Lithuania number dataset is a database of phone numbers collected from trusted sources. This means the numbers come from reliable places like government records, websites, or phone companies. The companies that provide this data work hard to ensure it is correct. They even offer source URLs, so you can see where the data came from. Moreover, you get 24/7 support, so if you have questions, help is always available. List to Data is a helpful website for finding important cell numbers quickly. Additionally, the phone numbers in the Lithuania number dataset follow an opt-in system. This means people agreed to share their phone numbers. This system is important because it keeps the data legal. It ensures that you are only contacting people who have given permission. Number data in Lithuania makes it easy to connect with the right people. Lithuania phone data is a special set of phone numbers that you can filter to meet your needs. You can easily filter the list by gender, age, and relationship status. For example, you can quickly sort the data to contact older adults or young singles easily. This flexibility makes it easier to communicate with the right audience. Therefore, you can connect with the people you want to reach. Also, the Lithuanian phone data follows strict GDPR rules. These rules protect people’s privacy and make sure their information stays safe. We collect and use the database of Lithuania in ways that respect everyone’s rights. Additionally, it removes any invalid numbers. You can find important phone numbers easily on our website, List to Data. Lithuania phone number list is a collection of phone numbers from people living in Lithuania. This list is completely correct and valid, meaning all numbers work properly. Companies check every phone number to ensure it is accurate. If you find a number that doesn’t work, you can get a new one for free. Moreover, Lithuania phone number list is about all numbers from authorized customers. People on this list agreed to share their numbers. As a result, you can use the data without worrying about legal issues. This makes the phonebook safe and useful for businesses that want to connect with people in Lithuania.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

In a multicommunity model, high-income families cluster together in any equilibrium, and cluster near effective schools if effectiveness is an important component of community desirability. Governmental fragmentation facilitates this residential sorting. Thus, if parents prefer effective schools, income correlates with effectiveness in high-choice-market equilibrium. I examine the distribution of student background and test scores across schools within metropolitan areas that differ in the structure of educational governance. I find little indication of the effectiveness sorting that is predicted if parents choose neighborhoods for the efficacy of the local schools. This suggests caution about the productivity implications of school choice policies. (JEL H73, I21, R21, R23)

Random Set Dataset Overview The Random_Set dataset contains a collection of randomly generated numerical and categorical values. This dataset is ideal for testing machine learning models, statistical analysis, and data preprocessing techniques. It includes a mix of integer, float, categorical, and boolean features, making it versatile for exploratory data analysis (EDA), feature engineering, and algorithm benchmarking.

Why Use This Dataset? ✅ Pre-cleaned & Structured – No missing values, well-organized data. ✅ Ideal for ML & Data Science Practice – Test different models and preprocessing techniques. ✅ Great for Feature Engineering – Work with different data types (categorical, numerical, boolean). ✅ Useful for Statistical & Algorithm Testing – Validate sorting, searching, clustering, and regression methods.

Potential Use Cases 📊 Machine Learning Pipeline Testing: Evaluate ML models on random structured data. 🧪 Feature Engineering Practice: Experiment with feature encoding, scaling, and transformations. 🎲 Algorithm Benchmarking: Test sorting, clustering, and classification algorithms. 📈 Data Visualization: Practice creating charts, graphs, and statistical summaries. 🛠️ Training for Data Science Competitions: Sharpen your skills with synthetic but structured data.

Source & Acknowledgment This dataset is randomly generated using statistical distributions and structured for usability. It is designed for practice, experimentation, and algorithm evaluation rather than real-world analysis.

In four public good game experiments, we study self-sorting as a means to facilitate cooperation in groups. When individuals can choose to join groups pre-committed to charity, such groups sustain cooperation towards the group’s local public good. By eliciting subjects’ conditional contribution profiles, we find that subjects who prefer the charity groups have higher average conditional contribution levels, but do not differ with respect to the slope of their profiles. The majority of subjects in both group types are conditional cooperators, whose willingness to contribute is stimulated by generous group members but undermined by free-riders. Charity groups thus seem better able to sustain cooperation because they attract more generous individuals, triggering generous responses by conditional cooperators.

