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160 results for “refugees”
CPTSD and PMLDs in Afghan refugees and asylum seekers (PIAAS-Study)
<p>Data set from the baseline assessment of the PIAAS study including all participants which completed at least one of the questionnaires (ITQ, PMLDC). Complex PTSD and post-migration living difficutlies (PMLDs) were assessed through a fully structured face-to-face and interpreter-assisted interview in Afghan refugees and asylum seekers (N=93).</p>
Supplementary material for the publication: J.D. Nixon, K. Bhargava and E. Gaura, Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps, 2020
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. </p> <p>As part of the project, we deployed a Standalone Solar System for a Community Hall in Nyabiheke camp, Rwanda, and a PV-battery Microgrid in Kigeme camp, Rwanda. The microgrid supplies power to a playground and two nursery buildings. It powers a total of 20 CPE (each with 3 LEDs) and 10 sockets. The standalone system at Hall powers 7 CPE (with 3 LEDs each) and 4 sockets. The aim of the study was to (a) understand the energy consumption behaviour, light usage and other enabled uses within the set location in each camp (b) create an evidence base on the value of energy and its benefits in displaced contexts (c) identify best practice in the construction, control and operation of the respective systems as a shared energy resource.</p> <p>The system data used for the performance analysis for this study (July 2019 and March 2020) is deposited here along with the metadata. The results from analysis are presented in a paper titled '<strong>Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps</strong>' (currently under submission). The scripts for analysis can be found at our Github account <a href="https://github.com/cogent-computing">Cogent Labs</a> under HEED-Microgrid and HEED-Hall repositories.</p>
Twiter Dataset on climate change discussions: COP27, IPCC, climate refugees and Doñana - Clint project
<p><strong>CLINT Data</strong></p> <p>This repository contains the date used in the project CLINT and the paper "<a href="https://arxiv.org/abs/2410.21187">A cross-platform analysis of polarization and echo chambers in climate change discussions</a>" </p> <p><strong>Open Twitter Data</strong></p> <p>We used the Twitter’s search to gather historical tweets and the streaming API to follow specified accounts and also collect in real-time tweets that mention specific keywords. To comply with <a href="https://developer.twitter.com/en/developer-terms/agreement-and-policy">Twitter’s Terms of Service</a>, we are only publicly releasing the tweet IDs of the collected tweets. The data is released for non-commercial research use. </p> <p><strong>With Twitter's changes to its Academic API policies, it’s no longer possible to collect or rehydrate tweets </strong><strong>as we usually did, however we open data in case at some point it will become feasible to do it.</strong></p> <table> <tbody> <tr> <td> </td> <td><strong>IPCC</strong></td> <td><strong>Doñana</strong></td> <td><strong>Climate Refugees</strong></td> <td><strong>COP27</strong></td> </tr> <tr> <td><strong>Number of tweets</strong></td> <td>352,723 </td> <td>1,487,425</td> <td>1,938,932</td> <td>6,225,508 </td> </tr> <tr> <td><strong>Number of authors</strong></td> <td>157,056</td> <td>290,782</td> <td>841,454 </td> <td>1,351,903 </td> </tr> <tr> <td><strong>First tweet date</strong></td> <td>2023-03-18</td> <td>2019-01-01</td> <td>2008-03-10 </td> <td>2022-09-01 </td> </tr> <tr> <td><strong>Last tweet date</strong></td> <td>2023-03-26</td> <td>2023-04-30</td> <td>2022-12-31 </td> <td>2022-11-27</td> </tr> </tbody> </table> <p> </p>
Data to support the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia"
<p>This dataset supports the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia". This dataset contains the delineated boundaries for 24 refugee camps in Ethiopia. Also included are refugee camp building footprint data (where available). All datasets are in shapefile format.</p>
Data and code for Decoding dynamic landslide hazard processes for a massive refugee camp (KTP) in Bangladesh
