Techsalerator’s News Event Data in Europe is a comprehensive and meticulously curated dataset designed to provide businesses, analysts, journalists, and researchers with an extensive view of significant news events across Europe. This dataset captures and categorizes key events reported from a variety of news sources, offering valuable insights into industry developments, economic changes, political shifts, and other noteworthy occurrences throughout the continent.

Key Features of the Dataset: Extensive Coverage:

The dataset aggregates news events from a wide range of sources including press releases, industry news sites, blogs, PR platforms, and traditional news outlets. This broad coverage ensures that users receive a diverse array of information from multiple reporting channels. Categorization of Events:

News events are meticulously categorized into various types such as business and financial updates, political developments, technological advancements, legal and regulatory changes, and cultural events. This categorization helps users quickly locate and analyze information relevant to specific interests or sectors. Real-Time Updates:

Data is updated regularly to include the most current events. This ensures that users have access to the latest information and can stay informed about recent developments as they unfold. Geographic Segmentation:

Events are tagged with their respective countries and regions within Europe. This geographic segmentation allows users to filter and analyze news events based on specific locations, facilitating targeted research and analysis. Event Details:

Each event entry includes detailed information such as the date of occurrence, source of the news, event description, and relevant keywords. This comprehensive detail aids in understanding the context and significance of each event. Historical Data:

The dataset includes historical news event data, enabling users to track trends and analyze changes over time. This feature supports longitudinal studies and comparative analysis of historical and recent events. Advanced Search and Filter Options:

Users can search and filter news events based on various criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. European Countries Covered: Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden Benefits of the Dataset: Informed Decision-Making: Businesses and analysts can leverage the dataset to stay updated on key developments that may impact their operations, market conditions, or strategic decisions. Market and Industry Analysis: The dataset provides valuable insights into industry trends, economic changes, and political events, helping users analyze market dynamics and make informed decisions. Media and PR Monitoring: Journalists and PR professionals can track relevant news and events across Europe, allowing them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Purposes: Researchers can use the dataset for longitudinal studies, trend analysis, and academic research on various topics related to European news and events. Techsalerator’s News Event Data in Europe is a vital resource for accessing and analyzing significant news events across the continent. By offering detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.

Techsalerator’s News Event Data in Asia offers a detailed and expansive dataset designed to provide businesses, analysts, journalists, and researchers with comprehensive insights into significant news events across the Asian continent. This dataset captures and categorizes major events reported from a diverse range of news sources, including press releases, industry news sites, blogs, and PR platforms, offering valuable perspectives on regional developments, economic shifts, political changes, and cultural occurrences.

Key Features of the Dataset: Extensive Coverage:

The dataset aggregates news events from a wide range of sources such as company press releases, industry-specific news outlets, blogs, PR sites, and traditional media. This broad coverage ensures a diverse array of information from multiple reporting channels. Categorization of Events:

News events are categorized into various types including business and economic updates, political developments, technological advancements, legal and regulatory changes, and cultural events. This categorization helps users quickly find and analyze information relevant to their interests or sectors. Real-Time Updates:

The dataset is updated regularly to include the most current events, ensuring users have access to the latest news and can stay informed about recent developments as they happen. Geographic Segmentation:

Events are tagged with their respective countries and regions within Asia. This geographic segmentation allows users to filter and analyze news events based on specific locations, facilitating targeted research and analysis. Event Details:

Each event entry includes comprehensive details such as the date of occurrence, source of the news, a description of the event, and relevant keywords. This thorough detailing helps users understand the context and significance of each event. Historical Data:

The dataset includes historical news event data, enabling users to track trends and perform comparative analysis over time. This feature supports longitudinal studies and provides insights into the evolution of news events. Advanced Search and Filter Options:

Users can search and filter news events based on criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. Asian Countries and Territories Covered: Central Asia: Kazakhstan Kyrgyzstan Tajikistan Turkmenistan Uzbekistan East Asia: China Hong Kong (Special Administrative Region of China) Japan Mongolia North Korea South Korea Taiwan South Asia: Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka Southeast Asia: Brunei Cambodia East Timor (Timor-Leste) Indonesia Laos Malaysia Myanmar (Burma) Philippines Singapore Thailand Vietnam Western Asia (Middle East): Armenia Azerbaijan Bahrain Cyprus Georgia Iraq Israel Jordan Kuwait Lebanon Oman Palestine Qatar Saudi Arabia Syria Turkey (partly in Europe, but often included in Asia contextually) United Arab Emirates Yemen Benefits of the Dataset: Strategic Insights: Businesses and analysts can use the dataset to gain insights into significant regional developments, economic conditions, and political changes, aiding in strategic decision-making and market analysis. Market and Industry Trends: The dataset provides valuable information on industry-specific trends and events, helping users understand market dynamics and identify emerging opportunities. Media and PR Monitoring: Journalists and PR professionals can track relevant news across Asia, enabling them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Use: Researchers can utilize the dataset for longitudinal studies, trend analysis, and academic research on various topics related to Asian news and events. Techsalerator’s News Event Data in Asia is a crucial resource for accessing and analyzing significant news events across the continent. By offering detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.

Techsalerator’s News Event Data in Latin America offers a detailed and extensive dataset designed to provide businesses, analysts, journalists, and researchers with an in-depth view of significant news events across the Latin American region. This dataset captures and categorizes key events reported from a wide array of news sources, including press releases, industry news sites, blogs, and PR platforms, offering valuable insights into regional developments, economic changes, political shifts, and cultural events.

Key Features of the Dataset: Comprehensive Coverage:

The dataset aggregates news events from numerous sources such as company press releases, industry news outlets, blogs, PR sites, and traditional news media. This broad coverage ensures a wide range of information from multiple reporting channels. Categorization of Events:

News events are categorized into various types including business and economic updates, political developments, technological advancements, legal and regulatory changes, and cultural events. This categorization helps users quickly locate and analyze information relevant to their interests or sectors. Real-Time Updates:

The dataset is updated regularly to include the most recent events, ensuring users have access to the latest news and can stay informed about current developments. Geographic Segmentation:

Events are tagged with their respective countries and regions within Latin America. This geographic segmentation allows users to filter and analyze news events based on specific locations, facilitating targeted research and analysis. Event Details:

Each event entry includes comprehensive details such as the date of occurrence, source of the news, a description of the event, and relevant keywords. This thorough detailing helps in understanding the context and significance of each event. Historical Data:

The dataset includes historical news event data, enabling users to track trends and perform comparative analysis over time. This feature supports longitudinal studies and provides insights into how news events evolve. Advanced Search and Filter Options:

Users can search and filter news events based on criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. Latin American Countries Covered: South America: Argentina Bolivia Brazil Chile Colombia Ecuador Guyana Paraguay Peru Suriname Uruguay Venezuela Central America: Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama Caribbean: Cuba Dominican Republic Haiti (Note: Primarily French-speaking but included due to geographic and cultural ties) Jamaica Trinidad and Tobago Benefits of the Dataset: Strategic Insights: Businesses and analysts can use the dataset to gain insights into significant regional developments, economic conditions, and political changes, aiding in strategic decision-making and market analysis. Market and Industry Trends: The dataset provides valuable information on industry-specific trends and events, helping users understand market dynamics and emerging opportunities. Media and PR Monitoring: Journalists and PR professionals can track relevant news across Latin America, enabling them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Use: Researchers can utilize the dataset for longitudinal studies, trend analysis, and academic research on various topics related to Latin American news and events. Techsalerator’s News Event Data in Latin America is a crucial resource for accessing and analyzing significant news events across the region. By providing detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.

