Data Quality Management Software Market size was valued at USD 4.32 Billion in 2023 and is projected to reach USD 10.73 Billion by 2030, growing at a CAGR of 17.75% during the forecast period 2024-2030.Global Data Quality Management Software Market DriversThe growth and development of the Data Quality Management Software Market can be credited with a few key market drivers. Several of the major market drivers are listed below:Growing Data Volumes: Organizations are facing difficulties in managing and guaranteeing the quality of massive volumes of data due to the exponential growth of data generated by consumers and businesses. Organizations can identify, clean up, and preserve high-quality data from a variety of data sources and formats with the use of data quality management software.Increasing Complexity of Data Ecosystems: Organizations function within ever-more-complex data ecosystems, which are made up of a variety of systems, formats, and data sources. Software for data quality management enables the integration, standardization, and validation of data from various sources, guaranteeing accuracy and consistency throughout the data landscape.Regulatory Compliance Requirements: Organizations must maintain accurate, complete, and secure data in order to comply with regulations like the GDPR, CCPA, HIPAA, and others. Data quality management software ensures data accuracy, integrity, and privacy, which assists organizations in meeting regulatory requirements.Growing Adoption of Business Intelligence and Analytics: As BI and analytics tools are used more frequently for data-driven decision-making, there is a greater need for high-quality data. With the help of data quality management software, businesses can extract actionable insights and generate significant business value by cleaning, enriching, and preparing data for analytics.Focus on Customer Experience: Put the Customer Experience First: Businesses understand that providing excellent customer experiences requires high-quality data. By ensuring data accuracy, consistency, and completeness across customer touchpoints, data quality management software assists businesses in fostering more individualized interactions and higher customer satisfaction.Initiatives for Data Migration and Integration: Organizations must clean up, transform, and move data across heterogeneous environments as part of data migration and integration projects like cloud migration, system upgrades, and mergers and acquisitions. Software for managing data quality offers procedures and instruments to guarantee the accuracy and consistency of transferred data.Need for Data Governance and Stewardship: The implementation of efficient data governance and stewardship practises is imperative to guarantee data quality, consistency, and compliance. Data governance initiatives are supported by data quality management software, which offers features like rule-based validation, data profiling, and lineage tracking.Operational Efficiency and Cost Reduction: Inadequate data quality can lead to errors, higher operating costs, and inefficiencies for organizations. By guaranteeing high-quality data across business processes, data quality management software helps organizations increase operational efficiency, decrease errors, and minimize rework.

The ckanext-dataquality extension for CKAN aims to provide tools and functionalities to assess and improve the quality of data within CKAN datasets. While the specific features and capabilities are not explicitly detailed in the provided README, the extension likely enables users to define, measure, and report on various data quality metrics. This can help data publishers maintain higher data standards and allow consumers to better understand the reliability and usability of the available data. Key Features (Inferred based on context, assuming common data quality features): Data Quality Checks: It is assumed that it includes features that can automatically check datasets for issues like missing values, incorrect formatting, or inconsistencies against predefined rules. Reporting Data Quality: Likely offers reporting capabilities to show the results of quality checks, providing users with insights into the quality of their datasets. Integration with CKAN: Integration with the CKAN UI is assumed, allowing users to view data quality reports directly within the CKAN interface. Customizable Rules: Users may be able to define custom data quality rules based on their specific needs and data formats. Technical Integration: The extension integrates with CKAN via plugins, as indicated by the installation instructions which involve adding dataquality to the ckan.plugins setting in the CKAN configuration file. This enables the functionality to be available and accessible within the CKAN environment. The installation process also requires basic familiarity with Python and CKAN's virtual environment management. Benefits & Impact (Inferred based on typical data quality extension benefits): The primary benefit of ckanext-dataquality is improved data quality within a CKAN instance. This leads to increased trust in the data, more effective data usage and better decision-making based on the data. By identifying and addressing data quality issues, publishers can enhance the value and impact of their datasets. It is also assumed that the extension reduces manual effort involved in data quality assessment. Note: The provided README offers limited details on the specific functionalities of this extension. The description provided above is based on common features expected of a data quality extension in a data catalog environment.

