cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12587702
title: Supplementary Material for: Real-Life Multimarker Monitoring in Patients with Heart Failure: Continuous Remote Monitoring of Mobility and Patient-Reported Outcomes as Digital End Points in Future Heart-Failure Trials

description: <b><i>Aims:</i></b> Heart failure (HF) affects approximately 26 million people worldwide. With an aging global population, innovative approaches to HF evaluation and management are needed to cope with the worsening HF epidemic. The aim of the Real-Life Multimarker Monitoring in Patients with Heart Failure (REALIsM-HF) study (NCT03507439) is to evaluate a composite instrument comprising remote, real-time, activity-monitoring devices combined with daily electronic patient-reported outcome (ePRO) items in patients who have been hospitalized for HF and are undergoing standard HF assessment (e.g., 6-min walking distance [6MWD], blood biomarkers, Kansas City Cardiomyopathy Questionnaire [KCCQ], and echocardiography). <b><i>Methods:</i></b> REALIsM-HF is an ongoing, 12-week, observational study enrolling 80–100 patients aged ≥45 years with HF with preserved ejection fraction (HFpEF; EF ≥45%) or reduced EF (HFrEF; EF ≤35%). Statistical analyses will include examining the association between data from wearables (the AVIVO<sup>©</sup> mobile patient management patch or VitalPatch<sup>©</sup> biosensor, and the DynaPort MoveMonitor<sup>©</sup>), daily ePROs, and conventional HF metrics (e.g., serum/plasma biomarkers, 6MWD, KCCQ, and echocardiographic parameters). The feasibility of and patient compliance with at-home devices will be documented, and the data captured for the purpose of establishing reference values in patients with HFpEF or HFrEF will be summarized. <b><i>Conclusions:</i></b> The REALIsM-HF study is to evaluate the longitudinal daily activity profiles of patients with HF and correlate these with changes in serum/plasma biomarker profiles, symptoms, quality of life, and cardiac function and morphology to inform the use of wearable activity monitors for developing novel therapies and managing patients.
DOI: 10.6084/m9.figshare.12587654
title: Supplementary Material for: Indoor Climate and Air Quality in a Neonatal Intensive Care Unit

description: <b><i>Introduction and Objective:</i></b> The skin and respiratory system of premature neonates are in permanent contact with indoor room air. We longitudinally analyzed the room air climate and quality in neonatal intensive care inside and outside an incubator. <b><i>Methods:</i></b> Sampling was performed in 2 patient rooms and inside a neonatal incubator (Caleo, Draeger Medical, Lübeck, Germany) over 6 weeks with 5-min resolution resulting in 12,090 samples (U-Monitor, U-Earth Biotech, London, UK). Temperature, humidity, and air pollutants, including particulate matter (&lt;1 μm [PM1] and &lt;2.5 μm [PM2.5]), volatile organic compounds (VOC), and odorous gases (OG), were recorded. Room air parameters were analyzed using time series analysis. A linear regression model was used to check for statistically significant linear trends. Statistical analysis was performed using decompensation of time series analysis and spectral analysis by fast Fourier transformation. <b><i>Results:</i></b> The indoor climate target values of the ward’s central ventilation system for temperature and humidity were not always met. Room air parameters (PM, VOC, and OG) showed significant daytime-dependent fluctuations with different oscillation frequencies per day. The daily mean (first quartile – third quartile) concentrations of PM2.5 were significantly higher inside the incubator compared to the surrounding ambient air (2,158 [1,948–2,298] pcs/L vs. 2,018 [1,852–2,058] pcs/L; <i>p</i> &lt; 0.001). OG were significantly lower inside the incubator compared to ambient air. VOC levels inside the incubator were substantially higher during the first 5 days of the observation period compared to VOC levels in the surrounding ambient air. <b><i>Conclusions:</i></b> The indoor climate of neonatal intensive care units should be monitored in real time to detect deviations from target parameters quickly. In our neonatal intensive care unit, indoor air quality fluctuated significantly depending on the time of day. We highly suspect that air pollutants are carried into the direct patient environment by visitors and medical staff. The incubator does not protect against PM and VOC exposure but reduces exposure to OG. Cleaning procedures may lead to substantially higher concentrations of VOC inside the incubator and may represent a potentially harmful factor for premature infants.
DOI: 10.6084/m9.figshare.12587702.v1
title: Supplementary Material for: Real-Life Multimarker Monitoring in Patients with Heart Failure: Continuous Remote Monitoring of Mobility and Patient-Reported Outcomes as Digital End Points in Future Heart-Failure Trials

