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9,727 results for “Japan”
Japan-Educated Officials in China's Wartime Central Administration (1944)
<p>This dataset is made up of two files:</p> <ol> <li>"CNKI-20231014003254589" is the raw extraction of bibliographical references from CNKI on the Chinese students who stuided in Japan before 1945. It consists in the main academic outputs on the topic, including journal article,s M.A. thesis, and doctoral dissertations.</li> <li>"CNKIjp2" is the pre-processed and cleaned file that was used for statistical analysis and topic modeling.</li> </ol> <p>The markdown script presents the core of the methodological framework for my study of Chinese historiography on the Japan-educated students in the late imperial and republican period. I developed this script as part of the paper titled "Japan-Educated Officials in China’s Wartime Central Administration (1944)". In this paper, I present a review of the literature on the Chinese students who went to Japan to study between 1896 and 1945. While the literature in English and Japanese is small and allwo for close reading, the literature in Chinese is massive. To explore this literature and identify research trends, I chose to apply topic modeling to the dataet of references extracted from CNKI (知網). The documentary basis consists in the abstracts of the academic outputs.</p>
Future hourly wet-bulb globe temperature dataset for 842 cities in Japan
We provide projected hourly wet-bulb globe temperature (WBGT) data for 842 cities in Japan for April to October in the period 2030-2100. The projection is generated by applying prediction models built by learning the relationship between historical hourly WBGT and the daily weather indices using a machine learning method called eXtreme Gradient Boosting, to a future climate scenario data for Japan area called NIES2020. Projection data for the historical period 1980-2014 are also available.
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Spectral dataset of daylights and surface properties of natural objects measured in Japan
<p>This is a spectral dataset of natural objects and daylights collected in Japan. </p> <p>We collected 359 natural objects and measured the reflectance of all objects and the transmittance of 75 leaves. We also measured daylights from dawn till dusk on four different days using a white plate placed (i) under the direct sun and (ii) under the casted shadow (in total 359 measurements). We also separately measured daylights at five different locations (including a sports ground, a space between tall buildings and a forest) with minimum time intervals to reveal the influence of surrounding environments on the spectral composition of daylights reaching the ground (in total 118 measurements).</p> <div> <div> <div> <p>If you use this dataset in your research, please cite the following publication.</p> </div> </div> </div> <div> <div> <div> </div> </div> </div> <div>Morimoto, T., Zhang, C., Fukuda, K., & Uchikawa, K. (2022). Spectral measurement of daylights and surface properties of natural objects in Japan. <em>Optics express</em>, <em>30</em>(3), 3183. https://doi.org/10.1364/OE.441063</div> <p> </p> <p>Dataset contains following Excel spread sheets and csv files:</p> <p><strong>(A) Surface properties of natural objects</strong></p> <p><strong> (A-1) Reflectance_ver1-2.xlsx and .csv</strong></p> <p><strong> (A-2) Transmittance_FrontSideUp_ver1-2.xlsx and .csv</strong></p> <p><strong> (A-2) Transmittance_BackSideUp_ver1-2.xlsx and .csv</strong></p> <p><strong>(B) Daylight measurements</strong></p> <p> <strong>(B-1) Daylight_TimeLapse_v1-2.xlsx and .csv</strong></p> <p> <strong>(B-2) Daylight_DifferentLocations_v1-2.xlsx and .csv</strong></p> <p> </p> <p>Data description</p> <p><strong>(A) Surface properties</strong></p> <p><strong>(A-1) Reflectance_ver1-2.xlsx and .csv</strong></p> <p>This file contains surface spectral reflectance data (380 - 780 nm, 5 nm step) of 359 natural objects, including 200 flowers, 113 leaves, 23 fruits, 6 vegetables, 8 barks, and 9 stones measured by a spectrophotometer (SR-2A, Topcon, Tokyo, Japan). Photos of all samples are included in the .xlsx file.