This study documents segregation within majors by examining student experiences at a student-by-course level. We measure the variation in the proportion of students’ classmates who are their same gender and which levels of sorting explain this variation. Our study offers a guide for measuring this phenomenon in various campus contexts, examining how curricular choice can affect student sorting within majors. This study suggests that previous work examining sorting only at the major level misses some important opportunities for understanding the mechanisms through which gender segregation occurs in higher education.

The dataset contains the data produced and used for the mapping of flood risk on the Flooding Lands of Lorraine on which the order fixing the flood area maps and flood risk maps has been taken. Sorting of the Rhine-Meuse Basin with Arrests: — NANCY_DAMELEVIERES: 13/06/2014 A series of geographical data produced by the GIS Flood Directive of territories at significant risk of flooding (TRI) of Lorraine and mapped for reporting purposes for the European Flood Directive. European Directive 2007/60/EC of 23 October 2007 on the assessment and management of flood risks (OJ L 288, 06-11-2007, p. 27) influences the flood prevention strategy in Europe. It requires the production of a flood risk management plan aimed at reducing the negative consequences of flooding on human health, the environment, cultural heritage and economic activity. The objectives and requirements for achievement are given by the Law of 12 July 2010 on the National Commitment for the Environment (LENE) and the Decree of 2 March 2011. Within this framework, the primary objective of the mapping of flood areas and flood risks for IRRs is to contribute, by homogenising and objecting knowledge of the exposure of issues to floods, to the development of Flood Risk Management Plans (IFMPs). This dataset is used to produce flood surface maps and flood risk maps that represent flood hazards and issues on an appropriate scale, respectively. Their objective is to provide quantitative elements to better assess the vulnerability of a territory for the three levels of flood probability (high, medium, low) (Source: standard COVADIS, Flood Directive of 26/09/2012). Tables contained in the lot: N_TRI_NANC_COMMUNE_S_054.TAB N_TRI_NANC_ENJEU_CRISE_P_054.TAB N_TRI_NANC_ENJEU_CRISE_L_054.TAB N_TRI_NANC_ENJEU_RAPPORT_054.TAB N_TRI_NANC_TRI_S_054.TAB N_TRI_NANC_ENJEU_IPPC_P_054.TAB N_TRI_NANC_ENJEU_STEU_P_054.TAB N_TRI_NANC_ENJEU_ECO_S_054.TAB N_TRI_NANC_CARTE_RISQ_S_054.TAB N_TRI_NANC_CARTE_INOND_S_054.TAB N_TRI_NANC_ISO_HT_S_054.TAB N_TRI_NANC_INONDABLE_S_054.TAB N_TRI_NANC_BATI_S_054.TAB N_TRI_NANC_SURFACE_EN_EAU_S_054.TAB

OVERVIEW

This data file, compiled from multiple online sources, presents 2013–2017 publication counts—articles, articles in high-impact journals, books, and books from high-impact publishers—for 2,132 professors and associate professors in 426 U.S. departments of sociology. It also includes information on institutional characteristics (e.g., institution type, highest sociology degree offered, department size) and individual characteristics (e.g., academic rank, gender, PhD year, PhD institution).

The data may be useful for investigations of scholarly productivity, the correlates of scholarly productivity, and the contributions of particular individuals and institutions. Complete population data are presented for the top 26 doctoral programs, doctoral institutions other than R1 universities, the top liberal arts colleges, and other bachelor's institutions. Sample data are presented for Carnegie R1 universities (other than the top 26) and master's institutions.

USER NOTES

Please see our paper in Scholarly Assessment Reports, freely available at https://doi.org/10.29024/sar.36 , for full information about the data set and the methods used in its compilation. The section numbers used here refer to the Appendix of that paper. See the References, below, for other papers that have made use of these data.

The data file is a single Excel file with five worksheets: Sampling, Articles, Books, Individuals, and Departments. Each worksheet has a simple rectangular format, and the cells include just text and values—no formulas or links. A few general notes apply to all five worksheets.

