The data set measures the processes of (a) liberalization, (b) democratization, and (c) consolidation of democracy in more than 30 countries from different regions of the world over the time period 1974-2000

The Data Democratization In Healthcare Market report segments the industry into By Component (Software, Services), By Deployment Mode (On-premise, Cloud-based), By Application (Clinical Decision Support, Patient Engagement, Operational Efficiency, and more), By End-User (Healthcare Providers, Payers, Pharmaceutical and Biotechnology Companies, and more), and Geography (North America, Europe, Asia-Pacific, and more).

This research note introduces the new Democratization Articles Dataset, a survey of peer-reviewed articles on democratization published over the past 25 years from five of the leading comparative politics journals. The data highlight significant gender and location authorship imbalances, while also noting a steady increase in the proportion of female authors as well as team collaboration over the past 25 years. Democratization studies also appear to be largely event driven, with more attention paid to the Post-Soviet countries and democratic transitions in the 1990s, and increased attention to MENA countries and the study of authoritarianism since 2000. The field is also evolving methodologically, with a greater proportion of articles investigating a causal claim, relying on statistical and experimental techniques, and detailing sample selection criteria. Yet, single and comparative cases studies still compose the overwhelming majority of published research articles.

This dataset is used in the paper entitle: "How Does Democratization Affect Economic Performance? A Case of Indonesia Using Synthetic Control Method"

Does the removal of salient external threats foster democratization? Recent research proposes an affirmative answer but either fails to examine democratization at the monadic level, to consider small-scale democratization, or to account for factors known to influence the democratization process. The current study addresses this deficit by (re)examining democratization during the period 1919–2006. The findings suggest a strong relationship between border settlement and democratization. A state that settles all of its interstate borders democratizes; any outstanding unsettled borders, however, prevent significant democratization. Furthermore, although border settlement contributes to democratization, it does not significantly affect democratic regime change. This empirical evidence cumulatively specifies a more precise relationship between external threat and democratization than previous work and thereby contributes directly to the recent debate between the territorial and democratic peace theories. It also suggests that democratization may proceed more readily if states address unsettled borders first.

Spatial omics technologies have revolutionized the field of biology by enabling the visualization of biomolecules within their native tissue context. However, the high costs associated with proprietary instrumentation, specialized reagents, and complex workflows have limited the broad application of these techniques. In this study, we introduce Python-based Robotic Imaging and Staining for Modular Spatial omics (PRISMS), an open-sourced, automated multiplexing pipeline compatible with several sample types and Nikon NIS Elements Basic Research software. PRISMS utilizes a liquid handling robot with thermal control to enable rapid, automated staining of RNA and protein samples. The modular sample holders and Python control facilitate high-throughput, single-molecule fluorescence imaging on widefield and confocal microscopes.We successfully demonstrate the versatility of PRISMS by imaging tissue slides and adherent cells. We also show that PRISMS can be used to perform super-resolved imaging, such as super-resolution radial fluctuations (SRRF) 1. PRISMS is a powerful tool that can be used to democratize spatial omics by providing researchers with an accessible, reproducible, and cost-effective solution for multiplex imaging. Specifically, PRISMS is an open-sourced, automated multiplexing pipeline for spatial omics, is compatible with several sample types and Nikon NIS Elements Basic Research software, performs high-throughput, single-molecule fluorescence imaging on widefield and confocal microscopes, and can be used to perform super-resolved imaging, such as SRRF. Overall, PRISMS is a powerful tool that can be used to democratize spatial omics by providing researchers with an accessible, reproducible, and cost-effective solution for multiplex imaging. This open-source platform will enable researchers to push the boundaries of spatial biology and make groundbreaking discoveries.