The present is a manually labeled data set for the task of Event Detection (ED). The task of ED consists of identifying event triggers, the word that most clearly indicates the occurrence of an event. The present data set consists of 2,200 news extracts from The New York Times (NYT) Annotated Corpus, separated into training (2,000) and testing (200) sets. Each news extract contains the plain text with the labels (event mentions), along with two metadata (publication date and an identifier). Labels description: We consider as event any ongoing real-world event or situation reported in the news articles. It is important to distinguish those events and situations that are in progress (or are reported as fresh events) at the moment the news is delivered from past events that are simply brought back, future events, hypothetical events, or events that will not take place. In our data set we only labeled as event the first type of event. Based on this criterion, some words that are typically considered as events are labeled as non-event triggers if they do not refer to ongoing events at the time the analyzed news is released. Take for instance the following news extract: "devaluation is not a realistic option to the current account deficit since it would only contribute to weakening the credibility of economic policies as it did during the last crisis." The only word that is labeled as event trigger in this example is "deficit" because it is the only ongoing event refereed in the news. Note that the words "devaluation", "weakening" and "crisis" could be labeled as event triggers in other news extracts, where the context of use of these words is different, but not in the given example. Further information: For a more detailed description of the data set and the data collection process please visit: https://cs.uns.edu.ar/~mmaisonnave/resources/ED_data. Data format: The dataset is split in two folders: training and testing. The first folder contains 2,000 XML files. The second folder contains 200 XML files. Each XML file has the following format. YYYYMMDDTHHMMSS ... ... ... The first three tags (pubdate, file-id and sent-idx) contain metadata information. The first one is the publication date of the news article that contained that text extract. The next two tags represent a unique identifier for the text extract. The file-id uniquely identifies a news article, that can hold several text extracts. The second one is the index that identifies that text extract inside the full article. The last tag (sentence) defines the beginning and end of the text extract. Inside that text are the tags. Each of these tags surrounds one word that was manually labeled as an event trigger.

This list contains information on approved event applications from 2008. Please note that Permitted Film Events only reflect those permits which will impact one or more streets for at least five days. For a current list of events, please refer to NYC Permitted Event Information dataset at https://data.cityofnewyork.us/City-Government/NYC-Permitted-Event-Information/tvpp-9vvx

In a survey conducted mid-June 2020, ** percent of U.S. adults stated that the coronavirus (COVID-19) pandemic made them more likely to vote for Democratic candidates in the U.S. election to be held in that year. A similar number - ** percent - also said that state governmental responses to the widespread protests made them more likely to vote for the Democrats. Conversely, for both of these current events, ** percent of respondents stated they were more likely to vote Republican.

Techsalerator’s News Event Data in North America offers a comprehensive and detailed dataset designed to provide businesses, analysts, journalists, and researchers with a thorough view of significant news events across North America. This dataset captures and categorizes major events reported from a diverse range of news sources, including press releases, industry news sites, blogs, and PR platforms, providing valuable insights into regional developments, economic shifts, political changes, and cultural events.

Key Features of the Dataset: Extensive Coverage:

The dataset aggregates news events from a wide array of sources, including company press releases, industry-specific news outlets, blogs, PR sites, and traditional media. This broad coverage ensures a diverse range of information from multiple reporting channels. Categorization of Events:

News events are categorized into various types such as business and economic updates, political developments, technological advancements, legal and regulatory changes, and cultural events. This categorization helps users quickly find and analyze information relevant to their interests or sectors. Real-Time Updates:

The dataset is updated regularly to include the most current events, ensuring that users have access to up-to-date news and can stay informed about recent developments as they happen. Geographic Segmentation:

Events are tagged with their respective countries and territories within North America. This geographic segmentation allows users to filter and analyze news events based on specific locations, facilitating targeted research and analysis. Event Details:

Each event entry includes comprehensive details such as the date of occurrence, source of the news, a description of the event, and relevant keywords. This thorough detailing helps users understand the context and significance of each event. Historical Data:

The dataset includes historical news event data, enabling users to track trends and conduct comparative analysis over time. This feature supports longitudinal studies and provides insights into how news events evolve. Advanced Search and Filter Options:

Users can search and filter news events based on criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. North American Countries and Territories Covered: Countries: Canada Mexico United States Territories: American Samoa (U.S. territory) French Polynesia (French overseas collectivity; included for regional relevance) Guam (U.S. territory) New Caledonia (French special collectivity; included for regional relevance) Northern Mariana Islands (U.S. territory) Puerto Rico (U.S. territory) Saint Pierre and Miquelon (French overseas territory; geographically close to North America and included for regional comprehensiveness) Wallis and Futuna (French overseas collectivity; included for regional relevance) Benefits of the Dataset: Strategic Insights: Businesses and analysts can use the dataset to gain insights into significant regional developments, economic conditions, and political changes, aiding in strategic decision-making and market analysis. Market and Industry Trends: The dataset provides valuable information on industry-specific trends and events, helping users understand market dynamics and identify emerging opportunities. Media and PR Monitoring: Journalists and PR professionals can track relevant news across North America, enabling them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Use: Researchers can utilize the dataset for longitudinal studies, trend analysis, and academic research on various topics related to North American news and events. Techsalerator’s News Event Data in North America is a crucial resource for accessing and analyzing significant news events across the continent. By providing detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Context

This dataset was created by an AP Statistics class. The purpose was to learn about survey methodology and analysis using R.

The participants are all AP Statistics students from schools within the US.

The purpose of their study was to learn more about the self-reported knowledge of current events among young adults.

Content

Below is a description of each variable. - Timestamp: The date and time that the response was recorded. - Gender: Self-reported gender of the respondent. - Age: Self-reported age of the respondent. - USCitizen: Response to the question "Are you a US citizen who has resided full time in the US for the last 5 years?" - Boarding: Self-reported residential status of the respondent. - NewsSource: Response to the question "What's your primary news source?" - HKProtests: Response to the question "How informed are you regarding the Hong Kong protests?" 0 = not informed and 5 = very informed. - BTaylor: Response to the question "How informed are you regarding the Breonna Taylor case?" 0 = not informed and 5 = very informed. - Explosion: Response to the question "How informed are you about the explosion that occurred in Beirut, Lebanon?" 0 = not informed and 5 = very informed. - SCNomination: Response to the question "How informed are you on the ongoing debate over the Supreme Court nomination in the US?" 0 = not informed and 5 = very informed.

These are the 28 files that are used for the analysis in the submitted manuscript entitled: An Error in a Recent Paper: gazeNet: End-to-end eye-movement event detection with deep neural Networks (Zemblys, Niehorster, & Holmqvist, 2019)

Normative learning theories dictate that we should preferentially attend to informative sources, but only up to the point that our limited learning systems can process their content. Humans, including infants, show this predicted strategic deployment of attention. Here we demonstrate that rhesus monkeys, much like humans, attend to events of moderate surprisingness over both more and less surprising events. They do this in the absence of any specific goal or contingent reward, indicating that the behavioral pattern is spontaneous. We suggest this U-shaped attentional preference represents an evolutionarily preserved strategy for guiding intelligent organisms toward material that is maximally useful for learning. Methods How the data were collected: In this project, we collected gaze data of 5 macaques when they watched sequential visual displays designed to elicit probabilistic expectations using the Eyelink Toolbox and were sampled at 1000 Hz by an infrared eye-monitoring camera system. Dataset:

"csv-combined.csv" is an aggregated dataset that includes one pop-up event per row for all original datasets for each trial. Here are descriptions of each column in the dataset:

subj: subject_ID = {"B":104, "C":102,"H":101,"J":103,"K":203} trialtime: start time of current trial in second trial: current trial number (each trial featured one of 80 possible visual-event sequences)(in order) seq current: sequence number (one of 80 sequences) seq_item: current item number in a seq (in order) active_item: pop-up item (active box) pre_active: prior pop-up item (actve box) {-1: "the first active object in the sequence/ no active object before the currently active object in the sequence"} next_active: next pop-up item (active box) {-1: "the last active object in the sequence/ no active object after the currently active object in the sequence"} firstappear: {0: "not first", 1: "first appear in the seq"} looks_blank: csv: total amount of time look at blank space for current event (ms); csv_timestamp: {1: "look blank at timestamp", 0: "not look blank at timestamp"} looks_offscreen: csv: total amount of time look offscreen for current event (ms); csv_timestamp: {1: "look offscreen at timestamp", 0: "not look offscreen at timestamp"} time till target: time spent to first start looking at the target object (ms) {-1: "never look at the target"} looks target: csv: time spent to look at the target object (ms);csv_timestamp: look at the target or not at current timestamp (1 or 0) look1,2,3: time spent look at each object (ms) location 123X, 123Y: location of each box (location of the three boxes for a given sequence were chosen randomly, but remained static throughout the sequence) item123id: pop-up item ID (remained static throughout a sequence) event time: total time spent for the whole event (pop-up and go back) (ms) eyeposX,Y: eye position at current timestamp