<p>The codes have been implemented using R 4.4.0. Landslide priority zonation using Monte Carlo simulation is implemented in Google Colab.</p> <p>A Dynamic Landslide Hazard Assessment has been conducted using a Generalized Additive Model (GAM). The results of the GAM are also compared with standard machine learning algorithms (MLs): NNET, RF, LDA, xgBoost, and SVM.</p> <p>The code is jointly developed by Dewan Haque and Ritu Roy, with collaboration from many others. The GAM code is an update from the study published by Zhice, F. (2023), <a href="https://doi.org/10.5281/zenodo.10395153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10395153</a>, adapted to apply it across settings. The ML code has been developed from scratch.</p> <p>The required data from intensive fieldwork and satellite image analysis is uploaded here to reproduce the results. Additionally, R Markdown files are provided.</p> <p>The ReadMe file here, as well as on GitHub, will be useful for further instructions.</p> <p>GitHub Link: https://github.com/Dewan-cpu/Decoding-Landslide-Hazard-Assessment</p>
SIRIUS Project - Comparative dataset on skills, qualifications and the employability of post-2014 migrants, refugees and asylum seekers
<p>The dataset provides harmonized country data on the skills and qualifications of post-2014 migrants, refugees and asylum seekers in all SIRIUS countries (Czech Republic, Denmark, Finland, Greece, Italy, Switzerland, United Kingdom). </p> <p>This dataset and the related codebook have been put together within the framework of Work package 1 of the SIRIUS Project "Skills and Integration of Migrants, Refugees and Asylum Applicants in European Labour markets". The work package, titled “Labour market barriers and enablers", aimed to determine (1) the position of post- 2014 migrants, refugees and asylum seekers in the labour market of their host country, (2) the main features of the host countries’ labour markets focusing on the sectoral structure and the relevant skills and occupations.</p> <p> </p> <p><strong>Acknowledgments and disclaimers</strong><br> This research was conducted under the Horizon 2020 project ‘SIRIUS’ (770515).<br> The sole responsibility of this publication lies with the author. The European Union is not responsible for any use that may be made of the information contained therein.</p>
Initial relevant routes and geospatial objects for refugees and asylum seekers in MS
<p>Open geospatial dataset with an initial collection of routes, landmarks, and decision and confirmation points relevant for young refugees and asylum seekers arriving to Münster (MS), Germany. The information here collected were the results of participatory workshops done with young forced migrants in 2016.</p> <p>The information of the routes, landmarks (reference objects), points (origin, destination, decision, and confirmation points) and the relationship between points and reference objects is available in .JSON format. It has as an example, the images collected for one of the relevant routes (R2) identified by the group of young forced migrants. This route is the one from the main mall downtown (Arkaden) MS to the central train station. The pictures are available in .zip format.</p> <p> </p>
CM-RWL1-O15S16 Daily refugee arrivals and weather data, Italy/Central Med., Oct.2015 - Sept.2016
<p>========================================================</p> <p> <strong>Dataset: CM-RWL1-O15S16</strong></p> <p><strong> Daily refugee arrivals and weather data<br> Italy/Central Med., Oct.2015 - Sept.2016</strong><br> <br> Release Notes</p> <p> Copyright (c) 2018 by Harris V. Georgiou</p> <p>========================================================<br> Release: Apr 14, 2018</p> <p> - Version: 1.1a<br> - Format: .xlsx/.csv/.txt<br> ========================================================</p> <p><br> This file contains important information about the<br> current version of the dataset package.</p> <p>Downloading and using this material hints that you<br> accept the EULA/Terms-of-Use (please read carefully).</p> <p>We welcome your comments and suggestions.</p> <p>_______________________________________________<br> WHAT'S IN THIS PACKAGE?</p> <p>- Overview<br> - Available file formats<br> - Files and Datasets<br> - License Agreement</p> <p>_______________________________________________<br> OVERVIEW</p> <p>Since early January 2015, Europe has witnessed an unprecedented influx of refugees<br> from regions of war and conflict in the Middle East, primarily Syria, Afghanistan<br> and Iraq. The rapid allocation of proper resources is the most critical factor in<br> the success or failure of any rescue and relief operations, especially in the "hot"<br> zones. In order to do so, proper tools of predictive analytics mus be available,<br> specifically for forecasting the intensity and, if possible, the location of the<br> next refugee influx waves, so that the rescue elements and the logistical support<br> is properly prepared beforehand.