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데이터셋

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  • 2021 해외 공개 English
    Coughs: ESC-50 and FSDKaggle2018
    • 데이터 제공처 OpenAIRE
    • 데이터 리포지터리
    • 생성자 Abdelkhalek, Mahmoud; Jinyi Qiu; Hernandez, Michelle; Bozkurt, Alper; Lobaton, Edgar;
    • 과제명
    • 과제책임자
    • 과제수행기관
    • 부처
    • 라이센스유형 CC-BY-4.0;
    • 주제분류
    • 인용횟수 0
    • doi 10.5281/zenodo.5136592
    • 버전 1.0

    This dataset consists of timestamps for coughs contained in files extracted from the ESC-50 and FSDKaggle2018 datasets. Citation This dataset was generated and used in our paper: Mahmoud Abdelkhalek, Jinyi Qiu, Michelle Hernandez, Alper Bozkurt, Edgar Lobaton, “Investigating the Relationship between Cough Detection and Sampling Frequency for Wearable Devices,” in the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2021. Please cite this paper if you use the timestamps.csv file in your work. Generation The cough timestamps given in the timestamps.csv file were generated using the cough templates given in figures 3 and 4 in the paper: A. H. Morice, G. A. Fontana, M. G. Belvisi, S. S. Birring, K. F. Chung, P. V. Dicpinigaitis, J. A. Kastelik, L. P. McGarvey, J. A. Smith, M. Tatar, J. Widdicombe, "ERS guidelines on the assessment of cough", European Respiratory Journal 2007 29: 1256-1276; DOI: 10.1183/09031936.00101006 More precisely, 40 files labelled as "coughing" in the ESC-50 dataset and 273 files labelled as "Cough" in the FSDKaggle2018 dataset were manually searched using Audacity for segments of audio that closely matched the aforementioned templates, both visually and auditorily. Some files did not contain any coughs at all, while other files contained several coughs. Therefore, only the files that contained at least one cough are included in the coughs directory. In total, the timestamps of 768 cough segments with lengths ranging from 0.2 seconds to 0.9 seconds were extracted. Description The audio files are presented in wav format in the coughs directory. Files named in the general format of "*-*-*-24.wav" were extracted from the ESC-50 dataset, while all other files were extracted from the FSDKaggle2018 dataset. The timestamps.csv file contains the timestamps for the coughs and it consists of four columns:
    file_name,cough_number,start_time,end_time
    Files in the file_name column can be found in the coughs directory. cough_number refers to the index of the cough in the corresponding file. For example, if the file X.wav contains 5 coughs, then X.wav will be repeated 5 times under the file_name column, and for each row, the cough_number will range from 1 to 5. start_time refers to the starting time of a cough segment measured in seconds, while end_time refers to the end time of a cough segment measured in seconds. Licensing The ESC-50 dataset as a whole is licensed under the Creative Commons Attribution-NonCommercial license. Individual files in the ESC-50 dataset are licensed under different Creative Commons licenses. For a list of these licenses, see LICENSE. The ESC-50 files in the cough directory are given for convenience only, and have not been modified from their original versions. To download the original files, see the ESC-50 dataset. The FSDKaggle2018 dataset as a whole is licensed under the Creative Commons Attribution 4.0 International license. Individual files in the FSDKaggle2018 dataset are licensed under different Creative Commons licenses. For a list of these licenses, see the License section in FSDKaggle2018. The FSDKaggle2018 files in the cough directory are given for convenience only, and have not been modified from their original versions. To download the original files, see the FSDKaggle2018 dataset. The timestamps.csv file is licensed under the Creative Commons Attribution-NonCommercial 4.0 International license.;This work was supported by the National Science Foundation under award IIS-1915599 and EEC-1160483 (ERC for ASSIST).

  • 2021 해외 공개 English
    KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle
    • 데이터 제공처 OpenAIRE
    • 데이터 리포지터리
    • 생성자 Quaranta, Luigi; Calefato, Fabio; Lanubile, Filippo;
    • 과제명
    • 과제책임자
    • 과제수행기관
    • 부처
    • 라이센스유형 CC-BY-4.0;
    • 주제분류
    • 인용횟수 0
    • doi 10.5281/zenodo.4468523
    • 버전 1.0

    KGTorrent is a dataset of Python Jupyter notebooks from the Kaggle platform.

    The dataset is accompanied by a MySQL database containing metadata about the notebooks and the activity of Kaggle users on the platform. The information to build the MySQL database has been derived from Meta Kaggle, a publicly available dataset containing Kaggle metadata.

    In this package, we share the complete KGTorrent dataset (consisting of the dataset itself plus its companion database), as well as the specific version of Meta Kaggle used to build the database.

    More specifically, the package comprises the following three compressed archives:

    1. KGT_dataset.tar.bz2, the dataset of Jupyter notebooks;

    2. KGTorrent_dump_10-2020.sql.tar.bz2, the dump of the MySQL companion database;

    3. MetaKaggle27Oct2020.tar.bz2, a copy of the Meta Kaggle version used to build the database.

    Moreover, we include KGTorrent_logical_schema.pdf, the logical schema of the KGTorrent MySQL database.

  • 2020 해외 공개 Korean
    COVID-19 in India
    • 데이터 제공처 코비드-19
    • 데이터 리포지터리
    • 생성자
    • 과제명
    • 과제책임자
    • 과제수행기관
    • 부처
    • 라이센스유형 CC-BY;
    • 주제분류 보건의료;
    • 인용횟수 0
    • doi 10.22711/0101ZZ013883525511.0
    • 버전 1.0

    Dataset on novel Covid-19 in India Coronaviruses are a large family of viruses which may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The most recently discovered coronavirus causes coronavirus disease COVID-19 - World Health Organization

  • 2020 해외 공개 English
    buds-lab/building-data-genome-project-2: v1.0
    • 데이터 제공처 OpenAIRE
    • 데이터 리포지터리
    • 생성자 Miller, Clayton; Anjukan Kathirgamanathan; Picchetti, Bianca; Pandarasamy Arjunan; Park, June Young; Zoltan Nagy; Raftery, Paul; Hobson, Brodie W.; Zixiao Shi; Meggers, Forrest;
    • 과제명
    • 과제책임자
    • 과제수행기관
    • 부처
    • 라이센스유형 CC-BY-4.0;
    • 주제분류
    • 인용횟수 0
    • doi 10.5281/zenodo.3887305
    • 버전 1.0

    The BDG2 open data set consists of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters are collected from 19 sites across North America and Europe, and they measure electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the ASHRAE organization in October-December 2019. This subset includes data from 2,380 meters from 1,448 buildings that were used in the GEPIII, a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.

  • 2020 해외 공개 English
    buds-lab/building-data-genome-project-2: v1.0
    • 데이터 제공처 OpenAIRE
    • 데이터 리포지터리
    • 생성자 Clayton Miller; Anjukan Kathirgamanathan; Bianca Picchetti; Pandarasamy Arjunan; June Young Park; Zoltan Nagy; Paul Raftery; Brodie W. Hobson; Zixiao Shi; Forrest Meggers;
    • 과제명
    • 과제책임자
    • 과제수행기관
    • 부처
    • 라이센스유형 CC-BY-4.0;
    • 주제분류
    • 인용횟수 0
    • doi 10.5281/zenodo.3887306
    • 버전 1.0

    The BDG2 open data set consists of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters are collected from 19 sites across North America and Europe, and they measure electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the ASHRAE organization in October-December 2019. This subset includes data from 2,380 meters from 1,448 buildings that were used in the GEPIII, a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.

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