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165 results for “politics”
Populist attitudes and other socio-political views and psychological traits of the UK population
<p>This dataset is the result of an original survey designed by an interdisciplinary team of researchers from National Universtity of Distance Education (UNED), University of Zaragoza, University of Córdoba and University of Valencia.</p> <p>The goal of the survey was to better understand the relationship between populist attitudes and relevant socio-political and psychology items and indexes. The UK was selected as case study given the lack of similar studies in this country and the relevance of the data to better understand the political context that had been heavily impacted by the Brexit referendum and proces of separation from the European Union.</p> <p>The survey was theoretically informed and included among others:</p> <ul> <li>Populism, Elitism and Pluralism items by Akkerman et al. (2014) (14 items)</li> <li>New items design for a new Multidimensional Scale of Populist Attitudes (37 items) (Olivas Osuna 2021; Olivas Osuna et al. 2024; Olivas Osuna et al. forthcoming)</li> <li>Conspiracy Beliefs items (8 items) (Bruder et al. 2013; Brotherton et al. 2013)</li> <li>Social alienation index (6 items) (Bélanger et al. 2019)</li> <li>Justification of violence index (6 items) (Bélanger et al. 2019)</li> <li>Radicalised network (3 items)(Moyano 2011)</li> <li>Meaning in life (presence and search)(4 items)(Steger et al. 2006)</li> <li>Bordering attitudes (6 items)(Olivas Osuna et al. forthcoming)</li> <li>Endorsement for political parties</li> <li>Items reflecting level of agreement with the main slogans and arguments used by British Eurosceptics (11 items)</li> <li>Items on satisfaction with democracy and importance of democracy and with illiberal views (ESS)</li> <li>Left-right ideological self-placement</li> <li>Socio-demographic variables (age, religion, education, etc.)</li> </ul> <p>Fieldwork was conducted between 17 November and 4 December 2020. Participants were recruited following socio-demographic representativity criteria via the online platform Prolific. Survey were collected via Google Forms (Survey title: <em>Political and social views in the UK</em>).</p> <p>Files uploaded include:</p> <ul> <li>Total responses received (N=849) (.xlsx file) </li> <li>Responses analysed once participants failing attention checks were eliminated from the sample (N=748) (.csv file)</li> <li>Survery questionnair (.pdf file)</li> </ul>
Dataset: The Role of News Consumption on Influencers' Facebook Pages in Threat Perception and Political Conservatism During Times of COVID-19: A Comparative Study between the USA, Spain, and Egypt
<p>Este archivo ofrece los datos en bruto de una encuesta examina el impacto del consumo de noticias en las páginas de Facebook de los influencers en la motivación del conservadurismo político durante amenazas como el terrorismo o las pandemias. Muestra: N=1309, jóvenes de entre 18 y 35 años en Estados Unidos, España y Egipto. Trabajo de campo realizado entre el 10 de agosto de 2021 y el 5 de septiembre de 2021.</p> <p><span>Dataset correspondiente al proyecto El rol de la ciudadanía en la comunicación política digital CI-COMPOL (PID2020-119492GB-I00) financiado por MCIN/AEI/10.13039/501100011033/. IP: Andreu Casero-Ripollés, Departamento de Ciencias de la Comunicación, Universitat Jaume I de Castellón</span></p>
Data for: Global political responsibility for the conservation of albatrosses and large petrels
<p>Data derivatives from analysis of seabird tracking data. These data allow one to reproduce the results of the paper "Global political responsibility for the conservation of albatrosses and large petrels by Beal et al (in press). </p>
Supplementary File: Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia's most popular social network VK
<p>Supplementary file and dataset for the paper "Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia’s most popular social network VK"</p>
Twitter analysis of the five main political leaders during the 2019 UK electoral campaign: from 12 October to 16 December 2019
<p>The analysis was conducted from 12 October to 16 December 2019 on Twitter through the study of the five main political leaders— Boris Johnson, Jeremy Corbyn, Jo Swinson, Nicola Sturgeon, and Nigel Farage — during the 2019 UK electoral campaign.</p>
Geospatial Dataset of GNSS Anomalies and Political Violence Events
<p><strong>Geospatial Dataset of GNSS Anomalies and Political Violence Events</strong></p> <p><strong>Overview</strong></p> <p>The <strong>Geospatial Dataset of GNSS Anomalies and Political Violence Events </strong>is a collection of data that integrates aircraft flight information, GNSS (Global Navigation Satellite System) anomalies, and political violence events from the ACLED (Armed Conflict Location & Event Data Project) database.</p> <p><strong>Dataset Files</strong></p> <p>The dataset consists of three CSV files:</p> <ol> <li><strong>Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv</strong></li> <ul> <li><strong>Description:</strong> Contains all grids and dates that had aircraft traffic during 2023.</li> <li><strong>Number of Records:</strong> 6,777,228</li> <li><strong>Purpose:</strong> Provides a complete view of aircraft movements and associated data, including grids without any GNSS anomalies.</li> </ul> <li><strong>Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv</strong></li> <ul> <li><strong>Description:</strong> A filtered version of V1, including only the grids and dates where GNSS anomalies (jumps or gaps) were reported.</li> <li><strong>Number of Records:</strong> 718,237</li> <li><strong>Purpose:</strong> Focuses on areas and times with GNSS anomalies for targeted analysis.</li> </ul> <li><strong>Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv</strong></li> <ul> <li><strong>Description:</strong> Contains aggregated monthly data for each grid cell, combining GNSS anomalies and ACLED political violence events. Summarizes aircraft traffic, anomaly counts, and conflict activity at a monthly resolution.</li> <li><strong>Number of Records:</strong> 25,770</li> <li><strong>Purpose:</strong> Enables temporal trend analysis and spatial correlation studies between GNSS interference and political violence, using reduced data volume suitable for modeling and visualization.