This large longitudinal study is the result of professor Tatu Vanhanen's long-term research on democratization and power resources. International scientific community knows this data also by the name "Vanhanen's Index of Power Resources". The data have been collected from several written sources and have been published as appendices of five different books. The books are listed in the section Data sources below. The original sources of the numerical data published in these books have been collected to a separate document containing background information. Vanhanen divides the variables of his dataset into two main groups. The first group consists of Measures of Democracy and includes three variables. The second group is called Measures of Resource Distribution. The variables in the first group (Measures of Democracy) are Competition, Participation and Index of Democratization. The value of Competition is calculated by subtracting the percentage of votes/seats gained by the largest political party in parliamentary elections and/or in presidential (executive) elections from 100%. The Participation variable is an aggregate of the turnout in elections (percentage of the total population who voted in the same election) and the number of referendums. Each national referendum raises the value of Participation by five percentage points and each state referendum by one percentage point for the year of the referendum. The upper limit for both variables is 70%. Index of Democratization is derived by first multiplying the above mentioned variables Competition and Participation and then dividing this product by 100. Six variables are used to measure resource distribution: 1) Urban Population (%) (as a percentage of total population). 2) Non-Agricultural Population (%) (derived by subtracting the percentage of agricultural population from 100%). 3) Number of students: the variable denotes how many students there are in universities and other higher education institutions per 100.000 inhabitants of the country. Two ways are used to calculate the percentage of Students (%): before the year 1988 the value 1000 of the variable Number of students is equivalent to 100% and between the years 1988-1998 the value 5000 of the same variable is equivalent to 100%. 4) Literates (%) (as a percentage of adult population). 5) Family Farms Are (%) (as a percentage of total cultivated area or of total area of holdings). 6) Degree of Decentralization of Non-Agricultural Economic Resources. This variable has been calculated from the 1970s. Three new variables have been derived from the above mentioned six variables. 1) Index of Occupational Diversification is derived by calculating the arithmetic mean of Urban Population and Non-Agricultural Population. 2) Index of Knowledge Distribution is derived by calculating the arithmetic mean of Students and Literates. 3) Index of Distribution of Economic Power Resources is derived by first multiplying the value of Family Farm Area with the percentage of agricultural population. Then the value of Degree of Decentralization of Non-Agricultural Economic Resources is multiplied with the percentage of Non-Agricultural Population. After this these two products are simply added up. Finally two new variables have derived from the above mentioned variables. First derived variable is Index of Power Resources, calculated by multiplying the values of Index of Occupational Diversification, Index of Knowledge Distribution and Index of the Distribution of Economic Power Resources and then dividing the product by 10 000. The second derived variable Mean is the arithmetic mean of the five (from the 1970s six) explanatory variables. This differs from Index of Power Resources in that a low value of any single variable does not reduce the value of Mean to any great extent.

The data were collected to assess levels of support among citizens of the Moscow Oblast for democratic rights, institutions, and processes, and to test several hypotheses about the democratic values within socialist political systems. The data cover a broad array of topics, including political tolerance, valuation of liberty, support for the norms of democracy, rights awareness, support for dissent, support for an independent media, support for the institution of competitive elections, and anti-Semitism. Questions were asked about the respondents' knowledge of current events in the Soviet Union, interest in politics, familiarity and contact with political leaders, level of political involvement, views on political issues, consumption of alcoholic beverages, and attitudes towards specific social, political, and ethnic groups. Demographic information includes age, education, occupation, birthplace, religion, and marital status. The self-administered portion of the data collection consists of a personality inventory and a word game.

The data contain three different variables, created by Tatu Vanhanen in his long-term research, for each year from 1810 to 2018. The variables in question are political competition, political participation and the index of democratization. The competition variable portrays the electoral success of smaller parties, that is, the percentage of votes gained by the smaller parties in parliamentary and/or presidential elections. The variable is calculated by subtracting from 100 the percentage of votes won by the largest party (the party which wins most votes) in parliamentary elections or by the party of the successful candidate in presidential elections. Depending on their importance, either parliamentary or presidential elections are used in the calculation of the variable, or both elections are used, with weights. If information on the distribution of votes is not available, or if the distribution does not portray the reality accurately, the distribution of parliamentary seats is used instead. If parliament members are elected but political parties are not allowed to take part in elections, it is assumed that one party has taken all votes or seats. In countries where parties are not banned but yet only independent candidates participate in elections, it is assumed that the share of the largest party is not over 30 percent. The political participation variable portrays the voting turnout in each election, and is calculated as the percentage of the total population who actually voted in the election. In the case of indirect elections, only votes cast in the final election are taken into account. If electors have not been elected by citizens, only the number of actual electors is taken into account, which means that the degree of participation drops to the value 0. If an election to choose electors has been held, the participation variable is calculated from the number and distribution of votes in that election. National referendums raise the variable value by five percent and state (regional) referendums by one percent for the year they are held. Referendums can add the degree of participation at maximum by 30 percent a year. The value of the combined degree of participation cannot be higher than 70 percent, even in cases where the sum of participation and referendums would be higher than 70. The index of democratization is formed by multiplying the competition and the participation variables and then dividing the outcome by 100.

The mechanism of the relationship between democratization and inequality has been one of the main focuses of political and economic research, albeit with no consensus. The presence of missing values, the voluminous social, economic, environmental measures across countries and the discretion needed to select proxies/measures, leading to difficulties in conducting multinational panel data analysis. This study utilizes one of the machine-learning techniques, the random forecast model, to interpolate missing values and select candidates of control variables based on importance ranking, which greatly reduces the variable selection bias and increases the continuity of data in time series. We then constructed unbalance panel data analysis on data from 151 countries (1993-2017). Results from different models come to a consensus that the promotion of democracy can significantly reduce income inequality, but this effect has declined slightly, both in terms of wealth and in the long run. However, such mechanism only exists in non-colonial nations and presidential states.

