Objective: Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.

Materials and Methods: We used the clinical documentation of 34 UK General Practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs. consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician's final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding.

Results: Supported documentation contained significantly more codes (IRR=5.76 [4.31, 7.70] P<0.001) and less free text (IRR = 0.32 [0.27, 0.40] P<0.001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b=-0.08 [-0.11, -0.05] P<0.001) in the supported consultations, and this was the case for both codes and free text.

Conclusions: We provide evidence that data entry in the EHR is incomplete and reflects physicians' cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.

Low data quality can seriously damage business operations as (potential) customers are not (properly) reached and unnecessary costs are incurred. It is therefore crucial that your customer base is complete, correct and up to date. That starts with measuring. For improving your data quality, it is essential that you map the status of your customer data and find out what is going right and wrong. We have therefore developed the Customer Data Quality Report with which you can find out where your improvement potential lies.

With the Customer Data Quality Report you get perfect insight into the status of your customer data. Our data specialists examine your (unstructured) data and translate the information into valuable insights into how you can improve your data quality, which missing data can be added and which new information you need.

Benefits - Insight into the status and improvement potential of your data file - Insight into how you can improve your data quality - Insight into the size of the required investment

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.

Replication Data for Measuring Service Quality based on Consumers' Evaluation of Services in Competitive Markets

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.

To present measurement data of the soil quality measurement network, the homogeneous areas have been compiled. These consist of certain combinations of soil type, land use and water management. Many combinations are ultimately possible, but it is agreed that only combinations are used whose surface covers more than 2 % of the province. (CSO, SNN.S01.02, Dec 1997).

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.

Data stream D includes the meta information on the area-related assessment methods that result from the assessment regime (data stream C). For stationary and indicative measurements, this is 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 made via the coordinates of the measuring stations.

Air Quality Management Zones (AQMZ) are zones or areas which are monitored and measured against the National Air Quality objectives. When pollutants exceed their air quality objectives, the specific area has to be declared and a Local Air Quality Action Plan drawn up as to how to improve the air quality at that location. There are 2 areas which have exceeded their national air quality objectives.

This dataset contains information on Programa Nacional Cuna Más (Cuna Mas, hereinafter), Peru’s largest early childhood development program established in 2012. It focuses on one of the two services provided by Cuna Mas known as Servicio de Acompanamiento a Familias (SAF), a home visiting program that operates in rural areas and provides one-hour weekly home visits to children aged 0-36 months and their caregiver. The objective of the study was to compare different instruments to measure the quality of home visiting programs. Between August and October 2015, three instruments were administered to a sample of 554 children enrolled in Cuna Mas and receiving home visits at the time of data collection, and on their 176 home visitors who regularly work with 80 supervisors.

Comprehensive dataset of 2 Measuring instruments suppliers in Nebraska, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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 indicative measurements, this is 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 made via the coordinates of the measuring stations.

To present measurement data of the groundwater quality measurement network, the homogeneous areas have been compiled. These consist of certain combinations of soil type, land use and torment/infiltration.