• The yellow column headings represent institutional (departmental) data. The blue column headings represent data for individual faculty.

• iType is institution type, as described in section A.2—TopR (top research universities), R1 (other R1 universities), OD (other doctoral universities), M (master's institutions), TopLA (top liberal arts colleges), or B (other bachelor's institutions). nType provides the same information, but as a single-digit code that is more useful for sorting the rows; 1=TopR, 2=R1, 3=OD, 4=M, 5=TopLA, and 6=B.

• Inst is a four-digit institution code. The first digit corresponds to nType, and the last three digits allow for alphabetical sorting by institution name. Indiv is a one- or two-digit code that can be used to sort the individuals by name within each department. The Inst, nType, and Indiv codes are consistent across the five worksheets.

• For binary variables such as Full professor and Female, 1 indicates yes (full professor or female) and 0 indicates no (associate professor or male).

The five worksheets represent five distinct stages in the data compilation process. First, the Sampling worksheet lists the 1,530 base-population institutions (see section A.3) and presents the characteristics of the faculty included in the data file. Each row with an entry in the Individual column represents a faculty member at one of the 426 institutions included in the data set. Each row without an entry in the Individual column represents an institution that either (a) did not meet the criteria for inclusion (section A.1) or (b) was not needed to attain the desired sample size for the R1 or M groups (section A.3).

The Articles worksheet includes the data compiled from SocINDEX, as described in section A.6. Each row with an entry in the Journal column represents an article written by one of the 2,132 faculty included in the data. Each row without an entry in the Journal column represents a faculty member without any article listings in SocINDEX for the 2013–2017 period. (Note that SocINDEX items other than peer-reviewed articles—editorials, letters, etc.—may be listed in the Journal column but assigned a value of 1 in the Excluded column and a value of 0 in the Article credit and HI article credit columns. We assigned no credit for items such as editorial and letters, but other researchers may wish to include them.) The N and i columns represent, for each article, the number of authors (N) and the faculty member's place in the byline (i), as described in section A.8. The CiteScore and Highest percentile columns were used to identify high-impact journals, as indicated in the HI journal column. The Article credit and HI article credit columns are article counts, adjusted for co-authorship.

The Books worksheet includes data compiled from Amazon and other sources, as described in section A.7. Each row with an entry in the Book column represents a book written by one of the 2,132 faculty. Each row without an entry in the Book column represents a faculty member without any book listings in Amazon during the 2013–2017 period. The publication counts in the Books worksheet—Book credit and HI book credit—follow the same format as those in the Articles worksheet.

The Individuals worksheet consolidates information from the Articles and Books worksheets so that each of the 2,132 individuals is represented by a single row. The worksheet also includes several categorical variables calculated or otherwise derived from the raw data—Years since PhD, for instance, and the three corresponding binary variables. We suspect that many data users will be most interested in the Individuals worksheet.

The Departments worksheet collapses the individual data so that each of the 426 institutions (departments) is represented by a single row. Individual characteristics such as Female and Years since PhD are presented as percentages or averages—% Female and Avg years since PhD, for instance. Each of the four productivity measures is represented by a departmental total, an average (the total divided by the number of full and associate professors), a departmental standard deviation, and a departmental median.

Americans are sorting ideologically: liberals and conservatives are more likely to respectively identify as Democrats and Republicans. They are sorting socially as well: partisans like and trust copartisans more than opposing partisans. Existing explanations for these phenomena rely on exogenous factors, such as elite polarization. But exogenous explanations cannot help explaining variation in sorting across individuals. We argue that psychological characteristics can help explain the tendency to sort ideologically or socially. Specifically, we investigate an individual’s responsiveness to internal values versus normative social pressures as a determinant of sorting. We test this theory with several nationally representative surveys, as well as one survey experiment, and find strong support that an individual’s own tendency to respond to social cues, as opposed to ideological values, has important consequences for this process. Our work allows for a better understanding of the psychological factors that promote partisan sorting and for interpreting variation in the degree to which citizens sort into partisan groups. The replication materials for this project are located here.

