100+ datasets found
  1. H

    Replication Data for: The Democratizing Effect of Education

    • dataverse.harvard.edu
    Updated Sep 29, 2015
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    Eduardo Aleman (2015). Replication Data for: The Democratizing Effect of Education [Dataset]. http://doi.org/10.7910/DVN/1R6OOC
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 29, 2015
    Dataset provided by
    Harvard Dataverse
    Authors
    Eduardo Aleman
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Replication data for Aleman and Kim 2015

  2. d

    Data from: Fit and Feasible: Why Democratizing States Form, not Join,...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 22, 2023
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    Poast, Paul; Urpelainen, Johannes (2023). Fit and Feasible: Why Democratizing States Form, not Join, International Organizations [Dataset]. http://doi.org/10.7910/DVN/SYYEVW
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Poast, Paul; Urpelainen, Johannes
    Description

    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.

  3. f

    Modular, Open-Sourced Multiplexing for Democratizing Spatial Multi-Omics

    • figshare.com
    tiff
    Updated Mar 24, 2025
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    Nicholas Zhang (2025). Modular, Open-Sourced Multiplexing for Democratizing Spatial Multi-Omics [Dataset]. http://doi.org/10.6084/m9.figshare.28646996.v1
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    tiffAvailable download formats
    Dataset updated
    Mar 24, 2025
    Dataset provided by
    figshare
    Authors
    Nicholas Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  4. f

    Table_1_Opportunities and Challenges in Democratizing Immunology...

    • frontiersin.figshare.com
    docx
    Updated Jun 6, 2023
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    Sanchita Bhattacharya; Zicheng Hu; Atul J. Butte (2023). Table_1_Opportunities and Challenges in Democratizing Immunology Datasets.docx [Dataset]. http://doi.org/10.3389/fimmu.2021.647536.s001
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    docxAvailable download formats
    Dataset updated
    Jun 6, 2023
    Dataset provided by
    Frontiers
    Authors
    Sanchita Bhattacharya; Zicheng Hu; Atul J. Butte
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more.

  5. Democratization and Power Resources 1850-2000

    • services.fsd.tuni.fi
    • datacatalogue.cessda.eu
    • +1more
    zip
    Updated Jan 16, 2025
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    Vanhanen, Tatu (2025). Democratization and Power Resources 1850-2000 [Dataset]. http://doi.org/10.60686/t-fsd1216
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    zipAvailable download formats
    Dataset updated
    Jan 16, 2025
    Dataset provided by
    Finnish Social Science Data Archive
    Authors
    Vanhanen, Tatu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  6. i

    Grant Giving Statistics for Democratizing Philanthropy Project

    • instrumentl.com
    Updated Dec 22, 2024
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    (2024). Grant Giving Statistics for Democratizing Philanthropy Project [Dataset]. https://www.instrumentl.com/990-report/democratizing-philanthropy-project
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    Dataset updated
    Dec 22, 2024
    Variables measured
    Total Assets, Total Giving, Average Grant Amount
    Description

    Financial overview and grant giving statistics of Democratizing Philanthropy Project

  7. g

    Democratization Data

    • datasearch.gesis.org
    • dataverse-staging.rdmc.unc.edu
    Updated Jan 22, 2020
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    Schneider, Carsten Q.; Schmitter, Philippe C. (2020). Democratization Data [Dataset]. https://datasearch.gesis.org/dataset/httpsdataverse.unc.eduoai--hdl1902.29D-33441
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    Dataset updated
    Jan 22, 2020
    Dataset provided by
    Odum Institute Dataverse Network
    Authors
    Schneider, Carsten Q.; Schmitter, Philippe C.
    Description

    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

  8. Data Democratization In Healthcare Market Size & Share Analysis - Industry...

    • mordorintelligence.com
    pdf,excel,csv,ppt
    Updated Feb 20, 2025
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    Mordor Intelligence (2025). Data Democratization In Healthcare Market Size & Share Analysis - Industry Research Report - Growth Trends [Dataset]. https://www.mordorintelligence.com/industry-reports/data-democratization-in-healthcare-market
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    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Feb 20, 2025
    Dataset authored and provided by
    Mordor Intelligence
    License

    https://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy

    Time period covered
    2022 - 2030
    Area covered
    Global
    Description

    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).

