cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7692935
title: Thermoelectric sample Cu2.3Mn0.7GeS4 - M289 - Enargite phase

description: <strong><em>M289-Enargite</em></strong> The following submission contains the data collection and processing of datasets for the sample M289, with composition Cu2.3Mn0.7GeS4 - Enargite phase, in the framework of the publication: Pavan Kumar, V., Passuti, S., Zhang, B., Fujii, S., Yoshizawa, K., Boullay, P., ... & Guilmeau, E. (2022). Engineering Transport Properties in Interconnected Enargite‐Stannite Type Cu2+ xMn1− xGeS4 Nanocomposites. <em>Angewandte Chemie International Edition</em>, <em>61</em>(49), e202210600. A crystal was identified, and precession data acquisition techniques were used to collect datasets on the same crystal. The data sets were processed with PETS2 software. The table below summarizes the data collection parameters for the data sets. The following data is also included as a text file in the data folder. <strong>Precession:</strong> <strong>General information:</strong> Project NanED (www.naned.eu) ESR Project ESR12 Project Label Cu2+xMn1-xGeS4 Sample Label Cu2.3Mn0.7GeS4 Data set Label Cu2.3Mn0.7GeS4-Enargite-PEDT <strong>Instrumental:</strong> Instrument Transmission electron microscope Jeol F200 Radiation source Cold FEG Accelerating voltage 200 kV Wavelength 0.0251 Å Probe Type Microdiffraction Beam Diameter 70nm Beam Convergence Parallel beam, convergence &lt;0.1mrad Detector Hybrid pixel detector ASI Cheetah M3 (bottom mounted) Number of pixels in the image 512 x 512 Pixel size 55 µm x 55 µm Effective camera length 200 mm Calibration constant 0.00 716 Å<sup>-1</sup>/pixel <strong>Sample description:</strong> Name Enargite Chemical composition Cu<sub>2.3</sub>Mn<sub>0.7</sub>GeS<sub>4</sub> Sample preparation Powder crushed on a mortar and dispersed in n-butanol, drop deposited on a Cu grid with holey C film <strong>Experimental:</strong> Data Type Electron diffraction data - 3D ED Data collection method PEDT Temperature (K) used during data collection 293 K Number of crystals contributing to the data set 1 Number of experimental frames 102 tilt range, tilt step, tilt per frame -39.9° to +60.5°, 1°, 0° Precession angle 1.2° Exposure time per frame 500ms <strong>Software:</strong> Software used for the data collection Instamatic software Software used for processing PETS2 (ver 2.1.20211012.1037) <strong>Authorship and bibliography</strong> Author(s) of the data Sara Passuti (ESR12) Related data Publication(s) Engineering Transport Properties in Interconnected Enargite-Stannite Type Cu2+xMn1-xGeS4 Nanocomposites DOI 10.1002/anie.202210600 <strong>Files and data formats</strong> Image folder raw-data\tiff: Folder containing images of the diffraction pattern from each frames. Image format tiff_16bit Additional folders/files M289.tif : image of the crystal M289-Enargite.pts2 :input file for the program PETS2 used for processing the data <strong>Notes:</strong>
DOI: 10.5517/ccdc.csd.cc2d081r
title: CCDC 2204783: Experimental Crystal Structure Determination

description: Related Article: Thomas Gedig, Roland Fröhlich, Constantin G. Daniliuc, Hans J. Schäfer
|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d9qr6
title: CCDC 2213888: Experimental Crystal Structure Determination

description: Related Article: Thanh Ha Nguyen, Inna Karatchevtseva, Mohan Bhadbhade, Yingjie Zhang|2023|Polyhedron|234|116338|doi:10.1016/j.poly.2023.116338
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dhkgy
title: CCDC 2219490: Experimental Crystal Structure Determination