description: <b><i>Aims:</i></b> Heart failure (HF) affects approximately 26 million people worldwide. With an aging global population, innovative approaches to HF evaluation and management are needed to cope with the worsening HF epidemic. The aim of the Real-Life Multimarker Monitoring in Patients with Heart Failure (REALIsM-HF) study (NCT03507439) is to evaluate a composite instrument comprising remote, real-time, activity-monitoring devices combined with daily electronic patient-reported outcome (ePRO) items in patients who have been hospitalized for HF and are undergoing standard HF assessment (e.g., 6-min walking distance [6MWD], blood biomarkers, Kansas City Cardiomyopathy Questionnaire [KCCQ], and echocardiography). <b><i>Methods:</i></b> REALIsM-HF is an ongoing, 12-week, observational study enrolling 80–100 patients aged ≥45 years with HF with preserved ejection fraction (HFpEF; EF ≥45%) or reduced EF (HFrEF; EF ≤35%). Statistical analyses will include examining the association between data from wearables (the AVIVO<sup>©</sup> mobile patient management patch or VitalPatch<sup>©</sup> biosensor, and the DynaPort MoveMonitor<sup>©</sup>), daily ePROs, and conventional HF metrics (e.g., serum/plasma biomarkers, 6MWD, KCCQ, and echocardiographic parameters). The feasibility of and patient compliance with at-home devices will be documented, and the data captured for the purpose of establishing reference values in patients with HFpEF or HFrEF will be summarized. <b><i>Conclusions:</i></b> The REALIsM-HF study is to evaluate the longitudinal daily activity profiles of patients with HF and correlate these with changes in serum/plasma biomarker profiles, symptoms, quality of life, and cardiac function and morphology to inform the use of wearable activity monitors for developing novel therapies and managing patients.
DOI: 10.6084/m9.figshare.12587621
title: Supplementary Material for: Impact of Body Position on Lung Deposition of Nebulized Surfactant in Newborn Piglets on Nasal Continuous Positive Airway Pressure

description: <b><i>Introduction:</i></b> The ideal body position during surfactant nebulization is not known. <b><i>Objective:</i></b> The aim of this study was to determine whether body positioning during surfactant nebulization influences surfactant distribution and deposition in the lungs. <b><i>Methods:</i></b> Twenty-four 12- to 36-h-old full-term<b><i></i></b>piglets (1.3–2.2 kg) on nasal continuous positive airway pressure (nCPAP) were randomized into four groups: lateral decubitus with right or left side up, prone or supine positions (<i>n</i> = 6 each). All animals received 200 mg kg<sup>–1</sup> of poractant alfa mixed with 200 MBq of <sup>99m</sup>technetium-nanocolloid via a customized eFlow-Neos investigational vibrating-membrane nebulizer. Surfactant deposition (percentage of the administered dose) was measured by gamma scintigraphy. <b><i>Results:</i></b> Comparing all groups, the mean total lung surfactant deposition was significantly higher in the prone position (32.4 ± 7.7%, <i>p</i> = 0.03). The deposition in this group was higher in the right lung (21.0 ± 8.6 vs. 11.3 ± 5.7%, <i>p</i> = 0.04). When nebulization was performed in the lateral decubitus, most of the surfactant was found in the dependent lung, regardless of which side the piglet lay on (right side up 15.3 ± 1.0 vs. 3.4 ± 1.0%, <i>p</i> = 0.06, and left side up 11.2 ± 9.8 vs. 1.8 ± 0.7%, <i>p</i> = 0.04). <b><i>Conclusions:</i></b> In spontaneously breathing animals on nCPAP, the prone position yielded the highest lung dose. Higher deposition rates in the dependent lung while on lateral decubitus indicates that deposition was also influenced by gravity.
DOI: 10.6084/m9.figshare.12587621.v1
title: Supplementary Material for: Impact of Body Position on Lung Deposition of Nebulized Surfactant in Newborn Piglets on Nasal Continuous Positive Airway Pressure

