Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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EventWiki is a knowledge base of major events happening throughout mankind history. It contains 21,275 events of 95 types. The details of event entries can be found in our paper submission and documentation file. Data in the knowledge base is mainly harvested from Wikipedia.As Wikipedia, this resource can be distributed and shared under CC-BY 3.0 license.
Comprehensive database of historical events, cultural movements, and technological innovations that shaped different generations from 1920s to present
Comprehensive database of event ticket sales data including pricing, zones, quantities, and timestamps for millions of historical ticket transactions across major venues and events. Ideal for analytics, market research, and AI/ML training.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries between 1870 and 2020. The data was collected by extensive data-collection from more than 800 sources ranging from news reports through government databases to scientific papers. The dataset includes 2521 events characterized by at least one impact statistic: area inundated, fatalities, persons affected or economic loss. Economic losses are presented both in the original currencies and price levels as well as inflation and exchange-rate adjusted to 2020 value of the euro. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Daily start and end dates, information on causes of the event, notes on data quality issues or associated non-flood impacts, and full bibliography of each record supplement the dataset. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: https://naturalhazards.eu. The dataset is designed to be complimentary to HANZE-Exposure, a high-resolution model of historical exposure changes (such as population and asset value), and be easily usable in statistical and spatial analyses.
The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders)
HANZE flood events database
HANZE_events.csv - Flood event data
HANZE_references.csv - List of all references
HANZE_events_regions_2010.zip - Flood event data as GIS file (regions v2010)
HANZE_events_regions_2021.zip - Flood event data as GIS file (regions v2021)
Supplementary data
S1_countries_codes_and_names.csv - Country codes/names
S2_regions_codes_and_names_v2010.csv - Region codes/names, v2010
S3_regions_codes_and_names_v2021.csv - Region codes/names, v2021
S4_list_of_all_currencies_by_country.csv - Data on all currencies used in the study area since 1870
S5_currency_conversion_rates.csv - Conversion rates applied to compute losses in 2020 euros
S6_GDP_deflators_by_country.csv - Gross domestic product deflator by country, 1870-2020
S7_floods_removed_from_HANZE.csv - Flood events in HANZE v1, which were excluded from v2
Regions_v2010_simplified.zip - Map of subnational regions used in the database, v2010
Regions_v2021_simplified.zip - Map of subnational regions used in the database, v2021
Note: this is a minor update of the original upload. It corrects the erroneous rendering of NUTS regions for event 2751, fixes some geometry problems with the GIS files and makes some small changes to the flood data (2 events were added and the regional codes for Kosovo in version 2021 were modified based on the upcoming NUTS 2024 classification).
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
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Warning: as of June 2020, this dataset is no longer updated and has been replaced. Please see https://www.donneesquebec.ca/recherche/fr/dataset/evenements-de-securite-civile for data on civil security events since June 2020. This database brings together in a structured way information related to past claims that have been systematically grouped and centralized by the Ministry of Public Security (MSP). The consequences and evolution of the events are documented and they have been categorized according to their level of impact on the safety of citizens, goods and services to the population based on criteria defined in the Canadian profile of the Common Alert Protocol. It is updated continuously by the MSP Operations Department (DO). This database will allow analyses to be carried out at regional and local levels and can be used by municipalities in the implementation of their emergency measures plans. The event history archives come from event reports and status reports that were produced by the Government Operations Center (COG) and by the regional directorates of the MSP. Among other things, it includes: 1- Observations entered directly into the Geoportal by civil security advisers from regional directorates; 2- A compilation of information recorded in COG event reports and DO status reports distributed to MSP partners since 1996; 3- A compilation of information contained in the files of the regional directorates. This may be information on