cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc22g4pd
title: CCDC 1920223: Experimental Crystal Structure Determination

description: Related Article: Li-Cheng Yang, Ya-Nong Wang, Ruoyang Liu, Yixin Luo, Xiao Qian Ng, Binmiao Yang, Zi-Qiang Rong, Yu Lan, Zhihui Shao, Yu Zhao|2020|Nature Chemistry|12|860|doi:10.1038/s41557-020-0503-7
DOI: 10.5517/ccdc.csd.cc22g4qf
title: CCDC 1920224: Experimental Crystal Structure Determination

description: Related Article: Li-Cheng Yang, Ya-Nong Wang, Ruoyang Liu, Yixin Luo, Xiao Qian Ng, Binmiao Yang, Zi-Qiang Rong, Yu Lan, Zhihui Shao, Yu Zhao|2020|Nature Chemistry|12|860|doi:10.1038/s41557-020-0503-7
DOI: 10.5517/ccdc.csd.cc22g4rg
title: CCDC 1920225: Experimental Crystal Structure Determination

description: Related Article: Li-Cheng Yang, Ya-Nong Wang, Ruoyang Liu, Yixin Luo, Xiao Qian Ng, Binmiao Yang, Zi-Qiang Rong, Yu Lan, Zhihui Shao, Yu Zhao|2020|Nature Chemistry|12|860|doi:10.1038/s41557-020-0503-7
DOI: 10.5517/ccdc.csd.cc22g4sh
title: CCDC 1920226: Experimental Crystal Structure Determination

description: Related Article: Li-Cheng Yang, Ya-Nong Wang, Ruoyang Liu, Yixin Luo, Xiao Qian Ng, Binmiao Yang, Zi-Qiang Rong, Yu Lan, Zhihui Shao, Yu Zhao|2020|Nature Chemistry|12|860|doi:10.1038/s41557-020-0503-7
DOI: 10.5517/ccdc.csd.cc22g4tj
title: CCDC 1920227: Experimental Crystal Structure Determination

description: Related Article: Li-Cheng Yang, Ya-Nong Wang, Ruoyang Liu, Yixin Luo, Xiao Qian Ng, Binmiao Yang, Zi-Qiang Rong, Yu Lan, Zhihui Shao, Yu Zhao|2020|Nature Chemistry|12|860|doi:10.1038/s41557-020-0503-7
DOI: 10.5517/ccdc.csd.cc22g4vk
title: CCDC 1920228: Experimental Crystal Structure Determination

description: Related Article: Li-Cheng Yang, Ya-Nong Wang, Ruoyang Liu, Yixin Luo, Xiao Qian Ng, Binmiao Yang, Zi-Qiang Rong, Yu Lan, Zhihui Shao, Yu Zhao|2020|Nature Chemistry|12|860|doi:10.1038/s41557-020-0503-7
DOI: 10.5517/ccdc.csd.cc22g50r
title: CCDC 1920233: Experimental Crystal Structure Determination

description: Related Article: Li-Cheng Yang, Ya-Nong Wang, Ruoyang Liu, Yixin Luo, Xiao Qian Ng, Binmiao Yang, Zi-Qiang Rong, Yu Lan, Zhihui Shao, Yu Zhao|2020|Nature Chemistry|12|860|doi:10.1038/s41557-020-0503-7
DOI: 10.5517/ccdc.csd.cc22g4nc
title: CCDC 1920222: Experimental Crystal Structure Determination

description: Related Article: Li-Cheng Yang, Ya-Nong Wang, Ruoyang Liu, Yixin Luo, Xiao Qian Ng, Binmiao Yang, Zi-Qiang Rong, Yu Lan, Zhihui Shao, Yu Zhao|2020|Nature Chemistry|12|860|doi:10.1038/s41557-020-0503-7
DOI: 10.5517/ccdc.csd.cc245t7d
title: CCDC 1971793: Experimental Crystal Structure Determination

