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.

In this seminar, you will learn about ArcGIS Data Reviewer tools that allow you to automate, centrally manage, and improve your GIS data quality control processes.This seminar was developed to support the following:ArcGIS 10.0 For Desktop (ArcView, ArcEditor, Or ArcInfo)ArcGIS Data Reviewer for Desktop

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

The size and share of this market is categorized based on Type (Data quality assessment tools, Data cleansing solutions, Data governance platforms, Data monitoring software, Data stewardship tools) and Application (Data cleansing, Data profiling, Data validation, Data enrichment, Data governance) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

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 Transmission Generation Heat Map. 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 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 data tables with the results when available.

Political scientists routinely face the challenge of assessing the quality (validity and reliability) of measures in order to use them in substantive research. While stand-alone assessment tools exist, researchers rarely combine them comprehensively. Further, while a large literature informs data producers, data consumers lack guidance on how to assess existing measures for use in substantive research. We delineate a three-component practical approach to data quality assessment that integrates complementary multi-method tools to assess: 1) content validity; 2) the validity and reliability of the data generation process; and 3) convergent validity. We apply our quality assessment approach to the corruption measures from the Varieties of Democracy (V-Dem) project, both illustrating our rubric and unearthing several quality advantages and disadvantages of the V-Dem measures, compared to other existing measures of corruption.

Have you ever assessed the quality of your data? Just as you would run spell check before publishing an important document, it is also beneficial to perform a quality control (QC) review before delivering data or map products. This course gives you the opportunity to learn how you can use ArcGIS Data Reviewer to manage and automate the quality control review process. While exploring the fundamental concepts of QC, you will gain hands-on experience configuring and running automated data checks. You will also practice organizing data review and building a comprehensive quality control model. You can easily modify and reuse this QC model over time as your organizational requirements change.After completing this course, you will be able to:Explain the importance of data quality.Select data checks to find specific errors.Apply a workflow to run individual data checks.Build a batch job to run cumulative data checks.

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.

Ontologies play an important role in the representation, standardization, and integration of biomedical data, but are known to have data quality (DQ) issues. We aimed to understand if the Harmonized Data Quality Framework (HDQF), developed to standardize electronic health record DQ assessment strategies, could be used to improve ontology quality assessment. A novel set of 14 ontology checks was developed. These DQ checks were aligned to the HDQF and examined by HDQF developers. The ontology checks were evaluated using 11 Open Biomedical Ontology Foundry ontologies. 85.7% of the ontology checks were successfully aligned to at least 1 HDQF category. Accommodating the unmapped DQ checks (n=2), required modifying an original HDQF category and adding a new Data Dependency category. While all of the ontology checks were mapped to an HDQF category, not all HDQF categories were represented by an ontology check presenting opportunities to strategically develop new ontology checks. The HDQF is a valuable resource and this work demonstrates its ability to categorize ontology quality assessment strategies.

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.

ADBNet is an online database tracking Iowa's water quality assessments. These assessments are prepared under guidance provided by the US EPA under Section 305b of the Clean Water Act. The assessments are intended to estimate the extent to which Iowa's waterbodies meet the goals of the Clean Water Act and attain state water quality standards, and share this information with planners, citizens and other partners in basin planning and watershed management activities. Water quality in Iowa is measured by comparisons of recent monitoring data to the Iowa Water Quality Standards. Results of recent water quality monitoring, special water quality studies, and other assessments of the quality of Iowa's waters are used to determine the degree to which Iowa's rivers, streams, lakes, and wetlands support the beneficial uses for which they are designated in the Iowa Water Quality Standards (for example, aquatic life (fishing), swimming, and/or use as a source of a public water supply). Other information from water quality monitoring and studies that are up to five years old are also used to expand the coverage of assessments in the report. Waters assessed as impaired (that is, either partially supporting or not supporting their designated uses) form the basis for the state's list of impaired waters as required by Section 303(d) of the Clean Water Act.

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.

This data table provides the detailed data quality assessment scores for the SPD DG Connections Network Info 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 refresehed 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.