Does democratization make states join existing international organizations (IOs)? Previous research suggests that democratization increases a state's propensity to join IOs capable of assisting in the distribution of public goods and establishing credibility for domestic reforms. We argue that this is not the case. Instead, recent democratization has a strong effect on a state's propensity to form new IOs. Since democratizing states face different governance problems than established democracies, existing IOs may not be a good “fit.” Additionally, established democracies might hesitate to allow democratizing states membership in the most lucrative existing IOs, thereby making immediate accession to such IOs not “feasible.” Quantitative analysis shows that democratization has a strong and consistently positive effect on the probability of forming a new IO, but not on the probability of joining an existing IO. The findings suggest that international cooperation theorists should begin to analyze forming new and joining existing IOs as alternative strategies that states can use to achieve their policy goals.

The mechanism for the association between democratic development and the wealth gap has always been the focus of political and economic research, yet with no consistent conclusion. The reasons for that often are, 1) challenges to generalize the results obtained from analyzing a single country’s time series studies or multinational cross-section data analysis, and 2) deviations in research results caused by missing values or variable selection in panel data analysis. When it comes to the latter one, there are two factors contribute to it. One is that the accuracy of estimation is interfered with the presence of missing values in variables, another is that subjective discretion that must be exercised to select suitable proxies amongst many candidates, which are likely to cause variable selection bias. In order to solve these problems, this study is the pioneeringly research to utilize the machine learning method to interpolate missing values efficiently through the random forest model in this topic, and effectively analyzed cross-country data from 151 countries covering the period 1993–2017. Since this paper measures the importance of different variables to the dependent variable, more appropriate and important variables could be selected to construct a complete regression model. Results from different models come to a consensus that the promotion of democracy can significantly narrow the gap between the rich and the poor, with marginally decreasing effect with respect to wealth. In addition, the study finds out that this mechanism exists only in non-colonial nations or presidential states. Finally, this paper discusses the potential theoretical and policy implications of results.

The National Elections across Democracy and Autocracy (NELDA) dataset provides detailed information on all election events from 1960-2006. To be included, elections must be for a national executive figure, such as a president, or for a national legislative body, such as a parliament, legislature, constituent assembly, or other directly elected representative bodies. In order for an election to be included, voters must directly elect the person or persons appearing on the ballot to the national post in question. Voting must also be direct, or “by the people” in the sense that mass voting takes place. That voting is “by the people” does not imply anything about the extent of the franchise: some regimes may construe this to mean a small portion of the population. However, when voting takes place by committee, institution or a coterie, the “election” is not included. By-elections are not counted as elections for the purpose of this project, unless they take the form of midterm elections occurring within a pre-established schedule. In federal systems, only elections to national-level bodies are included. Cases in which any portion of the seats in a national legislative body are filled through voting are included. Beyond these basic requirements, elections may or may not be competitive, and may have any number of other ostensible flaws. In fact, this last feature of the dataset is what separates NELDA most clearly from other available datasets on elections.

This dataset is about books. It has 12 rows and is filtered where the book subjects is Democratization-European Union countries. It features 9 columns including author, publication date, language, and book publisher.

Why do some states emerging from civil war take significant strides toward democracy while others do not? The existing literature comes to contradictory and puzzling findings, many of which, we argue, are driven by methodological problems. We examine the determinants of democratization in the short, medium, and long term after civil wars ending between 1945 and 1999. Other than a short-term effect of negotiated settlements, we find little support for the prominent claim that the outcome of the war shapes the prospects for postwar democratization. Neither does peacekeeping foster democratization. Meanwhile, consistent with the more general democratization literature, we find that economic development aids democratization while oil wealth hinders it. In short, we find the determinants of democratization to be much the same for post-civil war societies as for other societies.

Replication data for the paper "Democratization, Personal Wealth of Politicians and Voting Behavior" by Bas Machielsen

Data Democratization In Healthcare comes with extensive industry analysis of development components, patterns, flows, and sizes. The report calculates present and past market values to forecast potential market management during the forecast period between 2025 - 2033.

Replication data for Bounded Democratization: How Military-party Relations Shape Military-led Democratization.

The data contain three variables for each country for each year for the period of 1995-2010: index of democratization (ID), percentage of women representatives in national parliaments (W), and gender-weighted index of democratization (GID). The percentage of women in national parliaments (W) is used to indicate women's representation. The gender-weighted index of democratization has been calculated using the formula GID = ID x [1 + (W/100)]. If information on women's representation was not available for a parliamentary election, women's representation was estimated to be the same as in the previous or next election (see Appendix 1 in the Background file). If the state does not have a parliament chosen by popular election, the variable W has the value of 0. More information on the index on democratization in FSD1289 Measures of Democracy 1810-2010, and FSD2183 Women's Representation in National Parliaments 1970-2010.

Replication data for article in BJPolS. Derived partly from data in NELDA (Hyde and Marinov 2012 Political Analysis) and Archigos (Goemans, Henk E., Kristian Skrede Gleditsch, and Giacomo Chiozza. 2009. Journal of Peace Research).