In NLP Centre, dividing text into sentences is currently done with a tool which uses rule-based system. In order to make enough training data for machine learning, annotators manually split the corpus of contemporary text CBB.blog (1 million tokens) into sentences. Each file contains one hundredth of the whole corpus and all data were processed in parallel by two annotators.

Context

The data presented here was obtained in a Kali Machine from University of Cincinnati,Cincinnati,OHIO by carrying out packet captures for 1 hour during the evening on Oct 9th,2023 using Wireshark.This dataset consists of 394137 instances were obtained and stored in a CSV (Comma Separated Values) file.This large dataset could be used utilised for different machine learning applications for instance classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

The dataset can be used for a variety of machine learning tasks, such as network intrusion detection, traffic classification, and anomaly detection.

Content :

This network traffic dataset consists of 7 features.Each instance contains the information of source and destination IP addresses, The majority of the properties are numeric in nature, however there are also nominal and date kinds due to the Timestamp.

The network traffic flow statistics (No. Time Source Destination Protocol Length Info) were obtained using Wireshark (https://www.wireshark.org/).

Dataset Columns:

No : Number of Instance. Timestamp : Timestamp of instance of network traffic Source IP: IP address of Source Destination IP: IP address of Destination Portocol: Protocol used by the instance Length: Length of Instance Info: Information of Traffic Instance

Acknowledgements :

I would like thank University of Cincinnati for giving the infrastructure for generation of network traffic data set.

Ravikumar Gattu , Susmitha Choppadandi

Inspiration : This dataset goes beyond the majority of network traffic classification datasets, which only identify the type of application (WWW, DNS, ICMP,ARP,RARP) that an IP flow contains. Instead, it generates machine learning models that can identify specific applications (like Tiktok,Wikipedia,Instagram,Youtube,Websites,Blogs etc.) from IP flow statistics (there are currently 25 applications in total).

**Dataset License: ** CC0: Public Domain

Dataset Usages : This dataset can be used for different machine learning applications in the field of cybersecurity such as classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

ML techniques benefits from this Dataset :

This dataset is highly useful because it consists of 394137 instances of network traffic data obtained by using the 25 applications on a public,private and Enterprise networks.Also,the dataset consists of very important features that can be used for most of the applications of Machine learning in cybersecurity.Here are few of the potential machine learning applications that could be benefited from this dataset are :

1. Network Performance Monitoring : This large network traffic data set can be utilised for analysing the network traffic to identifying the network patterns in the network .This help in designing the network security algorithms for minimise the network probelms.

2. Anamoly Detection : Large network traffic dataset can be utilised training the machine learning models for finding the irregularitues in the traffic which could help identify the cyber attacks.

3.Network Intrusion Detection : This large dataset could be utilised for machine algorithms training and designing the models for detection of the traffic issues,Malicious traffic network attacks and DOS attacks as well.

A virtual database created by the Neuroscience Information Framework currently indexing Scientific Blog and News resources such as: Nature Network Blogs, Wired Science Blogs, The Guardian: Science, It Takes 30, Scientific American Cross-Check, Scientific American Bering in Mind, Research Blogging, CENtral Science, ScienceBlogs: Medicine and Health, American Guest Blog, Scientific American Observations, LabSpaces, RetractionWatch.com, Wired Science, Genomes Unzipped, PLoS Blogs, Daring Nucleic Adventures - genegeek, H2SO4Hurts - Brian Krueger PhD, and Sciblogs.

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 Bangla Sentiment Dataset is a curated collection of sentiment-rich textual data in Bangla, focused on recent and trending topics. This dataset has been compiled from diverse sources, including Bangladeshi online newspapers, social media platforms, and blogs, ensuring a wide spectrum of language styles and sentiment expressions.

Key Features: Focus on Recent Topics: The dataset emphasizes contemporary issues, trending discussions, and popular topics in Bangladeshi society. This includes sentiments on political developments, social movements, entertainment, cultural events, and other recent happenings.

Source Variety:

Online Newspapers: Articles, editorials, headlines, and reader comments provide structured and semi-formal sentiment data. Social Media: Posts, tweets, and comments reflect informal, conversational language with high emotional expressiveness. Blogs: Opinion pieces and discussions offer detailed and context-rich sentiment content. Sentiment Labels: Each entry in the dataset is annotated with one of the following sentiment categories:

Positive (1): Texts expressing happiness, agreement, or optimism. Negative (0): Texts reflecting criticism, disagreement, or pessimism. Neutral (2): Texts presenting balanced or factual statements with minimal emotional bias. Linguistic and Stylistic Diversity: The dataset captures a range of Bangla language variations, including:

Formal and informal Bangla usage. Regional dialects. Transliterated Bangla (Banglish) commonly used on social media. Real-World Context: The inclusion of recent topics ensures that the dataset is relevant for analyzing public sentiment around current events and trends. This makes it particularly useful for real-time sentiment analysis applications.

This dataset provides an invaluable resource for researchers and practitioners aiming to explore sentiment analysis in Bangla, with a special emphasis on modern-day relevance and real-world applicability.

