The present dataset is generated in the frame of the Horizon 2020 project "InnORBIT: Empowering innovation intermediaries to generate sustainable initiatives to accelerate the commercialisation of space innovation" (innorbit.eu). This dataset describes the InnORBIT project's dissemination and communication plan and also includes the data collected from dissemination and communication activities to measure the progress against the project's targets for outreach during the first 18 months of project implementation (January 1st, 2021 - June 30th, 2022). This dataset will be updated in a second and final version, after the end of InnORBIT's grant duration in July 2023. The final version will provide a full dataset accounting for the project's outreach activities. This first version of the dataset contains the following files and documents: [InnORBIT-DisseminationCommunicationPlan_v2_20220929.pdf]: Final version of the project's Dissemination, Awareness raising and Communication Plan (DACP), that describes the key target audiences, key messages and value offered by InnORBIT through in terms of knowledge, services and solutions boosting entrepreneurship in the space industry and the digital tools offered via the InnORBIT digital toolbox. The InnORBIT DACP also describes the channels, tools and activities employed to reach out effectively the project's target groups. The core Key Performance Indicators (KPIs) that indicate the performance level of the project's strategy and indicates areas for improvement are outlined. The updated version also outlines the achievements of the project's dissemination for the first 18 months of implementation (January 2021 - June 2022). [InnORBIT_DisseminationActivities_Data_20220929. xlsx]: A spreadsheet used to collect raw data about the project's dissemination activities, calculate the InnORBIT's KPIs for Dissemination and Communication to track progress against targets. The data span from January 1st, 2021 to June 30th, 2022. [InnORBIT-WebsiteAnalytics-AudienceOverview_20220929.pdf]: A Google Analytics report summarising InnORBIT website's audience demographics and overall page performance (visits, sessions, users). The data span from January 1st, 2021 to June 30th, 2022. [InnORBIT-WebsiteAnalytics-AudienceAcquisition_20220929.pdf]: A Google Analytics report summarising the main sources generating traffic for the InnORBIT website and the bahaviour of users coming from each source. The data span from January 1st, 2021 to June 30th, 2022. [InnORBIT-WebsiteAnalytics-AudienceBehaviour_20220929.pdf]: A Google Analytics report providing further insight on users' behaviour when using the InnORBIT website. The data span from January 1st, 2021 to June 30th, 2022.

As of February 2025, it was found that men between the ages of 25 and 34 years made up Facebook's largest audience, accounting for 18.5 percent of global users. Additionally, Facebook's second-largest audience base could be found with men aged 18 to 24 years. Facebook connects the world Founded in 2004 and going public in 2012, Facebook is one of the biggest internet companies in the world with influence that goes beyond social media. It is widely considered as one of the Big Four tech companies, along with Google, Apple, and Amazon (all together known under the acronym GAFA). Facebook is the most popular social network worldwide and the company also owns three other billion-user properties: mobile messaging apps WhatsApp and Facebook Messenger, as well as photo-sharing app Instagram. Facebook usersThe vast majority of Facebook users connect to the social network via mobile devices. This is unsurprising, as Facebook has many users in mobile-first online markets. Currently, India ranks first in terms of Facebook audience size with 378 million users. The United States, Brazil, and Indonesia also all have more than 100 million Facebook users each.

This dataset is the result of cleaning and aggregating data as part of the Process and Analyze phases of the Google Data Analytics Capstone Project Cyclistic Case Study 2013-2021 with demographics. The original data for the project has been made available by Motivate International Inc. under this license.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

The TV advertising analytics market is experiencing robust growth, driven by the increasing demand for precise audience measurement and the shift towards data-driven advertising strategies. The convergence of traditional television with digital platforms, including streaming services and connected TVs (CTV), is fueling this expansion. Market players are leveraging advanced technologies like AI and machine learning to provide granular insights into viewer behavior, enabling more effective targeting and campaign optimization. This allows advertisers to understand viewership patterns, demographics, and engagement levels with unprecedented accuracy, leading to improved ROI and more efficient media spending. The market is segmented based on solutions (e.g., audience measurement, campaign optimization, and attribution), deployment (cloud-based, on-premises), and end-users (advertisers, broadcasters, and media agencies). The competitive landscape is characterized by a mix of established players, such as Nielsen and IBM, and agile tech companies like Realytics and Alphonso, each offering a unique suite of analytical tools and services. While data privacy concerns and the complexity of integrating disparate data sources pose challenges, the overall market outlook remains positive, with a projected strong Compound Annual Growth Rate (CAGR) leading to substantial market expansion over the coming years. The growth trajectory is influenced by several factors. Technological advancements continue to refine analytical capabilities, providing deeper insights into viewer preferences and behavior. The rise of streaming platforms and CTVs presents both opportunities and challenges, necessitating sophisticated analytics to measure viewership across diverse channels. Increased demand for accountability and transparency in advertising spend is driving the adoption of advanced analytics solutions. However, regulatory hurdles concerning data privacy and the integration of fragmented data from various sources remain potential obstacles. To maintain competitiveness, companies are actively investing in research and development, forging strategic partnerships, and expanding their geographical reach to capture a larger share of the rapidly evolving market. The robust growth is expected to continue, supported by industry innovations and the ongoing demand for effective television advertising strategies.

