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167 results for “Disasters”
Dataset on Weather-related disasters in agriculture in Italy - WDA
<h1><strong>Abstract</strong></h1> <p>The dataset is the supplementary material for the following journal paper:</p> <p>Pontrandolfi A, Alilla R, De Natale F, Nuti R, Parisse B, Pepe AG, Dataset on Weather-related Disasters in Agriculture (WDA) in Italy 2005–2021, Data in Brief <br><a href="https://doi.org/10.1016/j.dib.2025.111323">https://doi.org/10.1016/j.dib.2025.111323</a></p> <p>The database on Weather-related disasters in agriculture (WDA) is a part of the cloud storage which hosts the materials of the <a href="https://agrometeo.crea.gov.it/">Observatory for agricultural meteorology and climatology</a> of the Research Center for Agriculture and Environment belonging to the Council for Agricultural Research and Economics (CREA). The Observatory website has a specific section devoted to <a href="https://agrometeo.crea.gov.it/dati-e-analisi__trashed/rischio-meteorologico-in-agricoltura/">weather-related risk in agriculture</a>.</p> <p>A specific relational SQL database has been created fo data entry information from the official decrees of WDA declaration in Italy.</p> <p>From this relational SQL database, a <strong>dataset </strong>of WDA has been extracted for the period from 2005 to 2021 and here published</p> <p>The WDA dataset aims to make available useful data for weather-related risk assessment and analysis in the Italian agricultural sector.</p> <h2>Attached content:</h2> <ul> <li>pdf file "A_Description_Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>csv file "Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>csv file "DiscoveryMD_Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>xlsx file "StructuralMD_Dataset_Weather_related_disasters_agriculture_v1.2"</li> </ul>
DISASTER database on hydro-geomorphologic disasters in Portugal
<p>In the last century, Portugal was affected by several natural disasters of hydrogeomorphologic origin that often caused high levels of destruction. However, data on past events related to floods and landslides were scattered. The DISASTER project created a consistent and validated hydro-geomorphologic database for Portugal, by creating, disseminating and exploiting a GIS database on disastrous floods and landslides for the period 1865–2010, further updated until 2020.</p> <p>Data collection was steered by the concept of disaster used within the DISASTER project. Therefore, any hydro-geomorphological case is stored in the database if the occurrence led to fatalities or injuries, and missing, evacuated or displaced people, independently of the number of people affected.</p> <p>The sources of information are 16 national, regional and local newspapers that implied the analysis of 145,344 individual newspapers. The hydro-geomorphologic occurrences were stored in a database containing two major parts: the characteristics of the hydro-geomorphologic case and the corresponding damages. We provide the main results of the DISASTER database for the public.</p> <p>Further details about the data collection and exploitation can be found in the following paper: </p> <p>Zêzere, J.L., Pereira, S., Tavares, A.O. <em>et al.</em> DISASTER: a GIS database on hydro-geomorphologic disasters in Portugal. <em>Natural Hazards</em> <strong>72</strong>, 503–532 (2014). https://doi.org/10.1007/s11069-013-1018-y</p> <p>We provide a shapefile with the location of the hydro-geomorphological hazard for the period 1865-2020 for mainland Portugal and additional details about the hydrogeomorphological hazard type and subtype, date of occcurrence, year, month, day, hour, georeferencing quality, source, source date, source type, page in the source, number of human damages (fatalities, injured, evaciated, displaced and missing people), district, municipality and parish.</p> <p>Also, we provide a word document with the database codes description.</p>
Open database to support forensic investigation of disasters in South East Asia: FORINSEA v1.0