</p> <p>This package contains a set of data regarding daily refugee arrivals at the general<br> area of the Central Mediterranean Sea, more specifically towards Italy, for the most<br> intense period of influx waves, from the beginning of October 2015 until the end of<br> September 2016 (one full year).</p> <p>The sources of the data are:<br> 1) For daily arrivals (Italy):<br> UNHCR Refugees/Migrants Emergency Response (Data mashups)<br> http://data2.unhcr.org/en/situations/mediterranean/location/5205<br> 2) For weather:<br> Weather Underground Database (mashup of NOAA, aviation, local)<br> https://www.wunderground.com/about/data</p> <p>The datasets from (1) have already been used in various publications describing<br> such predictive analytics models. Detailed description and related conclusions<br> can be found at:</p> <p>* Harris V. Georgiou, "Identification of refugee influx patterns in Greece via<br> model-theoretic analysis of daily arrivals" (9-May-2016),<br> https://arxiv.org/abs/1605.02784</p> <p>_______________________________________________<br> AVAILABLE FILE FORMATS</p> <p>The datasets are available in the following formats (included):</p> <p>*.xlsx : MS-Excel/LibreOffice native spreadsheets<br> *.csv : comma-separated plaintext spreadsheets<br> *.txt : raw plaintext files with full column headers</p> <p>These data formats are equivalent, i.e., they contain the exact same<br> sets of data. Normally, at least one of them should be compatible<br> with any major programming platform (e.g. Matlab, Octave, R) or any<br> native programming language for arbitrary handling (e.g. C, Java).</p> <p>_______________________________________________<br> FILES AND DATASETS</p> <p>Root folder: CM-RWL1-O15S16\</p> <p>Dataset 1: Arrivals\(csv,xlsx)<br> "Italy-DailyArrivals-Oct2015Sept2016.*"<br> : Complete data series for refugee influx arrivals for Italy</p> <p>Dataset 2: Weather\(xlsx,txt)<br> Habib Bourguiba, Tunisia<br> Sfax El-Maou, Tunisia<br> Lampedusa, Italy<br> Luqa, Malta<br> Tripoli Mitiga, Libya<br> : Weather data (temp,wind,gust,w.dir,...) at local airports</p> <p>The Mitiga airport station at Tripoli, Libya, is the most critical regarding<br> the construction of analytics and predictive modeling of the daily influx series<br> towards Italy. However, due to adverse conditions and lack of maintenance,<br> there are several blocks of consecutive days with missing weather data. Thus,<br> the other four reliable weather stations in the area should be used to build<br> regression models for filling-in these gaps.</p> <p>The satellite map(*) in the \Suppl folder shows the situation of Search & Rescue<br> (SAR) operations, density of shipwrecks by the end of Sept. 2015, as well as<br> the location of these weather stations and how these relate spatially to the<br> target area of Tripoli, which is still the departing spot with the highest<br> density of boats.</p> <p>(*) SAR map source: https://blamingtherescuers.org/report/</p> <p><br> _______________________________________________<br> LICENSE AGREEMENT</p> <p>This program was produced primarily for academic research and educational purposes.<br> Downloading and using this material implies acceptance of the Creative Commons<br> License: Attribution-NonCommercial-ShareAlike 4.0 International (BY-NC-SA), 2016.<br> * http://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p>Copyright (c) 2018 by Harris V. Georgiou (MSc,PhD) -- http://xgeorgio.info</p> <p><br> --</p> <p> </p>
Data of: Cross-sectional survey on Germans' awareness for refugees' information barriers
<p>The present dataset is the result of a cross-sectional online survey, which had been conducted to examine selected predictors of Germans' problem awareness in the form of perceived information barriers that refugees face, placing an emphasis on the role of positive intercultural contact experiences. The survey content is based on an extended version of the Empathy-Attitude-Action model and was carried out with a sample of Germans. </p> <p>The dataset is in xlsx-format and the variable-descriptions can be found in the headings of the spreadsheet. </p>