</p> <p>For the analysis presented in the paper, we identified reflectance pairs that have a Pearson’s correlation coefficient across 401 spectral channels of more than 0.999 and removed one of reflectances from each pair. The column 'Used in analysis' indicates whether or not each sample is used for the analysis (TRUE indicates used and FALSE indicate not used).</p> <p>At the time of collection, we noted the scientific names of flowers, leaves and barks from a name board provided by the Tokyo Institute of Technology in which samples are collected. If not available, we used a smartphone software which automatically identifies the scientific name from an input image (<em>PictureThis - Plant Identifier</em> developed by Glority Global Group Ltd.). The names of 2 flowers and 9 stones whose name could not be identified through either method were left blank.</p> <p><strong>(A-2) Transmittance_FrontSideUp_v1-2.xlsx and .csv</strong></p> <p>This file contains surface spectral transmittance data (380 - 780 nm, 5 nm step) for 75 leaves measured by a spectrophotometer (SR-2A, Topcon, Tokyo, Japan). Photos of all samples are included in the .xlsx file.</p> <p>For this data, the transmittance was measured with the front-side of leaves up (the light was transmitted from the back side of the leaves). This is the data presented in the associated article.</p> <p><strong>(A-3) Transmittance_BackSideUp_v1-2.xlsx and .csv</strong></p> <p>Spectral transmittance data of the same leaves presented in (A-2).</p> <p>For this data, the transmittance was measured with the back-side of leaves up (the light was transmitted from the front side of the leaves).</p> <p> </p> <p><strong>(B) Daylight measurements</strong></p> <p><strong>(B-1) Daylight_TimeLapse_ver1-2.xlsx and .csv</strong></p> <p>This file contains daylight spectra from sunrise to sunset on four different days (2013/11/20, 2013/12/24, 2014/07/03 and 2014/10/27) measured by a spectrophotometer (SR-LEDW, Topcon, Tokyo, Japan) with a wavelength range from 380 nm to 780 nm with 1 nm step. We measured the reflected light from the white calibration plate placed either under a direct sunlight or under a casted shadow.</p> <p>The column 'Cloud cover' provides visual estimate of percentage of cloud cover across the sky at the time of each measurement. The column 'Red lamp' indicates whether an aircraft warning lamp at the measurement site was on (circle) or off (blank).</p> <p><strong>(B-2) Daylight_DifferentLocations_ver1-2.xlsx and .csv</strong></p> <p>This file includes daylight spectra measured at five different sites within the Suzukakedai Campus of Tokyo Institute of Technology with minimum time gap on 2014/07/08, using a spectroradiometer (IM-1000, Topcon) from 380 nm to 780 nm with 1 nm step. The instrument was oriented either towards the sun or towards the zenith sky. When the instrument was oriented to the sun, we measured spectra in two ways: (i) one using a black cylinder covering the photodetector and (ii) the other without using a cylinder.</p> <p>The column 'Cylinder' indicates whether the black cylinder was used (circle) or not (cross). The column 'Cloud cover' shows the visual estimate of percentage of cloud cover at the time of each measurement. The column 'Sun hidden in clouds' denotes whether the measurement was taken when the sun was covered by clouds (circle) or not (blank).</p>
Field measurements of aerosol particles near a runway at Narita International Airport, Japan
