Populations, abbreviations, sample sizes (n), mean number of IBD blocks shared by a pair of individuals from that population (“self”), and mean IBD rate averaged across all other populations (“other”), sorted by regional groupings described in the text.

Yearly citation counts for the publication titled "Validation and Abbreviation of an HIV Stigma Scale in an Adult Spanish-Speaking Population in Urban Peru".

Data quality:Hamilton, City (C)Total non-response (TNR) rate, short-form census questionnaire: 2.5%Total non-response (TNR) rate, long-form census questionnaire: 3.5%Notes: 117 'Visible minority' refers to whether a person is a visible minority or not as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as "persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian Chinese Black Filipino Arab Latin American Southeast Asian West Asian Korean and Japanese.In 2021 Census analytical and communications products the term "visible minority" has been replaced by the terms "racialized population" or "racialized groups" reflecting the increased use of these terms in the public sphere. For more information on visible minority and population group variables including information on their classifications the questions from which they are derived data quality and their comparability with other sources of data please refer to the Visible Minority and Population Group Reference Guide Census of Population 2021. 118 In 2021 Census analytical and communications products the term "visible minority" has been replaced by the terms "racialized population" or "racialized groups" reflecting the increased use of these terms in the public sphere. 119 The abbreviation "n.i.e." means "not included elsewhere." This category includes persons who provided responses that are classified as a visible minority but that cannot be classified with a specific visible minority group. Such responses include for example "Guyanese " "Pacific Islander " "Polynesian " "Tibetan" and "West Indian." 120 In 2021 Census analytical and communications products this category is referred to as "the rest of the population." 121 'Ethnic or cultural origin' refers to the ethnic or cultural origins of the person's ancestors. Ancestors may have Indigenous origins origins that refer to different countries or other origins that may not refer to different countries.The sum of the ethnic or cultural origins in this table is greater than the total population estimate because a person may report more than one ethnic or cultural origin in the census. The ethnic groups selected are the most frequently reported at the Canada level. For more information on ethnic or cultural origin variables including information on their classifications the questions from which they are derived data quality and their comparability with other sources of data please refer to the Ethnic or Cultural Origin Reference Guide Census of Population 2021. 122 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating French origins not otherwise specified (e.g. "French"). 123 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating British Isles origins not otherwise specified (e.g. "British " "United Kingdom"). 124 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Caucasian (White) origins not otherwise specified (e.g. "Caucasian"). 125 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating First Nations (North American Indian) origins not otherwise specified (e.g. "First Nations " "North American Indian"). 126 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating European origins not otherwise specified (e.g. "European"). 127 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating African origins not otherwise specified (e.g. "African"). 128 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Arab origins not otherwise specified (e.g. "Arab"). 129 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Asian origins not otherwise specified (e.g. "Asian"). 130 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Cree origins not otherwise specified (e.g. "Cree"). 131 The abbreviation "n.i.e." means "not included elsewhere." This category includes responses indicating Christian origins not included elsewhere (e.g. "Christian " "Baptist " "Catholic"). 132 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating North American Indigenous origins not otherwise specified (e.g. "Aboriginal " "Indigenous"). 133 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating South Asian origins not otherwise specified (e.g. "South Asian"). 134 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Mi'kmaq origins not otherwise specified (e.g. "Mi'kmaq"). 135 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Northern European origins not otherwise specified (e.g. "Northern European " "Scandinavian"). 136 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Latin Central or South American origins not otherwise specified (e.g. "Latin American " "South American"). 137 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Black origins not otherwise specified (e.g. "Black"). 138 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Inuit origins not otherwise specified (e.g. "Inuit"). 139 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Eastern European origins not otherwise specified (e.g. "Eastern European"). 140 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating East or Southeast Asian origins not otherwise specified (e.g. "East Asian " "Southeast Asian"). 141 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating West or Central Asian or Middle Eastern origins not otherwise specified (e.g. "Central Asian " "Middle Eastern " "West Asian"). 142 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Caribbean origins not otherwise specified (e.g. "Caribbean"). 143 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating West Indian origins not otherwise specified (e.g. "West Indian"). 144 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Hispanic origins not otherwise specified (e.g. "Hispanic"). 145 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Western European origins not otherwise specified (e.g. "Western European"). 146 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Czechoslovakian origins not otherwise specified (e.g. "Czechoslovakian"). 147 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Yugoslavian origins not otherwise specified (e.g. "Yugoslavian"). 148 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Slavic origins not otherwise specified (e.g. "Slavic"). 149 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Innu origins not otherwise specified (e.g. "Innu " "Montagnais"). 150 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Celtic origins not otherwise specified (e.g. "Celtic"). 151 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating North American origins not otherwise specified (e.g. "North American"). 152 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Dene origins not otherwise specified (e.g. "Dene"). 153 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Blackfoot origins not otherwise specified (e.g. "Blackfoot"). 154 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Iroquoian (Haudenosaunee) origins not otherwise specified (e.g. "Iroquois " "Haudenosaunee"). 155 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating North African origins not otherwise specified (e.g. "North African"). 156 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Southern or East African origins not otherwise specified (e.g. "East African"). 157 The abbreviation "n.o.s." means "not otherwise specified.' This category includes responses indicating Anishinaabe origins not otherwise specified (e.g. "Anishinaabe"). 158 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Bantu origins not otherwise specified (e.g. "Bantu"). 159 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Akan origins not otherwise specified (e.g. "Akan"). 160 The abbreviation "n.o.s." means "not otherwise specified." This category includes responses indicating Central or West African origins not otherwise specified (e.g. "Central African " "West African"). 161 'Religion' refers to the person's self-identification as having a connection or affiliation with any religious denomination group body or other religiously defined community or system of belief. Religion is not limited to formal membership in a