We report the first demonstration of a microfluidics-based approach to measure lipids in single living cells using widely available liquid chromatography mass spectrometry (LC-MS) instrumentation. The method enables the rapid sorting of live cells into liquid chambers formed on standard Petri dishes and their subsequent dispensing into vials for analysis using LC-MS. This approach facilitates automated sampling, data acquisition, and analysis and carries the additional advantage of chromatographic separation, aimed at reducing matrix effects present in shotgun lipidomics approaches. We demonstrate that our method detects comparable numbers of features at around 200 lipids in populations of single cells versus established live single-cell capillary sampling methods and with greater throughput, albeit with the loss of spatial resolution. We also show the importance of optimization steps in addressing challenges from lipid contamination, especially in blanks, and demonstrate a 75% increase in the number of lipids identified. This work opens up a novel, accessible, and high-throughput way to obtain single-cell lipid profiles and also serves as an important validation of single-cell lipidomics through the use of different sampling methods.

Genomic studies of parallel (or convergent) evolution often compare multiple populations diverged into two ecologically different habitats to search for loci repeatedly involved in adaptation. Because the shared ancestor of these populations is generally unavailable, the source of the alleles at adaptation loci, and the direction in which their frequencies were shifted during evolution, remain elusive. To shed light on these issues, we here use multiple populations of stickleback fish adapted to two different types of derived freshwater habitats – basic and acidic lakes on the island of North Uist, Outer Hebrides, Scotland – and the present-day proxy of their marine ancestor. In a first step, we combine genome-wide pooled sequencing and targeted individual-level sequencing to demonstrate that ecological and phenotypic parallelism in basic-acidic divergence is reflected by genomic parallelism in dozens of genome regions. Exploiting data from the ancestor, we next show that the acidic populations, residing in ecologically more extreme derived habitats, have adapted by accumulating alleles rare in the ancestor, whereas the basic populations have retained alleles common in the ancestor. Genomic responses to selection are thus predictable from the ecological difference of each derived habitat type from the ancestral one. This asymmetric sorting of standing genetic variation at loci important to basic-acidic divergence has further resulted in more numerous selective sweeps in the acidic populations. Finally, our data suggest that the maintenance of standing variation important to adaptive basic-acidic differentiation in marine fish does not require extensive hybridization between the marine and freshwater populations. Overall, our study reveals striking genome-wide determinism in both the loci involved in parallel divergence, and in the direction in which alleles at these loci have been selected.

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Overview

This Extended Golf Play Dataset is a rich and detailed collection designed to extend the classic golf dataset. It includes a variety of features to cover many aspects of data science. This dataset is especially useful for teaching because it offers many small datasets within it, each one created for a different learning purpose.

Core Features:

• Outlook: Type of weather (sunny, cloudy, rainy, snowy).
• Temperature: How hot or cold it is, in Celsius.
• Humidity: How much moisture is in the air, as a percent.
• Windy: If it is windy or not (True or False).
• Play: If golf was played or not (Yes or No).

Extra Features:

• ID: Each player's unique number.
• Date: The day the data was recorded.
• Weekday: What day of the week it is.
• Holiday: If the day is a special holiday (Yes or No).
• Season: Time of the year (spring, summer, autumn, winter).
• Crowded-ness: How crowded the golf course is.
• PlayTime-Hour: How long people played golf, in hours.

Text Features:

• Review: What players said about their day at golf.
• EmailCampaign: Emails the golf place sent every day.
• MaintenanceTasks: Work done to take care of the golf course.

Mini Datasets Collection

This dataset includes a special set of mini datasets: - Each mini dataset focuses on a specific teaching point, like how to clean data or how to combine datasets. - They're perfect for beginners to practice with real examples. - Along with these datasets, you'll find notebooks with step-by-step guides that show you how to use the data.