<p>Field measurements of aerosol particles were conducted at an observation point ~180 m from the centerline of runway A (~140 m from the edge of the runway) at Narita International Airport (NRT), Japan, in February 2018. The online aerosol instruments used for the field measurements consisted of an ultrafine condensation particle counter (UCPC; Model 3776, TSI, d<sub>50</sub> = 2.5 nm), a condensation particle counter (CPC; Model 3771, TSI, d<sub>50</sub> = 10 nm), a scanning mobility particle sizer (SMPS; Model 3080, TSI), and an engine exhaust particle sizer (EEPS; TSI). The other instruments included a carbon dioxide (CO<sub>2</sub>) monitor (Model LI-840, Li-Cor Biosciences) and meteorological sensors. The sampling inlet for the UCPC, CPC, and SMPS was switched between an unheated (room temperature) mode and a 350°C heated mode every eight hours during selected time periods to measure the total and the non-volatile particles, respectively. The EEPS was operated independently from the UCPC/CPC/SMPS inlet system and it measured the unheated particle number size distributions during the entire period.</p>
Landslides from Space - Kumamoto Earthquake-triggered landslide, Japan (15th April 2016)
<p>The April 2016 Kumamoto earthquake in Japan triggered a huge landslide. This landslide appeared close to the village Tateno (Minamiaso, Aso District) and burried a railway line, a highway and destroyed a bridge.</p> <p>The pre-event acquisition is from 3rd March 2016 (Sentinel-2) and the post-event acquisition is from 25th May 2016 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2016)</em></p>
Moment rate functios of shallow very low frequency earthquakes off the Cape Muroto and Kii Channel, along the Nankai Trough, Japan
<p>Moment rate functions of shallow very low frequency earthquakes (VLFEs) that occurred off the Cape Muroto and Kii Channel. A similar catalog but for southeast off the Kii Penisula can be downloaded from <a href="https://doi.org/10.5281/zenodo.5211090">https://doi.org/10.5281/zenodo.5211090 </a></p> <p>This data set is the supplement of "Takemura, S., Baba, S., Yabe, S., Emoto, K., Shiomi, K., & Matsuzawa, T. (2022). Source characteristics and along-strike variations of shallow very low frequency earthquake swarms on the Nankai Trough shallow plate boundary. <em>Geophysical Research Letters</em>, 49, e2022GL097979. <a href="https://doi.org/10.1029/2022GL097979">https://doi.org/10.1029/2022GL097979</a>"</p> <p><strong>Included files</strong></p> <ul> <li>YYYY-MM-DDThhmmssparam.stf<br> Parameter file for the Monte-Carlo-based simulated annealing estimation for a shallow VLFE occurred at hh:mm:ss on DDth MM YYYY (JST). Detection time, correlation coefficient, longitude, latitude, ratio (internal parameter), template index (internal parameter), assumed strike angle, dip angle, rake angle, source grid index (internal parameter), the number of the used stations, station list are included.</li> <li>YYYY-MM-DDThhmmss_STF.dat<br> Moment rate function for a shallow VLFE occurred at hh:mm:ss on DDth MM YYYY (JST). The optimal and original simulated annealing estimations are listed in the 2nd and 3rd columns, respectively. The time from the origin is represented in the 1st column</li> <li>VLFE_catalog.csv<br> CSV format file of Shallow VLFE catalog from Apr. 2004 to Mar. 2021. Origin time (JST), origin time (UTC), longitude (ºE), latitude (ºN), seismic moment (Nm), duration (s), VR (%), and Mw are listed.</li> <li>Data Set S1<br> CSV format file of shallow VLFE swarm catalog. Origin time (JST), epicenter locations, seismic moments, durations, moment magnitudes, VRs, and swarm indexes of each shallow VLFE. The “-” in the swarm index column means that this shallow VLFE does not belong to shallow VLFE swarms.</li> </ul> <p><strong>Citation</strong></p> <ul> <li>Takemura, S., Baba, S., Yabe, S., Emoto, K., Shiomi, K., & Matsuzawa, T. (2022). Source characteristics and along-strike variations of shallow very low frequency earthquake swarms on the Nankai Trough shallow plate boundary. <em>Geophysical Research Letters</em>, 49, e2022GL097979. <a href="https://doi.org/10.1029/2022GL097979">https://doi.org/10.1029/2022GL097979</a></li> <li>This data doi</li> </ul>
Historical Reconstruction Dataset of Hourly Expected On-Shore Wind Generation in Japan
