The Gross Domestic Product (GDP) in South Korea was worth 1712.79 billion US dollars in 2023, according to official data from the World Bank. The GDP value of South Korea represents 1.62 percent of the world economy. This dataset provides - South Korea GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in South Korea was last recorded at 2.50 percent. This dataset provides - South Korea Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset is provided by AIxBlock, an unified platform for AI development and AI workflows automation. This dataset contains around 700k sentences in Korean, making it a valuable resource for a wide range of language technology applications. All data has undergone quality assurance (QA) checks to ensure clarity, correctness, and natural phrasing. The dataset is well-suited for: Speech data generation (e.g., recording short audio clips lasting 8–30 seconds per sentence) Natural Language… See the full description on the dataset page: https://huggingface.co/datasets/AIxBlock/Korean-short-utterances.

South Korea recorded a trade surplus of 6940 USD Million in May of 2025. This dataset provides the latest reported value for - South Korea Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

KorMedLawQA: Korean Medical Law Question Answering Dataset

Welcome to the official repository for KorMedLawQA, a dataset of multiple-choice questions focused on South Korean medical law. This dataset is designed to support the development and evaluation of Large Language Models (LLMs) in the medical-legal domain, particularly for applications within the Korean healthcare environment, and to serve as a preparatory resource for the Korean Medical Licensing Examination (KMLE). The… See the full description on the dataset page: https://huggingface.co/datasets/snuh/KorMedLawQA.

The Gross Domestic Product per capita in South Korea was last recorded at 49995.49 US dollars in 2023, when adjusted by purchasing power parity (PPP). The GDP per Capita, in South Korea, when adjusted by Purchasing Power Parity is equivalent to 281 percent of the world's average. This dataset provides the latest reported value for - South Korea GDP per capita PPP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Imports: Japan: CRP: Plastics in Primary Forms data was reported at 77.956 USD mn in Jun 2018. This records a decrease from the previous number of 82.452 USD mn for May 2018. Imports: Japan: CRP: Plastics in Primary Forms data is updated monthly, averaging 62.602 USD mn from Jan 1991 (Median) to Jun 2018, with 330 observations. The data reached an all-time high of 111.615 USD mn in Apr 2011 and a record low of 31.232 USD mn in Feb 1992. Imports: Japan: CRP: Plastics in Primary Forms data remains active status in CEIC and is reported by Korea International Trade Association. The data is categorized under Global Database’s Korea – Table KR.JA015: Trade Statistics: Import: Value: SITC Classification: USA and Japan.

This data package includes the underlying data to replicate the charts and calculations presented in Korea’s trade policy agenda in an uncertain US trade environment, PIIE Working Paper 24-19.

If you use the data, please cite as:

Wolff, Alan Wm., and Han-koo Yeo. 2024. Korea’s trade policy agenda in an uncertain US trade environment. PIIE Working Paper 24-19. Washington: Peterson Institute for International Economics.

Inflation Rate in South Korea increased to 2.20 percent in June from 1.90 percent in May of 2025. This dataset provides the latest reported value for - South Korea Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Deeply Korean Read Speech Corpus

Summary

Pairs of Korean speakers reading a script with , with , are recorded. The recordings took place in , which are an anechoic chamber, studio apartment, and dance studio, of which the level of reverberation differs. And in order to examine the effect of the distance of mic from the source and device, every experiment is recorded at with , iPhone X, and Galaxy S7.

There were two individuals in a group, and each group was distinguished by a unique key(subject ID). Two speakers(speaker a, speaker b) were facing each other 1.4m apart from each other (0.7m from the middle line). They read the allocated scripts alternating between speaker a and b, as if they were talking to each other.

Studio Apartment	Dance studio	Anechoic Chamber
https://github.com/deeplyinc/Korean-Read-Speech-Corpus/blob/main/etc/StudioApartment.jpeg?raw=true" alt="Studio">	https://github.com/deeplyinc/Korean-Read-Speech-Corpus/blob/main/etc/DanceStudio.jpeg?raw=true" alt="Dacne studio">	https://github.com/deeplyinc/Korean-Read-Speech-Corpus/blob/main/etc/AnechoicChamber.jpeg?raw=true" alt="Studio">

Refer to the dataset descriptions in 'docs' for detailed description and statistics of the full set of the dataset.

The dataset is a subset(approximately 1%) of a much bigger dataset which were recorded under the same circumstances as these open source datasets. Please contact us(contact@deeplyinc.com) for the full set with the commercial license.

Dataset statistics

The illustrations below are the statistics about the Deeply Korean Read Speech Corpus. The first three are from the sample dataset, And the others are from the full dataset. To attain more insight about the dataset, please refer to the detailed description in 'docs' and 'Korea_Read_Speech_Corpus.json' in 'Dataset'.

The sample is a partial set of recordings from a single subject group(sub1001), which consists of 20-year-old female(speaker a) and 49-year-old female(speaker b).

https://github.com/deeplyinc/Korean-Read-Speech-Corpus/blob/main/etc/fig0.png?raw=true"> https://github.com/deeplyinc/Korean-Read-Speech-Corpus/blob/main/etc/fig1.png?raw=true"> https://github.com/deeplyinc/Korean-Read-Speech-Corpus/blob/main/etc/fig2.png?raw=true">

https://github.com/deeplyinc/Korean-Read-Speech-Corpus/blob/main/etc/fig3.png?raw=true"> https://github.com/deeplyinc/Korean-Read-Speech-Corpus/blob/main/etc/fig4.png?raw=true"> https://github.com/deeplyinc/Korean-Read-Speech-Corpus/blob/main/etc/fig5.png?raw=true">

https://github.com/deeplyinc/Korean-Read-Speech-Corpus/blob/main/etc/fig6.png?raw=true">

Structure

├── Dataset
│  ├── AirbnbStudio
│  │  ├── sub100100a00000.wav
│  │  └── ...
│  ├── AnechoicChamber
│  │  ├── sub100120a00000.wav
│  │  └── ...
│  ├── DanceStudio
│  │  ├── sub100110a00000.wav
│  │  └── ...
│  └── Korean_Read_Speech_Corpus.json
└── docs
  ├── Deeply Korean Read Speech Corpus_Eng.pdf
  └── Deeply Korean Read Speech Corpus_Kor.pdf

Korean_Read_Speech_Corpus.json

{'AirbnbStudio': 
        {'sub100100a00000': {'text_sentiment': 0,
                  'voice_sentiment': -1,
                  'subjectID': 'sub1001',
                  'speaker': 'a',
                  'age': 20,
                  'sex': 0,
                  'noise': 0,
                  'location': 0,
                  'distance': 0,
                  'device': 0,
                  'text': '저 식당 음식이 정말 맛있나 봐요.',
                  'text_code': 'aa0',
                  'rms': 0.024304501712322235,
                  'length': 2.71825},
         ...
         },
  ...
}

How to decode

Text sentiment: {-1: negative, 0: neutral, 1: positive}

Vocie sentiment: {-1: negative, 0: neutral, 1: positive}

Subject ID: Unique 'sub + 4-digit' key allocated to each subject group

Speaker: unique key allocated to each indiivdual in the subject group.

Sex: {0: Female, 1: Male}

Noise: {0: Noiseless, 1: Indoor no...

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format. Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc. Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for south korea in the U.S.