Web traffic statistics for the top 2000 most visited pages on nyc.gov by month.

This dataset provides statistics on download counts, visitors, as well as the increase of participation of Government of Canada departments and agencies in supplying more open datasets. All statistics presented, unless otherwise noted, are as of June 18, 2013 to the end of the previous month.

The DART team is responsible for fulfilling ad hoc data requests that come in to the Analysis Division, FMCSA. The DART system tracks these requests, stores any coding and results, and performs internal reporting about requests received.

The Business Intelligence and Analytics (BI&A) platform provides technical services for and on behalf of US AID GH Bureau and the supply chain business initiatives. GH is interested in initiating, maintaining, and furthering its maturity level with enterprise data/information management and to build a fully integrated data warehouse by mapping data from multiple Implementing Partner (IP) application systems and using this data as primary data sources for data and trend analysis as well as predictive modeling. The USAID BIA Application receives data on a periodic basis from various Information Providers and integrates and transforms this data into a consolidated data repository. Software tools, including MicroStrategy, are utilized by USAID personnel to perform reporting and analytics on these data sets. USAID BI&A is a Software as a Service (SaaS) offering that most approximately is represented by Use Case 4 in the Guide to Understanding FedRAMP v2.0.

The Government of Alberta’s Enterprise Data Analytics Strategic Plan provides the framework that will enable a concerted and orchestrated approach to realize the value potential of the GoA’s data assets and to meet the emerging demands of a data driven enterprise.

High level website analytics from across the government domain.

Conduct data analysis

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Government Open Data Management Platform Market Size 2025-2029

The government open data management platform market size is forecast to increase by USD 189.4 million at a CAGR of 12.5% between 2024 and 2029.

The market is witnessing significant growth, driven by the increasing demand for digitalization in government operations. This trend is leading to an increased adoption of advanced technologies, such as artificial intelligence (AI) and machine learning, in open data management platforms. These technologies enable more efficient data processing, analysis, and dissemination, making it easier for governments to provide accessible and actionable data to the public. However, the market faces challenges related to data privacy concerns.
Additionally, there is a need for clear guidelines and regulations regarding the collection, storage, and sharing of open data to maintain transparency and trust with the public. Companies operating in this market can capitalize on the growing demand for digitalization and advanced technologies while addressing data privacy concerns to gain a competitive edge. With the growing availability of open data, ensuring the security and confidentiality of sensitive information is a major concern. Governments must implement robust security measures to protect data from unauthorized access, misuse, or theft. Computer vision and image recognition are transforming industries like healthcare and education.

What will be the Size of the Government Open Data Management Platform Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market for government open data management platforms continues to evolve, driven by the increasing importance of public data infrastructure and the need for effective data governance policies. Data privacy regulations are shaping the landscape, with a growing emphasis on data reuse promotion and performance benchmarking. Data aggregation methods and data usage patterns are under constant review, as transparency and system scalability become essential. Data storytelling techniques and data usability assessments are gaining traction, while data platform architecture and data integration tools are being refined. A recent study revealed a 25% increase in data accessibility features adoption among government agencies.

Industry growth is expected to reach 15% annually, as open data licensing, role-based access control, and data modeling techniques become standard. Data quality monitoring, data consistency, and data reliability remain key concerns, with data audit procedures and data integrity measures being implemented to address these challenges. Data contextualization and data visualization dashboards are essential for making sense of the vast amounts of data being generated, while open government initiatives continue to drive innovation and collaboration. Data security remains a priority, with privacy concerns driving the need for data mining and edge computing.

How is this Government Open Data Management Platform Industry segmented?

The government open data management platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Large enterprises
  SMEs


Deployment

  On-premises
  Cloud-based


Component

  Solutions
  Services


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    Australia
    China
    India


  Rest of World (ROW)

By End-user Insights

The Large enterprises segment is estimated to witness significant growth during the forecast period. In today's data-driven business landscape, large enterprises are increasingly turning to government open data management platforms to unlock valuable insights and fuel innovation. These platforms enable organizations to access, manage, and analyze vast amounts of data published by government agencies. By integrating government open data with their internal data, businesses can gain a deeper understanding of market trends, consumer behavior, and emerging opportunities. Data interoperability and version control ensure seamless integration of diverse data sources, while data migration strategies facilitate the transfer of data between systems. Data lineage tracking and metadata management provide transparency into the origin and evolution of data, enabling data provenance management and data discovery. Advanced process control and time series forecasting are integral to this evolution, with machine learning algorithms and deep learning frameworks powering predictive analytics tools.

