cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/14tk-9r41
title: Deep learning-based boundary detection and compensation technique for the accurate flow measurement near the vessel wall and fluid–structure interaction

description: An accurate analysis of fluid–structure interaction (FSI) at compliant arteries via ultrasound (US) imaging and numerical modeling is a limitation of several studies. In this study, we propose a deep learning-based boundary detection and compensation (DL-BDC) technique that can segment vessel boundaries by harnessing the convolutional neural network and wall motion compensation in near-wall flow dynamics. The segmentation performance of the technique is evaluated through numerical simulations with synthetic US images and in vitro experiments. The neural network algorithm performs well with a high structural similarity of over 0.9 and 0.82 for lumens and vessel walls, respectively. Then, the performance of the wall motion compensation is examined with or without applying DL-BDC to in vitro compliant phantoms. When DL-BDC is applied to flow influenced by wall motion, a bias error is less than 0.0002 cm/s compared with those of the theoretical values. The technique is utilized to analyze FSI with varying elasticities of the phantom. Results show that the flow dynamics and wall shear stress values are consistent with the expected values of the compliant phantoms, and their wall motion behavior is observed with pulse wave propagation. This technique is applied to US images of the human carotid artery to verify the clinical applicability of DL-BDC in analyzing FSI. As a result, DL-BDC can provide fast, accurate, and robust segmentations of vessel walls and high accuracy in near-wall flow measurement by compensating the wall motion. This proposed approach can be beneficial in the FSI analysis of various biomedical applications.
DOI: 10.21227/4nxd-w881
title: Deep learning-based boundary detection and compensation technique for the accurate flow measurement near the vessel wall and fluid–structure interaction

description: An accurate analysis of fluid–structure interaction (FSI) at compliant arteries via ultrasound (US) imaging and numerical modeling is a limitation of several studies. In this study, we propose a deep learning-based boundary detection and compensation (DL-BDC) technique that can segment vessel boundaries by harnessing the convolutional neural network and wall motion compensation in near-wall flow dynamics. The segmentation performance of the technique is evaluated through numerical simulations with synthetic US images and in vitro experiments. The neural network algorithm performs well with a high structural similarity of over 0.9 and 0.82 for lumens and vessel walls, respectively. Then, the performance of the wall motion compensation is examined with or without applying DL-BDC to in vitro compliant phantoms. When DL-BDC is applied to flow influenced by wall motion, a bias error is less than 0.0002 cm/s compared with those of the theoretical values. The technique is utilized to analyze FSI with varying elasticities of the phantom. Results show that the flow dynamics and wall shear stress values are consistent with the expected values of the compliant phantoms, and their wall motion behavior is observed with pulse wave propagation. This technique is applied to US images of the human carotid artery to verify the clinical applicability of DL-BDC in analyzing FSI. As a result, DL-BDC can provide fast, accurate, and robust segmentations of vessel walls and high accuracy in near-wall flow measurement by compensating the wall motion. This proposed approach can be beneficial in the FSI analysis of various biomedical applications.
DOI: 10.5281/zenodo.3533481
title: Example set of Arc Images

description: Example set of Arc Images
DOI: 10.5281/zenodo.3533482
title: Example set of Arc Images

description: Example set of Arc Images
DOI: 10.5281/zenodo.3533502
title: A Weighting Function Model for Unsteady Open Channel Friction: Raw code and output

description: Matlab source code and Raw Origin Lab file (.opj) for a weighting function model simulating unsteady open channel friction slopes. This material is provided as is. Methods (formulas) and Figures are explained further in the related article "A Weighting Function Model for Unsteady Open Channel Friction" by Junwei Zhou; Weimin Bao; Geoffrey R. Tick; Qing Cao; and Fanghong Ye.
DOI: 10.25345/c5w66x
title: MassIVE MSV000084563 - root nodules and nodule bacteria interactions
title: root nodules and rot nodule bacteria interaction metabolomics curated with msdial removed peaks less than 100k
DOI: 10.18112/openneuro.ds002312.v1.0.0
title: OcularLDT
DOI: 10.5258/soton/d1134
title: Datasets: Identifying nurse-staffing requirements using the Safer Nursing Care Tool. Modelling the costs and consequences of real world application to address variation in patient need on hospital wards

