Contain informative data related to COVID-19 pandemic. Specially, figure out about the First Case and First Death information for every single country. The datasets mainly focus on two major fields first one is First Case which consists of information of Date of First Case(s), Number of confirm Case(s) at First Day, Age of the patient(s) of First Case, Last Visited Country and the other one First Death information consist of Date of First Death and Age of the Patient who died first for every Country mentioning corresponding Continent. The datasets also contain the Binary Matrix of spread chain among different country and region.

*This is not a country. This is a ship. The name of the Cruise Ship was not given from the government.
"N+": the age is not specified but greater than N
“No Trace”: some data was not found
“Unspecified”: not available from the authority
“N/A”: for “Last Visited Country(s) of Confirmed Case(s)” column, “N/A” indicates that the confirmed case(s) of those countries do not have any travel history in recent past; in “Age of First Death(s)” column “N/A” indicates that those countries do not have may death case till May 16, 2020.

Concept: Difference between average cost of outstanding loans (ICC) and its average funding cost. Comprises both earmarked and nonearmarked operations. Source: Central Bank of Brazil – Statistics Department 27443-icc-spread 27443-icc-spread

Abstract:

Analyzing the spread of information related to a specific event in the news has many potential applications. Consequently, various systems have been developed to facilitate the analysis of information spreadings such as detection of disease propagation and identification of the spreading of fake news through social media. There are several open challenges in the process of discerning information propagation, among them the lack of resources for training and evaluation. This paper describes the process of compiling a corpus from the EventRegistry global media monitoring system. We focus on information spreading in three domains: sports (i.e. the FIFA WorldCup), natural disasters (i.e. earthquakes), and climate change (i.e.global warming). This corpus is a valuable addition to the currently available datasets to examine the spreading of information about various kinds of events.Introduction:Domain-specific gaps in information spreading are ubiquitous and may exist due to economic conditions, political factors, or linguistic, geographical, time-zone, cultural, and other barriers. These factors potentially contribute to obstructing the flow of local as well as international news. We believe that there is a lack of research studies that examine, identify, and uncover the reasons for barriers in information spreading. Additionally, there is limited availability of datasets containing news text and metadata including time, place, source, and other relevant information. When a piece of information starts spreading, it implicitly raises questions such as asHow far does the information in the form of news reach out to the public?Does the content of news remain the same or changes to a certain extent?Do the cultural values impact the information especially when the same news will get translated in other languages?Statistics about datasets:

Statistics about datasets:

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# Domain Event Type Articles Per Language Total Articles

1 Sports FIFA World Cup 983-en, 762-sp, 711-de, 10-sl, 216-pt 2679

2 Natural Disaster Earthquake 941-en, 999-sp, 937-de, 19-sl, 251-pt 3194

3 Climate Changes Global Warming 996-en, 298-sp, 545-de, 8-sl, 97-pt 1945

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This data set provides monthly average price values, and the differences among those values, at the farm, wholesale, and retail stages of the production and marketing chain for selected cuts of beef, pork, and broilers. In addition, retail prices are provided for beef and pork cuts, turkey, whole chickens, eggs, and dairy products. Price spreads are reported for last 6 years, 12 quarters, and 24 months. The retail price file provides monthly estimates for the last 6 months. The historical file provides data since 1970.

A class of discrete-time models of infectious disease spread, referred to as individual-level models (ILMs), are typically fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. These models quantify probabilistic outcomes regarding the risk of infection of susceptible individuals due to various susceptibility and transmissibility factors, including their spatial distance from infectious individuals. The infectious pressure from infected individuals exerted on susceptible individuals is intrinsic to these ILMs. Unfortunately, quantifying this infectious pressure for data sets containing many individuals can be computationally burdensome, leading to a time-consuming likelihood calculation and, thus, computationally prohibitive MCMC-based analysis. This problem worsens when using data augmentation to allow for uncertainty in infection times. In this paper, we develop sampling methods that can be used to calculate a fast, approximate likelihood when fitting such disease models. A simple random sampling approach is initially considered followed by various spatially-stratified schemes. We test and compare the performance of our methods with both simulated data and data from the 2001 foot-and-mouth disease (FMD) epidemic in the U.K. Our results indicate that substantial computation savings can be obtained—albeit, of course, with some information loss—suggesting that such techniques may be of use in the analysis of very large epidemic data sets.