Structured data management, data clea

This dataset contains data collected during a study "Smarter open government data for Society 5.0: are your open data smart enough" (Sensors. 2021; 21(15):5204) conducted by Anastasija Nikiforova (University of Latvia). It being made public both to act as supplementary data for "Smarter open government data for Society 5.0: are your open data smart enough" paper and in order for other researchers to use these data in their own work.

The data in this dataset were collected in the result of the inspection of 60 countries and their OGD portals (total of 51 OGD portal in May 2021) to find out whether they meet the trends of Society 5.0 and Industry 4.0 obtained by conducting an analysis of relevant OGD portals.

Each portal has been studied starting with a search for a data set of interest, i.e. “real-time”, “sensor” and “covid-19”, follwing by asking a list of additional questions. These questions were formulated on the basis of combination of (1) crucial open (government) data-related aspects, including open data principles, success factors, recent studies on the topic, PSI Directive etc., (2) trends and features of Society 5.0 and Industry 4.0, (3) elements of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use Model (UTAUT).

The method used belongs to typical / daily tasks of open data portals sometimes called “usability test” – keywords related to a research question are used to filter data sets, i.e. “real-time”, “real time” and “real time”, “sensor”, covid”, “covid-19”, “corona”, “coronavirus”, “virus”. In most cases, “real-time”, “sensor” and “covid” keywords were sufficient. The examination of the respective aspects for less user-friendly portals was adapted to particular case based on the portal or data set specifics, by checking: 1. are the open data related to the topic under question ({sensor; real-time; Covid-19}) published, i.e. available? 2. are these data available in a machine-readable format? 3. are these data current, i.e. regularly updated? Where the criteria on the currency depends on the nature of data, i.e. Covid-19 data on the number of cases per day is expected to be updated daily, which won’t be sufficient for real-time data as the title supposes etc. 4. is API ensured for these data? having most importance for real-time and sensor data; 5. have they been published in a timely manner? which was verified mainly for Covid-19 related data. The timeliness is assessed by comparing the dates of the first case identified in a given country and the first release of open data on this topic. 6. what is the total number of available data sets? 7. does the open government data portal provides use-cases / showcases?
8. does the open government portal provide an opportunity to gain insight into the popularity of the data, i.e. does the portal provide statistics of this nature, such as the number of views, downloads, reuses, rating etc.? 9. is there an opportunity to provide a feedback, comment, suggestion or complaint? 10. (9a) is the artifact, i.e. feedback, comment, suggestion or complaint, visible to other users?

Format of the file .xls, .ods, .csv (for the first spreadsheet only)

Licenses or restrictions CC-BY

For more info, see README.txt

Contains view count data for the top 20 pages each day on the Somerville MA city website dating back to 2020. Data is used in the City's dashboard which can be found at https://www.somervilledata.farm/.

This dataset provides information about access to public assets on the CT Open Data Portal by day. Types of access include:

-Grid view -Primer page view -Download -API read -Story page view -Visualization page view

It includes assets that meet the following criteria:

-Published on the data.ct.gov domain -Public -Official (ie published by a registered user) -Not a derived view

This dataset contains current job postings available on the City of New York’s official jobs site (http://www.nyc.gov/html/careers/html/search/search.shtml). Internal postings available to city employees and external postings available to the general public are included.

A catalog of high-value public science and research data sets from across the Federal Government.

Results of Pulse's survey of analytics participation on government agency websites.

The data were collected during the user-centered analysis of usability of 41 open government data portals including EU27, applying a common methodology to them, considering aspects such as specification of open data set, feedback and requests, further broken down into 14 sub-criteria. Each aspect was assessed using a three-level Likert scale (fulfilled - 3, partially fulfilled - 2, and unfulfilled – 1), that belongs to the acceptability tasks. This dataset summarises a total of 1640 protocols obtained during the analysis of the selected portals carried out by 40 participants, who were selected on a voluntary basis. This is complemented with 4 summaries of these protocols, which include calculated average scores by category, aspect and country. These data allow comparative analysis of the national open data portals, help to find the key challenges that can negatively impact users’ experience, and identifies portals that can be considered as an example for the less successful open data portals.