"csv-surprisal-prob.csv" is an output file from Monkilock_Data_Processing.ipynb. Surprisal values for each event were calculated and added to the "csv-combined.csv". Here are descriptions of each additional column:

rt: time till target {-1: "never look at the target"}. In data analysis, we included data that have rt > 0. already_there: {NA: "never look at the target object"}. In data analysis, we included events that are not the first event in a sequence, are not repeats of the previous event, and already_there is not NA. looks_away: {TRUE: "the subject was looking away from the currently active object at this time point", FALSE: "the subject was not looking away from the currently active object at this time point"} prob: the probability of the occurrence of object surprisal: unigram surprisal value bisurprisal: transitional surprisal value std_surprisal: standardized unigram surprisal value std_bisurprisal: standardized transitional surprisal value binned_surprisal_means: the means of unigram surprisal values binned to three groups of evenly spaced intervals according to surprisal values. binned_bisurprisal_means: the means of transitional surprisal values binned to three groups of evenly spaced intervals according to surprisal values.

"csv-surprisal-prob_updated.csv" is a ready-for-analysis dataset generated by Analysis_Code_final.Rmd after standardizing controlled variables, changing data types for categorical variables for analysts, etc. "AllSeq.csv" includes event information of all 80 sequences

Empty Values in Datasets:

There is no missing value in the original dataset "csv-combined.csv". Missing values (marked as NA in datasets) happen in columns "prev_active", "next_active", "already_there", "bisurprisal", "std_bisurprisal", "sq_std_bisurprisal" in "csv-surprisal-prob.csv" and "csv-surprisal-prob_updated.csv". NAs in columns "prev_active" and "next_active" mean that the first or the last active object in the sequence/no active object before or after the currently active object in the sequence. When we analyzed the variable "already_there", we eliminated data that their "prev_active" variable is NA. NAs in column "already there" mean that the subject never looks at the target object in the current event. When we analyzed the variable "already there", we eliminated data that their "already_there" variable is NA. Missing values happen in columns "bisurprisal", "std_bisurprisal", "sq_std_bisurprisal" when it is the first event in the sequence and the transitional probability of the event cannot be computed because there's no event happening before in this sequence. When we fitted models for transitional statistics, we eliminated data that their "bisurprisal", "std_bisurprisal", and "sq_std_bisurprisal" are NAs.

Codes:

In "Monkilock_Data_Processing.ipynb", we processed raw fixation data of 5 macaques and explored the relationship between their fixation patterns and the "surprisal" of events in each sequence. We computed the following variables which are necessary for further analysis, modeling, and visualizations in this notebook (see above for details): active_item, pre_active, next_active, firstappear ,looks_blank, looks_offscreen, time till target, looks target, look1,2,3, prob, surprisal, bisurprisal, std_surprisal, std_bisurprisal, binned_surprisal_means, binned_bisurprisal_means. "Analysis_Code_final.Rmd" is the main scripts that we further processed the data, built models, and created visualizations for data. We evaluated the statistical significance of variables using mixed effect linear and logistic regressions with random intercepts. The raw regression models include standardized linear and quadratic surprisal terms as predictors. The controlled regression models include covariate factors, such as whether an object is a repeat, the distance between the current and previous pop up object, trial number. A generalized additive model (GAM) was used to visualize the relationship between the surprisal estimate from the computational model and the behavioral data. "helper-lib.R" includes helper functions used in Analysis_Code_final.Rmd

Dataset (Stata v15.1) containing responses from a survey of UK Clinical Research Collaboration registered clinical trial units (CTUs) and industry statisticians from both pharmaceuticals and clinical research organisations (http://dx.doi. org/10.1136/bmjopen-2020- 036875) Data is de-identified. The dataset contains descriptive variables describing participant's experience, as well as responses to questions on current adverse event analysis practices, awareness of specialist methods for adverse event analysis and priorities, concerns and barriers participants experience when analysing adverse event data.