description: Related Article: Jie Luo, Michael Rauch, Liat Avram, Yael Diskin-Posner, Guy Shmul, Yehoshoa Ben-David, David Milstein|2020|Nat. Catal.|3|887|doi:10.1038/s41929-020-00514-9
DOI: 10.5517/ccdc.csd.cc24x92g
title: CCDC 1993395: Experimental Crystal Structure Determination

description: Related Article: Yue-Ming Yu, Ling-Yang Wang, Fan-Zhi Bu, Lin-Lin Wang, Yan-Tuan Li, Cheng Wang, Zhi-Yong Wu|2020|CrystEngComm|22|7992|doi:10.1039/D0CE01297A
DOI: 10.6084/m9.figshare.13258682
title: Sm3-2-Contig.fa

description: Qualcomm sequencing results
DOI: 10.6084/m9.figshare.13258682.v1
title: Sm3-2-Contig.fa

description: Qualcomm sequencing results
DOI: 10.6084/m9.figshare.13292369
title: data.xlsx

description: This dataset includes progression-free survival and overall survival data for 91 high-risk locations and 163 non-high-risk locations hepatocellular carcinoma patients treated with percutaneous microwave coagulation therapy.
DOI: 10.6084/m9.figshare.13292369.v1
title: data.xlsx

description: This dataset includes progression-free survival and overall survival data for 91 high-risk locations and 163 non-high-risk locations hepatocellular carcinoma patients treated with percutaneous microwave coagulation therapy.
DOI: 10.21227/7n0c-g453
title: Integrated transmission and distribution system expansion planning under uncertainty: Test data and results

description: Data and results for the 36- and the 334-node test systems used for the integrated expansion planning problem of transmission and distribution systems.
DOI: 10.21227/q15e-tr17
title: Integrated transmission and distribution system expansion planning under uncertainty: Test data and results

description: Data and results for the 36- and the 334-node test systems used for the integrated expansion planning problem of transmission and distribution systems.
DOI: 10.5519/qd.5xe4r0ng
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.4292047
title: Files and plotting scripts for "Parallel tridiagonal matrix inversion with a hybrid multigrid--Thomas algorithm method"

description: This archive contains the files required to reproduce the results and figures presented in <em>Parallel tridiagonal matrix inversion with a hybrid multigrid--Thomas algorithm method</em>, J. T. Parker, P. A. Hill, D. Dickinson and B. D. Dudson. Also available at the repository: https://gitlab.com/JosephThomasParker/files-and-plotting-scripts-for-parallel-tridiagonal-matrix-inversion-with-a-hybrid-multigrid-thomas-algorithm-method/ Questions to joseph.parker@ukaea.uk. This version is before submission to journal.
DOI: 10.5281/zenodo.4292046
title: Files and plotting scripts for "Parallel tridiagonal matrix inversion with a hybrid multigrid--Thomas algorithm method"

description: This archive contains the files required to reproduce the results and figures presented in <em>Parallel tridiagonal matrix inversion with a hybrid multigrid--Thomas algorithm method</em>, J. T. Parker, P. A. Hill, D. Dickinson and B. D. Dudson. Also available at the repository: https://gitlab.com/JosephThomasParker/files-and-plotting-scripts-for-parallel-tridiagonal-matrix-inversion-with-a-hybrid-multigrid-thomas-algorithm-method/ Questions to joseph.parker@ukaea.uk. This version is before submission to journal.
DOI: 10.6084/m9.figshare.13292411
title: Covid19 Fragebogen Datenbank.sav