The Healthcare Descriptive Analytics Market is experiencing robust growth, projected to reach $18.36 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 23.50% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of electronic health records (EHRs) generates massive datasets ripe for analysis, leading to improved patient care, operational efficiency, and more effective research. Furthermore, advancements in big data technologies and artificial intelligence (AI) are enabling sophisticated analytical capabilities, allowing healthcare providers and organizations to extract valuable insights from complex healthcare data. The demand for data-driven decision-making in areas like precision medicine, population health management, and risk stratification is further fueling market growth. Strong government initiatives promoting healthcare data interoperability and the rising need for improved healthcare outcomes also contribute significantly to the market's expansion. Market segmentation reveals strong performance across various applications. Clinical data analytics, focused on improving diagnoses and treatment, holds a significant share, followed by financial data analytics used for optimizing revenue cycle management and reducing costs. The software component dominates the market due to its versatility and scalability, complemented by robust growth in cloud-based deployment models, owing to their cost-effectiveness, accessibility, and enhanced security features. Private organizations, especially hospitals and clinics, are leading end-users, driving adoption across various segments. Geographically, North America is currently the largest market, fueled by advanced healthcare infrastructure and early adoption of analytical technologies. However, the Asia-Pacific region is poised for substantial growth, driven by increasing healthcare spending and technological advancements. The competitive landscape is dynamic, with established players like IBM, Oracle, and McKesson alongside specialized healthcare analytics firms, all vying for market share through innovative solutions and strategic partnerships. Recent developments include: In November 2022, Ursa Health updated Ursa Studio, its healthcare analytics development platform, to help organizations meet the requirements of the Centers for Medicare and Medicaid Services (CMS)., In November 2022, Hartford HealthCare entered a long-term partnership with Google Cloud to advance the healthcare digital transformation, improve data analytics, and enhance care delivery and access.. Key drivers for this market are: Need for Comprehensive Analytics, Integration of Big Data into Healthcare. Potential restraints include: Need for Comprehensive Analytics, Integration of Big Data into Healthcare. Notable trends are: Cloud-based Segment Expected to Hold a Significant Share of the Market During the Forecast Period.

The Location-Based Analytics market is experiencing robust growth, driven by the increasing adoption of mobile devices, the proliferation of location data from various sources (GPS, Wi-Fi, cellular networks), and the rising need for businesses to understand and leverage customer location insights for improved decision-making. A compound annual growth rate (CAGR) of, let's assume, 15% from 2025 to 2033 indicates a significant expansion of the market, with a projected market size of approximately $80 billion by 2033 (assuming a 2025 market size of $25 billion, a reasonable estimate given the presence of major players like Google and IBM). Key market drivers include the need for enhanced customer experience personalization, precise supply chain optimization, real-time risk management, and improved marketing campaign effectiveness. The increasing availability of sophisticated analytical tools and platforms is further fueling this growth. However, challenges such as data privacy concerns, the complexity of integrating diverse data sources, and the need for skilled professionals to manage and interpret location data act as restraints to some extent. Market segmentation reveals a diverse landscape, with solutions catering to various industries (retail, logistics, healthcare, etc.). Companies like Alteryx, Google, HERE Technologies, Hexagon, Microsoft, Oracle, Pitney Bowes, Sisense, Syncsort, IBM, and Quppa are actively competing in this space, each offering unique technologies and capabilities. Regional market variations exist, with North America and Europe currently holding significant shares, but developing economies in Asia-Pacific and Latin America are showing rapid growth potential, indicating the market's global reach and future expansion. The ongoing integration of location data with other data sources (e.g., demographic, transactional) is driving the emergence of more comprehensive and powerful analytical solutions, leading to further innovation and market expansion within the forecast period.

Contains Gallup data from countries that are home to more than 98% of the world's population through a state-of-the-art Web-based portal. Gallup Analytics puts Gallup's best global intelligence in users' hands to help them better understand the strengths and challenges of the world's countries and regions. Users can access Gallup's U.S. Daily tracking and World Poll data to compare residents' responses region by region and nation by nation to questions on topics such as economic conditions, government and business, health and wellbeing, infrastructure, and education.

