cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4672438
title: Raw Data for the article: Simulation study of transcatheter heart valve implantation in patients with stenotic bicuspid aortic valve

description: Bicuspid aortic valve (BAV) anatomy has routinely been considered an exclusion in the setting of transcatheter aortic valve implantation (TAVI) because of the large dimension of the aortic annulus having a more calcified, bulky, and irregular shape. The study aims to develop a patient-specific computational framework to virtually simulate TAVI in stenotic BAV patients using the Edwards SAPIEN 3 valve (S3) and its improved version SAPIEN 3 Ultra and quantify stent frame deformity as well as the severity of paravalvular leakage (PVL). Specifically, the aortic root anatomy of n.9 BAV patients who underwent TAVI was reconstructed from pre-operative CT imaging. Crimping and deployment of S3 frame were performed and then followed by fluid-solid interaction analysis to simulate valve leaflet dynamics throughout the entire cardiac cycle. Modeling revealed that the S3 stent frame expanded well on BAV anatomy with an elliptical shape at the aortic annulus. Comparison of predicted S3 deformity as assessed by eccentricity and expansion indices demonstrated a good agreement with the measurement obtained from CT imaging. Blood particle flow analysis demonstrated a backward blood jet during diastole, whereas the predicted PVL flows corresponded well with those determined by transesophageal echocardiography. This study represents a further step towards the use of personalized simulations to virtually plan TAVI, aiming at improving not only the efficacy of the implantation but also the exploration of "off-label" applications as the TAVI in the setting of BAV patients. Graphical abstract Computational frameworks of TAVI in patients with stenotic bicuspid aortic valve.
DOI: 10.5281/zenodo.51356
title: Laser Welding Multispectral Coaxial Monitoring

description: This is the first dataset containing thermopgraphy data of some single laser welding trials as they were  observed by coaxial integrated NIR and S/MWIR cameras and different IR filters.
DOI: 10.5281/zenodo.5710515
title: Dataset for typhoon paper by Wang&Xiu(2021)

description: Typhoon induced ocean surface change
DOI: 10.5281/zenodo.3541062
title: Geospatial data and 3D representation of Maungataketake, Auckland, New Zealand

description: <em>Context</em> Maungataketake (Ellett’s Mountain) was a volcanic cone on the shore of the Manukau Harbour, Mangere, New Zealand. In the second half of the twentieth century the mountain was quarried away. Maungataketake was a terraced Māori Pā, and archaeological excavations (only now in the process of being published) were undertaken there between 1972 and 1975, and in 1982, prior to its complete destruction. There is aerial imagery of the mountain available that depicts the mountain prior to quarrying. With these data a 3D model of the site was made using photogrammetry, which was also used to create a digital surface model (DSM) and contour map of the mountain. The resulting data is provided here and is aimed for further geospatial applications. In addition, the resolution of the provided DSM has analogues for the wider region and therefore could be incorporated to represent the landscape pre-destruction. Further to this a representation of the 3D model may be found on SketchFab. <em>Method</em> The photogrammetry model was created in Agisoft Metashape version 1.5.4. Ten aerial images were used of Maungataketake and the surrounding area, captured on 19<sup>th</sup> August 1960. These images were downloaded from http://retrolens.co.nz and are licensed by LINZ CC-BY 3.0. The model was aligned and the spare point cloud filtered by gradual selection with the following parameters: projection error = 0.2; reconstruction uncertainty = 10; projection accuracy = 2.5. The dense cloud was processed with depth maps of ultra high quality and aggressive filtering. The resulting points cloud was edited to remove outlying points and processed into a 3D model. The resulting 3D model was manually edited to remove faces representing trees on Maungataketake only, but not the surrounding area. This was done as to obtain representative surface contours of the mountain. The model was georeferenced by the identification of points on the landscape present on the 1960 composite image and contemporary satellite imagery. A 0.5 m DSM and contours at 1 m resolution were calculated of Maungataketake. <em>Contents of dataset</em> A geodatabase with: Control points used for georectification 1 m contours without elevation of Maungataketake 1 m contours with elevation of Maungataketake 0.5 m composite aerial image 0.5 m DSM of area covering control points 0.5 m DSM of Maungataketake Aerial photographs Crown_583-1924_22-26, Crown_583_1925_22-26 Licence for aerial photographs from http://retrolens.co.nz Attributes of aerial photographs
description: Please unzip the gdb file
DOI: 10.5281/zenodo.803508
title: A Dataset For Sanskrit Word Segmentation

description: The work was accepted in Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, colocated with ACL 2017

