cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7610236
title: PTB-XL (v1.0.1) Soft Segmentations (Delineation)

description: <strong>Overview</strong> The dataset provides (soft) segmentation/delineation masks for the PTB-XL ECG dataset. The underlying original dataset (v1.0.1) is available from PhysioNet (doi.org/10.13026/kfzx-aw45) For further details on the orginal dataset, consult the dataset descriptor (Wagner, P., Strodthoff, N., Bousseljot, RD. et al. PTB-XL, a large publicly available electrocardiography dataset. Sci Data 7, 154 (2020). https://doi.org/10.1038/s41597-020-0495-6). <strong>Dataset</strong> The dataset was obtained by training a U-net segmentation model using ECGDeli (Pilia, N., Nagel, C., et al. ECGdeli - An open source ECG delineation toolbox for MATLAB. SoftwareX 13, 100639 (2021).<br> https://doi.org/10.1016/j.softx.2020.100639.). as initial labels increased by adding intermediate segments. Details on the model architecture will be described in a forthcoming article (Wagner, P., Mehari, T. et al.). <strong>Dataset format</strong> In the folder ptbxl_segmentations_8bit, we provide files in numpy format (8 bit unsigned integer) with filenames corresponding to the ECG ID of the corresponding sample in PTB-XL. Each file contains an array of shape 1000x12x24, where the first axis corresponds to time steps in the sequence (10s at 100 Hz), the second refers to the 12 ECG leads (['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']) and the third axis refers to ['P-Onset', '(p', 'P-Peak', 'p)', 'P-Offset', 'pq', 'QRS-Onset', '(q', 'Q-Peak', 'qr', 'R-Peak', 'rs', 'S-Peak', 'q)', 'QRS-Offset', 'qj', 'J-point', 'jt', 'T-Onset', '(t', 'T-Peak', 't)', 'T-Offset', 'tp']. For every time step and channel it provides a soft output distribution over the 24 output classes of the segmentation model.
DOI: 10.5281/zenodo.7610235
title: PTB-XL (v1.0.1) Soft Segmentations (Delineation)

description: <strong>Overview</strong> The dataset provides (soft) segmentation/delineation masks for the PTB-XL ECG dataset. The underlying original dataset (v1.0.1) is available from PhysioNet (doi.org/10.13026/kfzx-aw45) For further details on the orginal dataset, consult the dataset descriptor (Wagner, P., Strodthoff, N., Bousseljot, RD. et al. PTB-XL, a large publicly available electrocardiography dataset. Sci Data 7, 154 (2020). https://doi.org/10.1038/s41597-020-0495-6). <strong>Dataset</strong> The dataset was obtained by training a U-net segmentation model using ECGDeli (Pilia, N., Nagel, C., et al. ECGdeli - An open source ECG delineation toolbox for MATLAB. SoftwareX 13, 100639 (2021).<br> https://doi.org/10.1016/j.softx.2020.100639.). as initial labels increased by adding intermediate segments. Details on the model architecture will be described in a forthcoming article (Wagner, P., Mehari, T. et al.). <strong>Dataset format</strong> In the folder ptbxl_segmentations_8bit, we provide files in numpy format (8 bit unsigned integer) with filenames corresponding to the ECG ID of the corresponding sample in PTB-XL. Each file contains an array of shape 1000x12x24, where the first axis corresponds to time steps in the sequence (10s at 100 Hz), the second refers to the 12 ECG leads (['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']) and the third axis refers to ['P-Onset', '(p', 'P-Peak', 'p)', 'P-Offset', 'pq', 'QRS-Onset', '(q', 'Q-Peak', 'qr', 'R-Peak', 'rs', 'S-Peak', 'q)', 'QRS-Offset', 'qj', 'J-point', 'jt', 'T-Onset', '(t', 'T-Peak', 't)', 'T-Offset', 'tp']. For every time step and channel it provides a soft output distribution over the 24 output classes of the segmentation model.
DOI: 10.34740/kaggle/dsv/4955785
title: Nifty-50 & Nifty Future 50 Dataset
DOI: 10.6084/m9.figshare.21718056
title: Dependence Model Assessment and Selection with DecoupleNets