Bathing Season is Now Closed for 2024Bathing Season runs from May to September each year and this is the raw data from 2023 - 2024 inclusive which is valuable for analyses it as it show the figures for each beach each year.Bathing waters are monitored, assessed and managed under the requirements of the 2008 Bathing Water Quality Regulations. Bathing waters are sampled every two weeks from the end of May to mid-September to assess the microbiological quality of the water and to minimise any public health risk. Samples are tested for two types of faecal bacteria Escherichia coli(also known as E. coli)and Intestinal Enterococci.E.coli (Escherichia coli)and Intestinal Enterococci, occur in very large numbers in the gut of warm blooded animal and human faeces. E.coli and Intestinal Enterococci are analysed in assessing bathing waters compliance and are used as “indicator” organisms where their presence in large numbers in bathing waters is a warning of a possible health risk from other harmful bacteria and viruses which might be present.The annual water quality of bathing waters are assessed and classified as 'Excellent', 'Good', 'Sufficient' or 'Poor'. In the case of Excellent water quality the risk of contracting gastro-intestinal illness is predicted to be ca. 3%, in Good waters ca. 5%, in Sufficient waters 8-9% and in Poor waters ca. >10%.The annual water quality status is determined from results covering a four year period rather than just the past season’s results using statistical methods rather than simple percentage compliance. This approach is more robust, as it averages out the impacts of seasonal variations and takes account of the spread of results.Annual Bathing Water Classifications include: * Excellent * Good Sufficient- PoorClassificationE.coliIntestinal EnterococciExcellent<250<100Good250-500100–200Sufficient500-1000200-250Poor Water>1000>250 The quality assessment is determined by the poorest of the two microbial indicators e.g. E.coli 400, Intestinal Enterococci 20 would result in a ‘Good’ outcome – the poorer of the two being the E.coliFurther InformationFurther information is available on the www.fingal.ieGraphs of the results are posted on notice boards at Fingal’s beaches throughout the season. All bathing water monitoring results are also available on the national www.beaches.ie which has the latest weekly datafor further data see www.beaches.ie which hosts the data from www.epa.ieRelated Data Set which we have published is Water Quality Beaches (Raw Data) 2010-2017 & Water Quality Beaches (Raw) 2018-2022Beach Water Quality - fair, good etc. for all beaches as they have now changed how the analyses this and only give good, fair etc. for each beach.

For more up to date quality metadata, please visit https://w3id.org/lodquator

This dataset is a collection of TRiG files with quality metadata for different datasets on the LOD cloud. Each dataset was assessed for

The length of URIs

Usage of RDF primitives

Re-use of existing terms

Usage of undefined terms

Usage of blank nodes

Indication for different serialisation formats

Usage of multiple languages

This data dump is part of the empirical study conducted for the paper "Are LOD Cloud Datasets Well Represented? A Data Representation Quality Survey."

For more information visit http://jerdeb.github.io/lodqa

Abstract

This dataset and its metadata were supplied to the Bioregional Assessments Programme by the NSW Office of Water and is presented here as originally supplied. The dataset includes daily water quality parameters time series for selected gauging stations within the Namoi subregion. Data includes, electrical conductivity, total suspended solids and temperature.

Gaps in data records

Gaps in data records are more to occur likely during extreme events, particularly for gauging stations, where the data is automatically audited and potentially erroneous data is prevented from being displayed (Unreported data).

Data validation

The Office of Water is endeavouring to validate data as quickly as possible. Our performance indicator aims for data to be validated is within 100 days.

With respect to use of data:

Unvalidated data (Quality Code 130) has not been rigorously assessed and the data is quality coded to indentify this. These data should be used with care as they may change after validation. These data should only be used by persons who are familiar with the characteristics of streamflow information.

Validated data are data that has been assessed and is the best available quality at the time, however the data should always be interpreted taking into account the quality codes that have been applied.

Hydrologic advice should be sought to assist with any interpretation.

For tables and codes refer to: http://realtimedata.water.nsw.gov.au/

Purpose

To show examples of water quality parameters for selected stations.

Dataset History

No history statement was provided with the dataset.

Dataset Citation

NSW Office of Water (2015) Namoi Water quality time series for selected stations. Bioregional Assessment Source Dataset. Viewed 02 May 2016, http://data.bioregionalassessments.gov.au/dataset/8b11a0d6-5f97-4e17-8cd1-75bb647b321a.