dataset on Refugees and labour market at Middle East countries
<p>Dataset with quality and quantity labour indicators and the number of refugees in host Middle Eastern countries (Egypt, Iran, Lebanon, Jordan and Turkiye) from 1991 to 2021. Data are collected from World Bank Open data and the statistics on working poverty by ILOSTAT. It includes:</p> <ul> <li>Refugee ratio (as a percentage of the total population of each country); Official development assistance.</li> <li>Quantitative labour market indicators: unemployment rate, labour force, number of own-account workers.</li> <li>Qualitative labour market indicators: distribution of employment by income level in developing countries.</li> </ul>
Rohingya Refugee Camp Fuel Load Dataset (Camp 4Ex, 5, KRC)
<p>A comprehensive survey was conducted on July 2, 2022, to prepare the fuel inventory map featuring dwelling structure, fuel load density, placement of fuel etc. In the 5-day long survey, three camps having contrasting fuel inventory have been examined. They are- Camp 4 Extension (Ex), Camp 5 and Kutupalong Registered Camp (KRC). These camps were selected based on the following features :</p> <ul> <li>Being the youngest of all the camp in Kutupalong, Camp 4 Ex followed a standard design and an average separation distance of 7m.</li> <li>On the other hand, Camp 5 is densely populated, having no visible space between two dwellings. It was one of the largest camps with a population of almost 25 thousand. The Rohingya community there are as equally impoverished as those living in Camp 4 Ext. The shelters of both of these camps are made of cellulose based structure material like wood, bamboo, timber etc., but the dwellings of camp 5 don’t follow any standard design.</li> <li>With a sharp contrast, dwellings in KRC follow a shaded structure consisting of six dwellings under one roof but separated by brick wall or corrugated steel sheet. KRC began informally in 1991, making it the oldest of all camps and its dwellers somewhat self-sufficient. A lot more permanent structure e.g., wooden furnitures, bed, wardrobe, showcase has been found here, increasing its fuel load larger than that of Camp 4 Ex and 5.</li> </ul>
GR-RWL1-O15J16 Daily refugee arrivals and weather data Greece Oct2015-Jan2016
<p>This package contains a set of data regarding daily refugee arrivals at the Greek islands of first-reception in teh Aegean Sea, for the most intense period of influx waves, from the beginning of October 2015 until the mid-January of 2016.</p> <p>The sources of the data are:<br /> 1) For daily arrivals:<br /> UNHCR Refugees/Migrants Emergency Response (Data mashups)<br /> http://data.unhcr.org/mediterranean/country.php?id=83<br /> 2) For weather:<br /> Searchable Weather Database - National Observatory (Greece)<br /> http://meteosearch.meteo.gr/</p> <p>The datasets from (1) have already been used in various publications describing<br /> such predictive analytics models. Detailed description and related conclusions<br /> can be found at:</p> <p>* Harris V. Georgiou, "Identification of refugee influx patterns in Greece via<br /> model-theoretic analysis of daily arrivals" (9-May-2016),<br /> https://arxiv.org/abs/1605.02784</p> <p>_______________________________________________<br /> AVAILABLE FILE FORMATS</p> <p>The datasets are available in the following formats (included):</p> <p>*.xlsx : MS-Excel/LibreOffice native spreadsheets<br /> *.csv : comma-separated plaintext spreadsheets<br /> *arff : WEKA native data source (plaintext)</p> <p>These data formats are equivalent, i.e., they contain the exact same sets of data. Normally, at least one of them should be compatible with any major programming platform (e.g. Matlab, Octave, R) or any native programming language for arbitrary handling (e.g. C, Java).</p>
International Protection for Refugees - World Map
<p>https://infchg.github.io/ProtecInt.html shows a map of current refugees asking asylum due to the situation in their countries of origin, the sites shows a live map.</p>
Business load profiles used in "Maximising the benefits of renewable energy infrastructure in displacement settings: Optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda"
<p>Version used in the submission of "Maximising the benefits of renewable energy infrastructure in displacement settings: Optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda" by Hamish Beath, Javier Baranda Alonso, Richard Mori, Ajay Gambhir, Jenny Nelson and Philip Sandwell.</p>
