cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25646/8262
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.5281/zenodo.3892918
title: Kaggle Wikipedia Web Traffic Daily Dataset (without Missing Values)

description: This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-10. The original dataset contains missing values. They have been simply replaced by zeros.
description: {"references": ["Google, 2017. Web traffic time series forecasting. URL https://www.kaggle.com/c/web-traffic-time-series-forecasting"]}
DOI: 10.5281/zenodo.3892813
title: Kaggle Wikipedia Web Traffic Daily Dataset (with Missing Values)

description: This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-10.
description: {"references": ["Google, 2017. Web traffic time series forecasting. URL https://www.kaggle.com/c/web-traffic-time-series-forecasting"]}
DOI: 10.5281/zenodo.4656093
title: Tourism Quarterly Dataset

description: This dataset contains 427 quarterly time series used in the Kaggle Tourism forecasting competition.
description: {"references": ["Athanasopoulos, G., Hyndman, R. J., Song, H., Wu, D. C., 2011. The tourism forecasting competition. International Journal of Forecasting 27 (3), 822\u2013844.", "Ellis, P., 2018. Tcomp: data from the 2010 tourism forecasting competition. https://cran.r-project.org/web/packages/Tcomp."]}
DOI: 10.5281/zenodo.3889386
title: Tourism Quarterly Dataset

description: This dataset contains 427 quarterly time series used in the Kaggle Tourism forecasting competition.
description: {"references": ["Athanasopoulos, G., Hyndman, R. J., Song, H., Wu, D. C., 2011. The tourism forecasting competition. International Journal of Forecasting 27 (3), 822\u2013844.", "Ellis, P., 2018. Tcomp: data from the 2010 tourism forecasting competition. https://cran.r-project.org/web/packages/Tcomp."]}
DOI: 10.5281/zenodo.4656096
title: Tourism Monthly Dataset

description: This dataset contains 366 monthly time series used in the Kaggle Tourism forecasting competition.
description: {"references": ["Athanasopoulos, G., Hyndman, R. J., Song, H., Wu, D. C., 2011. The tourism forecasting competition. International Journal of Forecasting 27 (3), 822\u2013844.", "Ellis, P., 2018. Tcomp: data from the 2010 tourism forecasting competition. https://cran.r-project.org/web/packages/Tcomp."]}
DOI: 10.5281/zenodo.3889425
title: Tourism Monthly Dataset

description: This dataset contains 366 monthly time series used in the Kaggle Tourism forecasting competition.
description: {"references": ["Athanasopoulos, G., Hyndman, R. J., Song, H., Wu, D. C., 2011. The tourism forecasting competition. International Journal of Forecasting 27 (3), 822\u2013844.", "Ellis, P., 2018. Tcomp: data from the 2010 tourism forecasting competition. https://cran.r-project.org/web/packages/Tcomp."]}
DOI: 10.18112/openneuro.ds003592.v1.0.1
title: Neurocognitive aging data release with behavioral, structural, and multi-echo functional MRI measures
DOI: 10.5281/zenodo.4656103
title: Tourism Yearly Dataset

description: This dataset contains 518 yearly time series used in the Kaggle Tourism forecasting competition.
description: {"references": ["Athanasopoulos, G., Hyndman, R. J., Song, H., Wu, D. C., 2011. The tourism forecasting competition. International Journal of Forecasting 27 (3), 822\u2013844.", "Ellis, P., 2018. Tcomp: data from the 2010 tourism forecasting competition. https://cran.r-project.org/web/packages/Tcomp."]}
DOI: 10.5281/zenodo.3887459
title: Tourism Yearly Dataset

description: This dataset contains 518 yearly time series used in the Kaggle Tourism forecasting competition.
description: {"references": ["Athanasopoulos, G., Hyndman, R. J., Song, H., Wu, D. C., 2011. The tourism forecasting competition. International Journal of Forecasting 27 (3), 822\u2013844.", "Ellis, P., 2018. Tcomp: data from the 2010 tourism forecasting competition. https://cran.r-project.org/web/packages/Tcomp."]}
DOI: 10.5285/dada967a-15b6-4c18-9e00-aee820a638e5
title: Persistent organic pollutant concentrations in artificial sea ice experiments conducted between 01-May-2017 to 01-Jun-2017

