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1,498 results for “South Korea”
Fig. 3. Loxoconcha tosaensis Ishizaki, 1968. A in Six species of the ostracod families Loxoconchidae and Cushmanideidae from South Korea
Fig. 3. Loxoconcha tosaensis Ishizaki, 1968. A, SEM photograph of Male left carapace; B, Right carapace.
Fig. 7 in Six species of the ostracod families Loxoconchidae and Cushmanideidae from South Korea
Fig. 7. Pontocythere miurensis (Hanai, 1959). Female. A, Left valve; B, Right valve; C, Antennule; D, Antenna; E, Copulatory organ.
Fig. 1 in Morphology and Molecular Phylogeny of Pseudocyrtohymenides lacunae nov. gen., nov. spec. (Ciliophora: Oxytrichidae) from South Korea
Fig. 1. Pseudocyrtohymenides lacunae nov. gen., nov. spec. (A, B), living specimens and (C–E), after protargol impregnation. (A, B), ventral (A) and dorsal (B) views of representative specimens; arrow denotes contractile vacuole. (C, D), ventral (C) and dorsal (D) views of the holotype specimen. (E), ventral views showing the variation of frontal-ventral-transverse cirri. AZM, adoral zone of membranelles; DK1, dorsal kinety 1; EM, endoral membrane; G, cortical granules; LMR, left marginal cirral row; Ma, macronuclear nodules; Mi, micronuclei; PM, paroral membrane; RMR, right marginal cirral row; TC, transverse cirri. Scale bars: 50 µm.
Fig. 2 in Morphology and Molecular Phylogeny of Pseudocyrtohymenides lacunae nov. gen., nov. spec. (Ciliophora: Oxytrichidae) from South Korea
Fig. 2. Pseudocyrtohymenides lacunae nov. gen., nov. spec. (A–E), living specimens; (F–K), after protargol impregnation. (A, D), ventral views of representative specimens. (B, C, E), dorsal views showing cortical granules and dorsal bristles. (F–K), dorsal (F, J) and ventral (G–I, K) views showing infraciliature and nuclear apparatus. AZM, adoral zone of membranelles; CV, contractile vacuole; DB, dorsal bristle; DK1–4, dorsal kineties 1–4; EM, endoral membrane; FC, frontal cirrus; G, cortical granules; LMR, left marginal cirral row; Ma, macronuclear nodules; Mi, micronuclei; PM, paroral membrane; RMR, right marginal cirral row; TC, transverse cirri; VC, ventral cirri. Scale bars: 100 µm in A–D and 50 µm in F–G.
Fig. 3 in Morphology and Molecular Phylogeny of Pseudocyrtohymenides lacunae nov. gen., nov. spec. (Ciliophora: Oxytrichidae) from South Korea
Fig. 3. Phylogenetic tree of SSU rRNA gene sequences, showing the position of Pseudocyrtohymenides lacunae nov. spec. on the basis of Maximum Likelihood (ML) and Bayesian Inference (BI). Bootstrap values of ML and posterior probabilities of BI were denoted on each interior branch. If the values of the bootstrap and the posterior probability were less than 50% and 0.50, respectively, they were excluded. The scale bar represents one nucleotide substitution per 100 nt.
Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
<p>Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer's, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people's daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question of whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments. To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (the United States and South Korea) with SARs deployed in each user's home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to generate a large-scale dataset of over 270 million data points representing 173 hours of randomly-sampled naturalistic interaction data between the human and robot pet. Interaction behaviors included activities like playing, petting, talking, cooking, etc.</p>
KF-metaweb: A trophic metaweb of freshwater ecosystems of South Korea (Version 1.1)
Open the record for dataset details and reuse information.
Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
Open the record for dataset details and reuse information.
The Situation of South Korea regarding the early stage of COVID-19 outbreak
<p>Drive-Through Screening Centers for COVID-19</p>
Impact of Different Nesting Methods on the Simulation of a Severe Convective Event Over South Korea Using the Weather Research and Forecasting Model
<p>The data from various platforms (NCEP FNL, TRMM, ERA5, AWS) and WRF Model output utilised to generate the figures in the current study (https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JD033084) are available at this Zenodo data repository.</p>
Figure 8. - Metasternal process (in the circle) and aedeagi of Dicronocephalusadamsidrumonti and Dicronocephalusadamsiadamsi. A, B, C, D Dicronocephalusadamsidrumonti (Tibet) E, F, G, H Dicronocephalusadamsidrumonti (Sichuan) I, J, K, L Dicronocephalusadamsiadamsi (South Korea) M, N, O, P Dicronocephalusadamsiadamsi (North Korea) Q, R, S, T Dicronocephalusadamsiadamsi (Dandong, China).
