This contains all reported and validated M&O Prime Contractor 1st tier subcontractor small business award dollars for the government’s most recently completed fiscal year. The file contains 55 data elements that represent a subset of FPDS-NG data.

This is a study to assess the application of process mining techniques on data from the Brazilian public services, made available on open data portals, aiming to identify bottlenecks and improvement opportunities in government processes. The datasets were obtained from the Brazilian Federal Government's Open Data Portal: dados.govCategorization:(1) event log(2) there is a complete date(3) list of data or information table(4) documents(5) no file founded(6) link to another portalLink of brasilian portal: https://dados.gov.br/homeList of content made available:open-data-sample.zip: all the files obtained from the representative sample of the studyopen-data-sample.xls: table categorizing the datasets obtained and classifying them as relevant for testing in the process mining toolsdataset137.csv: dataset with undergraduate degree records tested in the Disco, Celonis and ProM toolsdataset258.csv: dataset with software registration requests tested in the Disco, Celonis and ProM toolsdataset356.csv: dataset with public tender inspector registrations tested in the Disco, Celonis and ProM tools

The Data Extraction Software Market is projected to grow at 15.9% CAGR, reaching $3.64 Billion by 2029. Where is the industry heading next? Get the sample report now!

Increasing amounts of structured data can provide value for research and business if the relevant data can be located. Often the data is in a data lake without a consistent schema, making locating useful data challenging. Table search is a growing research area, but existing benchmarks have been limited to displayed tables. Tables sized and formatted for display in a Wikipedia page or ArXiv paper are considerably different from data tables in both scale and style. By using metadata associated with open data from government portals, we create the first dataset to benchmark search over data tables at scale. We demonstrate three styles of table-to-table related table search. The three notions of table relatedness are: tables produced by the same organization, tables distributed as part of the same dataset, and tables with a high degree of overlap in the annotated tags. The keyword tags provided with the metadata also permit the automatic creation of a keyword search over tables benchmark. We provide baselines on this dataset using existing methods including traditional and neural approaches.

As described by ASTM D7780-12: This feature class contains points that depict the location of environmental resource monitoring locations (ERMLs), including groundwater and surface water monitoring sites, NPDES locations, and certain types of sampling points. This dataset consists of coalmining related features as described by ASTM D7780-12, "Standard Practice for Geospatial Data for Representing Coal Mining Features". These data are gathered using automated processes from participating coalmining regulatory authorities, which are generally state government agencies. The data from the various sources are transformed into common schemas as described by the ASTM Standard above. The resultant feature classes represent seamless information covering the coal producing areas of the United States. Development of these data are ongoing and will become more complete as more cooperating regulatory authorities are added to the GeoMine system.

The Information Extraction (IE) technology market is experiencing robust growth, driven by the increasing need for automated data processing and analysis across diverse sectors. The market's expansion is fueled by the proliferation of unstructured data, the rising adoption of cloud-based solutions, and the growing demand for real-time insights in applications like cybersecurity, fraud detection, and market intelligence. While precise figures for market size and CAGR are not provided, a reasonable estimate, considering the market's dynamism and the involvement of major players like IBM and Cisco, could place the 2025 market size at approximately $5 billion, with a projected CAGR of 15% from 2025 to 2033. This growth is expected to be driven by the increasing adoption of AI and machine learning techniques within IE solutions, leading to more accurate and efficient data extraction. The integration of IE into existing enterprise systems is also a major growth driver, creating a seamless workflow for data processing and analysis. Segment-wise, the government and military sector are anticipated to show high growth due to the increasing need for intelligence gathering and threat analysis. Similarly, the standalone systems segment is expected to dominate initially, gradually giving way to the integrated systems segment as businesses move toward holistic data management solutions. Geographic expansion will likely be strong in North America and Europe initially, followed by rapid growth in Asia-Pacific, driven by technological advancements and digital transformation initiatives in emerging economies. Challenges remain in the form of data security concerns and the need for high-quality training data, but the overall outlook for the IE technology market remains positive. The competitive landscape is marked by the presence of both established technology giants and specialized players. Companies like IBM and Cisco leverage their existing infrastructure and expertise to integrate IE capabilities into their broader product offerings. Specialized players, including Palo Alto Networks and Check Point Software Technologies, focus on developing advanced IE solutions tailored to specific market needs, particularly in cybersecurity. The market is also expected to see increased consolidation and strategic partnerships as companies strive to expand their market share and offer comprehensive solutions. This includes both horizontal and vertical integration, with vendors moving to provide end-to-end solutions across the entire data lifecycle. Furthermore, the development of open-source IE tools is contributing to broader adoption and experimentation within the industry. These factors combined will shape the market's future, impacting both market size and adoption rates in the coming years.