<p>Forensic investigation of disasters (FORIN) is a conceptual framework and research guide that focuses on the investigation of root causes of disaster risk and occurrence. Underlying the FORIN conceptual framework is the understanding that historical processes, operating asynchronously at different spatial and temporal scales, configure the specific circumstances in which disasters occur. An objective of FORIN research is to accumulate lessons and experience in a systematic way that can lead to improved choices in the future. FORINSEA aims to support this objective by providing an open database suitable to FORIN enquiries in South East Asia region. FORINSEA1.0 provides a comprehensive and coherent historical record of disasters, from 1945 until 2020, socio-economic policies and development of key infrastructure at the hydrological catchments of the Red River Delta in Vietnam and the Marikina river basin in the Philippines. The FORINSEA1.0 dataset allows researchers, for the first time, to explore and make use of geocoded data on major disasters affecting the two large and rapidly expanding cities of Hanoi and Metro Manila and their catchment areas.</p>
Rapid Landslide Risk Zoning toward Multi-Slope Units of the Neikuihui Tribe for Preliminary Disaster Management repository
<p> Taiwan features steep terrain and a fragile geology environment accompanied by frequent earthquakes and typhoons annually. Meanwhile, with the booming economy and rapid population growth, activities pivot from metropolises to the Taiwan's suburban and mountain areas. However, for example, the Neikuihui tribe in northern Taiwan evolves landslide disasters during extreme rainfall events. To rapidly examine landslide risk in the tribe area for preliminary disaster management, the well-known principle of Risk, which comprises Hazard, Exposure, and Vulnerability, was carefully adapted to scrutinize 14 slope units around the Neikuihui tribe region. The framework of risk zoning is improved based on the previous quantified findings regarding the inventory of the deep-seated landslides in southern Taiwan. Moreover, the proposed procedures comprehensively assess susceptibility, activity, exposure, and vulnerability of each slope unit. The rapid risk zoning analysis of multi-slope units delivers a sloping unit with a high level of landslide risk, and this slope unit did suffer from landslide disasters in the 2016 typhoon event. This study preliminarily proves that the proposed framework and details of rapid risk zoning can help identify a relatively high-risk slope unit around a tribal region and address pre-countermeasures for disaster management.</p>
Two-wave Post-Disaster Survey on Climate Change Attitudes: Texas after Hurricane Harvey and the 2021 North American Winter Storms
<p><strong>Overview</strong></p> <p>This repository contains data needed to reproduce the analysis results from Chen et al. 2024. "Disaster Experience Mitigates the Partisan Divide on Climate Change: Evidence from Texas," <em>Global Environmental Change</em>. It is a study about climate change attitudes and experience with climate disasters across U.S. partisan groups. For details about the data, please see the published paper. Results reproduction code is available at <a href="https://github.com/tedhchen/floodStorm" target="_blank" rel="noopener">https://github.com/tedhchen/floodStorm</a>.</p> <p> </p> <p><strong>Data Set Details</strong></p> <p>`texas_climate_attitudes.csv` contains data from two waves of surveys of Democrats and Republicans living in Texas, with the following groups of variables.</p> <ul> <li>climate change attitudes</li> <li>self-reported exposure to climate disasters</li> <li>scientific information treatment condition and checks</li> <li>political leaning</li> <li>sociodemographics and residential location</li> <li>survey administration details</li> </ul> <p>`outage2021_data.RData` contains power outage data for counties and cities in Texas during Feb. 2020 and Feb. 2021.</p> <p>`outage2021_data_multithreshold.RData` contains power outage data for counties and cities in Texas during Feb. 2020 and Feb. 2021, aggregated to the county level based on different thresholds of uncertainty about which cities people live in.</p> <p>`gtrends_archive.RData` contains Google Trends data for "hurricane", "astros", and "power", in Texas between 2017 and 2021.</p> <p> </p> <p><strong>References</strong></p> <p>Please reference the original study when using this data set.</p> <p>Ted Hsuan Yun Chen, Christopher J. Fariss, Hwayong Shin, Xu Xu. 2024. "Disaster Experience Mitigates the Partisan Divide on Climate Change: Evidence from Texas." <em>Global Environmental Change</em>. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102918" target="_blank" rel="noopener">doi:10.1016/j.gloenvcha.2024.102918</a>.</p>
Respondents' perspectives on the impact of digital data-based health services on disaster risk management in Indonesia.
