Portugal Number of Companies: excl Size: Large data was reported at 1.000 Unit th in 2016. This stayed constant from the previous number of 1.000 Unit th for 2015. Portugal Number of Companies: excl Size: Large data is updated yearly, averaging 1.000 Unit th from Dec 2006 (Median) to 2016, with 11 observations. The data reached an all-time high of 1.000 Unit th in 2016 and a record low of 0.900 Unit th in 2014. Portugal Number of Companies: excl Size: Large data remains active status in CEIC and is reported by Bank of Portugal. The data is categorized under Global Database’s Portugal – Table PT.O001: Number of Companies.

To explore the application effect of the deep learning (DL) network model in the Internet of Things (IoT) database query and optimization. This study first analyzes the architecture of IoT database queries, then explores the DL network model, and finally optimizes the DL network model through optimization strategies. The advantages of the optimized model in this study are verified through experiments. Experimental results show that the optimized model has higher efficiency than other models in the model training and parameter optimization stages. Especially when the data volume is 2000, the model training time and parameter optimization time of the optimized model are remarkably lower than that of the traditional model. In terms of resource consumption, the Central Processing Unit and Graphics Processing Unit usage and memory usage of all models have increased as the data volume rises. However, the optimized model exhibits better performance on energy consumption. In throughput analysis, the optimized model can maintain high transaction numbers and data volumes per second when handling large data requests, especially at 4000 data volumes, and its peak time processing capacity exceeds that of other models. Regarding latency, although the latency of all models increases with data volume, the optimized model performs better in database query response time and data processing latency. The results of this study not only reveal the optimized model’s superior performance in processing IoT database queries and their optimization but also provide a valuable reference for IoT data processing and DL model optimization. These findings help to promote the application of DL technology in the IoT field, especially in the need to deal with large-scale data and require efficient processing scenarios, and offer a vital reference for the research and practice in related fields.

This 2001 Population Census dataset contains statistics relevant to demographic, household, educational, economic, housing and internal migration characteristics of the Hong Kong population residing in the 139 Large Tertiary Planning Unit Groups in 2001. The dataset also contains the boundaries of individual Large Tertiary Planning Unit Groups. Since 1961, a population census has been conducted in Hong Kong every 10 years and a by-census in the middle of the intercensal period. The 2001 Population Census, which was conducted in March 2001, provides benchmark statistics on the socio-economic characteristics of the Hong Kong population vital to the planning and policy formulation of the government. This dataset will be incorporated into Population Distribution Framework Spatial Data Theme.

Estonia Payments: Vol: Payable: Cross Border: Large data was reported at 0.500 Unit th in Jun 2018. This records an increase from the previous number of 0.400 Unit th for May 2018. Estonia Payments: Vol: Payable: Cross Border: Large data is updated monthly, averaging 0.900 Unit th from Dec 1997 (Median) to Jun 2018, with 247 observations. The data reached an all-time high of 4.800 Unit th in Dec 2003 and a record low of 0.300 Unit th in Apr 2017. Estonia Payments: Vol: Payable: Cross Border: Large data remains active status in CEIC and is reported by Bank of Estonia. The data is categorized under Global Database’s Estonia – Table EE.KA005: Payment Statistics: Value and Volume of Payments.

A variety of datasets for analysis of High Wealth individuals to assist HMRC's High Net Worth Unit in maintaining and refining its population. Matches 10 years of Inheritance Tax Data to the relevant in-life SA data. Updated: ad hoc.

