In 2023, online consumers in Hong Kong spent approximately *** billion U.S. dollars when shopping in mobile apps, increasing significantly compared to 2018. Estimates showed that mobile apps will continue to dominate Hong Kong's m-commerce market in 2028.

The mobile commerce market size in Japan stood at around *** trillion Japanese yen as of 2023. It almost tripled over the last decade. The mobile commerce market consists of the three segments shopping, services, and transactions.

Statistics for moving services in m including costs, move sizes, and other relevant data as of August 2025.

Number of businesses in routes of 1 000 m x 1 000 m as of 01 January. The breakdown indicates the total number of businesses in the routes. Historical versions back to 2013.

AM: School Enrollment: Primary: Male: % Net data was reported at 90.878 % in 2018. This records a decrease from the previous number of 91.973 % for 2017. AM: School Enrollment: Primary: Male: % Net data is updated yearly, averaging 91.973 % from Dec 2002 (Median) to 2018, with 13 observations. The data reached an all-time high of 96.006 % in 2012 and a record low of 80.021 % in 2005. AM: School Enrollment: Primary: Male: % Net data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Social: Education Statistics. Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.;UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020.;Weighted average;

Research Ship Laurence M. Gould Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~"ZZZ........Z." in your query. '=~' indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '' says to match the previous character 0 or more times. (Don't include backslashes in your query.) See the tutorial for regular expressions at https://www.vogella.com/tutorials/JavaRegularExpressions/article.html

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This chart provides a detailed overview of the number of Spain online retailers by Monthly Sales. Most Spain stores' Monthly Sales are Less than $100.00, there are 104.64K stores, which is 98.17% of total. In second place, 1.24K stores' Monthly Sales are $10.00M to $100.00M, which is 1.17% of total. Meanwhile, 534 stores' Monthly Sales are $100.00K to $1.00M, which is 0.50% of total. This breakdown reveals insights into Spain stores distribution, providing a comprehensive picture of the performance and efficient of online retailer.

The file CBS squares 100 m contains statistical data per 100 meters by 100 meters square. As of June 2023, the year 2022 has been added and the number of key figures for the years 2021 and 2020 has been significantly expanded to include data on demographics, housing, energy, income, social security, proximity to facilities and density where available. As of the publication year 2022, the classification of inhabitants by migrant background has been replaced by a breakdown by country of birth and origin. As of the publication year 2020, the category of standardised household income has expired.

This dataset contains daily fractional snow covered area (fSCA) at 30-m spatial resolution from August 1, 2015 - May 15, 2020 (five winters) for the National Elk Refuge in Wyoming, USA. The following summary statistics are also included: date of snow accumulation, date of snow melt, number of days in year with snow on ground, and percentage of "winter days" in year with snow on ground. The SNOWARP algorithm was used to produce these data (Berman et. al., 2018; see full citation below), in which dynamic time warping fuses together MODIS MOD10A1 with Landsat level-3 fSCA, resulting in daily 30-m fSCA estimates. All dates correspond to winter day of year (WDOY), which runs between August 1 - July 31 each season and has values between 1-365. Date of snow accumulation and date of snow melt provide an indication of when snow begins to persist and when it melts. Details are provided below. Number of days in year with snow on ground is the number of days, out of 365, with snow on ground between August 1 and July 31st each winter. The percentage of winter days in year with snow on ground is the percentage of days with snow each winter between date of snow accumulation and date of snow melt. Please note that winter 2018-2019 was only processed until May 31, 2019 (WDOY 304), and winter 2019-2020 was only processed until May 15, 2020 (WDOY 288), and therefore the only summary stat available for these winters is date of snow accumulation.

The file CBS squares 100 m contains statistical data per 100 meters by 100 meters square. As of March 2022, the year 2021 has been added and the number of key figures for the years 2020 and 2019 have been significantly expanded to include data on demographics, housing, energy, income, social security, proximity to facilities and density.

