Errors are given for both the training + unseen classifier and the unseen classifier, for 100, 1,000 and 18,000 unseen building patches per scan.

Demographic rates are rarely estimated over an entire species range, limiting empirical tests of ecological patterns and theories, and raising questions about the representativeness of studies that use data from a small part of a range. The uncertainty that results from using demographic rates from just a few sites is especially pervasive in population projections, which are critical for a wide range of questions in ecology and conservation. We developed a simple simulation to quantify how this lack of geographic representativeness can affect inferences about the global mean and variance of growth rates, which has implications for the robust design of a wide range of population studies. Using a coastal songbird, saltmarsh sparrow (Ammodramus caudacutus), as a case study, we first estimated survival, fecundity, and population growth rates at 21 sites distributed across much of their breeding range. We then subsampled this large, representative dataset according to five sampling scenarios in order to simulate a variety of geographic biases in study design. We found spatial variation in demographic rates, but no large systematic patterns. Estimating the global mean and variance of growth rates using subsets of the data suggested that at least 10-15 sites were required for reasonably unbiased estimates, highlighting how relying on demographic data from just a few sites can lead to biased results when extrapolating across a species range. Sampling at the full 21 sites, however, offered diminishing returns, raising the possibility that for some species accepting some geographical bias in sampling can still allow for robust range-wide inferences. The sub-sampling approach presented here, while conceptually simple, could be used with both new and existing data to encourage efficiency in the design of long-term or large-scale ecological studies.

This dataset is the supplement data of our publication inCVPR 2025: Deep Change Monitoring: A Hyperbolic Representative Learning Framework and a Dataset for Long-term Fine-grained Tree Change Detection.

The data includes "Train set" and "Test set", the pictures of the same tree are saved in the same folder, and each picture is named after their date, e.g., "20230425.jpg" means the picture is collected on 25th, April, 2023.

The "labelling.xlsx" contains the reference status of each tree, the meaning of status ID is below:

0 : The leaves have fallen out

1 : The leaves are green

2 : The leaves are yellow

3 : It starts to grow leaves or leaves starts to fall

4 : blossom

Abstract

The study included four separate surveys:

1. The LSMS survey of general population of Serbia in 2002

2. The survey of Family Income Support (MOP in Serbian) recipients in 2002 These two datasets are published together separately from the 2003 datasets.

3. The LSMS survey of general population of Serbia in 2003 (panel survey)

4. The survey of Roma from Roma settlements in 2003 These two datasets are published together.

Objectives

LSMS represents multi-topical study of household living standard and is based on international experience in designing and conducting this type of research. The basic survey was carried out in 2002 on a representative sample of households in Serbia (without Kosovo and Metohija). Its goal was to establish a poverty profile according to the comprehensive data on welfare of households and to identify vulnerable groups. Also its aim was to assess the targeting of safety net programs by collecting detailed information from individuals on participation in specific government social programs. This study was used as the basic document in developing Poverty Reduction Strategy (PRS) in Serbia which was adopted by the Government of the Republic of Serbia in October 2003.

The survey was repeated in 2003 on a panel sample (the households which participated in 2002 survey were re-interviewed).

Analysis of the take-up and profile of the population in 2003 was the first step towards formulating the system of monitoring in the Poverty Reduction Strategy (PRS). The survey was conducted in accordance with the same methodological principles used in 2002 survey, with necessary changes referring only to the content of certain modules and the reduction in sample size. The aim of the repeated survey was to obtain panel data to enable monitoring of the change in the living standard within a period of one year, thus indicating whether there had been a decrease or increase in poverty in Serbia in the course of 2003. [Note: Panel data are the data obtained on the sample of households which participated in the both surveys. These data made possible tracking of living standard of the same persons in the period of one year.]

