Vast records of our everyday interests and concerns are being generated by our frequent interactions with the Internet. Here, we investigate how the searches of Google users vary across U.S. states with different birth rates and infant mortality rates. We find that users in states with higher birth rates search for more information about pregnancy, while those in states with lower birth rates search for more information about cats. Similarly, we find that users in states with higher infant mortality rates search for more information about credit, loans and diseases. Our results provide evidence that Internet search data could offer new insight into the concerns of different demographics.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

This dataset includes the raw data from Google Trends, averaged data, the construct of (S)ARIMA models, and cross-correlation coefficients. Three sets of data are due to sensitivity analyses performed in 3 different time spans. The monthly rates of suicide by 3 differents means in the USA are also included.

The study elucidated 3 Google search terms whose search volume trends precede trends in means-specific suicide rate in the United States.

Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

List of search terms used to query Google Health Trends API, by category.

This dataset combines official unemployment rates in Jordan from 2020 to mid-2024 with Google Trends data capturing search behaviour in Arabic and English. Experts curated the search phrases to reflect terms associated with job searches, unemployment concerns, and expressions of economic frustration. This unique dataset offers a rich blend of traditional economic indicators and non-traditional data, such as search trends, providing a novel approach to understanding and predicting unemployment in an emergency context, particularly during and after the COVID-19 pandemic.

By leveraging search trends as proxies for real-time public sentiment and behaviours, this dataset opens the door to more immediate economic forecasts, bridging the gap between releasing official unemployment data and real-time indicators. Such insights are precious during economic uncertainty or crisis periods, where swift policy responses are critical. Researchers and policymakers can use this dataset to explore correlations between public search behaviour and unemployment rates, potentially allowing for earlier interventions based on predictive models incorporating traditional and non-traditional data.

This dataset combines official unemployment rates in Jordan from 2010 to the end of September 2024 with Google Trends data capturing search behaviour in Arabic and English. Experts curated the search phrases to reflect terms associated with job searches, unemployment concerns, and expressions of economic frustration. This unique dataset offers a rich blend of traditional economic indicators and non-traditional data, such as search trends, providing a novel approach to understanding and predicting unemployment in an emergency context, particularly during and after the COVID-19 pandemic.

By leveraging search trends as proxies for real-time public sentiment and behaviours, this dataset opens the door to more immediate economic forecasts, bridging the gap between releasing official unemployment data and real-time indicators. Such insights are precious during economic uncertainty or crisis periods, where swift policy responses are critical. Researchers and policymakers can use this dataset to explore correlations between public search behaviour and unemployment rates, potentially allowing for earlier interventions based on predictive models incorporating traditional and non-traditional data.

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

In 2020, more than ** percent of hedge fund managers classified as alternative data market leaders used ***** or more alternative data sets globally, while only ***** percent of the rest of the market used at least ***** alternative data sets. This highlights the difference between the level of alternative data experience between the two groups. Using *** or more alternative data sets was the most popular approach across both groups with ** percent of market leaders and ** percent of the rest of the market doing this.

ObjectivesTo systematically review recidivism rates internationally, report whether they are comparable and, on the basis of this, develop best reporting guidelines for recidivism.MethodsWe searched MEDLINE, Google Web, and Google Scholar search engines for recidivism rates around the world, using both non-country-specific searches as well as targeted searches for the 20 countries with the largest total prison populations worldwide.ResultsWe identified recidivism data for 18 countries. Of the 20 countries with the largest prison populations, only 2 reported repeat offending rates. The most commonly reported outcome was 2-year reconviction rates in prisoners. Sample selection and definitions of recidivism varied widely, and few countries were comparable.ConclusionsRecidivism data are currently not valid for international comparisons. Justice Departments should consider using the reporting guidelines developed in this paper to report their data.

This dataset is composed of 176 Excel files, downloaded via the Google Trends tool between June 11 and 15, 2025. The files include time series data on the relative frequency (in percentages) of Google searches conducted in European Union member states on four topics: corruption, immigration, security, and transexuality. They also include data related to right-wing, center-right, and center-left (used as a control group) political parties in each country. Each file contains one column with the date (on a weekly basis) and another with the series value (a percentage normalized by Google Trends). The time span covered in each file is five years.

