South Korea Google Search Trends: Online Classroom: Google Classroom data was reported at 81.000 Score in 14 May 2025. This records a decrease from the previous number of 98.000 Score for 13 May 2025. South Korea Google Search Trends: Online Classroom: Google Classroom data is updated daily, averaging 28.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 03 Apr 2025 and a record low of 0.000 Score in 29 Jan 2025. South Korea Google Search Trends: Online Classroom: Google Classroom data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s South Korea – Table KR.Google.GT: Google Search Trends: by Categories.

According to data collected in the fourth quarter of 2022, close to ********** of Google Ads' advertisement inflow was generated on mobile devices. Inflow via personal computers accounted for the remaining ** percent of ad inflow.

According to a survey conducted in South Korea in 2023, around ** percent of respondents stated that one of their most commonly used cloud services was Naver's NDrive. This was followed by Google Drive and Apple iCloud, at about ** percent and ** percent respectively. Naver Cloud Naver Cloud is a subsidiary of Naver, which is South Korea’s largest search portal. This cloud company offers IT platform services, such as storage, IT infrastructure consulting, data centers, networks, and security. Unlike other cloud services from foreign companies, which are provided in English, Naver Cloud supports its services in Korean. It also operates real-time customer service centers since it mainly targets Korean customers. Based on their localized service, Naver’s revenue from the cloud sector has been on the rise in recent years. Cloud services usage in South Korea Due to global IT trends, such as digitalization and remote working, the penetration rate of cloud services in South Korea grew in the latter half of the 2010s. In particular, Korean users utilize cloud services primarily for data and information management. Clouds can be used for both business and personal purposes. In the case of personal usage, most users were in their twenties and thirties, as they used cloud services for storing photos or documents online. As clouds are a digital-intensive service, it may require digital literacy to access and use, which can be an potential issue for the elderly population.

The South Korea cloud computing market is experiencing robust growth, projected to reach a market size of 5.52 million USD in 2025, exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 23.82% from 2019 to 2033. This expansion is fueled by several key drivers. Increased digital transformation initiatives across various sectors, including manufacturing, BFSI (Banking, Financial Services, and Insurance), and the public sector, are significantly boosting demand for cloud-based solutions. The rising adoption of cloud services by Small and Medium-sized Enterprises (SMEs) and large enterprises alike, driven by the need for enhanced scalability, cost-effectiveness, and agility, further contributes to market growth. Furthermore, government initiatives promoting digitalization and the expansion of high-speed internet infrastructure are creating a favorable environment for cloud adoption. The market is segmented across deployment models (Public, Private, and Hybrid Cloud) and end-user industries, with the public cloud segment dominating due to its flexibility and cost-efficiency. Leading players such as Amazon Web Services (AWS), Google LLC, Microsoft Corporation, and domestic players like Naver Cloud and Kakao Enterprise are vying for market share, fueling competition and innovation. Despite the promising growth trajectory, certain challenges exist. Data security concerns and regulatory compliance requirements pose potential restraints. The need for robust cybersecurity measures and adherence to data privacy regulations, particularly given the stringent data protection laws in South Korea, will influence future market dynamics. However, the ongoing technological advancements in areas such as artificial intelligence (AI) and the Internet of Things (IoT), along with increasing investment in cloud infrastructure, are expected to mitigate these challenges and propel further growth in the South Korean cloud computing market throughout the forecast period (2025-2033). The market's substantial growth potential makes it an attractive destination for both domestic and international cloud providers. This in-depth report provides a comprehensive analysis of the South Korea cloud computing market, encompassing its current state, future trends, and key growth drivers. With a study period spanning from 2019 to 2033, a base year of 2025, and a forecast period from 2025 to 2033, this report offers invaluable insights for businesses, investors, and policymakers seeking to understand this dynamic market. The report leverages data from the historical period (2019-2024) to provide a robust foundation for future projections, valued in millions of units. Recent developments include: June 2024: South Korean telecom giant KT Corp. partnered with Microsoft to bolster the local artificial intelligence (AI) and cloud computing sectors. Together, they will spearhead collaborative AI and cloud research initiatives.May 2024: Korean Air partnered with Amazon Web Services (AWS) to create an AI Contact Center (AICC) platform. This initiative leverages artificial intelligence (AI) technologies to enhance Korean Air's customer service capabilities. The AICC, a cloud-based platform, utilizes AI-driven voice bots and chatbots to address customer inquiries.. Key drivers for this market are: Increasing Adoption of Digital Technologies, Government Initiatives to Promote Cloud Adoption. Potential restraints include: Increasing Adoption of Digital Technologies, Government Initiatives to Promote Cloud Adoption. Notable trends are: Government Initiatives to Promote Cloud Adoption to Drive the Market.