The Bonnet Carré Spillway (BCS), located about 28 miles northwest of New Orleans, Louisiana, was constructed in the early 1930s as part of an integrated flood-control system for the lower Mississippi River Plain. The BCS is designed to divert water from the Mississippi River (MSR) into Lake Pontchartrain (LP), which then flows into Lake Borgne and the Mississippi Sound (MS Sound), thus relieving pressure on levees downstream. Opening of the spillway occurs when measured streamflow in the MSR at New Orleans exceeds approximately 1.25 million cubic feet per second, which normally occurs once a year in late spring. In 2019, for the first time, the spillway opened twice in one year; the first opening occurred between February 27th and April 11th and the second occurred between May 10th and July 22nd (Army Corps of Engineers, 2022). Monitoring the quality of estuary surface waters that receive inflows from the MSR diverted through the BCS is of vital importance to public and natural resource managers in Louisiana and Mississippi. These waterbodies provide habitat for many species of fish, shellfish, crabs, seagrass, and marine mammals, and are used for recreational activities and commercial fishing (U.S. Geological Survey, 2020). During the 2008–2020 BCS openings, MSR water entered LP and changed the brackish-estuarine system to a freshwater-dominated system, with some areas maintaining low salinity for 2 to 3 months. The introduction of nutrient-rich fresh river water into nutrient-poor brackish LP is known to substantially change the chemistry and ecology of the lake (Mize and Demcheck, 2009). Except for large openings during 2011 and 2019, algae blooms appear to be generally relegated to LP, particularly the northwest part of the lake, and in some instances, originating from Lake Maurepas. Although not normally an acute health hazard, these blooms can substantially limit the use of lake and sound waters for commercial and recreational activity. The U. S. Geological Survey (USGS), Lower Mississippi-Gulf Water Science Center, in cooperation with the U.S. Army Corp of Engineers (USACE) New Orleans District, sampled water from LP and the western MS Sound prior, during, and after the seven BCS openings that occurred between 2008 and 2020. Water samples were analyzed for major ions, nutrients, inorganic plus organic particulate carbon, total suspended solids, chlorophyll a, and algal toxins; results are available on the USGS National Water Information System (NWIS; U.S. Geological Survey, 2022). Vertical water column profiles of field water quality parameters, phytoplankton community sample results from 2008, 2011, 2013, 2016, 2018, 2019, and 2020, and oxygen and hydrogen stable freshwater isotopic composition sample results from 2019 and 2020 are reported in this data release. Field water-quality measurements were collected using a water quality sonde equipped with a depth transducer for measuring water depths and sensors for measuring water temperature, specific conductance (salinity), pH, dissolved oxygen, and oxygen percent saturation. Profile measurements were collected from 0.5 m above the water-sediment interface at the bottom, at mid-depth, and 0.5 m below the approximate water surface to help determine whether any water quality stratification is occurring, including salinity stratification and hypoxia in bottom waters that serve as habitat for bottom dwelling organisms, such as oysters. In addition, near-surface habitats, those found at the top of the water column, can exhibit elevated pH and oxygen saturation during the day when phytoplankton blooms are concentrated near the surface in the euphotic zone. Maximum depths in the euphotic zone, where photosynthesis occurs, were estimated from Secchi depth measurements. These measurements were used to guide sample collection depths for chlorophyll, phytoplankton, and algal toxins. Phytoplankton samples were collected using National Water Quality Assessment Program protocols (Moulton II et al., 2002). Samples collected in 2008 and 2011 were preserved with a 1% Lugol’s solution and sent to the Academy of Natural Sciences in Philadelphia, PA for taxonomic analysis, according to Charles et al. (2002). Samples collected in 2013, 2016, 2018, 2019, and 2020 were preserved with a 0.25-0.50% glutaraldehyde solution and sent to Phycotech, Inc. in St. Joseph, MI for taxonomic analysis. Research-grade microscopes ranging from 40-1,000x magnification were used to identify phytoplankton in samples to the most practical taxonomic levels (normally, species or genus level). Oxygen and hydrogen stable isotopic (ẟ 18O and ẟD) compositions were determined from samples collected in 2019 and 2020. The combination of salinity and isotope results were used to distinguish proportions of MSR water, water from local drainages, and seawater in LP and MS Sound throughout the sampling period. Water samples and salinity measurements were collected from the surface of the water column and filtered with a 0.45 µm syringe filter and stored in glass amber bottles with lids that were securely covered with parafilm to prevent evaporation. Isotopic analysis was performed using isotopic ratio infrared spectroscopy (Sanial et al., 2019). This data set provides profile measurement, phytoplankton, and oxygen and hydrogen stable freshwater isotopic composition data for Lake Pontchartrain and the Mississippi Sound collected between 2008 and 2020. "Table_1_Station_Data.txt" contains profile data (latitude, longitude, station name, etc) for all sites sampled and an overview of data available for each site by year. "Table_2_Field_physiochemical_profile_data_2008_2019.txt" contains physiochemical data (temperature, specific conductance, salinity, etc) for all sites sampled. "Table_3_Phytoplankton_Community_Data_2008_2020.txt" contains taxonomic data for all sites sampled. "Table_4_Salinity_and_stable_water_isotope_2019_2020.txt" contains oxygen and hydrogen stable isotopic composition data for all sites sampled. Reference Charles, D.F., Knowles, C., Davis, R.S., 2002, Protocols for the analysis of algal samples collected as part of the U.S. Geological Survey National Water-Quality Assessment Program. Patrick Center for Environmental Research, Academy of Natural Sciences, Philadelphia, PA. Mize, S.V., and Demcheck, D.K., 2009, Water quality and phytoplankton communities in Lake Pontchartrain during and after the Bonnet Carré Spillway opening, April to October 2008, in Louisiana, USA, Geo-Marine Letters, 29:431-440. Moulton II, S.R., Kennen, J.G, Goldstein, R.M., and Hambrook, J.A., 2002, Revised Protocols for Sampling Algal, Invertebrate, and Fish Communities as Part of the National Water-Quality assessment Program, US Geological Survey: Open File Report 02-150, accessed 2008, at https://doi.org/10.3133/ofr2002150. Sanial, V., Shiller, A.M., Joung, D., and Ho, P., 2019, Extent of Mississippi River water in the Mississippi Bight and Louisiana Shelf based on water isotopes, Estuarine, Coastal and Shelf Science, 226: 106196, accessed 2019, at https://doi.org/10.1016/j.ecss.2019.04.030. U.S. Army Corps of Engineers, 2022, Spillway Operation Information, accessed May 31, 2022, at https://www.mvn.usace.army.mil/Missions/Mississippi-River-Flood-Control/Bonnet-Carre-Spillway-Overview/Spillway-Operation-Information/. U.S. Geological Survey, 2020, Water Quality in Lake Pontchartrain and western Mississippi Sound during openings of Bonnet Carré Spillway, accessed March 9, 2020, at https://www.usgs.gov/centers/lmg-water/science/water-quality-lake-pontchartrain-and-western-mississippi-sound-during?qt-science_center_objects=0#qt-science_center_objects. U.S. Geological Survey, 2022, USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed July 12, 2022, at https://doi.org/10.5066/F7P55KJN.