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.

Over the past several years, the bicycle movement in Indianapolis has gained a great deal of momentum. Seventy-four miles of bicycle lanes and trails have been designed and implemented to support travel by bike. Despite the extensive investment in fostering a culture of cycling in the city, there is not yet a significant formal mechanism for documenting and analyzing the effects of these changes. In the case of the city’s bicycling community, records are being created in comments sections of blogs and online newspaper articles, and include personal snapshots and reflections published via social media platforms that are of a troublingly ephemeral nature. The residents of Indianapolis are divided in their estimations of the movement. There has been much debate and sides taken. Like parks, the streets of a community are shared public spaces whose use needs to be negotiated. The bicycle movement in Indianapolis presents an ideal issue around which to develop a digital community archive, as the geographic and mobile nature of the phenomenon will expose the challenges of capturing both place-bound and digital history as it is happening. Information regarding the movement is current and thus is mostly in a digital form. Much like changes to the physical landscape of a city, current digital information can be difficult to grasp all at once as it is widely-distributed.This paper will explore the legal issues related to the collection, organization, and preservation of relevant content that is available through the web, sometimes freely and sometimes behind pay walls. A comprehensive list of potential sources (e.g. newspapers, social media sites, blogs) needed to create an archive with the cycling community will be analyzed to identify the types of legal challenges (e.g. privacy, publicity rights, copyright licensing) would likely face. Recommendations for dealing with these challenges will be made.

Data and code for Malejka et al. (2021), "Correlation analysis to investigate unconscious mental processes". The present project focused on a particular domain of this literature, implicit learning. Studies conducted in this area try to determine whether we are able to detect regularities in our environment without awareness of those regularities. Finding evidence of awareness in these domains is important because it suggests that some degree of control may be available as well. In the present project we propose new methods for the study of unconscious learning. Many of the problems that we have detected in our previous research can be ameliorated by employing cutting-edge statistical analysis, including Bayesian and meta-analytic methods and model fitting. However, the validity of these approaches in the domain of implicit cognition remains untested.A consensus among researchers is that much of our behaviour is based on rather automatic processes we are barely aware of and over which we have little control. Research suggests that exposure to subtle cues can have dramatic effects on our decisions. For instance, asking people to provide the last 2 digits of their social security number biases how much they are willing to pay for products and commodities. Similarly, according to some researchers, people are more likely to be impolite and disrespectful if they have been exposed to words related to rudeness while solving anagrams. Another line of research suggests that we take many of our (important) decisions when distracted and thinking about other things and that this 'unconscious thought' process actually improves the quality of our decisions. These studies pertain to a larger area of research usually called 'implicit cognition', which explores how unconscious mechanisms contribute to cognitive processes including perception, learning, memory, and decision making. This area of research has attracted a great deal of attention from the media and features frequently in popular science books, blogs, and documentaries. Some authors have even suggested that parts of this research could be used to improve our decisions in different domains at a societal level (for example, in health behaviour and pension planning). The present project focuses on a particular domain of this literature, implicit learning. Studies conducted in this area try to determine whether we are able to detect regularities in our environment without awareness of those regularities. In other words, these studies address whether we can learn something without realising that we are indeed learning it. In recent years there have been thousands of demonstrations of implicit learning effects in the scientific literature and, not surprisingly, this literature has become increasingly influential in all areas of psychology, with an important impact in our understanding of human cognition and psychopathology. Unfortunately, our previous research suggests that much of this evidence is undermined by fundamental methodological problems that preclude any strong conclusions about the reliability of unconscious learning effects. We have shown that many of these studies find unconscious learning because researchers use weaker methods to assess whether people are conscious of what they have learned than to assess whether learning has taken place. Naturally, this implies that learning is easily detected but awareness is not, which creates the illusion that learning has taken place unconsciously. Finding evidence of awareness in these domains is important because it suggests that some degree of control may be available as well. In the present project we propose new methods for the study of unconscious learning. Many of the problems that we have detected in our previous research can be ameliorated by employing cutting-edge statistical analysis, including Bayesian and meta-analytic methods and model fitting. However, the validity of these approaches in the domain of implicit cognition remains untested. A second goal is to conduct a large-scale exploration of the prevalence and magnitude of these problems. Our previous studies have focused on a very particular effect studied in implicit learning research ('contextual cueing'). We suspect that many of these problems transcend this domain and affect a large proportion of current studies on implicit learning. The potential impact of this assessment is difficult to overestimate. Finally, we will set up a collaboration with other international laboratories working on this topic to gather the largest and most sensitive data set of implicit learning effects available so far. This data set will be publicly available for all researchers, which will make it a fundamental resource for the study of unconscious cognitive processes for many years to come.

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.

Thank you for your understanding and patience during this difficult and unprecedented period. You can find advice and guidance about accessing information from public bodies from the Information Commissioner’s Office (ICO) at www.ico.org.uk/your-data-matters/official-information Please also see the following information from the ICO at www.ico.org.uk/about-the-ico/news-and-events/news-and-blogs/2020/03/coronavirus-and-personal-data/ Details of Council spending from April 2016 onward. Please click 'Download' to view this data in a spreadsheet. This will enable you to filter the information more easily, for example by month. This dataset is updated monthly.

