cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3778442
title: Autoantibody Signature in Cardiac Arrest

description: <strong>Background:</strong> Cardiac arrest is a tragic event that causes one death roughly every 90 seconds worldwide. Survivors generally undergo a work-up to identify the etiology of arrest. However, 5- 10% of cardiac arrest remain unexplained. As cardiac arrhythmias mostly underlie cardiac arrest and increasing evidence strongly supports the involvement of autoantibodies in arrhythmogenesis, a large-panel autoantibody screening was performed in cardiac arrest patients. <strong>Methods:</strong> This is an observational, cross-sectional study of patients from the Montreal Heart Institute (MHI) hospital cohort, a single center registry of participants. A peptide microarray was designed to screen for IgG targeting epitopes from all known cardiac ion channels with extracellular domains. Plasma samples from 23 patients with unexplained cardiac arrest were compared to 22 cardiac arrest cases of ischemic origin and a group of 29 age-, sex- and BMImatched healthy subjects. The false discovery rate (FDR), LASSO logistic regression and random forest methods were jointly carried out to find significant differential IgG responses. <strong>Results: </strong>The autoantibody against the pore domain of the L-type voltage-gated calcium channel (Cav1.2) was consistently identified as a biomarker of idiopathic cardiac arrest (P=0.002, FDR=0.007, classification accuracies ≥0.83). Functional studies on human induced pluripotent stem cell-derived cardiomyocytes demonstrated that the anti-Cav1.2 IgG purified from patients with idiopathic cardiac arrest is proarrhythmogenic by reducing the action potential duration through calcium channel inhibition. <strong>Conclusions: </strong>The present report addresses the concept of autoimmunity and cardiac arrest. Hitherto unknown autoantibodies targeting extracellular sequences of cardiac ion channels were detected. Moreover, the study identified an autoantibody signature specific to patients with cardiac arrest.
description: Additional funding acknowledgment: Jubiläumsstiftung von SwissLife
DOI: 10.5281/zenodo.3778443
title: Autoantibody Signature in Cardiac Arrest

description: <strong>Background:</strong> Cardiac arrest is a tragic event that causes one death roughly every 90 seconds worldwide. Survivors generally undergo a work-up to identify the etiology of arrest. However, 5- 10% of cardiac arrest remain unexplained. As cardiac arrhythmias mostly underlie cardiac arrest and increasing evidence strongly supports the involvement of autoantibodies in arrhythmogenesis, a large-panel autoantibody screening was performed in cardiac arrest patients. <strong>Methods:</strong> This is an observational, cross-sectional study of patients from the Montreal Heart Institute (MHI) hospital cohort, a single center registry of participants. A peptide microarray was designed to screen for IgG targeting epitopes from all known cardiac ion channels with extracellular domains. Plasma samples from 23 patients with unexplained cardiac arrest were compared to 22 cardiac arrest cases of ischemic origin and a group of 29 age-, sex- and BMImatched healthy subjects. The false discovery rate (FDR), LASSO logistic regression and random forest methods were jointly carried out to find significant differential IgG responses. <strong>Results: </strong>The autoantibody against the pore domain of the L-type voltage-gated calcium channel (Cav1.2) was consistently identified as a biomarker of idiopathic cardiac arrest (P=0.002, FDR=0.007, classification accuracies ≥0.83). Functional studies on human induced pluripotent stem cell-derived cardiomyocytes demonstrated that the anti-Cav1.2 IgG purified from patients with idiopathic cardiac arrest is proarrhythmogenic by reducing the action potential duration through calcium channel inhibition. <strong>Conclusions: </strong>The present report addresses the concept of autoimmunity and cardiac arrest. Hitherto unknown autoantibodies targeting extracellular sequences of cardiac ion channels were detected. Moreover, the study identified an autoantibody signature specific to patients with cardiac arrest.
description: Additional funding acknowledgment: Jubiläumsstiftung von SwissLife
DOI: 10.21227/m2e5-mm88
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.21227/0d7w-r095
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.21227/x9hh-ed92
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment.The primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure.This study aims to collect and monitor the EEG activity of patients receiving intravenous medication. This dataset has been collected in the Patient Recovery Center (a 24-hour, 7-day nurse staffed facility) with medical consultant from the Mobile Healthcare Service of Hamad Medical Corporation. The collection of raw EEG data is made possible by the use of a non-invasive head-cap type device (i.e., EMOTIV EPOC+ with 14 EEG channels).The acquired EEG data in this study has been collected from 30 patients:before, during, and after receiving the medication. We remark that each column in our dataset files is mapping a particular channel of EMOTIV EPOC+, in addition to a class label column (the firstcolumn), i.e., refers to the data collection phase, and a patient identification column (the secondcolumn). Number of rows in our dataset represents time instance of the recording channels.
DOI: 10.5281/zenodo.3778416
title: 2019-20 Mexico fieldwork - Transcriptions backup