description: Persistent organic pollutant concentrations in artificial sea ice experiments at the Roland von Glasow Air-Sea-Ice Chamber (RvG-ASIC) at the University of East Anglia, UK. Experiments involved investigating chemical contaminant behaviours during sea ice formation and melt in order to assess possible exposure risk to sea ice biota. Funding was provided by: NERC ENVISION Doctoral Training Centre (NE/L002604/1). NERC and the German Federal Ministry of Education and Research (BMBF) funded Changing Arctic Ocean program EISPAC project (NE/R012857/1). British Antarctic Survey Collaboration Voucher. EUROCHAMP-2020 Infrastructure Activity under grant agreement (No 730997).
description: Experiments were conducted in the Roland von Glasow Air-Sea-Ice Chamber (RvG-ASIC) at the University of East Anglia, UK. Samples of sea ice were collected, and contaminant concentrations were measured.
description: Contaminant analysis was performed using a Thermo GC-MS (Trace GC Ultra - DSQ). Xcalibur software Version 1.4.x.
description: Mass-labelled chemical standards were used as recovery standards to assess analytical performance. A 10-point mixed-standard calibration curve was used for quantification Sample replicates (n = 3) were used to determine method performance. Method detection limits (MDL) were calculated from quality control blanks.
DOI: 10.5281/zenodo.4656110
title: NN5 Daily Dataset (with Missing Values)

description: This dataset was used in the NN5 forecasting competition. It contains 111 daily time series from the banking domain. The goal is predicting the daily cash withdrawals from ATMs in UK.<br>
description: {"references": ["Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A., 2012. A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition. Expert Systems withApplications 39(8), 7067 - 7083.", "Neural Forecasting Competitions, 2008. NN5 forecasting competition for artificial neural networks and computational intelligence. Accessed: 2020-05-10. URL http://www.neural-forecasting-competition.com/NN5/"]}
DOI: 10.5281/zenodo.3889633
title: NN5 Daily Dataset (with Missing Values)

description: This dataset was used in the NN5 forecasting competition. It contains 111 daily time series from the banking domain. The goal is predicting the daily cash withdrawals from ATMs in UK.<br>
description: {"references": ["Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A., 2012. A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition. Expert Systems withApplications 39(8), 7067 - 7083.", "Neural Forecasting Competitions, 2008. NN5 forecasting competition for artificial neural networks and computational intelligence. Accessed: 2020-05-10. URL http://www.neural-forecasting-competition.com/NN5/"]}
DOI: 10.5281/zenodo.4656117
title: NN5 Daily Dataset (without Missing Values)

description: This dataset was used in the NN5 forecasting competition. It contains 111 time series from the banking domain. The goal is predicting the daily cash withdrawals from ATMs in UK. <br> The original dataset contains missing values. A missing value on a particular day is replaced by the median across all the same days of the week along the whole series.
description: {"references": ["Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A., 2012. A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition. Expert Systems with Applications 39(8), 7067 - 7083.", "Neural Forecasting Competitions, 2008. NN5 forecasting competition for artificial neural networks and computational intelligence. Accessed: 2020-05-10. URL http://www.neural-forecasting-competition.com/NN5/"]}
DOI: 10.5281/zenodo.3889739
title: NN5 Daily Dataset (without Missing Values)

description: This dataset was used in the NN5 forecasting competition. It contains 111 time series from the banking domain. The goal is predicting the daily cash withdrawals from ATMs in UK. <br> The original dataset contains missing values. A missing value on a particular day is replaced by the median across all the same days of the week along the whole series.
description: {"references": ["Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A., 2012. A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition. Expert Systems with Applications 39(8), 7067 - 7083.", "Neural Forecasting Competitions, 2008. NN5 forecasting competition for artificial neural networks and computational intelligence. Accessed: 2020-05-10. URL http://www.neural-forecasting-competition.com/NN5/"]}
DOI: 10.5281/zenodo.4656125
title: NN5 Weekly Dataset

description: This is the aggregated version of the daily dataset used in the NN5 forecasting competition. It contains 111 weekly time series from the banking domain. The goal is predicting the weekly cash withdrawals from ATMs in UK. The original dataset contains missing values. A missing value on a particular day is replaced by the median across all the same days of the week along the whole series and then, have been aggregated into weekly.
description: {"references": ["Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A., 2012. A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition. Expert Systems with Applications 39(8), 7067 - 7083.", "Neural Forecasting Competitions, 2008. NN5 forecasting competition for artificial neural networks and computational intelligence. Accessed: 2020-05-10. URL http://www.neural-forecasting-competition.com/NN5/"]}
DOI: 10.5281/zenodo.3889749
title: NN5 Weekly Dataset

description: This is the aggregated version of the daily dataset used in the NN5 forecasting competition. It contains 111 weekly time series from the banking domain. The goal is predicting the weekly cash withdrawals from ATMs in UK. The original dataset contains missing values. A missing value on a particular day is replaced by the median across all the same days of the week along the whole series and then, have been aggregated into weekly.
description: {"references": ["Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A., 2012. A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition. Expert Systems with Applications 39(8), 7067 - 7083.", "Neural Forecasting Competitions, 2008. NN5 forecasting competition for artificial neural networks and computational intelligence. Accessed: 2020-05-10. URL http://www.neural-forecasting-competition.com/NN5/"]}
DOI: 10.5281/zenodo.4656101
title: Supplementary material

