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  • Authors: Ilko Kucheriv Democratic Initiatives Foundation (Kyiv), Center For Political Sociology (Kyiv);
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  • Authors: Andreas Heinrich, Heiko Pleines;

    This dataset covers the link between natural resources booms and social policy expenditure for all countries of the Global South.<br><br>All countries with “total natural resources rents” (World Bank data) above 15% have been checked. Economic booms have been defined as “GDP growth (annual %)” (World Bank data) showing at least three years of consecutive economic growth amounting to a total of at least 20% in GDP growth. For each country, all consecutive years where the total natural resource rent stays above 15% and GDP is growing have been taken together as one natural resource boom phase.<br><br>For each natural resource boom phase state social policy expenditure has been taken from the World Bank database, the IMF and the Global State Revenues and Expenditures Dataset (GSRE). For 45 cases from 37 countries there are sufficient data for analysis. For 23 cases, data on social spending are not sufficient to establish a trend over the full boom period for at least one type of social policy expenditure. <br><br>For the 45 cases, we calculate the slope of the linear trendline for all types of social expenditure as share of GDP for which data are available. We sort all cases for which sufficient data are available into three groups: (1) social spending increases in line with GDP (i.e., average increase = flat trendline for spending as share of GDP, marked as “=” in the dataset), (2) state social spending gets a substantially higher share of GDP (operationalized as a trendline slope higher than +7%, marked as “+” in the dataset) or (3) the share of GDP devoted to social spending by the state decreases (slope of the trendline below -7%, marked as “–” in the dataset).<br><br>This dataset contains the full data collection in one Excel file (sheet 1 contains all original data for all countries, sheet 2 shows the trendline scopes for all case countries and sheet 3 lists all sources used by country). This dataset also contains a pdf file with a detailed description of data collection.

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  • Authors: Kyiv International Institute Of Sociology (KIIS);

    The dataset contains data gathered from a series of public opinion polls conducted by the Kyiv International Institute of Sociology in the period from 1994 to 2023 on attitudes of the Ukrainian population towards specific ethnic groups. <br>The core set of ethnic categories included 13 groups: Americans, Africans, Belarusians, Jews, Canadians, Germans, Poles, Roma (Gypsies), Russians, Romanians, Ukrainian-speaking Ukrainians, Russian-speaking Ukrainians, and French. The survey data was also used to compute the xenophobia index, which was calculated as the mean social distance to those 13 ethnic and linguistic-ethnic groups.<br>All survey waves were conducted with samples representative of the adult population of Ukraine (18 years and older), with an average sample size of about 2000 respondents. <br>The merged dataset includes 32 polls for the period of 1994-2023 with a total of 60,701 respondents.<br>The data is available in an SAV format (Ukrainian, English) as well as a converted CSV format (with a codebook).<br>The Data Documentation (pdf file) also includes a short overview and discussion of survey results.

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  • Authors: Ilko Kucheriv Democratic Initiatives Foundation (Kyiv), Center For Political Sociology (Kyiv);

    Ilko Kucheriv Democratic Initiatives Foundation conducted the regional survey in cooperation with the Centre for Political Sociology on 10-21 July 2023. A total of 708 respondents aged 18 and older were interviewed face-to-face in Odesa region.<br><br>The sample is multi-stage with random selection of settlements at the first stage and quota selection of respondents at the final stage. The sample population reflects the demographic structure of the adult population of Odesa region as of the beginning of 2022.<br><br>The survey covers the issues of trust in Ukrainian authorities and volunteers, state language policy, media consumption, vulnerability to Russian disinformation, support for Ukraine's Euro-Atlantic course, assessment of the military situation and possible options for ending the war. <br><br>This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire is also included in the Documentation of Data Collection. An English translation of the questionnaire is currently in preparation and will be added to the next version of this data <br>collection.<br><br>Additionally, the data collection contains one file with "selected results" which document some major results of the survey in the form of a analytical summaries and descriptive statistics. The report has been preparted by the Ilko Kucheriv Democratic Initiatives Foundation.