The last decade saw a surge in digitisation efforts for ancient manuscripts in Sanskrit. Due to various linguistic peculiarities inherent to the language, even the preliminary tasks such as word segmentation are non-trivial in Sanskrit. Elegant models for Word Segmentation in Sanskrit are indispensable for further syntactic and semantic processing of the manuscripts. Current works in word segmentation for Sanskrit, though commendable in their novelty, often have variations in their objective and evaluation criteria. In this work, we set the record straight. We formally define the objectives and the requirements for the word segmentation task. In order to encourage research in the field and to alleviate the time and effort required in pre-processing, we release a dataset of 115,000 sentences for word segmentation. For each sentence in the dataset we include the input character sequence, ground truth segmentation, and additionally lexical and morphological information about all the phonetically possible segments for the given sentence. In this work, we also discuss the linguistic considerations made while generating the candidate space of the possible segments.
description: The work was accepted in Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, colocated with ACL 2017
DOI: 10.5281/zenodo.4432422
title: Sky View Factor layer of the metropolitan plain of Florence (Tuscany, Italy)

description: Sky View Factor (SVF) is a dimensionless parameter that returns, with values between 0 and 1, the degree of exposure of a surface to the celestial vault, or the portion of the sky visible from a certain point located at ground level (Oke, 1981). This factor is influenced by urban elements geometry: values close to 1 correspond to a large visible portion, while values close to 0 indicate a limited vision of the sky. The Florence SVF layer is a raster data (Reference System ETRS89-extended / LAEA Europe - EPSG:3035; extent: 4384619.05 - 4437783.05; columns number/rows number: 53164 / 36411) with 1 m spatial resolution: it was calculated by the Digital Surface Model (DSM), obtained from the Lidar dataset of the 2007-2010 period of the Tuscany Region. Lidar-DSM layers are available on the GEOscopio platform. Please cite as Giulia Guerri, Alfonso Crisci, & Marco Morabito. (2021). Sky View Factor layer of the metropolitan plain of Florence (Tuscany, Italy) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4432422
DOI: 10.5281/zenodo.4635349
title: DNS of a counter-flow channel configuration

description: Mean flow and turbulence statistics of a compressible turbulent counter-flow channel configuration. This dataset is based on direct numerical simulations conducted using OpenSBLI (https://opensbli.github.io/), a Python-based automatic source code generation and parallel computing framework for finite difference discretisation. #==============================================================================================<br> # Please cite the following paper when publishing using this dataset: <br> # Title: Direct numerical simulation of compressible turbulence in a counter-flow channel configuration <br> # Authors: Arash Hamzehloo, David Lusher, Sylvain Laizet and Neil Sandham<br> # Journal: Physical Review Fluids <br> # DOI: https://doi.org/10.1103/PhysRevFluids.6.094603 <br> # ============================================================================================== Please note: Tables 1 and 2 of the above paper provide more detailed information on the counter-flow channels of this dataset. Each folder name of this dataset includes the Mach number, Reynolds number, domain size and grid resolution of a particular case, respectively. In each file, the first column contains the grid-point coordinates in the wall-normal direction (\(y\)) with the channel centreline located at \(y=0\). The mean stresses are defined as \(\langle u_i^{\prime}u_j^{\prime}\rangle=\langle u_i u_j \rangle - \langle u_i \rangle \langle u_j \rangle \). Angle brackets denote averages over the homogeneous spatial directions (streamwise \(x\) and spanwise \(z\)) and time. The Favre average is related to the Reynolds average as \(\langle \rho \rangle \{u_i^{\prime\prime}u_j^{\prime\prime}\}=\langle \rho u_i u_j \rangle - \langle \rho \rangle \langle u_i \rangle \langle u_j \rangle\). The mean Mach number is defined as \(\langle M \rangle = {\sqrt{\langle u \rangle^2+\langle v \rangle^2+\langle w \rangle^2}}/{{\langle a \rangle}}\) where \(a\) is the local speed of sound. The turbulent Mach number is defined as \(M_t = {\sqrt{\langle u^{\prime}u^{\prime} \rangle+\langle v^{\prime}v^{\prime} \rangle+\langle w^{\prime}w^{\prime} \rangle}}/{{\langle a \rangle}}\). # ============================================================================================== Details of the OpenSBLI framework, its numerical methodology and existing flow configurations can be found in the following papers: OpenSBLI: Automated code-generation for heterogeneous computing architectures applied to compressible fluid dynamics on structured grids. (link) OpenSBLI: A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures. (link) On the performance of WENO/TENO schemes to resolve turbulence in DNS/LES of high‐speed compressible flows. (link)
DOI: 10.5281/zenodo.4643616
title: POPC-LARGE-WALL (6108 POPC @ 310K, 20 us)