description: Related Article: Yuna Kim, Clémence Allain, Régis Guillot, Pierre Audebert|2023|J.Photochem.Photobiol.A:Chem.|439|114629|doi:10.1016/j.jphotochem.2023.114629
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7689336
title: Raw Data for the publication of 'Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals'

description: The dataset used for the ML model described in <strong>Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals. </strong>
DOI: 10.5281/zenodo.7689337
title: Raw Data for the publication of 'Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals'

description: The dataset used for the ML model described in <strong>Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals. </strong> Composition ICSD Structure density (gm/cc) a (lattice Parameter a in Angstrom) b (lattice Parameter b in Angstrom) c (lattice Parameter c in Angstrom) z11* (Born Effective charge averaged over all atoms, component 1,1) z22 * (Born Effective charge averaged over al atoms, component 2,2) z33 * (Born Effective charge averaged over al atoms, component 3,3) E11 Dielectric tensor component 11 E22 Dielectric tensor component 11 E33 Dielectric tensor component 11 e15 Piezoelectric stress coefficient tensor component 15 in C/m2 e31 Piezoelectric stress coefficient tensor component 31 in C/m2 e33 Piezoelectric stress coefficient tensor component 33 in C/m2 max-rad in Angstrom min-rad in Angstrom Eneg-max Eneg-min Eneg-diff pol-max pol-min pol-diff C11 Elastic Modulus tensor Cij component 11 in GPa C12 Elastic Modulus tensor Cij component 12 in GPa C13 Elastic Modulus tensor Cij component 13 in GPa C33 Elastic Modulus tensor Cij component 33 in GPa C44 Elastic Modulus tensor Cij component 44 in GPa
DOI: 10.5281/zenodo.7689495
title: Raw Data for the publication of 'Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals'

description: The dataset used for the ML model described in <strong>Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals. </strong> Composition ICSD Structure density (gm/cc) a (lattice Parameter a in Angstrom) b (lattice Parameter b in Angstrom) c (lattice Parameter c in Angstrom) z11* (Born Effective charge averaged over all atoms, component 1,1) z22 * (Born Effective charge averaged over al atoms, component 2,2) z33 * (Born Effective charge averaged over al atoms, component 3,3) E11 Dielectric tensor component 11 E22 Dielectric tensor component 11 E33 Dielectric tensor component 11 e15 Piezoelectric stress coefficient tensor component 15 in C/m2 e31 Piezoelectric stress coefficient tensor component 31 in C/m2 e33 Piezoelectric stress coefficient tensor component 33 in C/m2 max-rad in Angstrom min-rad in Angstrom Eneg-max Eneg-min Eneg-diff pol-max pol-min pol-diff C11 Elastic Modulus tensor Cij component 11 in GPa C12 Elastic Modulus tensor Cij component 12 in GPa C13 Elastic Modulus tensor Cij component 13 in GPa C33 Elastic Modulus tensor Cij component 33 in GPa C44 Elastic Modulus tensor Cij component 44 in GPa
DOI: 10.5281/zenodo.7693206
title: Raw Data for the publication of 'Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals'

description: The dataset used for the ML model described in <strong>Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals. </strong> Composition ICSD Structure density (gm/cc) a (lattice Parameter a in Angstrom) b (lattice Parameter b in Angstrom) c (lattice Parameter c in Angstrom) z11* (Born Effective charge averaged over all atoms, component 1,1) z22 * (Born Effective charge averaged over al atoms, component 2,2) z33 * (Born Effective charge averaged over al atoms, component 3,3) E11 Dielectric tensor component 11 E22 Dielectric tensor component 11 E33 Dielectric tensor component 11 e15 Piezoelectric stress coefficient tensor component 15 in C/m2 e31 Piezoelectric stress coefficient tensor component 31 in C/m2 e33 Piezoelectric stress coefficient tensor component 33 in C/m2 max-rad in Angstrom min-rad in Angstrom Eneg-max Eneg-min Eneg-diff pol-max pol-min pol-diff C11 Elastic Modulus tensor Cij component 11 in GPa C12 Elastic Modulus tensor Cij component 12 in GPa C13 Elastic Modulus tensor Cij component 13 in GPa C33 Elastic Modulus tensor Cij component 33 in GPa C44 Elastic Modulus tensor Cij component 44 in GPa
DOI: 10.5281/zenodo.7693498
title: Raw Data for the publication of 'Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals'