We have used Analytic Hierarchy Process (AHP) to derive the priorities of all the factors in the evaluation framework for open government data (OGD) portals. The results of AHP process were shown in the uploaded pdf file. We have collected 2635 open government datasets of 15 different subject categories (local statistics, health, education, cultural activity, transportation, map, public safety, policies and legislation, weather, environment quality, registration, credit records, international trade, budget and spend, and government bid) from 9 OGD portals in China (Beijing, Zhejiang, Shanghai, Guangdong, Guizhou, Sichuan, XInjiang, Hong Kong and Taiwan). These datasets were used for the evaluation of these portals in our study. The records of the quality and open access of these datasets could be found in the uploaded Excel file.

This dataset includes information about state agencies publishing data on the CT Open Data Portal, including number of datasets, maps, external datasets (href), federated datasets (federated href), and data stories. It also includes a summary of the freshness of the data and the completeness of the metadata by agency.

Ethical Data ManagementExecutive SummaryIn the age of data and information, it is imperative that the City of Virginia Beach strategically utilize its data assets. Through expanding data access, improving quality, maintaining pace with advanced technologies, and strengthening capabilities, IT will ensure that the city remains at the forefront of digital transformation and innovation. The Data and Information Management team works under the purpose:“To promote a data-driven culture at all levels of the decision making process by supporting and enabling business capabilities with relevant and accurate information that can be accessed securely anytime, anywhere, and from any platform.”To fulfill this mission, IT will implement and utilize new and advanced technologies, enhanced data management and infrastructure, and will expand internal capabilities and regional collaboration.Introduction and JustificationThe Information technology (IT) department’s resources are integral features of the social, political and economic welfare of the City of Virginia Beach residents. In regard to local administration, the IT department makes it possible for the Data and Information Management Team to provide the general public with high-quality services, generate and disseminate knowledge, and facilitate growth through improved productivity.For the Data and Information Management Team, it is important to maximize the quality and security of the City’s data; to develop and apply the coherent management of information resources and management policies that aim to keep the general public constantly informed, protect their rights as subjects, improve the productivity, efficiency, effectiveness and public return of its projects and to promote responsible innovation. Furthermore, as technology evolves, it is important for public institutions to manage their information systems in such a way as to identify and minimize the security and privacy risks associated with the new capacities of those systems.The responsible and ethical use of data strategy is part of the City’s Master Technology Plan 2.0 (MTP), which establishes the roadmap designed by improve data and information accessibility, quality, and capabilities throughout the entire City. The strategy is being put into practice in the shape of a plan that involves various programs. Although these programs was specifically conceived as a conceptual framework for achieving a cultural change in terms of the public perception of data, it basically covers all the aspects of the MTP that concern data, and in particular the open-data and data-commons strategies, data-driven projects, with the aim of providing better urban services and interoperability based on metadata schemes and open-data formats, permanent access and data use and reuse, with the minimum possible legal, economic and technological barriers within current legislation.Fundamental valuesThe City of Virginia Beach’s data is a strategic asset and a valuable resource that enables our local government carry out its mission and its programs effectively. Appropriate access to municipal data significantly improves the value of the information and the return on the investment involved in generating it. In accordance with the Master Technology Plan 2.0 and its emphasis on public innovation, the digital economy and empowering city residents, this data-management strategy is based on the following considerations.Within this context, this new management and use of data has to respect and comply with the essential values applicable to data. For the Data and Information Team, these values are:Shared municipal knowledge. Municipal data, in its broadest sense, has a significant social dimension and provides the general public with past, present and future knowledge concerning the government, the city, society, the economy and the environment.The strategic value of data. The team must manage data as a strategic value, with an innovative vision, in order to turn it into an intellectual asset for the organization.Geared towards results. Municipal data is also a means of ensuring the administration’s accountability and transparency, for managing services and investments and for maintaining and improving the performance of the economy, wealth and the general public’s well-being.Data as a common asset. City residents and the common good have to be the central