description: <b><i>Introduction:</i></b> The ideal body position during surfactant nebulization is not known. <b><i>Objective:</i></b> The aim of this study was to determine whether body positioning during surfactant nebulization influences surfactant distribution and deposition in the lungs. <b><i>Methods:</i></b> Twenty-four 12- to 36-h-old full-term<b><i></i></b>piglets (1.3–2.2 kg) on nasal continuous positive airway pressure (nCPAP) were randomized into four groups: lateral decubitus with right or left side up, prone or supine positions (<i>n</i> = 6 each). All animals received 200 mg kg<sup>–1</sup> of poractant alfa mixed with 200 MBq of <sup>99m</sup>technetium-nanocolloid via a customized eFlow-Neos investigational vibrating-membrane nebulizer. Surfactant deposition (percentage of the administered dose) was measured by gamma scintigraphy. <b><i>Results:</i></b> Comparing all groups, the mean total lung surfactant deposition was significantly higher in the prone position (32.4 ± 7.7%, <i>p</i> = 0.03). The deposition in this group was higher in the right lung (21.0 ± 8.6 vs. 11.3 ± 5.7%, <i>p</i> = 0.04). When nebulization was performed in the lateral decubitus, most of the surfactant was found in the dependent lung, regardless of which side the piglet lay on (right side up 15.3 ± 1.0 vs. 3.4 ± 1.0%, <i>p</i> = 0.06, and left side up 11.2 ± 9.8 vs. 1.8 ± 0.7%, <i>p</i> = 0.04). <b><i>Conclusions:</i></b> In spontaneously breathing animals on nCPAP, the prone position yielded the highest lung dose. Higher deposition rates in the dependent lung while on lateral decubitus indicates that deposition was also influenced by gravity.
DOI: 10.5281/zenodo.3999305
title: Manually Annotated Instances of Ich ('I') from the German KoLas Corpus

description: Dataset used in Andresen/Knorr (2020). The dataset comprises 360 instances of <em>ich</em> ('I') taken from the German learner corpus KoLaS (Andresen/Knorr 2017, see http://hdl.handle.net/11022/0000-0001-B732-8 for full corpus access) and manually annotated with categories taken from Steinhoff (2007). Column descriptions: document: name of the document by which it can be found in the KoLaS corpus code_annotator1 - code_annotator4: Annotations by four annotators. Possible values: Verfasser-<em>Ich</em> (author <em>I</em>), Forscher-<em>Ich</em> (researcher <em>I</em>), Erzähler-<em>Ich</em> (narrator <em>I</em>) max_agreement_freq: Highest number of anntators that agreed on one label max_agreement_label: Label on which the highest number of annotators agreed context_before: 150 characters of context before the match match: the match itself (either <em>ich</em> or <em>Ich</em>) context_after: 150 characters of context after the match <strong>References</strong> Andresen M, Knorr D. KoLaS – Ein Lernendenkorpus in der Schreibberatungsausbildung einsetzen. <em>Zeitschrift Schreiben</em>. Published online July 5, 2017:10-17. Andresen M, Knorr D. Exploring the Use of the Pronoun I in German Academic Texts with Machine Learning. In: Burghardt M, Müller-Birn C, eds. <em>Methoden und Anwendungen der Computational Humanities</em>. Lecture Notes in Informatics (LNI). Gesellschaft für Informatik; 2020. Steinhoff T. Zum ich-Gebrauch in Wissenschaftstexten. <em>Zeitschrift für germanistische Linguistik</em>. 2007;35(1-2):1–26.
description: {"references": ["Andresen M, Knorr D. Exploring the Use of the Pronoun I in German Academic Texts with Machine Learning. In: Burghardt M, M\u00fcller-Birn C, eds. Methoden und Anwendungen der Computational Humanities. Lecture Notes in Informatics (LNI). Gesellschaft f\u00fcr Informatik; 2020."]}
DOI: 10.5281/zenodo.3999304
title: Manually Annotated Instances of Ich ('I') from the German KoLas Corpus

description: Dataset used in Andresen/Knorr (2020). The dataset comprises 360 instances of <em>ich</em> ('I') taken from the German learner corpus KoLaS (Andresen/Knorr 2017, see http://hdl.handle.net/11022/0000-0001-B732-8 for full corpus access) and manually annotated with categories taken from Steinhoff (2007). Column descriptions: document: name of the document by which it can be found in the KoLaS corpus code_annotator1 - code_annotator4: Annotations by four annotators. Possible values: Verfasser-<em>Ich</em> (author <em>I</em>), Forscher-<em>Ich</em> (researcher <em>I</em>), Erzähler-<em>Ich</em> (narrator <em>I</em>) max_agreement_freq: Highest number of anntators that agreed on one label max_agreement_label: Label on which the highest number of annotators agreed context_before: 150 characters of context before the match match: the match itself (either <em>ich</em> or <em>Ich</em>) context_after: 150 characters of context after the match <strong>References</strong> Andresen M, Knorr D. KoLaS – Ein Lernendenkorpus in der Schreibberatungsausbildung einsetzen. <em>Zeitschrift Schreiben</em>. Published online July 5, 2017:10-17. Andresen M, Knorr D. Exploring the Use of the Pronoun I in German Academic Texts with Machine Learning. In: Burghardt M, Müller-Birn C, eds. <em>Methoden und Anwendungen der Computational Humanities</em>. Lecture Notes in Informatics (LNI). Gesellschaft für Informatik; 2020. Steinhoff T. Zum ich-Gebrauch in Wissenschaftstexten. <em>Zeitschrift für germanistische Linguistik</em>. 2007;35(1-2):1–26.
description: {"references": ["Andresen M, Knorr D. Exploring the Use of the Pronoun I in German Academic Texts with Machine Learning. In: Burghardt M, M\u00fcller-Birn C, eds. Methoden und Anwendungen der Computational Humanities. Lecture Notes in Informatics (LNI). Gesellschaft f\u00fcr Informatik; 2020."]}
DOI: 10.6084/m9.figshare.12587495
title: van der Vinne et al. 2020 - Estimating core body temperature based on thermography in freely moving mice - Raw data Scripts Outputs