description: Dataset for:<br>Influence of COVID-19 Pandemic on Medical Students’ Academic Performance and Specialty Choice<br>Introduction The COVID-19 pandemic has wide ranging effects on people in training, including medical students. The authors wanted to evaluate the impact of the COVID-19 pandemic on subjective academic performance and choice of future specialty among medical students in Austria. Method An anonymous questionnaire-based survey was sent out to medical students in Austria via universities admissions departments and platforms on social media. The survey was available from the 8<sup>th</sup> of April 2020 until the 22<sup>nd</sup> of April 2020. Results Out of 1073 participants, who completed the survey, 969 (90%) fulfilled the inclusion criteria and were eligible for analysis. <br> 843 (87%) students claimed that the COVID-19 pandemic had an impact on everyday academic life. <br> A significant increase in interest in medical specialties which are essential during this pandemic was observed. Anesthesiology and internal medicine showed a 2% growth in interest (anesthesiology p=0.002; internal medicine p&lt;0.001), research had a 1% increase (p=0.015). Conclusion The authors evaluated that the COVID-19 pandemic had an impact on students’ choice of specialty, in favor of specialties that are more essential in this crisis. Furthermore, the subjective negative effect of the COVID-19 pandemic on academic performance due to distance learning was less impactful than expected. <br>
DOI: 10.6084/m9.figshare.13292411.v1
title: Covid19 Fragebogen Datenbank.sav

description: Dataset for:<br>Influence of COVID-19 Pandemic on Medical Students’ Academic Performance and Specialty Choice<br>Introduction The COVID-19 pandemic has wide ranging effects on people in training, including medical students. The authors wanted to evaluate the impact of the COVID-19 pandemic on subjective academic performance and choice of future specialty among medical students in Austria. Method An anonymous questionnaire-based survey was sent out to medical students in Austria via universities admissions departments and platforms on social media. The survey was available from the 8<sup>th</sup> of April 2020 until the 22<sup>nd</sup> of April 2020. Results Out of 1073 participants, who completed the survey, 969 (90%) fulfilled the inclusion criteria and were eligible for analysis. <br> 843 (87%) students claimed that the COVID-19 pandemic had an impact on everyday academic life. <br> A significant increase in interest in medical specialties which are essential during this pandemic was observed. Anesthesiology and internal medicine showed a 2% growth in interest (anesthesiology p=0.002; internal medicine p&lt;0.001), research had a 1% increase (p=0.015). Conclusion The authors evaluated that the COVID-19 pandemic had an impact on students’ choice of specialty, in favor of specialties that are more essential in this crisis. Furthermore, the subjective negative effect of the COVID-19 pandemic on academic performance due to distance learning was less impactful than expected. <br>
DOI: 10.6084/m9.figshare.13292135
title: Supplementary Material for: Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science

description: Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.
DOI: 10.6084/m9.figshare.13292135.v1
title: Supplementary Material for: Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science

description: Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.
DOI: 10.6084/m9.figshare.13292390
title: Supplementary Material for: Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies

description: Analyzing human gait with inertial sensors provides valuable insights into a wide range of health impairments, including many musculoskeletal and neurological diseases. A representative and reliable assessment of gait requires continuous monitoring over long periods and ideally takes place in the subjects’ habitual environment (real-world). An inconsistent sensor wearing position can affect gait characterization and influence clinical study results, thus clinical study protocols are typically highly proscriptive, instructing all participants to wear the sensor in a uniform manner. This restrictive approach improves data quality but reduces overall adherence. In this work, we analyze the impact of altering the sensor wearing position around the waist on sensor signal and step detection. We demonstrate that an asymmetrically worn sensor leads to additional odd-harmonic frequency components in the frequency spectrum. We propose a robust solution for step detection based on autocorrelation to overcome sensor position variation (sensitivity = 0.99, precision = 0.99). The proposed solution reduces the impact of inconsistent sensor positioning on gait characterization in clinical studies, thus providing more flexibility to protocol implementation and more freedom to participants to wear the sensor in the position most comfortable to them. This work is a first step towards truly position-agnostic gait assessment in clinical settings.
DOI: 10.6084/m9.figshare.13292390.v1
title: Supplementary Material for: Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies

description: Analyzing human gait with inertial sensors provides valuable insights into a wide range of health impairments, including many musculoskeletal and neurological diseases. A representative and reliable assessment of gait requires continuous monitoring over long periods and ideally takes place in the subjects’ habitual environment (real-world). An inconsistent sensor wearing position can affect gait characterization and influence clinical study results, thus clinical study protocols are typically highly proscriptive, instructing all participants to wear the sensor in a uniform manner. This restrictive approach improves data quality but reduces overall adherence. In this work, we analyze the impact of altering the sensor wearing position around the waist on sensor signal and step detection. We demonstrate that an asymmetrically worn sensor leads to additional odd-harmonic frequency components in the frequency spectrum. We propose a robust solution for step detection based on autocorrelation to overcome sensor position variation (sensitivity = 0.99, precision = 0.99). The proposed solution reduces the impact of inconsistent sensor positioning on gait characterization in clinical studies, thus providing more flexibility to protocol implementation and more freedom to participants to wear the sensor in the position most comfortable to them. This work is a first step towards truly position-agnostic gait assessment in clinical settings.
DOI: 10.6084/m9.figshare.13292333
title: Supplementary Material for: Assessment of Fatigue Using Wearable Sensors: A Pilot Study

description: <b><i>Background:</i></b> Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts patient health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are severely limited. <b><i>Methods:</i></b> After using a recurrent neural network-based algorithm to impute missing time series data form a multisensor wearable device, we compared supervised and unsupervised machine learning approaches to gain insights on the relationship between self-reported non-pathological fatigue and multimodal sensor data. <b><i>Results:</i></b> A total of 27 healthy subjects and 405 recording days were analyzed. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters, and daily questionnaires on fatigue. The best results were obtained when using the causal convolutional neural network model for unsupervised representation learning of multivariate sensor data, and random forest as a classifier trained on subject-reported physical fatigue labels (weighted precision of 0.70 ± 0.03 and recall of 0.73 ± 0.03). When using manually engineered features on sensor data to train our random forest (weighted precision of 0.70 ± 0.05 and recall of 0.72 ± 0.01), both physical activity (energy expenditure, activity counts, and steps) and vital signs (heart rate, heart rate variability, and respiratory rate) were important parameters to measure. Furthermore, vital signs contributed the most as top features for predicting mental fatigue compared to physical ones. These results support the idea that fatigue is a highly multimodal concept. Analysis of clusters from sensor data highlighted a digital phenotype indicating the presence of fatigue (95% of observations) characterized by a high intensity of physical activity. Mental fatigue followed similar trends but was less predictable. Potential future directions could focus on anomaly detection assuming longer individual monitoring periods. <b><i>Conclusion:</i></b> Taken together, these results are the first demonstration that multimodal digital data can be used to inform, quantify, and augment subjectively captured non-pathological fatigue measures.
DOI: 10.6084/m9.figshare.13292333.v1
title: Supplementary Material for: Assessment of Fatigue Using Wearable Sensors: A Pilot Study

description: <b><i>Background:</i></b> Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts patient health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are severely limited. <b><i>Methods:</i></b> After using a recurrent neural network-based algorithm to impute missing time series data form a multisensor wearable device, we compared supervised and unsupervised machine learning approaches to gain insights on the relationship between self-reported non-pathological fatigue and multimodal sensor data. <b><i>Results:</i></b> A total of 27 healthy subjects and 405 recording days were analyzed. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters, and daily questionnaires on fatigue. The best results were obtained when using the causal convolutional neural network model for unsupervised representation learning of multivariate sensor data, and random forest as a classifier trained on subject-reported physical fatigue labels (weighted precision of 0.70 ± 0.03 and recall of 0.73 ± 0.03). When using manually engineered features on sensor data to train our random forest (weighted precision of 0.70 ± 0.05 and recall of 0.72 ± 0.01), both physical activity (energy expenditure, activity counts, and steps) and vital signs (heart rate, heart rate variability, and respiratory rate) were important parameters to measure. Furthermore, vital signs contributed the most as top features for predicting mental fatigue compared to physical ones. These results support the idea that fatigue is a highly multimodal concept. Analysis of clusters from sensor data highlighted a digital phenotype indicating the presence of fatigue (95% of observations) characterized by a high intensity of physical activity. Mental fatigue followed similar trends but was less predictable. Potential future directions could focus on anomaly detection assuming longer individual monitoring periods. <b><i>Conclusion:</i></b> Taken together, these results are the first demonstration that multimodal digital data can be used to inform, quantify, and augment subjectively captured non-pathological fatigue measures.