In this seminar, the presenter introduces essential concepts of ArcGIS Data Reviewer and highlights automated and semi-automated methods to streamline and expedite data validation.This seminar was developed to support the following:ArcGIS Desktop 10.3 (Basic, Standard, or Advanced)ArcGIS Server 10.3 Workgroup (Standard Or Advanced)ArcGIS Data Reviewer for DesktopArcGIS Data Reviewer for Server

Dataset

This dataset was created by Isa Zeynalov

Contents

This data table provides the detailed data quality assessment scores for the Technical Limits dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

This data table provides the detailed data quality assessment scores for the Curtailment dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

This data table provides the detailed data quality assessment scores for the Historic Faults dataset. The quality assessment was carried out on the 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

Data Quality Solution Market Outlook

The global data quality solution market size is projected to grow significantly from USD 1.5 billion in 2023 to approximately USD 4.8 billion by 2032, reflecting a robust CAGR of 13.5%. This growth is driven primarily by the increasing adoption of data-driven decision-making processes across various industries. The surge in Big Data, coupled with the proliferation of IoT devices, has necessitated robust data quality solutions to ensure the accuracy, consistency, and reliability of data that organizations rely on for strategic insights.

One of the notable growth factors in this market is the exponential increase in data volumes, which calls for effective data management strategies. Businesses today are inundated with data from diverse sources such as social media, sensor data, transactional data, and more. Ensuring the quality of this data is paramount for gaining actionable insights and maintaining competitive advantage. Consequently, the demand for sophisticated data quality solutions has surged, propelling market growth. Additionally, stringent regulatory requirements across various sectors, including finance and healthcare, have further emphasized the need for data quality solutions to ensure compliance with data governance standards.

Another significant driver for the data quality solution market is the growing emphasis on digital transformation initiatives. Organizations across the globe are leveraging digital technologies to enhance operational efficiencies and customer experiences. However, the success of these initiatives largely depends on the quality of data being utilized. As a result, there is a burgeoning demand for data quality tools that can automate data cleansing, profiling, and enrichment processes, ensuring that the data is fit for purpose. This trend is particularly evident in sectors such as BFSI and retail, where accurate data is crucial for risk management, customer personalization, and strategic decision-making.

The rise of artificial intelligence and machine learning technologies also contributes significantly to the market's growth. These technologies rely heavily on high-quality data to train models and generate accurate predictions. Poor data quality can lead to erroneous insights and suboptimal decisions, thus undermining the potential benefits of AI and ML initiatives. Therefore, organizations are increasingly investing in advanced data quality solutions to enhance their AI capabilities and drive innovation. This trend is expected to further accelerate market growth over the forecast period.

Component Analysis

The data quality solution market can be segmented based on components, primarily into software and services. The software segment encompasses various tools and platforms designed to enhance data quality through cleansing, profiling, enrichment, and monitoring. These software solutions are equipped with advanced features like data matching, de-duplication, and standardization, which are crucial for maintaining high data quality standards. The increasing complexity of data environments and the need for real-time data quality management are driving the adoption of these sophisticated software solutions, making this segment a significant contributor to the market's growth.

In addition to the software, the services segment plays a crucial role in the data quality solution market. This segment includes professional services such as consulting, implementation, training, and support. Organizations often require expert guidance to deploy data quality solutions effectively and ensure they are tailored to specific business needs. Consulting services help in assessing current data quality issues, defining data governance frameworks, and developing customized solutions. Implementation services ensure seamless integration of data quality tools with existing systems, while training and support services empower users with the necessary skills to manage and maintain data quality effectively. The growth of the services segment is bolstered by the increasing complexity of data ecosystems and the need for specialized expertise.

Report Scope

Data Quality Management Service Market Outlook

The global data quality management service market size was valued at approximately USD 1.8 billion in 2023 and is projected to reach USD 5.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.1% during the forecast period. The primary growth factor driving this market is the increasing volume of data being generated across various industries, necessitating robust data quality management solutions to maintain data accuracy, reliability, and relevance.

One of the key growth drivers for the data quality management service market is the exponential increase in data generation due to the proliferation of digital technologies such as IoT, big data analytics, and AI. Organizations are increasingly recognizing the importance of maintaining high data quality to derive actionable insights and make informed business decisions. Poor data quality can lead to significant financial losses, inefficiencies, and missed opportunities, thereby driving the demand for comprehensive data quality management services.