description: The dataset used for the ML model described in <strong>Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals. </strong>
DOI: 10.17909/9j83-0841
title: TOI-904 paper data
DOI: 10.18120/y8wx-t660
title: DiffusionScans
DOI: 10.5281/zenodo.6149379
title: Repertorium van Nederlandse kaartmakers 1500 - 1900

description: This file is an HTML formatted version of the Repertorium of Dutch Map Makers (2003) by Marijke Donkersloot-de Vrij, which was published on the Internet in 2005 as a PDF file. At present, the file can only be retrieved via the Wayback Machine. This version of the dataset is mainly made to keep the file referable. With the exception of a few minor editorial changes, the contents of the original file have therefore been reproduced in full. The conversion to HTML has been made to improve machine readability relative to the original file. Although the <em>sui generis</em> database rights on the original file have expired, I think it is appropriate (and in accordance with the intentions of Donkersloot-de Vrij) to make this file available under a CC-BY license. When referring, please cite the original compiler.<br> <br> An updated version of the Repertorium of Dutch Map Makers is hosted at Explokart.
DOI: 10.5281/zenodo.6149380
title: Repertorium van Nederlandse kaartmakers 1500 - 1900

description: This file is an HTML formatted version of the Repertorium of Dutch Map Makers (2003) by Marijke Donkersloot-de Vrij, which was published on the Internet in 2005 as a PDF file. At present, the file can only be retrieved via the Wayback Machine. This version of the dataset is mainly made to keep the file referable. With the exception of a few minor editorial changes, the contents of the original file have therefore been reproduced in full. The conversion to HTML has been made to improve machine readability relative to the original file. Although the <em>sui generis</em> database rights on the original file have expired, I think it is appropriate (and in accordance with the intentions of Donkersloot-de Vrij) to make this file available under a CC-BY license. When referring, please cite the original compiler.<br> <br> An updated version of the Repertorium of Dutch Map Makers is hosted at Explokart.
DOI: 10.5281/zenodo.7693505
title: Repertorium van Nederlandse kaartmakers 1500 - 1900

description: This file is an HTML formatted version of the Repertorium of Dutch Map Makers (2003) by Marijke Donkersloot-de Vrij, which was published on the Internet in 2005 as a PDF file. At present, the file can only be retrieved via the Wayback Machine. This version of the dataset is mainly made to keep the file referable. With the exception of a few minor editorial changes, the contents of the original file have therefore been reproduced in full. The conversion to HTML has been made to improve machine readability relative to the original file. Although the <em>sui generis</em> database rights on the original file have expired, I think it is appropriate (and in accordance with the intentions of Donkersloot-de Vrij) to make this file available under a CC-BY license. When referring, please cite the original compiler.<br> <br> An updated version of the Repertorium of Dutch Map Makers is hosted at Explokart.
DOI: 10.34740/kaggle/ds/2959405
title: AdultoMayor_8_fc
DOI: 10.5281/zenodo.7693199
title: Kerr Quasi-Normal Mode Mathematica Data l=2--16, n=0--32

description: This tar.gz file contains the original Mathematica data files for the gravitational quasi-normal modes of the Kerr geometry. For n=0--15, all m values for the l=2--16 modes are present. For n=16--32, all m values for the l=2--4 modes are present. These data sets were constructed using the methods outlines in Cook & Zalutskiy, <em>Phys. Rev. D</em> <strong>90</strong> (2014) pp. 124021 (DOI: https://doi.org/10.1103/PhysRevD.90.124021).
DOI: 10.5281/zenodo.7693198
title: Kerr Quasi-Normal Mode Mathematica Data l=2--16, n=0--32