This resource contains Jupyter Notebooks with examples for conducting quality control post processing for in situ aquatic sensor data. The code uses the Python pyhydroqc package. The resource is part of set of materials for hydroinformatics and water data science instruction. Complete learning module materials are found in HydroLearn: Jones, A.S., Horsburgh, J.S., Bastidas Pacheco, C.J. (2022). Hydroinformatics and Water Data Science. HydroLearn. https://edx.hydrolearn.org/courses/course-v1:USU+CEE6110+2022/about.

This resources consists of 3 example notebooks and associated data files.

Notebooks: 1. Example 1: Import and plot data 2. Example 2: Perform rules-based quality control 3. Example 3: Perform model-based quality control (ARIMA)

Data files: Data files are available for 6 aquatic sites in the Logan River Observatory. Each file contains data for one site for a single year. Each file corresponds to a single year of data. The files are named according to monitoring site (FranklinBasin, TonyGrove, WaterLab, MainStreet, Mendon, BlackSmithFork) and year. The files were sourced by querying the Logan River Observatory relational database, and equivalent data could be obtained from the LRO website or on HydroShare. Additional information on sites, variables, and methods can be found on the LRO website (http://lrodata.usu.edu/tsa/) or HydroShare (https://www.hydroshare.org/search/?q=logan%20river%20observatory). Each file has the same structure indexed with a datetime column (mountain standard time) with three columns corresponding to each variable. Variable abbreviations and units are: - temp: water temperature, degrees C - cond: specific conductance, μS/cm - ph: pH, standard units - do: dissolved oxygen, mg/L - turb: turbidity, NTU - stage: stage height, cm

For each variable, there are 3 columns: - Raw data value measured by the sensor (column header is the variable abbreviation). - Technician quality controlled (corrected) value (column header is the variable abbreviation appended with '_cor'). - Technician labels/qualifiers (column header is the variable abbreviation appended with '_qual').

†Proportion obtained by removing parallel pairs with one empty and one non-empty values. ‡Proportion obtained by treating pairs with one empty and one non-empty values as symmetrical examples.

With recent technological advancements, quantitative analysis has become an increasingly important area within professional sports. However, the manual process of collecting data on relevant match events like passes, goals and tacklings comes with considerable costs and limited consistency across providers, affecting both research and practice. In football, while automatic detection of events from positional data of the players and the ball could alleviate these issues, it is not entirely clear what accuracy current state-of-the-art methods realistically achieve because there is a lack of high-quality validations on realistic and diverse data sets. This paper adds context to existing research by validating a two-step rule-based pass and shot detection algorithm on four different data sets using a comprehensive validation routine that accounts for the temporal, hierarchical and imbalanced nature of the task. Our evaluation shows that pass and shot detection performance is highly dependent on the specifics of the data set. In accordance with previous studies, we achieve F-scores of up to 0.92 for passes, but only when there is an inherent dependency between event and positional data. We find a significantly lower accuracy with F-scores of 0.71 for passes and 0.65 for shots if event and positional data are independent. This result, together with a critical evaluation of existing methodologies, suggests that the accuracy of current football event detection algorithms operating on positional data is currently overestimated. Further analysis reveals that the temporal extraction of passes and shots from positional data poses the main challenge for rule-based approaches. Our results further indicate that the classification of plays into shots and passes is a relatively straightforward task, achieving F-scores between 0.83 to 0.91 ro rule-based classifiers and up to 0.95 for machine learning classifiers. We show that there exist simple classifiers that accurately differentiate shots from passes in different data sets using a low number of human-understandable rules. Operating on basic spatial features, our classifiers provide a simple, objective event definition that can be used as a foundation for more reliable event-based match analysis.