Learning With This Dataset

Students can use this dataset to learn many skills: - Seeing Data: Learn how to make graphs and see patterns. - Sorting Data: Find out which data helps to predict if golf will be played. - Finding Odd Data: Spot data that doesn't look right. - Understanding Data Over Time: Look at how things change day by day or month by month. - Grouping Data: Learn how to put similar days together. - Learning From Text: Use players' reviews to get more insights. - Making Recommendations: Suggest the best time to play golf based on past data.

Who Can Use This Dataset

This dataset is for everyone: - New Learners: It's easy to understand and has guides to help you learn. - Teachers: Great for classes on how to see and understand data. - Researchers: Good for testing new ways to analyze data.

Disclaimer

This dataset can be shared and used by anyone under the Creative Commons Attribution 4.0 International License (CC BY 4.0). (Illustrations are AI-generated).

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Abstract

The Afrobarometer project assesses attitudes and public opinion on democracy, markets, and civil society in several sub-Saharan African.This dataset was compiled from the studies in Round 2 of the Afrobarometer, conducted from 2002-2004 in 16 countries, including Botswana, Cape Verde, Ghana, Kenya, Lesotho, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe

Geographic coverage

The Round 2 Afrobarometer surveys have national coverage for the following countries: Botswana, Ghana, Kenya, Lesotho, Malawi, Mali, Mozambique, Namibia, Nigeria, Republic of Cabo Verde, Senegal, South Africa, Tanzania, Uganda, Zambia, Zimbabwe.

Analysis unit

Individuals

Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Kind of data

Sample survey data [ssd]

Sampling procedure

Afrobarometer uses national probability samples designed to meet the following criteria. Samples are designed to generate a sample that is a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of being selected for an interview. They achieve this by:

• using random selection methods at every stage of sampling; • sampling at all stages with probability proportionate to population size wherever possible to ensure that larger (i.e., more populated) geographic units have a proportionally greater probability of being chosen into the sample.

The sampling universe normally includes all citizens age 18 and older. As a standard practice, we exclude people living in institutionalized settings, such as students in dormitories, patients in hospitals, and persons in prisons or nursing homes. Occasionally, we must also exclude people living in areas determined to be inaccessible due to conflict or insecurity. Any such exclusion is noted in the technical information report (TIR) that accompanies each data set.

Sample size and design Samples usually include either 1,200 or 2,400 cases. A randomly selected sample of n=1200 cases allows inferences to national adult populations with a margin of sampling error of no more than +/-2.8% with a confidence level of 95 percent. With a sample size of n=2400, the margin of error decreases to +/-2.0% at 95 percent confidence level.

The sample design is a clustered, stratified, multi-stage, area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location.

Area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analysed. Any oversamples is noted in the TIR.

Sample stages Samples are drawn in either four or five stages:

Stage 1: In rural areas only, the first stage is to draw secondary sampling units (SSUs). SSUs are not used in urban areas, and in some countries they are not used in rural areas. See the TIR that accompanies each data set for specific details on the sample in any given country. Stage 2: We randomly select primary sampling units (PSU). Stage 3: We then randomly select sampling start points. Stage 4: Interviewers then randomly select households. Stage 5: Within the household, the interviewer randomly selects an individual respondent. Each interviewer alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

To keep the costs and logistics of fieldwork within manageable limits, eight interviews are clustered within each selected PSU.

Data weights For some national surveys, data are weighted to correct for over or under-sampling or for household size. "Withinwt" should be turned on for all national -level descriptive statistics in countries that contain this weighting variable. It is included as the last variable in the data set, with details described in the codebook. For merged data sets, "Combinwt" should be turned on for cross-national comparisons of descriptive statistics. Note: this weighting variable standardizes each national sample as if it were equal in size.

Further information on sampling protocols, including full details of the methodologies used for each stage of sample selection, can be found at https://afrobarometer.org/surveys-and-methods/sampling-principles

Mode of data collection

Face-to-face [f2f]

Research instrument

Certain questions in the questionnaires for the Afrobarometer 2 survey addressed country-specific issues, but many of the same questions were asked across surveys. Citizens of the 16 countries were asked questions about their economic and social situations, and their opinions were elicited on recent political and economic changes within their country.

