cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3898358
title: User Stories made by Users Workshop Data Set

description: In order to enable members of a socio-technical evolutionary-teal organization to design their technical component, we conducted a workshop that structures the collaboration between technical trained participants and non-trained participants. The workshop aims to transform "vague needs" into technical descriptions in the form of user stories. The workshop is the second part of series of workshops all limited to two hours. It uses the methods of <em>Design Thinking</em> and <em>Participatory Design</em>. The workshop has been recorded in video and this data set contains a textual German transcript, transcripts of the moderation cards that have been created during the workshop, invitations of the participants, additional material needed to conduct the workshop (timetable, moderation concept, etc.), and material used during the workshop by the participants (leaflets, slides, etc.). We hope that the material can be used to (a) comprehend the interpretation used in our qualitative research, (b) to adapt the workshop model by other volunteers of our case study Viva con Agua de St. Pauli e.V. (https://www.vivaconagua.org/), and (c) investigate other interesting research questions.
description: Sometimes the data will contain the term "WASHEthno". This is an internal short name for the study that produces this workshop and thus also the current data.
DOI: 10.5281/zenodo.3686670
title: User Stories made by Users Workshop Data Set

description: In order to enable members of a socio-technical evolutionary-teal organization to design their technical component, we conducted a workshop that structures the collaboration between technical trained participants and non-trained participants. The workshop aims to transform "vague needs" into technical descriptions in the form of user stories. The workshop is the second part of series of workshops all limited to two hours. It uses the methods of <em>Design Thinking</em> and <em>Participatory Design</em>. The workshop has been recorded in video and this data set contains a textual German transcript, transcripts of the moderation cards that have been created during the workshop, invitations of the participants, additional material needed to conduct the workshop (timetable, moderation concept, etc.), and material used during the workshop by the participants (leaflets, slides, etc.). We hope that the material can be used to (a) comprehend the interpretation used in our qualitative research, (b) to adapt the workshop model by other volunteers of our case study Viva con Agua de St. Pauli e.V. (https://www.vivaconagua.org/), and (c) investigate other interesting research questions.
description: Sometimes the data will contain the term "WASHEthno". This is an internal short name for the study that produces this workshop and thus also the current data.
DOI: 10.5281/zenodo.3898376
title: M4 Weekly Dataset

description: This dataset contains 359 weekly time series used in the M4 forecasting competition.
description: {"references": ["Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2018. The m4 competition: results, findings, conclusion and way forward, International Journal of Forecasting 34 (4), 802 \u2013 808.", "Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2020. The m4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting 36 (1), 54 \u2013 74."]}
DOI: 10.5281/zenodo.3898377
title: Auto-segmentation DICOM

description: DICOM files for e2e testing of auto-segmentation
DOI: 10.5281/zenodo.3897979
title: Auto-segmentation DICOM

description: DICOM files for e2e testing of auto-segmentation
DOI: 10.5281/zenodo.3898380
title: M4 Monthly Dataset

description: This dataset contains 48000 monthly time series used in the M4 forecasting competition. The series are belonging to 6 different domains: demographic, micro, macro, industry, finance and other.
description: {"references": ["Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2018. The m4 competition: results, findings, conclusion and way forward, International Journal of Forecasting 34 (4), 802 \u2013 808.", "Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2020. The m4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting 36 (1), 54 \u2013 74."]}
DOI: 10.5281/zenodo.3898389
title: M4 Quarterly Dataset

description: This dataset contains 24000 quarterly time series used in the M4 forecasting competition.
description: {"references": ["Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2018. The m4 competition: results, findings, conclusion and way forward, International Journal of Forecasting 34 (4), 802 \u2013 808.", "Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2020. The m4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting 36 (1), 54 \u2013 74."]}
DOI: 10.5281/zenodo.3898393
title: M4 Yearly Dataset

description: This dataset contains 23000 yearly time series used in the M4 forecasting competition.
description: {"references": ["Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2020. The m4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting 36 (1), 54 \u2013 74.", "Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2018. The m4 competition: results, findings, conclusion and way forward, International Journal of Forecasting 34 (4), 802 \u2013 808."]}
DOI: 10.5281/zenodo.3898396
title: M3 Other Dataset

description: This dataset contains 174 time series used in the M3 forecasting competition other than the yearly, monthly and quarterly time series. The series are belonging to 3 different domains: micro, finance and other.
description: {"references": ["Makridakis, S., Hibon, M., 2000. The m3-competition: results, conclusions and implications. International Journal of Forecasting 16 (4), 451\u2013476."]}
DOI: 10.5281/zenodo.3898398
title: M3 Monthly Dataset

description: This dataset contains 1428 monthly time series used in the M3 forecasting competition. The series are belonging to 6 different domains: demographic, micro, macro, industry, finance and other.
description: {"references": ["Makridakis, S., Hibon, M., 2000. The m3-competition: results, conclusions and implications. International Journal of Forecasting 16 (4), 451\u2013476."]}
DOI: 10.5281/zenodo.3898401
title: M3 Quarterly Dataset

description: This dataset contains 756 quarterly time series used in the M3 forecasting competition. The series are belonging to 5 different domains: demographic, micro, macro, industry and finance.
description: {"references": ["Makridakis, S., Hibon, M., 2000. The m3-competition: results, conclusions and implications. International Journal of Forecasting 16 (4), 451\u2013476."]}
DOI: 10.5281/zenodo.3898405
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.3898408
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.3898409
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.3898404
title: M3 Yearly Dataset

description: This dataset contains 645 yearly time series used in the M3 forecasting competition. The series are belonging to 6 different domains: demographic, micro, macro, industry, finance and other.
description: {"references": ["Makridakis, S., Hibon, M., 2000. The m3-competition: results, conclusions and implications. International Journal of Forecasting 16 (4), 451\u2013476."]}
DOI: 10.5281/zenodo.3898420
title: Sunspot Daily Dataset (without Missing Values)