Along with these two comprehensive surveys, conducted on national and regional representative samples which were to give a picture of the general population, there were also two surveys with particular emphasis on vulnerable groups. In 2002, it was the survey of living standard of Family Income Support recipients with an aim to validate this state supported program of social welfare. In 2003 the survey of Roma from Roma settlements was conducted. Since all present experiences indicated that this was one of the most vulnerable groups on the territory of Serbia and Montenegro, but with no ample research of poverty of Roma population made, the aim of the survey was to compare poverty of this group with poverty of basic population and to establish which categories of Roma population were at the greatest risk of poverty in 2003. However, it is necessary to stress that the LSMS of the Roma population comprised potentially most imperilled Roma, while the Roma integrated in the main population were not included in this study.

Geographic coverage

The surveys were conducted on the whole territory of Serbia (without Kosovo and Metohija).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample frame for both surveys of general population (LSMS) in 2002 and 2003 consisted of all permanent residents of Serbia, without the population of Kosovo and Metohija, according to definition of permanently resident population contained in UN Recommendations for Population Censuses, which were applied in 2002 Census of Population in the Republic of Serbia. Therefore, permanent residents were all persons living in the territory Serbia longer than one year, with the exception of diplomatic and consular staff.

The sample frame for the survey of Family Income Support recipients included all current recipients of this program on the territory of Serbia based on the official list of recipients given by Ministry of Social affairs.

The definition of the Roma population from Roma settlements was faced with obstacles since precise data on the total number of Roma population in Serbia are not available. According to the last population Census from 2002 there were 108,000 Roma citizens, but the data from the Census are thought to significantly underestimate the total number of the Roma population. However, since no other more precise data were available, this number was taken as the basis for estimate on Roma population from Roma settlements. According to the 2002 Census, settlements with at least 7% of the total population who declared itself as belonging to Roma nationality were selected. A total of 83% or 90,000 self-declared Roma lived in the settlements that were defined in this way and this number was taken as the sample frame for Roma from Roma settlements.

Planned sample: In 2002 the planned size of the sample of general population included 6.500 households. The sample was both nationally and regionally representative (representative on each individual stratum). In 2003 the planned panel sample size was 3.000 households. In order to preserve the representative quality of the sample, we kept every other census block unit of the large sample realized in 2002. This way we kept the identical allocation by strata. In selected census block unit, the same households were interviewed as in the basic survey in 2002. The planned sample of Family Income Support recipients in 2002 and Roma from Roma settlements in 2003 was 500 households for each group.

Sample type: In both national surveys the implemented sample was a two-stage stratified sample. Units of the first stage were enumeration districts, and units of the second stage were the households. In the basic 2002 survey, enumeration districts were selected with probability proportional to number of households, so that the enumeration districts with bigger number of households have a higher probability of selection. In the repeated survey in 2003, first-stage units (census block units) were selected from the basic sample obtained in 2002 by including only even numbered census block units. In practice this meant that every second census block unit from the previous survey was included in the sample. In each selected enumeration district the same households interviewed in the previous round were included and interviewed. On finishing the survey in 2003 the cases were merged both on the level of households and members.

Stratification: Municipalities are stratified into the following six territorial strata: Vojvodina, Belgrade, Western Serbia, Central Serbia (Šumadija and Pomoravlje), Eastern Serbia and South-east Serbia. Primary units of selection are further stratified into enumeration districts which belong to urban type of settlements and enumeration districts which belong to rural type of settlement.

The sample of Family Income Support recipients represented the cases chosen randomly from the official list of recipients provided by Ministry of Social Affairs. The sample of Roma from Roma settlements was, as in the national survey, a two-staged stratified sample, but the units in the first stage were settlements where Roma population was represented in the percentage over 7%, and the units of the second stage were Roma households. Settlements are stratified in three territorial strata: Vojvodina, Beograd and Central Serbia.

Mode of data collection

Face-to-face [f2f]

Research instrument

In all surveys the same questionnaire with minimal changes was used. It included different modules, topically separate areas which had an aim of perceiving the living standard of households from different angles. Topic areas were the following: 1. Roster with demography. 2. Housing conditions and durables module with information on the age of durables owned by a household with a special block focused on collecting information on energy billing, payments, and usage. 3. Diary of food expenditures (weekly), including home production, gifts and transfers in kind. 4. Questionnaire of main expenditure-based recall periods sufficient to enable construction of annual consumption at the household level, including home production, gifts and transfers in kind. 5. Agricultural production for all households which cultivate 10+ acres of land or who breed cattle. 6. Participation and social transfers module with detailed breakdown by programs 7. Labour Market module in line with a simplified version of the Labour Force Survey (LFS), with special additional questions to capture various informal sector activities, and providing information on earnings 8. Health with a focus on utilization of services and expenditures (including informal payments) 9. Education module, which incorporated pre-school, compulsory primary education, secondary education and university education. 10. Special income block, focusing on sources of income not covered in other parts (with a focus on remittances).