The USDA-Agricultural Research Service High Plains Grasslands Research Station (HPGRS) is located in Cheyenne, Wyoming, USA. In 1982, a long-term stocking rate study on northern mixed-grass prairie was initiated with season-long (early June to October) grazing. Stocking rates defined as light (35% below NRCS recommended rate, 15 yearlings per 80 ha), moderate (NRCS recommended rate, 4 yearlings per 12ha), and heavy (33% above NRCS recommended rate, 4 yearlings per 9 ha). British- and continental-breed yearling cattle were used throughout the study years. When forage supply was limited due to drought, grazing seasons were shortened or cattle were not grazed for that season. Individual raw data on cattle entry and exit weights are available from 1982 to 2022. No grazing occurred in the years 1989, 2000, and 2002 due to drought conditions. Weight gain outliers (± 2 sd of treatment mean) were removed from the dataset. Resources in this dataset:Resource Title: Long-Term Grazing Intensity (LTGI) cattle weight gains. File Name: EastUnit_LTGI.csvResource Description: Cattle weight gain data from the Long-Term Grazing Intensity (LTGI) 1982-2022 on the USDA Agricultural Research Service High Plains Grasslands Research Station, near Cheyenne, WYResource Title: Data Dictionary for Long-Term Grazing Intensity (LTGI) cattle weight gains . File Name: EastUnit_LTGI_DataDictionary.csvResource Description: Data dictionary for cattle weight gain data from the Long-Term Grazing Intensity (LTGI) 1982-2022 on the USDA Agricultural Research Service High Plains Grasslands Research Station, near Cheyenne, WY

The booming 4U Rack-based CDU market is projected to reach $165.6 million in 2025, with a CAGR of 8-12% through 2033. Driven by cloud computing, edge computing, and high-density data centers, key players are innovating in liquid cooling to meet surging demand. Learn more about market trends, regional analysis, and key players.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey.
Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values.

Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

This Dynamics Explorer 2, DE 2, data set is a combination of the Vector Electric Field Instrument, VEFI, and Magnetometer-B, MAGB, high resolution data sets in spacecraft, SC, coordinates submitted to NSSDC. The following orbit-altitude, OA, parameters have been added to the data set: 1) Model magnetic field, SC coordinates 2) Satellite altitude 3) Geographic latitude and longitude 4) Magnetic local time 5) Invariant latitudeThe VEFI data set is described in the file VEFIVOLDESC.SFD and the MAGB data set is described in the file MAGBVOLDESC.SFD, these files are portions of the Standard Format Data Unit, SFDU, metadata files submitted with the VEFI and MAGB data to NSSDC and are included in each volume of this data set. This data set consists of daily files from 1981-08-15, day of year 227, to 1983-02-16, day of year 47. Each file contains all the data available for a given day. During the merging of the data sets it was found that although VEFI and MAGB should cover the same time spans, they do not, due perhaps to the fact that the original MAGB high resolution data set was created on the DE Sigma-9 in Sigma-9 format by using the DE telemetry tapes, while the VEFI high resolution data set was created on the DE MicroVAX system using the DE telemetry data base on optical disk. In order to keep the largest amount of data possible, the merged data set includes all the available VEFI and MAGB data, for those times when VEFI data was available but MAGB was not, 6.54% of the time spanned by this data product, a fill value of 9999999. was given to the MAGB data. Likewise, for those times when MAGB data was available but VEFI was not, 6.87% of the time, the fill value was assigned to the VEFI data. Times for which both VEFI and MAGB data were fill values in the original data sets were not included in the merged data set. There were also times when certain OA parameters were fill values in the OA data base and they are therefore also fill values in this merged data set. The model magnetic field had fill values for 8.55% of the data. Statistics were not kept for the other OA parameters. Each daily file contains a record per measurement. The total number of records in each file varies depending on the amount of data available for a given day.The DE 2 spacecraft, which was the low-altitude mission component, complemented the high-altitude mission DE 1 spacecraft and was placed into an orbit with a perigee sufficiently low to permit measurements of neutral composition, temperature, and wind. The apogee was high enough to permit measurements above the interaction regions of suprathermal ions, and also plasma flow measurements at the feet of the magnetospheric field lines. The general form of the spacecraft was a short polygon 137 cm in diameter and 115 cm high. The triaxial antennas were 23 m tip-to-tip. One 6 m boom was provided for remote measurements. The spacecraft weight was 403 kg. Power was supplied by a solar cell array, which charged two 6 ampere-hour nickel-cadmium batteries. The spacecraft was three-axis stabilized with the yaw axis aligned toward the center of the Earth to within 1°. The spin axis was normal to the orbit plane within 1° with a spin rate of one revolution per orbit. A single-axis scan platform was included in order to mount the low-altitude plasma instrument (ID: 81-070B-08). The platform rotated about the spin axis. A pulse code modulation telemetry data system was used that operated in real time or in a tape recorder mode. Data were acquired on a science-problem-oriented basis, with closely coordinated operations of the various instruments, both satellites, and supportive experiments. Measurements were temporarily stored on tape recorders before transmission at an 8:1 playback-to-record ratio. Since commands were also stored in a command memory unit, spacecraft operations were not real time. Additional details can be found in R.A. Hoffman et al., Space Sci. Instrum., 5(4), 349, 1981. DE-2 reentered the atmosphere on February 19, 1983. A triaxial fluxgate magnetometer onboard DE 2, MAG-B, similar to one on board DE 1 (ID: 81-070A-01), was used to obtain the magnetic field data needed to study the magnetosphere-ionosphere-atmosphere coupling.The primary objectives of this investigation were to measure field aligned currents in the auroral oval and over the polar cap at two different altitudes using the two spacecraft, and to correlate these measurements with observations of electric fields, plasma waves, suprathermal particles, thermal particles, and auroral images obtained from investigation (ID: 81-070A-03). The magnetometer had digital compensation of the ambient field in 8000 nT increments. The instrument incorporated its own 12-bit analog-to-digital, A/D, converter, a 4-bit digital compensation register for each axis, and a system control that generated a 48-bit data word consisting of a 16-bit representation of the field measured along each of three magnetometer axes. Track and hold modules were used to obtain simultaneous samples on all three axes. The instrument bandwidth was 25 Hz. The analog range was ±62000 nT, the accuracy was ±4 nT, and the resolution was 1.5 nT. The time resolution was 16 vector samples/s. More details can be found in W.H. Farthing et al., Space Sci. Instrum., 5(4), 551, 1981. The Vector Electric Field Instrument, VEFI, used flight-proven double-probe techniques with 20 m baselines to obtain measurements of DC electric fields.This electric field investigation had the following objectives: 1) obtain accurate and comprehensive triaxial DC electric field measurements at ionospheric altitudes in order to refine the basic spatial patterns, define the large-scale time history of these patterns, and study the small-scale temporal and spatial variations within the overall patterns 2) study the degree to which and in what region the electric field projects to the equatorial plane 3) obtain measurements of extreme low frequency, ELF, and lower frequency irregularity structures* 4) perform numerous correlative studiesThe VEFI instrument consisted of six cylindrical elements 11 m long and 28 mm in diameter. Each antenna was insulated from the plasma except for the outer 2 m. The baseline, or distance between the midpoints of these 2-m active elements, was 20 m. The antennas were interlocked along the edges to prevent oscillation and to increase their rigidity against drag forces. The basic electronic system was very similar in concept to those used on IMP-8 and ISEE 1, but modified for a three-axis measurement on a nonspinning spacecraft. At the core of the system were the high-impedance (10¹² ohm) preamplifiers, whose outputs were accurately subtracted and digitized with 14-bit A/D conversion for sensitivity to about 0.1 µV/m to maintain high resolution for subsequent removal of the cross product of the electric field, V, and magnetic field, B, vectors in data processing. This provided the basic DC measurement. Other circuitry was used to aid in interpreting the DC data and to measure rapid variations in the signals detected by the antennas. The planned DC electric field range was ±1 V/m, the planned resolution was 0.1 mV/m, and the variational AC electric field was measured from 4 Hz to 1024 Hz. The DC electric field was measured at 16 samples/s. The AC electric field was measured from 1 µV/m to 10 mV/m root mean square, rms. Note that the VEFI antenna pair perpendicular to the orbit plane onboard DE 2 did not deploy. Additional details are found in N.C. Maynard et al., Space Sci. Instrum., 5(4), 523, 1981.