Iran Energy Production: Average: ow Burning Oil data was reported at 20,061.000 Cub m/Day in 2016. This records a decrease from the previous number of 21,640.000 Cub m/Day for 2015. Iran Energy Production: Average: ow Burning Oil data is updated yearly, averaging 23,437.000 Cub m/Day from Mar 1997 (Median) to 2016, with 19 observations. The data reached an all-time high of 28,563.000 Cub m/Day in 2000 and a record low of 18,519.000 Cub m/Day in 2010. Iran Energy Production: Average: ow Burning Oil data remains active status in CEIC and is reported by Statistical Centre of Iran. The data is categorized under Global Database’s Iran – Table IR.RB001: Energy Statistics.

Abstract

The dataset contains eddy-covariance data from five i-Box stations in the Austrian Inn Valley, which have been processed to 1-min statistics. The i-Box is a long-term measurement platform, including a small network of eddy-covariance stations in the lower Inn Valley, to study boundary-layer processes in mountainous terrain. More information about the i-Box can be found at https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en and in Rotach et al. (2017).

Data description

Station locations

The present dataset contains processed data from five i-Box stations located in the Austrian Inn Valley. The Inn Valley is an approximately southwest-northeast oriented valley in the western part of Austria, with a depth of about 2000 m and a width of about 2 km at the valley floor. The locations of the sites are shown in the overview figure i-Box_sites.pdf.

• VF0 is located at the almost flat valley floor. The site is surrounded by grassland and agricultural fields. (47.305°N, 11.622°E, 545 m MSL)

• SF8 is located at the foot of the north sidewall next to a steep embankment between an agricultural field and a concrete parking lot. (47.326°N, 11.652°E, 575 m MSL)

• SF1 is located on an almost flat plateau running along the northern valley sidewall. The site is mainly surrounded by grassland and agricultural fields. (47.317°N, 11.616°E, 829 m MSL)

• NF10 is located on an approximately 10 deg slope on the south sidewall, covered by grassland. (47.300°N, 11.673°E, 930 m MSL)

• NF27 is located on a steep, grass-covered slope on the south sidewall, with a slope angle of about 25 deg. (47.288°N, 11.631°E, 1009 m MSL)

Further information about station locations can be found in Rotach et al. (2017) and Lehner et al. (2021).

Temporal coverage

The dataset contains processed data between 2014 and 2020. Some instruments were replaced and new instruments were added during this period. Data gaps occur as a result of instrument malfunctions and maintenance.

Instrumentation

Each station is equipped with at least one sonic anemometer and a gas analyzer. The instrumentation usually consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and KH20 Krypton hygrometer (Campbell Scientific) or an EC150 open-path infrared gas analyzer (Campbell Scientific). In 2020, several of the instruments were replaced with an Irgason (Campbell Scientific), which combines an open-path infrared gas analyzer with a sonic anemometer. Pressure, air temperature, and humidity used for calculating flux corrections are measured with Setra 278 sensors (Setra Systems, USA) and Rotronic HC2A-S temperature and humidity probes (Rotronic, Switzerland).

• VF0: CSAT3 and EC150 at 4.0 m, CSAT3 at 8.7 m, CSAT3 and KH20 (until July 2020) or Irgason (since July 2020) at 16.9 m

• SF8: CSAT3 at 6.1, CSAT3 and KH20 (until September 2020) or Irgason (since September 2020) at 11.2 m

• SF1: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 6.8 m

• NF10: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 5.7 m

• NF27: CSAT3 at 1.5 (since September 2017), CSAT3 and KH20 (until November 2016) or Irgason (since September 2017) 6.8 m

Further information about the instrumentation can be found in Rotach et al. (2017), Lehner et al. (2021), and in the ACINN database:

• VF0: https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html

• SF8: https://acinn-data.uibk.ac.at/pages/i-box-terfens.html

• SF1: https://acinn-data.uibk.ac.at/pages/i-box-eggen.html

• NF10: https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html

• NF27: https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html

Data processing

Raw 20-Hz data were quality controlled and rotated into a streamline coordinate system using double rotation before block averaging the data to 1-min statistics, without previous filtering. Flux corrections were applied to the turbulence statistics, including a frequency response correction (Aubinet et al. 2012) with spectral models following Moore (1986), Højstrup (1981), and Kaimal et al. (1972); a sonic heat-flux correction of the vertical heat flux and the temperature variance (Schotanus et al. 1983); a WPL correction of the vertical moisture flux (Webb et al. 1980); and an Oxygen correction of the vertical moisture flux for data from Krypton hygrometers (van Dijk et al. 2003).

The quality control procedures include the removal of data during periods of instrument malfunction as indicated by the instruments’ quality flags, a despiking, the removal of data points exceeding 30 m s^-1 for the horizontal wind components, 10 m s^-1 for the vertical wind velocity, and 50 g m³ for water vapor density, and the removal of sonic temperature data outside the range -20 – 40°C. The removed data are replaced with random values drawn from a Gaussian distribution, with its mean and standard deviation calculated over a 30-s data window.

Quality flags are based on the criteria described in Stiperski and Rotach (2016):

• -1: More than 10% of the raw data within the averaging period are replaced during the quality control.

• 0: More than 90% of the raw data fulfill the quality control criteria.

• 1: In addition to fulfilling the quality control criteria, the skewness is within the range -2–2 and the kurtosis is less than 8.

• 2: In addition to the above criteria, the stationarity test by Foken and Wichura (1996) is below 30% and the uncertainty is less than 50% based on Stiperski and Rotach (2016) and Wyngaard (1973)

Data files

• i-Box_sites.pdf contains a map of the i-Box stations.

• list_variables.pdf contains a list of variable names with a short description.

• SITENAME_1min.zip contains the processed turbulence statistics, split into yearly files. There is more than one file per year if the instrumentation changed during the year or because of memory restrictions during the processing.

Acknowledgments

Data processing was performed in the framework of the TExSMBL (Turbulent Exchange in the Stable Mountain Boundary Layer) project funded by the Austrian Science Fund (FWF) under grant V 791-N. Data were processed on the LEO HPC infrastructure of the University of Innsbruck.

References

Aubinet M, Vesala T, D P (eds) (2012) Eddy Covariance. A practical guide to measurements and data analysis. Springer, Dordrecht, DOI 10.1007/978-94-007-2351-1

Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Boundary-Layer Meteorol 21:341–356, DOI 10.1007/bf00119278

Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R M Soc 98:563–589, DOI 10.1002/qj.49709841707

Lehner M, Rotach MW, Sfyri E, Obleitner F (2021) Spatial and temporal variations in near-surface energy fluxes in an Alpine valley under synoptically undisturbed and clear-sky conditions. Q J R M Soc 147:2173–2196, DOI 10.1002/qj.4016

Moore CJ (1986) Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol 37:17–35, DOI 10.1007/BF00122754

Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography—the Innsbruck Box (i-Box). Bull Amer Meteorol Soc 98:787–805, DOI 10.1175/BAMS-D-15-00246.1

Schotanus P, Nieuwstadt FTM, de Bruijn HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26:81–93, DOI 10.1007/BF00164332

Stiperski, I. and Rotach, M.W. (2016) On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159, 97–121. DOI 10.1007/s10546-015-0103-z.

Van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen sensitivity of Krypton and Lyman-α hygrometers. J Atmos Ocean Technol 20:143–151, DOI 10.1175/1520-0426(2003)020¡0143:OSOKAL¿2.0.CO;2

Webb EK, Pearman GI, R L (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R M Soc 106:85–100, DOI 10.1002/qj.49710644707

Wyngaard, J.C. (1973). On surface layer turbulence. In D.A. Haugen (Ed.), Workshop on Micrometeorology, American Meteorological Society, pp. 101–150.