Response rate

During field work, interviewers kept a precise diary of interviews, recording both successful and unsuccessful visits. Particular attention was paid to reasons why some households were not interviewed. Separate marks were given for households which were not interviewed due to refusal and for cases when a given household could not be found on the territory of the chosen census block.

In 2002 a total of 7,491 households were contacted. Of this number a total of 6,386 households in 621 census rounds were interviewed. Interviewers did not manage to collect the data for 1,106 or 14.8% of selected households. Out of this number 634 households

Us House Congressional Representatives serving Macon-Bibb County.

Congressional districts are the 435 areas from which members are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states, which is based on decennial census population counts, each state with multiple seats is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The boundaries and numbers shown for the congressional districts are those specified in the state laws or court orders establishing the districts within each state.

Congressional districts for the 108th through 112th sessions were established by the states based on the result of the 2000 Census. Congressional districts for the 113th through 115th sessions were established by the states based on the result of the 2010 Census. Boundaries are effective until January of odd number years (for example, January 2015, January 2017, etc.), unless a state initiative or court ordered redistricting requires a change. All states established new congressional districts in 2011-2012, with the exception of the seven single member states (Alaska, Delaware, Montana, North Dakota, South Dakota, Vermont, and Wyoming).

For the states that have more than one representative, the Census Bureau requested a copy of the state laws or applicable court order(s) for each state from each secretary of state and each 2010 Redistricting Data Program state liaison requesting a copy of the state laws and/or applicable court order(s) for each state. Additionally, the states were asked to furnish their newly established congressional district boundaries and numbers by means of geographic equivalency files. States submitted equivalency files since most redistricting was based on whole census blocks. Kentucky was the only state where congressional district boundaries split some of the 2010 Census tabulation blocks. For further information on these blocks, please see the user-note at the bottom of the tables for this state.

The Census Bureau entered this information into its geographic database and produced tabulation block equivalency files that depicted the newly defined congressional district boundaries. Each state liaison was furnished with their file and requested to review, submit corrections, and certify the accuracy of the boundaries.

Content

A dataset of counties that are representative for Germany with regard to

• the average disposable income,
• the quota of divorces,
• the respective quotas of employees working in the services (excluding logistics, security, and cleaning) and the MINT sectors,
• the proportions of age groups in the total proportion of the respective population, with age groups in five-year strata for the population aged between 30 and 65 and the population in the age range between 65 and 75 each considered separately for the calculation of representativeness.
• population density, if the benchmark is defined as the national average density weighted by county population. This value is calculated by averaging the population-per-square-kilometer figures of all German counties, weighted by the population size of each county. In contrast to a simple arithmetic mean of county-level densities, which would disproportionately reflect sparsely populated areas, this method reflects the actual density of the environments where people live. The resulting value for the selected counties falls within the 95% confidence interval of the national weighted average.

In addition, data from the four big cities Berlin, München (Munich), Hamburg, and Köln (Cologne) were collected and reflected in the dataset.

The dataset is based on the most recent data available at the time of the creation of the dataset, mainly deriving from 2022, as set out in detail in the readme.md file.

Method applied

The selection of the representative counties, as reflected in the dataset, was performed on the basis of official statistics with the aim of obtaining a confidence rate of 95%. The selection was based on a principal component analysis of the statistical data available for Germany and the addition of the regions with the lowest population density and the highest and lowest per capita disposable income. A check of the representativity of the selected counties was performed.

In the case of Leipzig, the city and the district had to be treated together, in deviation from the official territorial division, with respect to a specific use case of the data.