This data package was created 2025-03-15 17:41:53 by NPSTORET and includes selected project, location, and result data. Data contained in Great Lakes Network NPSTORET back-end file (GLKNRVWQ_BE_20250303.ACCDB) were filtered to include: Station: - Include Trip QC And All Station Visit Results Value Status: - Accepted or Certified (exported as Final) or Final The data package is organized into five data tables: - Projects.csv - describes the purpose and background of the monitoring efforts - Locations.csv - documents the attributes of the monitoring locations/stations - Results.csv - contains the field measurements, observations, and/or lab analyses for each sample/event/data grouping - HUC.csv - enumerates the domain of allowed values for 8-digit and 12-digit hydrologic unit codes utilized by the Locations data table - Characteristics.csv - enumerates the domain of characteristics available in NPSTORET to identify what was sampled, measured or observed in Results Period of record for filtered data is 2006-04-11 to 2024-10-30. This data package is a snapshot in time of one National Park Service project. The most current data for this project, which may be more or less extensive than that in this data package, can be found on the Water Quality Portal at: https://www.waterqualitydata.us/data/Result/search?project=GLKNRVWQ&mimeType=csv&zip=yes&dataProfile=biological&providers=STORET

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The enormous size of the BIBE project area warranted the use of a modified or hybrid mapping approach. Early discussions determined the need to have an approach that included a coarse-level automated or machine-logic image processing stage and a fine-level stage that included vegetation signature interpretation and manual polygon delineation. Based on similar mapping work done by CTI in other desert environments, the automated stage would use multiresolution image segmentation routines to capture high contrast landforms and drainage/wash features, greatly reducing the time needed to delineate these by hand. The second phase would build off these segmented polygons to delineate the fine-level plant alliance/association based map units. For BIBE, 72 map units (62 vegetated and 10 land-use/land-cover) were developed. The final list of map classes/units was directly cross-walked or matched to corresponding plant associations and land use classes. BIBE map classes represent a compromise between the detail of the rUSNVC, new types found in the park (not currently in the rUSNVC), the needs of the resource management staff (e.g. detailed mapping of riparian, wetland, and non-native types), and the limitations of the imagery. An effort was made to crosswalk the final list of map classes/units to corresponding plant associations/alliances and land use classes. When a direct rUSNVC link to an association was not feasible, broader alliances or descriptive local map units (park specials) were created. In addition, some of the more widespread associations occurred across multiple map units.

This 2016 Population By-census dataset contains statistics relevant to demographic, household, educational, economic, housing and internal migration characteristics of the Hong Kong population residing in the 154 Large Tertiary Planning Unit Groups in 2016. The dataset also contains the boundaries of individual Large Tertiary Planning Unit Groups. Since 1961, a population census has been conducted in Hong Kong every 10 years and a by-census in the middle of the intercensal period. The 2016 Population By-census, which was conducted in June to August 2016, provides benchmark statistics on the socio-economic characteristics of the Hong Kong population vital to the planning and policy formulation of the government. This dataset will be incorporated into Population Distribution Framework Spatial Data Theme.

[NOTE - 2022-09-07: this dataset is superseded by an updated version https://doi.org/10.15482/USDA.ADC/1526433 ] This dataset consists of weather data for each year when maize was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Maize was grown for grain on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four square fields are themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Irrigation was by linear move sprinkler system in 1989, 1990, and 1994. In 2013, 2016, and 2018, two lysimeters and their respective fields (NE and SE) were irrigated using subsurface drip irrigation (SDI), and two lysimeters and their respective fields (NW and SW) were irrigated by a linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. The weather data include solar irradiance, barometric pressure, air temperature and relative humidity, and wind speed determined using sensors placed at 2-m height over a level, grass surface mowed to not exceed 12 cm height and irrigated and fertilized to maintain reference conditions as promulgated by ASCE (2005) and FAO (1996). Irrigation was by surface flood in 1989 through 1994, and by subsurface drip irrigation after 1994. Sensors were replicated and intercompared between replicates and with data from nearby weather stations, which were sometimes used for gap filling. Quality control and assurance methods are described by Evett et al. (2018). These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on maize ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used by the Agricultural Model Intercomparison and Improvement Project (AgMIP), by OPENET, and by many others for testing, and calibrating models of ET that use satellite and/or weather data. Resources in this dataset:

Resource Title: 1989 Bushland, TX, standard 15-minute weather data. File Name: 1989_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: The weather data are presented as 15-minute mean values of solar irradiance, air temperature, relative humidity, wind speed, and barometric pressure; and as 15-minute totals of precipitation (rain and snow). Daily total precipitation as determined by mass balance at each of the four large, precision weighing lysimeters is given in a separate tab along with the mean daily value of precipitation. Data dictionaries are in separate tabs with names corresponding to those of tabs containing data. A separate tab contains a visualization tool for missing data. Another tab contains a visualization tool for the weather data in five-day increments of the 15-minute data. An Introduction tab explains the other tabs, lists the authors, explains data time conventions, explains symbols, lists the sensors, and datalogging systems used, and gives geographic coordinates of sensing locations.

Resource Title: 1990 Bushland, TX, standard 15-minute weather data. File Name: 1990_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1990.

Resource Title: 1994 Bushland, TX, standard 15-minute weather data. File Name: 1994_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1994.

Resource Title: 2013 Bushland, TX, standard 15-minute weather data. File Name: 2013_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 2013.

Resource Title: 2016 Bushland, TX, standard 15-minute weather data. File Name: 2016_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 2016.

Resource Title: 2018 Bushland, TX, standard 15-minute weather data. File Name: 2018_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 2018.

Resource Title: 1996 Bushland, TX, standard 15-minute weather data. File Name: 1996_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1996.

Resource Title: 1997 Bushland, TX, standard 15-minute weather data. File Name: 1997_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1997.

Resource Title: 1998 Bushland, TX, standard 15-minute weather data. File Name: 1998_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1998.

Resource Title: 1999 Bushland, TX, standard 15-minute weather data. File Name: 1999_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1999.

A Planned Unit Development (PUD) is a large-scale development in which conventional zoning standards (such as setbacks and height limits) are relaxed in order to conserve sensitive areas, promote the creation of public amenities such as parks and plazas, and encourage the mixing of different land uses.

To advance the U.S. Geological Survey 3D National Topography Model (3DNTM) including the next generation of the 3D Elevation Program (3DEP) and the 3D Hydrography Program (3DHP), the USGS researched and created a Seamless 1-meter resolution (S1M) Digital Elevation Model (DEM) for the conterminous United States (CONUS). This dataset is a result of a joint project between the National Geospatial Technical Operations Center (NGTOC) and the Earth Resources Observation and Science Center (EROS) of the USGS National Geospatial Directorate (NGD). Scientists and resource managers can use the S1M data for global change research, hydrologic modeling, resource monitoring, mapping, visualization, and many other applications. A S1M DEM requires merging multiple lidar projects in which the lidar sensor, bare-earth DEM generation methodology, source resolution, datums/projection, unit of measure, and geoid (mean sea level model) can vary between projects. This tile of the Seamless 1-m DEM was created from the best available 3DEP Original Product Resolution source DEMs from one or several intersecting 3DEP data collection projects. Spatially referenced metadata are contained within an open-source GeoPackage that stores footprints for each of the input source DEMs along with source data characteristics. The source DEMs were processed to align vertically to North American Vertical Datum of 1988 (EPSG: 5703) updated to the current GEOID18 model and projected horizontally to North American Datum of 1983 (2011) USA Contiguous Albers Equal Area Conic projection (EPSG: 6350). Horizontal units and elevation values are in meters. Large data voids wider than 10 meters in the tile were backfilled with 1/9 arc-second or 1/3 arc-second DEMs in the 3DEP data repository while small data voids were interpolated across using bilinear interpolation. For tiles containing more than one 3DEP project or with large data voids, up to three blending routines were used: a simple blend, narrow blend, or a backfill blend. The spatial metadata GeoPackage contains information on where backfilling, void interpolation, and blending occurs within the tile. The tile spatial extent is 10 km x 10 km. The S1M DEM is available in a Cloud Optimized Georeferenced Tagged Image File Format (GeoTIFF). The S1M DEM has floating point numeric values and a spatial resolution of one meter. NoData values (areas where data is incomplete due to lack of full data coverage) are represented with the numeric value of -999999. Other 3DEP products are nationally seamless DEMs in resolutions of 1/3, 1, and 2 arc seconds. These seamless DEMs were referred to as the National Elevation Dataset (NED) from about 2000 through 2015 at which time they became the seamless DEM layers under the 3DEP program and the NED name and system were retired. Other 3DEP products include project-based one-meter DEMs in CONUS, five-meter DEMs in Alaska as well as various source datasets including the lidar point cloud and interferometric synthetic aperture radar (Ifsar) digital surface models and intensity images. All 3DEP products are public domain.