<p>This data contains respondents' perspectives on the impact of digital data-based health services on disaster risk management. Digital health services are the implementation of digital, information, and communication technologies in the context of health services. Digital health services include: mHealth, Health Information Technology, Wearable Devices, Telehealth and Telemedicine, and Personalized Medicine. </p> <p>Data was collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381) would be advisable.</p>
TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision
<p>This repository contains the silver standard dataset and code for the paper "TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision".</p> <p>The file "heuristic_uniq_terms_nd.txt" contains the list of terms used as the heuristic and the file "natural_disasters_ssd_tweetids.tsv" contains the tweet ids in the silver standard dataset. </p> <p>To hydrate the tweets, you can use tools like twarc or Social Media Mining toolkit - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362951/</p>
Worldwide CO2 emissions and natural disasters from 1960 to 2021
<p>A CSV file containing worldwide CO2 emissions as well as the number of natural disasters per year.</p> <p>Sources:</p> <ul> <li>Global Carbon Atlas <ul> <li>DOI: <a href="http://doi.org/10.17616/R3434K">http://doi.org/10.17616/R3434K</a></li> <li>URL: <a href="http://www.globalcarbonatlas.org/en/CO2-emissions">http://www.globalcarbonatlas.org/en/CO2-emissions</a></li> <li>Last accessed: 2023-05-09</li> </ul> </li> <li>EM-DAT <ul> <li>DOI: <a href="http://doi.org/10.17616/R3QQ1X">http://doi.org/10.17616/R3QQ1X</a></li> <li>URL: <a href="https://public.emdat.be/data">https://public.emdat.be/data</a> (registration necessary)</li> <li>Last accessed: 2023-05-14</li> </ul> </li> <li>GitHub Project <ul> <li>DOI: <a href="http://doi.org/10.5281/zenodo.7934702">http://doi.org/10.5281/zenodo.7934702</a> </li> <li>URL: <a href="https://github.com/jkopec/global-emission-and-disaster-analysis">https://github.com/jkopec/global-emission-and-disaster-analysis</a></li> </ul> </li> </ul>
Actionable Information During a Disaster (Self-organize Relief Efforts via #PorteOuverte)
<p><strong>Abstract</strong> (our paper)</p> <p>Web-based social and communication technologies enable citizens to self-organize relief efforts in response to crises. This work focuses on a question fundamental to the concept of collective intelligence: how effective are such self-organized channels, ungoverned by any central authority, in conforming to their intended function? In this study we examine the hashtag #PorteOuverte ("#OpenDoor") introduced during the 2015 Paris terrorist attacks, as an "improvised logistical channel" (ILC) to help individuals to find a safe shelter near the attack sites. We analyze the dynamics and effectiveness of #PorteOuverte by comparing its proportion of relevant logistical messages -- individuals requesting or offering shelter -- to other messages such as those offering emotional consolation or commenting on the hashtag itself. Our results reveal that the vast majority of messages are not relevant, however the crowd senses and spreads relevant messages more than others. We further demonstrate that relevant messages can be automatically detected and thus algorithmic promotion may be possible.</p> <p><strong>Data</strong></p> <p>The #PorteOuverte hashtag ("opendoor" in English), created right after the 2015 terrorist attacks in Paris, was used by individuals to offer shelter to strangers stranded by the attacks and by individuals in need of shelter to request help and post their whereabouts. The file #PorteOuverte _tweet_ids.txt contains all the original tweet ids that used this hashtag.</p> <p>The first tweet was posted on Friday, 13 Nov 2015 21:34:06 GMT.</p> <p>Duration: 2015-11-13 to 2015-11-16 (retweets not included).</p> <p>Total number of tweets: 75547</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:</p> <p>He, X., Lu, D., Margolin, D., Wang, M., Idrissi, S., Lin, Y.-R. (2017). "The Signals and Noise: Actionable Information in Improvised Social Media Channels During a Disaster," Proceedings of Web Science 2017 (WebSci 2017), 2017. doi:10.1145/3091478.3091501</p>
Global MVL loss map-induced by human expansions and natural disasters; Global mountain-PAs; Global AHRTMS
<p><span lang="EN-US">(1) Global MVL loss map-induced by human expansions and natural disasters</span></p> <p><span lang="EN-US">Global MVL loss map: a global mountain vegetated landscapes (MVL) loss map (during 2000-2020) at 30-m resolution was developed using global datasets on mountain boundaries, human land use, natural disasters together with Landsat imageries-derived NDVI. This map includes seven drivers that cause MVL loss (i.e. human expansions and natural disasters). The losses of MVL caused by human expansions include (i) human settlement growth, (ii) agriculture expansion, and (iii) mining. The losses of MVL caused by natural disasters (i.e. a net loss after deducting restored areas in disaster areas) include (vi) wildfires, (v) floods, (vi) landslides, and (xii) droughts. The data was stored in Global MVL loss map.gdb and can be opened through mxd file in ArcGIS software.