cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3902672
title: Benzene Concentration Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ This goal of this dataset is to predict benzene concentration in an Italian city. This dataset contains 8878 time series obtained from the Air Quality dataset from the UCI repository. The time series has 8 dimensions which consists of hourly averaged responses from an array of 5 metal oxide chemical sensors embedded in an Air Quality Chemical Multisensor Device, as well as temperature, relative humidity and absolute humidity. The Air Quality Chemical Multisensor device was located on the field in a significantly polluted area, at road level, within an Italian city. Data were recorded from March 2004 to February 2005 (one year) representing the longest freely available recordings of on field deployed air quality chemical sensor devices responses. Ground Truth hourly averaged concentrations for CO, Non Metanic Hydrocarbons, Benzene, Total Nitrogen Oxides (NOx) and Nitrogen Dioxide (NO2) and were provided by a co-located reference certified analyzer.<br> <br> Please refer to https://archive.ics.uci.edu/ml/datasets/Air+Quality for more details. Relevant papers<br> S. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005.<br> Saverio De Vito, Marco Piga, Luca Martinotto, Girolamo Di Francia, CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization, Sensors and Actuators B: Chemical, Volume 143, Issue 1, 4 December 2009, Pages 182-191, ISSN 0925-4005.<br> S. De Vito, G. Fattoruso, M. Pardo, F. Tortorella and G. Di Francia, Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction, in IEEE Sensors Journal, vol. 12, no. 11, pp. 3215-3224, Nov. 2012. doi: 10.1109/JSEN.2012.2192425 <br> Citation request<br> S. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005
DOI: 10.5281/zenodo.3902690
title: Covid-19 Death Rate Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict COVID-19's death rate on 1st April 2020 for each country using daily confirmed cases for the last three months. This dataset contains 201 time series, where each time series is the daily confirmed cases for a country. The data was obtained from WHO's COVID-19 database. <br> Please refer to https://covid19.who.int/ for more details
DOI: 10.5281/zenodo.3902689
title: Covid-19 Death Rate Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict COVID-19's death rate on 1st April 2020 for each country using daily confirmed cases for the last three months. This dataset contains 201 time series, where each time series is the daily confirmed cases for a country. The data was obtained from WHO's COVID-19 database. <br> Please refer to https://covid19.who.int/ for more details
DOI: 10.5281/zenodo.3902694
title: Flood Modeling Dataset 1

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict maximum water depth for flood modelling. The dataset contains 673 hourly rainfall events time series which are used to predict the maximum water depth of a domain (Digital Elevation Model, DEM). The rainfall events and DEM are generated synthetically by researchers at Monash University because real DEM data with accurate rainfall events are rare. <br> <br> Data Donor<br> Jihane Elyahyioui, jihane.elyahyioui@monash.edu
DOI: 10.5281/zenodo.3902693
title: Flood Modeling Dataset 1

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict maximum water depth for flood modelling. The dataset contains 673 hourly rainfall events time series which are used to predict the maximum water depth of a domain (Digital Elevation Model, DEM). The rainfall events and DEM are generated synthetically by researchers at Monash University because real DEM data with accurate rainfall events are rare. <br> <br> Data Donor<br> Jihane Elyahyioui, jihane.elyahyioui@monash.edu
DOI: 10.5281/zenodo.3902696
title: Flood Modeling Dataset 2

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict maximum water depth for flood modelling. The dataset contains 559 hourly rainfall events time series which are used to predict the maximum water depth of a domain (Digital Elevation Model, DEM). The rainfall events and DEM are generated synthetically by researchers at Monash University because real DEM data with accurate rainfall events are rare. <br> <br> Data Donor<br> Jihane Elyahyioui, jihane.elyahyioui@monash.edu
DOI: 10.5281/zenodo.3902695
title: Flood Modeling Dataset 2