paper, event reports or field visits, paper or digital maps, etc. The information in this database is in accordance with the Canadian Common Alert Protocol Profile (PC-PAC). The PC-PAC is a set of rules and controlled values that support the translation and composition of a message to make it possible to send it by different means and from different sources. The severity level is an attribute defined in the PC-PAC. It is used to characterize the severity level of the event based on the harm to the lives of people or damage to property. This severity level is defined by the following characteristics: Extreme: extraordinary threat to life or property; Important: significant threat to life or property; Moderate: possible threat to life or property; Minor: low or non-existent threat to life or property; Minor: low or non-existent threat to life or property; Unknown: unknown severity, used among other things during tests and exercises. The emergency level is determined based on the reactive measures that need to be taken in response to the current situation. It is defined by the following characteristics: Immediate: a reactive action must be taken immediately; Planned: a reactive action must be taken soon (within the next hour); Future: a reactive action must be taken in the near future; Past: a reactive measure is no longer necessary; Unknown: Unknown: Unknown emergency, to be used during tests and exercises. The state relates to the context of the event, real or simulated. It is defined by the following characteristics: Current: information on a real event or situation; Exercise: fictional or real information carried out as part of a civil security exercise; Test: technical tests only; to be ignored by all. Certainty is defined by the following characteristics: Observed: would have occurred or is currently taking place; Probable: probability of the event happening > 50%; Possible: probability of the event happening < 50%; Unlikely: probability of the event happening around 0%; Unlikely: probability of the event happening around 0%; Unknown: unknown certainty. When an event date was not known, the year 1900-01-01 was recorded. ATTRIBUTE DESCRIPTION: Date of observation: date of the event or observation; Type: name of the hazard; Name: name of the municipality; Municipality code: municipal code; State and certainty: as these are real events, the state is generally “current” and the certainty is generally “observed”; Emergency: the term “past” was generally used for events that occurred before the compilation work was carried out; Inprecision: imprecision: imprecision is generally “observed”; Urgency: the term “past” was generally used for events that occurred before the compilation work was carried out; Inaccuracy: imprecision: imprecision precision in a data (the date of the event, its location, the source of the data or none inaccuracy noted).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The CFTIlandslides, Italian database of historical earthquake-induced landslides by Istituto Nazionale di Geofisica e Vulcanologia (INGV), was developed within the project “Multi-scale, integrated approach for the definition of earthquake-induced landslide hazard in Italy”, funded by the Italian Ministry for the Environment, and completed in 2022. The goal of the project was to develop a multidisciplinary approach for assessing the earthquake-induced landslide hazard at national, regional, and local scales, combining existing databases and integrating them with the results from previous projects and research activities. The main objective was the investigation of the central Apennine region. One of the main scopes of the project was locating more accurately all recorded landslides. To this end we focused on the review of historical sources - either newly found or already archived in our database - and on the analysis of scientific articles and technical reports. In summary, our working group reviewed and integrated data relating to historical earthquake-induced landslides that were already included in the CFTI5Med. The combination of the relatively frequent seismic release with a very high landslide susceptibility, makes the Italian territory especially prone to the occurrence of earthquake-induced landslides. For this reason, over the past two years we continued our activity by reviewing all events included in the CFTI5Med for which landslide effects were reported, then we broadened the investigated area to the entire Italian territory. Data e Risorse Questo dataset non ha dati terremoti
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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The historical events calendar of the YLE Archive is a date database for events of the past. See http://fi.wikipedia.org/wiki/13._marraskuuta
The calendar includes events for years ending at -8, -9, -0, -1. It is no longer updated. The calendar is in Finnish.