Another significant growth factor is the rising regulatory and compliance requirements across various industry verticals such as BFSI, healthcare, and government. Regulations like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) necessitate organizations to maintain accurate and high-quality data. Non-compliance with these regulations can result in severe penalties and damage to the organization’s reputation, thus propelling the adoption of data quality management solutions.

Additionally, the increasing adoption of cloud-based solutions is further fueling the growth of the data quality management service market. Cloud-based data quality management solutions offer scalability, flexibility, and cost-effectiveness, making them an attractive option for organizations of all sizes. The availability of advanced data quality management tools that integrate seamlessly with existing IT infrastructure and cloud platforms is encouraging enterprises to invest in these services to enhance their data management capabilities.

From a regional perspective, North America is expected to hold the largest share of the data quality management service market, driven by the early adoption of advanced technologies and the presence of key market players. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, owing to the rapid digital transformation, increasing investments in IT infrastructure, and growing awareness about the importance of data quality management in enhancing business operations and decision-making processes.

Component Analysis

The data quality management service market is segmented by component into software and services. The software segment encompasses various data quality tools and platforms that help organizations assess, improve, and maintain the quality of their data. These tools include data profiling, data cleansing, data enrichment, and data monitoring solutions. The increasing complexity of data environments and the need for real-time data quality monitoring are driving the demand for sophisticated data quality software solutions.

Services, on the other hand, include consulting, implementation, and support services provided by data quality management service vendors. Consulting services assist organizations in identifying data quality issues, developing data governance frameworks, and implementing best practices for data quality management. Implementation services involve the deployment and integration of data quality tools with existing IT systems, while support services provide ongoing maintenance and troubleshooting assistance. The growing need for expert guidance and support in managing data quality is contributing to the growth of the services segment.

The software segment is expected to dominate the market due to the continuous advancements in data quality management tools and the increasing adoption of AI and machine learning technologies for automated data quality processes. Organizations are increasingly investing in advanced data quality software to streamline their data management operations, reduce manual intervention, and ensure data accuracy and consistency across various data sources.

Moreover, the services segment is anticipated to witness significant growth during the forecast period, driven by the increasing demand for professional services that can help organizations address complex dat

Organizations are increasingly accepting data quality (DQ) as a major key to their success. In order to assess and improve DQ, methods have been devised. Many of these methods attempt to raise DQ by directly manipulating low quality data. Such methods operate reactively and are suitable for organizations with highly developed integrated systems. However, there is a lack of a proactive DQ method for businesses with weak IT infrastructure where data quality is largely affected by tasks that are performed by human agents. This study aims to develop and evaluate a new method for structured data, which is simple and practical so that it can easily be applied to real world situations. The new method detects the potentially risky tasks within a process, and adds new improving tasks to counter them. To achieve continuous improvement, an award system is also developed to help with the better selection of the proposed improving tasks. The task-based DQ method (TBDQ) is most appropriate for small and medium organizations, and simplicity in implementation is one of its most prominent features. TBDQ is case studied in an international trade company. The case study shows that TBDQ is effective in selecting optimal activities for DQ improvement in terms of cost and improvement.

Under the Open Government Action Plan, and related National Action Plan, the FGP is required to report on its commitments related to: supporting a user-friendly open government platform; improving the quality of open data available on open.canada.ca; and reviewing additional geospatial datasets to assess their quality. This report summarizes the FGP’s action on meeting these commitments.

