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8 results for “Armed conflict”
A Dataset of Multilingual Facebook Comments on Moros and Armed Conflict in the Southern Philippines
<p>This dataset is a collection of 12,478 social media comments found on the official Facebook pages of ten Philippine newspapers, T<em>he Philippine Daily Inquirer, Manila Bulletin, The Philippine Star, The Manila Times, Sunstar Cebu, Sunstar Davao, Cebu Daily News, The Freeman, Sunstar Davao, MindaNews, </em>and <em>The Mindanao Times</em>, spanning the years 2015, 2017 and 2019. The comments contain terms related to the Moro identity and the Mamasapano Clash, the Marawi Siege and the establishment of BARMM in the southern Philippines, allowing researchers to study semantic fields with regard to Muslims and the relationship between the texts and the source newspaper, their region of origin, and political administration, among other variables. All comments in the dataset were downloaded through Facebook's Graph API via Facepager (Jünger & Keyling, 2019).</p> <p>One CSV file (MMB151719SOCMED_v2.csv) is provided, along with a codebook that contains descriptions of the variables and codes used in the CSV file, and a Readme document with a changelog. </p> <p>Each social media comment is annotated with the following metadata: </p> <ol> <li><strong>object_id</strong>: identifier associated with the comment;</li> <li><strong>message</strong>: the textual string of the comment;</li> <li><strong>message_proc:</strong> the textual string of the comment after pre-processing;</li> <li><strong>lang_label:</strong> categorical value for the language of the comment (Tagalog (Filipino), Cebuano, English, Taglish, Bislog, Bislish, Trilingual or Other);</li> <li><strong>from_name</strong>: identifier of public pages (not profiles of individuals) leaving comments (NaN for profiles of individuals, 'NAME' for public pages besides the newspapers, otherwise, the page name of the newspaper);</li> <li><strong>created_time</strong>: Facebook Graph API's-generated string for the date and time the comment was posted; </li> <li><strong>month_year</strong>: categorical value in the form string+YY (e.g. Jun-15) of the month and year when the comment was posted;</li> <li><strong>year</strong>: numerical value in the form YY; </li> <li><strong>newspaper</strong>: categorical value for the newspaper Facebook page under which the comment was found;</li> <li><strong>corpus</strong>: categorical value for comments from the main corpus or the side (control) corpus;</li> <li><strong>administration</strong>: categorical value for political administration (pbsa = President Benigno Aquino III, prrd = President Rodrigo Roa Duterte);</li> <li><strong>count</strong>: numerical value referring to the number of string sequences without spaces;</li> </ol> <p>The dataset may only be used for non-commercial purposes and is licensed under the <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank" rel="noopener">CC BY-NC-SA 4.0 DEED.</a></p> <p>_____________________________________________________________________________________</p> <p>V2 - 05/06/2024</p> <p>Corrections</p> <ul> <li>Corrections made to region to include Luzon, Visayas and Mindanao (as opposed to Mindanao, non-Mindanao);</li> <li>Corrections made to administration coding. </li> </ul> <p> </p> <p>This dataset is described by: </p> <p><span>Cruz, F. A. (2024). </span><span>A Multilingual Collection of Facebook Comments on the Moro Identity and Armed Conflict in the Southern Philippines<em>. Journal of Open Humanities Data, 10</em>(1), 41. </span><span>DOI: <a href="https://doi.org/10.5334/johd.219" target="_blank" rel="noopener">https://doi.org/10.5334/johd.219</a></span></p> <p> </p> <p><strong>Bibiliography</strong></p> <div> <div>Jünger, J., & Keyling, T. (2019). <em>Facepager: An application for automated data retrieval on the web</em> (4.5.3) [Computer software]. <a href="https://github.com/strohne/Facepager/">https://github.com/strohne/Facepager/</a></div> <div> </div> <div> </div> </div>
Figures for "Improving Satellite Monitoring of Armed Conflicts"
<p>Figure 1. From space, VHR imagery from Planet and Maxar reveals the landscape effects of Russia'’s invasion of Ukraine. 1. Cars waiting at the Ukrainian-Hungarian border (WorldView-3/Maxar); 2. Active fires in Chernihiv (PlanetScope2022/03/21); 3a. The North Crimean Channel without water preinvasion (PlanetScope-2022/02/21); 3b. Filled with water post-invasion (PlanetScope-2022/03/24); 4. Burning apartment buildings in Mariupol (WorldView-3/Maxar). Maxar imagery was obtained from https://www.maxar.com/ on 5 April 2022. PlanetScope data were obtained through University of Copenhagen license agreement PL0030875_2021/2022.</p> <p> </p> <p>Figure 2. A village and surrounding farmland in Ukraine'’s Lviv Oblast are more difficult to discern in imagery from medium-resolution civilian satellites Landsat 8 (USGS, 2021/05/04) and Sentinel-2 (ESA, 2021/05/11) than in commercial VHR imagery from Dove/PlanetScope (2021/05/09) and SkySat (2021/05/09), both of which belong to Planet. Landsat 8 and Sentinel-2 data are available via https://glovis.usgs.gov. PlanetScope data were obtained through University of Copenhagen license agreement PL-0030875_2021/2022. SkySat data were obtained by special request from Planet.</p>
Armed conflict avalanches and Voronoi grids for Africa