One re-use of the opened data could be complementing Wikidata, as it contains a lot of similar information. https://www.wikidata.org/
The Global Historical Tsunami Database provides information on over 2,400 tsunamis from 2100 BC to the present in the the Atlantic, Indian, and Pacific Oceans; and the Mediterranean and Caribbean Seas. The database includes two related files. The first file includes information on the tsunami source such as the date, time, and location of the source event; cause and validity of the source, tsunami magnitude and intensity; maximum water height; the total number of fatalities, injuries, houses destroyed, and houses damaged; and total damage estimate (in U.S. dollars). The second related file contains information on the runups (the locations where tsunami waves were observed by eyewitnesses, reconnaissance surveys, tide gauges, and deep-ocean sensors) such as name, location, arrival time, maximum water height and inundation distance, and socio-economic data (deaths, injuries, damage) for the specific runup location.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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We present the database (DB) of the eruptive activity of Etna from the 6th century BC to present as reconstructed through a detailed review of previous catalogs of Branca and Del Carlo (2005), Guidoboni et al. (2014) and Branca and Abate (2019). Concerning the eruptive activity from 1970 onward, we used different datasets from monitoring reports in addition to the scientific literature. Eruptive activity of Etna is distinguished in two broad categories - summit eruptions, which take place at the summit craters (also called terminal eruptions) and in their vicinity (subterminal eruptions), and flank eruptions, which occur at altitudes from approximately 2800 m to a few hundred metres above sea level. Summit activity is more or less continuous over periods of years or even decades, and occasionally punctuated by powerful explosive episodes (lava fountains and subplinian events). By contrast, flank eruptions take place at irregular intervals, producing lava effusion commonly associated with weak explosive activity. The DB is divided in two parts: (i) from the 6th century BC to the XVI century AD and (ii) from the XVII century to the present since the types and the quality of information available for the compilation of the datasets in these periods are different. In particular, the DB of the eruptions in the period 6th century BC to XVI century AD is mainly based on the stratigraphic, tephrostratigraphic and geochronological datasets of the geological map of the volcano (Branca et al., 2011) and following updates (Tanguy et al., 2012; Branca et al., 2015). In addition, we have integrated the geological data with the original sources extracted from the Guidoboni et al. (2014) catalog for the eruptions of the Lower Middle Age and Modern Age. The DB of the eruptions from the XVII century to the present is based on direct observations and documentation by scientists and volcanologists integrated with volcanological parameters as reported in the literature. The record of flank eruptions for this period is to be considered complete, whereas we cannot exclude some gaps in the information of summit activity up to the first half of the 20th century except for major explosive events. The DB unites in a single source the information thus far distributed in different scientific publications, not all of which are readily accessible to a broad public. Finally, this DB will be open to contributions from other sources and information on the past and ongoing eruptive activity of Etna, and thus continuously updated. Data e Risorse Questo dataset non ha dati vulcani
By US Open Data Portal, data.gov [source]
The National Weather Service (NWS) provides Storm Data, detailing the statistics of personal injuries and damage estimates resulting from numerous types of severe weather events that have occurred in the United States. Compiling records as early as 1950 to the present, Storm Data allows users to select storms by county or other custom criteria, listing hurricanes, tornadoes, thunderstorms, hail, floods, drought conditions, lightning strikes, blustering winds and snowfall accumulations among many other natural phenomena of varying intensities. All this raw material is organized chronologically by state and used selectively to gain a better understanding of our nation's diverse weather experiences. A maximum 120 day delay may exist in providing up-to-date Storm Data due to periodic updates released by NWS so users are afforded greater accuracy with their research efforts regardless for what purpose it’s being sought – whether for analysis or education. Making an informed decision about safety measures or studying historic trends related to climate change demands reliable data from trusted sources such as NCDC Storm Events Database; empowering us all with unimpeded access towards achieving a higher understanding of our environment
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- 🚨 Your notebook can be here! 🚨!
This dataset contains detailed information about various types of storms that have occured in the United States since 1950, including hurricanes, tornadoes, hail storms, and floods. The data is organized by county and can be used to analyze weather patterns or conduct research on severe weather events in particular regions of the country. Here are some steps to help you get started using this dataset:
Select a geographic area: You can start by selecting a specific state or county from where you would like to explore data on storm events. This will narrow down your search results so you can easily find more relevant data points.
Filter for desired storm type(s): Next step is to filter for the particular type of storm event that interests you—such as hurricanes, snowstorms, lightning strikes —to further refine your search results based on specific criteria such as date range and damage estimates etc..