description: These datasets underpin analyses relating to the project "Identifying nurse-staffing requirements using the Safer Nursing Care Tool. Modelling the costs and consequences of real world application to address variation in patient need on hospital wards", accessible at https://www.journalslibrary.nihr.ac.uk/programmes/hsdr/1419421/#/.The SAQX_Days tabs contain the data that were used to fit multilevel regression models linking staffing shortfalls with reported staffing adequacy.The Bootstrap_X tabs contain the data that were used for bootstrapping for calculating the precision of staffing estimates from different sample sizes.Disclaimer: This dataset was collected and processed as part of independent research funded by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, NETSCC, the PHR programme or the Department of Health and Social Care. If there are verbatim quotations included in this publication the views and opinions expressed by the interviewees are those of the interviewees and do not necessarily reflect those of the authors, those of the NHS, the NIHR, NETSCC, the PHR programme or the Department of Health and Social Care.
DOI: 10.5281/zenodo.3533446
title: Metalign:db_info

description: db_info.txt file for Metalign
DOI: 10.5281/zenodo.3533447
title: Metalign:db_info

description: db_info.txt file for Metalign
DOI: 10.5281/zenodo.3519373
title: A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis

description: The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file 'dst_2019_SJRW_TP_TDS.zip' for updated files..
DOI: 10.5281/zenodo.3519374
title: A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis

description: The uploaded contains the input and output data which can be used to reproduce the results in the research article 'A Decision Support Tool for Water Quality Modeling'.
DOI: 10.17632/vykc3wn39j.6
title: Data for: COMPARISON OF THE EFFECTIVENESS OF PAIN CONTROL WITH TWO REGIMENS OF ACETAMINOPHEN FOLLOWING UNCOMPLICATED INTRA-ALVEOLAR EXTRACTION

description: Data on the postoperative pain experience among subjects who had tooth extractions using two different analgesic regimens.
DOI: 10.14272/arancippbynjpl-uhfffaoysa-n.1
title: 4-(4'-amino)phenyl[2.2]paracyclophane
title: InChI=1S/C22H21N/c23-21-13-11-20(12-14-21)22-15-18-6-5-16-1-3-17(4-2-16)7-9-19(22)10-8-18/h1-4,8,10-15H,5-7,9,23H2

description: This dataset contains experimental data from a sample (InChI=1S/C22H21N/c23-21-13-11-20(12-14-21)22-15-18-6-5-16-1-3-17(4-2-16)7-9-19(22)10-8-18/h1-4,8,10-15H,5-7,9,23H2). The datasets from the following experiments are available: 13C nuclear magnetic resonance spectroscopy (13C NMR) 1H nuclear magnetic resonance spectroscopy (1H NMR) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.14272/arancippbynjpl-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C22H21N/c23-21-13-11-20(12-14-21)22-15-18-6-5-16-1-3-17(4-2-16)7-9-19(22)10-8-18/h1-4,8,10-15H,5-7,9,23H2

description: dataset for mass spectrometry (MS) EI (m/z, 70 eV, (%)): 300.3 (25), 299.3 (100), 195.2 (42), 194.2 (93), 181.1 (40), 180.1 (21), 131.0 (31), 106.1 (21), 69.0 (37). HRMS (C22H21N1) calcd. 299.1674, found 299.1674.
DOI: 10.14272/arancippbynjpl-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C22H21N/c23-21-13-11-20(12-14-21)22-15-18-6-5-16-1-3-17(4-2-16)7-9-19(22)10-8-18/h1-4,8,10-15H,5-7,9,23H2

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (500 MHz, Chloroform-d, ppm) δ = 7.34–7.29 (m, 2H), 6.85–6.79 (m, 2H), 6.65 (dt, J = 7.8 Hz, J = 1.8 Hz, 2H), 6.60 – 6.45 (m, 5H), 3.82 (br, 2H), 3.58–3.41 (m, 1H), 3.20–2.99 (m, 4H), 2.89 (dqt, J = 10.1 Hz, J = 6.9 Hz, J = 3.2 Hz, 2H), 2.77–2.65 (m, 1H).
DOI: 10.25919/5dc60ec5b78b0
title: Parkes observations for project P958 semester 2019OCTS_19

description: Fast radio bursts (FRBs) continue to raise more questions than they answer. Until last year, only one burst – the only one known to repeat - had been localized to a bright radio nebula (either a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. Over the last two year searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 31 bursts, five of which have been localised to host galaxies at redshifts ranging from 0.1 to 0.5. These bursts and their hosts are very unlike the repeater, suggesting there may be a dichotomy in the population. However one of the arcminute-localised ASKAP FRBs has recently been found to repeat. Here we propose to search and study repetitions in this FRB population. Our comprehensive sample and a dense monitoring campaign of well localised bursts, at a fluence limit more than 60 times lower than that of their detections to characterise the fraction of bursts that repeat. For bursts that repeat the unique wide bandwidth observations will be used to test leading models of burst emission.
DOI: 10.5281/zenodo.3533216
title: Ruwe data SNI - wave 1

