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.

The association between facility characteristics and the frequency of concordant data elements in paper records and KenyaEMR during baseline RDQAs.

The ETL (Extract, Transform, Load) testing services market is experiencing robust growth, driven by the increasing volume and complexity of data across industries. The market's expansion is fueled by the critical need for data quality and accuracy in business intelligence, analytics, and reporting. Organizations are prioritizing data integrity to ensure reliable decision-making, leading to heightened demand for comprehensive ETL testing solutions. The market is segmented by testing type (Data Completeness Testing, Data Accuracy Testing, Data Transformation Testing, Data Quality Testing) and application (Large Enterprises, SMEs). Large enterprises dominate the market currently, owing to their significant data volumes and higher budgets for quality assurance. However, SMEs are showing increasing adoption, driven by the growing affordability and accessibility of ETL testing services. The North American market holds a substantial share, propelled by early adoption of advanced data technologies and a strong emphasis on data governance. However, growth in regions like Asia-Pacific is accelerating rapidly, reflecting the region's burgeoning digital economy and expanding data infrastructure. The competitive landscape includes both established players like Infosys and Accenture and specialized ETL testing service providers. This competitive dynamic fosters innovation and ensures the provision of a diverse range of services tailored to specific client needs. The forecast period (2025-2033) projects sustained market growth, influenced by several key trends. The rising adoption of cloud-based data warehousing and big data analytics is a significant driver. Furthermore, the growing focus on data security and regulatory compliance necessitates robust ETL testing processes to safeguard sensitive information. While challenges like the complexity of ETL processes and skill shortages in data testing expertise exist, the overall outlook remains positive. Continued technological advancements in automation and AI-powered testing tools are expected to mitigate these restraints and drive efficiency in the market. The market's evolution will likely be marked by increased consolidation amongst service providers, as companies seek to expand their offerings and cater to a broader customer base. Overall, the ETL Testing Services market is poised for considerable expansion, presenting attractive opportunities for both established companies and new entrants.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class in the full geodatabase inventory (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to prioritize overlapping designations, avoiding massive overestimation in protected area statistics, and simplified by the following PAD-US attributes to support user needs for raster analysis data: Manager Type, Manager Name, Designation Type, GAP Status Code, Public Access, and State Name. The rasterization process (see processing steps below) prioritized overlapping designations previously identified (GAP_Prity field) in the Vector Analysis File (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation (e.g. GAP Status Code 1 over 2). The 30-meter Image (IMG) grid Raster Analysis Files area extents were defined by the Census state boundary file used to clip the Vector Analysis File, the data source for rasterization ("PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class from ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb"). Alaska (AK) and Hawaii (HI) raster data are separated from the contiguous U.S. (CONUS) to facilitate analyses at manageable scales. Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types (with a legal protection mechanism) represented in some manner, while work continues to maintain updates, improve data quality, and integrate new data as it becomes available (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, protection status represents a point-in-time and changes in status between versions of PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

CDFW BIOS GIS Dataset, Contact: Connie Shannon, Description: This dataset depicts observation-based stream-level geographic distribution of anadromous summer-run steelhead trout, Oncorhynchus mykiss irideus, in California. It was developed to assisting in steelhead recovery planning. The distributions reported in this dataset were derived from a subset of the data contained in the Aquatic Species Observation Database.This project is designed to capture statewide inland aquatic vertebrate species observation information.

Study-specific data quality testing is an essential part of minimizing analytic errors, particularly for studies making secondary use of clinical data. We applied a systematic and reproducible approach for study-specific data quality testing to the analysis plan for PRESERVE, a 15-site, EHR-based observational study of chronic kidney disease in children. This approach integrated widely adopted data quality concepts with healthcare-specific evaluation methods. We implemented two rounds of data quality assessment. The first produced high-level evaluation using aggregate results from a distributed query, focused on cohort identification and main analytic requirements. The second focused on extended testing of row-level data centralized for analysis. We systematized reporting and cataloguing of data quality issues, providing institutional teams with prioritized issues for resolution. We tracked improvements and documented anomalous data for consideration during analyses. The checks we developed identified 115 and 157 data quality issues in the two rounds, involving completeness, data model conformance, cross-variable concordance, consistency, and plausibility, extending traditional data quality approaches to address more complex stratification and temporal patterns. Resolution efforts focused on higher priority issues, given finite study resources. In many cases, institutional teams were able to correct data extraction errors or obtain additional data, avoiding exclusion of 2 institutions entirely and resolving 123 other gaps. Other results identified complexities in measures of kidney function, bearing on the study’s outcome definition. Where limitations such as these are intrinsic to clinical data, the study team must account for them in conducting analyses. This study rigorously evaluated fitness of data for intended use. The framework is reusable and built on a strong theoretical underpinning. Significant data quality issues that would have otherwise delayed analyses or made data unusable were addressed. This study highlights the need for teams combining subject-matter and informatics expertise to address data quality when working with real world data.