Analyze the resulting data set: Lastly you’ll need to analyze any additional fields available post filtering process like fatalities numbers , damage estimates , cities affected . Run analyses that could result in trends on severity of storms over time or location-based distribution etc.. Alternatively create charts / graphs which could help visualize any insights drawn from your findings better
- Looking into the correlation between severe weather events and climate change by tracking historical data points from 1950 onwards with the NCDC Storm Events Database.
- Identifying trends in storm damages, such as those caused by hail, high winds, and other weather phenomena that could lead to better preparedness strategies for businesses or individuals who are vulnerable to certain risks in their area.
- Analyzing which states or counties have experienced the most severe weather events over the years and use this information to inform better mitigation planning ahead of potential disasters in those areas
If you use this dataset in your research, please credit the original authors. Data Source
Unknown License - Please check the dataset description for more information.
If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit US Open Data Portal, data.gov.
The Economic Indicator Service (EIS) aims to deliver economic content to financial institutions on both buy and sell-side and service providers. This new service currently covers 34,351 recurring macro-economic indicators from 135 countries ( as of December 16, 2019 ) such as GDP data, unemployment releases, PMI numbers etc.
Economic Indicator Service gathers the major economic events from a variety of regions and countries around the globe and provides an Economic Events Data feed and Economic Calendar service to our clients. This service includes all previous historic data on economic indicators that are currently available on the database.
Depending on availability, information regarding economic indicators, including the details of the issuing agency as well as historical data series can be made accessible for the client. Key information about EIS: • Cloud-based service for Live Calendar – delivered via HTML/JavaScript application formats, which can then be embedded onto any website using iFrames • Alternatives methods available – such as API and JSON feed for the economic calendar that can be integrated into the company’s system • Live data – updated 24/5, immediately after the data has been released • Historical data – includes a feed of all previous economic indicators available We are currently adding additional indicators/countries from Africa as well as expanding our coverage of Indicators in G20. The calendar includes the following. • Recurring & Non-recurring indicators covering 136 countries across 21 regions. • Indicators showing high, medium, and low impact data. • Indicators showing actual, previous, and forecast data. • Indicators can be filtered across 16 subtypes. • News generation for selected high-impact data. • Indicator description and historical data up to the latest eight historical points with a chart.
This dataset contains event history for floods in Utah based on the NOAA weather database. It has been dis-aggregated to census place for alignment with other compatible databases in order to analyze local history and experience. The readme file elaborates on the methods of disaggregation. It includes data from 1997-2014, but more reliable data is separated for 2010-2014 after a change in NWS procedure and event recognition.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10.000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE are detected. All results are publicly available on www.globalfloodmonitor.org.
Please see the Tsunami Events (1850-Present) Time-Lapse map viewer.This is a web map displaying historical tsunami events from the Global Historical Tsunami Database at NOAA's National Centers for Environmental Information (NCEI).More information about tsunami data at NCEI
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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TEMPEST (Tracking Extremes of Meteorological Phenomena Experienced in Space and Time) is the major output of the Arts and Humanities Research Council (AHRC) funded project “Spaces of Experience and Horizons of Expectation: Extreme Weather in the UK, Past, Present and Future (which ran from 2013-2017)".
TEMPEST was designed as a freely accessible and user-friendly database resource on the UK’s weather and climate history. TEMPEST is comprised of narrative accounts of extreme weather events of all types, extracted from documentary materials located in a range of archival repositories in the UK (consequentially, please see the quality statement note below concerning data issues). The information has been extracted from a wide range of documents, including letters, diaries, church records, school log-books and many others. The entries span more than 400 years - some as early as 1346 - of weather history and relate to places across the UK, though the data search was focused in five case-study regions: Central England, Southwest England, East Anglia, Wales, and Northwest Scotland.
Each event entry or narrative has been assigned to at least one weather type, is dated (at least to a year), and is geographically referenced (using digital coordinates). Many also contain material relating to the impacts of the weather event and responses to it. In addition to information on extreme weather events, TEMPEST contains details of the original documents, their authors, and the collections and repositories in which they are held. TEMPEST is searchable by all of these fields.