This dataset contains the spatiotemporal data used to train the spatiotemporal deep neural networks described in "Modeling the Spread of a Livestock Disease With Semi-Supervised Spatiotemporal Deep Neural Networks". The dataset consists of two sets of NumPy arrays. The first set: X_grid.npy and Y_grid.npy were used to train the convolutional LSTM, while the second set: X_graph.npy, Y_graph.npy, and edge_index.npy were used to train the graph convolutional LSTM. The data consists of spatiotemporally varying environmental and anthropogenic variables along with case reports of vesicular stomatitis. Resources in this dataset:Resource Title: NumPy Arrays of Spatiotemporal Features and VS Cases. File Name: vs_data.zipResource Description: This is a ZIP archive containing five NumPy arrays of spatiotemporal features and geotagged VS cases.Resource Software Recommended: NumPy,url: https://numpy.org/

Concept: Difference between average cost of outstanding loans (ICC) and its average funding cost. Comprises both earmarked and nonearmarked operations. Source: Central Bank of Brazil – Statistics Department 27448-spread-of-the-icc---nonearmarked---individuals 27448-spread-of-the-icc---nonearmarked---individuals

These files are videos generated by a stochastic simulation that was created by Nikki Steenbakkers under the supervision of Marko Boon and Bert Zwart (all affiliated with Eindhoven University of Technology) for her bachelor final project "Simulating the Spread of COVID-19 in the Netherlands". The report can be found in the TU/e repository of bachelor project reports:
https://research.tue.nl/en/studentTheses/simulating-the-spread-of-covid-19-in-the-netherlands
The report contains more information about the project and the simulation. It explicitly refers to these files.

Dataset Overview

This dataset contains global COVID-19 case and death data by country, collected directly from the official World Health Organization (WHO) COVID-19 Dashboard. It provides a comprehensive view of the pandemic’s impact worldwide, covering the period up to 2025. The dataset is intended for researchers, analysts, and anyone interested in understanding the progression and global effects of COVID-19 through reliable, up-to-date information.

Source Information

• Website: WHO COVID-19 Dashboard
• Organization: World Health Organization (WHO)
• Data Coverage: Global (by country/territory)
• Time Period: Up to 2025

The World Health Organization is the United Nations agency responsible for international public health. The WHO COVID-19 Dashboard is a trusted source that aggregates official reports from countries and territories around the world, providing daily updates on cases, deaths, and other key metrics related to COVID-19.

Dataset Contents

• Country/Region: The name of the country or territory.
• Date: Reporting date.
• New Cases: Number of new confirmed COVID-19 cases.
• Cumulative Cases: Total confirmed COVID-19 cases to date.
• New Deaths: Number of new confirmed deaths due to COVID-19.
• Cumulative Deaths: Total deaths reported to date.
• Additional fields may include population, rates per 100,000, and more (see data files for details).

How to Use

This dataset can be used for: - Tracking the spread and trends of COVID-19 globally and by country - Modeling and forecasting pandemic progression - Comparative analysis of the pandemic’s impact across countries and regions - Visualization and reporting

Data Reliability

The data is sourced from the WHO, widely regarded as the most authoritative source for global health statistics. However, reporting practices and data completeness may vary by country and may be subject to revision as new information becomes available.

Acknowledgements

Special thanks to the WHO for making this data publicly available and to all those working to collect, verify, and report COVID-19 statistics.

This dataset illustrates the fluid dynamics of human coughing and breathing by using schlieren imaging. This dataset was used to help inform the general public about the importance of face coverings during the COVID-19 global pandemic.

This dataset provides the spread of the conflict globally in terms of population and country for the years 2000-2016.

Explore our meticulously curated Movies dataset and TV shows dataset, designed to cater to diverse analytical and research needs. Whether you're a data scientist, a student, or a business professional, these datasets provide valuable insights into the entertainment industry.