1. The role of predation in determining the metacommunity assembly model of prey communities is understudied relative to that of interspecific competition among prey. Previous work on metacommunity dynamics of competing species has shown that sorting by habitat patch type and spatial patterning can be affected by disturbances.
2. Microcosms offer a useful model system to test the effect of multi-trophic interactions and disturbance on metacommunity dynamics. Here, we investigated the potential role of predators in enhancing or disrupting sorting and spatial pattern among prey in experimental landscapes.
3. We exposed multi-trophic protist microcosm landscapes with one predator, two competing prey, two patch resource types, and localized dispersal to three disturbance regimes (none, low, and high). Then, we used variation partitioning and spatial clustering analysis to analyze the results.
4. In contrast with previous experiments that did not manipulate predators, we found that patch type did not structure prey communities very well. Instead, we found that it was the distribution of the predator that most strongly predicted the composition of the prey community.
5. The predator impacted species sorting by 1) preferentially consuming one prey, thereby acting as a strong local environmental driver, and by 2) indirectly magnifying the impact of patch food resources on the less preferred prey. The predator also enhanced spatial signal in the prey community because of its limited dispersal. Our results indicate that predators can strongly influence prey species sorting and spatial patterning in metacommunities in ways that would otherwise be attributed to stochastic effects, such as dispersal limitation or demographic drift. Therefore, whenever possible, predators should be explicitly included as separate explanatory factors in variation partitioning analyses.

The Dictionary of Algorithms and Data Structures (DADS) is an online, publicly accessible dictionary of generally useful algorithms, data structures, algorithmic techniques, archetypal problems, and related definitions. In addition to brief definitions, some entries have links to related entries, links to implementations, and additional information. DADS is meant to be a resource for the practicing programmer, although students and researchers may find it a useful starting point. DADS has fundamental entries in areas such as theory, cryptography and compression, graphs, trees, and searching, for instance, Ackermann's function, quick sort, traveling salesman, big O notation, merge sort, AVL tree, hash table, and Byzantine generals. DADS also has index pages that list entries by area and by type. Currently DADS does not include algorithms particular to business data processing, communications, operating systems or distributed algorithms, programming languages, AI, graphics, or numerical analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The evolutionary forces arising from differential dispersal are known as “spatial sorting,” distinguishing them from natural selection arising from differential survival or differential reproductive success. Spatial sorting is often considered to be transient, because it is offset by the return of dispersers in many cases. However, in riverine systems, spatial sorting by downstream dispersal can be cumulative in habitats upstream of migration barriers such as weirs or falls, which can block the return of the dispersers. Terraced rice paddies are often found on steep mountain slopes in Japan and often incorporate small streams with numerous migration barriers. This study investigated the morphological features of fat minnow, Rhynchocypris oxycephalus jouyi (Cyprinidae), inhabiting above-barrier habitats of the small streams flowing through flood-prone terraced rice paddies and examined their function via a mark–recapture experiment. Although this study did not reveal a consistent pattern across all local populations, some above-barrier populations were characterised by individuals with a thinner caudal peduncle, thinner body, and longer ventral caudal fin lobes than those in neighbouring mainstream populations. A mark–recapture experiment during flooding showed that a thinner caudal peduncle and deeper body helped fat minnow avoid downstream dispersal and ascend a small step, and suggested that a longer ventral caudal fin lobe was important for ascending. These results suggest that the caudal morphologies of some above-barrier populations avoid or reduce the risk of downstream dispersal, supporting the idea that spatial sorting shapes functional traits, enhancing the spatial persistence of individuals in upstream habitats.

