cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/2y5q-xa90
title: IMU raw and processed data for computing human joint angles

description: The goal of this study was to compute the relative angle of human joints such as the knee flex/extension angle using two IMUs. To do so, we utilized two 6-axis (accelerometer, gyroscope) low-cost IMUs (MPU6050, TDK-Invensense, CA, USA) that were mounted on a custom developed test apparatus that replicated the human knee motion. The custom test apparatus contained a single motor that repeatedly rotated one of the IMUs from 0 ~ 180 degrees at a one of three predefined speeds (slow - max speed of 25 deg/s, medium - max speed of 100 deg/s, fast = max speed of 200 deg/s) about one of the three rotation axes (yaw - rotation about gravity vector, pitch - rotation orthogonal to yaw and roll, roll - rotation orthogonal to yaw and pitch) for 25 minutes. One IMU was stationary and mounted securely on the side of the test apparatus while the other IMU was mounted on the motor axis and rotated about the motor-axis. The motor speed could be programmed via a microcontroller. The rotation axis could be changed by re-configuring the orientation of the IMUs. An optical encoder, which was placed on the motor shaft, was used to measure the true rotated angle. As the test apparatus rotated the IMU, raw accelerometer and gyroscopic data from the two IMUs as well as the encoder data were collected using a microcontroller. Five computational algorithms (i.e., accelerometer inclination angle, gyroscopic integration, Complementary Filter, Kalman Filter, Digital Motion Processing) IMU data were used (see https://github.com/ssong47/get_joint_angles_using_imus) to calculated the relative angle between the two IMUs. These computed angles were compared to the gold standard (i.e., encoder angle). A total of 9 trials were collected = three rotation axes (yaw, pitch, roll) x three speeds (slow, medium, fast). Each trial contained two phases: 1) a short calibration phase where the IMUs were stationary and placed such that the origins were parallel to each other, and 2) test phase when the one of the IMUs started to move at the pre-defined speed and rotation axis.
DOI: 10.21227/ptf2-zx95
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. 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 includedperformance comparison using Sliding Mode Observer (SMO) and flux observer isPWMCOmpensCLosed loopAll 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.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice. The author has also other 2-3 different complex models, may be uploaded later.
DOI: 10.21227/f2ph-0720
title: IMU raw and processed data for computing human joint angles

description: The goal of this study was to compute the relative angle of human joints such as the knee flex/extension angle using two IMUs. To do so, we utilized two 6-axis (accelerometer, gyroscope) low-cost IMUs (MPU6050, TDK-Invensense, CA, USA) that were mounted on a custom developed test apparatus that replicated the human knee motion. The custom test apparatus contained a single motor that repeatedly rotated one of the IMUs from 0 ~ 180 degrees at a one of three predefined speeds (slow - max speed of 25 deg/s, medium - max speed of 100 deg/s, fast = max speed of 200 deg/s) about one of the three rotation axes (yaw - rotation about gravity vector, pitch - rotation orthogonal to yaw and roll, roll - rotation orthogonal to yaw and pitch) for 25 minutes. One IMU was stationary and mounted securely on the side of the test apparatus while the other IMU was mounted on the motor axis and rotated about the motor-axis. The motor speed could be programmed via a microcontroller. The rotation axis could be changed by re-configuring the orientation of the IMUs. An optical encoder, which was placed on the motor shaft, was used to measure the true rotated angle. As the test apparatus rotated the IMU, raw accelerometer and gyroscopic data from the two IMUs as well as the encoder data were collected using a microcontroller. Five computational algorithms (i.e., accelerometer inclination angle, gyroscopic integration, Complementary Filter, Kalman Filter, Digital Motion Processing) IMU data were used (see https://github.com/ssong47/get_joint_angles_using_imus) to calculated the relative angle between the two IMUs. These computed angles were compared to the gold standard (i.e., encoder angle). A total of 9 trials were collected = three rotation axes (yaw, pitch, roll) x three speeds (slow, medium, fast). Each trial contained two phases: 1) a short calibration phase where the IMUs were stationary and placed such that the origins were parallel to each other, and 2) test phase when the one of the IMUs started to move at the pre-defined speed and rotation axis.
DOI: 10.21227/2wpe-pv19
title: IMU raw and processed data for computing human joint angles