  9. c

    DIY Biology: A Global Survey on Democratizing Science, 2021

    • datacatalogue.cessda.eu
    • beta.ukdataservice.ac.uk
    Updated Jun 7, 2025
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    Eireiner, A (2025). DIY Biology: A Global Survey on Democratizing Science, 2021 [Dataset]. http://doi.org/10.5255/UKDA-SN-857778
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    Dataset updated
    Jun 7, 2025
    Dataset provided by
    University of Cambridge
    Authors
    Eireiner, A
    Time period covered
    Mar 1, 2021 - Nov 30, 2021
    Area covered
    United Kingdom
    Variables measured
    Individual
    Measurement technique
    The survey data was collected between March and November 2021 using an online questionnaire designed and administered through Qualtrics. The survey targeted individuals who self-identified as DIY biologists, reaching them through social media platforms (Twitter, Facebook, Slack), DIY biology-specific email lists, and community meet-up groups. The sampling approach was voluntary and non-random, meaning the dataset reflects the perspectives of engaged community members rather than a statistically representative sample. A total of 154 individuals participated.
    Description

    This survey explores the evolving landscape of DIY biology, a grassroots movement that aims to democratize science. It builds on the first major quantitative study by Grushkin et al. (2013) and aims to: (1) assess how the movement has developed over the past decade, (2) examine its relationship with traditional scientific institutions, and (3) provide updated data for DIY biologists and policymakers. Conducted between March and November 2021, the survey included 28 questions across four key areas: demographics, professional affiliations, DIY biology activities, and perspectives on openness and safety. Some items mirrored Grushkin et al.’s (2013) survey for comparison, while others focused on DIY biology’s engagement with industry and academia. The questionnaire combined multiple-choice, short text responses, and time-allocation questions, ensuring a comprehensive dataset. The survey was distributed via social media, mailing lists, and community networks, garnering 154 responses. Of these, 124 participants completed the entire survey, while 30 answered at least two questions. The global DIY biology community is estimated to include thousands of individuals, but this study does not claim to represent the full diversity of the movement. Instead, it offers valuable insights into trends, challenges, and the professionalization of DIY biology. To encourage participation, respondents could enter a raffle for research vouchers. The median completion time was 20 minutes. Participants provided detailed free-text responses, which added depth to the findings. Preliminary results were shared with the community at the Global Community Summit 2021, reflecting the study’s commitment to open knowledge-sharing. This dataset serves as a resource for researchers, policymakers, and DIY biologists seeking to understand the movement’s trajectory and its intersections with mainstream science.

    This research project explores the Do-It-Yourself (DIY) biology movement, where biologists and enthusiasts set up laboratories in non-traditional spaces such as kitchens, garages, and community labs. DIY biologists engage in scientific experiments outside institutional settings, working with gene-editing technologies like CRISPR, engineering microorganisms, and developing bio-based projects. By studying this movement, the research aims to understand how extra-institutional science emerges, operates, and interacts with established scientific and regulatory frameworks. Specifically, it examines how DIY biologists create and sustain research spaces and how governments and institutions perceive and regulate these activities.

    This document presents data from the 2021 DIY Biology Community Survey, designed to capture insights into the evolving DIY biology landscape. The survey provides updated perspectives on the movement’s professionalization, relationship with traditional scientific institutions, and engagement with industry. Conducted between March and November 2021, the survey included 28 questions covering demographics, participants’ experiences in academia and industry, their DIY biology activities, and their views on openness and safety in the field. A total of 154 individuals participated, with 124 completing the entire survey. The findings offer valuable perspectives on the state of the DIY biology community, highlighting its diversity, challenges, and evolving role in the broader scientific ecosystem.

  10. H

    Replication data for: Democratization and International Border Agreements

    • data.niaid.nih.gov
    • dataverse.harvard.edu
    Updated Jul 7, 2013
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    Andrew P. Owsiak (2013). Replication data for: Democratization and International Border Agreements [Dataset]. http://doi.org/10.7910/DVN/C2RVUT
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    tsv, text/x-stata-syntax; charset=us-asciiAvailable download formats
    Dataset updated
    Jul 7, 2013
    Dataset provided by
    University of Georgia
    Authors
    Andrew P. Owsiak
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    All
    Description

    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.

  11. d

    Replication Data for: \"Technological Change and Political Turnover: The...

