The EOSC-A FAIR Metrics and Data Quality Task Force (TF) supported the European Open Science Cloud Association (EOSC-A) by providing strategic directions on FAIRness (Findable, Accessible, Interoperable, and Reusable) and data quality. The Task Force conducted a survey using the EUsurvey tool between 15.11.2022 and 18.01.2023, targeting both developers and users of FAIR assessment tools. The survey aimed at supporting the harmonisation of FAIR assessments, in terms of what it evaluated and how, across existing (and future) tools and services, as well as explore if and how a community-driven governance on these FAIR assessments would look like. The survey received 78 responses, mainly from academia, representing various domains and organisational roles. This is the anonymised survey dataset in csv format; most open-ended answers have been dropped. The codebook contains variable names, labels, and frequencies.

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

In 2013, the first of several Regional Stream Quality Assessments (RSQA) was done in the Midwest United States. The Midwest Stream Quality Assessment (MSQA) was a collaborative study by the U.S. Geological Survey National Water Quality Assessment and the U.S. Environmental Protection Agency National Rivers and Streams Assessment. One of the objectives of the RSQA, and thus the MSQA, is to characterize relations between stream ecology and water-quality stressors to determine the relative effects of these stressors on aquatic biota in streams. Data required to meet this objective included fish species and abundance data and physical and chemical water-quality characteristics of the ecological reaches of the sites that were sampled. This dataset comprises 135 fish species, 39,920 fish, 10 selected water-quality stressor metrics, and six selected fish community stressor response variables for 98 sites sampled for the MSQA.

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.

The USACE IENCs coverage area consists of 7,260 miles across 21 rivers primarily located in the Central United States. IENCs apply to inland waterways that are maintained for navigation by USACE for shallow-draft vessels (e.g., maintained at a depth of 9-14 feet, dependent upon the waterway project authorization). Generally, IENCs are produced for those commercially navigable waterways which the National Oceanic and Atmospheric Administration (NOAA) does not produce Electronic Navigational Charts (ENCs). However, Special Purpose IENCs may be produced in agreement with NOAA. IENC POC: IENC_POC@usace.army.mil

Homeland Infrastructure Foundation-Level Data (HIFLD) geospatial data sets containing information on Data Quality Assessment Areas (USACE IENC).

The Open Data Maturity (ODM) assessment is carried out yearly and provides a benchmark of European countries development in the field of open data. It is based on the following dimensions:

• Policy: focusing on countries’ open data policies and strategies;
• Impact: looking into the activities to monitor and measure open data reuse and its impact;
• Portal: assessing portal functions and features that enable users to access open data via the national portal and support interaction within the open data community;
• Quality: focusing on mechanisms that ensure the quality of the (meta)data..

This assessment helps the countries to better understand their level of maturity, to capture their progress over time and to find areas for improvement. Additionally, the study provides an overview of best practices implemented across Europe that could be transferred to other national and local contexts.

The 35 participant countries in the 2022 edition are the 27 EU Member States, 3 European Trade Association (EFTA) countries (Norway, Switzerland, Iceland), 4 candidate countries (Albania, Montenegro, Serbia, Ukraine) and Bosnia and Herzegovina.

The scores of the ODM assessment for each participating country and the questionnaire used in the survey are provided as a re-usable dataset. The complete report and the methodology can be found under documentation.

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.

A vocabulary for the specification and exchange of Data Quality Assessment Requirements

built on top of already well-established vocabularies such as the Data Quality Vocabulary (DQV)

Further description at http://purl.org/net/vsr/daqar

Contact:

André Langer
Professorship for Distruted and Self-Organizing Systems
Chemnitz University of Technology
Germany
[andre.langer@informatik.tu-chemnitz.de]

Summary of findings of a national data quality assessment of maternity data held by ISD, giving detailed findings about the maternity dataset, involvement of midwives, and quality of clinical coding.

Source agency: ISD Scotland (part of NHS National Services Scotland)

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Data Quality Assurance Assessment of Maternity Data (SMR02)

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.

description: This is a coverage of the boundaries and codes used for the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) Program Study-Unit investigations in the conterminous United States, excluding the High Plains Regional Ground-Water Study. The data set represents the areas studied during the first decade of the NAWQA Program, from 1991-2001 ("cycle 1").; abstract: This is a coverage of the boundaries and codes used for the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) Program Study-Unit investigations in the conterminous United States, excluding the High Plains Regional Ground-Water Study. The data set represents the areas studied during the first decade of the NAWQA Program, from 1991-2001 ("cycle 1").

DuQ Values after Execution 1.

Groundwater-quality data were collected from 748 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program from May 2012 through December 2013. 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, and radionuclides. These groundwater quality data are tabulated in a U.S. Geological Survey Data Series Report DS-997 which is available at http://dx.doi.org/10.3133/ds997 and in this data release. Quality-control samples also were collected; data from blank and replicate quality-control samples are included in the related report (DS-997) and this data release. This compressed file contains 28 files of groundwater-quality data in ASCII text tab-delimited format and 28 corresponding metadata in xml format for wells sampled for the U.S. Geological Survey National Water-Quality Assessment Project, May 2012 through December 2013.