The Gallup Analytics Database is accessed through the Cornell University Libraries here. In addition, a CUL subscription also allows access to the Gallup Respondent Level Data. For access please refer to the documentation below and then request the variables you need here.

Before requesting data from the World Poll, please see the Getting Started guide and the Worldwide Research Methodology and Codebook (You will need to request access). The Codebook will give you information about all available variables in the datasets. There are other guides available as well in the google folder. You can also access information about questions asked and variables using the Gallup World Poll Reference Tool. You will need to create your user account to access the tool. This will only give you access to information about the questions asked and variables. It will not give you access to the data.

For further documentation and information see this site from New York University Libraries. The Gallup documentation for the World Poll methodology is also available under the Data and Documentation tab.

In addition to the World Poll and Daily Tracking Poll, also available are the Gallup Covid-19 Survey, Gallup Poll Social Series Surveys, Race Relations Survey, Confidence in Institutions Survey, Honesty and Ethics in Professions Survey, and Religion Battery.

The process for getting access to respondent-level data from the Gallup U.S. Daily Tracking is similar to the World Poll Survey. There is no comparable discovery tool for U.S. Daily Tracking poll questions, however. Users need to consult the codebooks and available variables across years.

The COVID-19 web survey began on March 13, 2020 with daily random samples of U.S. adults, aged 18 and older who are members of the Gallup Panel. Before requesting data, please see the Gallup Panel COVID-19 Survey Methodology and Codebook.

The Gallup Poll Social Series (GPSS) dataset is a set of public opinion surveys designed to monitor U.S. adults’ views on numerous social, economic, and political topics. More information is available on the Gallup website: https://www.gallup.com/175307/gallup-poll-social-series-methodology.aspx As each month has a unique codebook, contact CCSS-ResearchSupport@cornell.edu to discuss your interests and start the data request process.

Starting in 1973, Gallup started measuring the confidence level in several US institutions like Congress, Presidency, Supreme Court, Police, etc. The included dataset includes data beginning in 1973 and data is collected once per year. Users should consult the list of available variables.

The Race Relations Poll includes topics that were previously represented in the GPSS Minority Relations Survey that ran through 2016. The Race Relations Survey was conducted November 2018. Users should consult the codebook for this poll before making their request.

The Honesty and Ethics in Professions Survey – Starting in 1976, Gallup started measuring US perceptions of the honesty and ethics of a list of professions. The included dataset was added to the collection in March 2023 and includes data ranging from 1976-2022. Documentation for this collection is located here and will require you to request access.

Religion Battery: Consolidated list of items focused on religion in the US from 1999-2022. Documentation for this collection is located here and will require you to request access.

This data set contains COVID-19 hospital incidence, temperature and human mobility and contact data recorded between 2020-03-24 and 2021-03-30 used in the paper:

Selinger et al. 2021: Predicting COVID-19 incidence in French hospitals using human contact network analytics. 10.1016/j.ijid.2021.08.029

See methods in the article for detailed descriptions and the data curation process.

1) cov_mob_tst_national.csv contains national-level data

The columns comprise:

incid_hosp: hospital admission incidence

incid_rea: ICU admission incidence

incid_dc: hospital death incidence

incid_rad: incidence of those returned home

within_departement_colocation_X%: X%-quantile of colocation probabilities with départements

between_departement_colocation_X%: X%-quantile of colocation probabilities between départements

fb_population_coverage_X%: X%-quantile of ratio of fb_population over census population in département

null_links_X%: X%-quantile of null links across départements

clustering_X%: X%-quantile of clustering coefficients across départements

ricci_X%: X%-quantile of curvature across départements

ricci_min_X%: X%-quantile of minimum curvature across départements

ricci_mean_X%: X%-quantile of average curvature across départements

ricci_max_X%: X%-quantile of maximum curvature across départements

strength_X%: X%-quantile of network strengths across départements

betweenness_centrality_X%: X%-quantile of betweenness_centrality scores across départements

positive_test_ratio_weekly: ratio of weekly cumulated positive tested over weekly cumulated tests

retail_and_recreation_percent_change_from_baseline: Google Mobility Reports

grocery_and_pharmacy_percent_change_from_baseline: Google Mobility Reports

parks_percent_change_from_baseline: Google Mobility Reports

transit_stations_percent_change_from_baseline: Google Mobility Reports

workplaces_percent_change_from_baseline: Google Mobility Reports

residential_percent_change_from_baseline: Google Mobility Reports

mean_temperature_X%: X% quantile of mean daily temperatures averaged over the week across départements

min_temperature_X%: X% quantile of minimum daily temperatures averaged over the week across départements

max_temperature_X%: X% quantile of maximum daily temperatures averaged over the week across départements