description: 2019-20 Mexico fieldwork - Interviews and transcriptions backup
DOI: 10.5281/zenodo.3778417
title: 2019-20 Mexico fieldwork - Transcriptions backup

description: 2019-20 Mexico fieldwork - Interviews and transcriptions backup
DOI: 10.21227/evmt-p369
title: Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT

description: This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training. The model improves the existing techniques used for low-dose HRCT image inspection through an application of Stacked Autoencoders (SAEs) structures using the segmentation function for the area object detection model on Mask-RCNN. As a result, the proposed approach can quickly analyze X-ray images in detecting abnormalities in patients with lab-confirmed coronavirus even before clinical symptoms appear. In addition to detecting early abnormalities, area object detection model reveals a finding not seen in the latest cases of COVID-19. Most noteworthy, the study has shown that all COVID-19 patients exhibit an associated bilateral pleural effusion. The features are augmented to the model for the improvement of detection quality improvement and the shorten of the examination period.
DOI: 10.21227/gn7q-pr07
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: This dataset has been collected in the Patient Recovery Center (a 24-hour, 7-day nurse staffed facility) with medical consultant from the Mobile Healthcare Service of Hamad Medical Corporation. The collection of raw EEG data is made possible by the use of a non-invasive head-cap type device (i.e., EMOTIV EPOC+ with 14 EEG channels).Using this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment.The primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure.This study aims to collect and monitor the EEG activity of patients receiving intravenous medication.The acquired EEG data in this study has been collected from 30 patients:before, during, and after receiving the medication. We remark that each column in our dataset files is mapping a particular channel of EMOTIV EPOC+, in addition to a class label column (the firstcolumn), i.e., refers to the data collection phase, and a patient identification column (the secondcolumn). Number of rows in our dataset represents time instance of the recording channels.
DOI: 10.21227/5crj-ck90
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: This dataset has been collected in the Patient Recovery Center (a 24-hour, 7-day nurse staffed facility) with medical consultant from the Mobile Healthcare Service of Hamad Medical Corporation. The collection of the raw EEG data is made possible by the use of a non-invasive head-cap type device (i.e., EMOTIV EPOC+ with 14 EEG channels).Using this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment.The primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure.This study aims to collect and monitor the EEG activity of patients receiving intravenous medication.The acquired EEG data in this study has been collected from 30 patients:before, during, and after receiving the medication. We remark that each column in our dataset files is mapping a particular channel of EMOTIV EPOC+, in addition to a class label column (the firstcolumn), i.e., refers to the data collection phase, and a patient identification column (the secondcolumn). Number of rows in our dataset represents time instance of the recording channels.
DOI: 10.21227/qknk-sq40
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.5281/zenodo.3778761
title: DNA Crookedness Regulates DNA Mechanical Properties at Short Length Scales

description: Data presented in the annual conference DPG conference 2019 (https://regensburg19.dpg-tagungen.de/). Here we discuss how DNA sequence allow us to modulate its structure and mechanical properties, thus proving for the first time a one to one map between sequence and mechanical code. This is intended to provide an overview of the results published in the following peer-reviewed freely available papers:<br> Phys. Rev. Lett. 122, 048102 (2019) [DOI: 10.1103/PhysRevLett.122.048102 or https://doi.org/10.1101/283648]<br> Nanoscale Nanoscale 11, 21471 (2019) [DOI: 10.1039/C9NR07516J]<br> PNAS 114, 7049 (2017) [DOI: 10.1073/pnas.1705642114]<br> Nucleic Acids Research, gkaa225 [DOI: 10.1093/nar/gkaa225]<br> (Please cite them, if you found this information useful.)
DOI: 10.5281/zenodo.3778762
title: DNA Crookedness Regulates DNA Mechanical Properties at Short Length Scales

