NSF information quality guidelines designed to fulfill the OMB guidelines.

Overview of the data quality framework and how the various sections/modules fit in.

Policy Framework

Administrative data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audits or comparison with a survey that is assumed perfect. We introduce the “generalized multitrait-multimethod” (GMTMM) model, which can be seen as a general framework for evaluating the quality of administrative and survey data simultaneously. This framework allows both survey and administrative data to contain random and systematic measurement errors. Moreover, it accommodates common features of administrative data such as discreteness, nonlinearity, and nonnormality, improving similar existing models. The use of the GMTMM model is demonstrated by application to linked survey-administrative data from the German Federal Employment Agency on income from of employment, and a simulation study evaluates the estimates obtained and their robustness to model misspecification. Supplementary materials for this article are available online.

This publication includes National level-specific data for QOF 2007-2008. For links to all QOF data for 2007-2008, see The Quality and Outcomes Framework, 2007-08. You can also browse the QOF online database to find results for your local surgery.

This RDF document contains a library of data quality constraints represented as SPARQL query templates based on the SPARQL Inferencing Framework (SPIN). The data quality constraint templates are especially useful for the identification of data quality problems during data entry and for periodic quality checks during data usage. @en

• Opposite valence factors, “Lack of abstractor training decreases accuracy of abstracted data,” “An incomplete review of the medical record (e.g., not reading all pages from the required time period) decreases the accuracy of abstracted data,” “Data element definitions that lack suggestions for where in the chart to find data values,” “Data abstracted from a complete medical record are more accurate than those that are abstracted from medical records with omissions,” “Abstractor (human) error is a factor in decreasing the accuracy of abstracted data,” and “Data abstracted from a medical record that is free from error are more accurate than those abstracted from a medical record containing errors,” were omitted from framework.† Combined factors “Misuse of the coding system” and “Misunderstanding the coding system,” and moved to the training category.‡ Original text “Abstractor human error” restated to create an actionable item.§ “Data elements requiring the abstractor to do calculations (e.g., convert units or score questionnaires) are less accurate than those that do not” and “Data elements that are abstracted directly from medical records) are more accurate than those requiring mapping or interpretation” were combined.Framework for increasing data accuracy in MRA.

Data related to the paper Everything you should know about process model quality- The Comprehensive Process Model Quality Framework

EPA has compiled state, territorial, and authorized tribal water quality standards that EPA has approved or are otherwise in effect for Clean Water Act purposes. This compilation is continuously updated as EPA approves new or revised WQS.Please note the water quality standards may contain additional provisions outside the scope of the Clean Water Act, its implementing federal regulations, or EPA's authority. In some cases, these additional provisions have been included as supplementary information. EPA is posting the water quality standards as a convenience to users and has made a reasonable effort to assure their accuracy. Additionally, EPA has made a reasonable effort to identify parts of the standards that are approved, disapproved, or are otherwise not in effect for Clean Water Act purposes.

The USAID-LMI COMET (Connecting the Mekong Through Education and Training) Quality Standards Self-Assessment contains data from trainers trained in the USAID-LMI COMET program's quality standards at endline. It is a self-assessment of their upholding of the USAID COMET quality standards.

If you are already familiar with QOF, you can go straight to the information you need using the resources listed above. This publication includes North East England level-specific data for QOF 2005-2006. For links to all QOF data for 2005-2006, see The Quality and Outcomes Framework, 2005-06. You can also browse the QOF online database to find results for your local surgery.

Counts of occurrence records in 2019-04-15 snapshot of GBIF-mediated data that fit the three categories of expected responses for each of the event date-related validation tests.