<p>This description outlines the dataset associated with the research paper "Discovering the Mesoscale for Chains of Conflict," published in PNAS Nexus (click <a href="https://doi.org/10.1093/pnasnexus/pgad228">here</a> to view the article). Please cite this paper if you use our dataset.</p> <p>The dataset is divided into two main folders:</p> <ol> <li> <p>"voronoi_grids": This folder contains 100 realizations of Voronoi tessellations over the African continent. These tessellations were generated during the research process detailed in the aforementioned paper.</p> </li> <li> <p>"avalanches": The "avalanches" folder comprises conflict avalanches derived from each of the realizations of the Voronoi tessellations. These avalanches were generated using the technique described in the research paper.</p> </li> </ol> <p>Reading avalanches from the pickle files:</p> <p>The avalanches are stored in pickle files, and the following Python code can be used to read them:</p> <pre><code class="language-python">import dill as pickle # Temporal scale. Choose one of the following values: 2, 4, 8, 16, 32, 64, 128, 256, 512 time = 32 # Spatial scale. Choose one of the following values: 20, 28, 40, 57, 80, 113, 160, 226, 320, 453, 640, 905, 1280 dx = 453 # Voronoi grid index. Choose from 0 to 99 gridix = 3 file_path = f"avalanches/battles/gridix_{gridix}/te/te_ava_{str(time)}_{str(dx)}.p" with open(file_path, "rb") as f: ava = pickle.load(f) ava_event = ava["ava_event"]</code></pre> <p> </p> <p>The extracted avalanches are in "event form," represented as a list of lists, where each element corresponds to the index of an individual conflict event in the ACLED dataset. Each sublist represents an avalanche. The total number of avalanches at the given temporal and spatial scale can be determined by the length of <code>ava_event</code> (<code>len(ava_event)</code>).</p> <p>ACLED Dataset:</p> <p>Due to copyright issues, we are providing a filtered version of the ACLED dataset that we used in our study. To obtain the full version of the ACLED dataset containing all the information regarding each conflict event, please refer to the final bullet point of the manual installation instructions of our python pacakge <a href="http://github.com/eltrompetero/armed_conflict_avalanche/tree/PNAS_Nexus_2023">arcolanche</a>.</p>
Figures for "Towards Improved Satellite Monitoring of Armed Conflicts"
<p>Figure 1. From space, VHR imagery from Planet and Maxar reveals the landscape effects of Russia’s invasion of Ukraine. 1. Cars waiting at the Ukrainian-Hungarian border (Maxar technologies); 2. Active fires in Chernihiv (PlanetScope-2022/03/21); 3a. The North Crimean Channel without water pre-invasion (PlanetScope-2022/02/21); 3b. Filled with water post-invasion (PlanetScope-2022/03/24); 4. Burning apartment buildings in Mariupol (Maxar technologies). Maxar technologies images were obtained from https://www.maxar.com/ on April 5th, 2022. PlanetScope data were obtained under the University of Copenhagen license agreement PL-0030875_2021/2022. Because Maxar and Planetscope original images are commercial they can not be publicly shared but can be requested from the Maxar and Planet with the additional assistance of authors.</p> <p>Figure 2. A village and surrounding farmland in Ukraine’s Lviv Oblast are more difficult to discern in imagery from medium-resolution civilian Landsat-8 (USGS-2021/05/04) and Sentinel-2 (ESA-2021/05/11) satellites than in imagery from their commercial VHR counterparts (Dove/PlanetScope -2021/05/09 and SkySat-2021/05/09, both of which belong to Planet). Landsat-8 (USGS) and Sentinel-2 (ESA) are available via glovis.usgs.gov. PlanetScope data were obtained under the University of Copenhagen license agreement PL-0030875_2021/2022. SkySat imagery was obtained under special request from Planet. PlanetScope and SkySat original images are commercial they can not be publicly shared but can be requested from Planet with the additional assistance of authors.</p> <p> </p> <p> </p>
Quality of life among Romanian Emergency Medicine Personnel during the COVID-19 Pandemic and the Russian–Ukrainian Armed Conflict
<p>The aim of the present study was to investigate the relationship between threat perception and professional quality of life in a sample of Emergency Medicine Personnel, during the COVID-19 pandemic and during the outbreak of the Russian-Ukrainian Armed Conflict. Two types of threats were investigated (war threat and pandemic threat) and three indicators of professional quality of life (i.e., secondary traumatic stress, burnout, and compassion satisfaction). The mediating role of worries in the relationship between threat perception and quality of life was also assessed. </p>
Unified Protocol for Emotional Problems in Victims of the Armed Conflict in Colombia
ClinicalTrials.gov study NCT03127982. IPD Sharing: NO. Countries: 1. Publications: 1.
Effectiveness of a Community - Based Cross-sector Network for the Management of Mental Health Problems and Disorders Associated With Forced Displacement Due to Armed Conflict in the Municipality of So
ClinicalTrials.gov study NCT03839225. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Psychological Impact of Armed Conflict on Sudanese Diabetic Patients 2024
ClinicalTrials.gov study NCT06682338. IPD Sharing: NO. Countries: 1. Publications: 0.
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