description: Dit zijn de ruwe data die voortkomen uit het sociaal netwerk instrument. Deze data werden verzameld in de periode september - november 2017.
description: This work was supported by Flanders Innovation and Entrepreneurship (www.vlaio.be/english) under grant 150011
DOI: 10.5281/zenodo.3533217
title: Ruwe data SNI - wave 1

description: Dit zijn de ruwe data die voortkomen uit het sociaal netwerk instrument. Deze data werden verzameld in de periode september - november 2017.
description: This work was supported by Flanders Innovation and Entrepreneurship (www.vlaio.be/english) under grant 150011
DOI: 10.5281/zenodo.3533462
title: Metalign:db_info

description: db_info.txt file for Metalign
DOI: 10.14272/arancippbynjpl-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C22H21N/c23-21-13-11-20(12-14-21)22-15-18-6-5-16-1-3-17(4-2-16)7-9-19(22)10-8-18/h1-4,8,10-15H,5-7,9,23H2

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (101 MHz, CDCl3, ppm) δ = 145.0, 141.9, 140.0, 139.7, 139.4, 136.9, 135.8, 133.2, 132.6, 132.3, 132.1, 131.9, 131.6, 130.8 (2C), 129.8, 115.5 (2C), 35.6, 35.4, 34.9, 34.4.
DOI: 10.14272/arancippbynjpl-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C22H21N/c23-21-13-11-20(12-14-21)22-15-18-6-5-16-1-3-17(4-2-16)7-9-19(22)10-8-18/h1-4,8,10-15H,5-7,9,23H2

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3445, 3356, 3213, 3024, 3009, 2961, 2948, 2925, 2915, 2890, 2849, 2776, 1622, 1605, 1589, 1514, 1500, 1477, 1451, 1431, 1411, 1401, 1283, 1208, 1179, 1153, 1128, 1091, 1067, 960, 952, 941, 901, 880, 853, 833, 815, 793, 735, 715, 700, 683, 653, 635, 589, 568, 552, 513, 489, 460, 419, 409, 387 cm–1.
DOI: 10.17632/cctdxs5zgs.1
title: Data for: Improving the Quality of Online Disaster Nursing Courses by Assessing Student Nurses’ Learning Perceived Needs: Instrument Development and Psychometric Evaluation
DOI: 10.17632/df7z8979nv.1
title: Data for: Improving the Quality of Online Disaster Nursing Courses by Assessing Student Nurses’ Learning Perceived Needs: Instrument Development and Psychometric Evaluation

description: The first file was used for confirmatory factor analysis, and the second file was used for exploratory factor analysis.
DOI: 10.3886/e115410v1
title: A Theory of Experimenters: Robustness, Randomization, and Balance

description: Readme for simulation/replication code for "A Theory of Experimenters:<br>Robustness, Randomization, and Balance" by Banerjee, Chassang, Montero<br>and Snowberg in the American Economic Review<br><br>Use Matlab R2019b or later to run simulations.m to produce fig1.csv,<br>fig2.csv, and fig3.csv.<br><br>Use Stata version 15 to run theoryOfExperimentersDrawFigures.do, which<br>will produce three figures: bayesLoss.eps, N100.eps, and<br>errorBalance.eps.<br><br>
DOI: 10.17632/df7z8979nv.2
title: Data for: Improving the Quality of Online Disaster Nursing Courses by Assessing Student Nurses’ Learning Perceived Needs: Instrument Development and Psychometric Evaluation

description: The first file was used for confirmatory factor analysis, and the second file was used for exploratory factor analysis.
DOI: 10.3886/e115410
title: A Theory of Experimenters: Robustness, Randomization, and Balance

description: Readme for simulation/replication code for "A Theory of Experimenters:<br>Robustness, Randomization, and Balance" by Banerjee, Chassang, Montero<br>and Snowberg in the American Economic Review<br><br>Use Matlab R2019b or later to run simulations.m to produce fig1.csv,<br>fig2.csv, and fig3.csv.<br><br>Use Stata version 15 to run theoryOfExperimentersDrawFigures.do, which<br>will produce three figures: bayesLoss.eps, N100.eps, and<br>errorBalance.eps.<br><br>
DOI: 10.21227/6xxn-d990
title: Supplement For "Morphological Prior Constrained Object Detection of Key Structures in Infant Fundus Image"

description: TO BE ADDED
DOI: 10.21227/4p60-3k93
title: Supplement For "Morphological Prior Constrained Object Detection of Key Structures in Infant Fundus Image"

description: TO BE ADDED
DOI: 10.21227/rpwt-tq13
title: Supplement For "Morphological Prior Constrained Object Detection of Key Structures in Infant Fundus Image"