This resource contains a video recording for a presentation given as part of the National Water Quality Monitoring Council conference in April 2021. The presentation covers the motivation for performing quality control for sensor data, the development of PyHydroQC, a Python package with functions for automating sensor quality control including anomaly detection and correction, and the performance of the algorithms applied to data from multiple sites in the Logan River Observatory.

The initial abstract for the presentation: Water quality sensors deployed to aquatic environments make measurements at high frequency and commonly include artifacts that do not represent the environmental phenomena targeted by the sensor. Sensors are subject to fouling from environmental conditions, often exhibit drift and calibration shifts, and report anomalies and erroneous readings due to issues with datalogging, transmission, and other unknown causes. The suitability of data for analyses and decision making often depend on subjective and time-consuming quality control processes consisting of manual review and adjustment of data. Data driven and machine learning techniques have the potential to automate identification and correction of anomalous data, streamlining the quality control process. We explored documented approaches and selected several for implementation in a reusable, extensible Python package designed for anomaly detection for aquatic sensor data. Implemented techniques include regression approaches that estimate values in a time series, flag a point as anomalous if the difference between the sensor measurement exceeds a threshold, and offer replacement values for correcting anomalies. Additional algorithms that scaffold the central regression approaches include rules-based preprocessing, thresholds for determining anomalies that adjust with data variability, and the ability to detect and correct anomalies using forecasted and backcasted estimation. The techniques were developed and tested based on several years of data from aquatic sensors deployed at multiple sites in the Logan River Observatory in northern Utah, USA. Performance was assessed based on labels and corrections applied previously by trained technicians. In this presentation, we describe the techniques for detection and correction, report their performance, illustrate the workflow for applying to high frequency aquatic sensor data, and demonstrate the possibility for additional approaches to help increase automation of aquatic sensor data post processing.

Accurate monitoring of air quality can reduce its adverse impact on earth. Ground-level sensors can provide fine particulate matter (PM2.5) concentrations and ground images. But, such sensors have limited spatial coverage and require deployment cost. PM2.5 can be estimated from satellite-retrieved Aerosol Optical Depth (AOD) too. However, AOD is subject to uncertainties associated with its retrieval algorithms and constrain the spatial resolution of estimated PM2.5. AOD is not retrievable under cloudy weather as well. In contrast, satellite images provide continuous spatial coverage with no separate deployment cost. Accuracy of monitoring from such satellite images is hindered due to uncertainties of sensor data of relevant environmental parameters, such as, relative humidity, temperature, wind speed and wind direction . Belief Rule Based Expert System (BRBES) is an efficient algorithm to address these uncertainties. Convolutional Neural Network (CNN) is suitable for image analytics. Hence, we propose a novel model by integrating CNN with BRBES to monitor air quality from satellite images with improved accuracy. We customized CNN and optimized BRBES to increase monitoring accuracy further. An obscure image has been differentiated between polluted air and cloud based on relationship of PM2.5 with relative humidity. Valid environmental data (temperature, wind speed and wind direction) have been adopted to further strengthen the monitoring performance of our proposed model. Three-year observation data (satellite images and environmental parameters) from 2014 to 2016 of Shanghai have been employed to analyze and design our proposed model.