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

Showing Abbreviations used for State name, annual (1961–2010) mean minimum and maximum values of temperature, humidity and rainfall averaged over each state.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

The census of population and housing, taken by the Census Bureau in years ending in 0 (zero). Article I of the Constitution requires that a census be taken every ten years for the purpose of reapportioning the U.S. House of Representatives. Title 13 of the U. S. Code provides the authorization for conducting the census in Puerto Rico and the Island Areas. After each decennial census, the results are released to the public in a variety of ways, including publishing multiple series of reports titled Census of Population and Housing. The abbreviation for these reports was CPH for some decades (including 1990 and 2010) and PHC for some decades (including 1970 and 2000).

Sampling information of the populations of Berberis trifoliolata studied: country and locality were provided followed by population abbreviation, number of individuals for DNA sequences, number of individuals for AFLPs, latitude and longitude and their respective haplotypes.

Contains a list of Arctic communities suitable for providing context in other geospatial data visualizations. Dataset is limited to communities greater than or equal to 55 degrees north latitude, with populations greater than or equal to 10,000 as of 2022, except for Alaska communities which allow populations as small as 500. The intent of this dataset is to provide intuitive landmarks that help with interpretation of other geospatial datasets. This dataset contains minimal fields: community name, two-letter country abbreviation, latitude and longitude geometry, estimated population as of 2022, and Geonames identifier. This dataset is visualized on the Permafrost Discovery Gateway (https://arcticdata.io/catalog/portals/permafrost/Imagery-Viewer), an online scientific gateway that makes information of changing permafrost conditions throughout the Arctic available by providing access to very high resolution satellite data products and new visualization tools that will allow exploration and discovery for researchers, educators, and the public at large.

MARF is the 1980 Census counterpart of the Master Enumeration District List (MEDList) prepared for the 1970 census. It links state or state equivalent, county or county equivalent, minor civil division (MCD)/census county division (CCD), and place names with their respective geographic codes. It is also an abbreviated summary file containing selected population and housing unit counts. MARF 2 has the same geographic coverage as the first MARF and includes the following additional information: FIPS place codes, latitude and longitude coordinates for geographic areas down to the BG/ED level, land area in square miles for geographic areas down to the level of places or minor civil divisions (for 11 selected states) with a population of 2,500 or more, total population and housing count estimates based on sample returns, and per capital income for all geographic areas included in the file. There are 51 files, one for each state and the District of Columbia. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08258.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Data are 3D landmarks and semilandmarks from the frontal bone (all_coord_f2.csv) and occipital bone (all_coord_o2.csv) of Homo erectus (extinct hominin) fossils and comparative Homo sapiens (recent humans). Data were collected with a 3D Microscribe digitizer. Missing landmarks were estimated using several procedures (described in SI of original article).