description: This tar.gz file contains the original Mathematica data files for the gravitational quasi-normal modes of the Kerr geometry. For n=0--15, all m values for the l=2--16 modes are present. For n=16--32, all m values for the l=2--4 modes are present. These data sets were constructed using the methods outlines in Cook & Zalutskiy, <em>Phys. Rev. D</em> <strong>90</strong> (2014) pp. 124021 (DOI: https://doi.org/10.1103/PhysRevD.90.124021).
DOI: 10.25345/c51g0j47f
title: MassIVE MSV000091397 - HEK293T cell degradome (pulse chase) time course (5h, 10, 15h) analysis upon Torin1 treatment. Related to Supplementary Table 3 from An et al (2020)
title: HEK293T Azidohomoalanine (AHA) Pulse Chase (degradome) time course experiment, were cells from various genetic background (WT, ATG7-/-, RB1CC1-/-) are treated or not with Torin1 for 5h, 10h or 15h. Samples are Biotin-click, and streptavidin enriched, digested and labeled with TMTpro 16plex.
DOI: 10.25345/c5wp9th2h
title: MassIVE MSV000091398 - Lipidomics-Zebrafish larvae exposed to Polyquaternium polymers - a dose-response study
title: Polyquaternium polymers are widely used in various applications, such as personal care products and wastewater treatment plants, and eventually end up in the aquatic environment. While polymers have been perceived of low toxicological concern due to their size, several studies have pointed towards cationic polymers being toxic towards aquatic organisms and that the toxicity largely is determined by the polymer charge density. The present study investigated the polyquaternium toxicological mechanism of action throughout lipidomic analysis of zebrafish larvae exposed continuously to two polymers a high charge density polyquaternium-6 and a low charge density polyquaternium-10, for 5 and 12 days upon fertilization. The results showed that the investigated polyquaterniums cause significant alterations of the zebrafish larvae lipidome. Alterations of the lipidome were additionally observed, with severe depletion of lipids (e.g. lyso-glycerophosphocholines and ceramides) in the 12 days old larvae exposed to high charge density polymer.
DOI: 10.5281/zenodo.7615623
title: Level statistics and entanglement entropy of Rydberg dressed bosons in a triple-well potential

description: We study the signatures of quantum chaos in Rydberg dressed bosonic atoms held in a 1 triple-well potential. Dynamics of the bosons are governed by an extended Bose-Hubbard model (EBHM) where long-range nearest-neighbor and next-nearest-neighbor interactions are induced by laser coupling the ground state to Rydberg state. We analyze the level statistics of the EBHM for finite number N of atoms through numerical diagonalization. In the presence of a tilting potential, the level statistics are Poissonian distribution for weak dressed interaction. It becomes a Wigner-Dyson distribution for strong interaction, signifying the emergence of quantum chaos. A hybrid distribution is obtained when the dressed interaction is much stronger than the hopping rate. Using the Fock basis, we further calculate dynamical evolution of the entanglement entropy. The maximal value (upper bound) of the entanglement entropy is proved to depend on particle numbers in the form ln(N + 1). It is found that the maximum of the time-averaged entanglement entropy appears when the chaos is strong. The location of the maximum as a function of the dressed interaction and tilting potential is independent of atom number N.
DOI: 10.5281/zenodo.7615622
title: Level statistics and entanglement entropy of Rydberg dressed bosons in a triple-well potential