Source code and dataset

We implement our proposed integrated algorithm with Python 3 and C++ programming language. We process the satellite images with OpenCV library. Keras library functions are used to implement our customized VGG Net. We write python script smallervggnet.py to build this VGG Net. Next, we train and test this network with a dataset of satellite images through train.py script. This dataset consists of 3-year satellite images of Oriental Pearl Tower, Shanghai, China from Planet from January-2014 till December-2016 (Planet Team, 2017). These images are captured by PlanetScope, which is a constellation composed by approximately 120 optical satellites operated by Planet (Planet Team, San Francisco, CA, USA, 2016). Based on the level of PM2.5, this dataset is divided into three classes: HighPM, MediumPM and LowPM. We classify a new satellite image (201612230949.png) with our trained VGG Net by classify.py script. Standard file I/O is used to feed this classification output to the first BRBES (cnn_brb_1.cpp) through a text file (cnn_prediction.txt). In addition to VGG Net classification output, cloud percentage and relative humidity are fed as input to first BRBES. We write cnn_brb_2.cpp to implement second BRBES, which takes the output of first BRBES, temperature and wind speed as its input. Wind direction based recalculation of the output of second BRBES is also performed in this cpp file to compute the final monitoring value of PM2.5. We demonstrate this code architecture through a flow chart in Figure 5 of the manuscript.Source code and dataset of the satellite images are made freely available through the published compute capsule (https://doi.org/10.24433/CO.8230207.v1).

Code: MIT license; Data: No Rights Reserved (CC0)

The dataset was originally published in DiVA and moved to SND in 2024.

This is the online appendix of my dissertation titled "Moving towards Principles-Based Accounting Standards: The Impact of the New Revenue Standard on the Quality of Accrual Accounting". It explains how I collected the data used in my disseration through the XBRL US API.My dissertation is open access via SMU InK at https://ink.library.smu.edu.sg/etd_coll/462/. The new revenue standard (ASU 2014-09, codified in ASC 606 and ASC 340-40) establishes a comprehensive framework on accounting for contracts with customers and replaces most existing revenue recognition rules. The new guidance removes the inconsistencies and weaknesses of legacy guidance, while is more principles-based and requires more managerial judgements. Using as-reported data from structured filings to construct aggregate accruals that are potentially affected by the new revenue standard (i.e., sales-related accruals), I find that the new revenue standard increases the quality of sales-related accruals, as measured by future cash flow predictability. The increased cash flow predictability comes not only from the guidance on contract revenue (ASC 606) but also from the guidance on contract costs (ASC 340-40). The effects concentrate among firms conducting long-term sales contracts, especially over longer forecast horizons. Further analysis shows that the new revenue standard also increases the combined information content of financial statements and the capital market efficiency. However, the discretion under the new standard opens avenue for earnings management when firms face strong manipulation incentives.

The global Risk-Based Monitoring Software market is projected to reach a value of approximately USD 342 million by 2033, expanding at a CAGR of 11.30%. The growing adoption of risk-based monitoring (RBM) approaches, stringent regulatory requirements, and increasing demand for efficient and cost-effective clinical trials are the primary drivers of market growth. Moreover, the advancements in technology, such as the integration of artificial intelligence (AI) and machine learning (ML) in RBM software, are further fueling market expansion. The market is segmented based on components (software, services), delivery modes (web-based, on-premise, cloud-based), and end users (pharma and biopharmaceutical companies, medical device companies, contract research organizations (CROs), other end users). The software segment holds a significant market share due to the increasing demand for advanced and comprehensive RBM solutions. The cloud-based delivery mode is gaining popularity as it offers flexibility, scalability, and cost-effectiveness. Pharma and biopharmaceutical companies are the largest end users of RBM software, driven by the need to comply with regulatory requirements and improve trial efficiency. Key market players include Medidata Solutions Inc, Parexel International Corporation, IBM Corporation, Veeva Systems, DSG Inc, MedNet Solutions Inc, Signant Health, OpenClinica LLC, Oracle, and Anju Software. Recent developments include: June 2024: Medidata unveiled its offering, the Medidata Clinical Data Studio. This innovative platform is designed to empower stakeholders, granting them enhanced control over data quality and, in turn, expediting the delivery of safer trials to patients. This Medidata Clinical Data Studio supports the principles of risk-based monitoring (RBM) by enhancing data quality control and accelerating trial timelines., April 2024: Parexel and Palantir Technologies Inc. unveiled a multi-year strategic alliance. The collaboration aims to harness artificial intelligence (AI) to expedite and improve the safety of clinical trials, catering specifically to the global biopharmaceutical clientele. This collaboration highlights Parexel's dedication to improving the efficiency of clinical trials while upholding strict safety and regulatory standards. Further, this strategic partnership supports the advancement of AI-driven efficiencies in clinical trials, aligning with the objectives of the RBM Software to improve trial outcomes and operational effectiveness.. Key drivers for this market are: High Efficiency of Risk-Based Monitoring Software Coupled with Growing Government Funding and Support for Clinical Trials, Advancements in Technology. Potential restraints include: High Efficiency of Risk-Based Monitoring Software Coupled with Growing Government Funding and Support for Clinical Trials, Advancements in Technology. Notable trends are: The Service Segment is Expected to Hold a Significant Share in the Market During the Forecast Period.

