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20 results for “natural disasters”
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>
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>
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>
Experimental Data for Natural Disaster Mobility Model and Typhoon Haiyan Scenario
<p>The experimental data set for running the <em>Typhoon Haiyan</em> scenario with the <em>Natural Disaster Mobility Model</em> presented in the paper:</p> <p>Milan Stute, Max Maass, Tom Schons, and Matthias Hollick, “<strong>Reverse Engineering Human Mobility in Large-scale Natural Disasters</strong>,” to appear in <em>ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)</em>, November 2017, Miami Beach, USA.</p>
Introgression dynamics from invasive pigs into wild boar following the March 2011 natural and anthropogenic disasters at Fukushima
<p>Natural and anthropogenic disasters have the capability to cause sudden extrinsic environmental changes and long-lasting perturbations including invasive species, species expansion, and influence evolution as selective pressures force adaption. Such disasters occurred on March 11th 2011, in Fukushima, Japan when an earthquake, tsunami, and meltdown of a nuclear power plant all drastically reformed anthropogenic land use. Here, we demonstrate, using genetic data, how wild boar (<em>Sus scrofa leucomystax</em>) have persevered against these environmental changes, including an invasion of escaped domestic pigs (<em>Sus scrofa domesticus</em>). Concurrently, we show evidence of successful hybridization between pigs and native wild boar in this area, however in future offspring, the pig legacy has been diluted through time. We speculate that the range expansion dynamics inhibit long-term introgression and introgressed alleles will continue to decrease at each generation while only maternally inherited organelles will persist. Using the gene flow data among wild boar, we assume that offspring from hybrid lineages will continue dispersal north at low frequencies as climates warm. We conclude that future risks for wild boar in this area include intraspecies competition, revitalization of human related disruptions, and disease outbreaks.</p>
Natural Disaster Trends
<p><strong>Data fields</strong></p> <ul> <li> <p><code>Disaster_Group</code>: EM-DAT stores different types of disasters: natural, technological and complex. The dataset has been filtered to contain only natural disasters so this column is "Natural" for all rows but kept for compatibility reasons.</p> </li> <li> <p><code>Disaster_Subgroup</code>: Every natural disaster is assigned to one of the following six subgroups: <em>Biological, Geophysical, Climatological, Hydrological, Meteorological and Extra-terrestrial</em> to describe the type of natural disaster. No missing values are present for this attribute.</p> </li> <li> <p><code>Disaster_type</code>: For every natural disaster event one main disaster type is identified. If two or more disasters are related because they are consequences of each other, then this information is encoded in the attributes <code>Associated_Dis</code> and <code>Associated_Dis2</code>. No missing values are present for this attribute.</p> </li> <li> <p><code>Disaster Sub-Type</code>: Subdivision related to the attribute <code>Disaster_type</code> so that a the disaster type Storm can be further classified as tropical, extra-tropical or convective storm.</p> </li> <li> <p><code>Disaster Sub-Sub Type</code>: Any appropriate sub-division of the disaster sub-type (not applicable for all disaster sub-types). Types of natural disasters could be further broken down using two more categories which would be available in the database. For example, the Disaster type <em>Storm</em> could be further subdivided into <em>Tropical storm</em>, <em>Extra-tropical storm</em> or <em>Convective storm</em>. Even a further subdivision of the category <em>Convective storm</em> would be possible. Since the analysis is aimed at detecting trends on a high level, the classification of each event based on the attributes <code>Disaster_Subgroup</code> and <code>Disaster _Type</code> was considered sufficient and the further subdivisions into <code>Disaster sub-type</code> and <code>Disaster Subsubtype</code> is only intended to be considered for detailed analysis. The full table is shown in the Appendix.</p> </li> <li> <p><code>Associated_Dis</code>: Secondary event triggered by a natural disaster (i.e. Landslide for a flood, explosion after an earthquake, ...)</p> </li> <li> <p><code>Associated_Dis2</code>: Another secondary event triggered by a natural disaster. (i.e. Landslide for a flood, explosion after an earthquake, ...)</p> <p>Example: If a tsunami is triggered by an earthquake, then the attribute <code>Disaster_Type</code> would be <em>Earthquake</em>, the attribute <code>Disaster_Subtype</code> would be <em>Ground movement</em> and the attribute <code>Associated_Dis</code> would be <em>Tsunami/Tidal wave</em>.</p> </li> <li> <p><code>Country</code>: The country in which the disaster has occurred or had an impact. If a disaster has affected more than one country, a seperate entry is created in the database for each country affected. No missing values are present for this attribute.</p> </li> <li> <p><code>ISO</code>: Unique 3-letter code for each country defined by ISO 3166. No missing values are present for this attribute.</p> </li> <li> <p><code>Region</code>: The region to which the country belongs, based on the UN regional division. No missing values are present for this attribute.</p> </li> <li> <p><code>Continent</code>: The continent to which the country belongs. No missing values are present for this attribute.</p> </li> <li> <p><code>Start_Year</code>: The year when the disaster occurred. No missing values are present for this attribute.