Groundwater samples were collected and analyzed from 782 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and the water-quality data and quality-control data are included in this data release. The samples were collected from three types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality, and major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including nutrients, major ions, trace elements, volatile organic compounds (VOCs), pesticides, radionuclides, and microbial indicators. Data from samples collected between 2012 and 2019 are associated with networks described in a collection of data series reports and associated data releases (Arnold and others, 2016a,b, 2017a,b, 2018a,b, 2020a,b; Kingsbury and others, 2020 and 2021). This data release includes data from networks sampled in 2019 through 2022. For some networks, certain constituent group data were not completely reviewed and released by the analyzing laboratory for all network sites in time for publication of this data release. For networks with incomplete data, no data were published for the incomplete constituent group(s). Datasets excluded from this data release because of incomplete results will be included in the earliest data release published after the dataset is complete. NOTE: While previous versions are available from the author, all the records in previous versions can be found in version 3.0. First posted - December 12, 2021 (available from author) Revised - January 27, 2023 (version 2.0: available from author) Revised - November 2, 2023 (version 3.0) The compressed file (NWQP_GW_QW_DataRelease_v3.zip) contains 24 files: 23 files of groundwater-quality, quality-control data, and general information in ASCII text tab-delimited format, and one corresponding metadata file in xml format that includes descriptions of all the tables and attributes. A shapefile containing study areas for each of the sampled groundwater networks also is provided as part of this data release and is described in the metadata (Network_Boundaries_v3.zip). The files are as follows: Description_of_Data_Field_v3.txt: Information for all constituents and ancillary information found in Tables 3 through 21. Network_Reference_List_v3.txt: References used for the description of the networks sampled by the USGS NAWQA Project. Table_1_site_list_v3.txt: Information about wells that have environmental data. Table_2_parameters_v3.txt: Constituent primary uses and sources; laboratory analytical schedules and sampling period; USGS parameter codes (pcodes); comparison thresholds; and reporting levels. Table_3_qw_indicators_v3.txt: Water-quality indicators in groundwater samples collected by the USGS NAWQA Project. Table_4_nutrients_v3.txt: Nutrients and dissolved organic carbon in groundwater samples collected by the USGS NAWQA Project. Table_5_major_ions_v3.txt: Major and minor ions in groundwater samples collected by the USGS NAWQA Project. Table_6_trace_elements_v3.txt: Trace elements in groundwater samples collected by the USGS NAWQA Project. Table_7_vocs_v3.txt: Volatile organic compounds (VOCs) in groundwater samples collected by the USGS NAWQA Project. Table_8_pesticides_v3.txt: Pesticides in groundwater samples collected by the USGS NAWQA Project. Table_9_radchem_v3.txt: Radionuclides in groundwater samples collected by the USGS NAWQA Project. Table_10_micro_v3.txt: Microbiological indicators in groundwater samples collected by the USGS NAWQA Project. Table_11_qw_ind_QC_v3.txt: Water-quality indicators in groundwater replicate samples collected by the USGS NAWQA Project. Table_12_nuts_QC_v3.txt: Nutrients and dissolved organic carbon in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_13_majors_QC_v3.txt: Major and minor ions in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_14_trace_element_QC_v3.txt: Trace elements in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_15_vocs_QC_v3.txt: Volatile organic compounds (VOCs) in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_16_pesticides_QC_v3.txt: Pesticide compounds in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_17_radchem_QC_v3.txt: Radionuclides in groundwater replicate samples collected by the USGS NAWQA Project. Table_18_micro_QC_v3.txt: Microbiological indicators in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_19_TE_SpikeStats_v3.txt: Statistics for trace elements in groundwater spike samples collected by the USGS NAWQA Project. Table_20_VOCLabSpikeStats_v3.txt: Statistics for volatile organic compounds (VOCs) in groundwater spike samples collected by the USGS NAWQA Project. Table_21_PestFieldSpikeStats_v3.txt: Statistics for pesticide compounds in groundwater spike samples collected by the USGS NAWQA Project. References Arnold, T.L., Bexfield, L.M., Musgrove, MaryLynn, Lindsey, B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, B.J., and Belitz, Kenneth, 2017a, Groundwater-quality data from the National Water-Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014: U.S. Geological Survey Data Series 1063, 83 p., https://doi.org/10.3133/ds1063. Arnold, T.L., Bexfield, L.M., Musgrove, MaryLynn, Lindsey, B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, B.J., and Belitz, Kenneth, 2017b, Datasets from Groundwater quality data from the National Water Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014: U.S. Geological Survey data release, https://doi.org/10.5066/F7W0942N. Arnold, T.L., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020a, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey Data Series 1124, 135 p., https://doi.org/10.3133/ds1124. Arnold, T.L., Bexfield, L.M., Musgrove, M., Lindsey, B.D., Stackelberg, P.E., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., and Belitz, K., 2018b, Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January through December 2015 and Previously Unpublished Data from 2013-2014, U.S. Geological Survey data release, https://doi.org/10.5066/F7XK8DHK. Arnold, T.L., Bexfield, L.M., Musgrove, M., Stackelberg, P.E., Lindsey, B.D., Kingsbury, J.A., Kulongoski, J.T., and Belitz, K., 2018a, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2015, and previously unpublished data from 2013 to 2014: U.S. Geological Survey Data Series 1087, 68 p., https://doi.org/10.3133/ds1087. Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., Musgrove, MaryLynn, Kingsbury, J.A., and Belitz, Kenneth, 2016a, Groundwater quality data from the National Water-Quality Assessment Project, May 2012 through December 2013 (ver. 1.1, November 2016): U.S. Geological Survey Data Series 997, 56 p., https://doi.org/10.3133/ds997. Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., Musgrove, MaryLynn, Kingsbury, J.A., and Belitz, Kenneth, 2016b, Groundwater quality data from the National Water Quality Assessment Project, May 2012 through December 2014 and select quality-control data from May 2012 through December 2013: U.S. Geological Survey data release, https://doi.org/10.5066/F7HQ3X18. Arnold, T.L., Sharpe, J.B., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020b, Datasets from groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey data release, https://doi.org/10.5066/P9W4RR74. Kingsbury, J.A., Sharpe, J.B., Bexfield, L.M., Arnold, T.L., Musgrove, M., Erickson, M.L., Degnan, J.R., Kulongoski, J.T., Lindsey, B.D., and Belitz, K., 2020, Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January 2017 through December 2019 (ver. 1.1, January 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P9XATXV1. Kingsbury, J.A., Bexfield, L.M., Arnold, T.L., Musgrove, M., Erickson, M.L., Degnan, J.R., Tesoriero, A.J., Lindsey B.D., and Belitz, K., 2021, Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January 2017 through December 2019: U.S. Geological Survey Data Series 1136, 97 p., https://doi.org/10.3133/ds1136.