This resource was created for the 2024 New Zealand Hydrological Society Data Workshop in Queenstown, NZ. This resource contains Jupyter Notebooks with examples for conducting quality control post processing for in situ aquatic sensor data. The code uses the Python pyhydroqc package to detect anomalies. This resource consists of 3 example notebooks and associated data files. For more information, see the original resource from which this was derived: http://www.hydroshare.org/resource/451c4f9697654b1682d87ee619cd7924.

Notebooks: 1. Example 1: Import and plot data 2. Example 2: Perform rules-based quality control 3. Example 3: Perform model-based quality control (ARIMA) 4. Example 4: Model-based quality control (ARIMA) with user data

Data files: Data files are available for 6 aquatic sites in the Logan River Observatory. Each file contains data for one site for a single year. Each file corresponds to a single year of data. The files are named according to monitoring site (FranklinBasin, TonyGrove, WaterLab, MainStreet, Mendon, BlackSmithFork) and year. The files were sourced by querying the Logan River Observatory relational database, and equivalent data could be obtained from the LRO website or on HydroShare. Additional information on sites, variables, and methods can be found on the LRO website (http://lrodata.usu.edu/tsa/) or HydroShare (https://www.hydroshare.org/search/?q=logan%20river%20observatory). Each file has the same structure indexed with a datetime column (mountain standard time) with three columns corresponding to each variable. Variable abbreviations and units are: - temp: water temperature, degrees C - cond: specific conductance, μS/cm - ph: pH, standard units - do: dissolved oxygen, mg/L - turb: turbidity, NTU - stage: stage height, cm

For each variable, there are 3 columns: - Raw data value measured by the sensor (column header is the variable abbreviation). - Technician quality controlled (corrected) value (column header is the variable abbreviation appended with '_cor'). - Technician labels/qualifiers (column header is the variable abbreviation appended with '_qual').

There is also a file "data.csv" for use with Example 4. If any user wants to bring their own data file, they should structure it similarly to this file with a single column of datetime values and a single column of numeric observations labeled "raw".

This resource contains an example script for using the software package pyhydroqc. pyhydroqc was developed to identify and correct anomalous values in time series data collected by in situ aquatic sensors. For more information, see the code repository: https://github.com/AmberSJones/pyhydroqc and the documentation: https://ambersjones.github.io/pyhydroqc/. The package may be installed from the Python Package Index.

This script applies the functions to data from a single site in the Logan River Observatory, which is included in the repository. The data collected in the Logan River Observatory are sourced at http://lrodata.usu.edu/tsa/ or on HydroShare: https://www.hydroshare.org/search/?q=logan%20river%20observatory.

Anomaly detection methods include ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short Term Memory). These are time series regression methods that detect anomalies by comparing model estimates to sensor observations and labeling points as anomalous when they exceed a threshold. There are multiple possible approaches for applying LSTM for anomaly detection/correction. - Vanilla LSTM: uses past values of a single variable to estimate the next value of that variable. - Multivariate Vanilla LSTM: uses past values of multiple variables to estimate the next value for all variables. - Bidirectional LSTM: uses past and future values of a single variable to estimate a value for that variable at the time step of interest. - Multivariate Bidirectional LSTM: uses past and future values of multiple variables to estimate a value for all variables at the time step of interest.

