cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/x6mt-3q70
title: 1.88kW BLDC Sensorless FOC with Adaptive Sliding Mode Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".The results contained in this dataset are graphs which give intutive idea on the control characteristics, whether the pole/zero is on the imaginary axis or the right half plane. The switching frequency for these controllers is generally 20 kHz or more.In this dataset, switching frequency has been increased from 20 kHz to 1MHZ , at 26 different PWM frequencies and the comparison of the resulting characteristics are drawn.BLDC motors ae used in medical industry ,ventillators, solar trackers, CNC servodrives, industrial robots, automative, electric vehicles- autos, rickshaws etc.Using this dataset, any industrial engineer can decide whether to run at that frequency or not. So, this study comes in handy to decide when designing in practice for industries and also for academia purposes.The speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) with the addition of SMO observer which gives the estimation of speed and the sensorless rotor position. The switching pattern of the 3-phase inverter is implemented using space vector modulation.DIfferences in this paper dataset can be seen as the author has included following , with analysis which can be drawn from seeing attached graphs -performance comparison using Sliding Mode Observer (SMO) at different frequencies.PWM switching frequency is varied 26 times from 20 kHz to 1 MHz , - will help decide MOSFETs switching frequency.Of course, the Rdson, dc link inverter voltage ,other practical parameters etc has been taken into consideration (not submitted here) bu some analysis related to it,you may find in the authors Transaction paper.Separate results at all these 26 frequenciesStability Margins - Gain/PhaseOpen Loop Control PerformanceTransfer Function CompensationClosed loop TuningRoot Locus of Uncompensated System and Compensated SystemBode Plot - magnitude and phasePole Zero Map of Uncompensated System and Compensated SystemUnit-Step Response of Uncompensated System and Compensated SystemNyquist plot of Uncompensated System and Compensated SystemNichols chart of Uncompensated System and Compensated SystemImpulse Response of Uncompensated System and Compensated SystemSinusoidal ExcitationAll this is implemented on 32-bit Real-Time microcontroller. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.
DOI: 10.21227/qjfh-9n48
title: 1.88kW BLDC Sensorless FOC with Adaptive Sliding Mode Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".The results contained in this dataset are graphs which give intutive idea on the control characteristics, whether the pole/zero is on the imaginary axis or the right half plane. The switching frequency for these controllers is generally 20 kHz or more.In this dataset, switching frequency has been increased from 20 kHz to 1MHZ , at 26 different PWM frequencies and the comparison of the resulting characteristics are drawn.BLDC motors ae used in medical industry ,ventillators, solar trackers, CNC servodrives, industrial robots, automative, electric vehicles- autos, rickshaws etc.Using this dataset, any industrial engineer can decide whether to run at that frequency or not. So, this study comes in handy to decide when designing in practice for industries and also for academia purposes.The speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) with the addition of SMO observer which gives the estimation of speed and the sensorless rotor position. The switching pattern of the 3-phase inverter is implemented using space vector modulation.DIfferences in this paper dataset can be seen as the author has included following , with analysis which can be drawn from seeing attached graphs -performance comparison using Sliding Mode Observer (SMO) at different frequencies.PWM switching frequency is varied 26 times from 20 kHz to 1 MHz , - will help decide MOSFETs switching frequency.Of course, the Rdson, dc link inverter voltage ,other practical parameters etc has been taken into consideration (not submitted here) bu some analysis related to it,you may find in the authors Transaction paper.Separate results at all these 26 frequenciesStability Margins - Gain/PhaseOpen Loop Control PerformanceTransfer Function CompensationClosed loop TuningRoot Locus of Uncompensated System and Compensated SystemBode Plot - magnitude and phasePole Zero Map of Uncompensated System and Compensated SystemUnit-Step Response of Uncompensated System and Compensated SystemNyquist plot of Uncompensated System and Compensated SystemNichols chart of Uncompensated System and Compensated SystemImpulse Response of Uncompensated System and Compensated SystemSinusoidal ExcitationAll this is implemented on 32-bit Real-Time microcontroller. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.
DOI: 10.6084/m9.figshare.13308455
title: Knowledge Hiding