The global data extraction market size was valued USD 2734.98 Million in 2022 and is expected to rise to USD 5691.02 Million by 2030 at a CAGR of 9.80%.

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...

The goal for Payroll Data Feed is to securely acquire pay data for all Federal Civilian employees by leveraging existing data extraction processes to the extent possible.Depending on the source of pay related data, one provider may submit payroll data for many agencies. Payroll data submissions from providers to EHRI represent actual payroll records in a given pay period. When a payroll data provider makes major system changes, it is responsible for ensuring that data accuracy and completeness are maintained. The Office of Personnel Management should be notified when any major system changes are planned. Then, the Office of Personnel Management will decide whether the payroll data provider should submit test data or continue to submit publication data.

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on known details at the time of acquisition.

The layer provides source data for groundwater to the Victorian Water Resources Data Warehouse which was closed down November 2013. It contains selected extraction wells attributed to the specific model layers used in the Gippsland regional Groundwater Model.

This was recently replaced by WMIS: http://data.water.vic.gov.au/monitoring.htm

The latest update (Oct 2013) for this dataset is from the GMS (as all previous datasets have been). Next update will be from WMIS.

Dataset History

This dataset was not accompanied by any description of how it was developed. It is assumed that it is a selection of bore locations extracted from the WMIS which have been attributed with other data relating to model layers, extraction rates and aquifer details. Please refer to the Principal investigator Craig Beverly for details (Gippsland Groundwater model, Beverly et al 2015)

The following description is provided by www.vvg.org.au. The WMIS is the statewide repository of groundwater bore information managed by the Department of Environment, Land, Water and Planning (DELWP) for the State Government Victoria. The data in the WMIS was migrated from the former Groundwater Management System (GMS) in 2013 and the current database holds groundwater information on approximately 220,000 boreholes throughout Victoria. The WMIS is also accessible directly through the DELWP groundwater portal.

Dataset Citation

Victorian Department of Economic Development, Jobs, Transport and Resources (2016) Extraction bores v1 Geodatabase. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/cca7757e-f108-4d09-ac5f-6ac6d30955b9.

description: These criminal justice expenditure and employment (CJEE) data are taken from a special compilation of sources available from the Census Bureau's Annual Surveys of Governments, Finance Statistics and Employment Statistics. Levels of government covered are; abstract: These criminal justice expenditure and employment (CJEE) data are taken from a special compilation of sources available from the Census Bureau's Annual Surveys of Governments, Finance Statistics and Employment Statistics. Levels of government covered are

This dataset was originally created in 2015 as a part of a stocktaking exercise initiated by the Integrated Digital Solutions (IDS) Group to present an inventory of all WBG investments including large ICT/e-Gov components for various sector reforms since 1995. The dataset includes the details of ICT investments in seven categories, and their mapping to four GovTech focus areas, together with the cost, duration, and outcome ratings of completed activities, in addition to key project data extracted from the WBG operations portal. The first version of the “DG Projects Database” including 1,100+ projects funded in 130+ countries was released publicly within the WBG Data Catalog in June 2015. There were several updates on the dataset since then (Aug 2017, Dec 2019, Jan 2020, and Jul 2020). The latest version (October 2022) presents the details of 1,449 projects funded in 147 countries. This dataset can be used by all practitoners involved in the design of digital government/GovTech activities to learn from relevant investments, search the contents of project documents (PAD, ICR, IEG review), and expand/customize the resulting data sets for various needs (operational support, project design, research, monitoring and quality assurance, training, etc.).