</li> </ul> </ol> <p><strong>Data Fields: </strong>Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv and Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv</p> <ol> <li><strong>grid_id</strong></li> <ul> <li><strong>Description:</strong> Unique identifier for a grid cell on Earth measuring 0.5 degrees latitude by 0.5 degrees longitude.</li> <li><strong>Format:</strong> String combining latitude and longitude (e.g., -10.0_-36.0).</li> </ul> <li><strong>day</strong></li> <ul> <li><strong>Description:</strong> Date of the recorded data.</li> <li><strong>Format:</strong> YYYY-MM-DD (e.g., 2023-03-28).</li> </ul> <li><strong>geometry</strong></li> <ul> <li><strong>Description:</strong> Polygon coordinates of the grid cell in Well-Known Text (WKT) format.</li> <li><strong>Format:</strong> POLYGON((longitude latitude, ...)) (e.g., POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))).</li> </ul> <li><strong>flights</strong></li> <ul> <li><strong>Description:</strong> Number of aircraft flights that passed through the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 28).</li> </ul> <li><strong>GPS_jumps</strong></li> <ul> <li><strong>Description:</strong> Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 1).</li> </ul> <li><strong>GPS_gaps</strong></li> <ul> <li><strong>Description:</strong> Number of reported GNSS "gap" anomalies, indicating gaps in aircraft routes, in the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 0).</li> </ul> <li><strong>gaps_density</strong></li> <ul> <li><strong>Description:</strong> Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.</li> <li><strong>Format:</strong> Decimal (e.g., 0).</li> </ul> <li><strong>jumps_density</strong></li> <ul> <li><strong>Description:</strong> Density of GNSS jumps, calculated as the number of jumps divided by the number of flights.</li> <li><strong>Format:</strong> Decimal (e.g., 0.035714286).</li> </ul> <li><strong>event_id_cnty</strong></li> <ul> <li><strong>Description:</strong> ACLED event ID corresponding to political violence events in the grid on that day.</li> <li><strong>Format:</strong> String (e.g., BRA69267).</li> </ul> <li><strong>disorder_type</strong></li> <ul> <li><strong>Description:</strong> Type of disorder as classified by ACLED (e.g., "Political violence").</li> <li><strong>Format:</strong> String.</li> </ul> <li><strong>event_type</strong></li> <ul> <li><strong>Description:</strong> General category of the event according to ACLED (e.g., "Violence against civilians").</li> <li><strong>Format:</strong> String.</li> </ul> <li><strong>sub_event_type</strong></li> <ul> <li><strong>Description:</strong> Specific subtype of the event as per ACLED classification (e.g., "Attack").</li> <li><strong>Format:</strong> String.</li> </ul> <li><strong>acled_count</strong></li> <ul> <li><strong>Description:</strong> Number of ACLED events in the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 1).</li> </ul> <li><strong>acled_flag</strong></li> <ul> <li><strong>Description:</strong> Indicator of ACLED event presence in the grid on that day (0 for no events, 1 for one or more events).</li> <li><strong>Format:</strong> Integer (0 or 1).</li> </ul> </ol> <p><strong> </strong></p> <p><strong>Data Fields: </strong>Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv</p> <p>The file contains monthly aggregated GNSS anomaly and ACLED event data per grid cell. The structure and meaning of each field are detailed below:</p> <ol> <li><strong>grid_id</strong></li> <ul> <li><strong>Description</strong>: Unique identifier for a grid cell on Earth measuring 0.5° latitude by 0.5° longitude.</li> <li><strong>Format</strong>: String combining latitude and longitude (e.g., -0.5_-79.0).</li> </ul> <li><strong>year_month</strong></li> <ul> <li><strong>Description</strong>: Month and year of the aggregated data.</li> <li><strong>Format</strong>: String in Mon-YY format (e.g., Jan-23).</li> </ul> <li><strong>geometry</strong></li> <ul> <li><strong>Description</strong>: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.</li> <li><strong>Format</strong>: POLYGON((longitude latitude, ...))<br>(e.g., POLYGON((-79.0 -0.5, -78.5 -0.5, -78.5 0.0, -79.0 0.0, -79.0 -0.5))).</li> </ul> <li><strong>flights</strong></li> <ul> <li><strong>Description</strong>: Total number of aircraft flights that passed through the grid cell during the month.</li> <li><strong>Format</strong>: Integer (e.g., 1230).</li> </ul> <li><strong>GPS_jumps</strong></li> <ul> <li><strong>Description</strong>: Total number of GNSS "jump" anomalies (possible spoofing events) in the grid cell during the month.</li> <li><strong>Format</strong>: Integer (e.g., 13).</li> </ul> <li><strong>GPS_gaps</strong></li> <ul> <li><strong>Description</strong>: Total number of GNSS "gap" anomalies, indicating interruptions in aircraft routes, during the month.</li> <li><strong>Format</strong>: Integer (e.g., 0).</li> </ul> <li><strong>event_id_cnty</strong></li> <ul> <li><strong>Description</strong>: Semicolon-separated list of ACLED event IDs associated with the grid cell during the month.</li> <li><strong>Format</strong>: String (e.g., ECU3151;ECU3158;ECU3150).</li> </ul> <li><strong>disorder_type</strong></li> <ul> <li><strong>Description</strong>: Semicolon-separated list of disorder types (e.g., "Political violence", "Demonstrations") reported by ACLED in that grid cell during the month.</li> <li><strong>Format</strong>: String.</li> </ul> <li><strong>event_type</strong></li> <ul> <li><strong>Description</strong>: Semicolon-separated list of high-level ACLED event types (e.g., "Riots", "Protests").</li> <li><strong>Format</strong>: String.</li> </ul> <li><strong>sub_event_type</strong></li> </ol> <ul> <li><strong>Description</strong>: Semicolon-separated list of detailed subtypes of ACLED events (e.g., "Mob violence", "Armed clash").</li> <li><strong>Format</strong>: String.</li> </ul> <ol> <li><strong>acled_count</strong></li> </ol> <ul> <li><strong>Description</strong>: Total number of ACLED conflict events in the grid cell during the month.</li> <li><strong>Format</strong>: Integer (e.g., 2).</li> </ul> <ol> <li><strong>acled_flag</strong></li> </ol> <ul> <li><strong>Description</strong>: Conflict presence indicator: 1 if any ACLED event occurred in the grid cell during the month, otherwise 0.</li> <li><strong>Format</strong>: Integer (0 or 1).