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.

The service enables the transmission of INSPIRE compliant data.

Chlorophyll-a is an essential climate variable. Chlorophyll-a in situ measurements are usually used for the validation of satellite images. Previous intercomparisons have shown that there can be substantial differences between in situ laboratories. In order to shed light on these differences, we arranged international chlorophyll-a intercomparisons with eight participating laboratories during 1–2 July 2021. We performed two dedicated transects through Bråviken bay (NW Baltic proper) and sampled four stations in each transect along a chlorophyll-a gradient. We took three surface replicates per laboratory and per station, i.e., 24 samples per laboratory. The samples were filtered through Whatman GF/F filters, and filters were frozen in liquid nitrogen and distributed in dry ice to all laboratories together with chlorophyll-a standards. The results between labs compared quite well. The mean normalized bias (MNB) of the standard measurements ranged between −23% and +19% for all laboratories and −7% to +19% for the Baltic Sea laboratories compared to high-performance liquid chromatography. The MNB of the two Bråviken transects ranged between −23 and +17% for all laboratories (compared to the median of all spectrophotometric and fluorometric measurements) and between −2 and +17% for the Baltic Sea laboratories. On average, the chlorophyll-a concentrations measured by the fluorometric method were about 13% higher than those measured by spectrophotometry, and fluorometry samples tended to have more scatter. The largest uncertainties seem to be caused by variable storage and extraction methods and are not fully captured in this intercomparison. This is demonstrated by analyzing historical comparisons revealing very large uncertainties (root mean square difference (RMSD) up to 109% and bias up to 68%), possibly due to too low filtration volumes and due to different extraction and storage methods. Our recommendation is to flash-freeze samples in liquid nitrogen and store them at −80°C. After storage, they should be extracted and measured at room temperature within 6–24 h. Our results also indicate that ethanol is much more efficient in extracting Chl-a than acetone. Last but not least, we would like to point out that the uncertainties in measuring chlorophyll-a by satellite are now within the range of in situ data, as shown here by comparing the in situ results from this study with published remote sensing results from the literature.

Owners/users of measuring stations, who have automatic imission measurement in place, shall ensure the continuous transmission of data via a computer network to the Agency. Owners/users of measuring stations who do not have automatic measurement in place shall submit annual air quality reports to the Agency. The database shall be continuously updated accordingly. Within the database there are two logical data units: 1) measurement data, i.e. individual hourly ‘raw’ and validated measurement values obtained by automatic continuous measurement of air quality, ‘met’ data on air quality monitoring networks (name, abbreviation, network type, body responsible for management and time notification), station data (name, location, name of the professional institution responsible for the station, body to which data is provided, measurement targets, geographical coordinates, pollutants to be measured, meteorological parameters, area type, station type in relation to the emission source, measurement equipment, sampling characteristics, etc.); 2) Reports: annual air quality reports.

I construct a new measure of candidate quality differentials using local newspaper endorsements. I argue that political endorsements made by newspapers can be used as expert opinions that reflect quality differences between the candidates in an election. Using a dataset of 21,095 local newspaper endorsements, I simultaneously estimate the quality differences between candidates in 6,432 elections, along with a dynamic measure of the partisan bias of 368 local newspapers. Using the new measure, I show that a one standard deviation increase in relative candidate quality increases a candidate’s two-party vote share by 3.4 percentage points, and that candidate quality accounts for about one-fourth of the incumbency advantage. These findings advance debates on the source of incumbency effects and demonstrate the broader electoral impact of candidate quality. I conclude by discussing the potential of these endorsement-based measures to enhance our understanding of candidate quality in electoral politics and governance.

This dataset contains the selected physical properties and chemical constituents that were measured at Puerto Mosquito, Isla de Vieques on June 22, 2017. A cross-sectional profile was conducted by measuring a total of ten selected site throughout the lagoon during the morning. Field water-quality measurements included the parameters of specific conductance, and dissolved oxygen.