Users are advised to read the quality statement carefully with regards to possible issues in date and location accuracy and the way "extreme" events were documented. Additionally, users should be aware that the period covered by the dataset includes the change from the Julian to Gregorian calendar. In order to manage that change 11 days were omitted from the year 1752, i.e. the day after the 2 September 1752 was 14 September, in accordance with the Calendar Act of 1751. Until September 1752 the New Year began on 25 March (Lady Day) but dual dating was commonplace for many years before, adding a further layer of complication to events that took place from 1 January to 24 March, and making 1751 a short year running from 25 March to 31 December! Scotland had changed the start of the year to 1 January in 1600. Where clear, the Gregorian calendar date has been used, providing further details in the notes section.
Vector database, obtained from the georeferencing of reports of historical evidence of the occurrence of surface aquifers in the Emilia-Romagna plain; among these, the fountains. The reports derive from historical topographic maps and bibliography, implementing data from the publication: Bonaposta D., Segadelli S., De Nardo M.T., Alessandrini A., Pezzoli S. (2011) - The geological potential of historical environmental data: the case of springs and fountains in Emilia Romagna. The degree of detail of the information layer is compatible with that required by territorial and urban planning in the municipal and supra-municipal areas.
I assembled this dataset from various published sources to evaluate how the social-ecological system (SES) in Adirondack Park, New York changed through time and the interplay of public goods (Common Pool Resources, CPRs), public land rules and private land rights, and related concepts over 260 years (1760-2020). The database was the basis for a doctoral dissertation titled "Blue Lining: Assessing the Resilience of Adirondack Park, New York Using Polycentricity and Panarchy Frameworks." The goal of the dissertation was to assess patterns and changes in institutional rules, actors and arrangements before and after establishment of the public Adirondack Forest Preserve in 1885 and Adirondack Park in 1892 as those actors and rules were modified and as both internal and external events influenced the SES as it moved through different phases of the adaptive cycle through space and time (see panarchy). Using the database, I identified which organizations and events contributed to natural resource and CPR policy. The dissertation can be downloaded here: https://experts.esf.edu/esploro/outputs/99917370604826.
Warning: as of June 2020, this dataset is no longer updated and has been replaced. Please see https://www.donneesquebec.ca/recherche/fr/dataset/evenements-de-securite-civile for data on civil security events since June 2020. This database brings together in a structured way information related to past claims that have been systematically grouped and centralized by the Ministry of Public Security (MSP). The consequences and evolution of the events are documented and they have been categorized according to their level of impact on the safety of citizens, goods and services to the population based on criteria defined in the Canadian profile of the Common Alert Protocol. It is updated continuously by the MSP Operations Department (DO). This database will allow analyses to be carried out at regional and local levels and can be used by municipalities in the implementation of their emergency measures plans. The event history archives come from event reports and status reports that were produced by the Government Operations Center (COG) and by the regional directorates of the MSP. Among other things, it includes: 1- Observations entered directly into the Geoportal by civil security advisers from regional directorates; 2- A compilation of information recorded in COG event reports and DO status reports distributed to MSP partners since 1996; 3- A compilation of information contained in the files of the regional directorates. This may be information on paper, event reports or field visits, paper or digital maps, etc. The information in this database is in accordance with the Canadian Common Alert Protocol Profile (PC-PAC). The PC-PAC is a set of rules and controlled values that support the translation and composition of a message to make it possible to send it by different means and from different sources. The severity level is an attribute defined in the PC-PAC. It is used to characterize the severity level of the event based on the harm to the lives of people or damage to property. This severity level is defined by the following characteristics: Extreme: extraordinary threat to life or property; Important: significant threat to life or property; Moderate: possible threat to life or property; Minor: low or non-existent threat to life or property; Minor: low or non-existent threat to life or property; Unknown: unknown severity, used among other things during tests and exercises. The emergency level is determined based on the reactive measures that need to be taken in response to the current situation. It is defined by the following characteristics: Immediate: a reactive action must be taken immediately; Planned: a reactive