</li> </ul> <ol> <li><strong>gaps_density</strong></li> </ol> <ul> <li><strong>Description</strong>: Monthly density of GNSS gaps, calculated as GPS_gaps / flights.</li> <li><strong>Format</strong>: Decimal (e.g., 0.0).</li> </ul> <ol> <li><strong>jumps_density</strong></li> </ol> <ul> <li><strong>Description</strong>: Monthly density of GNSS jumps, calculated as GPS_jumps / flights.</li> <li><strong>Format</strong>: Decimal (e.g., 0.0106).</li> </ul> <p><strong> </strong></p> <p><strong>Data Sources</strong></p> <ul> <li><strong>GNSS Anomalies Data:</strong></li> <ul> <li>Calculated from ADS-B (Automatic Dependent Surveillance-Broadcast) messages obtained via the OpenSky Network's Trino database.</li> <li>GNSS anomalies include "jumps" (potential spoofing incidents) and "gaps" (interruptions in aircraft route data).</li> </ul> <li><strong>Political Violence Events Data:</strong></li> <ul> <li>Sourced from the ACLED database, which provides detailed information on political violence and protest events worldwide.</li> </ul> </ul> <p><strong>Temporal and Spatial Coverage</strong></p> <ul> <li><strong>Temporal Coverage:</strong></li> <ul> <li>From January 1, 2023, to December 31, 2023.</li> <li>Daily records provide temporal granularity for time-series analysis.</li> </ul> <li><strong>Spatial Coverage:</strong></li> <ul> <li>Global coverage with grid cells measuring 0.5 degrees latitude by 0.5 degrees longitude.</li> <li>Each grid cell represents an area on Earth's surface, facilitating spatial analysis.</li> </ul> </ul> <p><strong>Usage and Applications</strong></p> <ul> <li><strong>Security Analysis:</strong></li> <ul> <li>Assess potential correlations between GNSS anomalies and political violence events.</li> <li>Identify regions with increased risk of GNSS spoofing or signal disruption.</li> </ul> <li><strong>Research and Development:</strong></li> <ul> <li>Develop models to predict socio-political events based on GNSS anomalies.</li> <li>Study the impact of political instability on aviation safety.</li> </ul> <li><strong>Policy and Decision Making:</strong></li> <ul> <li>Inform aviation authorities and policymakers about regions requiring enhanced navigation security measures.</li> <li>Support conflict analysis and monitoring efforts.</li> </ul> </ul> <p> </p> <p> </p>
Acoustics, Audibility and Political Culture in the House of Commons, 1800-34
<p>This dataset includes the auralization results obtained from the acoustic models of the House of Commons in 1800-34, as part of the research with the homonymous paper submitted in the special issue "Parliamentary History Journal" (first submission September 2023). </p><p>The auralization results represent the perceived result from the acoustic models for the two discussed scenarios (full-occupied and half-full-occupied House of Commons). We present the results from 3 different speakers at 5 listening positions as shown in the images. </p><p>The anechoic sample is an excerpt of Henry Beaufoy's speech to the House of Commons in 1792 on the subject of the slave trade, performed by John Cooper (co-author) in the anechoic chamber at the Audiolab, University of York. The perceived differences and similarities of the recorded/simulated spaces as heard in these audio files help to further verify the results of the acoustic parameters presented in this paper.</p>
Online Knowledge Production in Polarized Political Memes: The Case of Critical Race Theory (Dataset)
<p>This is the supplementary dataset to the article entitled "Online Knowledge Production in Polarized Political Memes: The Case of Critical Race Theory." This study, completed by Alyvia Walters, Tawfiq Ammari, Kiran Garimella, and Shagun Jhaver, was accepted for publication in <em>New Media & Society </em>in 2024.</p> <p>Description of files:</p> <p>Memes Codebook.dox - Codebook used for qualitative coding of memes.</p> <p>Memes Project.qdpx - NVivo coding project, exported.</p> <p>all_posts.jsonl, clusters.zip, images.zip, & image_data.csv - the complete collection of data and images used for this project. Please see publication in <em>New Media & Society </em>for more information on the use and collection of these items.</p>
Corpus of political tweets UK-EU-DEBATE-20-21
<p> </p> <p>The <em>UK-EU-DEBATE-20-21</em> corpus was collected within the framework of the collaborative research project OLiNDiNUM (<em><a href="https://olindinum.huma-num.fr">Observatoire LINguistique du DIscours NUMérique</a> / </em>Linguistic Observatory of Online Debate) to be part of a shared research archive of shared corpora and resources. </p> <p>The corpus was selected with a view to examining the UK-EU media debate on the COVID-19 vaccination campaign following a specific transformative moment: the signature of the Brexit withdrawal agreement by the UK and the EU at the end of January 2021.</p> <p>The data were retrieved through the Application Programming Interface of the social networking site Twitter, using the accounts of key political actors in the UK government and EU institutions over a period of 14 months (1 February 2020–31 March 2021). The composition of the corpus is illustrated in the table.</p> <p> </p> <table> <tbody> <tr> <td><em>Political Actor</em></td> <td><em>Role</em></td> <td><em>Account</em></td> <td><em>Tweets</em></td> </tr> <tr> <td>Boris Johnson</td> <td>UK Prime Minister</td> <td>@BorisJohnson</td> <td>1186</td> </tr> <tr> <td>Dominic R. Raab</td> <td>UK Foreign Secretary</td> <td>@DominicRaab</td> <td>1468</td> </tr> <tr> <td>Priti Patel</td> <td>UK Home Secretary</td> <td>@pritipatel</td> <td>941</td> </tr> <tr> <td>Ursula von der Leyen</td> <td>President of the European Commission</td> <td>@vonderleyen</td> <td>1338</td> </tr> <tr> <td>David Sassoli</td> <td>President of the European Parliament</td> <td>@EP_President</td> <td>554</td> </tr> <tr> <td>Charles Michel</td> <td>President of the Council of the European Union</td> <td>@eucopresident</td> <td>675</td> </tr> </tbody> </table> <p> </p> <p>The data are supplied in separate .csv files (tab-delimited format). Each row contains the text of the tweet (<em>data__text</em>) and the tweet identifier (<em>data__id</em>) as a header. The tweet identifier enables swift retrieval of the original tweet by searching https://twitter.com/anyuser/status/<em>data__id. </em></p> <p> </p>
Young people's media use and adherence to preventive measures in the "infodemic": Is it masked by political ideology?