There is a duality of trust in participatory science (citizen science) projects in which the data produced by volunteers must be trusted by the scientific community and participants must trust the scientists who lead projects. Facilitator organizations can diversify recruitment and broaden learning outcomes. We investigated the degree to which they can broker trust in participatory science projects. In Crowd the Tap, we recruited participants through partnerships with facilitators, including high schools, faith communities, universities, and a corporate volunteer program. We compared data quality (a proxy for scientists’ trust in the project) and participant privacy preferences (a proxy for participants’ trust in the project leaders) across the various facilitators as well as to those who came to the project independently (unfacilitated). In general, we found that data quality differed based on the project’s level of investment in the facilitation partner in terms of both time and money..., The data was collected through an IRB approved survey in which Crowd the Tap participants submitted data on the types of pipes they had, the age of their home, water aesthetics, and demographic information. As part of this process, participants also indicated if they came to the project through a partner organization (what we call facilitator organizations). Using the data available to us, we determined how completely, accurately, and informatively (understandability) they participated in the project to assess data quality. We also asked if they had interest in being publically associated with the project or if they referred to remain private. We used this and the number of times they selected "Prefer not to say" as indicators of privacy. We compared data quality and privacy preferences to the facilitator organization through which they came to the project. , , # Data from: The dual nature of trust in participatory science: An investigation into data quality and household privacy preferences

The dataset contains data on participation in Crowd the Tap, a large-scale participatory science (citizen science) project focused on identifying and addressing lead contamination in household drinking water. The project crowdsources information on plumbing materials, age of home, water aesthetics, and demographic data to learn more about the geographic spread of lead plumbing and social and environmental correlates to lead plumbing. We investigated how data quality (completeness, accuracy, and understandability) and participant privacy (whether or not they select to be public or private, the number times they select “prefer not to say†) preferences differed by facilitators. Data quality relates to scientists’ trust in the project, and privacy relates to the trust that participants have in the project leadership team. As participatory science projects inc...

Additional File 2. Demonstration of Equivalent deaths method (GBD standard life table) in Brazilian states, 2015.

Comprehensive dataset of 6,132 Organic food stores in 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.

Comprehensive dataset of 6 Accounting schools in Utah, United States as of August, 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.

Comprehensive dataset of 8 Sports in Maryland, 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.

Comprehensive dataset of 248 Information services in Massachusetts, United States as of August, 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.

Comparison of physician and CHT attributes affecting the completeness, accuracy and bias of data collected by each method for history-taking.

Antenatal care (ANC) is the care given to pregnant by qualified medical experts in order to guarantee the optimal health conditions for the mother and the unborn child during pregnancy. Four or fewer antenatal care (ANC) visits are strongly linked to maternal and perinatal death. Because of this, the World Health Organization created a new model known as minimum of eight antenatal care (ANC8+) contact. This study aims to focus on the current antenatal care contact which not previously addressed. Therefore, the aim of this to investigate time to first antenatal care contact and its predictors among pregnant women at Bishoftu General Hospital 2023/24Methods: An institutional-based cross-sectional study design was conducted among 347 study participants which was selected by systematic random sampling method. The data was collected using pretested, structured questionnaires. Data was entered into Epi Data version 4.6 and analyzed using STATA 15. Descriptive summary statistics like median survival time, Kaplan Meier survival curve, and Log-rank test were computed. Bivariate and multivariable Weibull regresion models were fitted to identify the time to first antenatal care contact and predictors. A hazard ratio with a 95% confidence interval was calculated and p-values < 0.05 were considered statistically significantEthical approval and informed consentEthical clearance was obtained from an institutional Research Ethics Review Board (IRB) of the University of Arsi University (with Reference number, A/CHS/18/2023). In addition, a letter of ethical approval was sent to Bishoftu General Hospital to be obtained from the hospital’s administrators. Informed, voluntary, and verbal were obtained from the head of the hospital and mothers. There are no study participants under the age of 18 years. Before conducting the interviews, information was given to the participants, and were assured of voluntary participation, confidentiality, and freedom to withdraw from the study at any time. The nature and significance of the study were explained to the participantsData collection tool and proceduresTo ensure the quality of data at the beginning, a data collection questionnaire was pre-tested on 5% of the calculated sample size at Chelelaka Health Center and necessary modifications will be made based on gaps identified in the questionnaire. Any error found during the process of checking will be corrected and modifications will be made to the final version of the data abstraction format. Training will be given to data collectors and supervisors for 01 days before the actual data collection task on the already existing records, half-day theoretical and half-day practical training. Data quality will be controlled by designing the proper data collection materials, through continuous supervision. All completed data collection forms will be examined for completeness and consistency during data management, storage, cleaning, and analysis. The data will be entered and cleaned by the principal investigator before analysis. Midwives, who are working in the maternity ward, will collect the data. The principal investigator of the study will control the overall activity.

Comprehensive dataset of 528 Study at home schools in 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.

Comprehensive dataset of 29,276 Mattress stores in 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.

Comprehensive dataset of 7,475 Athletic fields in United States as of August, 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.

Comprehensive dataset of 3 Data recovery services in Tula Oblast, Russia 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.

Comprehensive dataset of 2 Data recovery services in Bitlis, Turkey as of August, 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.