The correction approach uses piecewise ARIMA models. Each group of consecutive anomalous points is considered as a unit to be corrected. Separate ARIMA models are developed for valid points preceding and following the anomalous group. Model estimates are blended to achieve a correction.

The anomaly detection and correction workflow involves the following steps: 1. Retrieving data 2. Applying rules-based detection to screen data and apply initial corrections 3. Identifying and correcting sensor drift and calibration (if applicable) 4. Developing a model (i.e., ARIMA or LSTM) 5. Applying model to make time series predictions 6. Determining a threshold and detecting anomalies by comparing sensor observations to modeled results 7. Widening the window over which an anomaly is identified 8. Aggregating detections resulting from multiple models 9. Making corrections for anomalous events

Instructions to run the notebook through the CUAHSI JupyterHub: 1. Click "Open with..." at the top of the resource and select the CUAHSI JupyterHub. You may need to sign into CUAHSI JupyterHub using your HydroShare credentials. 2. Select 'Python 3.8 - Scientific' as the server and click Start. 2. From your JupyterHub directory, click on the ExampleNotebook.ipynb file. 3. Execute each cell in the code by clicking the Run button.

Dataset Description

Vietnamese Curated Text Dataset. This dataset is collected from multiple open Vietnamese datasets, and curated with NeMo Curator

• Developed by: Viettel Solutions
• Language: Vietnamese

Details

Please visit our Tech Blog post on NVIDIA's plog page for details. Link

Data Collection

We utilize a combination of datasets that contain samples in Vietnamese language, ensuring a robust and representative text corpus. These datasets include: - The Vietnamese subset of the C4 dataset . - The Vietnamese subset of the OSCAR dataset, version 23.01. - Wikipedia's Vietnamese articles. - Binhvq's Vietnamese news corpus.

Preprocessing

We use NeMo Curator to curate the collected data. The data curation pipeline includes these key steps: 1. Unicode Reformatting: Texts are standardized into a consistent Unicode format to avoid encoding issues. 2. Exact Deduplication: Removes exact duplicates to reduce redundancy. 3. Quality Filtering: 4. Heuristic Filtering: Applies rules-based filters to remove low-quality content. 5. Classifier-Based Filtering: Uses machine learning to classify and filter documents based on quality.

Notebook

Dataset Statistics

Content diversity https://cdn-uploads.huggingface.co/production/uploads/661766c00c68b375f3f0ccc3/mW6Pct3uyP_XDdGmE8EP3.png" alt="Domain proportion in curated dataset">

Character based metrics https://cdn-uploads.huggingface.co/production/uploads/661766c00c68b375f3f0ccc3/W9TQjM2vcC7uXozyERHSQ.png" alt="Box plots of percentage of symbols, numbers, and whitespace characters compared to the total characters, word counts and average word lengths">

Token count distribution https://cdn-uploads.huggingface.co/production/uploads/661766c00c68b375f3f0ccc3/PDelYpBI0DefSmQgFONgE.png" alt="Distribution of document sizes (in terms of token count)">

Embedding visualization https://cdn-uploads.huggingface.co/production/uploads/661766c00c68b375f3f0ccc3/sfeoZWuQ7DcSpbmUOJ12r.png" alt="UMAP visualization of 5% of the dataset"> UMAP visualization of 5% of the dataset