</span></p> <p><span lang="EN-US">(2) Global mountain-PAs; </span></p> <p><span lang="EN-US">Global mountain PAs: the mountain-protected areas (PAs) was mapped using World Database of Protected Areas (WDPA) and GMBA mountain boundaries (<a name="OLE_LINK1"></a>v2.0 standard). The data was stored in</span><span lang="EN-US"> </span><span lang="EN-US">Global mountain-PAs.gdb and can be opened through ArcGIS software.</span></p> <p><span lang="EN-US">(3) Global AHRTMS</span></p> <p><span lang="EN-US">Global AHRTMS: the areas with high richness of threatened mountain-occurring species (AHRTMS) was produced with IUCN Red List threatened species (including mammals, amphibians, reptiles, birds and plants) and GMBA mountain boundaries (v2.0 standard). The data was stored in</span><span lang="EN-US"> </span><span lang="EN-US">Global AHRTMS.gdb and can be opened through ArcGIS software.</span></p> <p><strong><span lang="EN-US">A manuscript related to above data analysis has submitted to a journal.</span></strong></p>
Datasets supporting the paper 'Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador'
<p>Datasets supporting the paper 'Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador'</p> <p>Contact: C. Scott Watson. c.s.watson@leeds.ac.uk</p> <p>DRR_greenspace<br> DRR_greenspace_polygon.shp - classified potential DRR greenspace (minimum 100 m2)<br> DRR_greenspace_zone_points.shp - classified potential DRR greenspace aggregated to zones<br> DRR_greenspace_zone_polygons_top10.shp - Top 10 maximum capacitated analysis of classified potential DRR greenspace aggregated to zones.<br> <br> Land_cover<br> rf_1986_mode_clipped.tif - 1986 land cover classification<br> rf_2020_mode_clipped.tif - 2020 land cover classification<br> landcover_classes.PNG - land cover classes<br> accuracy_assessment_points_1986 - 1986 land cover accuracy assessment points<br> accuracy_assessment_points_2020 - 2020 land cover accuracy assessment points<br> modified_urban_growth_scenario.shp - hazard-modified urban growth scenario</p> <p> </p>
Thermal demagnetization data of Risica et al. (Deposit-derived block-and-ash flows: the hazard posed by perched temporary tephra accumulations on volcanoes; 2018 Fuego disaster, Guatemala)
<p>Thermal demagnetization data (repository data) of Risica et al. "Deposit-derived block-and-ash flows: the hazard posed by perched temporary tephra accumulations on volcanoes; 2018 Fuego disaster, Guatemala".</p>
Mulberry Disasters in Chinese Local Gazetteers
<p>This dataset contains 404 mulberry disasters found in a digital collection of 4,000 Chinese local gazetteers (published in Erudition's Zhongguo Fangzhi Ku, or the Database of Chinese Local Gazetteers) that was curated during 2018 and 2019 by scholars at the Max Planck Institute for the History of Science to support their joint research paper entitled: “What Is Local Knowledge: Digital Humanities and Yuan Dynasty Disasters in Imperial China’s Local Gazetteers.” The paper appeared in the <em>Journal of Chinese History</em> in its 2020 spring issue. The authors use this dataset to ask what results the emerging methodology of analyzing data drawn from historical sources can produce for historical research, given that such data-driven analysis is inevitably quantitative and that many historians believe this would contradict the core value of studies in the humanities.</p> <p>This dataset is accompanied by a data paper to be published by Brill's Digital Concordances Platform. In the data paper, the authors give information about the full curation cycle of this dataset, including the research goal that motivated the curation, the sources that were used, and the curation/cleansing/preparation process. The authors also explain this dataset in detail, including the kind of information it contains and its overall temporal and geospatial distributions. In conclusion, they discuss the potential usages of the dataset. With this paper, we also hope to offer an exemplary data curation workflow for collecting data from historical sources that includes not only data curation (whether manual or semiautomatic), but also the practical steps of data cleansing and normalization that reflect the various historical considerations in the process.</p>
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
<p>Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based on social media contents (text and images). While notable progress has been made using texts, research on exploiting the images remains relatively under-explored. To advance image-based approaches, we propose MEDIC\footnote{Available~at: \url{https://crisisnlp.qcri.org/medic/index.html}}, which is the largest social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. This is the first dataset of its kind: social media images, disaster response, and multi-task learning research. An important property of this dataset is its high potential to facilitate research on \textit{multi-task learning}, which recently receives much interest from the machine learning community and has shown remarkable results in terms of memory, inference speed, performance, and generalization capability. Therefore, the proposed dataset is an important resource for advancing image-based disaster management and multi-task machine learning research. <br> </p>