Data Quality identifies FMCSA resources for evaluating, monitoring, and improving the quality of data submitted by States to the Motor Carrier Management Information System (MCMIS).

This data table provides the detailed data quality assessment scores for the Single Digital View dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

This data table provides the detailed data quality assessment scores for the Long Term Development Statement dataset. The quality assessment was carried out on 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality; to demonstrate our progress we conduct annual assessments of our data quality in line with the dataset refresh rate. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioral measures and compliance with measures to contain the pandemic, social science research has played a key role for policymaking during this global crisis.

This study addresses two key research questions: How FAIR (findable, accessible, interoperable, and reusable) are social science data on the COVID-19 pandemic? Which study features are related to the level of FAIRness scores of datasets? We assess the FAIRness of n=1,131 articles, retrieved through a keyword search in the Web of Science database, employing both automated and manual coding methods. Our study inclusion criteria encompass empirical studies on the COVID-19 pandemic published between 2019-2023 with a social science focus and explicit reference to the underlying dataset(s). Our analysis of n=45 datasets reveals substantial differences in FAIRness for different types of research on the COVID-19 pandemic. The overall FAIRness of data is acceptable, although particularly Reusability scores fall short, in both the manual and the automatic assessment. Further, articles explicitly linked to the Social Science concept in the OpenAlex database exhibit a higher mean overall FAIRness value. Based on these results, we derive recommendations for balancing ethical obligations and the potential tradeoff between speed and data (sharing) quality in social-scientific crisis research.

The replication data contains the manual and automatic coded values for FAIR criteria and the complete code to re-produce the results for the article.