The study deals with the changes in the regional structure of Germany’s foreign trade as well as with the causes of these changes between 1880 and 1938. In this context the regional development of german import and export by continents, regions and counties for the major German tradepartners is described. After that the regional development of the trade flows of all major import- and exportproducts is analysed. The German foreign trade therefore is examined on the basis of the goods on the one hand and on the other on the basis of the countries. For the analysis the researcher collected the data (time series) for the important goods and commodity groups. Further more he developed a consistent definition of the commodity groups, so that they are comparable.
Mehtodology a) Definition and Problem:The following processes hab been defined as structural change:- fundamental shifts in the composition of foreign trade partners, as well as- meaningful and sustained change of direction or volume of important commodities and products that affect the trade with countries or regions. b) Temporal Delimination:Period of investigation is from 1880 to 1938. The statistics for the war years 1914-1918 and 1939 and for the post-war years 1919-1924 have not been included in the analysis because values were not covered or values are very incomplete or unreliable coused by inflation and other circumstances of that period. c) Changes of Territory:The data of the German trade statistics refer from 1880 to February 1906 to the German custom territory, which comprised since 1872 the territory of the German Customs Union, consisting of the 26 states, the Grand Duchy of Luxembourg and the Austrian municipalities Jungholz and Mittelberg. The free port areas of Hamburg, Bremerhaven, Geestermünde and Helgoland and parts of the municipality of Hamburg and Cuxhaven did not belong to the German custum territory.Since March 1906 the german trade statistics collected data of the foreign merchandise traffic of the entire German economic area, consists until the Versailler contract of the area of the German Empire including the Grand Duchy of Luxembourg and the Austrian municipalities Jungholz and Mittelberg, excluding Helgoland and the badenese Custum boards. Since 1920 the official trade statistics reports the values of the foreign trade for the German Empire in its new borders. That is to say, the regions of Alsace-Lorraine, the Free City of Danzig, and parts of the Prussian provinces of East Prussia, West Prussia, Brandenburg, Pomerania, Silesia, Posen, Schleswig-Holstein, the Rhine province, the territory of Luxembourg and for the years 1919 to 1935, the Saarland no longer belong to the German economic territory. The expansion of the German Empire territory between 1938 and 1939 by the annexation of Austria, Sudetenland, Bohemia, Moravia, and the Memel territory has been kept out of consideration. For the analysis of the German foreign trade the values of german imports and exports published by the Statistical Office of the German Empire has been used. While comparing the pre-1914 values with values after the first World War, it is important to reconsider the lost of major agricultural areas of East-Germany, which restricts the comparison and it’s explanatory power or validity. On the other hand these changes reveals the changes of Germany’s foreign trade structure. Thus, it becomes obvious how the separation of large agricultural and farming land increased Germany’s import dependency in the food sector as well as Germany’s decreased export opportunities of agricultural products. d) System of commodity groups: The problem of published German trade values of the Official Statistics of the German Empire is, that commodity groups are not defined in terms of their content. Insofar as the information is about single goods (eg.: rye, copper, cotton, etc.), the values are reliable. This is not the case as soon as the information is about commodity groups, such as ‘food’, ‘textiles’, ‘metal goods’, etc., because the structure of the aggregation of specific goods to a commodity group has changed six times over the period of investigation. The list of countries in the german foreign trade statistics has changed as well. Therfore, the author had to revised commodity groups and country lists for the purpose of its analysis and to make them comparable. The author developed the following scheme in order to sort countries into groups or regions: - Europe:Denmarc, Norway, Sweden, Finnland = North EuropeNetherlands, Belgium/Luxembuorg, Great Britain, France, Swizerland = West EuropeJugoslawia, Hungary, Rumania, Bulgaria, Albania, Greek, european and asiatic Turkey = South-East EuropePortugal, Spane, Italy = South EuropePoland, Tschechoslowakia, Russia, Baltic States = East EuropAustria-Hungary - America:Canada, United States of America = North-AmericaMexico, Costarica, Duba, Dominican Republic, Guatemala, Honduras, Nicaragua, Haiti, El Salvador = Ce...

Abstract

The Afrobarometer is a comparative series of public attitude surveys that assess African citizen's attitudes to democracy and governance, markets, and civil society, among other topics.