    • dataone.org
    Updated Nov 22, 2023
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    Dasgupta, Aditya (2023). Replication Data for: \"Technological Change and Political Turnover: The Democratizing Effects of the Green Revolution in India\" [Dataset]. http://doi.org/10.7910/DVN/PSGCJH
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Dasgupta, Aditya
    Description

    Replication data for "Technological Change and Political Turnover: The Democratizing Effects of the Green Revolution in India".

  12. a

    Habitat, Drones, and Democratizing Science

    • africageoportal.com
    Updated Sep 2, 2019
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    World Wide Fund for Nature (2019). Habitat, Drones, and Democratizing Science [Dataset]. https://www.africageoportal.com/datasets/panda::habitat-drones-and-democratizing-science
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    Dataset updated
    Sep 2, 2019
    Dataset authored and provided by
    World Wide Fund for Nature
    Description

    This workshop was supported by a joint grant to WWF- Tanzania and Duke University, as well as from the Global Affairs Office at Duke University. These grants are intended to build relationships between Duke University and Tanzanian partners (WWF in particular). In addition to the workshop described below, this funding will also support fieldwork in communities in and around MBREMP focusing on the relationships between the marine protected area and community wellbeing. Dana Baker, a Duke University Phd student, will lead this part of the research. This workshop and fieldwork were preceded by two scoping trips (2018) which strengthened relationships, defined shared objectives, and initiated planning.

  13. H

    Replication Data for: Democratizing the Party: The Effects of Primary...

    • dataverse.harvard.edu
    Updated Apr 12, 2021
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    Nahomi Ichino; Noah L. Nathan (2021). Replication Data for: Democratizing the Party: The Effects of Primary Election Reforms in Ghana [Dataset]. http://doi.org/10.7910/DVN/FYOEWG
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 12, 2021
    Dataset provided by
    Harvard Dataverse
    Authors
    Nahomi Ichino; Noah L. Nathan
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Ghana
    Description

    A recent expansion of the primary electorate by one of Ghana's major parties offers a rare opportunity to assess the effects of franchise extensions in contemporary new democracies. Using an original dataset on candidate entry and nominations, we show that expanding the primary electorate opened paths to office for politicians from social groups that were previously excluded, such as women and ethnic groups outside the party's core national coalition. We propose that democratizing candidate selection has two consequences in patronage-oriented political systems: vote buying will become a less effective strategy and the electorate will become more diverse. These changes, in turn, affect the types of politicians who seek and win legislative nominations. This suggests that a simple shift in who votes in intra-party primaries can be a key institutional mechanism for improving the descriptive representation of women and other underrepresented groups.

  14. Measures of Democracy 1810-2018

    • services.fsd.tuni.fi
    zip
    Updated Apr 24, 2025
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    Vanhanen, Tatu (2025). Measures of Democracy 1810-2018 [Dataset]. http://doi.org/10.60686/t-fsd1289
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    zipAvailable download formats
    Dataset updated
    Apr 24, 2025
    Dataset provided by
    Finnish Social Science Data Archive
    Authors
    Vanhanen, Tatu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  15. f

    Breakdown of the filtered dataset by country and topic.

    • plos.figshare.com
    xls
    Updated May 8, 2024
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    Anees Baqir; Alessandro Galeazzi; Fabiana Zollo (2024). Breakdown of the filtered dataset by country and topic. [Dataset]. http://doi.org/10.1371/journal.pone.0302473.t003
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    xlsAvailable download formats
    Dataset updated
    May 8, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Anees Baqir; Alessandro Galeazzi; Fabiana Zollo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Breakdown of the filtered dataset by country and topic.

  16. q

    Democratizing Student Access to Help: the nationwide, virtual peer mentoring...

    • qubeshub.org
    Updated Nov 28, 2023
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    Katie Sandlin; Wilson Leung; D'Andrew Harrington; David Lopatto; S. Key; Melanie Stry; Jamie Siders; Laura Reed (2023). Democratizing Student Access to Help: the nationwide, virtual peer mentoring network of the Genomics Education Partnership [Dataset]. http://doi.org/10.25334/SQDZ-6T98
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    Dataset updated
    Nov 28, 2023
    Dataset provided by
    QUBES
    Authors
    Katie Sandlin; Wilson Leung; D'Andrew Harrington; David Lopatto; S. Key; Melanie Stry; Jamie Siders; Laura Reed
    Description

    Our data suggests that online delivery of the novel Virtual TA program allowed the GEP CURE to remain effective through the transition from onsite to online: the GEP CURE is resilient. This resilience aids in the democratization of science education by making research experiences available to all.