description: We study the signatures of quantum chaos in Rydberg dressed bosonic atoms held in a 1 triple-well potential. Dynamics of the bosons are governed by an extended Bose-Hubbard model (EBHM) where long-range nearest-neighbor and next-nearest-neighbor interactions are induced by laser coupling the ground state to Rydberg state. We analyze the level statistics of the EBHM for finite number N of atoms through numerical diagonalization. In the presence of a tilting potential, the level statistics are Poissonian distribution for weak dressed interaction. It becomes a Wigner-Dyson distribution for strong interaction, signifying the emergence of quantum chaos. A hybrid distribution is obtained when the dressed interaction is much stronger than the hopping rate. Using the Fock basis, we further calculate dynamical evolution of the entanglement entropy. The maximal value (upper bound) of the entanglement entropy is proved to depend on particle numbers in the form ln(N + 1). It is found that the maximum of the time-averaged entanglement entropy appears when the chaos is strong. The location of the maximum as a function of the dressed interaction and tilting potential is independent of atom number N.
DOI: 10.18434/mds2-2754
title: Nanoindentation study on the phase transformation of single crystal silicon of different crystallographic orientations using a spherical indenter probe
DOI: 10.6084/m9.figshare.21171442
title: Latent Curve Detrending for Disaggregating Between-Person Effect and Within-Person Effect

description: Longitudinal data analysis is gaining attention from researchers because it enables us to examine both between- and within-person effects simultaneously. Traditionally, centering has been used with multilevel models to estimate these two effects. However, recent studies found that centering could not disaggregate the between-person and within-person effects when a time-varying predictor shows time trends. This article develops methods for disaggregating the two effects in the presence of time trends using the latent curve model. The proposed methods reveal the link between centering and detrending, which are often seen as different preprocessing for different purposes. Two simulations are conducted to assess and compare the performance of the proposed and existing models. The results show that models with a slope factor behind a predictor can unbiasedly estimate the between- and within-person effects. Also, models with latent between-person predictors can unbiasedly estimate the between-person effect, while those with observed ones suffer from bias.
DOI: 10.6084/m9.figshare.21171442.v1
title: Latent Curve Detrending for Disaggregating Between-Person Effect and Within-Person Effect

description: Longitudinal data analysis is gaining attention from researchers because it enables us to examine both between- and within-person effects simultaneously. Traditionally, centering has been used with multilevel models to estimate these two effects. However, recent studies found that centering could not disaggregate the between-person and within-person effects when a time-varying predictor shows time trends. This article develops methods for disaggregating the two effects in the presence of time trends using the latent curve model. The proposed methods reveal the link between centering and detrending, which are often seen as different preprocessing for different purposes. Two simulations are conducted to assess and compare the performance of the proposed and existing models. The results show that models with a slope factor behind a predictor can unbiasedly estimate the between- and within-person effects. Also, models with latent between-person predictors can unbiasedly estimate the between-person effect, while those with observed ones suffer from bias.
DOI: 10.6084/m9.figshare.21350345
title: Testing Model Fit in Path Models with Dependent Errors Given Non-Normality, Non-Linearity and Hierarchical Data

description: We provide a generic method of testing path models that include dependent errors, nonlinear functional relationships and using nonnormal, hierarchically structured data. First, we provide a decomposition of the causal model into smaller, independent sets. These sets can be modeled independently of each other with methods that respect the type of data in these sets. Second, we introduce copulas to model the dependent errors between non-normally distributed variables. Our method yields identical results as classical covariance-based path modelling when meeting its assumptions of linearity and normality, outperforms classical SEM given nonlinear functional relationships, and can easily accommodate any parametric probability function and nonlinear functional relationships.
DOI: 10.6084/m9.figshare.21350345.v1
title: Testing Model Fit in Path Models with Dependent Errors Given Non-Normality, Non-Linearity and Hierarchical Data

description: We provide a generic method of testing path models that include dependent errors, nonlinear functional relationships and using nonnormal, hierarchically structured data. First, we provide a decomposition of the causal model into smaller, independent sets. These sets can be modeled independently of each other with methods that respect the type of data in these sets. Second, we introduce copulas to model the dependent errors between non-normally distributed variables. Our method yields identical results as classical covariance-based path modelling when meeting its assumptions of linearity and normality, outperforms classical SEM given nonlinear functional relationships, and can easily accommodate any parametric probability function and nonlinear functional relationships.