Abstract

Tunisia Rural Youth Survey (RYS) was implemented in 2012 in rural areas, building on the data collection in urban areas. The survey was conceived by a group of Tunisian professors and students, called Projet Citoyen, from various universities in Tunisia, particularly from Ecole Superieure des Sciences Economiques et Commerciales de Tunis (ESSECT). Motivated by the observed differences between different parts of the country, including neighborhoods in the Grand Tunis area, the aim of the survey was to scientifically understand urban inequality, with a specific focus on economic opportunities for young people. This effort led to collaboration between the Tunisian National Statistical Office (Institut National de la Statistique or INS), the General Commissariat for Regional Development, and the World Bank. The INS provided the sampling frame, the commissariat, as the main government counterpart, provided guidance for the scope of the survey and its urban focus, and the World Bank provided technical and financial support.

Geographic coverage

Rural Areas The first survey region covered the coast and included coastal governorates in the north and east of the country. The second survey region covered the south and included the southern governorates. The third survey region covered the rural interior of Tunisia and included the remote areas of central and western Tunisia, including the Algerian border.

Universe

The survey covered youth population aged 15-29 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Survey has a sample size of 1,400 households the entire rural area of Tunisia, as defined by the Tunisian Statistical Office, Institut National de la Statistique (INS). For the purpose of sampling, administrative governorates were grouped into 3 Survey Regions. The data is representative on the level of these Survey Regions, which largely correspond to socio-economically and geographically distinct rural zones. The first survey region covers the Coast and includes coastal governorates in the North and East of the country. The second survey region covers South and includes the southern governorates. The third survey region is covers the rural Interior of Tunisia and includes the remote areas of central and western Tunisia, incl. the Algerian border.

The sample was drawn from the latest available census, the 2004 General Census of Population and Housing, provided by the INS. This census also provided the sampling frame for the corresponding Urban and Peri-Urban Youth Survey. For determining the number of households in rural areas, proportionality of the possible locations was used to ensure representativeness. Because of the overall research focus on youth, the sampling design ensures representativeness of youth population, which is defined by ages 15-29. The proportionality to youth population size is based on the disaggregation of Tunisia into Enumeration Areas (EA). Each EA contains about 100-120 households. In total 70 EAs were randomly selected, with 29 EAs along the Coast, 10 EAs in the South, and 31 EAs in the Interior survey regions. The relative distribution between the survey regions corresponds to their respective shares of youth population. From each of these 70 EAs, 20 households were randomly selected, leading to a total sample size of 1,400 households.

The random sampling of PSUs was performed by experts from the INS, who were also responsible for the sample frame. The drawing of 20 households from each PSU is processed on a systematic and clearly defined approach. A random-walk procedure was conducted for each of the PSUs of the sample, which included 2 separate starting points at opposing ends of the east-west dimension of each PSU, and moving towards the population center of the PSU to allow a full coverage of both centrally and remotely located households.

Mode of data collection

Face-to-face [f2f]

The dataset contains information about the subject/object of the law and its properties, which is kept in the A824 agenda: Patents. Basic description: A person who, within the meaning of the Patent Attorneys Act, is authorised to represent, for consideration, parties to proceedings in matters of industrial property rights. The Patent Attorney is registered in the list of Patent Attorneys maintained by the Chamber of Patent Attorneys. Registered characteristics/data: 824-7-1: IČO representative PO 824-7-2: Company representative PO 824-7-3: Seat address of PO representative 824-7-4: Identification of the representative of the PO

This map shows Congressional District boundaries for the United States. The map is set to middle Georgia.

Congressional districts are the 435 areas from which members are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states, which is based on decennial census population counts, each state with multiple seats is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The boundaries and numbers shown for the congressional districts are those specified in the state laws or court orders establishing the districts within each state.

Congressional districts for the 108th through 112th sessions were established by the states based on the result of the 2000 Census. Congressional districts for the 113th through 115th sessions were established by the states based on the result of the 2010 Census. Boundaries are effective until January of odd number years (for example, January 2015, January 2017, etc.), unless a state initiative or court ordered redistricting requires a change. All states established new congressional districts in 2011-2012, with the exception of the seven single member states (Alaska, Delaware, Montana, North Dakota, South Dakota, Vermont, and Wyoming).

