European Union - Individuals using the internet for interaction with public authorities: Internet use: downloading official forms (last 12 months) was 44.07% in December of 2022, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for European Union - Individuals using the internet for interaction with public authorities: Internet use: downloading official forms (last 12 months) - last updated from the EUROSTAT on November of 2025. Historically, European Union - Individuals using the internet for interaction with public authorities: Internet use: downloading official forms (last 12 months) reached a record high of 44.07% in December of 2022 and a record low of 24.97% in December of 2011.

The National Data Asset Agency Limited Liability Company (NAVÜ Kft.) is continuously developing the National Open Data Portal to promote the utilization and further use of the national data asset, thereby supporting the establishment of the Hungarian data industry and the development of a national data ecosystem. The National Open Data Portal provides a platform for those interested in public data and for individuals and organizations initiating the further use of data within the scope of the national data asset. NAVÜ Kft.'s goal is that, through the continuously developing National Open Data Portal, Hungary's international perception of its IT development will improve, and that the Hungarian public administration will achieve the best possible results based on the European Union's Digital Economy and Society Index (DESI) assessment criteria for open access data. Since 2014, the European Commission has published its report annually, including specific country reports that help Member States identify key areas for action. It also publishes thematic chapters that provide EU-level analysis of key digital policy areas. The 2022 DESI report on Hungary is available by clicking here. The Open Data Maturity Report (ODMR) aims to serve as a benchmark and provide insight into the progress made in the field of open access data in Europe. The report assesses the maturity level of each country along four dimensions - policy, portal, impact and quality. Within these, it establishes indicators for each country according to 16 criteria. Based on the report, countries can be classified into four different groups: trendsetters, accelerators, followers and beginners. The report formulates recommendations tailored to the maturity level and characteristics of each group. The 2024 ODMR report on Hungary is available by clicking here. Read more. Translated from Hungarian Original Text: A Nemzeti Adatvagyon Ügynökség Korlátolt Felelősségű Társaság (NAVÜ Kft.) a nemzeti adatvagyon hasznosításának és további felhasználásának ösztönzése céljából folyamatosan fejleszti a Nemzeti Közadatportált, támogatva ezzel a magyarországi adatipar megalapozását és egy nemzeti adat-ökoszisztéma kialakulását. A Nemzeti Közadatportál a közadatok iránt érdeklődők, illetőleg a nemzeti adatvagyon körébe tartozó adatok további felhasználását kezdeményező személyek és szervezetek számára olyan felületet biztosít, amelyen keresztül A NAVÜ Kft. célja, hogy a folyamatosan fejlődő Nemzeti Közadatportál révén Magyarország informatikai fejlettségének nemzetközi megítélése is javuljon, illetve, hogy a magyar közigazgatás az Európai Unió Digitális Gazdaság és Társadalom Indexe (Digital Economy and Society Index: DESI) nyílt hozzáférésű adatokra vonatkozó értékelési szempontjai alapján minél jobb eredményeket érjen el. Az Európai Bizottság 2014 óta évente közzéteszi a jelentését, annak részeként specifikus országismertetőket, amelyek segítik a tagállamokat a kiemelt intézkedési területek azonosításában. Ezenkívül közzétesz olyan tematikus fejezeteket is, amelyek uniós szintű elemzést nyújtanak a kulcsfontosságú digitális szakpolitikai területeken. A 2022-es DESI jelentés Magyarországról ide kattintva érhető el. A nyíltadat-szolgáltatás fejlettségéről szóló jelentés (Open Data Maturity Report, ODMR) célja, hogy referenciaként szolgáljon, továbbá rálátást adjon a nyílt hozzáférésű adatok területén Európában elért fejlődésre. A jelentés az egyes országok érettségi szintjét 4 dimenzió - szakpolitika, portál, hatás és minőség - mentén értékeli. Ezeken belül 16 szempont szerint állapít meg mutatószámokat minden országra. A jelentés alapján 4 különböző csoportba sorolhatóak az országok: trendteremtők, gyorsítók, követők és kezdők. A jelentés az egyes csoportok érettségi szintjéhez és jellemzőihez igazított ajánlásokat fogalmaz meg. A 2024-es ODMR jelentés Magyarországról ide kattintva érhető el. Tovább olvasok

Snapshot img

Government Open Data Management Platform Market Size 2025-2029

The government open data management platform market size is valued to increase by USD 189.4 million, at a CAGR of 12.5% from 2024 to 2029. Rising demand for digitalization in government operations will drive the government open data management platform market.

Market Insights

North America dominated the market and accounted for a 38% growth during the 2025-2029.
By End-user - Large enterprises segment was valued at USD 108.50 million in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 138.56 million 
Market Future Opportunities 2024: USD 189.40 million
CAGR from 2024 to 2029 : 12.5%

Market Summary

The market witnesses significant growth due to the increasing demand for digitalization in government operations. Open data management platforms enable governments to make large volumes of data available to the public in a machine-readable format, fostering transparency and accountability. The adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in these platforms enhances data analysis capabilities, leading to more informed decision-making. However, data privacy concerns remain a major challenge in the open data management market. Governments must ensure the protection of sensitive information while making data publicly available. A real-world business scenario illustrating the importance of open data management platforms is supply chain optimization in the public sector.
By sharing data related to procurement, logistics, and inventory management, governments can streamline their operations and improve efficiency. For instance, a city government could share real-time traffic data to optimize public transportation routes, reducing travel time and improving overall service delivery. Despite these benefits, it is crucial for governments to address data security concerns and establish robust data management policies to ensure the safe and effective use of open data platforms.

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

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The market continues to evolve, with recent research indicating a significant increase in data reuse initiatives among government agencies. The use of open data platforms in the public sector has grown by over 25% in the last two years, driven by a need for transparency and improved data-driven decision making. This trend is particularly notable in areas such as compliance and budgeting, where accurate and accessible data is essential. Data replication strategies, data visualization libraries, and data portal design are key considerations for government agencies looking to optimize their open data management platforms.
Effective data discovery tools and metadata schema design are crucial for ensuring data silos are minimized and data usage patterns are easily understood. Data privacy regulations, such as GDPR and HIPAA, also require robust data governance frameworks and data security audits to maintain data privacy and protect against breaches. Data access logs, data consistency checks, and data quality dashboards are essential components of any open data management platform, ensuring data accuracy and reliability. Data integration services and data sharing platforms enable seamless data exchange between different agencies and departments, while data federation techniques allow for data to be accessed in its original source without the need for data replication.
Ultimately, these strategies contribute to a more efficient and effective data lifecycle, allowing government agencies to make informed decisions and deliver better services to their constituents.

Unpacking the Government Open Data Management Platform Market Landscape

The market encompasses a range of solutions designed to facilitate the efficient and secure handling of data throughout its lifecycle. According to recent studies, organizations adopting data lifecycle management practices experience a 30% reduction in data processing costs and a 25% improvement in ROI. Performance benchmarking is crucial for ensuring optimal system scalability, with leading platforms delivering up to 50% faster query response times than traditional systems. Data anonymization techniques and data modeling methods enable compliance with data protection regulations, while open data standards streamline data access and sharing. Data lineage tracking and metadata management are essential for maintaining data quality and ensuring data interoperability. API integration strategies and data transformation methods enable seamless data enrichment processes and knowledge graph implementation. Data access control, data versioning, and data security protocols

This data set contains the underlying data of the paper “The use of social media in EU policy communication and implications for the emergence of a European public sphere”, published by The Journal of Regional Research - Investigaciones Regionales (ISSN: 1695-7253; E-ISSN: 2340-2717) in 2019. Data openly available within this dataset are a subset of the two following data sets, which contains all the relevant data of Work Package 3 and Work Package 5 of PERCEIVE project: Data set: “PERCEIVE: WP3: Effectiveness of communication strategies of EU projects” https://doi.org/10.5281/zenodo.3371133 Data set: “PERCEIVE: WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels” https://doi.org/10.5281/zenodo.3371174 For the paper we collected Facebook posts referred to EU CP policies. We don’t have the permission to share these data (as they are protected by copyright), but all the sources are described in Deliverable 5.2, which is public (see http://doi.org/10.6092/unibo/amsacta/5726 or http://doi.org/10.5281/zenodo.1318184). We analyzed the textual content of data to construct a database of discursive topics in Task5.4. Data set includes the results of topic modeling and of a sentiment analysis performed on the Facebook homepages of Local Management Authorities (LMA) of PERCEIVE case study regions.

Snapshot img

Cloud Analytics Market Size 2024-2028

The cloud analytics market size is forecast to increase by USD 74.08 billion at a CAGR of 24.4% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. The adoption of hybrid and multi-cloud setups is on the rise, as these configurations enhance data connectivity and flexibility. Another trend driving market growth is the increasing use of cloud security applications to safeguard sensitive data.
However, concerns regarding confidential data security and privacy remain a challenge for market growth. Organizations must ensure robust security measures are in place to mitigate risks and maintain trust with their customers. Overall, the market is poised for continued expansion as businesses seek to leverage the benefits of cloud technologies for data processing and data analytics.

What will be the Size of the Cloud Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing volume of data generated by businesses and the demand for advanced analytics solutions. Cloud-based analytics enables organizations to process and analyze large datasets from various data sources, including unstructured data, in real-time. This is crucial for businesses looking to make data-driven decisions and gain valuable insights to optimize their operations and meet customer requirements. Key industries such as sales and marketing, customer service, and finance are adopting cloud analytics to improve key performance indicators and gain a competitive edge. Both Small and Medium-sized Enterprises (SMEs) and large enterprises are embracing cloud analytics, with solutions available on private, public, and multi-cloud platforms.
Big data technology, such as machine learning and artificial intelligence, are integral to cloud analytics, enabling advanced data analytics and business intelligence. Cloud analytics provides businesses with the flexibility to store and process data In the cloud, reducing the need for expensive on-premises data storage and computation. Hybrid environments are also gaining popularity, allowing businesses to leverage the benefits of both private and public clouds. Overall, the market is poised for continued growth as businesses increasingly rely on data-driven insights to inform their decision-making processes.

How is this Cloud Analytics Industry segmented and which is the largest segment?

The cloud analytics industry 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 2017-2022 for the following segments.

Solution

  Hosted data warehouse solutions
  Cloud BI tools
  Complex event processing
  Others


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Middle East and Africa



  South America

By Solution Insights

The hosted data warehouse solutions segment is estimated to witness significant growth during the forecast period.

Hosted data warehouses enable organizations to centralize and analyze large datasets from multiple sources, facilitating advanced analytics solutions and real-time insights. By utilizing cloud-based infrastructure, businesses can reduce operational costs through eliminating licensing expenses, hardware investments, and maintenance fees. Additionally, cloud solutions offer network security measures, such as Software Defined Networking and Network integration, ensuring data protection. Cloud analytics caters to diverse industries, including SMEs and large enterprises, addressing requirements for sales and marketing, customer service, and key performance indicators. Advanced analytics capabilities, including predictive analytics, automated decision making, and fraud prevention, are essential for data-driven decision making and business optimization.

Furthermore, cloud platforms provide access to specialized talent, big data technology, and AI, enhancing customer experiences and digital business opportunities. Data connectivity and data processing in real-time are crucial for network agility and application performance. Hosted data warehouses offer computational power and storage capabilities, ensuring efficient data utilization and enterprise information management. Cloud service providers offer various cloud environments, including private, public, multi-cloud, and hybrid, catering to diverse business needs. Compliance and security concerns are addressed through cybersecurity frameworks and data security measures, ensuring data breaches and thefts are minimized.

Get a glance at the Cloud Analytics Industry report of share of various segments Request Free Sample

The Hosted data warehouse solutions s

Collection of definitions of terms in English, French, German, Italian and Spanish extracted from the following data-related European laws:

Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE)

Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance)

Commission Recommendation (EU) 2018/790 of 25 April 2018 on access to and preservation of scientific information

Regulation (EU) 2018/1807 of the European Parliament and of the Council of 14 November 2018 on a framework for the free flow of non-personal data in the European Union (Text with EEA relevance)

Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC (Text with EEA relevance)

Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information (recast) (Open Data Directive)

Regulation (EU) 2021/695 of the European Parliament and of the Council of 28 April 2021 establishing Horizon Europe – the Framework Programme for Research and Innovation, laying down its rules for participation and dissemination, and repealing Regulations (EU) No 1290/2013 and (EU) No 1291/2013 (Text with EEA relevance)

Regulation (EU) 2022/868 of the European Parliament and of the Council of 30 May 2022 on European data governance and amending Regulation (EU) 2018/1724 (Data Governance Act) (Text with EEA relevance)

Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 (Digital Markets Act) (Text with EEA relevance)

Regulation (EU) 2022/2065 of the European Parliament and of the Council of 19 October 2022 on a Single Market For Digital Services and amending Directive 2000/31/EC (Digital Services Act) (Text with EEA relevance)

Commission Implementing Regulation (EU) 2023/138 of 21 December 2022 laying down a list of specific high-value datasets and the arrangements for their publication and re-use (Text with EEA relevance)

Regulation (EU) 2023/2854 of the European Parliament and of the Council of 13 December 2023 on harmonised rules on fair access to and use of data and amending Regulation (EU) 2017/2394 and Directive (EU) 2020/1828 (Data Act)

Regulation (EU) 2024/903 of the European Parliament and of the Council of 13 March 2024 laying down measures for a high level of public sector interoperability across the Union (Interoperable Europe Act)

Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) Text with EEA relevance.

Regulation (EU) 2024/2847 of the European Parliament and of the Council of 23 October 2024 on horizontal cybersecurity requirements for products with digital elements and amending Regulations (EU) No 168/2013 and (EU) 2019/1020 and Directive (EU) 2020/1828 (Cyber Resilience Act) (Text with EEA relevance)

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below:

corine/*

• CORINE Land Cover (CLC) database
• Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/
• Extract from Terms of Use:

Access to data is based on a principle of full, open and free access as established by the Copernicus data and information policy Regulation (EU) No 1159/2013 of 12 July 2013. This regulation establishes registration and licensing conditions for GMES/Copernicus users and can be found here. Free, full and open access to this data set is made on the conditions that:

• When distributing or communicating Copernicus dedicated data and Copernicus service information to the public, users shall inform the public of the source of that data and information.

• Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the Union.

• Where that data or information has been adapted or modified, the user shall clearly state this.

• The data remain the sole property of the European Union. Any information and data produced in the framework of the action shall be the sole property of the European Union. Any communication and publication by the beneficiary shall acknowledge that the data were produced “with funding by the European Union”.

• https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

eez/*

• World exclusive economic zones (EEZ)
• Source: http://www.marineregions.org/sources.php#unioneezcountry
• Extract from Terms of Use:

Marine Regions’ products are licensed under CC-BY-NC-SA. Please contact us for other uses of the Licensed Material beyond license terms. We kindly request our users not to make our products available for download elsewhere and to always refer to marineregions.org for the most up-to-date products and services.

• http://www.marineregions.org/disclaimer.php

natura/*

• Natura 2000 natural protection areas
• Source: https://www.eea.europa.eu/data-and-maps/data/natura-10
• Extract from Terms of Use:

EEA standard re-use policy: unless otherwise indicated, re-use of content on the EEA website for commercial or non-commercial purposes is permitted free of charge, provided that the source is acknowledged (https://www.eea.europa.eu/legal/copyright). Copyright holder: Directorate-General for Environment (DG ENV).

• https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata

naturalearth/*

• World country shapes
• Source: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/
• Extract from Terms of Use:

All versions of Natural Earth raster + vector map data found on this website are in the public domain. You may use the maps in any manner, including modifying the content and design, electronic dissemination, and offset printing. The primary authors, Tom Patterson and Nathaniel Vaughn Kelso, and all other contributors renounce all financial claim to the maps and invites you to use them for personal, educational, and commercial purposes.

No permission is needed to use Natural Earth. Crediting the authors is unnecessary.

• http://www.naturalearthdata.com/about/terms-of-use/

NUTS_2013_60M_SH/*

• Europe NUTS3 regions
• Source: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• Extract from Terms of Use:

In addition to the general copyright and licence policy applicable to the whole Eurostat website, the following specific provisions apply to the datasets you are downloading. The download and usage of these data is subject to the acceptance of the following clauses:

1. The Commission agrees to grant the non-exclusive and not transferable right to use and process the Eurostat/GISCO geographical data downloaded from this page (the "data").

2. The permission to use the data is granted on condition that: the data will not be used for commercial purposes; the source will be acknowledged. A copyright notice, as specified below, will have to be visible on any printed or electronic publication using the data downloaded from this page.

• https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• https://ec.europa.eu/eurostat/about/policies/copyright

gebco/GEBCO_2014_2D.nc

• GEBCO bathymetric dataset
• Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/
• Extract from Terms of Use:

The GEBCO Grid is placed in the public domain and may be used free of charge. Use of the GEBCO Grid indicates that the user accepts the conditions of use and disclaimer information given below.

Users are free to:

• Copy, publish, distribute and transmit The GEBCO Grid

• Adapt The GEBCO Grid

• Commercially exploit The GEBCO Grid, by, for example, combining it with other information, or by including it in their own product or application

Users must:

• Acknowledge the source of The GEBCO Grid. A suitable form of attribution is given in the documentation that accompanies The GEBCO Grid.

• Not use The GEBCO Grid in a way that suggests any official status or that GEBCO, or the IHO or IOC, endorses any particular application of The GEBCO Grid.

• Not mislead others or misrepresent The GEBCO Grid or its source.

• https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf

je-e-21.03.02.xls

• Population and GDP data for Swiss Cantons
• Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html
• Extract from Terms of Use:

Information on the websites of the Federal Authorities is accessible to the public. Downloading, copying or integrating content (texts, tables, graphics, maps, photos or any other data) does not entail any transfer of rights to the content.

Copyright and any other rights relating to content available on the websites of the Federal Authorities are the exclusive property of the Federal Authorities or of any other expressly mentioned owners.

Any reproduction requires the prior written consent of the copyright holder. The source of the content (statistical results) should always be given.

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Vision Europe includes consumer transaction data on 6.7M+ credit cards, debit cards, direct debit accounts, and direct transfer accounts, including 5.3M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 5K+ merchants, 3K+ brands mapped to 600 global parent companies (500 publicly traded), and deep geographic breakouts with demographic breakouts coming soon for UK. Brick & mortar and ecommerce direct-to-consumer sales are recorded on transaction date and purchase data is available for most companies as early as 5 days post-swipe.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

Private equity and venture capital firms can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights teams and retailers can gain visibility into transaction data’s potential for competitive analysis, shopper behavior, and market intelligence.

CE Vision Benefits • Discover new competitors • Compare sales, average ticket & transactions across competition • Evaluate demographic and geographic drivers of growth • Assess customer loyalty • Explore granularity by geos • Benchmark market share vs. competition • Analyze business performance with advanced cross-cut queries

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Purpose of data.gov.lv is to gather and to circulate Government institution and Government organization collected data in on place for public use, as this data is valuable for the development of innovations in the state. On this portal datasets can be browsed by category, keywords or institution. The portal is based on open source technologies including CKAN Open data catalogue. Developed add-ons are available at: https://github.com/dpp-dev The Latvian Open Data Portal was created by the European Regional Development Fund co-financed project Nr. 2.2.1.1/16/I/001 "Public Administration Information and Communication Technology Architecture Management System" (PIKTAPS) The Third Action Plan for Open Government Partnership of Latvia is taking an action towards openness, responsibility, publics’ participation and the use of ICT [1] This plan carries out the improvement and implementation of various services in the Internet environment. One of the 12 commitment plans of Latvia is the development of an open-source public data portal. But to fulfill the entire target of 2017-2019 OGP plan Latvia has committed to involve society in the selection of datasets. Consequently, the website has the ability to vote which data should be opened. So far in Latvia many valuable data were not available, since collecting them is a paid service, for that reason, access to re-usable data containing metadata has been difficult. Only a few institutions, on their own initiative, published open data on their websites. In 2013, the European Union (EU) adopted the Directive 2013/37/EU with a view to introducing uniform practices and rules in all Member States for the re-use of public administration information. In Latvia, in 2015, the relevant amendments were incorporated into the Information Disclosure Law. According to the directive, in Latvia the data that is open should be published "on the authority of its own initiative, if it is useful ", which means the voluntary principle in the publication of data and does not promote the general" open by default "compliance with the principle. This portal was opened within the OGP to facilitate the opening of data, of course, with respect to the protection of personal data. Involving society in choosing the datasets to be opened. From this commitment, an open data portal, gains all the groups of the public: Members of the society will not only be able to vote for data sets of interest to them, but also to obtain data without bureaucracy. Government institutions will increase the efficiency of work and improve their image by opening and publishing their data. Entrepreneurs will have more data available that can be used to create new products or services, thus contributing to overall economic growth.

This dataset contains the list of selected cases taken from the public sector institutions in Europe on adopting and implementing AI that has been used by the JRC Report 129301 “AI Watch. European landscape on the use of Artificial Intelligence by the Public Sector”. The cases have been collected within the AI for the public sector initiative from a series of activities (surveys, workshops, interviews, desk research on the web) to understand the type of AI solutions available in the public sector. Thus, the list must not be considered a complete and systematic inventory of current AI solutions in Europe and moreover this use cases list is not updated anymore. The actual published list is updated to 31/12/2021. The collection of AI cases in the Public Sector, together with other interesting technologies, is continued and updated by the Public Sector Technology Watch Observatory. For more information follow this link https://joinup.ec.europa.eu/collection/public-sector-tech-watch

The EU survey on the use of Information and Communication Technologies (ICT) in households and by individuals is an annual survey conducted since 2002 aiming at collecting and disseminating harmonised and comparable information on the use of ICT in households and by individuals.

Data presented in this domain are collected on a yearly basis by the National Statistical Institutes and are based on Eurostat's annual model questionnaire. This questionnaire is updated each year to reflect the evolving situation of information and communication technologies.

Indicators from this survey are used for benchmarking purposes and in particular, this survey supports measuring the implementation of one of the six priorities for the period 2019-2024 of the von der Leyen European Commission – A Europe fit for the digital age. The strategy is built on three pillars: (1) Technology that works for the people; (2) A fair and competitive digital economy; (3) An open, democratic and sustainable society. Furthermore, it facilitates monitoring of the EU’s digital targets for 2030 set by the Digital Compass for the EU's Digital Decade, evolving around four cardinal points: skills, digital transformation of businesses, secure and sustainable digital infrastructures, and digitalization of public services.

Data on the use of ICT are also used in the Consumer Conditions Scoreboard (purchases over the Internet) and in the Employment Guidelines (e-skills of individuals).

Coverage: The characteristics to be provided are drawn from the following list of subjects:

• access to and use of ICTs by individuals and/or in households,
• use of the internet and other electronic networks for different purposes (e-commerce for example) by individuals and/or in households,
• ICT security and trust,
• ICT competence and skills,
• barriers to the use of ICT and the Internet,
• perceived effects of ICT usage on individuals and/or on households,
• use of ICT by individuals to exchange information and services with governments and public administrations (e-government),
• access to and use of technologies enabling connection to the Internet or other networks from anywhere at any time (ubiquitous connectivity).

In 2024, the survey collects data on the access to information and communication technologies (ICT), on the use of the internet, e-government, e-commerce, internet of things and green ICT.

Breakdowns (see details of available breakdowns in the document stored in the Annexes):

Relating to households:

• by region of residence (NUTS 1, optional: NUTS 2)
• by geographical location: less developed regions, transition regions, more developed regions
• by degree of urbanisation (till 2012: densely/intermediate/sparsely populated areas; from 2012: densely/thinly populated area, intermediate density area)
• by type of household
• by household's net monthly income (optional until 2020, mandatory since 2021)

Relating to individuals:

• by region of residence (NUTS1, optional: NUTS 2)
• by geographical location: less developed regions, transition regions, more developed regions
• by degree of urbanisation: (till 2012: densely/intermediate/sparsely populated areas; from 2012: densely/thinly populated area, intermediate density area)
• by gender
• by country of birth, country of citizenship (as of 2010, optional in 2010)
• by educational level: ISCED 1997 up to 2013 and ISCED 2011 from 2014 onwards.
• by occupation: manual/non-manual; ICT/non-ICT worker; 2-digit ISCO categories (optional until 2020, mandatory since 2021)
• by employment situation
• by age (in completed years and by groups)
• legal / de facto marital status (2011-2014, optional)

Regional breakdowns (NUTS) are available only for a selection of indicators disseminated in the regional tables in Eurobase (Regional Information society statistics by NUTS regions (isoc_reg)):

• households with access to the internet at home
• households with broadband access
• individuals who have never used a computer
• individuals who used the internet, frequency of use and activities
• individuals who used the internet for interaction with public authorities
• individuals who ordered goods or services over the internet for private use
• individuals who accessed the internet away from home or work.

Context

Data containing life expectancy at birth by sex and geography in Europe. Source is https://ec.europa.eu/eurostat/data/database (official European Data Source). Data is downloaded.

Content

Original data

The original data (tps00208.tsv) is provided in TSV (tab delimited) format. The dataset life expectancy at birth by sex and geography in Europe (yearly data). The time unit used are years. Geography is at country level (in Europe) or aggregated on 2 indicators for Europe (EU27_2020 & EU28). Sex is F or M. Statistical indicator is LE_0, meaning life expectancy at birth (0 years old).

Transformed data

The transformed data (life_expectancy_at_birth.csv) is in csv format. The temporal data was pivoted using Starter Kernel: Life Expectancy in Europe at Birth Kernel.

How to

The original data has the temporal information given as columns (per year). In order to further use this data, it would be more easy to pivot first these columns to get instead date/value pairs. This pivot operation, using melt from pandas is done in the starter kernel: * Starter Kernel: Life Expectancy in Europe at Birth; we convert the year to an integer. Just run this Kernel to put the data in csv format, with yearly data pivoted.

Acknowledgements

All merit for data collection, curation, and initial publishing goes to Eurostat.

Inspiration

You can use this data for various demographic, public health, social aspects, combining with alternative data from Kaggle and other sources.

This dataset contains data collected during a study "Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries" conducted by Martin Lnenicka (University of Pardubice, Pardubice, Czech Republic), Anastasija Nikiforova (University of Tartu, Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Kosovska Mitrovica, Serbia), Daniel Rudmark (University of Gothenburg and RISE Research Institutes of Sweden, Gothenburg, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Caterina Santoro (KU Leuven, Leuven, Belgium), Cesar Casiano Flores (University of Twente, Twente, the Netherlands), Marijn Janssen (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

It is being made public both to act as supplementary data for "Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries", Government Information Quarterly*, and in order for other researchers to use these data in their own work.

Methodology

The paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems.

This study examines existing benchmarks, indices, and rankings of open (government) data initiatives to find the contexts by which these initiatives are shaped, both of which then outline a protocol to determine the patterns. The composite benchmarks-driven analytical protocol is used as an instrument to examine the understanding, effects, and expert opinions concerning the development patterns and current state of open data ecosystems implemented in eight European countries - Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. 3-round Delphi method is applied to identify, reach a consensus, and validate the observed development patterns and their effects that could lead to disparities and divides. Specifically, this study conducts a comparative analysis of different patterns of open (government) data initiatives and their effects in the eight selected countries using six open data benchmarks, two e-government reports (57 editions in total), and other relevant resources, covering the period of 2013–2022.

Description of the data in this data set

The file "OpenDataIndex_2013_2022" collects an overview of 27 editions of 6 open data indices - for all countries they cover, providing respective ranks and values for these countries. These indices are:

1) Global Open Data Index (GODI) (4 editions)

2) Open Data Maturity Report (ODMR) (8 editions)

3) Open Data Inventory (ODIN) (6 editions)

4) Open Data Barometer (ODB) (5 editions)

5) Open, Useful and Re-usable data (OURdata) Index (3 editions)

6) Open Government Development Index (OGDI) (2 editions)

These data shapes the third context - open data indices and rankings. The second sheet of this file covers countries covered by this study, namely, Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. It serves the basis for Section 4.2 of the paper.

Based on the analysis of selected countries, incl. the analysis of their specifics and performance over the years in the indices and benchmarks, covering 57 editions of OGD-oriented reports and indices and e-government-related reports (2013-2022) that shaped a protocol (see paper, Annex 1), 102 patterns that may lead to disparities and divides in the development and benchmarking of ODEs were identified, which after the assessment by expert panel were reduced to a final number of 94 patterns representing four contexts, from which the recommendations defined in the paper were obtained. These patterns are available in the file "OGDdevelopmentPatterns". The first sheet contains the list of patterns, while the second sheet - the list of patterns and their effect as assessed by expert panel.

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

Licenses or restrictionsCC-BY

For more info, see README.txt

AI Generated Summary: Data.gov.ro is Romania's national open data portal, established in 2013 to centralize open data published by Romanian institutions according to international standards. It serves as a central access point for open datasets from public authorities and institutions in Romania and acts as a liaison with the European Commission's European Data Portal, promoting the free use, reuse, and redistribution of data under the Open Government License - OGL ROU 1.0. About: The data.gov.ro portal was created in 2013 within the framework of open data efforts at the international level, with the aim of centralizing open data published by Romanian institutions in accordance with the principles and standards in the field. Currently, the General Secretariat of the Government ensures the coordination of the process of opening public data in Romania and manages the national portal data.gov.ro, the central access point for open datasets published by the authorities and institutions of the public administration in Romania and the point of contact in relation to the European Commission (europeandataportal.eu). Open data is data that can be freely used, reused, and redistributed by anyone, freely, without imposing restrictions such as copyright, patents, or other control mechanisms. In this sense, the portal provides users with the Open Government License - OGL ROU 1.0, issued in 2014 by the General Secretariat of the Government as an open license model. For data to be considered open, at least two conditions must be met: technical: the data is published online in file formats that can be automatically processed using computer programs (machine-readable), which are, as far as possible, available to anyone, free of charge (free and open source software). legal: at the time of publication, the data is attached to a license by which the data owner and publisher establishes the conditions for its reuse. In Romania, the legal framework for publishing open data was established by Law no. 109/2007 regarding the reuse of information from public institutions, amended and supplemented by Law no. 299/2015. More details can be found in the Methodology for publishing open data, developed by the General Secretariat of the Government. Open data visualization tool List of datasets assumed by public institutions through the OGP National Action Plan List 2016 Status 2017 Other useful resources DCAT application profile for data portals in Europe DCAT-AP: Information on the DCAT application profile for data portals in Europe Translated from Romanian Original Text: Portalul data.gov.ro a fost realizat în anul 2013 în marja demersurilor open data la nivel internațional, în scopul centralizării datelor deschise publicate de instituțiile din România conform principiilor și standardelor în domeniu. În prezent, Secretariatul General al Guvernului asigură coordonarea procesului de deschidere a datelor publice în România și administrează portalul național data.gov.ro, punctul central de acces pentru seturile de date deschise publicate de autoritățile și instituțiile administrației publice din România și punctul de legătură în relația cu Comisia Europeană (europeandataportal.eu). Datele deschise sunt date ce pot fi utilizate în mod liber, reutilizate și redistribuite de către oricine, în mod liber, fără a impune restricții de tipul drepturi de autor (copyright), patente sau alte mecanisme de control. În acest sens, portalul pune la dispoziția utilizatorilor Licența pentru o Guvernare Deschisă - OGL ROU 1.0, emisă în 2014 de Secretariatul General al Guvernului ca model de licență deschisă. Pentru ca datele să fie considerate deschise, trebuie îndeplinite minim două condiții: tehnic: datele sunt publicate online în formate de fișiere ce pot fi procesate în mod automat folosind programe de calculator (machine-readable), care sunt, pe cât posibil, disponibile oricui, în mod gratuit (free and open source software). legal: în momentul publicării, datelor li se atașează o licență prin care cel care deține și publică datele stabilește condițiile de reutilizare a acestora. În România, cadrul legal pentru publicarea datelor deschise a fost stabilit de Legea nr. 109/2007 privind reutilizarea informațiilor din instituții publice, modificată și completată de Legea nr. 299/ 2015. Mai multe detalii găsiți în Metodologia pentru publicarea datelor deschise, elaborată de Secretariatul General al Guvernului. Instrument de vizualizare a datelor deschise Lista seturilor de date asumate de instituțiile publice prin Planul Național de Acțiune OGP Listă 2016 Stadiu 2017 Alte resurse utile DCAT application profile for data portals in Europe DCAT-AP: Information on the DCAT application profile for data portals in Europe

Success.ai’s LinkedIn Data Solutions offer unparalleled access to a vast dataset of 700 million public LinkedIn profiles and 70 million LinkedIn company records, making it one of the most comprehensive and reliable LinkedIn datasets available on the market today. Our employee data and LinkedIn data are ideal for businesses looking to streamline recruitment efforts, build highly targeted lead lists, or develop personalized B2B marketing campaigns.

Whether you’re looking for recruiting data, conducting investment research, or seeking to enrich your CRM systems with accurate and up-to-date LinkedIn profile data, Success.ai provides everything you need with pinpoint precision. By tapping into LinkedIn company data, you’ll have access to over 40 critical data points per profile, including education, professional history, and skills.

Key Benefits of Success.ai’s LinkedIn Data: Our LinkedIn data solution offers more than just a dataset. With GDPR-compliant data, AI-enhanced accuracy, and a price match guarantee, Success.ai ensures you receive the highest-quality data at the best price in the market. Our datasets are delivered in Parquet format for easy integration into your systems, and with millions of profiles updated daily, you can trust that you’re always working with fresh, relevant data.

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Why Choose Success.ai’s LinkedIn Data: Success.ai stands out for its tailored approach and white-glove service, making it easy for businesses to receive exactly the data they need without managing complex data platforms. Our dedicated Success Managers will curate and deliver your dataset based on your specific requirements, so you can focus on what matters most—reaching the right audience. Whether you’re sourcing employee data, LinkedIn profile data, or recruiting data, our service ensures a seamless experience with 99% data accuracy.

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LinkedIn URL: Access direct links to LinkedIn profiles for immediate insights. Full Name: Verified first and last names. Job Title: Current job titles, and prior experience. Company Information: Company name, LinkedIn URL, domain, and location. Work and Per...

Context

Data containing education attainment level, also grouped by age group, sex and geography in Europe. Source is https://ec.europa.eu/eurostat/data/database (official European Data Source). Data is downloaded from the source, documented and uploaded to Kaggle.

Content

Original data

The original data is provided in TSV (tab delimited) format.

Data is grouped by sex, age group and geography. Education attainment is given by International Standard Classification of Education (ISCED11).

ISCED11 education levels are the following: X - No schooling
0 - Early childhood education
1 - Primary education
2 - Lower secondary education
3 - Upper secondary education
4 - Post-secondary non-tertiary education
5 - Short-cycle tertiary education
6 - Bachelor’s or equivalent level
7 - Master’s or equivalent level
8 - Doctoral or equivalent level
9 - Not elsewhere classified

Transformed data

For easiness of use, the original data was transformed using Starter Kernel: Population Education Levels in Europe in a csv format; if you want to replicate this process, you are welcome to fork this Kernel and implement your own data analysis.

How to

The data has the temporal information given as columns (per year). In order to further use this data, it would be more easy to pivot first these columns to get instead date/value pairs. This pivot operation can be done using melt from pandas is done in the starter kernel: * Starter Kernel: Population Education Levels in Europe; we convert the year to an integer. Just run this Kernel to put the data in csv format, with yearly data pivoted.