Figure 8. - Metasternal process (in the circle) and aedeagi of Dicronocephalusadamsidrumonti and Dicronocephalusadamsiadamsi. A, B, C, D Dicronocephalusadamsidrumonti (Tibet) E, F, G, H Dicronocephalusadamsidrumonti (Sichuan) I, J, K, L Dicronocephalusadamsiadamsi (South Korea) M, N, O, P Dicronocephalusadamsiadamsi (North Korea) Q, R, S, T Dicronocephalusadamsiadamsi (Dandong, China).
Figs 8–14 in New data on the chalcid wasps of the family Pteromalidae (Hymenoptera: Chalcidoidea) from South Korea
Figs 8–14. Miscogasteriella vladimiri Tselikh, Lee et Ku, 2023, male: 8 – body, lateral
Figs 1–7. Janssoniella kawabatai Tselikh, 2020 in New data on the chalcid wasps of the family Pteromalidae (Hymenoptera: Chalcidoidea) from South Korea
Figs 1–7. Janssoniella kawabatai Tselikh, 2020, male: 1 – body, lateral view; 2 –
Instrument response files for seismic stations in South Korea (accelerometer)
<p><strong>To do</strong></p><p>[1.01] I have found something wrong in poles and zeros for the KG and KN networks. Please do not use them until update. I am sorry for it. (14 November 2023)</p><p> </p><p><strong>Example 1 (Seismic Analysis Code)</strong></p><p>Examples to deconvolve the instrument response from raw data are below, using the Seismic Analysis Code (SAC, version sac-101.6a). </p><p> </p><p>1. A unit of output is 'm/s2'</p><p>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 w acc.sac q</p><p> </p><p>2. A unit of output is 'm/s'</p><p>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 TRANS FROM ACC TO VEL w vel.sac q</p><p> </p><p>3. A unit of output is 'm'</p><p>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 TRANS FROM ACC TO VEL rtr rmean taper TRANS FROM VEL TO NONE w disp.sac q</p><p> </p><p>Note that the unit of the output is incoherent with the SAC header 'IDEP'.</p><p><strong>Example 2 (StationXML with obspy)</strong></p><p>#this is same as the code for the velocity seismometers.</p><p> </p><p><strong>Update notes</strong></p><ul><li>[1.01]<ul><li>Typo at INPUT UNIT for KG is modified (M/S -> M/S**2)</li><li>Stations KS.CE2A, KS.HA2B are added</li><li>StationXML file is added (ksgn.xml)</li></ul></li><li>[1.00] The files for the KG network are made based on the logs until 12 April 2019 (personal communication with the Korea Institute of Geoscience and Mineral Resources).</li></ul><p><strong>Others</strong></p><p>Velocity seismometer <a href="https://doi.org/10.5281/zenodo.3700312">https://doi.org/10.5281/zenodo.3700312</a><br>Accelerometer <a href="https://doi.org/10.5281/zenodo.3872436">https://doi.org/10.5281/zenodo.3872436</a></p><p> </p><p><strong>License</strong></p><p>This distribution follows the Creative Commons Attribution 4.0 International. You are free to modify and redistribute the files.</p><p> </p>
Instrument response files for seismic stations in South Korea
<p> </p> <p><strong>Citation</strong></p> <p>If you use it, please cite the following article in your work: </p> <p>Lim and Kim (2020), A dataset of seismic sensor responses of South Korea seismic stations. Journal of the Geological Society of Korea, 56(4), 515-524, http://dx.doi.org/10.14770/jgsk.2020.56.4.515 (Korean with English abstract)</p> <p> </p> <p><strong>Example 1 (Seismic Analysis Code)</strong></p> <p>Examples to deconvolve the instrument response from raw data are below, using the Seismic Analysis Code (SAC, version sac-101.6a). </p> <p> </p> <p>1. A unit of output is 'm/s'</p> <pre><code>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 w vel.sac q</code></pre> <p> </p> <p>2. A unit of output is 'm'</p> <pre><code>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 TRANS FROM VEL TO NONE w disp.sac q</code></pre> <p> </p> <p>3. A unit of output is 'm/s2'</p> <pre><code>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 TRANS FROM VEL TO ACC w acc.sac q</code></pre> <p>Note that the unit of the output is incoherent with the SAC header 'IDEP'.</p> <p> </p> <p><strong>Example 2 (StationXML with obspy)</strong></p> <pre><code class="language-python">#!