A global study found that 55 percent of news avoiders were interested in positive news stories, making this the most interesting type of news for these consumers. News providing solutions or explaining a situation was also popular, whereas big stories of the day were deemed the least interesting.

The ratio of unique data to replicated cata is expected to slowly migrate from 1:9 to 1:10 from 2020 to 2024. Meanwhile, the amount of data created, captured, copied, and consumed worldwide is expected to grow from around 59 zettabytes (ZB) in 2020 to around 149 ZB in 2024.

In 2023, credential phishing was the most reported type of unique threat, with close to 940 thousand reports by end users. Malware ranked second, with over 52 thousand reports, followed by banking, with approximately 16 thousand reports.

The Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police. \r \r The CSA aims to provide an efficient and transparent information service to assist and inform policy makers, researchers and the Victorian public. \r \r The legal basis for the Crime Statistics Agency is the Crime Statistics Act 2014, which provides for the publication and release of crime statistics, research into crime trends, and the employment of a Chief Statistician for that purpose. \r \r Under the provisions of the Act, the Chief Statistician is empowered to receive law enforcement data from the Chief Commissioner of Police and is responsible for publishing and releasing statistical information relating to crime in Victoria.\r \r The number of unique victims recorded in Victoria, and demographic characteristics of victims.\r \r Data Classification - https://www.crimestatistics.vic.gov.au/about-the-data/classifications\r \r Glossary and Data Dictionary - https://www.crimestatistics.vic.gov.au/about-the-data/glossary-and-data-dictionary

Subject: EducationSpecific: Online Learning and FunType: Questionnaire survey data (csv / excel)Date: February - March 2020Content: Students' views about online learning and fun Data Source: Project OLAFValue: These data provide students' beliefs about how learning occurs and correlations with fun. Participants were 206 students from the OU

This bar chart displays books by publication date and is filtered where the author includes Jane Flinn and the book includes All things history : learning the past with fun facts. The data is about books.

Critical to any regression analysis is the identification of observations that exert a strong influence on the fitted regression model. Traditional regression influence statistics such as Cook's distance and DFFITS, each based on deleting single observations, can fail in the presence of multiple influential observations if these influential observations “mask” one another, or if other effects such as “swamping” occur. Masking refers to the situation where an observation reveals itself as influential only after one or more other observations are deleted. Swamping occurs when points that are not actually outliers/influential are declared to be so because of the effects on the model of other unusual observations. One computationally expensive solution to these problems is the use of influence statistics that delete multiple rather than single observations. In this article, we build on previous work to produce a computationally feasible algorithm for detecting an unknown number of influential observations in the presence of masking. An important difference between our proposed algorithm and existing methods is that we focus on the data that remain after observations are deleted, rather than on the deleted observations themselves. Further, our approach uses a novel confirmatory step designed to provide a secondary assessment of identified observations. Supplementary materials for this article are available online.

As of 2024, the most unconventional place for Brits to watch sports from Sky Mobile was at work, with 38 percent of respondents stating to do so. Further 30 percent of respondents to the survey admitted having consumed sporting actions during a family event.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Detailed Active addresses (weekly) metrics and analytics for pump.fun, including historical data and trends.

One-to-one correspondence of the distributions of simulated ΔCq and eΔCq data.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Financial overview and grant giving statistics of Independent Order of Odd Fellows Columbia Lodge 10

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

• An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.
• The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.
• The property’s Flood Factor as well as data on economic loss.
• The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.
• Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.
• Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

| Field Name | Type | Description | | fsid | int | First Street ID (FSID) is a unique identifier assigned to each location | | long | float | Longitude | | lat | float | Latitude | | zcta | int | ZIP code tabulation area as provided by the US Census Bureau | | blkgrp_fips | int | US Census Block Group FIPS Code | | tract_fips | int | US Census Tract FIPS Code | | county_fips | int | County FIPS Code | | cd_fips | int | Congressional District FIPS Code for the 116th Congress | | state_fips | int | State FIPS Code | | floodfactor | int | The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist) | | CS_depth_RP_YY | int | Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00 | | CS_chance_flood_YY | float | Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00 | | aal_YY_CS | int | The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low | | hist1_id | int | A unique First Street identifier assigned to a historic storm event modeled by First Street | | hist1_event | string | Short name of the modeled historic event | | hist1_year | int | Year the modeled historic event occurred | | hist1_depth | int | Depth (in cm) of flooding to the building from this historic event | | hist2_id | int | A unique First Street identifier assigned to a historic storm event modeled by First Street | | hist2_event | string | Short name of the modeled historic event | | hist2_year | int | Year the modeled historic event occurred | | hist2_depth | int | Depth (in cm) of flooding to the building from this historic event | | adapt_id | int | A unique First Street identifier assigned to each adaptation project | | adapt_name | string | Name of adaptation project | | adapt_rp | int | Return period of flood event structure provides protection for when applicable | | adapt_type | string | Specific flood adaptation structure type (can be one of many structures associated with a project) | | fema_zone | string | Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders | | footprint_flag | int | Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0) |

A 2024 survey on children's news consumption in the United Kingdom found that music was the most interesting news topic for girls aged 12 to 15 years old, with 59 percent saying that they found it interesting to read, watch, or listen to news about music, singers, and musicians. Interest in news topics often varied according to gender: boys were keener on news about science and technology or sports than their female counterparts, whereas girls were more likely to enjoy news about fashion and beauty or celebrities and famous people.