focus of the City of Virginia Beach’s plans and technological platforms. Data is a source of wealth that empowers people who have access to it. Making it possible for city residents to control the data, minimizing the digital gap and preventing discriminatory or unethical practices is the essence of municipal technological sovereignty.Transparency and interoperability. Public institutions must be open, transparent and responsible towards the general public. Promoting openness and interoperability, subject to technical and legal requirements, increases the efficiency of operations, reduces costs, improves services, supports needs and increases public access to valuable municipal information. In this way, it also promotes public participation in government.Reuse and open-source licenses. Making municipal information accessible, usable by everyone by default, without having to ask for prior permission, and analyzable by anyone who wishes to do so can foster entrepreneurship, social and digital innovation, jobs and excellence in scientific research, as well as improving the lives of Virginia Beach residents and making a significant contribution to the city’s stability and prosperity.Quality and security. The city government must take firm steps to ensure and maximize the quality, objectivity, usefulness, integrity and security of municipal information before disclosing it, and maintain processes to effectuate requests for amendments to the publicly-available information.Responsible organization. Adding value to the data and turning it into an asset, with the aim of promoting accountability and citizens’ rights, requires new actions, new integrated procedures, so that the new platforms can grow in an organic, transparent and cross-departmental way. A comprehensive governance strategy makes it possible to promote this revision and avoid redundancies, increased costs, inefficiency and bad practices.Care throughout the data’s life cycle. Paying attention to the management of municipal registers, from when they are created to when they are destroyed or preserved, is an essential part of data management and of promoting public responsibility. Being careful with the data throughout its life cycle combined with activities that ensure continued access to digital materials for as long as necessary, help with the analytic exploitation of the data, but also with the responsible protection of historic municipal government registers and safeguarding the economic and legal rights of the municipal government and the city’s residents.Privacy “by design”. Protecting privacy is of maximum importance. The Data and Information Management Team has to consider and protect individual and collective privacy during the data life cycle, systematically and verifiably, as specified in the general regulation for data protection.Security. Municipal information is a strategic asset subject to risks, and it has to be managed in such a way as to minimize those risks. This includes privacy, data protection, algorithmic discrimination and cybersecurity risks that must be specifically established, promoting ethical and responsible data architecture, techniques for improving privacy and evaluating the social effects. Although security and privacy are two separate, independent fields, they are closely related, and it is essential for the units to take a coordinated approach in order to identify and manage cybersecurity and risks to privacy with applicable requirements and standards.Open Source. It is obligatory for the Data and Information Management Team to maintain its Open Data- Open Source platform. The platform allows citizens to access open data from multiple cities in a central location, regional universities and colleges to foster continuous education, and aids in the development of data analytics skills for citizens. Continuing to uphold the Open Source platform with allow the City to continually offer citizens the ability to provide valuable input on the structure and availability of its data. Strategic areasIn order to deploy the strategy for the responsible and ethical use of data, the following areas of action have been established, which we will detail below, together with the actions and emblematic projects associated with them.In general, the strategy pivots on the following general principals, which form the basis for the strategic areas described in this section.Data sovereigntyOpen data and transparencyThe exchange and reuse of dataPolitical decision-making informed by dataThe life cycle of data and continual or permanent accessData GovernanceData quality and accessibility are crucial for meaningful data analysis, and must be ensured through the implementation of data governance. IT will establish a Data Governance Board, a collaborative organizational capability made up of the city’s data and analytics champions, who will work together to develop policies and practices to treat and use data as a strategic asset.Data governance is the overall management of the availability, usability, integrity and security of data used in the city. Increased data quality will positively impact overall trust in data, resulting in increased use and adoption. The ownership, accessibility, security, and quality, of the data is defined and maintained by the Data Governance Board.To improve operational efficiency, an enterprise-wide data catalog will be created to inventory data and track metadata from various data sources to allow for rapid data asset discovery. Through the data catalog, the city will

