorphism (0.9147). For population comparison, the exact test of population differentiation found that no significant difference was observed between the Panjin Han and the other Han populations, except for Guangdong Han and Jiangxi Han. The Panjin Han population showed significant differences with the other ethnic groups in China (Bouyei, Dong, Hui, Miao, Tibetan, and Uygur) and the foreign ethnic groups.

CCNP takes its pilot stage (2013 – 2022) of the first ten-year. It aims at establishing protocols on the Chinese normative brain development trajectories across the human lifespan. It implements a structured multi-cohort longitudinal design (or accelerated longitudinal design), which is particularly viable for lifespan trajectory studies, and optimal for recoverable missing data. The CCNP pilot comprises three connected components: developing CCNP (devCCNP, baseline age = 6-18 years, 12 age cohorts, 3 waves, interval = 15 months), maturing CCNP (matCCNP, baseline age = 18-60 years, 14 age cohorts, 3 waves, interval = 39 months) and ageing CCNP (ageCCNP, baseline age = 60-84 years, 12 age cohorts, 3 waves, interval = 27 months). The developmental component of CCNP (devCCNP, 2013-2022), also known as "Growing Up in China", a ten-year's pilot stage of CCNP, has established follow-up cohorts in Chongqing (,CKG, Southwest China) and Beijing (PEK, Northeast China). It is an ongoing project focused on longitudinal developmental research as creating and sharing a large-scale multimodal dataset for typically developing Chinese children and adolescents (ages 6.0-17.9 at enrollment) carried out in both school- and community-based samples. The devCCNP houses longitudinal data about demographics, biophysical measures, psychological and behavioral assessments, cognitive phenotyping, ocular-tracking, as well as multimodal magnetic resonance imaging (MRI) of brain's resting and naturalistic viewing function, diffusion structure and morphometry. With the collection of longitudinal structured images and psychobehavioral samples from school-age children and adolescents in multiple cohorts, devCCNP has constructed a full school-age brain template and its growth curve reference for Han Chinese which demonstrated the difference in brain development between Chinese and American school-aged children.To access the data, investigators must complete the application file Data Use Agreement on CCNP (DUA-CCNP) at http://deepneuro.bnu.edu.cn/?p=163 and have it reviewed and approved by the Chinese Color Nest Consortium (CCNC). All terms specified by the DUA-CCNP must be complied with. Meanwhile, the baseline CKG Sample on brain imaging are available to researchers via the International Data-sharing Neuroimaging Initiative (INDI) through the Consortium for Reliability and Reproducibility (CoRR). More information about CCNP can be found at: http://deepneuro.bnu.edu.cn/?p=163 or https://github.com/zuoxinian/CCNP. Requests for further information and collaboration are encouraged and considered by the CCNC, and please read the Data Use Agreement and contact us via deepneuro@bnu.edu.cn. The CCNP data will be fully available to the research community when acquisition is completed for the pilot CCNP. At this stage, the CCNP data are only available to researchers and collaborators of CCNC.

Soil fungi are a key component of terrestrial ecosystems and play a major role in soil biogeochemical cycling. Although the diversity and composition of fungal communities are regulated by many abiotic and biotic factors, the effect of elevation on soil fungal community diversity and composition remains largely unknown. In this study, the soil fungal composition and diversity in Deyeuxia angustifolia populations along an elevational gradient (1,690 m to 2020 m a.s.l.) were assessed, using Illumina MiSeq sequencing, on the north-facing slope of the Changbai Mountain, northeastern China. Our results showed that soil physicochemical parameters changed significantly along with the elevational gradients. The Ascomycota and Basidiomycota were the most dominant phyla along with the gradient. Alpha diversity of soil fungi decreased significantly with elevation. Soil nitrate nitrogen (NO3−-N) was positively correlated with fungal richness and phylogenetic diversity (PD), indicating that soil nitrate nitrogen (NO3−-N) is a key soil property determining fungal community diversity. In addition to soil nitrate content, soil pH and soil moisture were the most important environmental properties determining the soil fungal diversity. Our results suggest that the elevational changes in soil physicochemical properties play a key role in shaping the community composition and diversity of soil fungi. This study will allow us to better understand the biodiversity distribution patterns of soil microorganisms in mountain ecosystems.

About this dataset

Context

This list ranks the 1 cities in the Rock County, NE by Chinese population, as estimated by the United States Census Bureau. It also highlights population changes in each city 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 city in the Rock County, NE by their Chinese population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Chinese Population: The Chinese population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Chinese. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Rock County Chinese Population: This tells us how much of the entire Rock County, NE Chinese population lives in that city. 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/.

About this dataset

Context

This list ranks the 5 cities in the Dodge County, NE by Chinese population, as estimated by the United States Census Bureau. It also highlights population changes in each city 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 city in the Dodge County, NE by their Chinese population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Chinese Population: The Chinese population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Chinese. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Dodge County Chinese Population: This tells us how much of the entire Dodge County, NE Chinese population lives in that city. 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/.