description: Neural networks are suggested for learning a map from <i>d</i>-dimensional samples with any underlying dependence structure to multivariate uniformity in d′ dimensions. This map, termed DecoupleNet, is used for dependence model assessment and selection. If the data-generating dependence model was known, and if it was among the few analytically tractable ones, one such transformation for d′=d is Rosenblatt’s transform. DecoupleNets have multiple advantages. For example, they only require an available sample and are applicable to d′&lt;d, in particular d′=2. This allows for simpler model assessment and selection, both numerically and, because d′=2, especially graphically. A graphical assessment method has the advantage of being able to identify why, or in which region of the domain, a candidate model does not provide an adequate fit, thus, leading to model selection in particular regions of interest or improved model building strategies in such regions. Through simulation studies with data from various copulas, the feasibility and validity of this novel DecoupleNet approach is demonstrated. Applications to real world data illustrate its usefulness for model assessment and selection. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21718056.v2
title: Dependence Model Assessment and Selection with DecoupleNets

description: Neural networks are suggested for learning a map from <i>d</i>-dimensional samples with any underlying dependence structure to multivariate uniformity in d′ dimensions. This map, termed DecoupleNet, is used for dependence model assessment and selection. If the data-generating dependence model was known, and if it was among the few analytically tractable ones, one such transformation for d′=d is Rosenblatt’s transform. DecoupleNets have multiple advantages. For example, they only require an available sample and are applicable to d′&lt;d, in particular d′=2. This allows for simpler model assessment and selection, both numerically and, because d′=2, especially graphically. A graphical assessment method has the advantage of being able to identify why, or in which region of the domain, a candidate model does not provide an adequate fit, thus, leading to model selection in particular regions of interest or improved model building strategies in such regions. Through simulation studies with data from various copulas, the feasibility and validity of this novel DecoupleNet approach is demonstrated. Applications to real world data illustrate its usefulness for model assessment and selection. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21718056.v1
title: Dependence model assessment and selection with DecoupleNets

description: Neural networks are suggested for learning a map from d-dimensional samples with any underlying dependence structure to multivariate uniformity in d′ dimensions. This map, termed DecoupleNet, is used for dependence model assessment and selection. If the data-generating dependence model was known, and if it was among the few analytically tractable ones, one such transformation for d′=d is Rosenblatt’s transform. DecoupleNets have multiple advantages. For example, they only require an available sample and are applicable to d′&lt;d, in particular d′=2. This allows for simpler model assessment and selection, both numerically and, because d′=2, especially graphically. A graphical assessment method has the advantage of being able to identify why, or in which region of the domain, a candidate model does not provide an adequate fit, thus leading to model selection in particular regions of interest or improved model building strategies in such regions. Through simulation studies with data from various copulas, the feasibility and validity of this novel DecoupleNet approach is demonstrated. Applications to real world data illustrate its usefulness for model assessment and selection.
DOI: 10.5281/zenodo.7612627
title: Feasibility of Blood Pressure Measurement With a Wearable (Watch-Type) Monitor During Impending Syncopal Episodes