description: Neat lipid membrane of 6108 POPC @ 310K, 20 us long. WALL particles are used to suppress membrane undulations. Related flat simulations: POPC-LARGE-POSRES POPC-LARGE-WALL POPC-SMALL-POSRES POPC-SMALL-WALL Curved simulations: BUD / WAVE-LONG / WAVE-SHORT
DOI: 10.5281/zenodo.5173219
title: Dataset of hydrogen isotope record of 1F core from Lake Qinghai

description: The datasets include hydrogen isotope data (δDwax <em>n</em>-alkane C<sub>31</sub>) of 0 -9.8 m depth of 1F core from Lake Qinghai, representing the changes of the paleo-precipitation isotope over the northeastern Tibetan Plateau since the last glacial maximum.
DOI: 10.5281/zenodo.5510399
title: The EMSx dataset: historical photovoltaic and load scenarios and forecasts for 70 industrial sites

description: The EMSx dataset gathers a large collection of photovoltaic and load profiles collected by the company Schneider Electric on 70 anonymized industrial sites. This dataset is released as a component of the EMSx benchmark for energy management systems introduced in a sister publication DOI: 10.1007/s12667-020-00417-5 . Please refer to this related journal article for a detailed description of the dataset and if mentioning the EMSx dataset in your work. This dataset contains compressed files with a site ID ranging from 1 to 70. For these files, each line represents a timestep, gathering historical photovoltaic energy production and energy consumption data over the last 15 minutes, together with historical forecasts computed by Schneider Electric. The column naming follows the rules: <code>load_XX</code> in kWh: For XX from 00 to 95, a forecast for the consumption where each subsequent value is 15 minutes later. <code>load_00</code> is a forecast for the load in the next 15 minutes. These values are forecasts, and so will not exactly match the actual consumption at that timestep. <code>pv_XX</code> in kWh: For XX from 00 to 95, a forecast for the pv produced on site where each subsequent value is 15 minutes later. <code>pv_00</code> is a forecast for the pv production during the next 15 minutes. These values are forecasts, and so will not exactly match the actual_pv at that timestep. Besides files per sites, one file contains the unique historical photovoltaic production profile and forecasts employed in the data of all sites, after being rescaled appropriately. In this file, we have rescaled power values to [0,1] so that this data can serve the modeling of a photovoltaic unit beyond the scope of the EMSx benchmark. Finally, this dataset contains a metadata file providing battery storage capacities in kWh, battery flow capacities for 15 minutes in kWh, and battery efficiency coefficients, designed for the EMSx benchmark. This dataset contains information from Schneider's Microgrid Energy Management Benchmark, which is made available under the Open Database License (ODbL).
DOI: 10.5281/zenodo.5510400
title: The EMSx dataset: historical photovoltaic and load scenarios and forecasts for 70 industrial sites

