A combination of discrete and daily-aligned groundwater levels for the Mississippi River Valley alluvial aquifer clipped to the Mississippi Alluvial Plain, as defined by Painter and Westerman (2018), with corresponding metadata are based on processing of U.S. Geological Survey National Water Information System (NWIS) (U.S. Geological Survey, 2020) data. The processing was made after retrieval using aggregation and filtering through the infoGW2visGWDB software (Asquith and Seanor, 2019). The nomenclature GWmaster mimics that of the output from infoGW2visGWDB. Two separate data retrievals for NWIS were made. First, the discrete data were retrieved, and second, continuous records from recorder sites with daily-mean or other daily statistics codes were retrieved. Each dataset was separately passed through the infoGW2visGWDB software to create a "GWmaster discrete" and "GWmaster continuous" and these tables were combined and then sorted on the site identifier and date to form the data products described herein. A sweep through the combined dataset (the "database") was made to isolate duplicate observations, or observations for the same well and on the same day. If a discrete value was present, it was retained as authoritative for the day and in descending order of priority daily-mean, daily-maximum, and daily minimum. Therefore, only a single record for a well and day are present in the dataset. The duplicate search removed 876 records and 31 wells were involved; in total, this is about 0.3 percent of the database. References: Asquith, W.H., Seanor, R.C., 2019, infoGW2visGWDB—An R groundwater data-processing utility for manipulating, checking the veracity, and converting an "infoGW" object to the "GWmaster" object for the visGWDB software with demonstration for the Mississippi River Valley alluvial aquifer: U.S. Geological Survey software release, Reston, Va., https://doi.org/10.5066/P9MK0B6L. Painter, J.A., and Westerman, D.A., 2018. Mississippi Alluvial Plain extent, November 2017: U.S. Geological Survey data release, https://doi.org/10.5066/F70R9NMJ. U.S. Geological Survey, 2020, USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed April 2, 2020, at https://doi.org/10.5066/F7P55KJN.

The Discrete Semiconductor Devices market plays a crucial role in the electronics landscape, serving as the backbone for a variety of applications across multiple industries, including consumer electronics, automotive, telecommunications, and industrial equipment. These devices, which include diodes, transistors, an

This paper demonstrates the flexibility of a general approach for the analysis of discrete time competing risks data that can accommodate complex data structures, different time scales for different causes, and nonstandard sampling schemes. The data may involve a single data source where all individuals contribute to analyses of both cause-specific hazard functions, overlapping datasets where some individuals contribute to the analysis of the cause-specific hazard function of only one cause while other individuals contribute to analyses of both cause-specific hazard functions, or separate data sources where each individual contributes to the analysis of the cause-specific hazard function of only a single cause. The approach is modularized into estimation and prediction. For the estimation step, the parameters and the variance-covariance matrix can be estimated using widely available software. The prediction step utilizes a generic program with plug-in estimates from the estimation step. The approach is illustrated with three prognostic models for stage IV male oral cancer using different data structures. The first model uses only men with stage IV oral cancer from population-based registry data. The second model strategically extends the cohort to improve the efficiency of the estimates. The third model improves the accuracy for those with a lower risk of other causes of death, by bringing in an independent data source collected under a complex sampling design with additional other-cause covariates. These analyses represent novel extensions of existing methodology, broadly applicable for the development of prognostic models capturing both the cancer and non-cancer aspects of a patient's health.

Statistics Canada has developed a Web Data Service that provides access to data and metadata that we release each business day. This is a good option for users who want to consume a discrete amount of data points updates to Statistics Canada data. To obtain information on how to use and consume our Web Data Service, please read the Web Data Service User Guide.

China Semiconductor Discrete Device: YoY: Total Liability data was reported at 6.583 % in Oct 2015. This records a decrease from the previous number of 6.602 % for Sep 2015. China Semiconductor Discrete Device: YoY: Total Liability data is updated monthly, averaging 9.075 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 39.911 % in Sep 2011 and a record low of -2.957 % in Mar 2015. China Semiconductor Discrete Device: YoY: Total Liability data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.

Author affiliation: York University Unreviewed Faculty Non UBC

A DGGS is a type of spatial reference system that partitions the globe into many individual, evenly spaced, and well-aligned cells to encode location. We calculated normalized area and compactness of cell geometries for 5 open-source DGGS implementations - Uber H3, Google S2, RiskAware OpenEAGGR, rHEALPix by Landcare Research New Zealand, and DGGRID by Southern Oregon University - to evaluate their suitability for a global-level statistical data cube.