<h2>Description</h2> <p>This is a historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing the spatio-temporal impact of on-shore wind in Japan.</p> <p>The dataset consists of a set of <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF</a> files with yearly archives of reconstruction results from 1958 to 2012; hourly expected on-shore wind power potential in Japan with a spatial resolution of approximately 5 km mesh has been reconstructed from the numerical weather model reanalysis results. The expected per-unit output values at each location were calibrated using a nonparametric machine learning model that learns statistical relationships between spatial/meteorological features of target locations and actual wind farm outputs.</p> <p>A convenient way to handle this dataset would be to use a tool for manipulating netCDF files, such as <a href="https://code.mpimet.mpg.de/projects/cdo">CDO: Climate Data Operators</a>.</p> <h2>Associated Publication</h2> <ul> <li>Yu Fujimoto, Masamichi Ohba, Yujiro Tanno, Daisuke Nohara, Yuki Kanno, Akihisa Kaneko, Yasuhiro Hayashi, Yuki Itoda, and Wataru Wayama, "Historical Reconstruction Dataset of Hourly Expected Wind Generation Based on Dynamically Downscaled Atmospheric Reanalysis for Assessing Spatio-Temporal Impact of On-Shore Wind in Japan", <em>Big Earth Data</em>, doi: 10.1080/20964471.2024.2374044 </li> </ul> <h2>Version history</h2> <ul> <li>Ver. 1.0: Released.</li> <li>Ver. 1.1: The preprocessing of the source information used for dataset preparation has changed.</li> <li>Ver. 1.2: The hyperparameter tuning scheme for the post-processing model has changed.</li> </ul>
National Checklists 2017: Japan Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Japan collected using effechecka and geonames polygons
Water Body Checklists 2019: Sea of Japan Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Sea of Japan using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
National Checklists 2019: Japan Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Japan collected using effechecka and geonames polygons
Water Body Checklists: Sea of Japan Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Sea of Japan using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Risk-perception, attitudes and behavioural intentions to spend on experiences in the post-Corona crisis: data from Italy, Denmark, China and Japan
<p>A cross-sectional survey conducted in Japan (n=1,111), Denmark (n=1,028), China (n=1,019) and Italy (n=1,014) during 10-24th of July 2020.</p> <p>Data format: sav (SPSS) and csv.</p>
Dataset: Country-wide mapping of harvest areas and post-harvest forest recovery using Landsat time series data in Japan
<p><strong>Description:</strong><br>This version of the repository holds an updated dataset of the annual forest disturbance agents/stable land cover maps across Japan using Landsat time series data. This version covers the period for 1985-2023 (1985-2019 in the original dataset). Harvest, Conversion, Thinning, and Other disturbances are mapped as different disturbance agents. The maps cover the entire country of Japan, excluding several isolated islands. The maps are available in compressed GeoTIFF format in Albers Equal Area Conic projection. The maps can be browsed in <a href="https://dulvrq3317.users.earthengine.app/view/japan-harvest-year-1985-2023">Google Earth Engine Apps</a>.</p> <p><strong>Citation:</strong><br>Shimizu, K. and Saito, H. (2021) Country-wide mapping of harvest areas and post-harvest forest recovery using Landsat time series data in Japan. <em>International Journal of Applied Earth Observation and Geoinformation</em> 104: 102555. <a href="https://doi.org/10.1016/j.jag.2021.102555">https://doi.org/10.1016/j.jag.2021.102555</a></p> <p><strong>Period:</strong><br>1985 to 2023 annually</p> <p><strong>Spatial resolution:</strong><br>30m</p> <p><strong>Projection:</strong><br>Albers Equal Area Conic Projection (Two standard parallels: 33N and 44N, Central Meridian: 135E)</p>
Data and results in "Relationship between crustal structure and plate convergence around the Izu collision zone in central Japan"