<p>Data to replicate the publication "Young people's media use and adherence to preventive measures in the “infodemic”: Is it masked by political ideology?". This publication examines the role of political ideology and political extremism for COVID-19 information seeking and preventive behaviour with data of the COVIDisc project. COVIDisc investigates how young people aged 15 to 34 years perceive the discussion in the Coronavirus Pandemic, which messages reach them, what media they use to inform themselves and how they experience the situation. en</p>
Polidoc.net CODEBOOK: National and Regional Manifestos and other Political Documents Collected for the Research Projects "Representation in Europe: Congruence between Preferences of Elites and Voters" (REPCONG) and "The Impact of EU Cohesion Policy on European Identification" (COHESIFY)
<p>The Political Documents Archive http://www.polidoc.net/ contains election manifestos, coalition agreements, government declarations and various other documents of political actors from developed democracies. Currently, the archive builds on a stock of more than 3000 political documents from 20 European countries. The aim of the repository is to provide political texts in order to facilitate scholarly research in different areas of comparative politics such as party competition, coalition politics, legislative decision-making or electoral behavior.</p> <p>National electoral manifestos have been collected in the course of the REPCONG project ("Representation in Europe: Policy Congruence between Citizens and Elites"), and the archive includes party manifestos for regional elections in several European democracies. Because the process of European integration resulted in a strengthening of regions in EU member states and in countries that want to join the European Union, the relevance of the regional level for political decision-making has increased during the last decades. Therefore, also the policy profiles of regional parties are required to get a full picture of democratic responsiveness in European states across all levels of the political system. The collection of regional manifestos was supported by the COHESIFY project (www.cohesify.eu), funded under the Horizon 2020 Framework Programme for Research and Innovation. The aim of COHESIFY is to study whether the European Structural and Investment Funds affect people’s support for and identification with the European project.</p> <p>The archive is freely accessible (after a simple registration) and meant to foster rigorous research in these areas by enabling scholars to produce valid and reliable findings from empirical studies of textual data rather than unnecessarily struggling to obtain and process texts.</p>
Socio-political attitudes in France (2023)
<p>This dataset captures the responses of over 1500 participants in France to an original online survey.</p> <p>This online survey was designed by a group of experts in populism from Universidad Nacional de Educación a Distancia (UNED, Madrid), King's College London, Univerity of York, Universidad Diego Portales, Chile, Universidad Autónoma de Madrid (UAM) and University of Liverpool.</p> <p>The survey contains over a hundred items:</p> <ul> <li>Socio-demographic items: education, age, religion, gender, employment</li> <li>Populism items: including Akkerman et al.'s 2014 scale of populist attitudes, and a new items corresponding to a new multi-dimensional scale of populist attitudes (Olivas Osuna 2021; Olivas Osuna 2024; Olivas Osuna et al. forthcoming) (32 items)</li> <li>Items related to trust on institutions and media (9 items)</li> <li>Items related to satisfaction with the functioning of democracy, services and institutions (7 items)</li> <li>Authoritarian values (Feldman and Stenner 1997)</li> <li>Liberal democratic values (Zanotti and Rama 2021)</li> <li>Authoritarian personality indexes (Hibbing 2020)</li> <li>Conspiracy theories (3 items)</li> <li>Nationalism (5 items)</li> <li>Nativism (Young et al. 2019)</li> <li>Affective polarisation</li> <li>Support for political party (past vote and vote intention)</li> <li>Left-right ideological self-placement</li> <li>Other socio-political questions.</li> </ul> <p>Fieldwork was conducted by YouGov Spain in February 2023. The surveys was part of the projects: <em>Populism and Borders: a Supply- and Demand-Side Comparative Analysis of Discourses and Attitudes (PBSDCA) </em>and <em>Principal Investigator Interdisciplinary Comparative Project on Populism and Secessionism (ICPPS).</em></p> <p>The uploaded files contain:</p> <ul> <li>Detail of survey results (.sav)</li> <li>Questionnaire (.doc)</li> <li>Summary of results (.xls)</li> <li>Fieldwork summary file (.pdf)(this file is in Spanish)</li> </ul>
Socio-political attitudes in Spain (2023)
<p>This dataset captures the responses of over 1500 participants in Spain to an original online survey.</p> <p>This online survey was designed by a group of experts in populism from Universidad Nacional de Educación a Distancia (UNED, Madrid), King's College London, Univerity of York, Universidad Diego Portales, Chile, Universidad Autónoma de Madrid (UAM) and University of Liverpool.</p> <p>The survey contains over a hundred items:</p> <ul> <li>Socio-demographic items: education, age, religion, gender, employment</li> <li>Populism items: including Akkerman et al.'s 2014 scale of populist attitudes, and a new items corresponding to a new multi-dimensional scale of populist attitudes (Olivas Osuna 2021; Olivas Osuna et al. forthcoming) (32 items)</li> <li>Items related to trust on institutions and media (9 items)</li> <li>Items related to satisfaction with the functioning of democracy, services and institutions (7 items)</li> <li>Authoritarian values (Feldman and Stenner 1997)</li> <li>Liberal democratic values (Zanotti and Rama 2021)</li> <li>Authoritarian personality indexes (Hibbing 2020)</li> <li>Conspiracy theories (3 items)</li> <li>Nationalism (5 items)</li> <li>Nativism (Young et al. 2019)</li> <li>Affective polarisation</li> <li>Support for political party (past vote and vote intention)</li> <li>Left-right ideological self-placement</li> <li>Other socio-political questions.</li> </ul> <p>Fieldwork was conducted by YouGov Spain in February 2023. This surveys was part of the following projects: <em>Populism and Borders: a Supply- and Demand-Side Comparative Analysis of Discourses and Attitudes (PBSDCA) </em>and <em>Principal Investigator Interdisciplinary Comparative Project on Populism and Secessionism (ICPPS).</em></p> <p>The uploaded files contain:</p> <ul> <li>Detail of survey results (.sav)</li> <li>Questionnaire (.doc)</li> <li>Summary of results (.xls)</li> <li>Fieldwork summary file (.pdf)</li> </ul>