Repository to make datasets resulting from NIH funded research more accessible, citable, shareable, and discoverable. Data submitted will be reviewed to ensure there is no personally identifiable information in data and metadata prior to being published and in line with FAIR -Findable, Accessible, Interoperable, and Reusable principles. Data published on Figshare is assigned persistent, citable DOI (Digital Object Identifier) and is discoverable in Google, Google Scholar, Google Dataset Search, and more.Complited on July,2020. Researches can continue to share NIH funded data and other research product on figshare.com.

Data underlying: Miller, S., Preis, T., Mizzi, G., Bastos, L. S., Gomes, M. F. d. C., Coelho, F. C., Codeço, C. T., & Moat, H. S. (2022). Faster indicators of chikungunya incidence using Google searches. PLOS Neglected Tropical Diseases, 16, e0010441. doi:10.1371/journal.pntd.0010441.

MillerEtAl_ChikungunyaCaseCountData.csv This file contains data on weekly chikungunya case counts in the city of Rio de Janeiro, aggregated by the week in which the case was first diagnosed (the notification week) and the delay in number of weeks in entering the case in the surveillance system.

notification_week_commencing: the start date of the epidemiological week in which cases were notified notification_week: the epidemiological week in which cases were notified delay_in_weeks: the delay in number of weeks in entering the cases in the surveillance system case_count: the number of cases that were notified in the specified week with the specified delay in number of weeks

MillerEtAl_Fig1A.csv The data underlying Fig. 1A.

pct_entered: the percentage of cases notified in the specified epidemiological week that had been entered by the end of the week commencing 26 May 2019 notification_week_commencing: the start date of the epidemiological week in which cases were notified notified_cases: the number of cases notified in the specified epidemiological week entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the week commencing 26 May 2019

MillerEtAl_Fig1B.csv The data underlying Fig. 1B.

pct_entered: the percentage of cases notified in the specified epidemiological week that had been entered by the end of the week commencing 21 July 2019 notification_week_commencing: the start date of the epidemiological week in which cases were notified notified_cases: the number of cases notified in the specified epidemiological week entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the week commencing 21 July 2019

MillerEtAl_Fig1C.csv The data underlying Fig. 1C.

pct_entered: the percentage of cases notified in the specified epidemiological week that had been entered by the end of the week commencing 15 September 2019 notification_week_commencing: the start date of the epidemiological week in which cases were notified notified_cases: the number of cases notified in the specified epidemiological week entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the week commencing 15 September 2019

MillerEtAl_Fig2A.csv The data underlying Fig. 2A.

notification_week_commencing: the start date of the epidemiological week in which cases were notified notified_cases: the number of cases notified in the specified epidemiological week entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the same week

MillerEtAl_Fig3FigS1A.csv The data underlying Fig. 3 in the main text and Fig. A in S1 Appendix.

notification_week_commencing: the start date of the epidemiological week in which cases were notified notification_week: the epidemiological week in which cases were notified notified_cases: the number of cases notified in the specified epidemiological week baseline_mean: the baseline nowcasting model's mean estimate of the number of cases notified in the specified epidemiological week baseline_2.5: the lower bound of the baseline nowcasting model's 95% prediction interval for the number of cases notified in the specified epidemiological week baseline_97.5: the upper bound of the baseline nowcasting model's 95% prediction interval for the number of cases notified in the specified epidemiological week baseline_in_interval: whether the true number of notified cases for the specified epidemiological week fell within the baseline nowcasting model's 95% prediction interval baseline_error: the difference between the baseline nowcasting model's mean estimate of the number of cases notified in the specified epidemiological week and the true number of notified cases baseline_interval_width: the size of the baseline nowcasting model's 95% prediction interval for the number of cases notified in the specified epidemiological week google_mean: the mean estimate of the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches google_2.5: the lower bound of the 95% prediction interval for the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches google_97.5: the upper bound of the 95% prediction interval for the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches google_in_interval: whether the true number of notified cases for the specified epidemiological week fell within the 95% prediction interval produced by the nowcasting model using Google searches google_error: the difference between the mean estimate of the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches and the true number of notified cases google_interval_width: the size of the 95% prediction interval for the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches heuristic: the heuristic model's estimate of the number of cases notified in the specified epidemiological week

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for 5 age groups and the overall population during 5 seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

The table is arranged by alphabetical order of the location entity and category.

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.