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.

As part of the Mayor's Social Integration Strategy published in March 2018, one of the commitments was to develop a more comprehensive set of measures for social integration and to carry out bespoke and innovative data collection for London to achieve this. This scoping study conducted by the Centre for Analysis of Social Media at Demos and commissioned by the GLA is the first step towards looking at more innovative data collection methods. The report highlights the potential opportunities and pitfalls in using digital and online data in measuring social integration in London, identifies a diverse selection of sources available and provides an outline of potential use cases for this data across the breadth of social integration measures. A handy one-page matrix of all of the evaluated sources is also available to download separately.

Data set pertaining to the article "How to measure work functions from aqueous solutions", https://doi.org/10.1039/D3SC01740K (Chemical Science 14, 9574-9588 (2023)). A new protocol for energy referencing of photoemission data from liquids (https://doi.org/10.1039/D1SC01908B, Chemical Science 12, 10558-10582 (2021)) is refined towards determining work functions from liquids.

Files with extension .h5 are hdf5-files structured according to the NeXus v2020.10 standard using the NXmpes user contributed format suggested by the Fairmat consortium, seehttps://www.nexusformat.org/https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.htmlA few extensions specific to liquid jet-experiments were added to the standard, and are explained in the notes-group on the top level of each file.NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:* nexpy (distributed with python)* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)

In each NeXus file-entry, two types of spectra are included:1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').2. As-measured data ('raw').

Files with extension .txt are tab-separated ascii-files.

The following files are provided:

Photoemission data pertaining to solute measurements and reference measurements using a gold wire:'Figure 3.h5''Figure 4.h5''Figure S1.h5''Figure S2.h5'Kinetic energies are presented as measured. The scale offset of our spectrometer, determined as E_kin(corrected) = E_kin(measured) + 0.224 eV for data sets 'Figure 3.h5', 'Figure 4.h5' ,'Figure S2.h5', has not been taken into account.

Numeric representations of the analysis results shown in the article's figures in graphical form:'Figure 5.txt''Figure 6B.txt''Figure 7.txt''Figure S4B.txt''Figure S5.txt'

In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .

The EPA Control Measure Dataset is a collection of documents describing air pollution control available to regulated facilities for the control and abatement of air pollution emissions from a range of regulated source types, whether directly through the use of technical measures, or indirectly through economic or other measures.

Although it is recommended that the Tableau online dashboards be the primary method of access to Measure Up! LOCUS, the underlying data is available for download. Submit a request for access via the contact page.

As per data from a recent report, in 2019, ** percent of respondents claimed that overall performance or utilization was their primary method of measuring success in relation to data center infrastructure, whilst a return on investment (ROI) was cited by ** percent. These measures do not have a focus on reducing energy usage or lowering the environmental impact - measurements like total cost to the environment (TCE) or IT assets lifecycle, which would contribute toward this, were cited less often by respondents. Just ** percent stated IT asset lifecycles as being a method of measuring the success of data center infrastructure.

Data/code files for the following project: I study the viability of Twitter-based measures for measuring public attitudes about the police. I find that Twitter-based measures track Gallup's measure of public attitudes starting around 2014, when Twitter user base stabilized, but not before 2014. Increases in Black Lives Matter protests are also associated with increases in negative sentiment measures from Twitter. The findings suggest that Twitter-based measures can be used to acquire granular evaluations of police performance, but they can be more useful in analyzing panel data of multiple agencies over time than in tracking a single geographical area over time.

During a global October 2023 survey among communications specialists, almost **** out of five (or ** percent) of respondents said they used engagement data such as comments, views, shares and likes, to measure the individual success of each influencer marketing campaign. Around ** percent of interviewees mentioned product sales, while ** turned to impressions as a metric for success rate.

This article provides the necessary tools to advance comparative research studying the substantive representation of ethnic minorities and women. Firstly, I clarify how the various indicators for individual representatives’ and parliaments’ considerateness of the interests of traditionally excluded groups used in earlier (mostly single-country) studies relate to each other and discuss the advantages and drawbacks of different measures for quantitative comparative research. Secondly, the present article introduces new data comprising three indicators for the substantive representation of ethnic minorities.

The dataset contains all the current and historical performance measures submitted as part of ONC;s annual budget formulation process. These measures track agency priorities for electronic health record adoption, health information exchange, patient engagement, and privacy and security. Each measure contains the annual estimate and a measure target, if applicable, for all the years the measure was reported in the ONC Budget.