description: The EMSx dataset gathers a large collection of photovoltaic and load profiles collected by the company Schneider Electric on 70 anonymized industrial sites. This dataset is released as a component of the EMSx benchmark for energy management systems introduced in a sister publication DOI: 10.1007/s12667-020-00417-5 . Please refer to this related journal article for a detailed description of the dataset and if mentioning the EMSx dataset in your work. This dataset contains compressed files with a site ID ranging from 1 to 70. For these files, each line represents a timestep, gathering historical photovoltaic energy production and energy consumption data over the last 15 minutes, together with historical forecasts computed by Schneider Electric. The column naming follows the rules: <code>load_XX</code> in kWh: For XX from 00 to 95, a forecast for the consumption where each subsequent value is 15 minutes later. <code>load_00</code> is a forecast for the load in the next 15 minutes. These values are forecasts, and so will not exactly match the actual consumption at that timestep. <code>pv_XX</code> in kWh: For XX from 00 to 95, a forecast for the pv produced on site where each subsequent value is 15 minutes later. <code>pv_00</code> is a forecast for the pv production during the next 15 minutes. These values are forecasts, and so will not exactly match the actual_pv at that timestep. Besides files per sites, one file contains the unique historical photovoltaic production profile and forecasts employed in the data of all sites, after being rescaled appropriately. In this file, we have rescaled power values to [0,1] so that this data can serve the modeling of a photovoltaic unit beyond the scope of the EMSx benchmark. Finally, this dataset contains a metadata file providing battery storage capacities in kWh, battery flow capacities for 15 minutes in kWh, and battery efficiency coefficients, designed for the EMSx benchmark. This dataset contains information from Schneider's Microgrid Energy Management Benchmark, which is made available under the Open Database License (ODbL).
DOI: 10.5281/zenodo.7032381
title: COVID-19: data and indicators to measure the return to work in Area Science Park after the emergency phase

description: Data to measure the impact of SARS-CoV-2 virus on work, activities and services of Area Science Park and the return to work after the emergency epidemiological phase. Files available: Report Restart Area Stima_Presenze_Area_Science_Park
DOI: 10.5281/zenodo.7032380
title: COVID-19: data and indicators to measure the return to work in Area Science Park after the emergency phase

description: Data to measure the impact of SARS-CoV-2 virus on work, activities and services of Area Science Park and the return to work after the emergency epidemiological phase. Files available: Report Restart Area Stima_Presenze_Area_Science_Park
DOI: 10.21227/pdpn-1b78
title: Dataset for Optical fiber faults 

description: The dataset includes processed sequences of optical time domain reflectometry (OTDR) traces incorporating different types of fiber faults namely fiber cut, fiber eavesdropping (fiber tapping), dirty connector and bad splice. The dataset can be used for developping ML-based approaches for optical fiber fault detection, localization, idenification, and characterization.
DOI: 10.5281/zenodo.7214872
title: Raw_Data_Responses to salinity in the littoral earthworm genus Pontodrilus

description: <strong>A raw data file of "Responses to salinity in the littoral earthworm genus <em>Pontodrilus</em>"</strong>
DOI: 10.5281/zenodo.7214871
title: Raw_Data_Responses to salinity in the littoral earthworm genus Pontodrilus

description: <strong>A raw data file of "Responses to salinity in the littoral earthworm genus <em>Pontodrilus</em>"</strong>
DOI: 10.5281/zenodo.3386859
title: Redistribution of map of the water surfaces of the Flemish Region (status 2018-03-01)