action must be taken soon (within the next hour); Future: a reactive action must be taken in the near future; Past: a reactive measure is no longer necessary; Unknown: Unknown: Unknown emergency, to be used during tests and exercises. The state relates to the context of the event, real or simulated. It is defined by the following characteristics: Current: information on a real event or situation; Exercise: fictional or real information carried out as part of a civil security exercise; Test: technical tests only; to be ignored by all. Certainty is defined by the following characteristics: Observed: would have happened or is currently taking place; Probable: probability of the event happening > 50%; Possible: probability of the event happening < 50%; Unlikely: probability of the event happening around 0%; Unlikely: probability of the event happening around 0%; Unknown: unknown certainty. When an event date was not known, the year 1900-01-01 was recorded. ATTRIBUTE DESCRIPTION: Date of observation: date of the event or observation; Type: name of the hazard; Name: name of the municipality; Municipality code: municipal code; State and certainty: as these are real events, the state is generally “current” and the certainty is generally “observed”; Emergency: the term “past” was generally used for events that occurred before the compilation work was carried out; Inprecision: imprecision: imprecision is generally “observed”; Urgency: the term “past” was generally used for events that occurred before the compilation work was carried out; Inaccuracy: imprecision: imprecision precision in a data (the date of the event, its location, the source of the data or none inaccuracy noted).This third party metadata element was translated using an automated translation tool (Amazon Translate).
The Global Historical Tsunami Database provides information on over 2,400 tsunamis from 2000 BC to the present around the globe. The dataset includes two related tables. The historical source events table includes information on the tsunami source such as the date, time, and location of the source event; cause and validity of the source, tsunami magnitude and intensity; maximum water height; the total number of fatalities, injuries, houses destroyed, and houses damaged; and total damage estimate (in U.S. dollars). The second table, historical runups ,contains information on the observations of the tsunamis, or "runups". This is variables such as the locations where tsunami waves were observed by eyewitnesses, reconnaissance surveys, tide gauges, and deep-ocean sensors, as well as name, location, arrival time, maximum water height and inundation distance, and socio-economic data (deaths, injuries, damage) for the specific runup location. Please cite this dataset as: National Geophysical Data Center / World Data Service: NCEI/WDS Global Historical Tsunami Database. NOAA National Centers for Environmental Information. doi:10.7289/V5PN93H7 [access date] See the National Centers for Environmental Information (NCEI) website for more details on this dataset. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.2.0 adds 94 additional coup events. 66 of these came from examining Powell and Thyne’s “discarded” events and 28 of these events were added to the data set in the normal annual review of potential new coup events. This version also updates the coding to events in Brazil in 1945 and the Congo in 1968. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 as a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011) Version 1.0.0 was released in 2013. This version consolidated coup data taken from the following sources: • The Center for Systemic Peace (Marshall and Marshall, 2007) • The World Handbook of Political and Social Indicators (Taylor and Jodice, 1983) • Coup d’Ètat: A Practical Handbook (Luttwak, 1979) • The Cline Center’s Social, Political and Economic Event Database (SPEED) Project (Nardulli, Althaus and Hayes, 2015) • Government Change in Authoritarian Regimes – 2010 Update (Svolik and Akcinaroglu, 2006)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.2.0 Codebook.pdf - This 17-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised January 2025 2. Coup Data v2.2.0.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1094 observations. Revised January 2025 3. Source Document v2.2.0.pdf - This 347-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised January 2025 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised January 2025
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2025. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.2.0. Janurary 30. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V8 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Michael Martin, Sam Alahi, Norah Fadell, and Maddie Jeralds. 2025. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.2.0. Janurary 30. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V8
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EventWiki is a knowledge base of major events happening throughout mankind history. It contains 21,275 events of 95 types. The details of event entries can be found in our paper submission and documentation file. Data in the knowledge base is mainly harvested from Wikipedia.As Wikipedia, this resource can be distributed and shared under CC-BY 3.0 license.