The 12 country datasetis a combined dataset for the 12 African countries surveyed during round 1 of the survey, conducted between 1999-2000 (Botswana, Ghana, Lesotho, Mali, Malawi, Namibia, Nigeria South Africa, Tanzania, Uganda, Zambia and Zimbabwe), plus data from the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

Geographic coverage

The Round 1 Afrobarometer surveys have national coverage for the following countries: Botswana, Ghana, Lesotho, Malawi, Mali, Namibia, Nigeria, South Africa, Tanzania, Uganda, Zambia, Zimbabwe.

Analysis unit

Individuals

Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Kind of data

Sample survey data [ssd]

Sampling procedure

Afrobarometer uses national probability samples designed to meet the following criteria. Samples are designed to generate a sample that is a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of being selected for an interview. They achieve this by:

• using random selection methods at every stage of sampling; • sampling at all stages with probability proportionate to population size wherever possible to ensure that larger (i.e., more populated) geographic units have a proportionally greater probability of being chosen into the sample.

The sampling universe normally includes all citizens age 18 and older. As a standard practice, we exclude people living in institutionalized settings, such as students in dormitories, patients in hospitals, and persons in prisons or nursing homes. Occasionally, we must also exclude people living in areas determined to be inaccessible due to conflict or insecurity. Any such exclusion is noted in the technical information report (TIR) that accompanies each data set.

Sample size and design Samples usually include either 1,200 or 2,400 cases. A randomly selected sample of n=1200 cases allows inferences to national adult populations with a margin of sampling error of no more than +/-2.8% with a confidence level of 95 percent. With a sample size of n=2400, the margin of error decreases to +/-2.0% at 95 percent confidence level.

The sample design is a clustered, stratified, multi-stage, area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location.

Area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analysed. Any oversamples is noted in the TIR.

Sample stages Samples are drawn in either four or five stages:

Stage 1: In rural areas only, the first stage is to draw secondary sampling units (SSUs). SSUs are not used in urban areas, and in some countries they are not used in rural areas. See the TIR that accompanies each data set for specific details on the sample in any given country. Stage 2: We randomly select primary sampling units (PSU). Stage 3: We then randomly select sampling start points. Stage 4: Interviewers then randomly select households. Stage 5: Within the household, the interviewer randomly selects an individual respondent. Each interviewer alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

To keep the costs and logistics of fieldwork within manageable limits, eight interviews are clustered within each selected PSU.

Data weights For some national surveys, data are weighted to correct for over or under-sampling or for household size. "Withinwt" should be turned on for all national -level descriptive statistics in countries that contain this weighting variable. It is included as the last variable in the data set, with details described in the codebook. For merged data sets, "Combinwt" should be turned on for cross-national comparisons of descriptive statistics. Note: this weighting variable standardizes each national sample as if it were equal in size.

Further information on sampling protocols, including full details of the methodologies used for each stage of sample selection, can be found at https://afrobarometer.org/surveys-and-methods/sampling-principles

Mode of data collection

Face-to-face [f2f]

Research instrument

Because Afrobarometer Round 1 emerged out of several different survey research efforts, survey instruments were not standardized across all countries, there are a number of features of the questionnaires that should be noted, as follows: • In most cases, the data set only includes those questions/variables that were asked in nine or more countries. Complete Round 1 data sets for each individual country have already been released, and are available from ICPSR or from the Afrobarometer website at www.afrobarometer.org. • In the seven countries that originally formed the Southern Africa Barometer (SAB) - Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe - a standardized questionnaire was used, so question wording and response categories are the generally the same for all of these countries. The questionnaires in Mali and Tanzania were also essentially identical (in the original English version). Ghana, Uganda and Nigeria each had distinct questionnaires. • This merged dataset combines, into a single variable, responses from across these different countries where either identical or very similar questions were used, or where conceptually equivalent questions can be found in at least nine of the different countries. For each variable, the exact question text from each of the countries or groups of countries ("SAB" refers to the Southern Africa Barometer countries) is listed. • Response options also varied on some questions, and where applicable, these differences are also noted.