Financial overview and grant giving statistics of Henry M Jackson Foundation

The file CBS squares 100 m contains statistical data per 100 meters by 100 meters square. As of March 2022, the year 2021 has been added and the number of key figures for the years 2020 and 2019 have been significantly expanded to include data on demographics, housing, energy, income, social security, proximity to facilities and density.

Dwellings Completed: ytd: Housing Cooperatives: Avg Usable Floor Space: Kujawsko Pomorskie data was reported at 54.900 sq m in Dec 2017. This records a decrease from the previous number of 58.700 sq m for Sep 2017. Dwellings Completed: ytd: Housing Cooperatives: Avg Usable Floor Space: Kujawsko Pomorskie data is updated quarterly, averaging 53.800 sq m from Dec 2003 (Median) to Dec 2017, with 42 observations. The data reached an all-time high of 65.300 sq m in Sep 2010 and a record low of 27.000 sq m in Mar 2014. Dwellings Completed: ytd: Housing Cooperatives: Avg Usable Floor Space: Kujawsko Pomorskie data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Poland – Table PL.EB005: Dwellings Completed Statistics: ytd: Quarterly.

Brazil Highways Statistics: Achievements in Safety: Steel Fenders and Concrete Barriers: Rio Grande do Sul data was reported at 2,559.000 m in 2013. This records a decrease from the previous number of 4,436.000 m for 2012. Brazil Highways Statistics: Achievements in Safety: Steel Fenders and Concrete Barriers: Rio Grande do Sul data is updated yearly, averaging 7,581.500 m from Dec 1998 (Median) to 2013, with 16 observations. The data reached an all-time high of 21,445.000 m in 2003 and a record low of 1,162.000 m in 2001. Brazil Highways Statistics: Achievements in Safety: Steel Fenders and Concrete Barriers: Rio Grande do Sul data remains active status in CEIC and is reported by Brazilian Association of Highway Concessionaires. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAW007: Highways Statistics: Achievements in Safety. The Brazilian Association of Highway Concessionaires-ABCR represents the highway concession sector.

Housing statistics on routes 5 000 m X 5 000 m for all vintages from 2008, in separate CSV files. Newer vintages available in several formats. The data sets contain statistics on the number of dwellings and associated variables as of 1 January. Housing statistics on routes belong to the thematic group “Construction/Housing” in Statistics Norway’s product group “Statistics on grids”. In the same Theme group there are also dataset Building mass statistics on routes Other themes available are “Population”, “Businesses”, and “Earth, Forests, Hunting and Fisheries”

Overview Sequences S, T, and U: Upwind, No Probes (F); Upwind 2° Pitch (F); Upwind 4° Pitch (F) This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speed ranged from 5 m/s to 25 m/s. Yaw angles of 0° to 180° were achieved for Sequence S, but the yaw angle remained at 0° for Sequences T and U. The blade tip pitch was 3° for Sequence S, 2° for Sequence T, and 4° for Sequence U. These three sequences were interleaved during testing because the pitch angle change was easily made by the turbine operator. The rotor rotated at 72 RPM. Blade pressure measurements were collected. The five-hole probes were removed and the plugs were installed. Plastic tape 0.03-mm-thick was used to smooth the interface between the plugs and the blade. The teeter dampers were replaced with rigid links, and these two channels were flagged as not applicable by setting the measured values in the data file to -99999.99 Nm. The teeter link load cell was pre-tensioned to 40,000 N. During post-processing, the probe channels were set to read -99999.99. In addition to the standard 30-second campaigns, yaw sweeps were done at 7 m/s and 10 m/s for the Sequence S configuration. These 6-minute campaigns were collected while the yaw drive rotated the turbine 360° at a rate of 1°/s. The file names for these campaigns use the letter designation, followed by two digits for wind speed, followed by YSU, followed by 00. Data Details File naming information can be found in the attached Word document "Sequence T Filename Key", copied from the Phase VI Test Report.

There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.