For the states that have more than one representative, the Census Bureau requested a copy of the state laws or applicable court order(s) for each state from each secretary of state and each 2010 Redistricting Data Program state liaison requesting a copy of the state laws and/or applicable court order(s) for each state. Additionally, the states were asked to furnish their newly established congressional district boundaries and numbers by means of geographic equivalency files. States submitted equivalency files since most redistricting was based on whole census blocks. Kentucky was the only state where congressional district boundaries split some of the 2010 Census tabulation blocks. For further information on these blocks, please see the user-note at the bottom of the tables for this state.

The Census Bureau entered this information into its geographic database and produced tabulation block equivalency files that depicted the newly defined congressional district boundaries. Each state liaison was furnished with their file and requested to review, submit corrections, and certify the accuracy of the boundaries.

Descriptive statistics and mean differences in a study sample (Study 1).

Abstract

The survey on financial literacy among the citizens of Bosnia and Herzegovina was conducted within a larger project that aims at creating the Action Plan for Consumer Protection in Financial Services.

The conclusion about the need for an Action Plan was reached by the representatives of the World Bank, the Federal Ministry of Finance, the Central Bank of Bosnia and Herzegovina, supervisory authorities for entity financial institutions and non-governmental organizations for the protection of consumer rights, based on the Diagnostic Review on Consumer Protection and Financial Literacy in Bosnia and Herzegovina conducted by the World Bank in 2009-2010. This diagnostic review was conducted at the request of the Federal Ministry of Finance, as part of a larger World Bank pilot program to assess consumer protection and financial literacy in developing countries and middle-income countries. The diagnostic review in Bosnia and Herzegovina was the eighth within this project.

The financial literacy survey, whose results are presented in this report, aims at establishing the basic situation with respect to financial literacy, serving on the one hand as a preparation for the educational activities plan, and on the other as a basis for measuring the efficiency of activities undertaken.

Geographic coverage

Data collection was based on a random, nation-wide sample of citizens of Bosnia and Herzegovina aged 18 or older (N = 1036).

Analysis unit

Household, individual

Universe

Population aged 18 or older

Kind of data

Sample survey data [ssd]

Sampling procedure

SUMMARY

In Bosnia and Herzegovina, as is well known, there is no completely reliable sample frame or information about universe. The main reasons for such a situation are migrations caused by war and lack of recent census data. The last census dates back to 1991, but since then the size and distribution of population has significantly changed. In such a situation, researchers have to combine all available sources of population data to estimate the present size and structure of the population: estimates by official statistical offices and international organizations, voters? lists, list of polling stations, registries of passport and ID holders, data from large random surveys etc.

The sample was three-stage stratified: in the first stage by entity, in the second by county/region and in the third by type of settlement (urban/rural). This means that, in the first stage, the total sample size was divided in two parts proportionally to number of inhabitants by entity, while in the second stage the subsample size for each entity was further divided by regions/counties. In the third stage, the subsample for each region/county was divided in two categories according to settlement type (rural/urban).

Taking into the account the lack of a reliable and complete list of citizens to be used as a sample frame, a multistage sampling method was applied. The list of polling stations was used as a frame for the selection of primary sampling units (PSU). Polling station territories are a good choice for such a procedure since they have been recently updated, for the general elections held in October 2010. The list of polling station territories contains a list of addresses of housing units that are certainly occupied.

In the second stage, households were used as a secondary sampling unit. Households were selected randomly by a random route technique. In total, 104 PSU were selected with an average of 10 respondents per PSU. The respondent from the selected household was selected randomly using the Trohdal-Bryant scheme.

In total, 1036 citizens were interviewed with a satisfactory response rate of around 60% (table 1). A higher refusal rate is recorded among middle-age groups (table 2). The theoretical margin of error for a random sample of this size is +/-3.0%.

Due to refusals, the sample structure deviated from the estimated population structure by gender, age and education level. Deviations were corrected by RIM weighting procedure.