1.Framework overview. This paper proposed a pipeline to construct high-quality datasets for text mining in materials science. Firstly, we utilize the traceable automatic acquisition scheme of literature to ensure the traceability of textual data. Then, a data processing method driven by downstream tasks is performed to generate high-quality pre-annotated corpora conditioned on the characteristics of materials texts. On this basis, we define a general annotation scheme derived from materials science tetrahedron to complete high-quality annotation. Finally, a conditional data augmentation model incorporating materials domain knowledge (cDA-DK) is constructed to augment the data quantity.2.Dataset information. The experimental datasets used in this paper include: the Matscholar dataset publicly published by Weston et al. (DOI: 10.1021/acs.jcim.9b00470), and the NASICON entity recognition dataset constructed by ourselves. Herein, we mainly introduce the details of NASICON entity recognition dataset.2.1 Data collection and preprocessing. Firstly, 55 materials science literature related to NASICON system are collected through Crystallographic Information File (CIF), which contains a wealth of structure-activity relationship information. Note that materials science literature is mostly stored as portable document format (PDF), with content arranged in columns and mixed with tables, images, and formulas, which significantly compromises the readability of the text sequence. To tackle this issue, we employ the text parser PDFMiner (a Python toolkit) to standardize, segment, and parse the original documents, thereby converting PDF literature into plain text. In this process, the entire textual information of literature, encompassing title, author, abstract, keywords, institution, publisher, and publication year, is retained and stored as a unified TXT document. Subsequently, we apply rules based on Python regular expressions to remove redundant information, such as garbled characters and line breaks caused by figures, tables, and formulas. This results in a cleaner text corpus, enhancing its readability and enabling more efficient data analysis. Note that special symbols may also appear as garbled characters, but we refrain from directly deleting them, as they may contain valuable information such as chemical units. Therefore, we converted all such symbols to a special token

Additional file 1: DD-KARB Case-Study Java Code.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Workers include members of the Armed Forces and civilians who were at work last week..Industry titles and their 4-digit codes are based on the 2017 North American Industry Classification System. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..Several means of transportation to work categories were updated in 2019. For more information, see: Change to Means of Transportation..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, and the visual format of the categories on the questionnaire. The format for the class of worker categories are now listed under the headings "Private Sector Employee," "Government Employee," and "Self-Employed or Other." Additionally, the category of Active Duty was added as one of the response categories under the "Government Employee" section for the mail questionnaire. For more detailed information about the 2019 changes, see the 2016 American Community Survey Content Test Report for Class of Worker located at http://www.census.gov/library/working-papers/2017/acs/2017_Martinez_01.html..Beginning in data year 2019, respondents to the Weeks Worked question provided an integer value for the number of wee...

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Workers include members of the Armed Forces and civilians who were at work last week..Industry titles and their 4-digit codes are based on the North American Industry Classification System (NAICS). The Census industry codes for 2018 and later years are based on the 2017 revision of the NAICS. To allow for the creation of multiyear tables, industry data in the multiyear files (prior to data year 2018) were recoded to the 2017 Census industry codes. We recommend using caution when comparing data coded using 2017 Census industry codes with data coded using Census industry codes prior to data year 2018. For more information on the Census industry code changes, please visit our website at https://www.census.gov/topics/employment/industry-occupation/guidance/code-lists.html..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..Several means of transportation to work categories were updated in 2019. For more information, see: Change to Means of Transportation..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..Occupation titles and their 4-digit codes are based on the Standard Occupational Classification (SOC). The Census occupation codes for 2018 and later years are based on the 2018 revision of the SOC. To allow for the creation of the multiyear tables, occupation data in the multiyear files (prior to data year 2018) were recoded to the 2018 Census occupation codes. We recommend using caution when comparing data coded using 2018 Census occupation codes with data coded using Census occupation codes prior to data year 2018. For more information on the Census occupation code changes, please visit our website at https://www.census.gov/topics/employment /industry-occupation/guidance/code-lists.html..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, a...

