The experiment here was to demonstrate that we can reliably measure the Reference Waveforms designed in the IEEE P1765 proposed standard and calculate EVM along with the associated uncertainties. The measurements were performed using NIST's calibrated sampling oscilloscope and were traceable to the primary standards.We have uploaded the following two datasets. (1) Table 3 contains the EVM values (in %) for the Reference Waveforms 1--7 after performing the uncertainty analyses. The Monte Carlo means are also compared with the ideal values from the calculations in the IEEE P1765 standard.(2) Figure 3 shows the complete EVM distribution upon performing uncertainty analysis for Reference Waveform 3 as an example. Each of the entries in Table 3 is associated with an EVM distribution similar to that shown in Fig. 3.

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.

ABSTRACT The ultimate goal of the present work lay in creating a vector methodology for measuring QoL. Application of an integrated approach to the results of the classification analysis and SWOT analysis enabled elaborating a vector methodology of a recommendatory type aimed at improving QoL measurement approaches. It was established that this methodology should include four major updates taking into account the challenges of tomorrow. The study results may be of interest to public authorities responsible for taking measures directed at raising the country’s international ranking as well as be used for reducing contradictions on the part of QoL measuring procedures.

Percentage of participants who respond to an on-site survey reporting that the activity that they just witnessed contributed to their overall knowledge and understanding of world cultures, world history and/or arts of every discipline.

This dataset includes soil wet aggregate stability measurements from the Upper Mississippi River Basin LTAR site in Ames, Iowa. Samples were collected in 2021 from this long-term tillage and cover crop trial in a corn-based agroecosystem. We measured wet aggregate stability using digital photography to quantify disintegration (slaking) of submerged aggregates over time, similar to the technique described by Fajardo et al. (2016) and Rieke et al. (2021). However, we adapted the technique to larger sample numbers by using a multi-well tray to submerge 20-36 aggregates simultaneously. We used this approach to measure slaking index of 160 soil samples (2120 aggregates). This dataset includes slaking index calculated for each aggregates, and also summarized by samples. There were usually 10-12 aggregates measured per sample. We focused primarily on methodological issues, assessing the statistical power of slaking index, needed replication, sensitivity to cultural practices, and sensitivity to sample collection date. We found that small numbers of highly unstable aggregates lead to skewed distributions for slaking index. We concluded at least 20 aggregates per sample were preferred to provide confidence in measurement precision. However, the experiment had high statistical power with only 10-12 replicates per sample. Slaking index was not sensitive to the initial size of dry aggregates (3 to 10 mm diameter); therefore, pre-sieving soils was not necessary. The field trial showed greater aggregate stability under no-till than chisel plow practice, and changing stability over a growing season. These results will be useful to researchers and agricultural practitioners who want a simple, fast, low-cost method for measuring wet aggregate stability on many samples.

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.

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.

Performance measures are data metrics defined and tracked by city departments to measure the city government’s effectiveness and efficiency of service delivery. Data for the performance measures are derived from department data tracking systems. Each performance measure is connected to one of the strategic goals and objectives that the City has defined as a high priority. The performance measures will be reviewed and refined annually to ensure they are representative of the priorities set out by City Council and the community.

In 2024, ** percent of respondents in a global survey had implemented multifactor authentication as their main data protection measure, both in the cloud and on-premises. Furthermore, over ** percent of respondents stated that their company had already implemented backups in the cloud.

Transparency is often viewed as crucial to government accountability, but its measurement remains elusive. This concept encompasses many dimensions, which have distinct effects. In this paper, we focus on a specific dimension of transparency: governments' collection and dissemination of aggregate data. We construct a measure of this aspect of transparency, using an item response model that treats transparency as a latent predictor of the reporting of data to the World Bank's World Development Indicators. The resultant index covers 125 countries from 1980-2010. Unlike some alternatives (e.g., Freedom House), our measure -- the HRV Index -- is based on objective criteria rather than subjective expert judgments. Unlike newspaper circulation numbers, HRV reflects the dissemination of credible content -- in that it has survived the World Bank's quality control assessment. In a validation exercise, we find that our measure outperforms newspaper circulation as a predictor of Law and Order and Bureaucratic Quality as measured by the ICRG, particularly in autocracies. It performs as well as newspaper circulation in predicting Corruption. These findings suggest that data dissemination is a distinct, and politically relevant, form of transparency.

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.

As part of a larger study examining stream conditions and the effect of Best Management Practices in the Chesapeake Bay watershed, thirty small streams on the Delmarva Peninsula were instrumented and monitored for gage height (water level), water temperature, and air temperature using Onset HOBO sensors from March to September 2022. In addition, two discrete discharge measurements were made at baseflow at each site. This data release contains four .csv files with time-series for gage height, water temperature, and air temperature for all thirty monitoring locations and a table of discrete discharge measurements and associated field measurement metadata: Delmarva_2022_Continuous_Air_Temperature.csv Delmarva_2022_Continuous_Gage_Height.csv Delmarva_2022_Continuous_Water_Temperature.csv Delmarva_2022_Discharge_Measurements.csv

This CODMAC level 3 data set contains the key parameters of the Inertial Measurement Package. In particular, it provides information on the gyroscope attitude measurements on a global scale and individual. It covers the period from launch in 2004, through the 3 Earth and 1 Mars flyby, plus the hibernation phases, plus the asteroid flybys and finally covers the Prelanding, comet escort & Extension phases of the prime target of the mission. The prime target is comet 67P/Churyumov-Gerasimenko 1 (1969 R1). This version V1.0 is the first version of this dataset.