This is the artifact page for the TSE 2023 paper titled "An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation" by Max Schaefer, Sarah Nadi, Aryaz Eghbali, and Frank Tip. Please unzip the artifact and check the included ReadMe file for how to interpret this data and how to use the scripts.

China Industrial Enterprise: Large & Medium: Number of Enterprise data was reported at 58,882.000 Unit in Oct 2018. This records a decrease from the previous number of 58,923.000 Unit for Sep 2018. China Industrial Enterprise: Large & Medium: Number of Enterprise data is updated monthly, averaging 41,580.500 Unit from Jan 2001 (Median) to Oct 2018, with 190 observations. The data reached an all-time high of 65,514.000 Unit in Dec 2013 and a record low of 20,648.000 Unit in Feb 2001. China Industrial Enterprise: Large & Medium: Number of Enterprise 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.BF: Industrial Financial Data: Large and Medium Enterprise.

As part of the VERTIGO project, Multiple Unit Large Volume in-situ Filtration System (MULVFS) sampling took place during two-week-long intensive study periods at ALOHA (in 2004) and K2 (in 2005). This dataset includes data from station ALOHA.

Final review by the data submitter was not received after it was imported into the BCO-DMO data system. Data have been published \"as is\".

Associated Publication:
Bishop, J.K.B.and Wood, T.J. (2008) Particulate Matter Chemistry and Dynamics in the Twilight Zone at VERTIGO ALOHA and K2 Sites. Deep-Sea Research I 55, 1684-1706. doi: 10.1016/j.dsr.2008.07.012

The crystal structure of Na3MnF6 has been investigated at high pressures by means of single-crystal x-ray diffraction, and its Mn(III) coordination environment has been studied by means of single-crystal optical absorption spectroscopy using diamond anvil techniques. Compressibility data (unit cell parameters) were collected in the pressure range from ambient to 4.06 GPa, and structural refinements based on single-crystal diffraction data were performed at 0.12, 0.91, 2.27, and 2.79 GPa. The monoclinic space group symmetry (P21/n) is retained in the entire pressure range, but, at increasing pressure, a discontinuous phase transition is observed at ∼2.2 GPa. This is interpreted as an effect of a reversible, isosymmetric phase transition with a hysteresis width of 0.5 GPa, observed when the pressure is successively lowered. The structure refinements show that the phase transition involves a reorientation of the static prolate distortion of the coordination around manganese(III). The angle between the elongation axis (z) of the MnF63- octahedron with [0 0 1] flips from ∼20° at ambient pressures to ∼70° at 2.79 GPa. Polarized single-crystal absorption spectra of Na3MnF6 show drastic changes of the polarization of bands due to spin-allowed d−d transitions in Mn(III) when passing the transition pressure, which confirm the results of the single-crystal structure refinements. A possible explanation for this transition is discussed in terms of structure packing arguments. The isothermal bulk modulus at ambient pressure and its pressure derivative were determined to B0 = 47.8(1) GPa and B0‘ = 1.2(1), respectively.

China Industrial Enterprise: Large & Medium: State Holding: Number of Enterprise data was reported at 7,035.000 Unit in Oct 2018. This records a decrease from the previous number of 7,074.000 Unit for Sep 2018. China Industrial Enterprise: Large & Medium: State Holding: Number of Enterprise data is updated monthly, averaging 7,987.500 Unit from Jan 2001 (Median) to Oct 2018, with 186 observations. The data reached an all-time high of 12,720.000 Unit in Jun 2001 and a record low of 6,969.000 Unit in Feb 2008. China Industrial Enterprise: Large & Medium: State Holding: Number of Enterprise 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.BF: Industrial Financial Data: Large and Medium: State Holding Enterprise.