MORE DETAILED INFORMATION

IPSOS designed a representative sample of approximately 1.000 residents age 18 and over, proportional to the adult populations of each region, based on age, sex, region and town (settlement) type.

For this research we designed three-stage stratified representative sample. First we stratify sample at entity level, regional level and then at settlement type level for each region.

Sample universe:

Population of B&H -18+; 1991 Census figures and estimated population dynamics, census figures of refugees and IDPs, 1996. Central Election Commision - 2008; CIPS - 2008;

Sampling frame:

Polling stations territory (approximate size of census units) within strata defined by regions and type of settlements (urban and rural) Polling stations territories are chosen to be used as primary units because it enables the most reliable sample selection, due to the fact that for these units the most complete data are available (dwelling register - addresses)

Type of sample:

Three stage random representative stratified sample

Definition and number of PSU, SSU, TSU, and sampling points

• PSU - Polling station territory Definition: Polling stations territories are defined by street(s) name(s) and dwelling numbers; each polling station territory comprises approximately 300 households, with exception of the settlements with less than 300 HH which are defined as one unite. Number of PSUs in sample universe: 4710
• SSU - Household Definition: One household comprises people living in the same apartment and sharing the expenditure for food
• TSU - Respondent Definition: Member of the HH , 18+ Number of TSUs in sample universe: = 2.966.766
• Sampling points Approximately 10 respondents per one PSU, total 104

Stratification, purpose and method

• First level strata: Federation of B&H Republika Srpska Brc ko District
• Second level strata: 10 cantons 2 regions -
• Third level strata: urban and rural settlements
• Purpose: Optimisation of the sample plan, and reducing the sampling error

• Method: The strata are defined by criteria of optimal geographical and cultural uniformity

• Selection procedure of PSU, SSU, and respondent Stratification, purpose and method

• PSU Type of sampling of the PSU: Polling station territory chosen with probability proportional to size (PPS) Method of selection: Cumulative (Lachirie method)

• SSU Type of sampling of the SSU: Sample random sampling without replacement Method of selection: Random walk - Random choice of the starting point

• TSU - Respondent Type of sampling of respondent: Sample random sampling without replacement Method of selection: TCB (Trohdal-Bryant scheme)

• Sample size N=1036 respondents

• Sampling error Marginal error +/-3.0%

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was modelled after the identical survey conducted in Romania. The questionnaire used in the Financial Literacy Survey in Romania was localized for Bosnia and Herzegovina, including adaptations to match the Bosnian context and methodological improvements in wording of questions.

Cleaning operations

Before data entry, 100% logic and consistency controls are performed first by local supervisors and once later by staff in central office.

Verification of correct data entry is assured by using BLAISE system for data entry (commercial product of Netherlands statistics), where criteria for logical and consistency control are defined in advance.

Response rate

• Nobody at home: 2,8%
• Eligible person is not home: 2,8%
• Refusal : 32,79%
• Given up after a minimum of two visits: 0,82%
• Other (excluded after control): 0,29%
• Finished: 60,5%

Environmental challenges of the twenty-first-century led some institutions to foster populations to use less polluting modes of transport. Nevertheless, cars remain a representative means of locomotion for getting to work for a lot of people. Despite a slight decrease from 73.7 percent in 2015 to 72.8 percent in 2020, the car is the most means of transport used for getting to work among the French population. On the other hand, "soft" modes of locomotion were still little used by French people for getting to work in 2020.

This dataset is about: (Table 2) Mean major and trace element compositions of representative tephras from the four melt types identified in ODP Site 181-1123. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.815949 for more information. […]

Mean differences between the parents regretting and not regretting parenthood in Study 1.