This data release contains groundwater-quality data and well information for the glacial aquifer system in the northern USA. Water-quality data and well information were derived from a dataset compiled from three sources: The U.S. Geological Survey (USGS) National Water Information System (NWIS; USGS, 1998, 2002), the U.S. Environmental Protection Agency (USEPA) Safe Drinking Water Information System (SDWIS; USEPA, 2013), and numerous agencies and organizations at the state, regional, and local level. The data compilation of the National Water Quality Program’s groundwater assessment team is an internal dataset informally referred to as the National Groundwater Aggregation (NGA). The current study of groundwater quality in the glaciated U.S. (Erickson and others, 2019) considers only parameters with benchmarks from wells in the national groundwater aggregation—data from springs were not used. Data were screened for sample dates of 2005 or later, and the most recent sample at each site was used. This data release includes a table of benchmarks and thresholds. “Benchmark” is a generic term for any standard, regulation, guideline, or criteria against which constituent concentrations are compared. The threshold is the value against which measured concentrations of constituents in water samples can be compared to help assess the potential effects of contaminants on water quality. The table of water-quality results includes the concentration of constituents relative to their health-based or non-health benchmark, and a flag to indicate if the concentration is low, medium, or high relative to the benchmark. A table of site information includes attributes for each well such as the source of the water-quality data and well information, the state, water use code, depth (if available), and the 17 hydrogeologic terrane from Yager and others (2018). Each hydrogeologic terrane contains Quaternary sediment that is derived from a common depositional history and can be characterized by similar texture and thickness. Each of the 17 hydrogeologic terranes was divided into 30 equal-areas (cells) based on the method of Scott (1990). This cell number for each well is included in the table of site information. An equal-area assessment was used to show the proportion of the aquifer affected by high, medium, and low concentrations of selected constituents at the aquifer scale and terrane scale (Belitz and others, 2010). The equal-area cells were also used with population data (Erickson and others, 2019, supplemental information) to determine aquifer- and terrane-scale proportions of the population affected by high, medium, and low concentrations of selected constituents. A shape file of the hydrogeologic terranes and equal-area cells is included in this data release. A table of well construction information includes attributes for each well such as the source of the well information, the state, well depth, screen length (if available), and the hydrogeologic terrane from Yager and others (2018). Information in this table is from a well construction database compiled from several sources to obtain information on well depths and screened intervals of domestic and public supply wells producing groundwater from Quaternary sediments in the U.S. within the glacial extent. Domestic-supply well data were compiled from a lithologic database (Bayless and others, 2017) as modified by Yager and others (2018), the USGS NWIS (USGS, 2016), and several state well log databases (Erickson and others, 2019, supplemental information). The state databases were accessed to add well records in areas where information from the lithologic and NWIS databases was sparse. Public-supply well data were compiled from the list of public water-supply wells in the water-use database of Yager and others (2018). This data release contains four tables and one shape file: Drinking_Water_QW_Glacial_Aquifer_System_Results.txt Drinking_Water_QW_Glacial_Aquifer_System_Sites.txt Well_Construction.txt Benchmarks.txt TerraneEqualAreas shape file

Access to continuous, quality assessed meteorological data is critical for understanding the climatology and atmospheric dynamics of a region. Research facilities like Oak Ridge National Laboratory (ORNL) rely on such data to assess site-specific climatology, model potential emissions, establish safety baselines, and prepare for emergency scenarios. To meet these needs, on-site towers at ORNL collect meteorological data at 15-minute and hourly intervals. However, data measurements from meteorological towers are affected by sensor sensitivity, degradation, lightning strikes, power fluctuations, glitching, and sensor failures, all of which can affect data quality. To address these challenges, we conducted a comprehensive quality assessment and processing of five years of meteorological data collected from ORNL at 15-minute intervals, including measurements of temperature, pressure, humidity, wind, and solar radiation. The time series of each variable was pre-processed and gap-filled using established meteorological data collection and cleaning techniques, i.e., the time series were subjected to structural standardization, data integrity testing, automated and manual outlier detection, and gap-filling. The data product and highly generalizable processing workflow developed in Python Jupyter notebooks are publicly accessible online. As a key contribution of this study, the evaluated 5-year data will be used to train atmospheric dispersion models that simulate dispersion dynamics across the complex ridge-and-valley topography of the Oak Ridge Reservation in East Tennessee.

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.