Excel data collection template on descriptive political representation in national parliaments of the projects Pathways to Power and InclusiveParl adapted for the ActEU project
<p>This file contains the empty data collection template and variable and value labels to code biographical data on legislators for WP4 in the ActEU project. It is an abbreviated version of the codebooks produced by the Pathways to Power project and by the InclusiveParl project.</p>
Migration on the Chessboard: Political Violence as a Decisive Factor in Coercive Migration Diplomacy (Data and Associated Files for Dissertation)
<p>This publication contains files associated with analysis for my dissertation, "Migration on the Chessboard: Political Violence as a Decisive Factor in Coercive Migration Diplomacy." The dissertation explores a potential relationship between political violence and a state leader's choice to use migration as a bargaining chip in pursuit of foreign policy objectives. "Key to datasets.docx" and "dataframes_viz.png" explain the contents of the five datasets used. These five datasets are the five .dta files. There are five log files (.txt) and five do files containing code (.do) corresponding to the five datasets. Finally, each dataset has three associated results tables (.xls) for a total of fifteen .xls files. </p>
MIGR-TWIT Corpus. Migration Tweets of right and far-right politics in Europe
<p><strong>Description</strong></p> <p>The <strong>MIGR-TWIT Corpus</strong> is a multilingual corpus of tweets about the topic of migration in Europe. Within the framework of the collaborative research project OLiNDiNUM (Observatoire LINguistique du DIscours NUMérique, Linguistic Observatory of Online Debate) the MIGR-TWIT Corpus is created with the aim of developing language databases of online debate. Considering the global issue of migration in line with British and French political contexts of last dozen years from 2011 to 2022, the corpus consists of two sub-corpora: </p> <ul> <li> <p><strong>FR-R-MIGR-TWIT-2011-2022 Corpus </strong>for French language data (1 January 2011 - 30 June 2022) and </p> </li> <li> <p><strong>UK-R-MIGR-RA-TWIT-2012-2022 Corpus </strong>for English language data (1 January 2012 - 5 September 2022) <strong> </strong></p> </li> </ul> <p>Using the Twitter API v2 Academic Research, tweets containing at least one occurrence of migration or refugee related words are retrieved automatically from 28 right and far-right political figures and parties. The whole corpus contains 18,233 tweets and 533,198 words. </p> <p><strong>Scientific reference:</strong></p> <p>Pietrandrea, P., Battaglia, E. (2022). “Migrants and the EU”. The diachronic construction of ad hoc categories in French far-right discourse. Journal of Pragmatics 192, 139-157.</p> <p>Blandino, G. (2023). <em>10 years of public debate on immigration: combining topic modeling and corpus linguistics to examine the British (far-)right discourse on Twitter</em>, MA University of Wolverhampton</p> <p>Jeon, S. (2025). Le discours numérique sur l'immigration en France entre 2011 et 2022. Une analyse de corpus (Online Discourse on Immigration in France between 2011 and 2022. A Corpus Analysis), PhD Thesis, Université de Lille, France.</p> <p><strong>Contents</strong></p> <p>The whole corpus contains two CSV Zip files (tabular format) corresponding to each sub-corpus. The complete corpus is presented in two versions, one version with the tweet identifier (<strong><em>data__id</em></strong>) and the text of the tweet (<strong><em>data__text</em></strong>) as a header (folders named <em>FR-R-MIGR-TWIT-2011-2022_textonly</em> and <em>UK-R-MIGR-RA-TWIT-2012-2022_textonly</em>, respectively composed of 12 and 11 Zip files of every single year), and the other version with all tweet fields information included as a header, such as the posting date (<em><strong>data__created__at</strong></em>), the username (<strong><em>author__name</em></strong>), the number of retweets (<em><strong>data__public_metrics__retweet_count</strong></em>), etc., with two folders named <em>FR-R-MIGR-TWIT-2011-2022_meta</em> and <em>UK-R-MIGR-RA-TWIT-2012-2022_meta</em>. Detailed information for each sub-corpus is illustrated below.</p> <p><strong>1. FR-R-MIGR-TWIT-2011-2022 </strong></p> <ul> <li><strong>Created at: </strong>2022-08-08</li> <li> <p><strong>Language: </strong>FR<strong> </strong></p> </li> <li> <p><strong>Coverage: </strong>16 user accounts; 11,761 tweets; 358,491 words</p> </li> <li> <p><strong>Time of data collection: </strong>start=2011-01-01; end=2022-06-30 </p> </li> <li> <p><strong>Keywords: </strong>words derived from a latin root “<em><strong>migr</strong></em>” of <em>migrare</em></p> </li> <li> <p><strong>Corpus composition: </strong></p> </li> </ul> <table> <tbody> <tr> <th> </th> <th>Political figure/party</th> <th>Username</th> <th>Tweets</th> <th>Year concerned</th> </tr> <tr> <th>1</th> <td>Michel Barnier</td> <td>@MichelBarnier</td> <td>31</td> <td>2017-22</td> </tr> <tr> <th>2</th> <td>Valérie Pécresse</td> <td>@vpecresse</td> <td>81</td> <td>2017-22</td> </tr> <tr> <th>3</th> <td>Rassemblement National</td> <td>@RNational_off</td> <td>3,347</td> <td>2017-22</td> </tr> <tr> <th>4</th> <td>Nicolas Dupont-aignan</td> <td>@dupontaignan</td> <td>663</td> <td>2011-22</td> </tr> <tr> <th>5</th> <td>Éric Ciotti</td> <td>@ECiotti</td> <td>1,007</td> <td>2012-22</td> </tr> <tr> <th>6</th> <td>Christian Estrosi</td> <td>@cestrosi</td> <td>137</td> <td>2011-22</td> </tr> <tr> <th>7</th> <td>Marine Le Pen</td> <td>@MLP_officiel</td> <td>1,650</td> <td>2011-22</td> </tr> <tr> <th>8</th> <td>Valérie Boyer</td> <td>@valerieboyer13</td> <td>837</td> <td>2012-22</td> </tr> <tr> <th>9</th> <td>Florian Philippot</td> <td>@f_philippot</td> <td>485</td> <td>2012-22</td> </tr> <tr> <th>10</th> <td>Xavier Bertrand</td> <td>@xavierbertrand</td> <td>70</td> <td>2017-22</td> </tr> <tr> <th>11</th> <td>Marion Maréchal</td> <td>@MarionMarechal</td> <td>479</td> <td>2012-17,19-22</td> </tr> <tr> <th>12</th> <td>Philippe Meunier</td> <td>@Meunier_Ph</td> <td>245</td> <td>2013-22</td> </tr> <tr> <th>13</th> <td>Jordan Bardella</td> <td>@J_Bardella</td> <td>1,095</td> <td>2013-22</td> </tr> <tr> <th>14</th> <td>Nicolas Bay</td> <td>@NicolasBay_</td> <td>1,260</td> <td>2017-22</td> </tr> <tr> <th>15</th> <td>Emmanuel Macron</td> <td>@EmmanuelMacron</td> <td>72</td> <td>2017-22</td> </tr> <tr> <th>16</th> <td>Éric Zemmour</td> <td>@ZemmourEric</td> <td>302</td> <td>2019-22</td> </tr> <tr> <th>17</th> <td>Jean Messiha*</td> <td>Banned from Twitter (since July 2021)</td> <td>-</td> <td>-</td> </tr> </tbody> </table> <ul> <li>Political figures and parties of table above are listed in chronological order according to the dates on which they posted their first tweet.