[ Derived from parent entry - See data hierarchy tab ]

Each year (in ~July/August) the TRENDY group runs their DGVMs (to the end of the previous year) in support of the Global Carbon Budget (GCB) annual assessment. TRENDY is currently led by Stephen Sitch, with GCB led by Pierre Friedlingstein (Exeter).

TRENDY delivers global & regional – Northern, tropics, and southern- NBP data from all models for the GCB ESSD publication (these regional data are then freely available). However TRENDY models output a wider range of carbon and hydrological data (e.g. all gridded data) which are available for spin-off studies. The modeling teams have priority, but these data are also available to external collaborators on the agreement (for further information see https://blogs.exeter.ac.uk/trendy/).

This dataset group includes the contribution of MPI-ESM/JSBACH3.2 to the TRENDY project.

This map was updated in April of 2022. To see the archived version of this map, click here: https://nmcdc.maps.arcgis.com/home/item.html?id=2e4c4c4cafcc49db80837f32912e66a5#overviewThis map displays data from the Selected Economic Indicators (DP03) dataset from the 2020 American Community Survey 5-Yr Estimates, U.S. Census Bureau. Data is shown at the level of Census Tract and County levels. Small Areas are not on this map at this time (aggregation of Census Tracts developed by the New Mexico Department of Health). Measuring poverty is a topic of much current discussion. See the following links: A Different Way to Measure Poverty - https://www.sanders.senate.gov/imo/media/image/census.jpg"Few topics in American society have more myths and stereotypes surrounding them than poverty, misconceptions that distort both our politics and our domestic policy making."They include the notion that poverty affects a relatively small number of Americans, that the poor are impoverished for years at a time, that most of those in poverty live in inner cities, that too much welfare assistance is provided and that poverty is ultimately a result of not working hard enough. Although pervasive, each assumption is flat-out wrong." -Mark Rank, Professor of Social Welfare at Washington University: https://opinionator.blogs.nytimes.com/2013/11/02/poverty-in-america-is-mainstream/

Techsalerator’s News Event Data in Oceania provides a thorough and detailed dataset designed to offer businesses, analysts, journalists, and researchers with comprehensive insights into significant news events across the Oceania region. This dataset captures and categorizes major events reported from a variety 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 latest events, ensuring users have access to the most current news and can stay informed about recent developments as they occur. Geographic Segmentation:

Events are tagged with their respective countries and territories within Oceania. 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 various criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. Oceania Countries and Territories Covered: Australia and New Zealand: Australia New Zealand Pacific Island Countries and Territories: Fiji Kiribati Marshall Islands Micronesia (Federated States of) Nauru Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu French Overseas Territories: New Caledonia (French special collectivity) French Polynesia (French overseas collectivity) Wallis and Futuna (French overseas collectivity) U.S. Territories: American Samoa (U.S. territory) Guam (U.S. territory) Northern Mariana Islands (U.S. territory) Benefits of the Dataset: Strategic Insights: Businesses and analysts can utilize 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 Oceania, enabling them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Use: Researchers can leverage the dataset for longitudinal studies, trend analysis, and academic research on various topics related to Oceania’s news and events. Techsalerator’s News Event Data in Oceania is an essential resource for accessing and analyzing significant news events across the region. 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 Africa offers a comprehensive and detailed dataset designed to provide businesses, analysts, journalists, and researchers with a thorough view of significant news events across the African continent. This dataset captures and categorizes important events reported from a wide variety 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 occurrences.

Key Features of the Dataset: Extensive Coverage:

The dataset aggregates news events from numerous 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 latest events, ensuring that users have access to current news and can stay informed about recent developments as they occur. Geographic Segmentation:

Events are tagged with their respective countries and regions within Africa. 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 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 the evolution of news 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. African Countries Covered: Northern Africa: Algeria Egypt Libya Mauritania Morocco Sudan Tunisia Sub-Saharan Africa: West Africa: Benin Burkina Faso Cape Verde Ivory Coast (Côte d'Ivoire) Gambia Ghana Guinea Guinea-Bissau Liberia Mali Niger Nigeria Senegal Sierra Leone Togo Central Africa: Angola Cameroon Central African Republic Chad Congo, Republic of the Congo, Democratic Republic of the Equatorial Guinea Gabon São Tomé and Príncipe East Africa: Burundi Comoros Djibouti Eritrea Eswatini (Swaziland) Ethiopia Kenya Lesotho Malawi Mauritius Rwanda Seychelles Somalia Tanzania Uganda Southern Africa: Botswana Lesotho Namibia South Africa Eswatini (Swaziland) Zimbabwe Benefits of the Dataset: Strategic Insights: Businesses and analysts can leverage 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 Africa, 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 African news and events. Techsalerator’s News Event Data in Africa is an essential 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.