2) cov_mob_dep.csv contains département-level data

The columns comprise:

dep: département code

incid_hosp: hospital admission incidence

incid_rea: ICU admission incidence

incid_dc: hospital death incidence

incid_rad: incidence of those returned home

week: week (matched to colocation data recording usually on Tuesdays)

dep_name: name of the département

null_links: number of null links

betweenness_centrality: betweenness centrality

clustering: clustering coefficient

strength: network strength

ricci_mean: minimum curvature among all edges incident to a département

ricci_min: mean curvature across all edges incident to a département

ricci_X%: X%-quantile curvature among all edges incident to a département

fb_population: number of facebook users

facebook_colocation_within_dep: colocation probability within département

fb_population_coverage: ratio of fb_population over census population in département

facebook_colocation_between_dep_X%: X%-quantile of facebook colocation among all edges incident to the département

min_temperature: minimum daily temperature averaged over the week

max_temperature: maximum daily temperature averaged over the week

mean_temperature: mean daily temperature averaged over the week

incid_hosp_Y: incidence of hospital admission from Ynd most colocated département

incid_rea_Y: incidence of ICU admission from Ynd most colocated département

incid_dc_Y: incidence of hospital deaths from Ynd most colocated département

incid_rad_Y: incidence of returned home from Ynd most colocated département

The Dataflix COVID dataset is a centralized repository of up-to-date and curated data focused on key tracking metics and U.S. census data. The dataset is publicly-readable & accessible on Google BigQuery – ready for analysis, analytics and machine learning initiatives. The dataset is built on data sourced from trusted sources like CSSE at Johns Hopkins University and government agencies, covering a wide range of metrics including confirmed cases, new cases, % population, mortality rate and deaths, aggregated at various geographic levels including city, county, state and country. New data is published on daily basis. Our objective is to make structured COVID data available for organizations and individuals to help in the fight against COVID-19. Example, health authorities will be able to build reports & dashboards to efficiently deploy vital resources like hospital beds and ventilators as they track the spread of the disease. Or epidemiologists can use the dataset to complement their existing models & datasets, and generate better forecasts of hotspots and trends. 了解详情

The geolocation marketing market is experiencing robust growth, driven by the increasing adoption of location-based services and the proliferation of smartphones. The market's expansion is fueled by several key factors. Firstly, the enhanced ability to target specific demographics based on location significantly improves marketing campaign effectiveness and ROI. Businesses are increasingly leveraging geolocation technologies like geofencing and beacons to deliver personalized and contextualized advertisements, promotions, and services to consumers in real-time. This personalized approach fosters stronger customer engagement and loyalty, leading to improved conversion rates. Secondly, the advanced analytics capabilities associated with geolocation marketing provide valuable insights into consumer behavior, preferences, and movement patterns. This data-driven approach allows businesses to refine their marketing strategies, optimize resource allocation, and make more informed decisions. The market segmentation reveals a strong demand from both large enterprises and SMEs, with geolocation, beacon, and geofencing technologies being widely utilized. The North American market currently holds a significant share, but rapid growth is anticipated in the Asia-Pacific region driven by increasing smartphone penetration and expanding digital economies. However, challenges remain. Data privacy concerns and regulations surrounding the collection and use of location data pose a significant restraint to market expansion. Maintaining consumer trust and ensuring compliance with relevant data protection laws are crucial for sustainable growth. Additionally, the accuracy and reliability of geolocation data can be affected by various factors, including GPS signal strength and environmental interference. Overcoming these technological limitations and ensuring data integrity are important considerations for market players. Despite these challenges, the overall outlook for the geolocation marketing market remains positive, with continued innovation in location-based technologies and growing demand for targeted advertising expected to drive significant growth throughout the forecast period (2025-2033). Competition among established tech giants and emerging players is fierce, prompting continuous advancements and innovation within the space.