description: This is a redistribution of the dataset 'Watervlakken - versie 1.0’ (Water surfaces - version 1.0), originally published by the Research Institute for Nature and Forest (INBO) and distributed by 'Informatie Vlaanderen' under a CC-BY compatible license. It is redistributed for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes. The digital map of standing water surfaces (version 1.0) is a new georeferenced digital file of standing surface waters in Flanders (Flemish region). The file, built by combining existing topographic map layers, ortho-photos and a detailed digital terrain model of Flanders (version 2), comprises 86 026 polygons with sizes ranging from 1.45 square metres to 2.4 square kilometres and can be considered the most complete representation of standing surface waters currently available for the Flemish region. It can be used for a wide range of applications in research, policy preparation and implementation in which knowledge about the distribution and characteristics of standing waters is important. Also, this data source provides a unique reference base for further data management regarding these water bodies. The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, Department of Environment of the Flemish government).
description: This is a redistribution of data originally published by 'Informatie Vlaanderen'.
description: {"references": ["Packet J., Scheers K., Smeekens V., Leyssen A., Wils C., Denys L. (2018). Watervlakken versie 1.0: polygonenkaart van stilstaand water in Vlaanderen. Een nieuw instrument voor onderzoek, water-, milieu- en natuurbeleid. Rapporten van het Instituut voor Natuur- en Bosonderzoek 2018 (Rapporten van het Instituut voor Natuur- en Bosonderzoek 2018 (14)). Instituut voor Natuur- en Bosonderzoek, Brussel. DOI: doi.org/10.21436/inbor.14178464"]}
DOI: 10.5281/zenodo.7214894
title: Dataset: Architecture-based Attack Propagation and Variation Analysis for Identifying Confidentiality Issues in Industry 4.0

description: Dataset for the publication <em>Architecture-based Attack Propagation and Variation Analysis for Identifying Confidentiality Issues in Industry 4.0</em> More Information can be found in the zipped Readme
DOI: 10.5281/zenodo.7214893
title: Dataset: Architecture-based Attack Propagation and Variation Analysis for Identifying Confidentiality Issues in Industry 4.0

description: Dataset for the publication <em>Architecture-based Attack Propagation and Variation Analysis for Identifying Confidentiality Issues in Industry 4.0</em> More Information can be found in the zipped Readme
DOI: 10.25442/hku.20711581
title: Supporting data for "Virus discovery and surveillance at the human-animal interface for characterization of viruses with cross-species transmission potential"

description: Complete list of samples collected from wildlife and confiscated animals examined as part of research work led by Brian M. Worthington and conducted in Hong Kong. Samples collected as part of this One Health suveillance program were subjected to virological investigation paired with metatranscriptomic profiling for virus discovery and characterization. Phylogenomic methods were applied to alucidate host-virus relationships and to infer cross-species transmission events.
DOI: 10.25442/hku.20711581.v1
title: Supporting data for "Virus discovery and surveillance at the human-animal interface for characterization of viruses with cross-species transmission potential"

description: Complete list of samples collected from wildlife and confiscated animals examined as part of research work led by Brian M. Worthington and conducted in Hong Kong. Samples collected as part of this One Health suveillance program were subjected to virological investigation paired with metatranscriptomic profiling for virus discovery and characterization. Phylogenomic methods were applied to alucidate host-virus relationships and to infer cross-species transmission events.
DOI: 10.25442/hku.21222035
title: Supporting data for Cellular and molecular dysregulations of neutrophils in systemic lupus erythematosus.zip

description: (1) Clinical research data: Database containing patients’ data including patients’ clinical parameters are organized by our clinical research assistants with restricted access. Patients’ clinical data are anonymised and used for correlation analysis with laboratory data by GraphPad prism. (2) Laboratory research data 1) Flow cytometry: The data are automatically saved in .LMD or .fcs files. Then, the raw data will be copied by thumb drives and analysed by FlowJo software. After analysis, data can be saved in .wsp files. 2) qPCR: The raw data are saved in the .eds format and can be exported as .xls files. Excel and GraphPad prism are used for further analysis. Data collection of qPCR will follow the minimum information for publication of quantitative real-time PCR experiments (MIQE) guidelines. 3) Western Blot: PVDF membranes are scanned by ChemiDoc Imaging System (Bio-Rad). The raw data are saved in .scn files, the images are saved in .jpg and .tif formats. Further quantification is performed by Bio-Rad New Image Lab Software. 4) Immunofluorescence staining: Images are taken by Nikon Ni-U fluorescence microscope and saved in .tif files. 5) ELISA, Bradford and Endotoxin detection assay: Microplate reader is used to detect and process biological and chemical data using absorbance. Data are exported in .xls files. 6)Pyrosequencing: Data are processed and exported in .xls files. 7) ATAC-sequencing assay: Data are processed and exported in .xls and .gz files.