description: Supplementary material Nutrients
DOI: 10.5281/zenodo.4656100
title: Supplementary material

description: Supplementary material Nutrients
DOI: 10.5517/ccdc.csd.cc23p511
title: CCDC 1956752: Experimental Crystal Structure Determination

description: Related Article: Yang Feng, Zhikun Wang, Weidong Fan, Zixi Kang, Shou Feng, Lili Fan, Songqing Hu, Daofeng Sun|2020|J.Mater.Chem.A|8|13132|doi:10.1039/C9TA13547B
DOI: 10.5281/zenodo.3889785
title: Traffic Hourly Dataset

description: This dataset contains the San Francisco Traffic dataset used by Lai et al. (2017). It contains 862 hourly time series showing the road occupancy rates on the San Francisco Bay area freeways from 2015 to 2016.
description: {"references": ["Caltrans, 2020. Caltrans performance measurement system, california department of transportation. Accessed: 2020-04-30. URL http://pems.dot.ca.gov/", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.3889805
title: Traffic Weekly Dataset

description: This dataset contains an aggregated version of the San Francisco Traffic dataset used by Lai et al. (2017). It contains 862 weekly time series showing the road occupancy rates on the San Francisco Bay area freeways from 2015 to 2016.
description: {"references": ["Caltrans, 2020. Caltrans performance measurement system, california department of transportation. Accessed: 2020-04-30. URL http://pems.dot.ca.gov/", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.4656140
title: Electricity Hourly Dataset

description: The electricity dataset represents the electricity consumption of 370 clients recorded in 15-minutes periods in Kilowatt (kW) from 2011 to 2014. The uploaded dataset is an aggregated version of the original dataset used by Lai et al. (2017). It contains 321 hourly time series from 2012 to 2014.
description: {"references": ["UCI, 2020. Electricityloaddiagrams20112014 data set, uci machine learning repository. Accessed: 2020-04-30. URL https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.3889829
title: Electricity Hourly Dataset

description: The electricity dataset represents the electricity consumption of 370 clients recorded in 15-minutes periods in Kilowatt (kW) from 2011 to 2014. The uploaded dataset is an aggregated version of the original dataset used by Lai et al. (2017). It contains 321 hourly time series from 2012 to 2014.
description: {"references": ["UCI, 2020. Electricityloaddiagrams20112014 data set, uci machine learning repository. Accessed: 2020-04-30. URL https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.3889952
title: Electricity Weekly Dataset

description: The electricity dataset represents the electricity consumption of 370 clients recorded in 15-minutes periods in Kilowatt (kW) from 2011 to 2014. The uploaded dataset is an aggregated version of the hourly electricity dataset used by Lai et al. (2017). It contains 321 weekly time series from 2012 to 2014.
description: {"references": ["UCI, 2020. Electricityloaddiagrams20112014 data set, uci machine learning repository. Accessed: 2020-04-30. URL https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.3889973
title: Solar Dataset (10 Minutes Observations)

description: The solar dataset contains approximately 6000 simulated time series representing 5-minute solar power and hourly day-ahead forecasts of photovoltaic (PV) power plants in United States in 2006. <br> The uploaded dataset contains the aggregated version of a subset of the original dataset used by Lai et al. (2017). It contains 137 time series representing the solar power production recorded per every 10 minutes in Alabama state in 2006.
description: {"references": ["National Renewable Energy Laboratory, 2020. Solar power data for integration studies. Accessed: 2020-04-30. URL https://www.nrel.gov/grid/solar-power-data.html", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.3890009
title: Solar Weekly Dataset

description: The solar dataset contains approximately 6000 simulated time series representing 5-minute solar power and hourly day-ahead forecasts of photovoltaic (PV) power plants in United States in 2006. <br> The uploaded dataset contains the aggregated version of a subset of the original dataset. It contains 137 time series representing the weekly solar power production in Alabama state in 2006.
description: {"references": ["National Renewable Energy Laboratory, 2020. Solar power data for integration studies. Accessed: 2020-04-30. URL https://www.nrel.gov/grid/solar-power-data.html", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.4656154
title: M1 Quarterly Dataset