The California System Performance Measures (CA SPMs) are a series of metrics developed by the California Interagency Council on Homelessness (Cal ICH), pursuant to Health and Safety Code §50220.7, that help the state and local jurisdictions assess their progress toward preventing, reducing, and ending homelessness. All measures except for Measure 1b are generated using data from the state’s Homelessness Data Integration System. Measure 1b and Point in Time (PIT) Count data are sourced from each Continuum of Care’s PIT Count. Measure 1b and PIT Count data are not shown for 2021 because of irregularities in that year’s counts. Measure 3 is not shown for the most recent period (period from 4/1/25 - 3/31/25) due to data discrepancies.

For more information about the measures and how they are calculated, please see the California System Performance Measures Guide and Glossary: https://www.bcsh.ca.gov/calich/documents/california_system_performance_measures_guide.pdf

For more information about Measure 1b and PIT Count data, please see the Department of Housing and Urban Development’s website: https://www.hudexchange.info/programs/hdx/pit-hic.

Replication data for How to Measure a Constitutional Moment http://www.yalelawjournal.org/the-yale-law-journal/note/how-do-you-measure-a-constitutional-moment?-using-algorithmic-topic-modeling-to-evaluate--bruce-ackerman%E2%80%99s-theory-of-constitutional-change/

Physical Media (Oceanography and meteorology) datasets focused on the port area served through OPeNDAP. Currently available Measure Data from Radares located in different coastal areas, and Data of Sea Level Models, Circulation (Regional, Coastal and Port) and Swell (Great Scale, Regional, Coastal and Port).

BackgroundNational Health Systems managers have been subject in recent years to considerable pressure to increase concentration and allow mergers. This pressure has been justified by a belief that larger hospitals lead to lower average costs and better clinical outcomes through the exploitation of economies of scale. In this context, the opportunity to measure scale efficiency is crucial to address the question of optimal productive size and to manage a fair allocation of resources.Methods and findingsThis paper analyses the stance of existing research on scale efficiency and optimal size of the hospital sector. We performed a systematic search of 45 past years (1969–2014) of research published in peer-reviewed scientific journals recorded by the Social Sciences Citation Index concerning this topic. We classified articles by the journal’s category, research topic, hospital setting, method and primary data analysis technique. Results showed that most of the studies were focussed on the analysis of technical and scale efficiency or on input / output ratio using Data Envelopment Analysis. We also find increasing interest concerning the effect of possible changes in hospital size on quality of care.ConclusionsStudies analysed in this review showed that economies of scale are present for merging hospitals. Results supported the current policy of expanding larger hospitals and restructuring/closing smaller hospitals. In terms of beds, studies reported consistent evidence of economies of scale for hospitals with 200–300 beds. Diseconomies of scale can be expected to occur below 200 beds and above 600 beds.

This service displays data from 2021 and 2024 for the Federal Performance Measures (FPM) reported to the Federal Highway Administration (FHWA). These measures support performance-based planning and programming, and are updated annually as part of Federal Performance Reporting (FPR). Data are updated annually.This data is used in the Federal Performance Measures Story Map.

Learn how to measure dark social traffic in Google Analytics, which drives 40% of blog visitors. Essential tracking insights for startup founders and content marketers.

Europe has the highest rate of some data governance measures taken regarding AI in 2024. Latin America had the least number of measures against data governance, with nearly ** percent of respondents saying no measures were taken.

American Community Survey Public Use Micro Sample, augmented by NYC Opportunity.

This zip file contains data files for 3 activities described in the accompanying PPT slides 1. an excel spreadsheet for analysing gain scores in a 2 group, 2 times data array. this activity requires access to –https://campbellcollaboration.org/research-resources/effect-size-calculator.html to calculate effect size.2. an AMOS path model and SPSS data set for an autoregressive, bivariate path model with cross-lagging. This activity is related to the following article: Brown, G. T. L., & Marshall, J. C. (2012). The impact of training students how to write introductions for academic essays: An exploratory, longitudinal study. Assessment & Evaluation in Higher Education, 37(6), 653-670. doi:10.1080/02602938.2011.5632773. an AMOS latent curve model and SPSS data set for a 3-time latent factor model with an interaction mixed model that uses GPA as a predictor of the LCM start and slope or change factors. This activity makes use of data reported previously and a published data analysis case: Peterson, E. R., Brown, G. T. L., & Jun, M. C. (2015). Achievement emotions in higher education: A diary study exploring emotions across an assessment event. Contemporary Educational Psychology, 42, 82-96. doi:10.1016/j.cedpsych.2015.05.002andBrown, G. T. L., & Peterson, E. R. (2018). Evaluating repeated diary study responses: Latent curve modeling. In SAGE Research Methods Cases Part 2. Retrieved from http://methods.sagepub.com/case/evaluating-repeated-diary-study-responses-latent-curve-modeling doi:10.4135/9781526431592