Key Features of the Movies Dataset:

1. Extensive collection of global movies across various genres and languages.

2. Detailed metadata, including titles, release dates, genres, directors, cast, and ratings.

3. Regularly updated to ensure relevance and accuracy.

Why Choose Our TV Shows Dataset?

Our TV shows dataset is your gateway to understanding trends in episodic content. It includes:

• Comprehensive details about popular and niche TV shows.

• Information on episode counts, seasons, ratings, and networks.

• Insights into audience preferences and regional programming.

Applications of These Datasets

These datasets are perfect for:

• Machine learning models for recommendation systems.

• Academic research on media trends and audience behavior.

• Business strategies for entertainment platforms.

Unlock the power of TV show data with our Crawl Feeds TV Shows Dataset. Start analyzing today and gain valuable insights into your favorite shows!

The Novel Coronavirus (COVID-19) daily data of confirmed cases for affected countries and provinces of China reported between 31st December 2019 and 31st May 2020. The data was collected from the European Centre for Disease Prevention and Control (ECDC), and John Hopkin CSSA.

The monthly mean temperature of February to May 2020 of capital cities for the various nations.

We present a multi-temporal, multi-modal remote-sensing dataset for predicting how active wildfires will spread at a resolution of 24 hours. The dataset consists of 13.607 images across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a full time series of daily observations, containing detected active fires and variables related to fuel, topography and weather conditions. Documentation WildfireSpreadTS_Documentation.pdf includes further details about the dataset, following Gebru et al.'s "Datasheets for Datasets" framework. This documentation is similar to the supplementary material of the associated NeurIPS paper, excluding only information about experimental setup and results. For full details, please refer to the associated paper. Code: Getting started Get started working with the dataset at https://github.com/SebastianGer/WildfireSpreadTS. The code includes a PyTorch Dataset and Lightning DataModule to allow for easy access. We recommend converting the GeoTIFF files provided here to HDF5 files (bigger files, but much faster). The necessary code is also available in the repository.

This work is funded by Digital Futures in the project EO-AI4GlobalChange. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at C3SE partially funded by the Swedish Research Council through grant agreement no. 2022-06725.

Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. The first 9 weeks of data (from January 1st, 2020 to March 11th, 2020) contain very low tweet counts as we filtered other data we were collecting for other research purposes, however, one can see the dramatic increase as the awareness for the virus spread. Dedicated data gathering started from March 11th to March 29th which yielded over 4 million tweets a day.

The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (70,569,368 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (13,535,912 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the statistics-full_dataset.tsv and statistics-full_dataset-clean.tsv files.

More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter)

As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data. The need to be hydrated to be used.

Concept: Difference between average cost of outstanding loans (ICC) and its average funding cost. Comprises both earmarked and nonearmarked operations. Source: Central Bank of Brazil – Statistics Department 27451-spread-of-the-icc---earmarked---individuals 27451-spread-of-the-icc---earmarked---individuals

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6). CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), with PCIC later adding SSP3-7.0 to the CanDCS-M6 dataset. The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset). Statistically downscaled individual model output and ensembles are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios. Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale. Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

This dataset provides values for BANK LENDING DEPOSIT SPREAD WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset contains metadata about all Covid-related YouTube videos which circulated on public social media, but which YouTube eventually removed because they contained false information. It describes 8,122 videos that were shared between November 2019 and June 2020. The dataset contains unique identifiers for the videos and social media accounts that shared the videos, statistics on social media engagement and metadata such as video titles and view counts where they were recoverable. We publish the data alongside the code used to produce on Github. The dataset has reuse potential for research studying narratives related to the coronavirus, the impact of social media on knowledge about health and the politics of social media platforms.

This dataset supports the research article "The Spread of Synthetic Media on X". In compliance with X's terms of service, we are only able to provide the tweet IDs used in our analysis rather than the full annotated tweet data. However, to enable reproducibility and further research, we are also making available the Python Jupyter notebook used for data collection and analysis, as well as the validation dataset used to assess the performance of our classification models. Additional, the Community Notes datasets are public and accessible at: https://communitynotes.x.com/guide/en/under-the-hood/download-data