Changes in the use of 'refugees' and 'migrants' in international news media and academic publications 2010–2022
<p>The dataset contains source data for Figure 2 in the article, based on search results from Factiva and Web of Science.</p>
Dataset for High Self-Selection of Ukrainian Refugees into Europa: Evidence from Kraków and Vienna
<p>This dataset contains selected columns of the pooled datasets from the UkrAiA and UkrPL surveys (N_combined=1,566) conducted in 2022 in csv, R and STATA dta file formats. The selected variables accompany the paper "High Self-Selection of Ukrainian Refugees into Europa: Evidence from Kraków and Vienna" published in PLOS ONE. Due to data confidentiality reaons, some variables were grouped (reflected in the variable name: _gr). Furthermore, questions reporting details of partners (husbands / wives) and children were de-coupled from the respondents' records and added as additional cases. Thus, the majority of the cases in the dataset (N_relatives=2,479) contain only information on the few fields that respondents had reported about these closest relatives.</p><p>The research projects Ukrainian Arrivals in Austria (UkrAiA) and Ukrainian Arrivals in Poland (UkrPL) aimed to shed light on Ukrainian displaced persons in Austria and Poland who left their homes due to the Russian war of aggression. These two projects sought to establish an evidence base for understanding the needs and resources of displaced individuals in the areas of integration, education, labour market and housing. They were led by researchers from the Vienna University of Economics and Business (WU) and the Austrian Academy of Sciences (OeAW) in Austria and from the Multiculturalism and Migration Observatory (MMO), as well as the Centre for Advanced Studies of Population and Religion (CASPAR) at the Cracow University of Economics in Poland. </p><p>The UkrAiA survey was a rapid-response survey and provided the first reliable data on Ukrainian displaced persons in Austria. The field phase took place between April and June 2022, during the early stages of the war. Data collection was carried out using a multi-mode approach (PAPI and CAWI) following convenience sampling. The final sample consisted of N=1,094 Ukrainian individuals aged 18 and above. The survey design was approved by the ethics committee of the Vienna University of Economics and Business and follows the university's as well as international refugee studies' ethical guidelines. During the field phase of the survey financial support was provided by the City of Vienna and the Vienna Social Fund. Furthermore, the University of Applied Sciences Salzburg supported the CAWI design. </p><p>Similar to Austria, the field phase of the UkrPL survey in Poland took place between May and June 2022. The final sample consisted of N=472 Ukrainian individuals aged 18 and above. In addition to the items included in the UkrAiA survey, the UkrPL survey also incorporated a set of questions assessing refugees' perception of various elements of the reception system as well as actors involved in the support of Ukrainian refugees. The survey design was approved by the ethics committee of the Cracow University of Economics and the field phase of the survey was supported financially by the Cracow University of Economics and the Multiculturalism and Migration Observatory. </p>
Self-efficacy and Knowledge (SEEK) Trial to Improve Sexual Reproductive Health and Well-being for Syrian Refugee Women and Girls in Lebanon
ClinicalTrials.gov study NCT07008950. IPD Sharing: NO. Countries: 1. Publications: 1.
Implementing and Evaluating a Social-Emotional Learning Program for Refugee Children During the COVID-19 Pandemic
ClinicalTrials.gov study NCT04931888. IPD Sharing: NO. Countries: 1. Publications: 1.
Early Community Client-LED ART Delivery in Nakivale Refugee Settlement
ClinicalTrials.gov study NCT04736316. IPD Sharing: NO. Countries: 1. Publications: 0.
Self-Help Plus to Enhance Early Development: A Cluster-Randomized Controlled Trial of Maternal Mental Health, Child Cognitive Abilities, and Socio-Behavioral Skills Among South Sudanese Refugee Mother
ClinicalTrials.gov study NCT07062341. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
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.