description: BACKGROUND: We assessed the reliability and feasibility of blood pressure (BP) measurements by means of a new wearable<br> watch-type<br> BP monitor (HeartGuide) in detecting episodes of hypotensive (pre)syncope induced by tilt table test.<br> METHODS AND RESULTS: An intrapatient comparison between systolic BP (SBP) measured by means of the HeartGuide device<br> and noninvasive finger beat-to-<br> beat<br> BP monitoring was undertaken both at baseline in supine position and repeatedly during<br> tilt table test in patients evaluated for reflex syncope. Intrapatient fall of systolic BP from baseline was measured. Eighty-one<br> patients (mean age, 61±19 years; 46 women) were included. Overall, HeartGuide was able to yield BP values at the time of<br> BP nadir in 58 (72%) patients (average HeartGuide SBP 102±18 mm Hg, versus finger SBP 101±19 mm Hg). Compared with<br> baseline, the maximum SBP decrease was on average −28.5±27.8 and −30.3±33.9 mm Hg respectively (Lin’s concordance<br> correlation coefficient=0.78, r=0.79, P=0.001). In the subgroup of 38 patients with tilt table test induced (pre)syncope, the average<br> HeartGuide SBP during symptoms was 97±16 mm Hg, and the finger SBP was 94±18 mm Hg. Compared with baseline,<br> the maximum SBP decrease was on average −35.2±29.3 and −43.3±31.8 mm Hg, respectively (Lin’s concordance correlation<br> coefficient=0.83, r=0.87, P=0.001).<br> CONCLUSIONS: Our data indicate that the HeartGuide BP monitor can detect low BP during presyncope and that its measure<br> of SBP change is consistent with that simultaneously obtained through continuous BP monitoring, despite some intrapatient<br> variability. Thus, this device might be useful in determining the hypotensive nature of spontaneous (pre)syncopal symptoms, a<br> possibility that should be verified by field studies.
DOI: 10.5281/zenodo.7612626
title: Feasibility of Blood Pressure Measurement With a Wearable (Watch-Type) Monitor During Impending Syncopal Episodes

description: BACKGROUND: We assessed the reliability and feasibility of blood pressure (BP) measurements by means of a new wearable<br> watch-type<br> BP monitor (HeartGuide) in detecting episodes of hypotensive (pre)syncope induced by tilt table test.<br> METHODS AND RESULTS: An intrapatient comparison between systolic BP (SBP) measured by means of the HeartGuide device<br> and noninvasive finger beat-to-<br> beat<br> BP monitoring was undertaken both at baseline in supine position and repeatedly during<br> tilt table test in patients evaluated for reflex syncope. Intrapatient fall of systolic BP from baseline was measured. Eighty-one<br> patients (mean age, 61±19 years; 46 women) were included. Overall, HeartGuide was able to yield BP values at the time of<br> BP nadir in 58 (72%) patients (average HeartGuide SBP 102±18 mm Hg, versus finger SBP 101±19 mm Hg). Compared with<br> baseline, the maximum SBP decrease was on average −28.5±27.8 and −30.3±33.9 mm Hg respectively (Lin’s concordance<br> correlation coefficient=0.78, r=0.79, P=0.001). In the subgroup of 38 patients with tilt table test induced (pre)syncope, the average<br> HeartGuide SBP during symptoms was 97±16 mm Hg, and the finger SBP was 94±18 mm Hg. Compared with baseline,<br> the maximum SBP decrease was on average −35.2±29.3 and −43.3±31.8 mm Hg, respectively (Lin’s concordance correlation<br> coefficient=0.83, r=0.87, P=0.001).<br> CONCLUSIONS: Our data indicate that the HeartGuide BP monitor can detect low BP during presyncope and that its measure<br> of SBP change is consistent with that simultaneously obtained through continuous BP monitoring, despite some intrapatient<br> variability. Thus, this device might be useful in determining the hypotensive nature of spontaneous (pre)syncopal symptoms, a<br> possibility that should be verified by field studies.
DOI: 10.25500/edata.bham.00000916
title: Illuminating an Uncatalogued Micropalaeontological Collection at the Lapworth Museum of Geology, University of Birmingham - Supplementary Information
DOI: 10.18728/igb-fred-815.2
title: Demnitz Isotope Hydrological Data

description: Meteorological data were measured at Hasenfelde - reported here as hourly data - Wind speed, air temperature, air pressure, humidity, precipitation, radiation Precipitation isotopes - Daily samples at Hasenfelde - Weekly samples at various locations (stream locations) in the catchment Throughfall and throughfall isotopes - Taken at soil moisture measurement sites - weekly samples Discharge - Daily measurements reported at Demnitz Mill (and rating curve) and Berkenbruck Groundwater Level at 5 locations - Elevation of surface, below ground, bottom of well, and time-series of water level (translated to m a.s.l.) Groundwater isotopes at 5 locations - Roughly monthly samples at various locations in the catchment, spread through the center and south reaches of catchment Soil Moisture - Measured at 2 locations (Forest A and Grass A) at 3 depths 20,60, and 100cm with soil temperature at each depth Soil Isotopes - Measured through bulk soil sampling at locations (Forest A and Grass A) at multiple depths - Depth ranges indicate bulk soil samples taken between specified depths Sapflow - Measured average sapflow of forest stand at Forest A Vegetation Isotopes <br><br>- Twig samples were taken from (Forest A) woody vegetation and analyzed using cryogenic extraction techniques. GIS Data - Please see the "GeoNode References" section in the metadata.
DOI: 10.5281/zenodo.7600139
title: Input data for oktoberfest