Google Search Trends: Online Games: Call of Duty data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. Google Search Trends: Online Games: Call of Duty data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 27.000 Score in 19 Dec 2021 and a record low of 0.000 Score in 14 May 2025. Google Search Trends: Online Games: Call of Duty data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s South Korea – Table KR.Google.GT: Google Search Trends: by Categories.

🇰🇷📚 korean-textbooks-edu

maywell/korean_textbooks의 모든 subset을 devngho/ko_edu_classifier_v2_nlpai-lab_KoE5 모델로 평가한 데이터셋

  불러오기

from datasets import load_dataset

ds = load_dataset("devngho/korean-textbooks-edu", name="scored_over_3", split="train")

  성능

예정

  컴퓨팅

Google Cloud TPU, transformers, JAX, tpuswarm

  하드웨어

TPU v4-8 x 4 instances, 약 2시간 소요 이 연구는 Google의 TPU Research Cloud (TRC)의 Cloud TPU 제공으로 수행되었습니다. ⚡

  라이선스

원본… See the full description on the dataset page: https://huggingface.co/datasets/devngho/korean-textbooks-edu.

Based on data collected by Google on search queries throughout 2022, South Korean Google search engine users seemed to search more for terms that challenge traditional norms and that focus more on self-identity. For example, "plus size" related online searches increased by over 110 percent, interest for office workers' education doubled, and web searches concerning work-life balance increased by over 40 percent in 2022.

Based on data collected by Google on search queries throughout 2022, terms searched by South Korean Google search engine users showed the influence of inflation, with around a *** and ** percent increase in the use of related keywords such as "interest rates" and "thrifty", respectively. Carbon neutrality was also a point of interest for South Korean consumers as the search term grew over *** percent during the same time period.

Kor-Learner is a Korean grammatical error correction (GEC) dataset collected grammatically from two sources, and the correct sentences were read using Google Text-to-Speech(TTS) system. The general public was tasked with dictating grammatically correct sentences and transcribe them. It contains more than 17K sentence pairs.

South Korea Google Search Trends: Economic Measures: Unemployment data was reported at 80.000 Score in 14 May 2025. This records a decrease from the previous number of 82.000 Score for 13 May 2025. South Korea Google Search Trends: Economic Measures: Unemployment data is updated daily, averaging 51.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 06 Jan 2025 and a record low of 0.000 Score in 12 Aug 2023. South Korea Google Search Trends: Economic Measures: Unemployment data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s South Korea – Table KR.Google.GT: Google Search Trends: by Categories.

Based on data collected by Google on search queries throughout 2022, South Korean Google search engine users had a heightened interest in travelling Europe as a leisure activity, with a *** percent surge in web searches. Aside from being able to travel more comfortably again, technological advancements like VR chatting also gathered interest with a search query increase of around *** percent.

Google Search Trends: Travel & Accommodations: American Airlines data was reported at 1.000 Score in 14 May 2025. This stayed constant from the previous number of 1.000 Score for 13 May 2025. Google Search Trends: Travel & Accommodations: American Airlines data is updated daily, averaging 1.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 7.000 Score in 30 Jan 2025 and a record low of 0.000 Score in 30 Apr 2025. Google Search Trends: Travel & Accommodations: American Airlines data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s South Korea – Table KR.Google.GT: Google Search Trends: by Categories.