</p> </li> <li> <p><code>End Year</code>: The year when the disaster ended. No missing values are present for this attribute. For sudden-impact disasters also the month and the day are well defined and available. For disaster situations developing gradually over a longer time period (i.e. drought) with no specific start date the day attribute is empty. For our questions the exact date plays a subordinate role and therefore the year of the beginning of the disaster is completely sufficient for our analysis.</p> </li> <li> <p><code>Total_Deaths</code>: Number of people who lost their life because the event happened plus the number of people whose whereabouts since the disaster are unknown, and presumed dead based on official figures. Missing values present for approx. 25% of all events.</p> </li> <li> <p><code>No_Affected</code>: Number of people which requiring immediate assistance during an emergency situation. The indicator affected is often reported and is widely used by different actors to convey the extent, impact, or severity of a disaster in non-spatial terms. In case that no values for the attribute <code>Total_Deaths</code> are available this attribute could be used as a proxy.</p> </li> </ul> <p><strong>Appendix</strong></p> <table> <tbody> <tr> <td> <p><strong>Disaster </strong></p> <p><strong>Group</strong></p> </td> <td> <p><strong>Disaster </strong></p> <p><strong>Sub-Group</strong></p> </td> <td> <p><strong>Disaster </strong></p> <p><strong>Type</strong></p> </td> <td> <p><strong>Disaster </strong></p> <p><strong>Sub-Type</strong></p> </td> <td> <p><strong>Disaster </strong></p> <p><strong>Sub-Sub Type</strong></p> </td> </tr> <tr> <td> <p><strong>Natural</strong></p> </td> <td> <p>Geophysical</p> </td> <td> <p>Earthquake</p> </td> <td> <p>Ground movement</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Tsunami</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Volcanic activity</p> </td> <td> <p>Ash fall</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Lahar</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Pyroclastic flow</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Lava flow</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Mass Movement</p> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Meteorological</p> </td> <td> <p>Storm</p> </td> <td> <p>Tropical storm</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Extra-tropical storm</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Convective storm</p> </td> <td> <p>Derecho</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Hail</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Lightning/thunderstorm</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Rain</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Tornado</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Sand/dust storm</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Winter storm/blizzard</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Storm/surge</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Wind</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Severe Storm</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Extreme Temperature</p> </td> <td> <p>Cold wave</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Heat Wave</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Severe winter conditions</p> </td> <td> <p>Snow/ice</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Frost/freeze</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Fog</p> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Hydrological</p> </td> <td> <p>Flood</p> </td> <td> <p>Coastal flood</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Riverine flood</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Flash flood</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Ice jam flood</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Landslide</p> </td> <td> <p>Avalanche (snow, debris, mudflow, rock fall)</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Wave action</p> </td> <td> <p>Rogue wave</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Seiche</p> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Climatological</p> </td> <td> <p>Drought</p> </td> <td> <p>Drought</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Glacial Lake outburst</p> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Wildfire</p> </td> <td> <p>Forest fires</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Land fire: Brush, bush, pasture</p> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Biological</p> </td> <td> <p>Epidemic</p> </td> <td> <p>Viral diseases</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Bacterial diseases</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Parasitic diseases</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Fungal diseases</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Prion diseases</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Insect Infestation</p> </td> <td> <p>Locust</p> <p>Grasshopper</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Animal accident</p> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Extra-terrestrial</p> </td> <td> <p>Impact</p> </td> <td> <p>Airburst</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Space weather</p> </td> <td> <p>Energic particles</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Geomagnetic storm</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Shockwave</p> </td> <td> </td> </tr> </tbody> </table> <p>Sourced from https://public.emdat.be/about.</p>
Data for: Spatial-temporal analysis of natural hazards and disasters in the Greater Horn of Africa between 2010 and 2024 to inform disaster risk reduction, and surveillance and control strategies for climate and environmentally sensitive diseases
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Introgression dynamics from invasive pigs into wild boar following the March 2011 natural and anthropogenic disasters at Fukushima
Open the record for dataset details and reuse information.