  17. Cultural Democratization in the Union of Soviet Socialist Republics (USSR):...

    • icpsr.umich.edu
    ascii, sas, spss
    Updated May 18, 1992
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    Gibson, James L.; Duch, Raymond M.; Denisovsky, Gennady; Kozyreva, Polina; Matskovsky, Michail (1992). Cultural Democratization in the Union of Soviet Socialist Republics (USSR): Moscow Oblast Survey, 1990 [Dataset]. http://doi.org/10.3886/ICPSR09726.v1
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    spss, sas, asciiAvailable download formats
    Dataset updated
    May 18, 1992
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Gibson, James L.; Duch, Raymond M.; Denisovsky, Gennady; Kozyreva, Polina; Matskovsky, Michail
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/9726/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/9726/terms

    Time period covered
    Feb 17, 1990 - Mar 4, 1990
    Area covered
    Soviet Union, Russia, Global, Moscow
    Description

    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.

  18. d

    Replication Data for: Democratization and Economic Output in Sub-Saharan...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 21, 2023
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    Wittels, Stephen B.; De Kadt, Daniel (2023). Replication Data for: Democratization and Economic Output in Sub-Saharan Africa [Dataset]. http://doi.org/10.7910/DVN/I42PPJ
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    Dataset updated
    Nov 21, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Wittels, Stephen B.; De Kadt, Daniel
    Description

    Does democratization increase economic output? Answers to this question are inconsistent partly due to the challenges of examining the causal forces behind political and economic phenomena that occur at the national level. We employ a new empirical approach, the synthetic control method, to study the economic effects of democratization in sub-Saharan Africa over the period 1975-2008. This method yields case-specific causal estimates which show that political reform associated with the “third wave” of democracy had highly heterogeneous, yet often substantively important effects in Africa. In some countries democratization adversely affected economic output while in others it exerted an analogous positive effect.

  19. w

    Dataset of author, BNB id, book publisher, and publication date of...

    • workwithdata.com
    Updated Apr 17, 2025
    + more versions
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    Work With Data (2025). Dataset of author, BNB id, book publisher, and publication date of Democratization and research methods [Dataset]. https://www.workwithdata.com/datasets/books?col=author%2Cbnb_id%2Cbook%2Cbook_publisher&f=1&fcol0=book&fop0=%3D&fval0=Democratization+and+research+methods
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    Dataset updated
    Apr 17, 2025
    Dataset authored and provided by
    Work With Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset is about books. It has 1 row and is filtered where the book is Democratization and research methods. It features 4 columns: author, book publisher, and BNB id.

  20. d

    Replication Data for: Does Public Support Help Democracy Survive?

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 22, 2023
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    Claassen, Christopher (2023). Replication Data for: Does Public Support Help Democracy Survive? [Dataset]. http://doi.org/10.7910/DVN/HWLW0J
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Claassen, Christopher
    Description

    It is widely believed that democracy requires public support to survive. The empirical evidence for this hypothesis is weak, however, with existing tests resting on small cross-sectional samples and producing contradictory results. The underlying problem is that survey measures of support for democracy are fragmented across time, space, and different survey questions. In response, this article uses a Bayesian latent variable model to estimate a smooth country-year panel of democratic support for 135 countries and up to 29 years. The article then demonstrates a positive effect of support on subsequent democratic change, while adjusting for the possible confounding effects of prior levels of democracy and unobservable time-invariant factors. Support is, moreover, more robustly linked with the endurance of democracy than its emergence in the first place. As Lipset and Easton hypothesized over 50 years ago, public support does indeed help democracy survive. ERRATUM: An erratum was approved by AJPS Editors for this manuscript. Updated data and code files are all included with this version of the published record.

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Eduardo Aleman (2015). Replication Data for: The Democratizing Effect of Education [Dataset]. http://doi.org/10.7910/DVN/1R6OOC

Replication Data for: The Democratizing Effect of Education

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CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Sep 29, 2015
Dataset provided by
Harvard Dataverse
Authors
Eduardo Aleman
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Description

Replication data for Aleman and Kim 2015

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