Several quality control measures were taken during the project. These included: - Central provision of sampling equipment and sample bags to all field teams - Randomised sample identification scheme so that samples were presented to the laboratories in a sequence unrelated to the order in which they were collected (as much as practically feasible) - Prevention of contamination in the field and in the lab - Prevention of sample mix-up in the field and in the lab - Field duplicates: every 10th site, a field duplicate sample was collected to help quantify total (sampling + analytical) precision (not identified as such to the lab) - Certified Reference Materials (CRMs) TILL-1, TILL-2 (Natural Resources Canada) were run with every batch on GA's XRF & ICP-MS to help quantify analytical precision and bias - Laboratory duplicates (splits), internal project standards (MRIS, WRIS, ORIS, MRIS2, WRIS2), exchanged project standards (GEMAS-Ap, GEMAS-Gr from EuroGeoSurveys; SoNE-1 from United States Geological Survey), and international CRMs (TILL-1, TILL-3, LKSD-1, STSD-3 from Natural Resources Canada) were covertly inserted in the analytical suites for in-house and external analyses to help quantify analytical precision and bias (not identified as such to the lab) - Internal project standard (GRIS) for pH 1:5, EC 1:5 and grain size measurements (not identified as such to the lab) In addition to the above measures, the analytical labs applied their own QA/QC procedures, including running CRMs and/or internal standards, replicating digests and/or analysis, and analysis of blanks. The present report uses some of the above data to quantitatively assess the quality of the NGSA data, which allows a quality statement to be made about the NGSA data.

Statewide River Water Quality Assessment (SRWQA) 2004 & 2008 uses water quality data collected as far back as 1998 to determine the status and trends of nine water quality parameters for all …Show full descriptionStatewide River Water Quality Assessment (SRWQA) 2004 & 2008 uses water quality data collected as far back as 1998 to determine the status and trends of nine water quality parameters for all waterways in the state, where consistent data is available. The project was undertaken by the Water Science Branch of the Department of Water and Environmental Regulation in 1999 and 2004 has now been updated to include water quality information up to the end of 2007. This dataset only shows the classifications and trends from the 2004 and 2008 assessment. The Assessment focused on colour, dissolved organic carbon, dissolved oxygen, pH, total nitrogen, total phosphorus, total dissolved salts, total suspended solids and turbidity. A total of 255 sites from 23 basins in Western Australia (out of a total of 44) were included in the 2008 update with 126 of these being assessed for the first time in 2008. In 2004 232 sites were assessed. Due to a lack of data numerous sites that were assessed in 2004 are not included in the 2008 update. Many basins had no data, whilst the others lacked recent monitoring data. The status and trend results were compiled into an excel spreadsheet. Dataset was formally known as Statewide River Water Quality Assessment (DOW-056)

Abstract Four documents describe the specifications, methods and scripts of the Impact and Risk Analysis Databases developed for the Bioregional Assessments Programme. They are: Bioregional Assessment Impact and Risk Databases Installation Advice (IMIA Database Installation Advice v1.docx). Naming Convention of the Bioregional Assessment Impact and Risk Databases (IMIA Project Naming Convention v39.docx). Data treatments for the Bioregional Assessment Impact and Risk Databases (IMIA Project …Show full descriptionAbstract Four documents describe the specifications, methods and scripts of the Impact and Risk Analysis Databases developed for the Bioregional Assessments Programme. They are: Bioregional Assessment Impact and Risk Databases Installation Advice (IMIA Database Installation Advice v1.docx). Naming Convention of the Bioregional Assessment Impact and Risk Databases (IMIA Project Naming Convention v39.docx). Data treatments for the Bioregional Assessment Impact and Risk Databases (IMIA Project Data Treatments v02.docx). Quality Assurance of the Bioregional Assessment Impact and Risk Databases (IMIA Project Quality Assurance Protocol v17.docx). This dataset also includes the Materialised View Information Manager (MatInfoManager.zip). This Microsoft Access database is used to manage the overlay definitions of materialized views of the Impact and Risk Analysis Databases. For more information about this tool, refer to the Data Treatments document. The documentation supports all five Impact and Risk Analysis Databases developed for the assessment areas: Maranoa-Balonne-Condamine: http://data.bioregionalassessments.gov.au/dataset/69075f3e-67ba-405b-8640-96e6cb2a189a Gloucester: http://data.bioregionalassessments.gov.au/dataset/d78c474c-5177-42c2-873c-64c7fe2b178c Hunter: http://data.bioregionalassessments.gov.au/dataset/7c170d60-ff09-4982-bd89-dd3998a88a47 Namoi: http://data.bioregionalassessments.gov.au/dataset/1549c88d-927b-4cb5-b531-1d584d59be58 Galilee: http://data.bioregionalassessments.gov.au/dataset/3dbb5380-2956-4f40-a535-cbdcda129045​ Purpose These documents describe end-to-end treatments of scientific data for the Impact and Risk Analysis Databases, developed and published by the Bioregional Assessment Programme. The applied approach to data quality assurance is also described. These documents are intended for people with an advanced knowledge in geospatial analysis and database administration, who seek to understand, restore or utilise the Analysis Databases and their underlying methods of analysis. Dataset History The Impact and Risk Analysis Database Documentation was created for and by the Information Modelling and Impact Assessment Project (IMIA Project). Dataset Citation Bioregional Assessment Programme (2018) Impact and Risk Analysis Database Documentation. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c.

Data stream D comprises the meta-information on the area-based assessment methods resulting from the assessment regime (data stream C). For the fixed and indicative measurements, this is the material-specific meta-information about the measuring stations, such as name, code, measurement configuration, station classification, data quality objectives, etc. The link to the assessment areas (data stream B) is made via the coordinates of the measuring stations.

Data stream D includes the meta-information on the area-related assessment methods that result from the assessment regime (data stream C). For the stationary and orienting measurements, this is the substance-specific meta information about the measuring stations, such as name, code, measurement configuration, station classification, data quality goals, etc. The link to the assessment areas (data stream B) is done via the coordinates of the measuring stations.