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  • Authors: Center For Political Sociology (Kyiv), Ilko Kucheriv Democratic Initiatives Foundation (Kyiv);
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  • Authors: Center For Political Sociology (Kyiv), Ilko Kucheriv Democratic Initiatives Foundation (Kyiv);

    The face-to-face survey was conducted by Ilko Kucheriv Democratic Initiatives Foundation in collaboration with Center for Political Sociology on June 5–15, 2023. <br><br>In total, 2,001 respondents aged 18 or older took part in the survey in Vinnytsia, Volyn, Dnipropetrovsk, Zhytomyr, Zakarpattia, Zaporizhzhia, Ivano-Frankivsk, Kyiv, Kirovohrad, Lviv, Mykolaiiv, Odesa, Poltava, Rivne, Sumy, Ternopil, Kharkiv, Kherson, Khmelnytskyi, Cherkasy, Chernihiv, and Chernivtsi regions, and the city of Kyiv (in Zaporizhzhia, Kharkiv, and Kherson regions – only in the territories controlled by Ukraine and not affected by hostilities).<br><br>The sampling technique used in the survey is multistage, involving a random selection of localities at the initial stages and a quota-based selection of respondents at the final stage. The random selection represents the demographic structure of the adult population in the territories covered by the survey as of the beginning of 2022. <br><br>The maximum sample random error does not exceed 2.3%. At the same time, it is necessary to consider systematic deviations in the sample caused by the forced migration of millions of citizens due to the Russian-Ukrainian war.<br><br>MACROREGION COMPOSITION:<br><br>West – Volyn, Zakarpattia, Ivano-Frankivsk, Lviv, Rivne, Ternopil, and Chernivtsi regions; <br>Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy, and Chernihiv regions, and the city of Kyiv; <br>South – Zaporizhzhia, Mykolaiiv, Kherson, and Odesa regions; and <br>East – Dnipropetrovsk and Kharkiv regions.<br><br>This data collection contains the original survey data. The SPSS file (.sav) is the original file. It has been exported into an Excel file. The content of the respective xlsx-file should is identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire is also included in this data collection as separate pdf-file.<br><br>An English translation of the questionnaire is currently in preparation and will be added to the next version of this data collection. Additionally, the data collection contains one file with "selected results" which document some major results of the survey in the form of a analytical summaries and descriptive statistics. The report has been prepared by the civil society organisation Opora.

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  • Authors: Andreas Heinrich, Heiko Pleines;
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bellamy, Katherine; Kahn, Rebecca; Hacıgüzeller, Piraye; Murrieta-Flores, Patricia;

    These datasets are part of the Geographical Text Analysis tutorial hosted on DARIAH-Campus, and produced as part of the GeoHumanities Working Group activities. 

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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  • Authors: Viganò, Antonio;
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  • Authors: Ilko Kucheriv Democratic Initiatives Foundation (Kyiv), Center For Political Sociology (Kyiv);
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  • Authors: Andreas Heinrich, Heiko Pleines;

    This dataset covers the link between natural resources booms and social policy expenditure for all countries of the Global South.<br><br>All countries with “total natural resources rents” (World Bank data) above 15% have been checked. Economic booms have been defined as “GDP growth (annual %)” (World Bank data) showing at least three years of consecutive economic growth amounting to a total of at least 20% in GDP growth. For each country, all consecutive years where the total natural resource rent stays above 15% and GDP is growing have been taken together as one natural resource boom phase.<br><br>For each natural resource boom phase state social policy expenditure has been taken from the World Bank database, the IMF and the Global State Revenues and Expenditures Dataset (GSRE). For 45 cases from 37 countries there are sufficient data for analysis. For 23 cases, data on social spending are not sufficient to establish a trend over the full boom period for at least one type of social policy expenditure. <br><br>For the 45 cases, we calculate the slope of the linear trendline for all types of social expenditure as share of GDP for which data are available. We sort all cases for which sufficient data are available into three groups: (1) social spending increases in line with GDP (i.e., average increase = flat trendline for spending as share of GDP, marked as “=” in the dataset), (2) state social spending gets a substantially higher share of GDP (operationalized as a trendline slope higher than +7%, marked as “+” in the dataset) or (3) the share of GDP devoted to social spending by the state decreases (slope of the trendline below -7%, marked as “–” in the dataset).<br><br>This dataset contains the full data collection in one Excel file (sheet 1 contains all original data for all countries, sheet 2 shows the trendline scopes for all case countries and sheet 3 lists all sources used by country). This dataset also contains a pdf file with a detailed description of data collection.