South Korea Google Search Trends: Government Measures: Unemployment Benefits data was reported at 34.000 Score in 14 May 2025. This records a decrease from the previous number of 39.000 Score for 13 May 2025. South Korea Google Search Trends: Government Measures: Unemployment Benefits data is updated daily, averaging 40.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 06 Mar 2023 and a record low of 0.000 Score in 29 Jan 2025. South Korea Google Search Trends: Government Measures: Unemployment Benefits data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s South Korea – Table KR.Google.GT: Google Search Trends: by Categories.

Overview

The Korea University Camera-LIDAR (KUCL) dataset contains images and point clouds acquired in indoor and outdoor environments for various applications (e.g., calibration of rigid-body transformation between camera and LIDAR) in robotics and computer vision communities.

• Indoor dataset: contains 63 pairs of images and point clouds ('indoor.zip'). We collected the indoor dataset in a static indoor environment with walls, floor, and ceiling.
• Outdoor dataset: 61 pairs of images and point clouds ('outdoor.zip'). We collected the outdoor dataset in an outdoor environment including buildings and trees.

Setup

The images were taken using a Point Grey Ladybug5 (specifications) camera and point clouds were acquired with a Velodyne VLP-16 LIDAR (specifications). We rigidly mounted both sensors on the sensor frame during the overall data acquisition. Each pair of images and point clouds was discretely acquired while maintaining the sensor system standing still to reduce time-synchronization problems.

Description

Each dataset (zip file) is organized as follows:

• images/pano: This folder contains spherical panorama images (8000 X 4000) collected using the Ladybug5.
• images/pinhole/cam0~cam5: These folders contain rectified pinhole images (2448 X 2048) collected using six cameras (cam0~cam5) of the Ladybug5.
• images/pinhole/mask: This folder contains the mask (BW image) of each camera of the Ladybug5.
• images/pinhole/cam_param_pinhole.txt: This file contains extrinsic (transformation from the Ladybug5 to each lens) and intrinsic (focal length and center) parameters of each lens of the Ladybug5. For details of Ladybug5 coordinate system, please refer to the technical application note.
• scans: This folder contains point clouds collected using the VLP-16 LIDAR in text files. The first line of each file is the number of points (N), and the remaining lines are points and corresponding reflectivities (N X 4).

We also provide MATLAB functions projecting point cloud onto spherical panorama and pinhole images. Before running the following functions, please unzip the dataset file ('indoor.zip' or 'outdoor.zip') under the main directory.

• run_pano_projection.m: This function projects points onto a spherical panorama image. Lines 19-20 select dataset and index of an image and a point cloud.
• run_pinhole_projection.m: This function projects points onto a pinhole image. Lines 19-21 select dataset, index of an image and a point cloud, and pinhole camera index.

The rigid-body transformation between the Ladybug5 and the VLP-16 in each function is acquired using our edge-based Camera-LIDAR calibration method with Gaussian Mixture Model (GMM). For the details, please refer to our paper (https://doi.org/10.1002/rob.21893).

Citation

Please cite the following paper when using this dataset in your work.

• Jaehyeon Kang and Nakju L. Doh, "Automatic Targetless Camera-LIDAR Calibration by Aligning Edge with Gaussian Mixture Model," Journal of Field Robotics, vol. 37, no. 1, pp.158-179, 2020.
• @ARTICLE {kang-2020-jfr,
  AUTHOR = {Jaehyeon Kang and Nakju Lett Doh},
  TITLE = {Automatic Targetless Camera–{LIDAR} Calibration by Aligning Edge with {Gaussian} Mixture Model},
  JOURNAL = {Journal of Field Robotics},
  YEAR = {2020},
  VOLUME = {37},
  NUMBER = {1},
  PAGES = {158--179},
  }

License information

The KUCL dataset is released under a Creative Commons Attribution 4.0 International License, CC BY 4.0

Contact Information

If you have any issues about the KUCL dataset, please contact us at kangjae07@gmail.com.