The Indonesia Big Data Analytics Software market is experiencing robust growth, projected to reach a market size of $43.15 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 9.35% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of cloud computing within Indonesian businesses provides a scalable and cost-effective platform for big data analytics solutions. Furthermore, the Indonesian government's ongoing digitalization initiatives are creating a demand for sophisticated data analysis tools to support policy decisions and improve public services. The growth of e-commerce and the rise of digitally native businesses within the country are also contributing significantly to the market's expansion, as companies require advanced analytical capabilities to understand consumer behavior, optimize marketing campaigns, and enhance operational efficiency. Competitive landscape analysis reveals that major players such as Teradata, SAS, SAP, Tableau, IBM, Oracle, Google, Microsoft, and Cloudera are actively competing for market share, offering a diverse range of solutions to cater to various industry verticals and business needs. While challenges like data security concerns and the need for skilled data analysts exist, the overall market outlook remains positive due to the sustained growth of the digital economy in Indonesia. The continued growth of the Indonesian economy and the increasing focus on data-driven decision-making across multiple sectors suggest that the market's trajectory will remain upward throughout the forecast period. Strong government support for technology adoption and investments in digital infrastructure are key factors contributing to this optimistic outlook. The expansion of internet and mobile penetration, coupled with a growing young and tech-savvy population, further strengthens the market's growth potential. While market saturation and the emergence of new, agile competitors might introduce some challenges, the long-term potential for big data analytics software in Indonesia remains considerable, presenting lucrative opportunities for established players and emerging businesses alike. The market's segmentation, while not explicitly detailed, likely includes various industry verticals such as finance, retail, healthcare, and telecommunications, each exhibiting different levels of adoption and specific software requirements. Key drivers for this market are: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Potential restraints include: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Notable trends are: Small and Medium Enterprises to Hold Major Market Share.

The global social networking market is experiencing robust growth, driven by increasing smartphone penetration, rising internet usage, and the expanding adoption of social media platforms across diverse demographics and sectors. The market, estimated at $250 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated value of $800 billion by 2033. This expansion is fueled by several key trends, including the rise of short-form video content, the increasing integration of social commerce, and the evolution of metaverse-related features on social media platforms. The BFSI (Banking, Financial Services, and Insurance) and Retail/Wholesale sectors are major contributors to this growth, leveraging social networking for marketing, customer engagement, and sales. However, concerns around data privacy, misinformation, and cybersecurity pose significant challenges, acting as restraints on the market's unfettered expansion. The market is segmented across various platforms (mobile applications, digital platforms) and applications (public sector, BFSI, telecom and media, retail/wholesale, others), with mobile applications dominating due to their accessibility and convenience. Geographic distribution shows North America and Asia-Pacific as leading regions, benefiting from high internet and smartphone penetration rates. Competition in the social networking market is intense, with established giants like Facebook, Instagram, Google, LinkedIn, Twitter, Tencent, Pinterest, and Tumblr vying for market share. Future growth will depend on platforms' ability to innovate, adapt to evolving user preferences, and effectively address regulatory concerns regarding data privacy and content moderation. The integration of artificial intelligence and machine learning is expected to play a crucial role in enhancing user experience and targeted advertising. Furthermore, the emergence of new social media platforms and innovative features will continue to shape the competitive landscape. The market's future trajectory hinges on navigating the challenges of misinformation, cybersecurity threats, and regulatory scrutiny while simultaneously capitalizing on the immense potential offered by emerging technologies and user demands.

The global mobile app distribution platform market is experiencing robust growth, driven by the ever-increasing adoption of smartphones and the burgeoning mobile app economy. While precise market size figures for 2025 aren't provided, a reasonable estimate, considering typical growth rates in this sector and the substantial existing market, would place the 2025 market value at approximately $50 billion. This is projected to grow at a Compound Annual Growth Rate (CAGR) of 15% through 2033, reaching an estimated $175 billion by the end of the forecast period. Key drivers include the rising number of app developers seeking efficient distribution channels, the expanding user base of mobile applications across diverse demographics, and the ongoing development of sophisticated in-app advertising and monetization strategies. The market is segmented by platform (third-party vs. native) and operating system (iOS vs. Android), with Android holding a larger market share due to its wider global adoption. Leading players like Apple, Google, Amazon, and Samsung are heavily invested in their app stores, creating a competitive landscape. The rise of alternative app stores and the increasing focus on app privacy and security will continue to shape market dynamics in the years to come. The regional distribution reveals significant variations in market penetration. North America and Asia Pacific (particularly China and India) are currently the dominant regions, although the European and Middle Eastern markets are poised for significant growth. The emergence of new technologies like 5G, the expansion of augmented reality (AR) and virtual reality (VR) applications, and the growing integration of mobile apps with other digital services like IoT and cloud computing are all contributing to the continued expansion of this dynamic market. Constraints might include regulatory hurdles surrounding data privacy, concerns about app store fees and policies, and the increasing competition among developers for user attention. The successful players will be those who can adapt to these changing dynamics and offer efficient, secure, and innovative distribution solutions.

Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.