Also included are files required to 'slide' the semilandmarks (sliders_Frt.csv and sliders_occ.csv)

The final file is R code for performing anlyses presented in article.

Potential pollen donor (PPD) genotypes present at the study site, including their abbreviation codes, population sizes (number of adult trees) and relative amounts.

As of 2023, an estimated ******* households in New York City lived in properties with lead or possible lead water service lines (LSL). This equates to some *** million people. New York City has a population of roughly *** million, meaning that roughly one in five New Yorkers could be drinking lead-contaminated water. The water supplier for NYC is the Department of Environmental Protection (DEP). Lead exposure can have serious adverse health impacts in humans of all ages, though children are the most susceptible. One of the most high-profile examples of lead-contamination in the U.S. is Flint, Michigan, where high concentrations of lead were detected in the city's drinking water in 2014.

In the affiliated paper we compare likely the oldest populations of Aedes aegypti in continental North America with some of the newest to illuminate the range of genetic diversity and structure that can be found within the invasive range of this important disease vector. Aedes aegypti populations in Florida have likely persisted since the 1600-1700s, while populations in southern California derive from new invasions that occurred in the last ten years. For this comparison, we genotyped 1,193 individuals from 29 sites at 12 highly variable microsatellites and a subset of these individuals at 23,961 single nucleotide polymorphisms (SNPs).

Introduction

This dataset contains information related to Brazilian states, like names, abbreviations, population size, latitude, longitude, capitals, area, GDP, HDI and much more. This data was compiled extracting several datasets from IBGE.

The corresponding information for the Swedish and Finnish provinces is given in Table S1.aUtah residents with Northern and Western European ancestry from the CEPH collection.

Measures of genetic diversity and divergence for 15 population and three groups based on haplotype frequencies using CONTRIB 1.02 (see Table 1 for site and group abbreviations).

List of rural municipalities within the meaning of “Eligibility to the GIP”, a global allocation of equipment paid to the department of Saône and Loire. Prefectural Order No. 2017103-001 of 13 April 2017. Article D3334-8-1 of the General Code of Local and Regional Authorities: The following municipalities in metropolitan France are considered to be rural municipalities for the purposes of Articles L. 3334-10 and R. 3334-8: — municipalities whose population does not exceed 2 000 inhabitants; — municipalities whose population exceeds 2 000 inhabitants and does not exceed 5 000 inhabitants, if they do not belong to an urban unit or if they belong to an urban unit whose population does not exceed 5000 inhabitants. The urban reference unit is that defined by the National Institute of Statistics and Economic Studies. The population taken into account is the total population authenticated at the end of the population census.

The California Monitoring Plan (CMP) salmonid monitoring areas and associated population data are part of an ongoing effort to summarize existing and past salmonid monitoring efforts in the areas identified by Adams et al. 2011. These data are compiled and maintained by the California Department of Fish and Wildlife with the cooperation of monitoring practitioners. Updates and associated outreach are intended to occur on an annual basis. Data were created from several sources and existing datasets: some monitoring areas were accurately depicted using the USGS National Hydrography Dataset (NHD), other monitoring areas were approximated using the monitoring point location and the USGS StreamStats tool to depict the watershed area above that point. The areas are intended to represent the approximate extent of sampling within sub-basins, watershed areas, or regions. For example, the spatial extent of monitoring using a fixed count station is approximated by accounting for all anadromous fish habitat upstream of the sampling location. Therefore, the area is approximated by entering the monitoring location coordinates into the StreamStats tool. The resulting shapefile is then examined to ensure the watershed area did not include habitat above dams or barriers to migration. Areas were clipped when needed. The data user should recognize that errors may have occurred during production of this dataset, changes may have occurred to the external sources used post transfer, and for other possible reasons. The population metrics summarized in the associated tabular data may be regarded as spatially limited, temporally limited, and not considered a complete estimate for the population being described. The data user is advised to refer to the annual reports cited in the Source field from the tabular data for additional details regarding monitoring within the area spatially depicted.Abbreviation Definitions: SGS = Spawning Ground Survey, RM = River Mile, RST = Rotary Screw Trap, RKM