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict maximum water depth for flood modelling. The dataset contains 559 hourly rainfall events time series which are used to predict the maximum water depth of a domain (Digital Elevation Model, DEM). The rainfall events and DEM are generated synthetically by researchers at Monash University because real DEM data with accurate rainfall events are rare. <br> <br> Data Donor<br> Jihane Elyahyioui, jihane.elyahyioui@monash.edu
DOI: 10.5281/zenodo.3902698
title: Flood Modeling Dataset 3

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict maximum water depth for flood modelling. The dataset contains 613hourly rainfall events time series which are used to predict the maximum water depth of a domain (Digital Elevation Model, DEM). The rainfall events and DEM are generated synthetically by researchers at Monash University because real DEM data with accurate rainfall events are rare. <br> <br> Data Donor<br> Jihane Elyahyioui, jihane.elyahyioui@monash.edu
DOI: 10.5281/zenodo.3902697
title: Flood Modeling Dataset 3

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict maximum water depth for flood modelling. The dataset contains 613hourly rainfall events time series which are used to predict the maximum water depth of a domain (Digital Elevation Model, DEM). The rainfall events and DEM are generated synthetically by researchers at Monash University because real DEM data with accurate rainfall events are rare. <br> <br> Data Donor<br> Jihane Elyahyioui, jihane.elyahyioui@monash.edu
DOI: 10.5281/zenodo.3902704
title: Household Active Power Consumption Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict total active power consumption in a household. This dataset contains 1440 time series obtained from the Individual household electric power consumption dataset from the UCI repository. The time series has 5 dimensions. This includes measurements for voltage, current annd 3 sub-metering energy usage. <br> Please refer to https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption for more details<br> <br> Source<br> Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France<br> Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France
DOI: 10.5281/zenodo.3902703
title: Household Active Power Consumption Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict total active power consumption in a household. This dataset contains 1440 time series obtained from the Individual household electric power consumption dataset from the UCI repository. The time series has 5 dimensions. This includes measurements for voltage, current annd 3 sub-metering energy usage. <br> Please refer to https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption for more details<br> <br> Source<br> Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France<br> Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France
DOI: 10.5281/zenodo.3902706
title: Household Reactive Power Consumption Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict total reactive power consumption in a household. This dataset contains 1440 time series obtained from the Individual household electric power consumption dataset from the UCI repository. The time series has 5 dimensions. This includes measurements for voltage, current annd 3 sub-metering energy usage. <br> Please refer to https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption for more details<br> <br> Source<br> Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France<br> Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France
DOI: 10.5281/zenodo.3902705
title: Household Reactive Power Consumption Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict total reactive power consumption in a household. This dataset contains 1440 time series obtained from the Individual household electric power consumption dataset from the UCI repository. The time series has 5 dimensions. This includes measurements for voltage, current annd 3 sub-metering energy usage. <br> Please refer to https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption for more details<br> <br> Source<br> Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France<br> Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France
DOI: 10.5281/zenodo.3902709
title: IEEEPPG Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to estimate heart rate using PPG sensors. This dataset contains 3096, 5 dimensional time series obtained from the IEEE Signal Processing Cup 2015: Heart Rate Monitoring During Physical Exercise Using Wrist-Type Photoplethysmographic (PPG) Signals. Two-channel PPG signals, three-axis acceleration signals, and one-channel ECG signals were simultaneously recorded from subjects with age from 18 to 35. For each subject, the PPG signals were recorded from wrist by two pulse oximeters with green LEDs (wavelength: 515nm). Their distance (from center to center) was 2 cm. The acceleration signal was also recorded from wrist by a three-axis accelerometer. Both the pulse oximeter and the accelerometer were embedded in a wristband, which was comfortably worn. The ECG signal was recorded simultaneously from the chest using wet ECG sensors. All signals were sampled at 125 Hz and sent to a nearby computer via Bluetooth. Please refer to https://sites.google.com/site/researchbyzhang/ieeespcup2015 for more details.<br> <br> Copyright<br> All datasets have copyrights. But you can freely use them for the Signal Processing Cup or your own academic research, as long as you suitably cite the data in your works.<br> <br> Citation request<br> Z. Zhang, Z. Pi, B. Liu, TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise, IEEE Transactions on Biomedical Engineering, vol. 62, no. 2, pp. 522-531, February 2015, DOI: 10.1109/TBME.2014.2359372
DOI: 10.5281/zenodo.3902717
title: News Headline Sentiment Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict sentiment score for news headline. This dataset contains 83164 time series obtained from the News Popularity in Multiple Social Media Platforms dataset from the UCI repository. This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation. The time series has 3 dimensions. <br> <br> Please refer to https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms for more details<br> <br> Citation request<br> Nuno Moniz and Luis Torgo (2018), Multi-Source Social Feedback of Online News Feeds, CoRR
DOI: 10.5281/zenodo.3902726
title: News Title Sentiment Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict sentiment score for news title. This dataset contains 83164 time series obtained from the News Popularity in Multiple Social Media Platforms dataset from the UCI repository. This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation. The time series has 3 dimensions. <br> <br> Please refer to https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms for more details<br> <br> Citation request<br> Nuno Moniz and Luis Torgo (2018), Multi-Source Social Feedback of Online News Feeds, CoRR
DOI: 10.5281/zenodo.3902725
title: News Title Sentiment Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict sentiment score for news title. This dataset contains 83164 time series obtained from the News Popularity in Multiple Social Media Platforms dataset from the UCI repository. This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation. The time series has 3 dimensions. <br> <br> Please refer to https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms for more details<br> <br> Citation request<br> Nuno Moniz and Luis Torgo (2018), Multi-Source Social Feedback of Online News Feeds, CoRR
DOI: 10.5281/zenodo.4001456
title: BIDMC Heart Rate Dataset (32 seconds window)