description: Fieldwork was carried out in the summer of 2019, through a questionnaire sent by e-mail and physically applied to the management staff of a total of 200 companies that participated in training spaces on new technology management provided by a university.
DOI: 10.5281/zenodo.4299056
title: VESIcal supplementary datasets

description: Example and supplementary datasets associated with VESIcal. The example dataset used for worked examples in Section 3 of the VESIcal manuscript (example data.xlsx file; Supplemental Dataset S1) contains compositional information for basalts (Roggensack, 2001; Tucker et al., 2019), andesites (Moore et al., 1998), rhyolites (Mercer et al., 2015; Myers et al., 2019), and alkaline melts (phototephrite, basaltic-trachyandesite, and basanite from Iacovino et al. 2016). Several additional example datasets from the literature are also included. These include experimentally produced alkaline magmas from Iacovino et al. (2016, alkaline.xlsx), basaltic melt inclusions from Kilauea (Tucker et al., 2019) and Gakkel Ridge (Bennett et al., 2019, basalts.xlsx), basaltic melt inclusions from Cerro Negro volcano, Nicaragua (Roggensack, 2001, cerro negro.xlsx), and rhyolite melt inclusions from the Taupo Volcanic Center, New Zealand (Myers et al., 2019) and a topaz rhyolite from the Rio Grande Rift (Mercer et al., 2015, rhyolites.xlsx).
DOI: 10.5281/zenodo.4299055
title: VESIcal supplementary datasets

description: Example and supplementary datasets associated with VESIcal. The example dataset used for worked examples in Section 3 of the VESIcal manuscript (example data.xlsx file; Supplemental Dataset S1) contains compositional information for basalts (Roggensack, 2001; Tucker et al., 2019), andesites (Moore et al., 1998), rhyolites (Mercer et al., 2015; Myers et al., 2019), and alkaline melts (phototephrite, basaltic-trachyandesite, and basanite from Iacovino et al. 2016). Several additional example datasets from the literature are also included. These include experimentally produced alkaline magmas from Iacovino et al. (2016, alkaline.xlsx), basaltic melt inclusions from Kilauea (Tucker et al., 2019) and Gakkel Ridge (Bennett et al., 2019, basalts.xlsx), basaltic melt inclusions from Cerro Negro volcano, Nicaragua (Roggensack, 2001, cerro negro.xlsx), and rhyolite melt inclusions from the Taupo Volcanic Center, New Zealand (Myers et al., 2019) and a topaz rhyolite from the Rio Grande Rift (Mercer et al., 2015, rhyolites.xlsx).
DOI: 10.21227/rnjx-v724
title: 1.88kW BLDC Sensorless FOC with Adaptive Sliding Mode Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".The results contained in this dataset are graphs which give intutive idea on the control characteristics, whether the pole/zero is on the imaginary axis or the right half plane. The switching frequency for these controllers is generally 20 kHz or more.In this dataset, switching frequency has been increased from 20 kHz to 1MHZ , at 26 different PWM frequencies and the comparison of the resulting characteristics are drawn.BLDC motors ae used in medical industry ,ventillators, solar trackers, CNC servodrives, industrial robots, automative, electric vehicles- autos, rickshaws etc.Using this dataset, any industrial engineer can decide whether to run at that frequency or not. So, this study comes in handy to decide when designing in practice for industries and also for academia purposes.The speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) with the addition of SMO observer which gives the estimation of speed and the sensorless rotor position. The switching pattern of the 3-phase inverter is implemented using space vector modulation.DIfferences in this paper dataset can be seen as the author has included following , with analysis which can be drawn from seeing attached graphs -performance comparison using Sliding Mode Observer (SMO) at different frequencies.PWM switching frequency is varied 26 times from 20 kHz to 1 MHz , - will help decide MOSFETs switching frequency.Of course, the Rdson, dc link inverter voltage ,other practical parameters etc has been taken into consideration (not submitted here) bu some analysis related to it,you may find in the authors Transaction paper.Separate results at all these 26 frequenciesStability Margins - Gain/PhaseOpen Loop Control PerformanceTransfer Function CompensationClosed loop TuningRoot Locus of Uncompensated System and Compensated SystemBode Plot - magnitude and phasePole Zero Map of Uncompensated System and Compensated SystemUnit-Step Response of Uncompensated System and Compensated SystemNyquist plot of Uncompensated System and Compensated SystemNichols chart of Uncompensated System and Compensated SystemImpulse Response of Uncompensated System and Compensated SystemSinusoidal ExcitationAll this is implemented on 32-bit Real-Time microcontroller. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.
DOI: 10.5517/ccdc.csd.cc1ltzr4
title: CCDC 1484737: Experimental Crystal Structure Determination

description: Related Article: Hao-Ran Xie, Ya-Qi Gu, Li Liu, Jing-Cao Dai|2020|New J.Chem.|44|19713|doi:10.1039/D0NJ04124F
DOI: 10.5517/ccdc.csd.cc229b6y
title: CCDC 1915589: Experimental Crystal Structure Determination

description: Related Article: Hao-Ran Xie, Ya-Qi Gu, Li Liu, Jing-Cao Dai|2020|New J.Chem.|44|19713|doi:10.1039/D0NJ04124F
DOI: 10.5517/ccdc.csd.cc22b90r
title: CCDC 1916513: Experimental Crystal Structure Determination

description: Related Article: Praveen Kumar, Sergey V. Lindeman, Adam T. Fiedler|2019|J.Am.Chem.Soc.|141|10984|doi:10.1021/jacs.9b05320
DOI: 10.5517/ccdc.csd.cc22b91s
title: CCDC 1916514: Experimental Crystal Structure Determination

description: Related Article: Praveen Kumar, Sergey V. Lindeman, Adam T. Fiedler|2019|J.Am.Chem.Soc.|141|10984|doi:10.1021/jacs.9b05320
DOI: 10.5517/ccdc.csd.cc22b92t
title: CCDC 1916515: Experimental Crystal Structure Determination

description: Related Article: Praveen Kumar, Sergey V. Lindeman, Adam T. Fiedler|2019|J.Am.Chem.Soc.|141|10984|doi:10.1021/jacs.9b05320
DOI: 10.5517/ccdc.csd.cc22b93v
title: CCDC 1916516: Experimental Crystal Structure Determination

description: Related Article: Praveen Kumar, Sergey V. Lindeman, Adam T. Fiedler|2019|J.Am.Chem.Soc.|141|10984|doi:10.1021/jacs.9b05320
DOI: 10.5517/ccdc.csd.cc25pw81
title: CCDC 2017023: Experimental Crystal Structure Determination

description: Related Article: Myron Wilde, Danielle Arzur, Blandine Baratte, Dorian Lefebvre, Thomas Robert, Thierry Roisnel, Catherine Le Jossic-Corcos, Stéphane Bach, Laurent Corcos, William Erb|2020|New J.Chem.|44|19723|doi:10.1039/D0NJ05334A
DOI: 10.5517/ccdc.csd.cc25pw92
title: CCDC 2017024: Experimental Crystal Structure Determination

description: Related Article: Myron Wilde, Danielle Arzur, Blandine Baratte, Dorian Lefebvre, Thomas Robert, Thierry Roisnel, Catherine Le Jossic-Corcos, Stéphane Bach, Laurent Corcos, William Erb|2020|New J.Chem.|44|19723|doi:10.1039/D0NJ05334A
DOI: 10.5517/ccdc.csd.cc25pwb3
title: CCDC 2017025: Experimental Crystal Structure Determination

description: Related Article: Myron Wilde, Danielle Arzur, Blandine Baratte, Dorian Lefebvre, Thomas Robert, Thierry Roisnel, Catherine Le Jossic-Corcos, Stéphane Bach, Laurent Corcos, William Erb|2020|New J.Chem.|44|19723|doi:10.1039/D0NJ05334A
DOI: 10.5517/ccdc.csd.cc25pwc4
title: CCDC 2017026: Experimental Crystal Structure Determination

description: Related Article: Myron Wilde, Danielle Arzur, Blandine Baratte, Dorian Lefebvre, Thomas Robert, Thierry Roisnel, Catherine Le Jossic-Corcos, Stéphane Bach, Laurent Corcos, William Erb|2020|New J.Chem.|44|19723|doi:10.1039/D0NJ05334A
DOI: 10.5517/ccdc.csd.cc25pwd5
title: CCDC 2017027: Experimental Crystal Structure Determination

description: Related Article: Myron Wilde, Danielle Arzur, Blandine Baratte, Dorian Lefebvre, Thomas Robert, Thierry Roisnel, Catherine Le Jossic-Corcos, Stéphane Bach, Laurent Corcos, William Erb|2020|New J.Chem.|44|19723|doi:10.1039/D0NJ05334A
DOI: 10.5517/ccdc.csd.cc25pwf6
title: CCDC 2017028: Experimental Crystal Structure Determination

description: Related Article: Myron Wilde, Danielle Arzur, Blandine Baratte, Dorian Lefebvre, Thomas Robert, Thierry Roisnel, Catherine Le Jossic-Corcos, Stéphane Bach, Laurent Corcos, William Erb|2020|New J.Chem.|44|19723|doi:10.1039/D0NJ05334A
DOI: 10.5281/zenodo.4299086
title: Three-systems for visual numerosity: A single case study

description: MOT TASK Each file refers to a session with a particular condition. It is spelt out in the file name the number of objects to track as well as the total amount of objects on the screen Within each file it is found a variable called “MatriceRisultati”. Which contains: Number of targets to follow Number of correct answers Number of trials at the condition Percent correct responses (i.e. value_2 / value_3) NECKLACE – DISTANCE TASK Each file contains raw data for each session. All the data are store in a variable called RESP, which contains parameters for each trial. The crucial columns are Inter dot distance in the reference (in pixels – typically 1 pixel =~0.03 cm) Interdot distance in the test (in pixels) Subject choice to the question “which contains closer dots” For analysis one has to draw a psychometric curve (i.e. a cumulative gaussian) that fits the data of column 3, as a function of interdot distance (column 2). Varinat may include dividing column 2 by column 1 (so to have the ratio between test and reference) and run the psychometric curve on such normalized dimension Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors. Numerosity discrimination The relevant columns in the matrix ‘a’ contain the following information: 1<sup>st</sup>: Numerosity 2<sup>nd</sup>: Log10 Numerosity 3<sup>rd</sup>: Response Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors. Numerosity estimation The relevant columns in the matrix ‘ContengoRisultati’ contain the following information: 1<sup>st</sup>: Numerosity 2<sup>nd</sup>: Response Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors.
DOI: 10.5281/zenodo.4299085
title: Three-systems for visual numerosity: A single case study

description: MOT TASK Each file refers to a session with a particular condition. It is spelt out in the file name the number of objects to track as well as the total amount of objects on the screen Within each file it is found a variable called “MatriceRisultati”. Which contains: Number of targets to follow Number of correct answers Number of trials at the condition Percent correct responses (i.e. value_2 / value_3) NECKLACE – DISTANCE TASK Each file contains raw data for each session. All the data are store in a variable called RESP, which contains parameters for each trial. The crucial columns are Inter dot distance in the reference (in pixels – typically 1 pixel =~0.03 cm) Interdot distance in the test (in pixels) Subject choice to the question “which contains closer dots” For analysis one has to draw a psychometric curve (i.e. a cumulative gaussian) that fits the data of column 3, as a function of interdot distance (column 2). Varinat may include dividing column 2 by column 1 (so to have the ratio between test and reference) and run the psychometric curve on such normalized dimension Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors. Numerosity discrimination The relevant columns in the matrix ‘a’ contain the following information: 1<sup>st</sup>: Numerosity 2<sup>nd</sup>: Log10 Numerosity 3<sup>rd</sup>: Response Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors. Numerosity estimation The relevant columns in the matrix ‘ContengoRisultati’ contain the following information: 1<sup>st</sup>: Numerosity 2<sup>nd</sup>: Response Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors.
DOI: 10.5281/zenodo.4298999
title: LocaRDS: A Localization Reference Data Set

description: Accompanying dataset to the LocaRDS paper. With this work, we attempt to improve the current state of the art in localization research and put it on a solid scientific grounding for the future. Concretely, LocaRDS is an open reference dataset of real-world crowdsourced flight data from the OpenSky Network featuring more than 222 million measurements from over 50 million transmissions recorded by 323 sensors. LocaRDS can be used to test, analyze and directly compare different localization techniques. It is intended to answer in particular the open question of the aircraft localization problem in crowdsourced sensor networks.
description: Set 1 is yet incomplete, as it is used in the Aircraft Localization Competition at https://www.aicrowd.com/challenges/cyd-campus-aircraft-localization-competition It will be updated at the end of the competition in February 2021.
DOI: 10.6084/m9.figshare.13151567
title: Energetic scaling across different host densities and its consequences for pathogen proliferation - raw data

description: Raw data used for analysis and figures. Fig_2_data: data for energy intake, expenditure and discretionary energy. <br>Fig_3_data: data for host mass, spore load and mass corrected spore load. <br>Fig_4_data: data used to explore changes in pathogen spore load across density. Data is analysed on block level for spore load, host mass and discretionary energy.
DOI: 10.6084/m9.figshare.13151567.v1
title: Energetic scaling across different host densities and its consequences for pathogen proliferation - raw data

description: Raw data used for analysis and figures. Fig_2_data: data for energy intake, expenditure and discretionary energy. <br>Fig_3_data: data for host mass, spore load and mass corrected spore load. <br>Fig_4_data: data used to explore changes in pathogen spore load across density. Data is analysed on block level for spore load, host mass and discretionary energy.
DOI: 10.6084/m9.figshare.13308602
title: CFA Master.sav

description: CFA Master is the data set Todd Gilson used to analyze the CRAVE data with the confirmatory factor analysis. "data from January 2015 used for the CFA analysis". Data sent back to us from Todd Gilson on 11/30/2020.
DOI: 10.25934/00004521.1
title: IPD datapackage for study 'MONARCH 2: A Randomized, Double-Blind, Placebo-Controlled, Phase 3 Study of Fulvestrant With or Without Abemaciclib, a CDK4/6 Inhibitor, for Women With Hormone Receptor Positive, HER2 Negative Locally Advanced or Metastatic Breast Cancer' and research request: 'Predictors of therapeutic and adverse effects of medicines used in the treatment of breast cancer'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00004523.1
title: IPD datapackage for study 'A Randomized, Double-Blind, Placebo-Controlled, Phase 3 Study of Nonsteroidal Aromatase Inhibitors (Anastrozole or Letrozole) Plus LY2835219, a CDK4/6 Inhibitor, or Placebo in Postmenopausal Women With Hormone Receptor-Positive, HER2-Negative Locoregionally Recurrent or Metastatic Breast Cancer With No Prior Systemic Therapy in This Disease Setting' and research request: 'Predictors of therapeutic and adverse effects of medicines used in the treatment of breast cancer'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00004520.1
title: IPD datapackage for study 'A Phase 2 Study of LY2835219 for Patients With Previously Treated Hormone Receptor Positive, HER2 Negative Metastatic Breast Cancer' and research request: 'Predictors of therapeutic and adverse effects of medicines used in the treatment of breast cancer'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5281/zenodo.4299093
title: Climate Forced Hydropower Simulations Using NASA NEX-GDDP

description: * This update includes the corrected values for all Peruvian Hydropower Plants included in the original dataset. This dataset includes the results of simulations of future hydropower usable capacity for power plants in Brazil, Colombia, and Peru. These simulations have been forced using NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, which includes maximum temperature, minimum temperature, and precipitation simulations from 21 Global Climate Models (GCM) and three scenarios. The scenarios include a retrospective run (1950-2005) and two projection runs for Representative Concentration Pathways (RCP) 4.5 and 8.5. There is a folder for each country that includes a power plant characteristics file, with a list of all the power plants and the characteristics used for the analysis (installed capacity, effective height, reservoir specifications, etc.). Additionally, there is a folder including the dates for the usable capacity files. Each file inside the usable capacity folder is labeled "power_", followed by the power plant name (e.g. "tres_irmaos"), and the scenario (e.g. "rcp45_2006_2099"). This work is based on the future publication: Caceres, A.L., Jaramillo, P., Matthews, H.S., Samaras, C. & Nijssen B. "Hydropower under climate uncertainty: characterizing the usable capacity of Brazilian, Colombian and Peruvian power plants under climate scenarios".