Indonesia Government Revenue: Domestic: Non Tax: Natural Resources: Geothermal Mining data was reported at 2,280.577 IDR bn in 2018. This records an increase from the previous number of 932.981 IDR bn for 2017. Indonesia Government Revenue: Domestic: Non Tax: Natural Resources: Geothermal Mining data is updated yearly, averaging 866.552 IDR bn from Dec 2008 (Median) to 2018, with 11 observations. The data reached an all-time high of 2,280.577 IDR bn in 2018 and a record low of 343.800 IDR bn in 2010. Indonesia Government Revenue: Domestic: Non Tax: Natural Resources: Geothermal Mining data remains active status in CEIC and is reported by The Audit Board of The Republic of Indonesia. The data is categorized under Indonesia Premium Database’s Government and Public Finance – Table ID.FA003: Government Budget: Realization.

This dataset contains the results of a survey about the use of open government data applied to public agents working in public institutions in Brazil. It has two sets, one with questionnaire responses and metadata and the second with a coding table with interview extracts: 1) In the first dataset, each row holds a response to a questionnaire about the public agent's perceptions of the use and reuse of open government data in Brazilian public institutions. Columns store the questionnaire questions. Data were collected between 8 June and 13 July 2021, and this sample is composed of responses from 40 federal, state, and municipal public administrators. Thus, this dataset contains 40 rows and 158 columns. Data were collected on the LimeSurvey platform, where it was screened for missing values and incomplete responses. After cleaning, data were exported to Excel in tabular format. Questionnaire responses are provided in two files ResultsSurvey_OGDUseBRPubInstitutions_DataSet_PT and ResultsSurvey_OGDUseBRPubInstitutions_DataSet_EN. They contain the same information in Portuguese and English. 2) The second dataset records the code table of the interviews about the benefits, barriers, enablers, and drivers of open government data (OGD) use in Brazilian public institutions. A questionnaire applied to public agents working in Brazilian public institutions was followed up by interviews to broaden an understanding of the use of OGD. Nine interviews were conducted between May 17-31, 2022. This dataset represents the perspective of these public agents. The dataset contains 97 lines and six columns. Each row of the dataset lists the factor code used in the questionnaire, the factor descriptions in Portuguese and English, the interviewee code, the transcription extract of an interviewee narration collected in Portuguese, and the English translation. After collection in Portuguese, interviews were automatically transcribed using the NVivo Transcription software. Then, they were anonymized, and a human reviewed the transcriptions. Interviews were coded using NVivo and used the questionnaire factors to guide coding. Coded extracts were translated to English using Google and Microsoft translators. Then, translated extracts were revised by a human and were used for reporting. The coding table was exported to Excel. Interviews extracts are provided in one file, InterviewsExtracts_OGDUseBR_PublicInstitutions_Dataset.

This data contains a comprehensive District of Columbia government extract of fees and fines. These include fees for Department of Consumer and Regulatory Affairs (DCRA) permits, Department of Health (DOH) licenses, and Department of Motor Vehicles (DMV) services. DC government will update the data yearly as fees and fines are re-evaluated.