A key aim of the FNS-Cloud project (grant agreement no. 863059) was to overcome fragmentation within food, nutrition and health data through development of tools and services facilitating matching and merging of data to promote increased reuse. However, in an era of increasing data reuse, it is imperative that the scientific quality of data analysis is maintained. Whilst it is true that many datasets can be reused, questions remain regarding whether they should be, thus, there is a need to support researchers making such a decision. This paper describes the development and evaluation of the FNS-Cloud data quality assessment tool for dietary intake datasets. Markers of quality were identified from the literature for dietary intake, lifestyle, demographic, anthropometric, and consumer behavior data at all levels of data generation (data collection, underlying data sources used, dataset management and data analysis). These markers informed the development of a quality assessment framework, which comprised of decision trees and feedback messages relating to each quality parameter. These fed into a report provided to the researcher on completion of the assessment, with considerations to support them in deciding whether the dataset is appropriate for reuse. This quality assessment framework was transformed into an online tool and a user evaluation study undertaken. Participants recruited from three centres (N = 13) were observed and interviewed while using the tool to assess the quality of a dataset they were familiar with. Participants positively rated the assessment format and feedback messages in helping them assess the quality of a dataset. Several participants quoted the tool as being potentially useful in training students and inexperienced researchers in the use of secondary datasets. This quality assessment tool, deployed within FNS-Cloud, is openly accessible to users as one of the first steps in identifying datasets suitable for use in their specific analyses. It is intended to support researchers in their decision-making process of whether previously collected datasets under consideration for reuse are fit their new intended research purposes. While it has been developed and evaluated, further testing and refinement of this resource would improve its applicability to a broader range of users.

Groundwater-quality data were collected from 559 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program from January through December 2014. The data were collected from four types of well networks: principal aquifer study networks, which assess the quality of groundwater used for public water supply; land-use study networks, which assess land-use effects on shallow groundwater quality; major aquifer study networks, which assess the quality of groundwater used for domestic supply; and enhanced trends networks, which evaluate the time scales during which groundwater quality changes. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some special interest constituents (arsenic speciation, chromium (VI) and perchlorate). These groundwater quality data are tabulated in a U.S. Geological Survey Data Series Report DS-1063 which is available at https://dx.doi.org/10.3133/ds1063 and in this data release. Quality-control samples also were collected and select data from 2012-2014 are included in the report DS1063 and this data release. Data from the environmental and QC blank and replicate samples from the 2012-2013 sampling period were presented in Arnold and others (2016). Data from spike QC samples have not previously been published Data from VOC spike QC data from May 2012-December 2014 are published in this report (see larger work citation) and data release along with an analysis of QC data where detections in field blank samples, variability in replicate samples, and recoveries in VOC spike samples are described for the entire sampling period through the date covered in this report (May 2012-December 2014). Pesticide spike samples and analysis of recoveries will be presented in a later report. There are 42 tables that are part of the larger work citation. There are 9 tables in the text of the larger work citation and 33 external tables included in this data release. The 9 tables not included in the data release are summary tables derived from some of the other 33 tables. Tables in the text include table 1, Appendix 1 tables 1-1 and 1-2; Appendix 2 table 2-1, and Appendix 4 tables 4-1 through 4-6. Tables that are external files are tables 1 through 12; Appendix 4 tables 4-7 through 4-27. This compressed file contains 33 files of groundwater-quality data in ASCII text tab-delimited format and 33 corresponding metadata in xml format for wells sampled for the U.S. Geological Survey National Water-Quality Assessment Project.

This data table provides the detailed data quality assessment scores for the Embedded Capacity Register dataset. The quality assessment was carried out on 30th April. Please note, this assessment only covers 1MW and above data. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please not that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

This dataset provides the detailed data quality assessment scores for the Voltage dataset. The quality assessment was carried out on the 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please not that the quality assessment may be based on an earlier version of the dataset. To access our full suite of aggregated quality assessments and learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding our approach to data quality. Our Open Data team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the dataset schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the datasets with the results when available.

Technical and behavioural factors of HMIS data quality in health centers of Hadiya zone, southern Ethiopia 2018.

Code and data for master's thesis on quality assessment of the size property in research data repositories. Research questions:

• For what semantic concepts is the size property of repositories being used?

• What kind of quality factors can be detected when assessing the size property in a registry for research data repositories?

• Which automated and intellectual measures can improve the quality of the size property?

Method 1: Data analysis of size and related properties over all re3data records

• Property selection

• Data extraction from API

• Data normalization

• Typing of patterns: mainly units of size

• Analysis: ~quantitative, mainly univariate, but also some multivariate / time

[Included in the publication:

Method 2: Case Study of size in individual repositories

• Repository selection: purposive sampling

• Data capture from GUI / API

• Analysis: ~qualitative]