the arcachon bay is a meso- / macro-tidal (0.8 to 4.6 m), semi-enclosed lagoon of 180 km² located on the south-western coast of france. three main water masses are described in this bay: (i) the external neritic waters (enw) directly influenced by the adjacent oceanic waters, (ii) the intermediate neritic waters (itnw) and (iii) the inner neritic waters (innw) more influenced by the continental inputs. the watershed of the arcachon bay, mainly covered by forests, has an area of 3500 km² and the bay is considered as poorly anthropised. it hosts the largest zostera noltei seagrass meadow in western europe and is an important site for oyster farming and manilla clam production.since 1997, arcachon bay waters are monitored for hydrological and bio-geochemical parameters by the “environnements et paléoenvironnements océaniques et continentaux” (epoc) research unit of the university of bordeaux-cnrs, first in one single station (eyrac), then on 2 complementary sites since 2005 (bouee13 and comprian). the monitoring is carried out within the national framework of the “somlit” (“service d’observation en milieu littoral”) which is a french multi-site monitoring network initiated in the mid-1990s. somlit is based on a joint strategy for 19 sites belonging to 12 ecosystems that are distributed over the three maritime facades of mainland france, i.e. the english channel, the atlantic ocean and the mediterranean sea. sampling of surface water samples is performed fortnightly at high tide for a group of 15 parameters (temperature, salinity, dissolved oxygen, ph, nitrate, nitrite, ammonium, phosphate, silicate, suspended matter, chlorophyll a, concentrations and isotopic ratios of particulate organic carbon and nitrogen) and 8 flow cytometry biological variables of pico- and nanoplankton. vertical profiles of multiparametric probes concerning 4 parameters (temperature, salinity, fluorescence, par) are also performed.given the significant diversity of coastal ecosystems where somlit’s stations are located, strict and joint guidelines with regards to sampling strategy, measurement methods and data qualification and storage are paramount in order to make fair data available to users. the whole data acquisition strategy is carried out within the framework of the somlit quality system formalized in 2006-2007 by referring to the iso 17025: 2017 standard “general requirements for the competence of testing and calibration laboratories”. unified sampling and analysis protocols are based on recognized disciplinary standards and on the expertise of the research teams.the scientific objectives of somlit are 1) to characterize the multi-decadal evolution of coastal ecosystems; 2) to determine the climatic and anthropogenic forcings and 3) to make data and logistical support available for research activities and other observation activities. somlit is therefore a research tool providing large datasets that also serve as logistical support for related research actions (from seasonal to long-term studies).two additional national networks operate at the same somlit sites: “coast-hf” network performs high-frequency measurements (automated in situ measurements every 10 to 20 minutes) and “phytobs-network” provides microphytoplankton biodiversity data. somlit, coast-hf and phytobs are elementary networks of the research infrastructure “infrastructure littorale et côtière” (ilico) and are national observation services (sno) of the institut national des sciences de l'univers (insu).

Tempe’s Housing Choice Voucher program provides federally subsidized housing to eligible low-income families. The program is designed to provide housing that is affordable decent, safe and sanitary. All dwelling units are inspected prior to a tenant moving into the unit and then inspected annually. Tenants and landlords may request inspections for maintenance issues that impact health and safety.This data represents the resolution rate for all Housing Quality Standard issues related to life, health or safety within 24 hours.This page provides data for the Housing Quality Standards performance measure. The performance measure dashboard is available at 1.19 Housing Quality StandardsAdditional InformationSource: Manually maintained data, Housing Pro and ExcelContact: Irma Hollamby CainContact E-Mail: irma_hollambycain@tempe.govData Source Type: Preparation Method: A tracking log in maintained for units that fail the initial inspection. The required fix timeframe of 24 hour or 30 days is documented and then the unit is manually tracked for re-inspection.Publish Frequency: QuarterlyPublish Method: ManualData Dictionary

A list by county of the current attainment status of the National Ambient Air Quality Standards (NAAQS).

Description and specifications for the tests following the conventions of the Fitness For Use Framework. This supplement is a copy of https://github.com/tdwg/bdq/blob/master/tg2/core/TG2_tests.csv as of commit 941e774 2019-Aug-20.

This operations dashboard shows historic and current data related to this performance measure.The performance measure dashboard is available at 1.19 Housing Quality Standards. Data Dictionary

This publication includes England-specific data for QOF 2010-2011. For links to all QOF data for 2010-2011, see The Quality and Outcomes Framework, 2010-11. You can also browse the QOF online database to find results for your local surgery.

Open the Data Resource: https://www.chesapeakeprogress.com/clean-water/water-quality This Chesapeake Bay Program indicator of progress toward the Water Quality Standards Attainment and Monitoring Outcome shows the estimated percentage of the tidal Chesapeake Bay that is considered to be "in attainment" of water quality standards. Water quality is evaluated using three parameters: dissolved oxygen, water clarity or underwater grass abundance, and chlorophyll a (a measure of algae growth). For a more detailed look at water quality standards attainment, open the Chesapeake Bay Water Quality Standards Attainment Indicator Visualization Tool or the Chesapeake Bay Water Quality Standards Attainment Deficit Visualization Tool.

Comparison of factors mentioned in the Delphi top 27% and the literature top 26%.