This paper provides a novel model that is more relevant than the well-known conventional distributions, which stand for the two-parameter distribution of the lifetime modified Kies Topp–Leone (MKTL) model. Compared to the current distributions, the most recent one gives an unusually varied collection of probability functions. The density and hazard rate functions exhibit features, demonstrating that the model is flexible to several kinds of data. Multiple statistical characteristics have been obtained. To estimate the parameters of the MKTL model, we employed various estimation techniques, including maximum likelihood estimators (MLEs) and the Bayesian estimation approach. We compared the traditional reliability function model to the fuzzy reliability function model within the reliability analysis framework. A complete Monte Carlo simulation analysis is conducted to determine the precision of these estimators. The suggested model outperforms competing models in real-world applications and may be chosen as an enhanced model for building a statistical model for the COVID-19 data and other data sets with similar features.

Portail Open data CDC - nombre d'utilisateur unique par mois

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Anomaly Detection Market Size 2024-2028

The anomaly detection market size is forecast to increase by USD 3.71 billion at a CAGR of 13.63% between 2023 and 2028. Anomaly detection is a critical aspect of cybersecurity, particularly in sectors like healthcare where abnormal patient conditions or unusual network activity can have significant consequences. The market for anomaly detection solutions is experiencing significant growth due to several factors. Firstly, the increasing incidence of internal threats and cyber frauds has led organizations to invest in advanced tools for detecting and responding to anomalous behavior. Secondly, the infrastructural requirements for implementing these solutions are becoming more accessible, making them a viable option for businesses of all sizes. Data science and machine learning algorithms play a crucial role in anomaly detection, enabling accurate identification of anomalies and minimizing the risk of incorrect or misleading conclusions.

However, data quality is a significant challenge in this field, as poor quality data can lead to false positives or false negatives, undermining the effectiveness of the solution. Overall, the market for anomaly detection solutions is expected to grow steadily in the coming years, driven by the need for enhanced cybersecurity and the increasing availability of advanced technologies.

What will be the Anomaly Detection Market Size During the Forecast Period?

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Anomaly detection, also known as outlier detection, is a critical data analysis technique used to identify observations or events that deviate significantly from the normal behavior or expected patterns in data. These deviations, referred to as anomalies or outliers, can indicate infrastructure failures, breaking changes, manufacturing defects, equipment malfunctions, or unusual network activity. In various industries, including manufacturing, cybersecurity, healthcare, and data science, anomaly detection plays a crucial role in preventing incorrect or misleading conclusions. Artificial intelligence and machine learning algorithms, such as statistical tests (Grubbs test, Kolmogorov-Smirnov test), decision trees, isolation forest, naive Bayesian, autoencoders, local outlier factor, and k-means clustering, are commonly used for anomaly detection.

Furthermore, these techniques help identify anomalies by analyzing data points and their statistical properties using charts, visualization, and ML models. For instance, in manufacturing, anomaly detection can help identify defective products, while in cybersecurity, it can detect unusual network activity. In healthcare, it can be used to identify abnormal patient conditions. By applying anomaly detection techniques, organizations can proactively address potential issues and mitigate risks, ensuring optimal performance and security.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premise


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period. The market is witnessing a notable shift towards cloud-based solutions due to their numerous advantages over traditional on-premises systems. Cloud-based anomaly detection offers breaking changes such as quicker deployment, enhanced flexibility, and scalability, real-time data visibility, and customization capabilities. These features are provided by service providers with flexible payment models like monthly subscriptions and pay-as-you-go, making cloud-based software a cost-effective and economical choice. Anodot, Ltd, Cisco Systems Inc, IBM Corp, and SAS Institute Inc are some prominent companies offering cloud-based anomaly detection solutions in addition to on-premise alternatives. In the context of security threats, architectural optimization, marketing strategies, finance, fraud detection, manufacturing, and defects, equipment malfunctions, cloud-based anomaly detection is becoming increasingly popular due to its ability to provide real-time insights and swift response to anomalies.

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The cloud segment accounted for USD 1.59 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

When it comes to Anomaly Detection Market growth, North America is estimated to contribute 37% to the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast per

Financial overview and grant giving statistics of Just for Fun Baseball