October 31, 2025 (Final DWR Data)

The 2018 Legislation required DWR to provide or otherwise identify data regarding the unique local conditions to support the calculation of an urban water use objective (CWC 10609. (b)(2) (C)). The urban water use objective (UWUO) is an estimate of aggregate efficient water use for the previous year based on adopted water use efficiency standards and local service area characteristics for that year.

UWUO is calculated as the sum of efficient indoor residential water use, efficient outdoor residential water use, efficient outdoor irrigation of landscape areas with dedicated irrigation meter for Commercial, Industrial, and Institutional (CII) water use, efficient water losses, and an estimated water use in accordance with variances, as appropriate. Details of urban water use objective calculations can be obtained from DWR’s Recommendations for Guidelines and Methodologies document (Recommendations for Guidelines and Methodologies for Calculating Urban Water Use Objective - https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/Performance-Measures/UWUO_GM_WUES-DWR-2021-01B_COMPLETE.pdf).

The datasets provided in the links below enable urban retail water suppliers calculate efficient outdoor water uses (both residential and CII), agricultural variances, variances for significant uses of water for dust control for horse corals, and temporary provisions for water use for existing pools (as stated in Water Boards’ draft regulation). DWR will provide technical assistance for estimating the remaining UWUO components, as needed. Data for calculating outdoor water uses include:

• Reference evapotranspiration (ETo) – ETo is evaporation plant and soil surface plus transpiration through the leaves of standardized grass surfaces over which weather stations stand. Standardization of the surfaces is required because evapotranspiration (ET) depends on combinations of several factors, making it impractical to take measurements under all sets of conditions. Plant factors, known as crop coefficients (Kc) or landscape coefficients (KL), are used to convert ETo to actual water use by specific crop/plant. The ETo data that DWR provides to urban retail water suppliers for urban water use objective calculation purposes is derived from the California Irrigation Management Information System (CIMIS) program (https://cimis.water.ca.gov/). CIMIS is a network of over 150 automated weather stations throughout the state that measure weather data that are used to estimate ETo. CIMIS also provides daily maps of ETo at 2-km grid using the Spatial CIMIS modeling approach that couples satellite data with point measurements. The ETo data provided below for each urban retail water supplier is an area weighted average value from the Spatial CIMIS ETo.

• Effective precipitation (Peff) - Peff is the portion of total precipitation which becomes available for plant growth. Peff is affected by soil type, slope, land cover type, and intensity and duration of rainfall. DWR is using a soil water balance model, known as Cal-SIMETAW, to estimate daily Peff at 4-km grid and an area weighted average value is calculated at the service area level. Cal-SIMETAW is a model that was developed by UC Davis and DWR and it is widely used to quantify agricultural, and to some extent urban, water uses for the publication of DWR’s Water Plan Update. Peff from Cal-SIMETAW is capped at 25% of total precipitation to account for potential uncertainties in its estimation. Daily Peff at each grid point is aggregated to produce weighted average annual or seasonal Peff at the service area level. The total precipitation that Cal-SIMETAW uses to estimate Peff comes from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), which is a climate mapping model developed by the PRISM Climate Group at Oregon State University.

• Residential Landscape Area Measurement (LAM) – The 2018 Legislation required DWR to provide each urban retail water supplier with data regarding the area of residential irrigable lands in a manner that can reasonably be applied to the standards (CWC 10609.6.(b)). DWR delivered the LAM data to all retail water suppliers, and a tabular summary of selected data types will be provided here. The data summary that is provided in this file contains irrigable-irrigated (II), irrigable-not-irrigated (INI), and not irrigable (NI) irrigation status classes, as well as horse corral areas (HCL_area), agricultural areas (Ag_area), and pool areas (Pool_area) for all retail suppliers.