Data from: Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disaster
Natural disasters pose serious threats to large urban areas, therefore understanding and predicting human movements is critical for evaluating a population's vulnerability and resilience and developing plans for disaster evacuation, response and relief. However, only limited research has been conducted into the effect of natural disasters on human mobility. This study examines how natural disasters influence human mobility patterns in urban populations using individuals' movement data collected from Twitter. We selected fifteen destructive cases across five types of natural disaster and analyzed the human movement data before, during, and after each event, comparing the perturbed and steady state movement data. The results suggest that the power-law can describe human mobility in most cases and that human mobility patterns observed in steady states are often correlated with those in perturbed states, highlighting their inherent resilience. However, the quantitative analysis shows that this resilience has its limits and can fail in more powerful natural disasters. The findings from this study will deepen our understanding of the interaction between urban dwellers and civil infrastructure, improve our ability to predict human movement patterns during natural disasters, and facilitate contingency planning by policymakers.
simulation scenes of natural disasters and highway constructions in Unreal Engine 4
<p>This is a zip file which contains two simulation scenes built in UE4. One shows natural disasters' impacts on surrounding objects. Another shows highway constructions' impacts on surrounding objects.</p>
Data and Code for "Mapping of Global Research Trend on GIS in Natural Disasters: A Bibliometric Approach during 2004-2023"
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Coda and data for "A natural disaster exacerbates and redistributes disease risk across free-ranging macaques by altering social structure"
<p>This Zenodo repository contains the data and code for the article entitled "<strong>A natural disaster exacerbates and redistributes disease risk across free-ranging macaques by altering social structure</strong>". </p>
Data from: Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disaster
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Dataset-From a natural disaster to a geothermal opportunity: the Lusi geothermal system investigated with Ambient Noise Attenuation Tomography
<p>Dataset and results of the Ambient Noise Attenuation Tomography of Lusi volcano.</p>
DNA methylation signatures triggered by prenatal maternal stress exposure to a natural disaster: Project Ice Storm
GEO Series GSE72354. Homo sapiens. 34 samples. Type: Methylation profiling by genome tiling array.
Analysis of Emergency Department (ED) Volumes During Natural Disasters
ClinicalTrials.gov study NCT01059981. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Mid-Term Follow-up Assessment After Exposure to Natural Disaster
ClinicalTrials.gov study NCT05473962. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Development of Post Traumatic Stress Disorder After Natural Disaster
ClinicalTrials.gov study NCT01661452. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Business grant following natural disasters and its different impact on the performance of female and male-owned microenterprises: Evidence from Sri Lanka
<p><strong><em>Objective</em></strong></p> <p>This paper investigates gender differences in the treatment effects of business grants on firm performance following natural disasters, and seeks to identify the mechanisms underlying the unequal effects.</p> <p><strong><em>Method</em></strong></p> <p>A panel dataset from an experiment in Sri Lanka is used to measure the difference in the treatment effects of a business grant on the performance of female and male-owned firms following the 2004 Indian Ocean tsunami. The sample of 608 microenterprises includes 297 female-owned firms and 311 male-owned firms. There are 338 firms (male = 176, female = 162) in the treatment group that received the grant and 270 firms (male = 135, female = 135) in the control group that did not receive the grant. Data on firm performance, firm characteristics and owner characteristics were collected in 13 survey waves from April 2005 to December 2010. Firm performance, which is measured by firm profit, is assessed by employing linear regression with fixed effects in an intention-to-treat analysis.</p> <p><strong><em>Findings</em></strong></p> <p>The results suggest that the business grant has a positive impact on the performance of male-owned firms, but zero effect on that of female-owned firms. Several potential mechanisms drive the results, including gender differences in business investment, household expenditures and initial business closures. The results also show a positive treatment effect of the business grant on the psychological recovery of recipients, but there is no evidence supporting gender differences in this dimension.</p> <p><strong><em>Contribution</em></strong></p> <p>This paper provides new evidence on gender differences in the treatment effects of business grants on firm performance in the context of post-disasters, and has implications for business recovery programs aimed at supporting female microentrepreneurs in the aftermath of large-scale catastrophes.</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.