The Knowledge Graph Technology market is experiencing robust growth, driven by the increasing need for enhanced data organization, improved search capabilities, and the rise of artificial intelligence (AI) and machine learning (ML) applications. The market's expansion is fueled by several key factors, including the growing volume of unstructured data, the need for better data integration across disparate sources, and the demand for more intelligent and context-aware applications. Businesses across various sectors, including healthcare, finance, and e-commerce, are adopting knowledge graphs to enhance decision-making, improve customer experiences, and gain a competitive advantage. The market is witnessing significant advancements in graph database technologies, semantic technologies, and knowledge representation techniques, further accelerating its growth trajectory. While challenges such as data quality issues and the complexity of implementing and maintaining knowledge graphs exist, the substantial benefits are driving widespread adoption. We project a substantial increase in market size over the next decade, with particular growth anticipated in regions with advanced digital infrastructures and strong investments in AI and data analytics. The segmentation of the market by application (e.g., customer relationship management, fraud detection, supply chain optimization) and type (e.g., ontology-based, rule-based) reflects the diverse use cases driving adoption across different sectors. The forecast for Knowledge Graph Technology demonstrates continued, albeit potentially moderating, growth through 2033. While the initial years will likely see strong expansion driven by early adoption and technological advancements, the growth rate might stabilize as the market matures. However, continued innovation, particularly in areas like integrating knowledge graphs with emerging technologies such as the metaverse and Web3, and expansion into new applications within industries like personalized medicine and smart manufacturing, will ensure sustained, though potentially less rapid, growth. Geographical expansion, particularly into developing economies with increasing digitalization, presents a significant opportunity for market expansion. Competitive pressures among vendors will drive further innovation and potentially lead to consolidation within the market. Therefore, a thorough understanding of market segmentation, competitive dynamics, and technological advancements is crucial for stakeholders to navigate the evolving landscape and capitalize on emerging opportunities.

‘class’ is equivalent to ‘highway’ in OSM, ‘structure’ indicates whether a road is bridge or tunnel.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Workers include members of the Armed Forces and civilians who were at work last week..Industry titles and their 4-digit codes are based on the North American Industry Classification System (NAICS). The Census industry codes for 2018 and later years are based on the 2017 revision of the NAICS. To allow for the creation of multiyear tables, industry data in the multiyear files (prior to data year 2018) were recoded to the 2017 Census industry codes. We recommend using caution when comparing data coded using 2017 Census industry codes with data coded using Census industry codes prior to data year 2018. For more information on the Census industry code changes, please visit our website at https://www.census.gov/topics/employment/industry-occupation/guidance/code-lists.html..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..Several means of transportation to work categories were updated in 2019. For more information, see: Change to Means of Transportation..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..Occupation titles and their 4-digit codes are based on the Standard Occupational Classification (SOC). The Census occupation codes for 2018 and later years are based on the 2018 revision of the SOC. To allow for the creation of the multiyear tables, occupation data in the multiyear files (prior to data year 2018) were recoded to the 2018 Census occupation codes. We recommend using caution when comparing data coded using 2018 Census occupation codes with data coded using Census occupation codes prior to data year 2018. For more information on the Census occupation code changes, please visit our website at https://www.census.gov/topics/employment /industry-occupation/guidance/code-lists.html..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, a...

Central African Republic CF: CPIA: Public Sector Management and Institutions Cluster Average: 1=Low To 6=High data was reported at 2.400 NA in 2023. This stayed constant from the previous number of 2.400 NA for 2022. Central African Republic CF: CPIA: Public Sector Management and Institutions Cluster Average: 1=Low To 6=High data is updated yearly, averaging 2.400 NA from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 2.600 NA in 2011 and a record low of 2.200 NA in 2016. Central African Republic CF: CPIA: Public Sector Management and Institutions Cluster Average: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Governance: Policy and Institutions. The public sector management and institutions cluster includes property rights and rule-based governance, quality of budgetary and financial management, efficiency of revenue mobilization, quality of public administration, and transparency, accountability, and corruption in the public sector.;World Bank Group, CPIA database (http://www.worldbank.org/ida).;Unweighted average;