</li> <li> <p><strong>*</strong>Before the launching of Twitter API v2 Academic Research, migr-tweets were collected from the database of Europresse.com including 1,453 tweets of Jean Messiha as part of the reference study (Pietrandrea & Battaglia 2022). However, the Twitter account in question has been permanently banned since July 2021. For our data collection using the Twitter API started in September 2021, we could not access this account. Therefore, we decided not to include his tweets in the FR-R-MIGR-TWIT-2011-2022 for the sake of consistency with the rest of twitter data that are automatically retrieved.</p> </li> <li> <p>The sub-corpus FR-R-MIGR-TWIT-2017-2022 is developed, annotated and analyzed as part of a doctoral thesis in progress (<a href="https://theses.fr/s360032">Jeon, 2025</a>) with the aim of studying the semantic construction of migr-lexicon over the period between 2011 and 2022. </p> </li> </ul> <p><strong> </strong></p> <p><strong>2. UK-R-MIGR-RA-TWIT-2012-2022 </strong></p> <ul> <li> <p><strong>Created at: </strong>2022-09-06</p> </li> <li> <p><strong>Language: </strong>EN</p> </li> <li> <p><strong>Coverage: </strong>12 user accounts; 6,472 tweets; 174,707 words </p> </li> <li> <p><strong>Time of data collection: </strong>start=2012-01-01; end=2022-09-05</p> </li> <li> <p><strong>Keywords: </strong>words derived from a latin root “<strong><em>migr</em></strong>” of <em>migrare </em>in addition to the keywords “<strong><em>refugee</em></strong>(<strong><em>s</em></strong>)” and “<strong><em>asylum</em></strong>”.</p> </li> <li> <p><strong>Corpus composition:</strong></p> </li> </ul> <table> <tbody> <tr> <th> </th> <th>Political figure/party</th> <th>Username</th> <th>Tweets</th> <th>Year concerned</th> </tr> </tbody> <tbody> <tr> <th>1</th> <td>David Cameron</td> <td>@David_Cameron</td> <td>32</td> <td>2012-22</td> </tr> <tr> <th>2</th> <td>Amber Rudd</td> <td>@AmberRuddUK</td> <td>29</td> <td>2012-22</td> </tr> <tr> <th>3</th> <td>Sajid Javid</td> <td>@sajidjavid</td> <td>84</td> <td>2012-22</td> </tr> <tr> <th>4</th> <td>Boris johnson</td> <td>@BorisJohnson</td> <td>80</td> <td>2015-22</td> </tr> <tr> <th>5</th> <td>Priti Patel</td> <td>@pritipatel</td> <td>304</td> <td>2012-22</td> </tr> <tr> <th>6</th> <td>UK Home Office</td> <td>@ukhomeoffice</td> <td>909</td> <td>2012-22</td> </tr> <tr> <th>7</th> <td>Nigel Farage</td> <td>@Nigel_Farage</td> <td>1,010</td> <td>2012-22</td> </tr> <tr> <th>8</th> <td>Richard Tice</td> <td>@TiceRichard</td> <td>180</td> <td>2013-22</td> </tr> <tr> <th>9</th> <td>UKIP</td> <td>@UKIP</td> <td>2,746</td> <td>2012-22</td> </tr> <tr> <th>10</th> <td>Neil Hamilton</td> <td>@NeilUKIP</td> <td>252</td> <td>2013-22</td> </tr> <tr> <th>11</th> <td>Nick Griffin</td> <td>@NickGriffinBU</td> <td>542</td> <td>2012-22</td> </tr> <tr> <th>12</th> <td>Robin Tilbrook</td> <td>@RobinTilbrook</td> <td>304</td> <td>2012-22</td> </tr> </tbody> </table> <p> </p> <ul> <li> <p>2 out of 12 accounts are official accounts belonging to the” UK Home Office” department and the “UKIP” (United Kingdom Independence Party) party. 10 out of 12 accounts are political figures’ accounts.</p> </li> <li> <p>The corpus UK-R-MIGR-RA-TWIT-2012-2022 will be exploited for the following master’s thesis: Blandino, G. (2023). <em>10 years of public debate on immigration: combining topic modeling and corpus linguistics to examine the British (far-)right discourse on Twitter</em>, MA University of Wolverhampton.</p> </li> </ul> <p> </p>
Twitter Dataset for Pakistani Political Discourse
<p>This is one of the largest dataset of Pakistani Twitter political discourse and consists of more than 49 million tweets, collected during April 2022.</p> <p>Please use the following citation if you use this this dataset: MLA format: Haq, Ehsan-Ul, et al. "A Twitter Dataset for Pakistani Political Discourse." arXiv preprint arXiv:<a href="https://doi.org/10.48550/arXiv.2301.06316">2301.06316</a> (2023), doi: <a href="https://doi.org/10.48550/arXiv.2301.06316">https://doi.org/10.48550/arXiv.2301.06316</a></p> <p>Bibtex: @misc{2301.06316, Author = {Ehsan-Ul Haq and Haris Bin Zia and Reza Hadi Mogavi and Gareth Tyson and Yang K. Lu and Tristan Braud and Pan Hui}, Title = {A Twitter Dataset for Pakistani Political Discourse}, Year = {2023}, Eprint = {arXiv:2301.06316}, }</p> <p>Relevant paper highlight the dataset collection, and any possible changes is accessible at: https://arxiv.org/abs/2301.06316</p>
SM: Economy-wide impacts of socio-politically driven net-zero energy in Europe
<p><strong>Supplementary Material (SM): Economy-wide impacts of socio-politically driven net-zero energy in Europe</strong></p> <p>Two zipped folders</p> <p>(a) Euro-Calliope.zip includes</p> <p>-Energy system configurations by storyline (market-driven, government-directed, people-powered) and year (2030, 2050).</p> <p>(b) WEGDYN.zip includes</p> <p>-Supplementary Material (SM_Regionaleconomiceffects.pdf)<br>-Processed Euro-Calliope output data to WEGDYN input data (EC2WD_data.xlsx)<br>-WEGDYN results (WEGDYN_data.xlsx)<br>-WEGDYN resolution, nesting trees, elasticities (WEGDYN_model.xlsx)</p>
MIGR-TWIT CORPORA. Migration Tweets of French Left-wing Politics.