description: TO BE ADDED
DOI: 10.7910/dvn/woqcc5/0bg3le
title: NEST_Data_Readme_File.txt

description: Readme file
DOI: 10.7910/dvn/woqcc5/6a1b6j
title: PMwangala_NEST_Data_Codebook.pdf

description: contains variable description and value labels
DOI: 10.7910/dvn/woqcc5/b6swb2
title: Nest_Do_file.do

description: Analysis code written in STATA software
DOI: 10.7910/dvn/woqcc5/bprbi7
title: NEST_Data_WithLabels.tab

description: STATA analysis dataset (v13) with variable and value labels
DOI: 10.7910/dvn/woqcc5/z0r6cu
title: NEST_Data.tab

description: Dataset in CSV format
DOI: 10.17632/p8jbvtt54n.1
title: Data for: Paleocene initial indentation and early growth of the Pamir as recorded in the western Tarim Basin

description: - Table S1 Sandstone petrography data support the Figure 4 in the main article. - Table S2 U-Pb ages of detrital zircons support the Figure 6 in the main article. - Table S3 Hf isotope of detrital zircons support the Figure 7 in the main article.
DOI: 10.17632/zz94z6k4gr.1
title: Data for: The Cyclostratigraphy Intercomparison Project (CIP): consistency, merits and pitfalls

description: Supplementary materials for Submission: Sinnesael et al., Earth-Science Reviews "The Cyclostratigraphy Intercomparison Project (CIP): consistency, merits and pitfalls ".
DOI: 10.17632/b9f6yp5b27.1
title: Data for: Direct Gaze Enhances Interoceptive Accuracy
DOI: 10.5281/zenodo.3522318
title: Finite element mesh of fusion energy heat exchange component: hybrid CAD/IBSim model including a graphite foam interlayer

description: Image-Based Simulation (IBSim) mesh:<br> A finite element mesh of a conceptual design for a fusion energy heat exchange component (monoblock). The mesh is a hybrid from a computer aided design (CAD) drawing for the pipe and armour and IBSim for the interlayer. The IBSim interlayer is generated directly from a 3D volumetric image of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Conversion of the data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA). The FE mesh data uses the EnSight Gold file format and may be visualised using Paraview (https://www.<strong>paraview</strong>.org). The CT data used for the mesh is available as a separate dataset: This data was used originally for the following publications (please cite if re-using the data):<br> Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. https://doi.org/10.1016/j.carbon.2018.10.031 Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf
DOI: 10.5281/zenodo.3532907
title: Finite element analysis results from simulation of fusion energy heat exchange component: hybrid CAD/IBSim model including a graphite foam interlayer

description: Temperature profile data from a finite element analysis of a conceptual design for a fusion energy heat exchange component (monoblock). The mesh is a hybrid from a computer aided design (CAD) drawing for the pipe and armour and IBSim for the interlayer. The IBSim interlayer is generated directly from a 3D volumetric image of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Conversion of the data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA). The mesh used for the analysis is available as a separate dataset: https://doi.org/10.5281/zenodo.3522319 This data was used originally for the following publications (please cite if re-using the data): Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. https://doi.org/10.1016/j.carbon.2018.10.031 Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf
DOI: 10.5281/zenodo.3532908
title: Finite element analysis results from simulation of fusion energy heat exchange component: hybrid CAD/IBSim model including a graphite foam interlayer

description: Temperature profile data from a finite element analysis of a conceptual design for a fusion energy heat exchange component (monoblock). The mesh is a hybrid from a computer aided design (CAD) drawing for the pipe and armour and IBSim for the interlayer. The IBSim interlayer is generated directly from a 3D volumetric image of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Conversion of the data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA). The mesh used for the analysis is available as a separate dataset: https://doi.org/10.5281/zenodo.3522319 This data was used originally for the following publications (please cite if re-using the data): Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. https://doi.org/10.1016/j.carbon.2018.10.031 Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf
DOI: 10.5281/zenodo.3532934
title: X-ray tomography image data of a graphite foam block (KFoam) and tortuosity analysis

description: X-ray tomography (CT) image data of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (NMT_15_229_LLME_DivInterlayer.raw) is in binary format and has the following characteristics: 1586 x 1567 x 1588; 8-bit; little-endian byte order. The second .zip file is a 200 x 200 x 200 subset of this dataset. This was used to perform a tortuosity analysis on the foam. This dataset includes three sets of tiff images; tomographic slices; binarised slices; skeletonised slices. It also includes an excel file with the results of the tortuosity analysis performed with ImageJ. This data was used originally for the following publications (please cite if re-using the data): Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. https://doi.org/10.1016/j.carbon.2018.10.031 Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf