The Register provides information on the availability of surplus land for those government departments and their sponsored bodies which fall under the responsibility of English Ministers. The Register is also used on a voluntary basis by NHS trusts and Welsh Government. The land records are presented as points data. This dataset does not include the land parcel boundaries. The Register helps to ensure that wider Government objectives, including housing needs are factored into land disposal decisions. Through the Register, the disposing body provides details of the site and there is a window of 40 working days during which certain public sector bodies can identify new uses for the land.

The dataset available on data.gov.uk covers all sites that are outside the 40 working day ‘window’. Such sites may be included in the disposal strategies that have been published by a number of individual government departments. In addition, these sites may now be ‘on the market’ and being actively marketed. The ‘Register of Public Sector Land’ dataset provides details of any sites that are being disposed of through the Register of Surplus Public Sector Land and are outside the 40 working day ‘window’ Certain properties may have more than one entry in the data extract as government has more than one ‘interest’ in that property. Again, the extract provides information about the ‘owning’ government department and the ‘property centre’, i.e. that part of the government department responsible for that property. In addition, it has a property reference (the ‘ePIMS Property Ref’) that allows it to be linked to the other data extracts.

The scope of the data includes land and property information for those government departments, together with any arms’ length bodies for which they are responsible, including their non-departmental public bodies (NDPBs), which fall under the responsibility of English Ministers. These assets are primarily located in England, but are also located in the devolved administrations of Northern Ireland, Scotland and Wales as well as overseas. Also, some Local Authorities have chosen to publish their property data as part of our transparency exercise. The Register helps to ensure that wider Government objectives, including housing needs are factored into land disposal decisions. Through the Register, the disposing body provides details of the site and there is a window of 40 working days during which certain public sector bodies can identify new uses for the land. The dataset available on data.gov.uk covers all sites that are outside the 40 working day ‘window’. Such sites may be included in the disposal strategies that have been published by a number of individual government departments. In addition, these sites may now be ‘on the market’ and being actively marketed. The ‘Register of Public Sector Land’ dataset provides details of any sites that are being disposed of through the Register of Surplus Public Sector Land and are outside the 40 working day ‘window’ Certain properties may have more than one entry in the data extract as government has more than one ‘interest’ in that property. Again, the extract provides information about the ‘owning’ government department and the ‘property centre’, i.e. that part of the government department responsible for that property. In addition, it has a property reference (the ‘ePIMS Property Ref’) that allows it to be linked to the other datasets.

Three spatiotemporal data sets of drinking water hardness in Denmark (version 1) are presented here: (1) annual drinking water hardness at public waterworks (1905–2023); (2) annual drinking water hardness at their water supply areas (1978–2023) and (3) the latest drinking water hardness at the water supply areas (1980–2023). Raw data were extracted from the Jupiter database for groundwater and drinking water data in Denmark, and were quality-assured. Hardness was calculated after semi-automatic outlier exclusion based on Ca and Mg, or if not available, the reported total hardness. Data were further aggregated at the waterworks level by the annual mean and at the supply area level by the weighted mean (weighted to waterworks annual abstraction volumes). Temporal and spatial gaps were filled prior to these aggregations. Various stakeholders could benefit from these open access data. They provide a societal service in response to increased public interest in drinking water hardness. The research community could use the data in environmental, exposure or epidemiological assessments. Finally, the water supplies and the public sector could benefit from these data as they provide a nationwide overview of current and past drinking water hardness in Denmark and highlight the geographic areas that lack recent data, most probably due to de-regulation.

Two validation approaches to ensure the reliability of this foundational data: the first is on verifying the internal accuracy of data extraction and integration processes, and the second is on assessing consistency with external benchmark data sources.

Scottish Public Sector Employment Statistics are published by the Scottish Government and includes results from the work of a cross-departmental project, led by ONS, to improve the quality and timeliness of public sector employment statistics. Standard definitions for public sector employment across departmental statistics were agreed and a single definitive set of quarterly PSE estimates were introduced. Methodology Data reproduced here is an extract of Public Sector Employment Statistics, filtered for Glasgow local authority. The full Scottish dataset can be download from here 2014-07-17T17:00:00 Licence: None

This land dataset includes land parcel boundaries for e-PIMS records marked on the Register. This may be extended to other land records in the future. Currently it provides information on the availability of surplus land for those government departments and their sponsored bodies which fall under the responsibility of English Ministers. The Register is also used on a voluntary basis by NHS trusts and Welsh Government. The Register helps to ensure that wider Government objectives, including housing needs are factored into land disposal decisions. Through the Register, the disposing body provides details of the site and there is a window of 40 working days during which certain public sector bodies can identify new uses for the land.

The dataset available on data.gov.uk covers all sites that are outside the 40 working day ‘window’. Such sites may be included in the disposal strategies that have been published by a number of individual government departments. In addition, these sites may now be ‘on the market’ and being actively marketed. The ‘Register of Public Sector Land’ dataset provides details of any sites that are being disposed of through the Register of Surplus Public Sector Land and are outside the 40 working day ‘window’ Certain properties may have more than one entry in the data extract as government has more than one ‘interest’ in that property. Again, the extract provides information about the ‘owning’ government department and the ‘property centre’, i.e. that part of the government department responsible for that property. In addition, it has a property reference (the ‘ePIMS Property Ref’) that allows it to be linked to the other data extracts.

The scope of the data includes land and property information for those government departments, together with any arms’ length bodies for which they are responsible, including their non-departmental public bodies (NDPBs), which fall under the responsibility of English Ministers. These assets are primarily located in England, but are also located in the devolved administrations of Northern Ireland, Scotland and Wales as well as overseas. Also, some Local Authorities have chosen to publish their property data as part of our transparency exercise. The Register helps to ensure that wider Government objectives, including housing needs are factored into land disposal decisions. Through the Register, the disposing body provides details of the site and there is a window of 40 working days during which certain public sector bodies can identify new uses for the land. The dataset available on data.gov.uk covers all sites that are outside the 40 working day ‘window’. Such sites may be included in the disposal strategies that have been published by a number of individual government departments. In addition, these sites may now be ‘on the market’ and being actively marketed. The ‘Register of Public Sector Land’ dataset provides details of any sites that are being disposed of through the Register of Surplus Public Sector Land and are outside the 40 working day ‘window’ Certain properties may have more than one entry in the data extract as government has more than one ‘interest’ in that property. Again, the extract provides information about the ‘owning’ government department and the ‘property centre’, i.e. that part of the government department responsible for that property. In addition, it has a property reference (the ‘ePIMS Property Ref’) that allows it to be linked to the other datasets.

Global Data Extraction Software market size is expected to reach $3.64 billion by 2029 at 15.9%, segmented as by tools, web-based extraction tools, cloud-based extraction tools, on-premise extraction tools

🇬🇧 영국 English *** THIS DATA IS UPDATED DAILY *** The Register provides information on the availability of surplus land for those government departments and their sponsored bodies which fall under the responsibility of English Ministers. The Register is also used on a voluntary basis by NHS trusts and Welsh Government. The land records are presented as points data. This dataset does not include the land parcel boundaries. The Register helps to ensure that wider Government objectives, including housing needs are factored into land disposal decisions. Through the Register, the disposing body provides details of the site and there is a window of 40 working days during which certain public sector bodies can identify new uses for the land. The dataset available on data.gov.uk covers all sites that are outside the 40 working day ‘window’. Such sites may be included in the disposal strategies that have been published by a number of individual government departments. In addition, these sites may now be ‘on the market’ and being actively marketed. The ‘Register of Public Sector Land’ dataset provides details of any sites that are being disposed of through the Register of Surplus Public Sector Land and are outside the 40 working day ‘window’ Certain properties may have more than one entry in the data extract as government has more than one ‘interest’ in that property. Again, the extract provides information about the ‘owning’ government department and the ‘property centre’, i.e. that part of the government department responsible for that property. In addition, it has a property reference (the ‘ePIMS Property Ref’) that allows it to be linked to the other data extracts. The scope of the data includes land and property information for those government departments, together with any arms’ length bodies for which they are responsible, including their non-departmental public bodies (NDPBs), which fall under the responsibility of English Ministers. These assets are primarily located in England, but are also located in the devolved administrations of Northern Ireland, Scotland and Wales as well as overseas. Also, some Local Authorities have chosen to publish their property data as part of our transparency exercise. The Register helps to ensure that wider Government objectives, including housing needs are factored into land disposal decisions. Through the Register, the disposing body provides details of the site and there is a window of 40 working days during which certain public sector bodies can identify new uses for the land. The dataset available on data.gov.uk covers all sites that are outside the 40 working day ‘window’. Such sites may be included in the disposal strategies that have been published by a number of individual government departments. In addition, these sites may now be ‘on the market’ and being actively marketed. The ‘Register of Public Sector Land’ dataset provides details of any sites that are being disposed of through the Register of Surplus Public Sector Land and are outside the 40 working day ‘window’ Certain properties may have more than one entry in the data extract as government has more than one ‘interest’ in that property. Again, the extract provides information about the ‘owning’ government department and the ‘property centre’, i.e. that part of the government department responsible for that property. In addition, it has a property reference (the ‘ePIMS Property Ref’) that allows it to be linked to the other datasets.

Tenders are powerful means of investment of public funds and represent a strategic development resource. Despite the efforts made so far by governments at national and international levels to digitalise documents related to the Public Administration sector, most of the information is still available in an unstructured format only. With the aim of bridging this gap, we present OIE4PA, our latest study on extracting and classifying relations from tenders of the Public Administration. Our work focuses on the Italian language, where the availability of linguistic resources to perform Natural Language Processing tasks is considerably limited. For evaluation purposes, we built a dataset composed of 2,000 triples extracted from Italian tenders, which have been manually annotated by two human experts.

The dataset, compressed in a single zip file, is composed of:

The corpus of 6,262 texts extracted from Italian public tenders (corpus_tenders)

The training set of 1,600 annotated triples (training_set)

The test set of 400 annotated triples (test_set)

The set U of 14,096 triples used for the self-training (u_triples_dd)

a compressed archive that contains both the extracted triples and the index for each supervised approach (extraction)

Outcome data extracted for meta-analysis of the Mental Health First Aid training program.

The online encyclopedia Wikipedia aggregates a large amount of data on chemistry, encompassing well over 20,000 individual Wikipedia pages and serves the general public as well as the chemistry community. Many other chemical databases and services utilize these data, and previous projects have focused on methods to index, search, and extract it for review and use. We present a comprehensive effort that combines bulk automated data extraction over tens of thousands of pages, semiautomated data extraction over hundreds of pages, and fine-grained manual extraction of individual lists and compounds of interest. We then correlate these data with the existing contents of the U.S. Environmental Protection Agency’s (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database. This was performed with a number of intentions including ensuring as complete a mapping as possible between the Dashboard and Wikipedia so that relevant snippets of the article are loaded for the user to review. Conflicts between Dashboard content and Wikipedia in terms of, for example, identifiers such as chemical registry numbers, names, and InChIs and structure-based collisions such as SMILES were identified and used as the basis of curation of both DSSTox and Wikipedia. This work also allowed us to evaluate available data for sets of chemicals of interest to the Agency, such as synthetic cannabinoids, and expand the content in DSSTox as appropriate. This work also led to improved bidirectional linkage of the detailed chemistry and usage information from Wikipedia with expert-curated structure and identifier data from DSSTox for a new list of nearly 20,000 chemicals. All of this work ultimately enhances the data mappings that allow for the display of the introduction of the Wikipedia article in the community-accessible web-based EPA Comptox Chemicals Dashboard, enhancing the user experience for the thousands of users per day accessing the resource.

Hiring activities refers to indeterminate and term appointments to the public service, the hiring of casuals as per subsection 50(1) of the Public Service Employment Act (PSEA) and the hiring of students under the Student Employment Programs Participants Exclusion Approval Order. Indeterminate and term appointments to the public service include appointments from the general public, including former casuals, students and employees of government organizations that are not subject to the PSEA. Staffing activities to and within the public service include appointments to the public service as well as promotions, lateral and downward movements and acting appointments of indeterminate and term employees. Deployments of employees within or between organizations that are subject to the PSEA are counted in lateral and downward movements. Hiring and staffing activities data are derived from information received from the Treasury Board Secretariat (TBS) Incumbent File. This file is extracted from the Public Services and Procurement Canada (PSPC) pay system. The Public Service Commission (PSC) has developed a series of algorithms that are used to produce the PSC’s official record of hiring and staffing activities across the federal public service, based on pay records submitted by organizations. Recruitment data for the Recruitment of Policy Leaders Initiative and the Post-Secondary Recruitment Program are based on individuals who have applied to these programs through the PSC’s Public Service Resourcing System (PSRS) and where a match was found in the PSC hiring and staffing activities file covering the current fiscal year.

EXTOPIA was a project that has been executed in 2020 by the Administration du cadastre et de la topographie in order to evaluate the feasability of automatic extraction of topographic objects from aerial imagery using artificial intelligence, especially deep learning tools. The project was funded by the Ministry of Digitalization in the context of the AI4GOV Program 2020. Several contractors had been invited in a tender process and the project has finally been executed by GIM nv from Leuven, Belgium. The results are very satisfying and the tools are available for the ACT on the Govcloud infrastructure of the CTIE. This dataset is meant to describe the project and to publish the final report.

Ecuador Annual Freshwater Withdrawals: Domestic: % of Total Freshwater Withdrawal data was reported at 13.037 % in 2021. This stayed constant from the previous number of 13.037 % for 2020. Ecuador Annual Freshwater Withdrawals: Domestic: % of Total Freshwater Withdrawal data is updated yearly, averaging 13.037 % from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 13.037 % in 2021 and a record low of 9.685 % in 2000. Ecuador Annual Freshwater Withdrawals: Domestic: % of Total Freshwater Withdrawal data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ecuador – Table EC.World Bank.WDI: Environmental: Water and Wastewater Management. Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002.;Food and Agriculture Organization, AQUASTAT data.;Weighted average;

The Systematic Review Management Software market, currently valued at $345 million in 2025, is projected to experience robust growth, driven by the increasing demand for efficient and accurate research methodologies across academia, corporations, and the public sector. The 6.2% CAGR indicates a steady expansion over the forecast period (2025-2033), fueled by several key factors. The rising volume of research publications necessitates streamlined review processes, pushing organizations to adopt sophisticated software solutions. Furthermore, the increasing complexity of systematic reviews, coupled with the need for reproducible research, is driving adoption. Cloud-based solutions are gaining significant traction due to their accessibility, scalability, and collaborative features, further propelling market growth. While the on-premise segment retains a market share, the trend clearly favors cloud-based offerings. Competition is intense, with established players like Clarivate (EndNote, RefWorks) and Elsevier (Mendeley) facing challenges from newer, agile companies like Rayyan and DistillerSR that offer specialized features. Geographic distribution shows a concentration in North America and Europe, reflecting the higher research activity and funding in these regions. However, growth opportunities exist in developing markets like Asia Pacific and Middle East & Africa as research capabilities expand. The market segmentation reveals that the academic sector currently holds the largest share, followed by the corporate and public sectors. This distribution reflects the critical role systematic reviews play in academic research, evidence-based policy-making, and pharmaceutical development. The evolution of the software itself is also a key driver, with features like advanced data extraction, AI-powered analysis, and improved collaboration tools becoming increasingly prevalent. However, factors such as the high initial cost of software implementation, the need for user training, and the potential for data security concerns act as restraints. Despite these challenges, the market's long-term outlook remains positive, driven by the sustained increase in research output and the crucial role of systematic reviews in evidence-based decision-making across various sectors.

According to Cognitive Market Research, the global Government Open Data Management Platform Market size will be USD XX million in 2024. It will expand at a compound annual growth rate (CAGR) of 9.90% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.1% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD XX million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.9% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.3% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.6% from 2024 to 2031.
The large enterprises held the highest Government Open Data Management Platform Market revenue share in 2024.

Market Dynamics of Government Open Data Management Platform Market

Key Drivers for Government Open Data Management Platform Market

Streamlining Procedures and Increasing Productivity to Increase the Demand Globally

Operational effectiveness and process optimization are propelling market expansion. Organizations can increase operational efficiency and streamline procedures by implementing open data management solutions. Organizational data is gathered, managed, organized, and stored with the use of open data management platforms to increase accessibility and usability. These kinds of solutions are commonly applied to business process automation as well as operational optimization and streamlining. For instance, by significantly reducing human engagement and contact during the data extraction procedures, open data management platforms are often used to automate corporate processes. In response to advancements in technology and the creation of increasingly complicated data sets, open data management platforms have developed.

Advancements in Technology to Propel Market Growth

The Government Open Data Management Platform Market has witnessed steady growth, driven by advancements in technology, such as improving analytics, security, and data accessibility. Governments can more effectively manage and use huge volumes of public data because of advances in AI, cloud computing, and big data analytics. By enhancing the integration of data, real-time analysis, and visualization, these technologies promote availability and well-informed decision-making. Furthermore, improvements in cybersecurity guarantee data security, encouraging public confidence. The need for advanced data management platforms in the public sector is being driven by the increasing capacity to handle and exploit open data as a result of technological advancements.

Restraint Factor for the Government Open Data Management Platform Market

Lack of Skilled Workforce in Government Open Data Management Platform to Limit the Sales

The government's open data management platform needs skilled workers to oversee its operations, but a key hindrance to its expansion is the need for a skilled workforce. Understanding HTML, CSS, and JavaScript is necessary for the developer to execute data platform management. Thus, lacking in this fundamental knowledge makes it more difficult to hire the proper specialists, which lowers productivity inside the firm. These important problems make it harder for the market for government open data platform management to expand.

Impact of Covid-19 on the Government Open Data Management Platform Market

The Government Open Data Management Platform Market has witnessed growth. In order for researchers and policymakers to follow the virus's transmission, locate hotspots, and make defensible decisions, open data management technologies were essential in the collection, analysis, and visualization of COVID-19 data. Consequently, the outbreak had a favorable effect on the expansion of the local market. The need for improved data security, the growing focus on data-driven decision-making, the need for transparent and accessible government data, changing...

Each row of the dataset lists the responses to a questionnaire about the use (consumption) of open government data (OGD) in the Brazilian states and federal district public administrations. The questionnaire was applied between June 10 and July 9, 2021, in a sample of focal points designated by the GTD.GOV. The dataset contains 26 lines, one response per Federation Unit, except for the State of São Paulo, which did not answer the survey. Thus, this dataset represents the perspective of each federation unit participating in the survey. Respondents were instructed to offer the view of their Federation Unit, as they are state officials responsible for open data in their administrations. After collection, we exported the data to Excel. The resulting dataset was cleaned and is in a table format. Questionnaire responses are provided in two files ResultsSurvey_OGDUseBRStatesDF_DataSet_PT and ResultsSurvey_OGDUseBRStatesDF_DataSet_EN. They contain the same information in Portuguese and English. V2 - Added a dataset with the extracts of interviews conducted with three states. Each row of this dataset has six columns: Factor Code shows the factor code as it appears in the questionnaire. A factor code represents benefit (BE), barrier (BA), enabler (E), driver (D), or N/A when the factor does not exist in the questionnaire or when it is the response to a question, such as why OGD is used in the public sector. The column Factor Description Portuguese describes the factor in Portuguese. The column Factor Description English presents the factor description in English. The column Interviewee holds the interviewee code. The column Extract Portuguese has an excerpt of the transcribed interview in Portuguese that provide evidence of the factor. Finally, the column Extract English presents the translation of the Portuguese extract. Interviews extracts are provided in the file InterviewExtracts_BR_StatesDF_Dataset and metadata in the AboutBR_StatesDF.

