This dataset includes discrete sample and profile data collected from DISCOVERY in the Indian Ocean and Southern Oceans (> 60 degrees South) from 1994-02-19 to 1994-03-30. These data include CHLOROFLUOROCARBON-11 (CFC-11), CHLOROFLUOROCARBON-113 (CFC-113), CHLOROFLUOROCARBON-12 (CFC-12), Carbon tetrachloride (CCL4), DISSOLVED OXYGEN, Delta Oxygen-18, HYDROSTATIC PRESSURE, NITRATE, Potential temperature (theta), SALINITY, WATER TEMPERATURE, phosphate and silicate. The instruments used to collect these data include CTD and bottle. These data were collected by Robert R. Dickson of Fisheries Laboratory - Lowestoft as part of the WOCE_ISS01h_74DI19940219 dataset. CDIAC associated the following cruise ID(s) with this dataset: DIS94 and WOCE_ISS01h_1994 The World Ocean Circulation Experiment (WOCE) was a major component of the World Climate Research Program with the overall goal of better understanding the ocean's role in climate and climatic changes resulting from both natural and anthropogenic causes. The CO2 survey took advantage of the sampling opportunities provided by the WOCE Hydrographic Program (WHP) cruises during this period between 1990 and 1998. The final collection covers approximately 23,000 stations from 94 WOCE cruises.

Averaged performance of rule classifiers for supervised discretisation with transformations of the test sets based on discrete data models obtained for the training samples [%].

Discrete water quality monitoring data from the Sacramento-San Joaquin Bay-Delta collected by the Environmental Monitoring Program. Data is also accessible via the Environmental Data Initiative.

In this paper it is shown that a convenient score test against non-nested alternatives can be constructed from the linear combination of the likelihood functions of the competing models. This is essentially a test for the correct specification of the conditional distribution of the variable of interest. Given its characteristics, the proposed test is particularly attractive to check the distributional assumptions in models for discrete data. The usefulness of the test is illustrated with an application to models for recreational boating trips.

Usage note: Time zone information was not received by BCO-DMO so it is unknown if the dates and times in this dataset are local or UTC. Please contact the dataset PI if you have questions about this.

This data release includes water-quality data collected at up to thirteen locations along the Merrimack River and Merrimack River Estuary in Massachusetts. In this study, conducted by the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Department of Environmental Protection, discrete samples were collected, and continuous monitoring was completed from June to September 2020. The data include results of measured field properties (water temperature, specific conductivity, pH, dissolved oxygen) and laboratory concentrations of nitrogen and phosphorus species, total carbon, pheophytin-a, and chlorophyll-a. These data were collected to assess selected (mainly nutrients) water-quality conditions in the Merrimack River and Merrimack River Estuary at the thirteen locations and identify areas where more water-quality monitoring is needed. The discrete samples and continuous-monitoring data are also available in the USGS National Water Information System at https://waterdata.usgs.gov/nwis. This data release consists of (1) Table of the discrete water-quality data collected (Merrimack_DiscreteWQ_Data.csv); (2) Statistical summaries including the minimum, median, and maximum of the discrete water-quality data collected (Merrimack_DiscreteWQ_Statistical_Data.original.csv); (3) Statistical summaries including the minimum, median, and maximum of the continuous water-quality data collected (Merrimack_ContinuousWQ_Statistical_Data.csv); (4) Table of vertical profile data (Merrimack_VerticalWQ_Profiles_Data.csv); (5) Table of continuous monitor deployment location and dates (Merrimack_ContinuousWQ_Deployment_Dates.csv); (6) Time-series plots of continuous water-quality data (Continuous_QW_Plots_All.zip); (7) Vertical profile plots (Vertical Profiles_QW_Plots.zip).

Unclassified three-dimensional point cloud by flightline and classified point cloud by 1 km tile, provided in LAZ format. Classifications follow standard ASPRS definitions. All point coordinates are provided in meters. Horizontal coordinates are referenced in the appropriate UTM zone and the ITRF00 datum. Elevations are referenced to Geoid12A.

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.

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.