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  • Authors: Kyiv International Institute Of Sociology (KIIS);

    The dataset contains data gathered from a series of public opinion polls conducted by the Kyiv International Institute of Sociology in the period from 1994 to 2023 on attitudes of the Ukrainian population towards specific ethnic groups. <br>The core set of ethnic categories included 13 groups: Americans, Africans, Belarusians, Jews, Canadians, Germans, Poles, Roma (Gypsies), Russians, Romanians, Ukrainian-speaking Ukrainians, Russian-speaking Ukrainians, and French. The survey data was also used to compute the xenophobia index, which was calculated as the mean social distance to those 13 ethnic and linguistic-ethnic groups.<br>All survey waves were conducted with samples representative of the adult population of Ukraine (18 years and older), with an average sample size of about 2000 respondents. <br>The merged dataset includes 32 polls for the period of 1994-2023 with a total of 60,701 respondents.<br>The data is available in an SAV format (Ukrainian, English) as well as a converted CSV format (with a codebook).<br>The Data Documentation (pdf file) also includes a short overview and discussion of survey results.

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  • Authors: Ilko Kucheriv Democratic Initiatives Foundation (Kyiv), Center For Political Sociology (Kyiv);

    Ilko Kucheriv Democratic Initiatives Foundation conducted the regional survey in cooperation with the Centre for Political Sociology on 10-21 July 2023. A total of 708 respondents aged 18 and older were interviewed face-to-face in Odesa region.<br><br>The sample is multi-stage with random selection of settlements at the first stage and quota selection of respondents at the final stage. The sample population reflects the demographic structure of the adult population of Odesa region as of the beginning of 2022.<br><br>The survey covers the issues of trust in Ukrainian authorities and volunteers, state language policy, media consumption, vulnerability to Russian disinformation, support for Ukraine's Euro-Atlantic course, assessment of the military situation and possible options for ending the war. <br><br>This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire is also included in the Documentation of Data Collection. An English translation of the questionnaire is currently in preparation and will be added to the next version of this data <br>collection.<br><br>Additionally, the data collection contains one file with "selected results" which document some major results of the survey in the form of a analytical summaries and descriptive statistics. The report has been preparted by the Ilko Kucheriv Democratic Initiatives Foundation.

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  • Authors: Center For Political Sociology (Kyiv), Ilko Kucheriv Democratic Initiatives Foundation (Kyiv);
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  • Authors: Center For Political Sociology (Kyiv), Ilko Kucheriv Democratic Initiatives Foundation (Kyiv);

    The face-to-face survey was conducted by Ilko Kucheriv Democratic Initiatives Foundation in collaboration with Center for Political Sociology on June 5–15, 2023. <br><br>In total, 2,001 respondents aged 18 or older took part in the survey in Vinnytsia, Volyn, Dnipropetrovsk, Zhytomyr, Zakarpattia, Zaporizhzhia, Ivano-Frankivsk, Kyiv, Kirovohrad, Lviv, Mykolaiiv, Odesa, Poltava, Rivne, Sumy, Ternopil, Kharkiv, Kherson, Khmelnytskyi, Cherkasy, Chernihiv, and Chernivtsi regions, and the city of Kyiv (in Zaporizhzhia, Kharkiv, and Kherson regions – only in the territories controlled by Ukraine and not affected by hostilities).<br><br>The sampling technique used in the survey is multistage, involving a random selection of localities at the initial stages and a quota-based selection of respondents at the final stage. The random selection represents the demographic structure of the adult population in the territories covered by the survey as of the beginning of 2022. <br><br>The maximum sample random error does not exceed 2.3%. At the same time, it is necessary to consider systematic deviations in the sample caused by the forced migration of millions of citizens due to the Russian-Ukrainian war.<br><br>MACROREGION COMPOSITION:<br><br>West – Volyn, Zakarpattia, Ivano-Frankivsk, Lviv, Rivne, Ternopil, and Chernivtsi regions; <br>Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy, and Chernihiv regions, and the city of Kyiv; <br>South – Zaporizhzhia, Mykolaiiv, Kherson, and Odesa regions; and <br>East – Dnipropetrovsk and Kharkiv regions.<br><br>This data collection contains the original survey data. The SPSS file (.sav) is the original file. It has been exported into an Excel file. The content of the respective xlsx-file should is identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire is also included in this data collection as separate pdf-file.<br><br>An English translation of the questionnaire is currently in preparation and will be added to the next version of this data collection. Additionally, the data collection contains one file with "selected results" which document some major results of the survey in the form of a analytical summaries and descriptive statistics. The report has been prepared by the civil society organisation Opora.

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  • Authors: Andreas Heinrich, Heiko Pleines;
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bellamy, Katherine; Kahn, Rebecca; Hacıgüzeller, Piraye; Murrieta-Flores, Patricia;

    These datasets are part of the Geographical Text Analysis tutorial hosted on DARIAH-Campus, and produced as part of the GeoHumanities Working Group activities. 

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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  • Authors: Viganò, Antonio;
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