The repository contains public line list and summaries of the COVID-19 outbreak in South Korea from official sources (the Korean Centers for Disease Control, the websites of the city governments of Seoul, Busan, Daegu, Gwangju, and Ulsan, and the website of Gyeonggi Province government) translated and transcribed by Sang Woo Park. The data are available in excel file and into Google Sheets. The data contains detailed information about each case including dates of onset, dates of hospitalization, dates of recovery or death, dates of discharge, location, contacts information, demographic information, and other notes in a line list format. The data also include aggregated information about testing, total cases, and deaths at state level, by geographic region, by community and by age-group and source files in pdf format.

South Korea Google Search Trends: Online Classroom: Zoom data was reported at 1.000 Score in 24 Nov 2024. This stayed constant from the previous number of 1.000 Score for 23 Nov 2024. South Korea Google Search Trends: Online Classroom: Zoom data is updated daily, averaging 1.000 Score from Dec 2021 (Median) to 24 Nov 2024, with 1090 observations. The data reached an all-time high of 9.000 Score in 06 Sep 2022 and a record low of 0.000 Score in 18 Nov 2024. South Korea Google Search Trends: Online Classroom: Zoom data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s South Korea – Table KR.Google.GT: Google Search Trends: by Categories.

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(https://map.naver.com, https://map.kakao.com) and updated to the state of 2018.

Supported by the CALLISTO (No. 101004152) project, which has been funded by EU Horizon 2020 programs.

The purpose of this project is to write a large and in sync dataset focused patient characteristics for identify the Risk groups and characteristics human-level that impact on infection, Complication and Death as a result of the disease

for more detail about the data:

https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

last date for update 06.12.2020

4535323 rows

Version 5:

A version that includes cleaning the data and engineering new features for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

Version 6:

Machine-ready version of machine learning model Consists only of INT and FLOAT for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

problem with dataset

• There may be duplicate cases (which come from different data systems) Focusing on countries: France, Korea, Indonesia, Tunisia, Japan, canada, new_zealand, singapore, guatemala, philippines, india, vietnam, hong kong , Toronto, Mexico.

• I did not check the credibility of the sources

• Concerns of the credibility of the Mexican government's data

• Concerns about the credibility of the data of the Chinese government

Acknowledgements and Sources

india_wiki https://www.kaggle.com/karthikcs1/covid19-coronavirus-patient-list-karnataka-india

philippines https://www.kaggle.com/sundiver/covid19-philippines-edges

france https://www.kaggle.com/lperez/coronavirus-france-dataset

korea https://www.kaggle.com/kimjihoo/coronavirusdataset

indonesia https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

tunisia https://www.kaggle.com/ghassen1302/coronavirus-tunisia

japan https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan

world https://github.com/beoutbreakprepared/nCoV2019/tree/master/latest_data

canada https://www.kaggle.com/ryanxjhan/coronaviruscovid19-canada

new_zealand https://www.kaggle.com/madhavkru/covid19-nz

singapore https://www.kaggle.com/rhodiumbeng/singapores-covid19-cases

guatemala https://www.kaggle.com/ncovgt2020/covid19-guatemala

colombia https://www.kaggle.com/sebaxtian/covid19co

mexico https://www.kaggle.com/lalish99/covid19-mx

india_data https://www.kaggle.com/samacker77k/covid19india

vietnam https://www.kaggle.com/nh

kerla https://www.kaggle.com/baburajr/covid19inkerala

hong_kong https://www.kaggle.com/teddyteddywu/covid-19-hong-kong-cases

toronto https://www.kaggle.com/divyansh22/toronto-covid19-cases

Determining the severity illness according to WHO: https://www.who.int/publications/i/item/clinical-management-of-covid-19