Acknowledgements

All merit for data collection, curation, and initial publishing goes to Eurostat.

Inspiration

You can use this data for various demographic, economic, public health, social aspects, combining with alternative data from Kaggle and other sources.

Differential Privacy Tools for Public Data Market Outlook

According to our latest research, the global market size for Differential Privacy Tools for Public Data reached USD 1.24 billion in 2024, reflecting a robust adoption curve as organizations worldwide prioritize data privacy. The market is expected to grow at a CAGR of 19.7% from 2025 to 2033, reaching a projected value of USD 5.24 billion by 2033. This impressive growth is driven by an escalating demand for privacy-preserving technologies amidst tightening regulatory frameworks and increasing public awareness around data confidentiality. The market's upward trajectory is further underpinned by technological advancements and the expanding use of sensitive public datasets across sectors such as healthcare, finance, and government.

The primary growth factor propelling the Differential Privacy Tools for Public Data Market is the intensifying regulatory landscape. Governments worldwide are enacting stringent data protection laws, including the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations mandate organizations to adopt robust privacy-preserving mechanisms, making differential privacy tools essential for compliance. Enterprises and public agencies are increasingly leveraging these solutions to ensure the safe sharing and analysis of public data without compromising individual privacy. Furthermore, the growing frequency of data breaches and cyber threats has heightened the urgency to adopt advanced privacy-enhancing technologies, further driving market expansion.

Another significant driver is the rapid digitization and the exponential growth of data generation across industries. As organizations collect and analyze vast volumes of public data for research, policy-making, and business intelligence, the risk of inadvertently exposing sensitive information increases. Differential privacy tools provide a mathematically rigorous approach to anonymizing datasets, enabling organizations to extract valuable insights while safeguarding individual privacy. The adoption of artificial intelligence and machine learning in data analytics has also fueled demand for privacy-preserving solutions, as these technologies often require access to large, sensitive datasets. This convergence of big data and privacy concerns is accelerating the integration of differential privacy tools into enterprise data infrastructures.

Additionally, the market is witnessing a surge in collaborative data-sharing initiatives, particularly in sectors such as healthcare and research. The COVID-19 pandemic underscored the importance of sharing public health data for epidemiological modeling and policy response, while simultaneously highlighting the need to protect patient confidentiality. Differential privacy tools have emerged as a critical enabler for secure data-sharing consortia, facilitating cross-institutional collaboration without compromising privacy. This trend is expected to persist, with ongoing investments in privacy-preserving data infrastructure supporting the market’s sustained growth. The increasing availability of open-source differential privacy frameworks and the proliferation of cloud-based solutions are also reducing barriers to adoption, further broadening the market’s reach.

Regionally, North America dominates the Differential Privacy Tools for Public Data Market, accounting for the largest revenue share in 2024. The region’s leadership is attributed to the presence of major technology vendors, early adoption of privacy-enhancing technologies, and a proactive regulatory environment. Europe follows closely, driven by the GDPR and robust public sector investments in data privacy. The Asia Pacific region is rapidly emerging as a high-growth market, fueled by digital transformation initiatives and increasing awareness of data privacy issues. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by evolving regulatory frameworks and growing digital economies. Each region presents unique opportunities and challenges, shaping the global market’s competitive and technological landscape.

Context

Data containing life expectancy at birth by sex and geography in Europe. Source is https://ec.europa.eu/eurostat/data/database (official European Data Source). Data is downloaded from the source, documented and uploaded to Kaggle.

Content

Original data

The original data is provided in TSV (tab delimited) format. The dataset gives the number of deaths by cancer (icd10 classification ac C) by sex and geography in Europe (yearly data). The time unit used are years. Geography is at country level (in Europe) or aggregated on 2 indicators for Europe (EU27_2020 & EU28). Sex is F or M. ICD-10 indicator is C, meaning neoplasm (Cancer).

Transformed data

For your convenience, we transformed the data in csv format; see procedure in Starter Kernel: Deaths by Cancer in Europe.

How to

The data has the temporal information given as columns (per year). In order to further use this data, it would be more easy to pivot first these columns to get instead date/value pairs. This pivot operation, using melt from pandas is done in the starter kernel: * Starter Kernel: Deaths by Cancer in Europe; we convert the year to an integer. Just run this Kernel to put the data in csv format, with yearly data pivoted.

Acknowledgements

All merit for data collection, curation, and initial publishing goes to Eurostat.

Inspiration

You can use this data for various demographic, public health, social aspects, combining with alternative data from Kaggle and other sources.

The EMA publishes an EPAR for every medicine granted a central marketing authorisation by the European Commission following an assessment by the EMA's Committee for Medicinal Products for Veterinary Use (CVMP). EPARs are full scientific assessment reports of medicines authorised at a European Union level.

Use this search to find key information on medicines authorised for veterinary use, including a public-friendly summary in question-and-answer format and the package leaflet. You can also find information on medicines that have been refused a marketing authorisation or that have been suspended or withdrawn after being approved.

The Agency does not evaluate all medicines currently in use in Europe. If you cannot find the medicine you need through this search, please visit the website of your national health authority. More information is also available on the central authorisation procedure and on European public assessment reports.

Search results can be exported in Excel format.