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ <br> The goal of this dataset is to estimate heart rate using PPG and ECG data. This dataset contains 7949 time series obtained from the Physionet's BIDMC PPG and Respiration dataset, which was extracted from the much larger MIMIC II waveform database.<br> <br> Please refer to https://physionet.org/content/bidmc/1.0.0/ for more details<br> <br> Relevant papers<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> <br> Citation request<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
DOI: 10.5281/zenodo.3902675
title: BIDMC Heart Rate Dataset (32 seconds window)

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ <br> The goal of this dataset is to estimate heart rate using PPG and ECG data. This dataset contains 7949 time series obtained from the Physionet's BIDMC PPG and Respiration dataset, which was extracted from the much larger MIMIC II waveform database.<br> <br> Please refer to https://physionet.org/content/bidmc/1.0.0/ for more details<br> <br> Relevant papers<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> <br> Citation request<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
DOI: 10.5281/zenodo.4001463
title: BIDMC Respiratory Rate Dataset (32 seconds window)

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ <br> The goal of this dataset is to estimate respiratory rate using PPG and ECG data. This dataset contains 7949 time series obtained from the Physionet's BIDMC PPG and Respiration dataset, which was extracted from the much larger MIMIC II waveform database.<br> <br> Please refer to https://physionet.org/content/bidmc/1.0.0/ for more details<br> <br> Relevant papers<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> <br> Citation request<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
DOI: 10.5281/zenodo.3902684
title: BIDMC Respiratory Rate Dataset (32 seconds window)

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ <br> The goal of this dataset is to estimate respiratory rate using PPG and ECG data. This dataset contains 7949 time series obtained from the Physionet's BIDMC PPG and Respiration dataset, which was extracted from the much larger MIMIC II waveform database.<br> <br> Please refer to https://physionet.org/content/bidmc/1.0.0/ for more details<br> <br> Relevant papers<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> <br> Citation request<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
DOI: 10.5281/zenodo.4001464
title: BIDMC Blood Oxygen Saturation Dataset (32 seconds window)

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ <br> The goal of this dataset is to estimate blood oxygen saturation level using PPG and ECG data. This dataset contains 7949 time series obtained from the Physionet's BIDMC PPG and Respiration dataset, which was extracted from the much larger MIMIC II waveform database.<br> <br> Please refer to https://physionet.org/content/bidmc/1.0.0/ for more details<br> <br> Relevant papers<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> <br> Citation request<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.