description: Data presented in the annual conference DPG conference 2019 (https://regensburg19.dpg-tagungen.de/). Here we discuss how DNA sequence allow us to modulate its structure and mechanical properties, thus proving for the first time a one to one map between sequence and mechanical code. This is intended to provide an overview of the results published in the following peer-reviewed freely available papers:<br> Phys. Rev. Lett. 122, 048102 (2019) [DOI: 10.1103/PhysRevLett.122.048102 or https://doi.org/10.1101/283648]<br> Nanoscale Nanoscale 11, 21471 (2019) [DOI: 10.1039/C9NR07516J]<br> PNAS 114, 7049 (2017) [DOI: 10.1073/pnas.1705642114]<br> Nucleic Acids Research, gkaa225 [DOI: 10.1093/nar/gkaa225]<br> (Please cite them, if you found this information useful.)
DOI: 10.21227/j72r-v789
title: NREL 30-Second ASI Timeseries April 4-21, 2019

description: All Sky Imagers or Total Sky Imagers (ASIs or TSIs) have substantial promise as scalable sensors for solar resource estimation and forecasting. ASI images are high-resolution fisheye images containing large amounts of sky content. This dataset contains ASI images taken at a 30-second time resolution, over a period of 17 days during April 2019 using an all-sky imager model ASI-16 by EKO Instrument. These images were used to test an automated clear sky image selection algorithm, however they can be used for a variety of other purposes including cloud position forecasting or solar irradiance estimation.
DOI: 10.21227/c3pc-fc18
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.5281/zenodo.3778677
title: Single molecule nanotribology: understanding friction and adhesion at a single molecule level

description: Data presented in the annual conference SAOG2019 (Surface Science and Thin Films Community of Switzerland 2019). Here we discuss the effect of molecular vibrations on its friction properties. This is intended to provide an overview of the results published in the following peer-reviewed freely available papers:<br> Nature Communications 10, 685 (2019). [DOI:10.1038/s41467-019-08531-4 ]<br> Nano Lett. 20, 652 (2020). [DOI: 10.1021/acs.nanolett.9b04418]<br> (Please cite them, if you found this information useful.)
DOI: 10.5281/zenodo.3778676
title: Single molecule nanotribology: understanding friction and adhesion at a single molecule level

description: Data presented in the annual conference SAOG2019 (Surface Science and Thin Films Community of Switzerland 2019). Here we discuss the effect of molecular vibrations on its friction properties. This is intended to provide an overview of the results published in the following peer-reviewed freely available papers:<br> Nature Communications 10, 685 (2019). [DOI:10.1038/s41467-019-08531-4 ]<br> Nano Lett. 20, 652 (2020). [DOI: 10.1021/acs.nanolett.9b04418]<br> (Please cite them, if you found this information useful.)
DOI: 10.15468/huhjzw
title: The short-legged Andean cosmetids revisited: the genus Libitia Simon, 1879 with description of two new species (Opiliones, Cosmetidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Medrano, Miguel, Ázara, Ludson Neves de, Kury, Adriano Brilhante (2020): The short-legged Andean cosmetids revisited: the genus Libitia Simon, 1879 with description of two new species (Opiliones, Cosmetidae). European Journal of Taxonomy 634: 1-25, DOI: https://doi.org/10.5852/ejt.2020.634
DOI: 10.21227/07xe-2c12
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.5281/zenodo.3726740
title: Raw SNR data for Manuscript "GPS Interferometric Reflectometry : Using a Low Cost Antenna to Measure Water Levels"

description: Raw GPS L1 SNR (and ancillary) data for an experiment to use a low-cost GPS antenna/receiver to measure water levels using the GNSS - Interferometric Reflectometry technique. The data were recorded at the RNLI lifeboat station in Sligo, Ireland (N 54<sup>o </sup>18' 17.8'', W 8<sup>o</sup> 34' 5.4'' ) using a Globalsat BU353S4 USB puck that uses a SirfStar IV receiver with patch antenna (2018 data) and a Maestro A2200A SirfStar IV module (2019 data). Both systems were mounted to a radio mast at around 16m above sea level. The data are stored in daily files with the naming convention sligDDD0.YY.TNR.gz where DDD is the Day of Year and YY is the year in short format (18,19). Each file is gzipped. The files are flat text files with fixed width columns in the following order 1) PRN GPS satellite code 2) Elevation (degrees) 3) Azimuth (degrees) 4) Seconds of Day 5) change in elevation angle with time (degrees/second) : needed for reflector height change corrections 6) Blank 7) S1 SNR signal (dB-Hz) 8) Blank reserved for S2 SNR signal 9) Blank reserved for S5 SNR signal
DOI: 10.5281/zenodo.3726741
title: Raw SNR data for Manuscript "GPS Interferometric Reflectometry : Using a Low Cost Antenna to Measure Water Levels"