description: This file contains all raw data, scripts and outputs per mouse underlying the analyses presented in the article: van der Vinne et al. "Optimisation and validation of thermal imaging to continuously and non-invasively assess core body temperature changes in mice".<br><br>All scripts were written in SciLab 6.0.1.
DOI: 10.6084/m9.figshare.12587351
title: Supplementary Tables. The effect of APOL1 risk variants on emergent outcomes in kidney disorders: A meta-analysis of individual participant data

description: Dataset is attributed to Journal article entitled Effect of APOL1 risk variants on emergent outcomes in kidney disorders in Figshare.These datasets is presented in supplementary tables 1-8 and its subtables.
DOI: 10.6084/m9.figshare.12587324
title: result

description: This .zip file includes the prediction results of hydrological streamline detection using the U-net and attention U-net models.
DOI: 10.6084/m9.figshare.12587351.v1
title: Supplementary Tables. The effect of APOL1 risk variants on emergent outcomes in kidney disorders: A meta-analysis of individual participant data

description: Dataset is attributed to Journal article entitled Effect of APOL1 risk variants on emergent outcomes in kidney disorders in Figshare.These datasets is presented in supplementary tables 1-8 and its subtables.
DOI: 10.6084/m9.figshare.12587318
title: models

description: This .zip includes deep learning models trained for hydrological streamline detection.
DOI: 10.6084/m9.figshare.12587381.v1
title: CBMIstudy2_fulloutcome_measures.xlsx

description: This data is for Computerized Cognitive Bias Modification for Interpretation (CBM-I) for Japanese university students.This file contains the total scores on all 14 outcomes measuresat all time points for the CBM-I and control groups in Study 2.
DOI: 10.6084/m9.figshare.12587318.v1
title: models

description: This .zip includes deep learning models trained for hydrological streamline detection.
DOI: 10.6084/m9.figshare.12587324.v1
title: result

description: This .zip file includes the prediction results of hydrological streamline detection using the U-net and attention U-net models.
DOI: 10.6084/m9.figshare.12587306
title: Metagenome_2

description: Raw data of metagenome analysis
DOI: 10.6084/m9.figshare.12587381.v2
title: CBMIstudy2_full_outcome_measures.xlsx

description: This data is for Computerized Cognitive Bias Modification for Interpretation (CBM-I) for Japanese university students.This file contains the total scores on all 14 outcomes measuresat all time points for the CBM-I and control groups in Study 2.
DOI: 10.6084/m9.figshare.12587306.v1
title: Metagenome_2

description: Raw data of metagenome analysis
DOI: 10.6084/m9.figshare.12587294
title: Metagenome_1

description: Raw data metagenome analysis_1
DOI: 10.6084/m9.figshare.12587294.v1
title: Metagenome_1

description: Raw data metagenome analysis_1
DOI: 10.6084/m9.figshare.12587276.v1
title: CBMIstudy2_outcome_measures(1m_followup).xlsx

description: This data is for Computerized Cognitive Bias Modification for Interpretation (CBM-I) for Japanese university students.This file contains the data for pre-treatment, the post-treatment and 1 month follow up data for all participants.These data were used to compute the included in the file "CBMIstudy2_alldata.xlsx".
DOI: 10.6084/m9.figshare.12587048
title: Data for Deep-Learning based Inverse Modeling Approaches

description: The supporting data for the paper "Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example".
DOI: 10.6084/m9.figshare.12587048.v1
title: Data for Deep-Learning based Inverse Modeling Approaches