Norway Central Government Revenue: NI: Petroleum Extraction Tax: CO2 Emissions data was reported at 0.000 NOK mn in May 2018. This records a decrease from the previous number of 2,192.300 NOK mn for Apr 2018. Norway Central Government Revenue: NI: Petroleum Extraction Tax: CO2 Emissions data is updated monthly, averaging 0.000 NOK mn from Jan 1980 (Median) to May 2018, with 448 observations. The data reached an all-time high of 2,404.000 NOK mn in Oct 2015 and a record low of -19.900 NOK mn in Dec 2009. Norway Central Government Revenue: NI: Petroleum Extraction Tax: CO2 Emissions data remains active status in CEIC and is reported by Statistics Norway. The data is categorized under Global Database’s Norway – Table NO.F018: Central Government Revenue and Expenditure: Including National Insurance Scheme.

This dataset consists of sentences extracted from BGS memoirs, DECC/OGA onshore hydrocarbons well reports and Mineral Reconnaissance Programme (MRP) reports. The sentences have been annotated to enable the dataset to be used as labelled training data for a Named Entity Recognition model and Entity Relation Extraction model, both of which are Natural Language Processing (NLP) techniques that assist with extracting structured data from unstructured text. The entities of interest are rock formations, geological ages, rock types, physical properties and locations, with inter-relations such as overlies, observedIn. The entity labels for rock formations and geological ages in the BGS memoirs were an extract from earlier published work https://github.com/BritishGeologicalSurvey/geo-ner-model https://zenodo.org/records/4181488 . The data can be used to fine tune a pre-trained large language model using transfer learning, to create a model that can be used in inference mode to automatically create the labels, thereby creating structured data useful for geological modelling and subsurface characterisation. The data is provided in JSONL(Relation) format which is the export format from doccano open source text annotation software (https://doccano.github.io/doccano/) used to create the labels. The source documents are already publicly available, but the MRP and DECC reports are only published in pdf image form. These latter documents had to undergo OCR and resulted in lower quality text and a lower quality training data. The majority of the labelled data is from the higher quality BGS memoirs text. The dataset is a proof of concept. Minimal peer review of the labelling has been conducted so this should not be treated as a gold standard labelled dataset, and it is of insufficient volume to build a performant model. The development of this training data and the text processing scripts were supported by a grant from UK Government Office for Technology Transfer (GOTT) Knowledge Asset Grant Fund Project 10083604

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The groundwater database extract holds groundwater bore location, construction, aquifer details, level and quality information from the State's core groundwater system. The system is provided in text files and as Microsoft Access database. There are twenty one main tables: aquifer -bore_condition -casing -elevations -facility …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The groundwater database extract holds groundwater bore location, construction, aquifer details, level and quality information from the State's core groundwater system. The system is provided in text files and as Microsoft Access database. There are twenty one main tables: aquifer -bore_condition -casing -elevations -facility roles field_water _quality -flow irregularities -lithology multiple conductivity precipitates -pump test readings -pump test summary -registrations -sample record -sample results sample variables -strata logs stratigraphy water analysis water levels wireline logs For further explanation on these tables, information is found in the Queensland Government Department of Natural Resources and Mining, Groundwater Database : Data Dictionary and Standards. A read-me file is also provided that allows the user to import each text file to excel or access. All tables have already been imported to the microsoft access database. Dataset History The database was created by the staff at the Queensland Government as the States repository of groundwater bore and monitoring information. Dataset Citation Queensland Department of Natural Resources and Mines (2013) QLD Department of Natural Resources and Mining Groundwater Database Extract 20131111. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/827e70b3-29d5-4e89-8d59-a93bc499de60.

As described by ASTM D7780-12: This feature class contains polygons that depict post mining land use for a reclaimed Coal Mining Operation. This dataset consists of coalmining related features as described by ASTM D7780-12, "Standard Practice for Geospatial Data for Representing Coal Mining Features". These data are gathered using automated processes from participating coalmining regulatory authorities, which are generally state government agencies. The data from the various sources are transformed into common schemas as described by the ASTM Standard above. The resultant feature classes represent seamless information covering the coal producing areas of the United States. Development of these data are ongoing and will become more complete as more cooperating regulatory authorities are added to the GeoMine system.

Statistical Monitoring of The Green Deal. Data available through MRA legislation