description: This dataset contains 203 quarterly time series used in the M1 forecasting competition. The series are belonging to 7 different domains: macro 1, macro 2, micro 1, micro 2, micro 3, industry and demographic.
description: {"references": ["Makridakis, S., Andersen, A., Carbone, R. F., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen,E., Winkler, R. L., 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting 1 (2), 111\u2013153."]}
DOI: 10.5281/zenodo.3890100
title: M1 Quarterly Dataset

description: This dataset contains 203 quarterly time series used in the M1 forecasting competition. The series are belonging to 7 different domains: macro 1, macro 2, micro 1, micro 2, micro 3, industry and demographic.
description: {"references": ["Makridakis, S., Andersen, A., Carbone, R. F., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen,E., Winkler, R. L., 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting 1 (2), 111\u2013153."]}
DOI: 10.5281/zenodo.4656159
title: M1 Monthly Dataset

description: This dataset contains 617 monthly time series used in the M1 forecasting competition. The series are belonging to 7 different domains: macro 1, macro 2, micro 1, micro 2, micro 3, industry and demographic.
description: {"references": ["Makridakis, S., Andersen, A., Carbone, R. F., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen,E., Winkler, R. L., 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting 1 (2), 111\u2013153."]}
DOI: 10.5281/zenodo.3890136
title: M1 Monthly Dataset

description: This dataset contains 617 monthly time series used in the M1 forecasting competition. The series are belonging to 7 different domains: macro 1, macro 2, micro 1, micro 2, micro 3, industry and demographic.
description: {"references": ["Makridakis, S., Andersen, A., Carbone, R. F., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen,E., Winkler, R. L., 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting 1 (2), 111\u2013153."]}
DOI: 10.5281/zenodo.4650243
title: Multispectral photos of Lippman Plates

description: Hyperspectral measurements of historical Lippmann photographs and simulate the Lippmann process, analysed in https://github.com/LCAV/lippmann-photography. <strong>Contents</strong><br> The multispectral cubes are in the directory `Cubes`.<br> It contains the photos of the following plates: Saasfee: made by Gabriel Lippmann between 1891 and 1899 in Saas-Fee, Switzerland. Parrot: made by Richard Neuhauss in 1899 and is of a parrot. Modern plates made at LCAV: two photos of the same colour checker plated + multispectral measurements of the colour checker in question a photo of a plate made with light dispersed on a prism <br> <strong>Data formats:</strong> .hdr: header files, contains e.g. wavelengths at which the data was acquired by a SPECIM camera (see http://www.specim.fi/) .dat: data cubes acquired by a SPECIM camera (see http://www.specim.fi/) _cut.txt: text file with four numbers describing how to crop the image to have only the interesting part .png: three colour preview
DOI: 10.5281/zenodo.4650242
title: Multispectral photos of Lippman Plates

description: Hyperspectral measurements of historical Lippmann photographs and simulate the Lippmann process, analysed in https://github.com/LCAV/lippmann-photography. <strong>Contents</strong><br> The multispectral cubes are in the directory `Cubes`.<br> It contains the photos of the following plates: Saasfee: made by Gabriel Lippmann between 1891 and 1899 in Saas-Fee, Switzerland. Parrot: made by Richard Neuhauss in 1899 and is of a parrot. Modern plates made at LCAV: two photos of the same colour checker plated + multispectral measurements of the colour checker in question a photo of a plate made with light dispersed on a prism <br> <strong>Data formats:</strong> .hdr: header files, contains e.g. wavelengths at which the data was acquired by a SPECIM camera (see http://www.specim.fi/) .dat: data cubes acquired by a SPECIM camera (see http://www.specim.fi/) _cut.txt: text file with four numbers describing how to crop the image to have only the interesting part .png: three colour preview
DOI: 10.5281/zenodo.4656193
title: M1 Yearly Dataset

description: This dataset contains 181 yearly time series used in the M1 forecasting competition. The series are belonging to 7 different domains: macro 1, macro 2, micro 1, micro 2, micro 3, industry and demographic.
description: {"references": ["Makridakis, S., Andersen, A., Carbone, R. F., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen,E., Winkler, R. L., 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting 1 (2), 111\u2013153."]}
DOI: 10.5281/zenodo.3890087
title: M1 Yearly Dataset

description: This dataset contains 181 yearly time series used in the M1 forecasting competition. The series are belonging to 7 different domains: macro 1, macro 2, micro 1, micro 2, micro 3, industry and demographic.
description: {"references": ["Makridakis, S., Andersen, A., Carbone, R. F., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen,E., Winkler, R. L., 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting 1 (2), 111\u2013153."]}
DOI: 10.25430/researchdata.cab.unipd.it.00000470
title: A Fresh View on Limestone Calcined Clay Cement (LC3) - Supplementary Material

description: Plastic shrinkage videos