description: This dataset contains a single very long daily time series of sunspot numbers from 1818-01-01 to 2020-05-31. The original dataset contains missing values. The leading missing values were replaced by zeros and the remaining missing values were replaced by carrying forward the corresponding last observations (LOCF method).
description: {"references": ["Sunspot, 2015. Sunspot number version 2.0: new data and conventions. URL http://www.sidc.be/silso/newdataset"]}
DOI: 10.5281/zenodo.3898423
title: Sunspot Daily Dataset (with Missing Values)

description: This dataset contains a single very long daily time series of sunspot numbers from 1818-01-01 to 2020-05-31.
description: {"references": ["Sunspot, 2015. Sunspot number version 2.0: new data and conventions. URL http://www.sidc.be/silso/newdataset"]}
DOI: 10.5281/zenodo.3898424
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.3898425
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.3898419
title: Data supplement for: "Open Access für die Maschinen"

description: Structured bibliographical metadata for the paper: "Open Access für die Maschinen".
DOI: 10.5281/zenodo.3898418
title: Data supplement for: "Open Access für die Maschinen"

description: Structured bibliographical metadata for the paper: "Open Access für die Maschinen".
DOI: 10.15129/791fa14e-8aa1-489f-83a3-777b62c77f00
title: Data for: "Noise-related polarization dynamics for femto and picosecond pulses in normal dispersion fibers"

description: The data includes all simulation results which support the paper. Data sets for figures 2 to 7 are are in the form of a zipped archive created using python's numpy.savez_compressed method. Data for figure 1 is given in two tab-delimited text files under filenames dispersionData.txt and lossData.txt. Basic python scripts for opening and plotting all of the data are provided. The supplementary documentation and Visualisations 1 and 2 (referenced in the main article) are also included here.
DOI: 10.5281/zenodo.3891388
title: PKC Alpha C2 domain interacting with POPC/POPS membranes,

description: Simulations of a single copy of the PKC alpha C2 domain (PDB:1DSY) with a lipid membrane consisting of 40 POPS and 160 POPC molecules. The system is solvated by 20000 water molecules (100 per lipid), and there is 100 mM CaCl_2. Potassium ions are used as counter-ions for POPS charges. The system is simulated for 500 ns using three force fields: Full charge (original) CHARMM36, full charge CHARMM36 with NBFIX corrections, and with scaled CHARMM36 force field based on the electronic continuum correction (ECC) concept. The standard CHARMM36 simulation parameters for GROMACS are used. Files required to generate simulation inputs (tpr) are proviedd: initial structures (gro), simulation parameters (md.mdp), topology files (top, itp), and index files (ndx). Output trajectories (xtc) and energy files (edr) are also provided. The binding of PKC alpha C2 domain to the bilayers is reported in DOI: [ADDED UPON ACCEPTANCE].
DOI: 10.5281/zenodo.3891387
title: PKC Alpha C2 domain interacting with POPC/POPS membranes,

description: Simulations of a single copy of the PKC alpha C2 domain (PDB:1DSY) with a lipid membrane consisting of 40 POPS and 160 POPC molecules. The system is solvated by 20000 water molecules (100 per lipid), and there is 100 mM CaCl_2. Potassium ions are used as counter-ions for POPS charges. The system is simulated for 500 ns using three force fields: Full charge (original) CHARMM36, full charge CHARMM36 with NBFIX corrections, and with scaled CHARMM36 force field based on the electronic continuum correction (ECC) concept. The standard CHARMM36 simulation parameters for GROMACS are used. Files required to generate simulation inputs (tpr) are proviedd: initial structures (gro), simulation parameters (md.mdp), topology files (top, itp), and index files (ndx). Output trajectories (xtc) and energy files (edr) are also provided. The binding of PKC alpha C2 domain to the bilayers is reported in DOI: [ADDED UPON ACCEPTANCE].
DOI: 10.5281/zenodo.3898436
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.3874309
title: Capital use matrices

description: Matrices describing the total use of capital goods by industries in MEur, in industry (pxi) and product classification (pxp). Details on compilation procedure is described in: Södersten, Carl-Johan H., Richard Wood, and Edgar G. Hertwich. "Endogenizing capital in MRIO models: the implications for consumption-based accounting." <em>Environmental science & technology</em> 52.22 (2018): 13250-13259 The capital flows stem from the consumption of capital (CFC) from EXIOBASE3, and have been benchmarked to CFC measures from the World Bank, as described in: Södersten, Carl-Johan, Richard Wood, and Thomas Wiedmann. "The capital load of global material footprints." <em>Resources, Conservation and Recycling</em> 158 (2020): 104811.
DOI: 10.5281/zenodo.3874308
title: Capital use matrices

description: Matrices describing the total use of capital goods by industries in MEur, in industry (pxi) and product classification (pxp). Details on compilation procedure is described in: Södersten, Carl-Johan H., Richard Wood, and Edgar G. Hertwich. "Endogenizing capital in MRIO models: the implications for consumption-based accounting." <em>Environmental science & technology</em> 52.22 (2018): 13250-13259 The capital flows stem from the consumption of capital (CFC) from EXIOBASE3, and have been benchmarked to CFC measures from the World Bank, as described in: Södersten, Carl-Johan, Richard Wood, and Thomas Wiedmann. "The capital load of global material footprints." <em>Resources, Conservation and Recycling</em> 158 (2020): 104811.
DOI: 10.5281/zenodo.3898439
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.3898444
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.3898445
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.3898446
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.3898449
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/"]}