description: Raw GPS L1 SNR (and ancillary) data for an experiment to use a low-cost GPS antenna/receiver to measure water levels using the GNSS - Interferometric Reflectometry technique. The data were recorded at the RNLI lifeboat station in Sligo, Ireland (N 54<sup>o </sup>18' 17.8'', W 8<sup>o</sup> 34' 5.4'' ) using a Globalsat BU353S4 USB puck that uses a SirfStar IV receiver with patch antenna (2018 data) and a Maestro A2200A SirfStar IV module (2019 data). Both systems were mounted to a radio mast at around 16m above sea level. The data are stored in daily files with the naming convention sligDDD0.YY.TNR.gz where DDD is the Day of Year and YY is the year in short format (18,19). Each file is gzipped. The files are flat text files with fixed width columns in the following order 1) PRN GPS satellite code 2) Elevation (degrees) 3) Azimuth (degrees) 4) Seconds of Day 5) change in elevation angle with time (degrees/second) : needed for reflector height change corrections 6) Blank 7) S1 SNR signal (dB-Hz) 8) Blank reserved for S2 SNR signal 9) Blank reserved for S5 SNR signal
DOI: 10.5281/zenodo.3778483
title: COEMS EU2020 Open Data

description: Open Data generated in the context of the COEMS H2020 project for the monitoring of execution traces. The Open Data includes the software source code, the instrumented binary files that we monitor, the execution traces and the output analysis that the TeSSLa specifications generated. The experiments are executed both in x86/ARM architectures. The x86 experiments corresponds to the offline monitoring of the execution traces; while the ARM experiments corresponds to the online monitoring via the COEMS hardware specific infrastructure. The Open Data includes 4 case of study from a COEMS industrial partner.
DOI: 10.15468/4pg6qt
title: New collections of freshwater crabs from northern Madagascar, with the description of a new species of Foza Reed & Cumberlidge, 2006 (Brachyura, Potamonautidae), and comments on their conservation status

description: This dataset contains the digitized treatments in Plazi based on the original journal article Cumberlidge, Neil, Klaus, Sebastian, Meyer, Kirstin S., Koppin, Jennifer C. (2015): New collections of freshwater crabs from northern Madagascar, with the description of a new species of Foza Reed & Cumberlidge, 2006 (Brachyura, Potamonautidae), and comments on their conservation status. European Journal of Taxonomy 109: 1-15, DOI: http://dx.doi.org/10.5852/ejt.2015.109
DOI: 10.17632/x6gj5ygjmm.1
title: Data for: Changing Trend of Land Coverage Allocation in India

description: Land forms the foundation of all economies and has a direct connotation to the socio-economic development of the region It becomes increasingly important to manage the land usage and coverage distribution in order to ensure continuation of ecological equilibrium; especially after the drastic changes with urbanization. The study was conducted to understand the changing trends in land usage and coverage in India in the 21st century with a focus on urbanization. This was observed with a magnified focus for the region of Delhi and surrounding states of Haryana and Uttar Pradesh (acting as auxiliary). The main tools utilized were regression analysis and moving average model. It was noticed that land has continuously been reallocated towards non-agricultural purposes.
DOI: 10.17632/wgd2whkxgn.2
title: Data for: Ultrafast synthesis of Pb-doped BiCuSeO oxyselenides by high-energy ball milling doi: 10.1016/j.matlet.2019.127184

description: It contains some raw data obtained during the study of phase formation mechanism for BiCuSeO based oxyselenides doi: 10.1016/j.matlet.2019.127184
DOI: 10.17632/x3vsxpspn4.2
title: Somatic evolution of the non-neoplastic IBD affected colon

description: Data supporting the manuscript 'Somatic evolution of the non-neoplastic IBD affected colon' by Sigurgeir Olafsson et al. https://www.biorxiv.org/content/10.1101/832014v1.article-metrics
DOI: 10.3886/e119198v2
title: Covid App

description: Covid-19 app study