This repository contains all generated data and statistics.

• EAGGR doesn't seem to have a predefined logic of hierarchical cell resolutions for ISEA3H
• EAGGR doesn't seem to have a region filling algorithm available, neither for ISEA4T nor ISEA3H
• rHEALPix is pure Python (with Numpy/Scipy support), but cell generation/conversion is slower than the other C/C++ based implementations
• DGGRID is a commandline tool and can predominantly only be used to generate a grid and fill with sampling data, the Python API is only a wrapper

The Discrete and Power Devices market plays a pivotal role in the modern electronics landscape, encompassing a wide range of components used to manage and control electrical power. These devices, which include transistors, diodes, rectifiers, and thyristors, serve essential functions in various applications, such as

There is a growing appreciation of the multiple social and nonsocial factors influencing the foraging behavior of social animals, but little understanding of how these factors depend on habitat characteristics or individual traits. This partly reflects the difficulties inherent in using conventional statistical techniques to analyze multi-factor, multi-context foraging decisions. Discrete choice models provide a way to do so, and we demonstrate this by using them to investigate patch preference in a wild population of social foragers (chacma baboons, Papio ursinus). Data were collected from 29 adults across two social groups encompassing 683 foraging decisions over a six-month period, and the results interpreted using an information theoretic approach. Baboon foraging decisions were influenced by multiple nonsocial and social factors, and were often contingent on the characteristics of the habitat or individual. Differences in decision-making between habitats were consistent with changes in interference competition costs but not changes in social foraging benefits. Individual differences in decision-making were suggestive of a trade-off between dominance rank and social capital. Our findings emphasize that taking a multi-factor, multi-context approach is important to fully understand animal decision-making. We also demonstrate how discrete choice models can be used to achieve this.

Monthly rollup of the discrete and daily-aligned groundwater levels were created from Robinson, Asquith, and Seanor (2020) data products with removal of the paired groundwater and surface-water sites listed by Robinson, Killian, and Asquith (2020). The monthly rollup is composed of (1) computed monthly "mean" values regardless of whether a well had one measurement in the month or up to about 30 days of daily-mean values, (2) standard deviation of the water levels within the month (sample size is generally just one day but for recorder sites could be up to about 30 days), (3) the last water level in the month, and (4) monthly counts of water levels. The algorithm is available within the sources of visGWDBmrva (Asquith and others, 2019). A comment is made that the string 1980-01-01_2019-12-31 is retained in the file naming to parallel that for Robinson, Asquith, and Seanor (2020) files although the day of the month has no meaning for a monthly rollup. There are 18,736 unique wells of statistics; 18,736 wells in the metadata; and 107,568 year-month entries in the monthly rollup product. References: Asquith, W.H., Seanor, R.C., McGuire, V.L. (contributor), and Kress, W.H. (contributor), 2019, Source code in R to quality assure, plot, summarize, interpolate, and extend groundwater-level information, visGWDB—Groundwater-level informatics with demonstration for the Mississippi River Valley alluvial aquifer: U.S. Geological Survey software release, Reston, Va., https://doi.org/10.5066/P9W004O6.

China Semiconductor Discrete Device: Asset Contribution Ratio: Year to Date data was reported at 7.309 % in Oct 2015. This records an increase from the previous number of 7.149 % for Sep 2015. China Semiconductor Discrete Device: Asset Contribution Ratio: Year to Date data is updated monthly, averaging 5.358 % from Dec 2006 (Median) to Oct 2015, with 83 observations. The data reached an all-time high of 10.476 % in Nov 2008 and a record low of 0.404 % in Feb 2009. China Semiconductor Discrete Device: Asset Contribution Ratio: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.

China Semiconductor Discrete Device: Current Asset data was reported at 47.833 RMB bn in Oct 2015. This records a decrease from the previous number of 52.456 RMB bn for Sep 2015. China Semiconductor Discrete Device: Current Asset data is updated monthly, averaging 37.896 RMB bn from Dec 1998 (Median) to Oct 2015, with 102 observations. The data reached an all-time high of 52.456 RMB bn in Sep 2015 and a record low of 5.788 RMB bn in Dec 1998. China Semiconductor Discrete Device: Current Asset data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.