Description

This is a public release of the dataset described in Kennedy et al. (2020), consisting of 39,565 comments annotated by 7,912 annotators, for 135,556 combined rows. The primary outcome variable is the "hate speech score" but the 10 constituent labels can also be treated as outcomes.

The original paper can be found here: Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application

Original dataset link at HuggingFace: https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech

Acknowledgemen to the original work:

@article{kennedy2020constructing, title={Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application}, author={Kennedy, Chris J and Bacon, Geoff and Sahn, Alexander and von Vacano, Claudia}, journal={arXiv preprint arXiv:2009.10277}, year={2020} }

The Intercantonal Measurement and Information System (IMIS) consists of nearly 200 automatic measuring stations. They are distributed throughout the Swiss Alps and the Jura region and, in most cases, are situated above the tree line, most frequently between 2000 and 3000 m. The stations record the conditions around the clock, in general every 30 minutes. Most IMIS stations are located in the vicinity of starting zones of potentially destructive avalanches, and provide essential information to local safety officers for public safety in settlements and on the roads. They are also used for snow-hydrological and research purposes and by the avalanche warning service of the SLF. This dataset comprises data from IMIS snow and wind stations. The snow and wind stations are usually situated close to each other and measure the key weather data required for assessing the avalanche danger.

IMIS snow stations Snow stations are located on wind-protected flat terrain. The snowpack model SNOWPACK calculates the layers and properties of the snowpack throughout the winter for each of the IMIS snow stations. The following variables are measured or simulated in the standard programme of IMIS snow stations and are available in this dataset: - Snow depth - 24-hour new snow (SNOWPACK simulation) - Air and surface temperature - Wind speed and direction - Relative humidity - Reflected shortwave radiation - Ground temperature - Snow temperature 25 cm, 50 cm and 100 cm above the ground IMIS wind stations Wind stations are generally situated at higher altitudes on exposed summits or ridges. The following variables are measured in the standard programme of IMIS wind stations and are available in this dataset: - Wind speed and direction - Air temperature - Relative humidity

When using the data, please consider and adhere to the associated Terms of Use. To download live data use our API. To download data older than 7 days use our File Download.

We collected grapevine shoot growth over a growing season of 2024 (April to June) in a vineyard of the horticultural unit 2 farm of the Ohio State University (40.73866822022149, -81.90273359323078). The measurements were made with a measuring tape.

Public health-related decision-making on policies aimed at controlling the COVID-19 pandemic outbreak depends on complex epidemiological models that are compelled to be robust and use all relevant available data. This data article provides a new combined worldwide COVID-19 dataset obtained from official data sources with improved systematic measurement errors and a dedicated dashboard for online data visualization and summary. The dataset adds new measures and attributes to the normal attributes of official data sources, such as daily mortality, and fatality rates. We used comparative statistical analysis to evaluate the measurement errors of COVID-19 official data collections from the Chinese Center for Disease Control and Prevention (Chinese CDC), World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC). The data is collected by using text mining techniques and reviewing pdf reports, metadata, and reference data. The combined dataset includes complete spatial data such as countries area, international number of countries, Alpha-2 code, Alpha-3 code, latitude, longitude, and some additional attributes such as population. The improved dataset benefits from major corrections on the referenced data sets and official reports such as adjustments in the reporting dates, which suffered from a one to two days lag, removing negative values, detecting unreasonable changes in historical data in new reports and corrections on systematic measurement errors, which have been increasing as the pandemic outbreak spreads and more countries contribute data for the official repositories. Additionally, the root mean square error of attributes in the paired comparison of datasets was used to identify the main data problems. The data for China is presented separately and in more detail, and it has been extracted from the attached reports available on the main page of the CCDC website. This dataset is a comprehensive and reliable source of worldwide COVID-19 data that can be used in epidemiological models assessing the magnitude and timeline for confirmed cases, long-term predictions of deaths or hospital utilization, the effects of quarantine, stay-at-home orders and other social distancing measures, the pandemic’s turning point or in economic and social impact analysis, helping to inform national and local authorities on how to implement an adaptive response approach to re-opening the economy, re-open schools, alleviate business and social distancing restrictions, design economic programs or allow sports events to resume.

This dataset represents the total number of Open Data Portal assets and the frequency of how often the asset is accessed. This data is collected by using Socrata Analytics. This dataset supports measure GTW.G.4 of SD23. Data Source: Socrata. Calculations: (GTW.G.4) Percentage of datasets published in the Open Data portal that are being accessed frequently (such as through a website views, API interactions, embeds or mobile views). Measure Time Period: Fiscal Year Annually Automated: No Date of Last description update: 4/1/2020 For questions please contact CTMCollaborationServices@austintexas.gov

This document is the Standard Area Measurements (2025) User Guide. It provides information regarding the Standard Area Measurements (SAM) products including: types of measurement; data tolerance, accuracy and currency; guidance on the use of measurements for statistical purposes; and conditions of use. (File Size - 408 KB)