Mygalomorph spiders (trapdoor spiders and their kin) have long been associated with high levels of homoplasy, and many convergent features can be intuitively associated with different behavioral niches. This dataset includes two discrete behavioral characters and 55 somatic morphological characters (scored from adult females), for 110 genera of mygalomorph spiders, along with a complete reference list and exemplar list used when constructing the dataset. This dataset was used to reconstruct the evolution of burrowing behavior in the Mygalomorphae, compare the influence of behavior and evolutionary history on somatic morphology, and test hypotheses of correlated evolution between specific morphological features and behavior. The results revealed the simplicity of the mygalomorph adaptive landscape, with opportunistic, web-building taxa at one end, and burrowing/nesting taxa with structurally-modified burrow entrances (e.g., a trapdoor) at the other. Shifts in behavioral niche, in both directions, are common across the evolutionary history of the Mygalomorphae, and several major clades include taxa inhabiting both behavioral extremes. Somatic morphology is heavily influenced by behavior, with taxa inhabiting the same behavioral niche often more similar morphologically than more closely-related but behaviorally-divergent taxa. Methods We scored this dataset by combining a semi-exhaustive literature review with exemplar cross-checking. Morphological characters were scored exclusively from adult females because adult male morphology is at least partially adapted for the terrestrial dispersal phase that they undergo, whereas female morphology is more representative of the general morphology of the species (in that juveniles of both sexes resemble adult females) and is presumably adapted to the sedentary lifestyle of the species. Most of our morphological characters correspond closely with those scored in previous morphological analyses of the Mygalomorphae, with some modifications to decrease ambiguity. Many genera are polymorphic for behavior and/or morphology, and this is reflected in the dataset. A list of relevant references and an exemplar specimen list are included in this dataset.

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.

Discrete fourier transforms for turbulence is a book. It was written by Stewart Cant and published by University of Cambridge, Dept. of Engineering in 2012.

musscrat_dryadSupplemental data and scripts.

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.

Example of bias resulting from fitting discrete data with continuous maximum likelihood solutions.

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.

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.

The FORGE team is making these fracture models available to researchers wanting a set of natural fractures in the FORGE reservoir for use in their own modeling work. They have been used to predict stimulation distances during hydraulic stimulation at the open toe section of well 16A(78)-32. This is a simplified DFN (discrete fracture network) dataset, that was generated using FracMan, for Utah FORGE well 16A(78)-32. A short, well-illustrated, report describing the data is also included in the provided archive file.

This project was a collaboration between Dr. Christopher W. Hunt and Dr. Joseph Salisbury (of the University of New Hampshire) and Dr. Xuewu Liu and Dr. Robert H. Byrne (of the University of South Florida).

The goal of this study was to develop a suite of inter-related water quality monitoring approaches capable of modeling and estimating the spatial and temporal gradients of particulate and dissolved total mercury (THg) concentration, and particulate and dissolved methyl mercury (MeHg), concentration, in surface waters across the Sacramento / San Joaquin River Delta (SSJRD). This suite of monitoring approaches included: a) data collection at fixed continuous monitoring stations (CMS) outfitted with in-situ sensors, b) spatial mapping using boat-mounted flow-through sensors, and c) satellite-based remote sensing. The focus of this specific Child Page is to present laboratory measured spectral data associated with discrete surface water samples collected as part of both the CMS and boat mapping sampling efforts. All laboratory-based measurement presented herein were conducted by the U.S. Geological Survey (USGS) Organic Matter Research Laboratory (OMRL) in Sacramento, Calif. The machine-readable (comma separated value, *.csv) files presented herein include spectral data collected using two different instruments: 1) Laboratory-based absorbance and fluorescence measurements on filtered water using an Aqualog (Hansen and others, 2018) and 2) Laboratory-based absorption measurements using a Varian Cary spectrophotometer on particulate samples collected on glass fiber filters (Kishino and others, 1985; Roesler, 1998). The reported spectral data includes: 1) fluorescence intensities across a wide range of excitation (240 to 800 nm) and emission (250 to 800 nm) wavelengths expressed as an excitation-emission matrix (EEM), 2) absorbance of light (from 239 nm to 800 nm) due to dissolved and colloidal substances, and 3) absorption coefficients (from 350 nm to 715 nm) for particulates using the quantitative filter technique (QFT).