Indicator 14.3.1Average marine acidity (pH) measured at agreed suite of representative sampling stations.Methodology:This indicator calls for the collection of multiple observations, in the form of individual data points, to capture the variability in ocean acidity. Individual data points for pH either are measured directly or can be calculated based on data for two of the other carbonate chemistry parameters, these being TA (AT), DIC (CT) and pCO2. Calculation tools developed by experts in the field are freely available, and they are introduced and linked in the methodology. Average pH is defined as the annual equally weighed mean of multiple data points at representative sampling stations. The exact number of samples and data points depends on the level of variability of ocean acidity at the site in question. The minimum number of samples should enable the characterisation of a seasonal cycle at the site. Detailed guidelines on the minimum number of observations required are provided in the Methodology (https://oa.iode.org). In addition to the data value, standard deviation and the total range (minimum and maximum values measured), as well as underlying data used to provide traceability and transparency (metadata information) should be reported. All reported values should have gone through a first level quality control by the data provider. If historical data is available, this should be released to enable calculations about the rate of change and to compare natural variability and anthropogenic effects. Relevant data from 2010 onwards are accepted.Note :The national maximum is between 6.5 and 8.3Data Source:Ministry of Environment and Climate Change .

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

Surface ocean velocities estimated from HF-Radar are representative of the upper 0.3 - 2.5 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Near real-time surface current maps produced on a 6km grid of the U.S. East and Gulf Coast are averaged on a monthly basis for climatological applications.Surface ocean velocities estimated from HF-Radar are representative of the upper 0.3 - 2.5 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Near real-time surface current maps produced on a 6km grid of the U.S. East and Gulf Coast are averaged on a monthly basis for climatological applications.Surface ocean velocities estimated from HF-Radar are representative of the upper 0.3 - 2.5 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Near real-time surface current maps produced on a 6km grid of the U.S. East and Gulf Coast are averaged on a monthly basis for climatological applications.Surface ocean velocities estimated from HF-Radar are representative of the upper 0.3 - 2.5 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Near real-time surface current maps produced on a 6km grid of the U.S. East and Gulf Coast are averaged on a monthly basis for climatological applications.Surface ocean velocities estimated from HF-Radar are representative of the upper 0.3 - 2.5 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Near real-time surface current maps produced on a 6km grid of the U.S. East and Gulf Coast are averaged on a monthly basis for climatological applications.

The data provided are synthetic hourly electricity load profiles for the paper and food industries for one year. The data have been synthetized from two years of measured data from industries in Chile using a comprehensive clustering analysis. The synthetic data possess the same statistical characteristics as the measured data but are provided normalized to one kW and anonymized in order to be used without confidentiality issues. Three CSV files are provided: food_i.csv, paper_i_small.csv and paper_i_large.csv containing the data of a small food processing industry, a small paper industry, and a medium-large paper industry, respectively. All the three files contain seven columns of data: weekday, month, hour, cluster, min, max, mean. The four first columns index the data in the following way:

Month: it includes the range of integer values between 1 and 12 accounting for the consecutive calendar months of a year starting in January (1) and ending in December (12).
Weekday: this column has integer values in the range 1 to 7 that are equivalent to the consecutive days of the week starting on Monday (1) and ending on Sunday (7). Hour: it consist of integer values ranging between 1 and 24, which describe the hours of a day. Cluster: The column “cluster” represents the cluster to which this data is associated to. The number of clusters is different for each load profile, as well as the number of days included in each cluster. Since the cluster were calculated for days, a cluster number covers 24 consecutive points of data.

The load profile data are provided in the three different columns: min, max and mean:

Min: this column provides the min value of the cluster at that time of the day. Therefore, it represents the minimum demand of electricity recorded in all the days belonging to this representative group of data.

Max. This column provides the maximum electric load of the cluster at that time of the day. It represents the maximum demand of electricity in all the days belonging to this representative group of data at that hour of the day.

Mean: This column provides the average electric load of the cluster at that time of the day. It represents the mean demand for electricity belonging to this representative group of data at that hour of the day.

The min, max and mean values are different for each hour of the day. All values are provided in values from 0 to 1 with the unit kW.
For details on the clustering procedure or the data itself please refer to the associated paper published in the journal Energy and the one published in Data in Brief journal.

The study was supported by the German Federal Ministry of Education and Research - BMBF and the Chilean National Commission for Scientific Research and Technology - CONICYT (grant number BMBF150075) , the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH through the Energy Program in Chile, and the European Research Council (“reFUEL” ERC-2017-STG 758149).