China Semiconductor Discrete Device: YoY: Total Asset data was reported at 4.633 % in Oct 2015. This records a decrease from the previous number of 5.007 % for Sep 2015. China Semiconductor Discrete Device: YoY: Total Asset data is updated monthly, averaging 12.110 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 28.800 % in Nov 2009 and a record low of 3.469 % in Apr 2014. China Semiconductor Discrete Device: YoY: Total Asset data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.

The Hype Cycle for Discrete Manufacturing and Product Lifecycle Management (PLM) serves as a pivotal framework for understanding the evolving landscape of technologies and innovations within the manufacturing sector. This cycle illustrates the maturation of technologies from their initial inception to widespread ado

China Semiconductor Discrete Device: Current Asset Turnover Ratio data was reported at 1.912 Times in Oct 2015. This records an increase from the previous number of 1.805 Times for Sep 2015. China Semiconductor Discrete Device: Current Asset Turnover Ratio data is updated monthly, averaging 1.651 Times from Dec 2006 (Median) to Oct 2015, with 83 observations. The data reached an all-time high of 2.036 Times in Nov 2010 and a record low of 0.219 Times in Feb 2009. China Semiconductor Discrete Device: Current Asset Turnover Ratio data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.

China Semiconductor Discrete Device: YoY: Sales Revenue: Year to Date data was reported at 10.182 % in Oct 2015. This records an increase from the previous number of 8.846 % for Sep 2015. China Semiconductor Discrete Device: YoY: Sales Revenue: Year to Date data is updated monthly, averaging 8.957 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 57.680 % in Feb 2010 and a record low of -32.270 % in Feb 2009. China Semiconductor Discrete Device: YoY: Sales Revenue: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.

China Semiconductor Discrete Device: YoY: Total Tax: Year to Date data was reported at 4.596 % in Oct 2015. This records a decrease from the previous number of 11.609 % for Sep 2015. China Semiconductor Discrete Device: YoY: Total Tax: Year to Date data is updated monthly, averaging 16.783 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 100.660 % in Aug 2008 and a record low of -31.070 % in Feb 2009. China Semiconductor Discrete Device: YoY: Total Tax: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.

Experimental data can broadly be divided in discrete or continuous data. Continuous data are obtained from measurements that are performed as a function of another quantitative variable, e.g., time, length, concentration, or wavelength. The results from these types of experiments are often used to generate plots that visualize the measured variable on a continuous, quantitative scale. To simplify state-of-the-art data visualization and annotation of data from such experiments, an open-source tool was created with R/shiny that does not require coding skills to operate it. The freely available web app accepts wide (spreadsheet) and tidy data and offers a range of options to normalize the data. The data from individual objects can be shown in 3 different ways: (1) lines with unique colors, (2) small multiples, and (3) heatmap-style display. Next to this, the mean can be displayed with a 95% confidence interval for the visual comparison of different conditions. Several color-blind-friendly palettes are available to label the data and/or statistics. The plots can be annotated with graphical features and/or text to indicate any perturbations that are relevant. All user-defined settings can be stored for reproducibility of the data visualization. The app is dubbed PlotTwist and runs locally or online: https://huygens.science.uva.nl/PlotTwist

This table provides statistics on the Distribution of Discrete Patients by Payment Range for Services Provided by Physicians, based on fee-for-service payments under the Alberta Health Care Insurance Plan (AHCIP). This table is an Excel version of a table in the “Alberta Health Care Insurance Plan Statistical Supplement” report published annually by Alberta Health.

It is natural to regard most animal movement as a continuous-time process, generally observed at discrete times. Most existing statistical methods for movement data ignore this; the remainder mostly use discrete-time approximations, the statistical properties of which have not been widely studied, or are limited to special cases. We aim to facilitate wider use of continuous-time modelling for realistic problems. We develop novel methodology which allows exact Bayesian statistical analysis for a rich class of movement models with behavioural switching in continuous time, without any need for time discretization error. We represent the times of changes in behaviour as forming a thinned Poisson process, allowing exact simulation and Markov chain Monte Carlo inference. The methodology applies to data that are regular or irregular in time, with or without missing values. We apply these methods to GPS data from two animals, a fisher (Pekania [Martes] pennanti) and a wild boar (Sus scrofa), using models with both spatial and temporal heterogeneity. We are able to identify and describe differences in movement behaviour across habitats and over time. Our methods allow exact fitting of realistically complex movement models, incorporating environmental information. They also provide an essential point of reference for evaluating other existing and future approximate methods for continuous-time inference.