/usr/bin/env python3 from obspy import read_inventory from obspy import read st = read('/path/to/waveforms') inv = read_inventory('ksgn.xml') st.detrend(type="linear") st.detrend(type="demean") pref = [0.1, 0.2, 15, 20] #output = m/s**2 st2 = st.copy() st2.remove_response(inventory=inv,output='ACC',pre_filt=pref,taper=True,zero_mean=False) st2[0].write('acc.sac',format="SAC") #... #output = m/s st2 = st.copy() st2.remove_response(inventory=inv,output='VEL',pre_filt=pref,taper=True,zero_mean=False) st2[0].write('vel.sac',format="SAC") #... #output = m st2 = st.copy() st2.remove_response(inventory=inv,output='DISP',pre_filt=pref,taper=True,zero_mean=False) st2[0].write('disp.sac',format="SAC") #...</code></pre> <p> </p> <p><strong>Update notes</strong></p> <ul> <li>[1.02] Typo in station name of KS.HA2B is modified (KS.HA2B -> HA2B)</li> <li>[1.01] <ul> <li>Files for the sensor JC-V100 are provided from Won-Young Kim (2 Dec 2021)</li> <li>Station KS.HA2B is added</li> <li>StationXML file is added (ksgn.xml)</li> </ul> </li> <li>[1.00] The files for the KG network are made based on the logs until 12 April 2019 (personal communication with the Korea Institute of Geoscience and Mineral Resources). </li> </ul> <p> </p> <p><strong>Pending works</strong></p> <p>Reflecting a change in logger of KS.DGY2</p> <p> </p> <p><strong>Others</strong></p> <p>Velocity seismometer <a href="https://doi.org/10.5281/zenodo.3700312">https://doi.org/10.5281/zenodo.3700312</a><br> Accelerometer <a href="https://doi.org/10.5281/zenodo.3872436">https://doi.org/10.5281/zenodo.3872436</a></p> <p> </p> <p><strong>License</strong></p> <p>This distribution follows the Creative Commons Attribution 4.0 International. You are free to modify and redistribute the files.</p>
Paddy Rice Labeling Sites in South Korea (2018)
<p>The paddy rice was visually interpreted at 30 sites in South Korea. The sites were selected at each province by a proportional stratified sampling method according to the paddy rice area statistics(Statistics Korea), so the dataset can be used for the validation on model generalization over the entire country. The paddy rice areas were visually interpreted by using Google Earth Pro and street view services(<a href="https://map.naver.com">https://map.naver.com</a>, <a href="https://map.kakao.com">https://map.kakao.com</a>) and updated to the state of 2018.</p> <p>Supported by the CALLISTO (No. 101004152) project, which has been funded by EU Horizon 2020 programs.</p>
Paddy Rice Maps South Korea (2017-2021)
<p>This dataset includes paddy rice maps in South Korea from 2017 to 2021 with 10 m resolution. The paddy rice maps are a product of deep learning model predictions and DO NOT represent ground truth information. The predictions were made by analyzing time series Sentinel-1 images based on the deep learning architecture that integrates U-Net and RNNs layers desined by eGIS/RS lab., Korea University. The deep learning model has been trained on the farm map produced by Korean Ministry of Agriculture, Food and Rural Affairs(MAFRA). The validation accuracy and Cohen's kappa value are 96.50%, 0.7857 each which were calculated from the 40% of the farm map. For more information please contact to the KU-eGIS/RS lab.</p> <p><br> Supported by the CALLISTO (No. 101004152) project, which has been funded by EU Horizon 2020 programs.</p>
Fig. 4 in First Deep-Sea Shark Fossil Teeth From The Miocene Of South Korea
Fig. 4. Life reconstruction of deep-water sharks lived in the East sea of
Figs 7–10 in Empis (Planempis) tripotini sp. n. from South Korea: one of the largest empidoid flies ever discovered (Diptera: Empididae)
Figs 7–10. Males of new species with their prey, dorsal view. 7, 9 – E. (P.) tripotini sp. n.;
Figs 5–6. Male habitus, lateral view. 5 in Empis (Planempis) tripotini sp. n. from South Korea: one of the largest empidoid flies ever discovered (Diptera: Empididae)
Figs 5–6. Male habitus, lateral view. 5 – Empis (Planempis) tripotini sp. n.; 6 – E. (P.)
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.