<p>“allrfstationlist.dat” contains the list of the used seismic stations. The four columns indicate the name, latitude, longitude, and altitude (m) of each station, respectively.</p> <p>“allrfevent.dat” contains the list of the used teleseismic events. From left to right, the 10 columns indicate the year, month (in number), day, hour, minute, and second of the origin time (Japan Standard Time), and the latitude (from –90 to 90), longitude (from –180 to 180), depth of the hypocenter, and magnitude of each event, respectively.</p> <p>“RFmoho.dat” contains the depth distribution of the Moho determined by our RF analysis. The third column indicates the depth (km) of the Moho at the given latitude (the second column) and longitude (the first column)</p> <p>“Tomo_depth_limited.txt” contains the depth distribution of the lower boundary of a layer with a P-wave velocity of 7.5–7.7 km/s in the model by Ishise et al. (2021), which was assumed as the Moho. The third column indicates its depth (km) at the given latitude (the first column) and longitude (the second column)</p> <p>“crustthickness_tomorf.dat” contains the thickness distribution of the crust of the Philippine Sea Plate determined from the geometry of its upper surface estimated by Hirose et al. (2008a, b) and Nakajima et al. (2009) and the Moho depth distribution shown in RFmoho.dat and Tomo_depth_limited.txt. The third column indicates the thickness (km) of the crust at the given latitude (the second column) and longitude (the first column). “RF” and “tomo” in the fourth column indicate the corresponding thickness determined based on the RF analysis and the model by Ishise et al. (2021), respectively.</p>
Multi-model Ensemble for Robust Verification of hydrological modeling in Japan (MERV-Jp)
<p>MERV-Jp is the dataset of meteorological forcing and multi-model runoff simulation in 135 (ver1.1) / 87 (ver2.0) Japanese basins, and contributes to carrying out a large sample rainfall-runoff simulation in Japan. In addition, MERV-Jp can be used as a benchmark to evaluate user's hydrological modeling. <br> The detailed description of MERV-Jp can be found at "Y. Sawada, S. Okugawa and T. Kimizuka (2022): Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins, Hydrological Research Letters, 16, 73-79" (https://doi.org/10.3178/hrl.16.73).</p>
Fig. 1 in Trionymus okiensis sp. nov., a new species of mealybug from Japan (Hemiptera: Coccomorpha: Pseudococcidae)
Fig. 1. Trionymus okiensis sp. nov., left side dorsal view, right side ventral view. ALC – anal lobe cerarius; ANT – antenna; DP– discoidal pore; LG – hind tibia, tarsus and claw; MP – multilocular pore; OCD I – large type of oral-collar tubular duct; OCD II – small type of oral-collar tubular duct; PC– penultimate cerarius; TP – trilocular pore. Scale bars: 200 μm for ANT, LG; 100 μm for ALC, PC; 10 μm for others.
Fig. 1 in A new species of the genus Liljeborgia Spence Bate, 1862 (Crustacea: Amphipoda: Liljeborgiidae) associated with the burrows of the spoon worm Urechis unicinctus in the Sea of Japan
Fig. 1. Habitus of live female specimen of Liljeborgia associata sp. nov. from Vostok Bay of the Sea of Japan with geographical indication of the collection sites in the Peter the Great Bay and Posjeta Bay of the Sea of Japan.
Fig. 4 in A new species of the genus Liljeborgia Spence Bate, 1862 (Crustacea: Amphipoda: Liljeborgiidae) associated with the burrows of the spoon worm Urechis unicinctus in the Sea of Japan
Fig. 4. Liljeborgia associata sp. nov., female (LEMMI) from Vostok Bay of the Sea of Japan. a. Pereopod 3. b. Pereopod 4. c. Pereopod 5. d. Distal segments of P5. e. Pereopod 6. f. Distal segments of P6. g. Pereopod 7.
Fig. 3 in A new species of the genus Liljeborgia Spence Bate, 1862 (Crustacea: Amphipoda: Liljeborgiidae) associated with the burrows of the spoon worm Urechis unicinctus in the Sea of Japan
Fig. 3. Liljeborgia associata sp. nov., female (LEMMI) from Vostok Bay of the Sea of Japan. a. Gnathopod 1. b. Chela of Gn1. c. Gnathopod 2. d. Chela of Gn2.
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