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).**

Techsalerator's News Events Data for Greece: A Comprehensive Overview

Techsalerator's News Events Data for Greece is an essential resource for businesses, researchers, and media organizations. This dataset aggregates information on significant news events across Greece, drawing from diverse media sources such as news outlets, online publications, and social platforms. It offers valuable insights for those looking to track trends, analyze public sentiment, or monitor industry-specific developments.

Key Data Fields - Event Date: Captures the exact date of the news event, crucial for analysts monitoring trends over time or businesses responding to market shifts. - Event Title: A brief headline describing the event, allowing users to quickly categorize and assess news content based on relevance. - Source: Identifies the news outlet or platform where the event was reported, helping users track credible sources and assess the reach and influence of the event. - Location: Provides geographic information on where the event took place within Greece, valuable for regional analysis or localized marketing efforts. - Event Description: A detailed summary of the event, outlining key developments, participants, and potential impact. Researchers and businesses use this to understand the context and implications of the event.

Top 5 News Categories in Greece - Politics: Major news coverage on government decisions, political movements, elections, and policy changes affecting the national landscape. - Economy: Focuses on Greece’s economic indicators, inflation rates, international trade, and corporate activities influencing business and finance sectors. - Social Issues: News events covering protests, public health, education, and other societal concerns driving public discourse. - Sports: Highlights events in football, basketball, and other popular sports, often drawing widespread attention and engagement across the country. - Technology and Innovation: Reports on tech developments, startups, and innovations in Greece’s growing tech ecosystem, featuring companies like Vodafone Greece and up-and-coming startups.

Top 5 News Sources in Greece - Kathimerini: A major newspaper providing in-depth coverage of politics, economy, and social issues. - Ta Nea: A prominent source for news related to politics, business, and cultural events across Greece. - Mega Channel: A leading TV network offering real-time updates on current affairs, sports, and entertainment. - Protagon: A well-regarded online news platform known for its investigative journalism and analysis of political and social issues. - Ethnos: A key newspaper providing extensive coverage of national politics, economy, and public affairs.

Accessing Techsalerator’s News Events Data for Greece To access Techsalerator’s News Events Data for Greece, please contact info@techsalerator.com with your specific needs. We will provide a customized quote based on the data fields and records you require, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields - Event Date - Event Title - Source - Location - Event Description - Event Category (Politics, Economy, Sports, etc.) - Participants (if applicable) - Event Impact (Social, Economic, etc.)

Techsalerator’s dataset is an invaluable tool for tracking significant events in Greece. It aids in making informed decisions, whether for business strategy, market analysis, or academic research, offering a clear picture of the country’s news landscape.

BFD stands for the benchmark with full data analyzed with the current standard method, which includes t-tests for two-group comparisons. CTOT stands for the cycle-to-threshold method, while CO denotes the complete-observation method and MC denotes the method that sets uncertain and incomplete observations equal to the assay-specific maximum cycle threshold C1. Uncertain qPCR data may occur in one or both groups under comparison. (CSV)

This dataset includes past causalities and their categories to connect similar past and present causalities. We report how to use this dataset in the following papers.

Ryohei Ikejiri, Yasunobu Sumikawa: "Developing world history lessons to foster authentic social participation by searching for historical causation in relation to current issues dominating the news". Journal of Educational Research on Social Studies 84, 37–48 (2016). (in Japanese).

Yasunobu Sumikawa and Ryohei Ikejiri, "Mining Historical Social Issues", Intelligent Decision Technologies, Smart Innovation, IDT'15, Systems and Technologies, Vol. 39, Springer, pp. 587--597, 2015.