The state level data contains different factors from population density, comorbid conditions and social distancing score.

The market for systematic review tools is experiencing robust growth, driven by the increasing demand for evidence-based decision-making across academic, corporate, and public sectors. The rising volume of research publications and the need for efficient, reliable methods to synthesize this information are key factors fueling market expansion. Cloud-based solutions are gaining significant traction due to their accessibility, scalability, and collaborative features, surpassing on-premises deployments in market share. While North America currently holds a dominant position, driven by strong research infrastructure and funding, regions like Asia Pacific are exhibiting rapid growth potential, reflecting increasing research activity and adoption of digital tools. Competition is intense, with established players like Clarivate and Elsevier competing with specialized startups and open-source options. The market is segmented by application (academic, corporate, public sector) and type (cloud-based, on-premises), offering diverse solutions to meet specific needs. Future growth will be influenced by advancements in artificial intelligence (AI) and machine learning (ML) to automate aspects of systematic review processes, improving efficiency and reducing bias. Integration with other research management tools and enhanced collaboration features will further shape the market landscape. The market is projected to maintain a healthy CAGR (let's assume a conservative 8% based on typical software market growth), resulting in substantial market expansion over the forecast period (2025-2033). Challenges remain, including the need for user-friendly interfaces, robust data security, and cost considerations, particularly for smaller institutions and organizations. However, the ongoing emphasis on rigorous research methodologies and the increasing accessibility of digital tools suggest a positive outlook for the systematic review tools market. The continued development of advanced features, such as AI-powered tools for identifying relevant literature and automating data extraction, will further drive market expansion and solidify the role of these tools in research and evidence-based decision-making.

*** THIS DATA IS A SNAPSHOT AS AT 31ST MARCH 2025 *** Basic information about the occupation. A property may consist of a single building or many buildings, associated with one or many holdings. A holding should have at least one occupation record relating to the holding owner. If there is more than one occupier then multiple occupation records may exist.

The ‘Occupation’ dataset provides key information about the tenant of each property, the amount of space that they occupy and the nature of that occupation. As certain properties may have more than one tenant, there may be more than one entry in the data extract as government has more than one ‘interest’ in that property. It should be noted that the list of tenants only includes government tenants. It does not include either commercial tenants or tenants from other parts of the public sector. Again, the extract provides information about the ‘owning’ government department and the ‘property centre’, i.e. that part of the government department responsible for that property. In addition, it has a property reference (the ‘ePIMS Property Ref’) that allows it to be linked to the other data extracts.

The scope of the data includes land and property information for those government departments, together with any arms’ length bodies for which they are responsible, including their non-departmental public bodies (NDPBs), which fall under the responsibility of English Ministers. These assets are primarily located in England, but are also located in the devolved administrations of Northern Ireland, Scotland and Wales as well as overseas.

Also, some Local Authorities have chosen to publish their property data as part of our transparency exercise.

Population data refers to the number of active employees in organizations under the exclusive appointment authority of the Public Service Commission (PSC) (employees of organizations named in the Financial Administration Act — Schedule I, most of Schedule IV and some agencies in Schedule V). This differs from numbers reported by the Treasury Board Secretariat (TBS) that reflect employment in organizations under the Public Service Staff Relations Act. In addition, a number of separate agencies are subject to Part 7 of the Public Service Employment Act (PSEA), which administers the political activities of public servants. The population count represents the number of active employees at a specific point in time. Population data are derived from the TBS Incumbent File. This file is extracted from the Public Services and Procurement Canada (PSPC) pay system.