• Each update contains the information found in the previous version

*Thanks to all sources

*If you have any helpful information or suggestions for improvement, write

Building notebook

• netbook PART A - cleaning and conact the data: https://www.kaggle.com/shirmani/characteristics-of-corona-patient-ds-v4

• netbook PART B- features Engineering: https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-b/edit

• part C data QA https://www.kaggle.com/shirmani/qa-characteristics-corona-patients-part-c

• netbook PART D - format the data to int and float cols (model preparation): https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-d

Authors of the Dataset:

Pratik Bhowal (B.E., Dept of Electronics and Instrumentation Engineering, Jadavpur University Kolkata, India) [LinkedIn], [Github] Subhankar Sen (B.Tech, Dept of Computer Science Engineering, Manipal University Jaipur, India) [LinkedIn], [Github], [Google Scholar] Jin Hee Yoon (faculty of the Dept. of Mathematics and Statistics at Sejong University, Seoul, South Korea) [LinkedIn], [Google Scholar] Zong Woo Geem (faculty of College of IT Convergence at Gachon University, South Korea) [LinkedIn], [Google Scholar] Ram Sarkar( Professor at Dept. of Computer Science Engineering, Jadavpur Univeristy Kolkata, India) [LinkedIn], [Google Scholar]

Overview The authors have created a new dataset known as Novel COVID-19 Chestxray Repository by the fusion of publicly available chest-xray image repositories. In creating this combined dataset, three different datasets obtained from the Github and Kaggle databases,created by the authors of other research studies in this field, were utilized.In our study,frontal and lateral chest X-ray images are used since this view of radiography is widely used by radiologist in clinical diagnosis.In the following section, authors have summarized how this dataset is created.

COVID-19 Radiography Database: The first release of this dataset reports 219 COVID-19,1345 viral pneumonia and 1341 normal radiographic chest X-ray images. This dataset was created by a team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh in collaboration with medical doctors and specialists from Pakistan and Malaysia.This database is regularly updated with the emergence of new cases of COVID-19 patients worldwide.Related Paper:https://arxiv.org/abs/2003.13145

COVID-Chestxray set:Joseph Paul Cohen and Paul Morrison and Lan Dao have created a public image repository on Github which consists both CT scans and digital chest x-rays.The data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Children’s medical center.With the aid of metadata information provided along with the dataset,we were able to extract 521 COVID-19 positive,239 viral and bacterial pneumonias;which are of the following three broad categories:Middle East Respiratory Syndrome (MERS),Severe Acute Respiratory Syndrome (SARS), and Acute Respiratory Distress syndrome (ARDS);and 218 normal radiographic chest X-ray images of varying image resolutions. Related Paper: https://arxiv.org/abs/2006.11988

Actualmed COVID chestxray dataset:Actualmed-COVID-chestxray-dataset comprises of 12 COVID-19 positive and 80 normal radiographic chest x-ray images.

The combined dataset includes chest X-ray images of COVID-19,Pneumonia and Normal (healthy) classes, with a total of 752, 1584, and 1639 images respectively. Information about the Novel COVID-19 Chestxray Database and its parent image repositories is provided in Table 1.

Table 1: Dataset Description | Dataset| COVID-19 |Pneumonia | Normal | | ------------- | ------------- | ------------- | -------------| | COVID Chestxray set | 521 |239|218| | COVID-19 Radiography Database(first release) | 219 |1345|1341| | Actualmed COVID chestxray dataset| 12 |0|80| | Total|752|1584|1639|

DATA ACCESS AND USE: Academic/Non-Commercial Use Dataset License : Database: Open Database, Contents: Database Contents

Google Search Trends: Online Movie: YouTube data was reported at 74.000 Score in 14 May 2025. This records an increase from the previous number of 73.000 Score for 13 May 2025. Google Search Trends: Online Movie: YouTube data is updated daily, averaging 83.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 25 Dec 2024 and a record low of 0.000 Score in 31 Jan 2023. Google Search Trends: Online Movie: YouTube data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s South Korea – Table KR.Google.GT: Google Search Trends: by Categories.