This dataset is based on some textbooks that are popular ones in Japanese high-school. We first collect past causalities by referencing the textbooks. We then select the causalities if they can be useful for considering solutions for present social issues. To enhance the analogy, we describe each causality in three kinds of texts: background including problems, solution ways, and their results. From the selected causalities and an Encyclopedia of Historiography, we define categories for them. Finally, the created dataset contains 138 past causalities and 13 categories. Each past causality has more than one categories.

File contents:

• FAQ data (*.csv)
  1. historical_causalities_data.tsv: Detail of stored causalities.
  2. historical_causalities_regions.tsv: Regions where the causalities happened.
  3. historical_causalities_categories.tsv: Categories of the causalities.


• Statistics (Statistics.tsv)

  Results of statistical analyses for the dataset. We used Calinski and Harabaz method, mutual information, Jaccard Index, TF-IDF+JS divergence, and Meta-data Similarity that counts how many common categories two causalities share in order to measure qualities of the dataset.

Grants: JSPS KAKENHI Grant Number 26750076 and 17K12792

In 2024, the most popular way for teenagers in Germany to find information about current events was by speaking with family members. ** percent of teenagers used TikTok to inform themselves about current events.

This dataset includes past causalities and their categories to connect similar past and present causalities. We report how to use this dataset in the following papers.

Ryohei Ikejiri, Yasunobu Sumikawa: "Developing world history lessons to foster authentic social participation by searching for historical causation in relation to current issues dominating the news". Journal of Educational Research on Social Studies 84, 37–48 (2016). (in Japanese).

Yasunobu Sumikawa and Ryohei Ikejiri, "Mining Historical Social Issues", Intelligent Decision Technologies, Smart Innovation, IDT'15, Systems and Technologies, Vol. 39, Springer, pp. 587--597, 2015.

This dataset is based on some textbooks that are popular ones in Japanese high-school. We first collect past causalities by referencing the textbooks. We then select the causalities if they can be useful for considering solutions for present social issues. To enhance the analogy, we describe each causality in three kinds of texts: background including problems, solution ways, and their results. From the selected causalities and an Encyclopedia of Historiography, we define categories for them. Finally, the created dataset contains 138 past causalities and 13 categories. Each past causality has more than one categories.

To help training machine learning models, this dataset additionally provides 900 past event data in past_events_wikipedia.tsv. The event data were collected from Wikipedia, and then were assigned one or more categories from the above 13 ones. We have confirmed that SVM-RBF equipped with the above all categorized data obtained 73.6% precision, 55.8% recall and 63.5% F1 score

File contents:

• Past causality data
  1. historical_causalities_data.tsv: Detail of stored causalities.
  2. historical_causalities_regions.tsv: Regions where the causalities happened.
  3. historical_causalities_categories.tsv: Categories of the causalities.
• Past event data
  1. past_events_wikipedia.tsv: Descriptions of past events stored in Wikipedia. This file is useful for training machine learning model such as SVM.
• Statistics (Statistics.tsv)

  Results of statistical analyses for the dataset. We used Calinski and Harabaz method, mutual information, Jaccard Index, TF-IDF+JS divergence, and Meta-data Similarity that counts how many common categories two causalities share in order to measure qualities of the dataset.

Grants: JSPS KAKENHI Grant Number 26750076, 17K12792, and 19K20631

Descriptive statistics for (clusters of) individual life events.

WikiEvents is a knowledge graph based dataset for NLP and event-related machine learning tasks.

This dataset includes RDF data in JSON-LD about events between January 2020 and December 2022. It was extracted from the Wikipedia Current events portal, Wikidata, OpenStreetMaps Nominatim and Falcon 2.0. The extractor is available on GitHub under semantic-systems/current-events-to-kg.

The RDF data for each month is split onto four graph modules each:

• The base graph module contains events, event summaries with references from named entities to Wikipedia articles.
• The ohg graph module with all one-hop graphs (ohg) around the referencend Wikidata entities.
• The osm graph module which contains spartial data from OpenStreetMap (OSM).
• The raw graph module containing the raw HTML objects of events and article infoboxes.

This repository additionally includes two JSON files with training samples used for entity linking and event-related location extraction. They were created using queries to the WikiEvents dataset uploaded into this repository.