Datasets for input and output of INFORM Severity-based SMAA study of resource allocation in humanitarian aid and disaster management under climatic losses and damages
<p>The landscape of climate change and extreme events will remain a wicked problem for equitable and forward-looking resource prioritisation. The question of how to couple climate and multi-risk information remains. IPCC has considered that multi-criteria decision analysis (MCDA) can help.</p> <p>We use stochastic multi-attribute analysis (SMAA), a variant of MCDA, to compute prioritisations of climatic losses & damages (l&d) for fragile countries with a humanitarian response plan. SMAA is combined with the INFORM Severity index, measuring the status of crises and disasters, and preferences gathered from stakeholders (e.g., United Nations, European Union, World Bank, the research and public sector, civil society).</p> <ul> <li><strong>Dataset S1. </strong>XLS-file with all the input data compiled from sources, concurrent data manipulation, and descriptions of steps taken until ready for the SMAA.</li> <li><strong>Dataset S2.</strong> XLS-file with results of the SMAA for all weight schemes and concurrent analysis, such as sensitivity heat mapping, correlations, regressions, and Tukey mean-difference plot.</li> </ul>
Dataset: Procure Disaster Recovery Strategy ETF (FIXT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Assessment of risk and vulnerability to disasters by field protocols and classification trees: an analysis of the Municipal Risk Reduction Plans (MRRP) of São Bernardo do Campo and Franco da Rocha (2020-2021), in Brazil
<p><span>Most disaster risk assessment methodologies were developed by natural science professionals. The UN Sendai Framework confirms the need to include these methods as interweaving complex networks of socially vulnerable financial processes, especially in developing countries. This work aimed to analyze the experience of including social vulnerability information in field protocols of the Municipal Risk Reduction Plans (MRRP) of São Bernardo do Campo and Franco da Rocha (2020-2021). A georeferenced database was structured with data from field protocols on risk sectors and then the relationship between vulnerability and risk was analyzed using the classification tree method. The results show that the geotechnical and social vulnerability variables relevant to risk attribution are largely overlapping over the sectors, but that the integrated analysis of the two dimensions allows a better interpretation of the risk level. However, the models with information from field protocols cannot replace a broader holistic assessment by the specialist in the field. Finally, qualitative reflections are made on the limitations and potential of including aspects of vulnerability in the specific case study.</span></p>
Results and figures from "Evaluating the robustness of the ARIO model for a local disaster: 2021 Flooding in Germany"
<p>These files and notebook allow reproducing the figures from "Evaluating the robustness of the ARIO model for a local disaster: 2021 Flooding in Germany".</p> <p>The main result file is a pandas DataFrame saved in parquet format under "results/general-plot_df.parquet".</p> <p>Two notebooks allow post-processing these results and plot the figures.</p> <p>Reproduction of the raw results can be achieved with the Snakemake pipeline, available here: https://github.com/spjuhel/BoARIO-Sensitivity</p> <p> </p>
Interpersonal Support (ISEL) in Hungarian communities experienced past disasters
<p>The dataset was generated from answers collected among citizens living in Hungarian Settlements that experienced natural disasters in the recent decades. The basis of the dataset is the Interpersonal Support Evaluation List, extended with questions related to the disaster experiences, as well as social and demographic background.</p>
Worldwide CO2 emissions and natural disasters from 1960 to 2021
<p>A PDF file containing a plot visualizing worldwide CO2 emissions as well as the number of natural disasters per year.</p> <p>Sources:</p> <ul> <li>Global Carbon Atlas <ul> <li>DOI: <a href="http://doi.org/10.17616/R3434K">http://doi.org/10.17616/R3434K</a></li> <li>URL: <a href="http://www.globalcarbonatlas.org/en/CO2-emissions">http://www.globalcarbonatlas.org/en/CO2-emissions</a></li> <li>Last accessed: 2023-05-09</li> </ul> </li> <li>EM-DAT <ul> <li>DOI: <a href="http://doi.org/10.17616/R3QQ1X">http://doi.org/10.17616/R3QQ1X</a></li> <li>URL: <a href="https://public.emdat.be/data">https://public.emdat.be/data</a> (registration necessary)</li> <li>Last accessed: 2023-05-14</li> </ul> </li> <li>GitHub Project <ul> <li>DOI: <a href="http://doi.org/10.5281/zenodo.7934702">http://doi.org/10.5281/zenodo.7934702</a> </li> <li>URL: <a href="https://github.com/jkopec/global-emission-and-disaster-analysis">https://github.com/jkopec/global-emission-and-disaster-analysis</a></li> </ul> </li> </ul>
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.