description: The supporting data for the paper "Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example".
DOI: 10.6084/m9.figshare.12587048.v2
title: Data for Deep-Learning based Inverse Modeling Approaches

description: The supporting data for the paper "Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example".
DOI: 10.6084/m9.figshare.12587048.v3
title: Data for Deep-Learning based Inverse Modeling Approaches

description: The supporting data for the paper "Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example".
DOI: 10.6084/m9.figshare.12587048.v4
title: Data for Deep-Learning based Inverse Modeling Approaches

description: The supporting data for the paper "Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example".
DOI: 10.6084/m9.figshare.12586727
title: Additional file 1 of Development of a multivariable improvement measure for gout

description: Additional file 1.
DOI: 10.6084/m9.figshare.12586717
title: Additional file 2 of Genome-wide identification of the class III POD gene family and their expression profiling in grapevine (Vitis vinifera L)

description: Additional file 2 Table S1. The basic information of POD genes identified in grapevine. Table S2. The gene ontology of POD gene in grapevine. Table S3. Kyoto Encyclopedia of Genes and Genomics (KEGG) POD genes in grapevine. Table S4. Cis-Elements of POD genes in grapevine. Table S5. The FPKM based values of POD genes in grapevine including, different organs, tissues, and developmental stages. Table S6. Relative expression patterns (log2) of POD genes in grapevine by qRT-PCR. Table S7. Sequences of the POD gene primers used for quantitative real-time PCR.
DOI: 10.6084/m9.figshare.12586727.v1
title: Additional file 1 of Development of a multivariable improvement measure for gout

description: Additional file 1.
DOI: 10.6084/m9.figshare.12586717.v1
title: Additional file 2 of Genome-wide identification of the class III POD gene family and their expression profiling in grapevine (Vitis vinifera L)

description: Additional file 2 Table S1. The basic information of POD genes identified in grapevine. Table S2. The gene ontology of POD gene in grapevine. Table S3. Kyoto Encyclopedia of Genes and Genomics (KEGG) POD genes in grapevine. Table S4. Cis-Elements of POD genes in grapevine. Table S5. The FPKM based values of POD genes in grapevine including, different organs, tissues, and developmental stages. Table S6. Relative expression patterns (log2) of POD genes in grapevine by qRT-PCR. Table S7. Sequences of the POD gene primers used for quantitative real-time PCR.
DOI: 10.6084/m9.figshare.12587048.v5
title: Data for Deep-Learning based Inverse Modeling Approaches

description: The supporting data for the paper "Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example".
DOI: 10.6084/m9.figshare.12587048.v6
title: Data for Deep-Learning based Inverse Modeling Approaches

description: The supporting data for the paper "Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example".
DOI: 10.6084/m9.figshare.12586645
title: Additional file 6 of Effect of phyB and phyC loss-of-function mutations on the wheat transcriptome under short and long day photoperiods

description: Additional file 6. Alternative splicing data from all pairwise comparisons.
DOI: 10.6084/m9.figshare.12586642
title: Additional file 5 of Effect of phyB and phyC loss-of-function mutations on the wheat transcriptome under short and long day photoperiods

description: Additional file 5. RNA-seq data comparing SD and LD within genotypes.
DOI: 10.6084/m9.figshare.12586639
title: Additional file 4 of Effect of phyB and phyC loss-of-function mutations on the wheat transcriptome under short and long day photoperiods

description: Additional file 4 RNA-seq data and annotations of genes regulated by PHYB or PHYC under SD or LD, divided into mutually exclusive categories.
DOI: 10.6084/m9.figshare.12586642.v1
title: Additional file 5 of Effect of phyB and phyC loss-of-function mutations on the wheat transcriptome under short and long day photoperiods

description: Additional file 5. RNA-seq data comparing SD and LD within genotypes.
DOI: 10.6084/m9.figshare.12586645.v1
title: Additional file 6 of Effect of phyB and phyC loss-of-function mutations on the wheat transcriptome under short and long day photoperiods

description: Additional file 6. Alternative splicing data from all pairwise comparisons.
DOI: 10.6084/m9.figshare.12586637
title: Additional file 4 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol

description: Additional file 4: 4.0 Biopsy SOP. PDF file. Biopsy and pathology SOPs. Protocol for storing and capturing meta-data regarding the renal biopsy tissue for the iBEAt study. (EXE 543 kb)
DOI: 10.6084/m9.figshare.12586633
title: Additional file 2 of Effect of phyB and phyC loss-of-function mutations on the wheat transcriptome under short and long day photoperiods

description: Additional file 2. RNA-seq data for all samples from SD photoperiods.