description: Example input data for Oktoberfest https://github.com/wilhelm-lab/oktoberfest
DOI: 10.5281/zenodo.7607438
title: Sample output from from Oktoberfest

description: Oktoberfest output for the 3 available tasks: 1- Spectral Library Generation 2- CE Calibration 3- Rescoring This is the result of running Oktoberfest with the input files available at https://zenodo.org/deposit/7600139
DOI: 10.5281/zenodo.7607437
title: Sample output from from Oktoberfest

description: Oktoberfest output for the 3 available tasks: 1- Spectral Library Generation 2- CE Calibration 3- Rescoring This is the result of running Oktoberfest with the input files available at https://zenodo.org/deposit/7600139
DOI: 10.5281/zenodo.7612524
title: Lung Ultrasound in Patients With SARS-COV-2 Pneumonia Correlations With Chest Computed Tomography, Respiratory Impairment, and Inflammatory Cascade

description: Objectives—Lung ultrasound (LUS) might be comparable to chest computed tomography (CT) in detecting parenchymal and pleural pathology, and in monitoring interstitial lung disease. We aimed to describe LUS characteristics of patients during the hospitalization for COVID-19 pneumonia, and to compare the extent of lung involvement at LUS and chest-CT with inflammatory response and the severity of respiration impairment.<br> Methods—During a 2-week period, we performed LUS and chest CT in hospitalized patients affected by COVID-19 pneumonia. Dosages of high sensitivity C-reactive protein (HS-CRP), D-dimer, and interleukin-6 (IL-6) were also obtained. The index of lung function (P/F ratio) was calculated from the blood gas test. LUS and CT scoring were assessed using previously validated scores. <br> Results—Twenty-six consecutive patients (3 women) underwent LUS 34 - 14 days from the early symptoms. Among them, 21 underwent CT on the same day of LUS. A fair association was found between LUS and CT scores (R = 0.45, P = .049), which became stronger if the B-lines score on LUS was not considered (R = 0.57, P = .024). LUS B-lines score correlated with IL-6 levels (R = 0.75, P = 0.011), and the number of involved lung segments detected by LUS correlated with the P/F ratio (R = 0.60, P = .019) but not with HS-CRP and D-Dimer levels. No correlations were found between CT scores and inflammations markers or P/F.<br> Conclusion—In patients with COVID-19 pneumonia, LUS was correlated with both the extent of the inflammatory response and the P/F ratio.
DOI: 10.5281/zenodo.7612523
title: Lung Ultrasound in Patients With SARS-COV-2 Pneumonia Correlations With Chest Computed Tomography, Respiratory Impairment, and Inflammatory Cascade

description: Objectives—Lung ultrasound (LUS) might be comparable to chest computed tomography (CT) in detecting parenchymal and pleural pathology, and in monitoring interstitial lung disease. We aimed to describe LUS characteristics of patients during the hospitalization for COVID-19 pneumonia, and to compare the extent of lung involvement at LUS and chest-CT with inflammatory response and the severity of respiration impairment.<br> Methods—During a 2-week period, we performed LUS and chest CT in hospitalized patients affected by COVID-19 pneumonia. Dosages of high sensitivity C-reactive protein (HS-CRP), D-dimer, and interleukin-6 (IL-6) were also obtained. The index of lung function (P/F ratio) was calculated from the blood gas test. LUS and CT scoring were assessed using previously validated scores. <br> Results—Twenty-six consecutive patients (3 women) underwent LUS 34 - 14 days from the early symptoms. Among them, 21 underwent CT on the same day of LUS. A fair association was found between LUS and CT scores (R = 0.45, P = .049), which became stronger if the B-lines score on LUS was not considered (R = 0.57, P = .024). LUS B-lines score correlated with IL-6 levels (R = 0.75, P = 0.011), and the number of involved lung segments detected by LUS correlated with the P/F ratio (R = 0.60, P = .019) but not with HS-CRP and D-Dimer levels. No correlations were found between CT scores and inflammations markers or P/F.<br> Conclusion—In patients with COVID-19 pneumonia, LUS was correlated with both the extent of the inflammatory response and the P/F ratio.
DOI: 10.25446/oxford.21382251
title: Impact of district education policies on learning: School admission system in Yogyakarta