The global Big Data Analytics in Defense market is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) exceeding 13% from 2025 to 2033. This expansion is fueled by several key factors. The increasing reliance on advanced technologies for enhanced situational awareness and improved decision-making within military operations is a primary driver. The need to analyze vast quantities of data from diverse sources, including sensor networks, satellite imagery, and social media, is pushing the adoption of sophisticated big data analytics solutions. Furthermore, the growing demand for predictive intelligence and improved cybersecurity within defense organizations is further accelerating market growth. Technological advancements in artificial intelligence (AI), machine learning (ML), and cloud computing are continuously enhancing the capabilities of big data analytics platforms, making them more efficient and effective. Segmentation reveals a strong demand across all platforms (Army, Navy, Air Force), with hardware, software, and services all contributing significantly to the overall market value. While the market faces some restraints, such as data security concerns and the high cost of implementation, these are being mitigated by ongoing innovation and government investment in defense modernization initiatives. The North American market currently holds a substantial share, driven by significant defense spending and the presence of major technology players. However, the Asia-Pacific region is poised for rapid expansion due to increasing military modernization efforts in countries like China and India. The competitive landscape is dominated by established defense contractors and technology giants, indicating a robust ecosystem fostering further innovation and market penetration. The market's trajectory suggests continued high growth over the forecast period, driven by the increasing strategic importance of big data analytics in national security and defense operations. The market's future is characterized by a strong focus on developing AI-powered analytics solutions for real-time threat detection, predictive maintenance of defense equipment, and optimized resource allocation. Furthermore, the integration of big data analytics with other emerging technologies, such as the Internet of Things (IoT) and blockchain, will further expand its capabilities and applications. The increasing emphasis on cybersecurity and data privacy is likely to drive demand for robust and secure data analytics solutions. Collaborative partnerships between defense organizations and technology providers are crucial for developing and deploying effective big data analytics solutions. Government initiatives to encourage innovation and investment in the defense technology sector will play a significant role in shaping the market's future trajectory. The continued growth in defense budgets globally will further support the market's expansion, making it a highly attractive investment opportunity for both established players and emerging technology companies. Recent developments include: September 2022: The United States Air Force signed a contract worth USD 1.25 million with ZeroEyto procure an AI gun detection solution for the service's unmanned aerial vehicles (UAVs) at the Dover Air Force Base, Delaware. ZeroEyes' technology will enable drones to detect handheld weapons for base protection., July 2022: The Indian Ministry of Defense launched 75 newly developed artificial intelligence (AI) products and technologies during the first-ever 'AI in Defense symposium and exhibition in New Delhi. These include autonomous systems, AI platform automation, command, control, communication, computer (C4), blockchain-based automation, intelligence, surveillance & reconnaissance (ISR), intelligent monitoring systems, cyber security, and others.. Notable trends are: Software Segment Will Showcase Remarkable Growth During the Forecast Period.

The open data portal catalogue is a downloadable dataset containing some key metadata for the general datasets available on the Government of Canada's Open Data portal. Resource 1 is generated using the ckanapi tool (external link) Resources 2 - 8 are generated using the Flatterer (external link) utility. ###Description of resources: 1. Dataset is a JSON Lines (external link) file where the metadata of each Dataset/Open Information Record is one line of JSON. The file is compressed with GZip. The file is heavily nested and recommended for users familiar with working with nested JSON. 2. Catalogue is a XLSX workbook where the nested metadata of each Dataset/Open Information Record is flattened into worksheets for each type of metadata. 3. datasets metadata contains metadata at the dataset level. This is also referred to as the package in some CKAN documentation. This is the main table/worksheet in the SQLite database and XLSX output. 4. Resources Metadata contains the metadata for the resources contained within each dataset. 5. resource views metadata contains the metadata for the views applied to each resource, if a resource has a view configured. 6. datastore fields metadata contains the DataStore information for CSV datasets that have been loaded into the DataStore. This information is displayed in the Data Dictionary for DataStore enabled CSVs. 7. Data Package Fields contains a description of the fields available in each of the tables within the Catalogue, as well as the count of the number of records each table contains. 8. data package entity relation diagram Displays the title and format for column, in each table in the Data Package in the form of a ERD Diagram. The Data Package resource offers a text based version. 9. SQLite Database is a .db database, similar in structure to Catalogue. This can be queried with database or analytical software tools for doing analysis.