<p><strong>Description</strong></p> <p>The <strong>FR-L-MIGR-TWIT Corpus</strong> is part of the <strong><a href="https://www.ortolang.fr/market/corpora/migr-twit-corpus">MIGR-TWIT CORPORA</a></strong>, diachronic bilingual corpus of Tweets about the topic of migration in Europe.<br>Within the framework of the collaborative research project <a href="https://olindinum.huma-num.fr/recherche/">OLiNDiNUM</a> (Observatoire LINguistique du DIscours NUMérique, [Linguistic Observatory of Online Debate]), the MIGR-TWIT Corpora are created with the aim to study the evolution of the public discourse on migration in Europe during the past dozen years from 2011 to 2022. First two components of the corpus represent migration discourse of right-wing politics in France and in the UK. The FR-L-MIGR-TWIT Corpus represents French left-wing politics' migration discourse on Twitter. </p> <p>Using the <em>Twitter API v2 Academic Research</em>, the Tweets containing at least one occurrence of lexicon derived from a latin root "<em>migr</em>" of <em>migrare </em>are automatically retrieved from 23 Twitter accounts of French left-wing political figures and parties.<br> </p> <p><strong>Scientific reference : </strong>Jeon, S. (2025). Le discours numérique sur l'immigration en France entre 2011 et 2022. Une analyse de corpus (Online Discourse on Immigration in France between 2011 and 2022. A Corpus Analysis), PhD Thesis, Université de Lille, France.</p> <p><strong>Contents</strong><br>The downloadable version of <strong>FR-L-MIGR-TWIT-2011-2022</strong> <strong>Corpus </strong>contains 32 CSV files (tabular format). The corpus is presented in simplified and complete versions in terms of metadata. The simplified version corresponds to one single file named <em><strong>FR-L-MIGR-TWIT-2011-2022.csv</strong></em>, containing four basic (meta)data, <em>i.e</em>. identifier, text, posting date and username (that is, <em><strong>data__id</strong></em>, <strong>data__text</strong>, <em><strong>data__created_at</strong></em> and <strong><em>author__name</em></strong><em> </em>as the table hearder elements). In addition to these four (meta)data, the elaborate version is provided with all Tweet fields information included as a header element, such as the numbers of Replies, Retweets, Likes and Quotes, etc. This version is also available in one single CSV file named <em><strong>FR-L-MIGR-TWIT-2011-2022_meta.csv</strong></em>.</p> <p>Besides, the elaborate version is provided with three CSV Zip files: 7 CSV files in the zip file named <em>FR-L-MIGR-TWIT-</em><strong><em>YEAR</em></strong><em>_meta</em> correspond to grouped years (<em>i.e. FR-L-MIGR-TWIT-<strong>2011-2016</strong>_meta.csv</em>) or each and every year (<em>e.g. FR-L-MIGR-TWIT-<strong>2017</strong>_meta.csv, </em>and so on) for the last dozen years. 23 files in the zip file named <em>FR-L-<strong>NAME</strong>-MIGR-TWIT_meta</em> for each and every component of selected French left-wing political figures and parties (<em>e.g. FR-L-<strong>Arthaud</strong>-TWIT_meta.csv</em>). The zip file named FR-L-MIGR-TWIT-2011-2022_meta contains yearly Tweets of each and every component of political figures and parties.</p> <p>Detailed information of the FR-L-MIGR-TWIT-2011-2022 CORPUS is illustrated below.</p> <ul> <li><strong>Created at:</strong> 2023-04-18</li> <li><strong>Language:</strong> FR</li> <li><strong>Coverage:</strong> <strong>23</strong> <strong>user accounts</strong> ; <strong>5,636 Tweets</strong> ; <strong>169,818 words</strong></li> <li><strong>Time of data collection:</strong> start=2011-01-01 ; end=2022-06-30</li> <li><strong>Keywords: </strong>words derived from a latine root “<strong><em>migr</em></strong>” of <em>migrare</em></li> <li><strong>Corpus composition:</strong></li> </ul> <table> <tbody> <tr> <td> <p> </p> </td> <td> <p><strong>Political Figure/party</strong></p> </td> <td> <p><strong>Type of representative</strong></p> </td> <td> <p><strong>Username</strong></p> </td> <td> <p><strong><em>migr</em>-Tweets</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p><strong>Adrien Quatennens</strong></p> </td> <td> <p><strong>PERSON (M)</strong></p> </td> <td> <p><strong>@AQuatennens</strong></p> </td> <td> <p><strong>315</strong></p> </td> </tr> <tr> <td> <p><strong>2</strong></p> </td> <td> <p><strong>Alexis Corbière</strong></p> </td> <td> <p><strong>PERSON(M)</strong></p> </td> <td> <p><strong>@Alexiscorbiere</strong></p> </td> <td> <p><strong>209</strong></p> </td> </tr> <tr> <td> <p><strong>3</strong></p> </td> <td> <p><strong>Anne Hidalgo</strong></p> </td> <td> <p><strong>PERSON (F)</strong></p> </td> <td> <p><strong>@Anne_Hidalgo</strong></p> </td> <td> <p><strong>801</strong></p> </td> </tr> <tr> <td> <p><strong>4</strong></p> </td> <td> <p><strong>Arnaud Montebourg*</strong></p> </td> <td> <p><strong>PERSON (M)</strong></p> </td> <td> <p><strong>@montebourg</strong></p> </td> <td> <p><strong>7</strong></p> </td> </tr> <tr> <td> <p><strong>5</strong></p> </td> <td> <p><strong>Benoît Hamon</strong></p> </td> <td> <p><strong>PERSON (M)</strong></p> </td> <td> <p><strong>@benoithamon</strong></p> </td> <td> <p><strong>172</strong></p> </td> </tr> <tr> <td> <p><strong>6</strong></p> </td> <td> <p><strong>Christiane Taubira</strong></p> </td> <td> <p><strong>PERSON (F)</strong></p> </td> <td> <p><strong>@ChTaubira</strong></p> </td> <td> <p><strong>11</strong></p> </td> </tr> <tr> <td> <p><strong>7</strong></p> </td> <td> <p><strong>Clémentine Autain</strong></p> </td> <td> <p><strong>PERSON (F)</strong></p> </td> <td> <p><strong>@Clem_Autain</strong></p> </td> <td> <p><strong>102</strong></p> </td> </tr> <tr> <td> <p><strong>8</strong></p> </td> <td> <p><strong>Danièle Obono</strong></p> </td> <td> <p><strong>PERSON (F)</strong></p> </td> <td> <p><strong>@Deputee_Obono</strong></p> </td> <td> <p><strong>415</strong></p> </td> </tr> <tr> <td> <p><strong>9</strong></p> </td> <td> <p><strong>Esther Benbassa**</strong></p> </td> <td> <p><strong>PERSON (F)</strong></p> </td> <td> <p><strong>@EstherBenbassa</strong></p> </td> <td> <p><strong>936</strong></p> </td> </tr> <tr> <td> <p><strong>10</strong></p> </td> <td> <p><strong>François Hollande</strong></p> </td> <td> <p><strong>PERSON (M)</strong></p> </td> <td> <p><strong>@fhollande</strong></p> </td> <td> <p><strong>28</strong></p> </td> </tr> <tr> <td> <p><strong>11</strong></p> </td> <td> <p><strong>François_Ruffin</strong></p> </td> <td> <p><strong>PERSON (M)</strong></p> </td> <td> <p><strong>@Francois_Ruffin</strong></p> </td> <td> <p><strong>19</strong></p> </td> </tr> <tr> <td> <p><strong>12</strong></p> </td> <td> <p><strong>Jean-Luc Mélenchon</strong></p> </td> <td> <p><strong>PERSON (M)</strong></p> </td> <td> <p><strong>@JLMelenchon</strong></p> </td> <td> <p><strong>240</strong></p> </td> </tr> <tr> <td> <p><strong>13</strong></p> </td> <td> <p><strong>Manon Aubry</strong></p> </td> <td> <p><strong>PERSON (F)</strong></p> </td> <td> <p><strong>@ManonAubryFr</strong></p> </td> <td> <p><strong>182</strong></p> </td> </tr> <tr> <td> <p><strong>14</strong></p> </td> <td> <p><strong>Natalie Arthaud</strong></p> </td> <td> <p><strong>PERSON (F)</strong></p> </td> <td> <p><strong>@n_arthaud</strong></p> </td> <td> <p><strong>165</strong></p> </td> </tr> <tr> <td> <p><strong>15</strong></p> </td> <td> <p><strong>Philippe Poutou</strong></p> </td> <td> <p><strong>PERSON (M)</strong></p> </td> <td> <p><strong>@PhilippePoutou</strong></p> </td> <td> <p><strong>83</strong></p> </td> </tr> <tr> <td> <p><strong>16</strong></p> </td> <td> <p><strong>Raphael Glucksmann</strong></p> </td> <td> <p><strong>PERSON (M)</strong></p> </td> <td> <p><strong>@rglucks1</strong></p> </td> <td> <p><strong>142</strong></p> </td> </tr> <tr> <td> <p><strong>17</strong></p> </td> <td> <p><strong>Yannick Jadot</strong></p> </td> <td> <p><strong>PERSON (M)</strong></p> </td> <td> <p><strong>@yjadot</strong></p> </td> <td> <p><strong>374</strong></p> </td> </tr> <tr> <td> <p><strong>18</strong></p> </td> <td> <p><strong>Europe Écologie-Les Verts</strong></p> </td> <td> <p><strong>ORGANIZATION</strong></p> </td> <td> <p><strong>@EELV</strong></p> </td> <td> <p><strong>484</strong></p> </td> </tr> <tr> <td> <p><strong>19</strong></p> </td> <td> <p><strong>Gauche Républicaine et Socialiste</strong></p> </td> <td> <p><strong>ORGANIZATION</strong></p> </td> <td> <p><strong>@Gauche_RS</strong></p> </td> <td> <p><strong>73</strong></p> </td> </tr> <tr> <td> <p><strong>20</strong></p> </td> <td> <p><strong>Génération.s</strong></p> </td> <td> <p><strong>ORGANIZATION</strong></p> </td> <td> <p><strong>@GenerationsMvt</strong></p> </td> <td> <p><strong>165</strong></p> </td> </tr> <tr> <td> <p><strong>21</strong></p> </td> <td> <p><strong>La France Insoumise</strong></p> </td> <td> <p><strong>ORGANIZATION</strong></p> </td> <td> <p><strong>@FranceInsoumise</strong></p> </td> <td> <p><strong>300</strong></p> </td> </tr> <tr> <td> <p><strong>22</strong></p> </td> <td> <p><strong>Parti Radical Gauche</strong></p> </td> <td> <p><strong>ORGANIZATION</strong></p> </td> <td> <p><strong>@PartiRadicalG</strong></p> </td> <td> <p><strong>37</strong></p> </td> </tr> <tr> <td> <p><strong>23</strong></p> </td> <td> <p><strong>Parti Socialiste</strong></p> </td> <td> <p><strong>ORGANIZATION</strong></p> </td> <td> <p><strong>@partisocialiste</strong></p> </td> <td> <p><strong>376</strong></p> </td> </tr> </tbody> </table> <ul> <li>Political figures and parties, listed in alphabetical order, are selected according to the four criteria: (1) the high number of <em>migr</em>-tweets, (2) the political affiliation, (3) the political careers, that is, the Member of the European Parliament or (4) the presidential candidate during the period between 2011 and 2022. These four criteria are not mutually exclusive.</li> <li>As part of a doctoral thesis (<a href="https://theses.fr/s360032">Jeon, 2025</a>), the FR-L-MIGR-TWIT and FR-R-MIGR-TWIT corpora are compiled, annotated and analyzed through a comparative discourse analysis approach, with the aim to study the semantic construction of <em>migr</em>-lexicon over the period between 2011 and 2022.</li> <li>*One migration Tweet retrieved from the user account @montebourg for the year of 2019 was removed and is not included in his 7 <em>migr</em>-tweets because it refers to the issue of the migration of honey bees.</li> <li>**We later added the user account @EstherBenbassa represented by Esther Benbassa, senator and former member of political party Europe Écologie-Les Verts (representative of the user account @EELV), because of the high number of her <em>migr</em>-tweets that were retweeted by @EELV.</li> </ul> <p>The <strong>MIGR-TWIT</strong> <strong>Corpus</strong> consists of three subcorpora for a total amount of <strong>23,869 Tweets </strong>and <strong>703,016</strong> <strong>words</strong>:</p> <ul> <li>FR-R-MIGR-TWIT-2011-2022 Corpus: <em>French Right-wing</em> politics' <em>migr</em>-tweets</li> <li>UK-R-MIGR-RA-TWIT-2011-2022 Corpus: <em>British Right-wing</em> politics' <em>migr</em>-tweets</li> <li>FR-L-MIGR-TWIT-2011-2022 Corpus: <em>French Left-wing</em> politics' <em>migr</em>-tweets </li> </ul> <p> </p>
The effect of a political crisis on performance of community- and state-managed forests in Madagascar
<p>Data associated with paper: "The effect of a political crisis on performance of community forests and protected areas in Madagascar"</p> <p>For code and selected tabular data outputs, also see: https://github.com/raenb0/madagascar</p> <p>Includes a number of files with raster (tif) data. All data is for Madagascar:</p> <p>⦁ for2000.tif is forest cover in the year 2000<br>⦁ for2000_0.tif is the same as above but contains 0 values instead of NA values (better for analysis)<br>⦁ defor_year_90m is annual deforestation as a proportion of each 90 m pixel that is deforested, values range from 0-1.</p> <p>data on all time-invariant covariates used for matching, including:<br>⦁ dist_cart (distance from cart tracks, meters)<br>⦁ dist_road (distance from roads, meters)<br>⦁ dist_urb (distance from villages, meters)<br>⦁ dist_urb (distance from urban centers, meters)<br>⦁ dist_vil (distance from villages, meters)<br>⦁ edge_05 (distance from forest edge in 2005, meters)<br>⦁ elev (elevation, meters)<br>⦁ q1_materials (index of self-reported development level, based on material assets)<br>⦁ rain (average precipitation 1970-2000, mm)<br>⦁ rice (rice suitability, 0 for unsuitable or 1 for suitable)<br>⦁ slope (slope, meters)<br>⦁ v7_security (self-reported indicator of security and risk of theft)<br>⦁ veg_type (vegetation type, 1= eastern humid forest, 2= western deciduous forest, 3 = southern dry spiny forest)</p> <p>time-variant covariates include (for years 2005-2020):<br>⦁ distance_year (distance from forest edge of each forest pixel, in meters, in each year)<br>⦁ drght_year (drought severity, Palmer Index Score)<br>⦁ pop_year (human population density, people per sq km)<br>⦁ precip_year (maximum accumulated precipitation, mm)<br>⦁ rice_av_year (annual average rice prices, in USD)<br>⦁ rice_sd_year (standard deviation of rice price, in USD)<br>⦁ temp_year (maximum annual temperature, degrees C)<br>⦁ wind_year (maximum annual windspeed, meters/sec)</p> <p>Shapefile polygons for Community Forest Managed areas (CFM) and protected areas administered by Madagascar National Parks can be requested from the corresponding author: ran63 (at) cornell (dot) edu</p> <p>Shapefile polygons for protected areas in Madagascar are available from the World Database of Protected Areas: https://www.protectedplanet.net/country/MDG</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.