For the Konrad Adenauer Foundation´s Department of Politics and Consultancy, Ipsos conducted a representative multi-topic survey among the German voting population aged 18 and over. The focus of the survey was to determine opinions and attitudes on various political topics.

Topics: The future of Germany, Concerns and trust in Germany, Social and cultural identity and security: particularly important aspects of what makes us in Germany (open, three mentions); assignment of various terms rather as something good, rather as something bad or ignorance of the meaning of the term (split half, 10 terms each: Opportunity Republic of Germany, Lifelong Learning, Black Zero, Personal Responsibility, Video Surveillance, Cold Progression, Middle Class Belly, Promote and Demand, Reliable State, Security, Spousal Splitting, Guiding Culture, Strong State, Responsibility, Social Market Economy, Prosperity, Solid Finances, Tax Justice, Consolidation Course, Flexi Pension); personally most important topic (open); most important topic for Germany´s future (open); assessment of Germany´s preparation for the future based on school grades; worries about Germany and trust in Germany based on opposing statements (often fear of what will come vs. everything will be fine, if possible no change vs. further development for prosperity, see black for Germany vs. trust in Germany); respondent strongly agrees or somewhat agrees with the statement; agree with statements on social, cultural identity and security (All in all, I´m doing well, can live well on my pension, must not forget socially weaker people in Germany, own job is secure, politicians don´t think about people like me, you can live well in Germany, family is where parents take responsibility for children, people who have no right to stay here must be consistently sent back, those who want to live here must adapt to German culture, respect for each other has been lost in our society, I´m afraid that Germany will change too much because of immigration, the state has the refugee crisis under control, you can no longer say what you actually think).

Digitalisation: Attitude towards digitalisation (in Germany, digitalisation will create new jobs, endangers jobs, makes work easier, self-driving cars will increase road safety, technology has become so complicated that I often don´t know how to use it, technology will help me to continue living independently in old age, desire for more help in everyday life through technical innovations); security: Opinion on the topic of security (enforce the law much more harshly, harsher punishments for many crimes, as a woman you can no longer go out alone on the street at night, more video surveillance in public, fear chaotic conditions in Germany); open mention of the feared chaotic conditions in Germany.

Political parties and voting intention: Party (CDU/CSU, SPD, FDP, Bündnis 90/Die Grünen, Die Linke, NPD, Piratenpartei, AfD -Alternative für Deutschland, other party) to which various characteristics can most likely be assigned (Christian party, party of the centre, conservative party, solves problem, can be trusted in the future, people´s party, social, protects citizens from crime, moderate, able to compromise, reasonable) and importance that the party has this characteristic; Left-right self-rating; left-right rating of the aforementioned parties; voting behaviour in the last five years (second vote); voting behaviour in the last election in which the respondent participated; Importance of various reasons for voting for this party (party has good politicians, party can be trusted in principle, party offers good solutions, was pissed off at another party/parties, always vote for this party, vote for another party every time, party knows what people like me think, wanted to show the other parties, party is setting the right course for the future); parties considered in principle; likelihood of voting for this party in the next election; party preference in the next federal election (Sunday question).

Demography: Sex; age (year of birth or age groups; education: highest school-leaving qualification; occupation or activity if not in employment; occupational status; denomination (religious denomination); frequency of churchgoing; household size; number of persons in the household aged 18 and over;

Additionally coded: Respondent ID; weighting factor; Interview date; Interview duration; city size (BIK and political city size); Information for dual-frame weighting: number of landline numbers and mobile phone numbers via which the respondent can be reached by phone; private mobile phone use; number of mobile phone numbers via which the respondent can be reached in person; mobile phone use exclusively alone or by other persons; number of other persons aged 18 and over who use the mobile phone regularly; private telephone accessibility on an average weekday (exclusively via landline, predominantly via landline, in equal parts via landline and...

Representative mean square displacements (MSDs) for Y_2x1x5/L_0/Y_0 and Y_1x7/L_0/Y_0 diplayed from different temperatures.Python script can sort and display up to 20 curves with the largest diffusion coefficients in each case.