description: This study forms part of the teams Reform Area B workstream that focusses on decentralisation and district innovations through four specially selected innovative districts that will be used as "learning laboratories" to generate findings. In each district, the team studies a particular system innovation and its impact on learning outcomes. Some of these innovations are co-designed with the district government.
DOI: 10.25446/oxford.21382251.v1
title: Impact of district education policies on learning: School admission system in Yogyakarta

description: This study forms part of the teams Reform Area B workstream that focusses on decentralisation and district innovations through four specially selected innovative districts that will be used as "learning laboratories" to generate findings. In each district, the team studies a particular system innovation and its impact on learning outcomes. Some of these innovations are co-designed with the district government.
DOI: 10.25446/oxford.21382272
title: Impact of district education policies on learning: School admission system in Yogyakarta

description: This study forms part of the teams Reform Area B workstream that focusses on decentralisation and district innovations through four specially selected innovative districts that will be used as "learning laboratories" to generate findings. In each district, the team studies a particular system innovation and its impact on learning outcomes. Some of these innovations are co-designed with the district government.
DOI: 10.25446/oxford.21382272.v1
title: Impact of district education policies on learning: School admission system in Yogyakarta

description: This study forms part of the teams Reform Area B workstream that focusses on decentralisation and district innovations through four specially selected innovative districts that will be used as "learning laboratories" to generate findings. In each district, the team studies a particular system innovation and its impact on learning outcomes. Some of these innovations are co-designed with the district government.
DOI: 10.5281/zenodo.7612698
title: Data set from Fischertechnik Smart Factory Model at University of St.Gallen (Custom Python Configuration)

description: This is about 60 mins worth of data collected from Fischertechnik Industry 9.0V smart factory model available at the University of St.Gallen. In this data set, we used a custom Python-based software stack to control the smart factory via a business process system (Camunda Platform) that calls the functionality of the smart factory via web services implemented in Python flask. MQTT is used to collect the data. Each entry in the file (low-level_log_20230206-140808.txt) corresponds to one message (as JSON object) received on a specific topic via MQTT. Each line contains all the readings of all the sensors, actuators and additional data from <strong>one </strong>CPS component (i.e., production station) at <strong>one </strong>point in time. The data set contains the following files low-level_log_20230206-140808.txt: low-level IoT data from all the sensors and actuators *.bpmn: executable BPMN 2.0 models of three different processes that have been executed several times via the Camunda Platform BPM system to control the smart factory camunda_process-instance.json: event log generated by the BPM system regarding the process instance execution camunda_activity-instance.json: event log generated by the BPM system regarding the activity instance execution Check the following publications to learn more about our research using the model factory: Malburg, L., Seiger, R., Bergmann, R., & Weber, B. (2020). Using physical factory simulation models for business process management research. In <em>Business Process Management Workshops: BPM 2020 International Workshops, Seville, Spain, September 13–18, 2020, Revised Selected Papers 18</em> (pp. 95-107). Springer International Publishing. Seiger, R., Zerbato, F., Burattin, A., García-Bañuelos, L., & Weber, B. (2020, October). Towards iot-driven process event log generation for conformance checking in smart factories. In <em>2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)</em> (pp. 20-26). IEEE. Seiger, R., Malburg, L., Weber, B., & Bergmann, R. (2022). Integrating process management and event processing in smart factories: A systems architecture and use cases. <em>Journal of Manufacturing Systems</em>, <em>63</em>, 575-592.