description: The goal of this study was to compute the relative angle of human joints such as the knee flex/extension angle using two IMUs. To do so, we utilized two 6-axis (accelerometer, gyroscope) low-cost IMUs (MPU6050, TDK-Invensense, CA, USA) that were mounted on a custom developed test apparatus that replicated the human knee motion. The custom test apparatus contained a single motor that repeatedly rotated one of the IMUs from 0 ~ 180 degrees at a one of three predefined speeds (slow - max speed of 25 deg/s, medium - max speed of 100 deg/s, fast = max speed of 200 deg/s) about one of the three rotation axes (yaw - rotation about gravity vector, pitch - rotation orthogonal to yaw and roll, roll - rotation orthogonal to yaw and pitch) for 25 minutes. One IMU was stationary and mounted securely on the side of the test apparatus while the other IMU was mounted on the motor axis and rotated about the motor-axis. The motor speed could be programmed via a microcontroller. The rotation axis could be changed by re-configuring the orientation of the IMUs. An optical encoder, which was placed on the motor shaft, was used to measure the true rotated angle. As the test apparatus rotated the IMU, raw accelerometer and gyroscopic data from the two IMUs as well as the encoder data were collected using a microcontroller. Five computational algorithms (i.e., accelerometer inclination angle, gyroscopic integration, Complementary Filter, Kalman Filter, Digital Motion Processing) IMU data were used (see https://github.com/ssong47/get_joint_angles_using_imus) to calculated the relative angle between the two IMUs. These computed angles were compared to the gold standard (i.e., encoder angle). A total of 9 trials were collected = three rotation axes (yaw, pitch, roll) x three speeds (slow, medium, fast). Each trial contained two phases: 1) a short calibration phase where the IMUs were stationary and placed such that the origins were parallel to each other, and 2) test phase when the one of the IMUs started to move at the pre-defined speed and rotation axis.
DOI: 10.6084/m9.figshare.13297655
title: 2bRAD-M species database (default)

description: The default 2bRAD-M unique tag database
DOI: 10.6084/m9.figshare.13298732.v1
title: Anthropogenically modified habitats favour bigger and bolder lizards

description: Abundance, size, and behavioural datasets of lace monitors (<i>Varanus varius) </i>in areas with and without resource subsidy and with and without cane toads (<i>Rhinella marina</i>).
DOI: 10.6084/m9.figshare.13298732
title: Anthropogenically modified habitats favour bigger and bolder lizards

description: Abundance, size, and behavioural datasets of lace monitors (<i>Varanus varius) </i>in areas with and without resource subsidy and with and without cane toads (<i>Rhinella marina</i>).
DOI: 10.34740/kaggle/dsv/1694841
title: Cause of Death in Indonesia
DOI: 10.21227/stjv-9c37
title: Surface Electromyography-controlled Pedestrian Collision Avoidance: A Driving Simulator Study

description: The data are associated with a submitted journal paper.
DOI: 10.21227/5r07-ds52
title: Surface Electromyography-controlled Pedestrian Collision Avoidance: A Driving Simulator Study

description: The data are associated with a submitted journal paper.
DOI: 10.6084/m9.figshare.13298807.v1
title: HKIA 2005 2020

description: Impact of Covid-19 on HK Air Transport
DOI: 10.6084/m9.figshare.13298807
title: HKIA 2005 2020

description: Impact of Covid-19 on HK Air Transport
DOI: 10.6084/m9.figshare.13297880
title: Seed synteny blocks (high quality) between M. lutarioriparius and S.biocolor

description: Seed synteny blocks (high quality) between M. lutarioriparius and S.biocolor generated by JCVI (MCScan python version)
DOI: 10.6084/m9.figshare.13297880.v1
title: Seed synteny blocks (high quality) between M. lutarioriparius and S.biocolor

description: Seed synteny blocks (high quality) between M. lutarioriparius and S.biocolor generated by JCVI (MCScan python version)
DOI: 10.5281/zenodo.4295742
title: The role of environmental factors in affecting species distribution: A joint analysis of GBIF data and virtual species

description: A vigorous debate among ecologists concerns two contrasting theories of species distribution and diversity, the niche theory and the neutral theory. The 'continuum hypothesis', supported by modelling results, maintains that rather than being mutually exclusive, these theories represent two ends of a continuum. Here we develop the first empirical test capable of distinguishing between these three theories using continental-scale occurrence data from GBIF and a novel simulation framework of corresponding virtual species; application of this test to a set of 84 Australian mammals supported the continuum hypothesis over the two competing theories. Repository contains: - Manuscript supplementary information (Sp.Dis_F1000-Supplementary.pdf)<br> - All analysis data and code (analysis_data_and_code.zip)<br> - GBIF raw data in a DwC-A format (0054618-160910150852091.zip). Data is also publicly available via GBIF, with the following DOI: https://doi.org/10.15468/dl.3poqxs
DOI: 10.6084/m9.figshare.13075097
title: Banni Canid Data

description: Camera trapping data for four canids species (desert fox, Indian fox, domestic dog, and golden jackal) along with habitat covariates measured.<br>
DOI: 10.6084/m9.figshare.13298870.v1
title: Data for "Digital rock reconstruction with user-defined properties using conditional generative adversarial networks"

description: The training data and results data for "Digital rock reconstruction with user-defined properties using conditional generative adversarial networks"
DOI: 10.6084/m9.figshare.13298870
title: Data for "Digital rock reconstruction with user-defined properties using conditional generative adversarial networks"

description: The training data and results data for "Digital rock reconstruction with user-defined properties using conditional generative adversarial networks"
DOI: 10.15161/oar.it/73293
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.21227/xz73-e493
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. 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 includedperformance comparison using Sliding Mode Observer (SMO) and flux observer isPWMCOmpensCLosed loopAll 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.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice. The author has also other 2-3 different complex models, may be uploaded later.
DOI: 10.21227/vxvv-dm73
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. 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 includedperformance comparison using Sliding Mode Observer (SMO) and flux observer isPWMCOmpensCLosed loopAll 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.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice. The author has also other 2-3 different complex models, may be uploaded later.
DOI: 10.21227/k2aq-4874
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. 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 includedperformance comparison using Sliding Mode Observer (SMO) and flux observer isPWMCOmpensCLosed loopAll 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.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice. The author has also other 2-3 different complex models, may be uploaded later.
DOI: 10.21227/n7fw-eh21
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. 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 includedperformance comparison using Sliding Mode Observer (SMO) and flux observer isPWMCOmpensCLosed loopAll 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.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice. The author has also other 2-3 different complex models, may be uploaded later.
DOI: 10.5281/zenodo.4295859
title: Long-Term Surgical Results of Skip Pedicle Screw Fixation for Patients with Adolescent Idiopathic Scoliosis: A Minimum Ten-Year Follow-Up Study

description: Dataset
DOI: 10.5281/zenodo.4295858
title: Long-Term Surgical Results of Skip Pedicle Screw Fixation for Patients with Adolescent Idiopathic Scoliosis: A Minimum Ten-Year Follow-Up Study

description: Dataset
DOI: 10.6084/m9.figshare.13298906.v1
title: 4 materials-Acc.xlsx

description: RTs for caucasian, afro-american, houses and words-change detection vs. change localization
DOI: 10.6084/m9.figshare.13298906
title: 4 materials-Acc.xlsx

description: RTs for caucasian, afro-american, houses and words-change detection vs. change localization
DOI: 10.6084/m9.figshare.13298909
title: 4 materials-RTs.xlsx

description: Accuracy for caucasian, afro-american, houses and words-change detection vs. change localization
DOI: 10.6084/m9.figshare.13298909.v1
title: 4 materials-RTs.xlsx

description: Accuracy for caucasian, afro-american, houses and words-change detection vs. change localization
DOI: 10.6084/m9.figshare.13298912
title: material_change detection vs. change localization

description: All material for caucasian, afro-american, houses and words-change detection vs. change localization
DOI: 10.5281/zenodo.4295823
title: MacroLip_A2016

description: BMDM treated with a BGal or Srebp1c adenovirus
DOI: 10.5281/zenodo.4295822
title: MacroLip_A2016

description: BMDM treated with a BGal or Srebp1c adenovirus
DOI: 10.6084/m9.figshare.5555002
title: Adaptive Selection of Classifiers for Bug Prediction: A Large-Scale Empirical Analysis of Its Performance and a Benchmark Study

description: Bug prediction aims at locating defective source code components relying on machine learning models. Although some previous work showed that selecting the machine-learning classifier is crucial, the results are contrasting. Therefore, several ensemble techniques, \ie approaches able to mix the output of different classifiers, have been proposed.<br>In this paper, we present a benchmark study in which we compare the performance of seven ensemble techniques on 21 open-source software projects. Our aim is twofold. On the one hand, we aim at bridging the limitations of previous empirical studies that compared the accuracy of ensemble approaches in bug prediction. On the other hand, our goal is to verify how ensemble techniques perform in different settings such as cross- and local-project defect prediction.<br>Our empirical experimentation results show that ensemble techniques are not a silver bullet for bug prediction.In within-project bug prediction, using ensemble techniques improves the prediction performance with respect to the best stand-alone classifier. We confirm that the models based on Validation and Voting achieve slightly better results. However, they are similar to those obtained by other ensemble techniques.Identifying buggy classes using external sources of information is still an open problem. In this setting, the use of ensemble techniques does not provide evident benefits with respect to stand-alone classifiers.The statistical analysis highlights that local and global models are mostly equivalent in terms of performance. Only one ensemble technique (\ie ASCI) slightly exploits local learning to improve performance.
DOI: 10.25646/7696
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: 10.34740/kaggle/dsv/1695987
title: covid 19
DOI: 10.5517/ccdc.csd.cc24sh0g
title: CCDC 1989735: Experimental Crystal Structure Determination

description: Related Article: Kerim Samedov, Yannic Heider, Yuanjing Cai, Philipp Willmes, Daniel Mühlhausen, Volker Huch, Robert West, David Scheschkewitz, Paul W. Percival|2020|Angew.Chem.,Int.Ed.|59|16007|doi:10.1002/anie.202006289
DOI: 10.5517/ccdc.csd.cc24sh1h
title: CCDC 1989736: Experimental Crystal Structure Determination

description: Related Article: Kerim Samedov, Yannic Heider, Yuanjing Cai, Philipp Willmes, Daniel Mühlhausen, Volker Huch, Robert West, David Scheschkewitz, Paul W. Percival|2020|Angew.Chem.,Int.Ed.|59|16007|doi:10.1002/anie.202006289
DOI: 10.5517/ccdc.csd.cc252fg5
title: CCDC 1998336: Experimental Crystal Structure Determination

description: Related Article: Shintaro Takahashi, Kazuki Nakaya, María Frutos, Antoine Baceiredo, Nathalie Saffon-Merceron, Stéphane Massou, Norio Nakata, Daisuke Hashizume, Vicenç Branchadell, Tsuyoshi Kato|2020|Angew.Chem.,Int.Ed.|59|15937|doi:10.1002/anie.202006088
DOI: 10.5517/ccdc.csd.cc252fh6
title: CCDC 1998337: Experimental Crystal Structure Determination

description: Related Article: Shintaro Takahashi, Kazuki Nakaya, María Frutos, Antoine Baceiredo, Nathalie Saffon-Merceron, Stéphane Massou, Norio Nakata, Daisuke Hashizume, Vicenç Branchadell, Tsuyoshi Kato|2020|Angew.Chem.,Int.Ed.|59|15937|doi:10.1002/anie.202006088