cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/5zbc-8g23
title: Dataset for classification of handwritten and printed text in a Doctor's prescription ext

description: Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to erratic writing style of the individuals. Problem becomes more severe when the input image is doctor's prescription. Before feeding such image to the OCR engine, the classification of printed and handwritten texts is a necessity as doctor's prescription contains both handwritten and printed texts which are to be processed separately. Much work have been done in the domain of handwritten and printed text separation albeit work related to doctor's handwriting. This dataset consists of various localized and extracted images of handwritten and printed texts from various prescriptions of doctors.The images are categorized into 4 categories namely:HandwrittenMixedOtherPrintedThere are 11340 images in total after using augmentation techniques. Our model has achieved 99.5% accuracy in classifying the images. The publication link for the same will be added soon. To try out the gui for the same visit:https://garain.vision/Authentication/prescriptionPlease cite the dataset if it comes to any use.
DOI: 10.21227/zpv8-qr98
title: Dataset for classification of handwritten and printed text in a Doctor's prescription ext

description: Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to erratic writing style of the individuals. Problem becomes more severe when the input image is doctor's prescription. Before feeding such image to the OCR engine, the classification of printed and handwritten texts is a necessity as doctor's prescription contains both handwritten and printed texts which are to be processed separately. Much work have been done in the domain of handwritten and printed text separation albeit work related to doctor's handwriting. This dataset consists of various localized and extracted images of handwritten and printed texts from various prescriptions of doctors.The images are categorized into 4 categories namely:HandwrittenMixedOtherPrintedThere are 11340 images in total after using augmentation techniques. Our model has achieved 99.5% accuracy in classifying the images. The publication link for the same will be added soon. To try out the gui for the same visit:https://garain.vision/Authentication/prescriptionPlease cite the dataset if it comes to any use.
DOI: 10.26182/12r5-6z79
title: The Global Sedimentary Sulfur Isotope Database (GSSID)
DOI: 10.21227/hbmh-5c43
title: Dataset for classification of handwritten and printed text in a Doctor's prescription

description: Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to erratic writing style of the individuals. Problem becomes more severe when the input image is doctor's prescription. Before feeding such image to the OCR engine, the classification of printed and handwritten texts is a necessity as doctor's prescription contains both handwritten and printed texts which are to be processed separately. Much work have been done in the domain of handwritten and printed text separation albeit work related to doctor's handwriting. This dataset consists of various localized and extracted images of handwritten and printed texts from various prescriptions of doctors.The images are categorized into 4 categories namely:HandwrittenMixedOtherPrintedThere are 11340 images in total after using augmentation techniques. Our model has achieved 99.5% accuracy in classifying the images. The publication link for the same will be added soon. To try out the gui for the same visit:https://garain.vision/Authentication/prescriptionPlease cite the dataset if it comes to any use.
DOI: 10.5281/zenodo.3871080
title: Raw diffraction images of heliorhodopsin E108D mutant

description: Crystal structure of <em>Thermoplasmatales archaeon</em> heliorhodopsin E108D mutant<em> </em>(PDB code: 7CLJ). 79 small-wedge (10°/crystal) datasets collected from loop-harvested microcrystals using EIGER X 9M detector at a wavelength of 1 Å on BL32XU, SPring-8. Beam size was around 5×5 µm<sup>2</sup> and oscillation step was 0.1°. The crystals belonged to space group P4<sub>2</sub>2<sub>1</sub>2 with unit cell parameters a~72.9, c~115 Å. 52 datasets were indexed and integrated using XDS with KAMO pipeline. Finally 12 integrated results were merged at 2.6 Å resolution after CC-based clustering and outlier rejection by KAMO in the published result (Tanaka et al. BBRC, 2020).
DOI: 10.5281/zenodo.3871079
title: Raw diffraction images of heliorhodopsin E108D mutant

description: Crystal structure of <em>Thermoplasmatales archaeon</em> heliorhodopsin E108D mutant<em> </em>(PDB code: 7CLJ). 79 small-wedge (10°/crystal) datasets collected from loop-harvested microcrystals using EIGER X 9M detector at a wavelength of 1 Å on BL32XU, SPring-8. Beam size was around 5×5 µm<sup>2</sup> and oscillation step was 0.1°. The crystals belonged to space group P4<sub>2</sub>2<sub>1</sub>2 with unit cell parameters a~72.9, c~115 Å. 52 datasets were indexed and integrated using XDS with KAMO pipeline. Finally 12 integrated results were merged at 2.6 Å resolution after CC-based clustering and outlier rejection by KAMO in the published result (Tanaka et al. BBRC, 2020).
DOI: 10.5517/ccdc.csd.cc21fqqy
title: CCDC 1890030: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fqt1
title: CCDC 1890033: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fqv2
title: CCDC 1890034: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fqx4
title: CCDC 1890036: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fqy5
title: CCDC 1890037: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fqz6
title: CCDC 1890038: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fr08
title: CCDC 1890039: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fr19
title: CCDC 1890040: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fr2b
title: CCDC 1890041: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fr3c
title: CCDC 1890042: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fr4d
title: CCDC 1890043: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fr5f
title: CCDC 1890044: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fr6g
title: CCDC 1890045: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21fqw3
title: CCDC 1890035: Experimental Crystal Structure Determination

description: Related Article: Orestes Rivada-Wheelaghan, Aleix Comas-Vives, Robert R. Fayzullin, Agustí Lledós, Julia R. Khusnutdinova|2020|Chem.-Eur.J.|26|12168|doi:10.1002/chem.202002013
DOI: 10.5517/ccdc.csd.cc21tmcw
title: CCDC 1901458: Experimental Crystal Structure Determination

description: Related Article: David Gerven, Mathias S. Wickleder|2020|Angew.Chem.,Int.Ed.|59|17169|doi:10.1002/anie.202005056
DOI: 10.5517/ccdc.csd.cc24gj8f
title: CCDC 1980164: Experimental Crystal Structure Determination

description: Related Article: Andrés Luengo, Isabel Marzo, Matthew Reback, Isabelle M. Daubit, Vanesa Fernández-Moreira, Nils Metzler-Nolte, M. Concepción Gimeno|2020|Chem.-Eur.J.|26|12158|doi:10.1002/chem.202002067
DOI: 10.5517/ccdc.csd.cc25djz3
title: CCDC 2008055: Experimental Crystal Structure Determination

description: Related Article: Fuxing Pan, Lukas Guggolz, Florian Weigend, Stefanie Dehnen|2020|Angew.Chem.,Int.Ed.|59|16638|doi:10.1002/anie.202008108
DOI: 10.5517/ccdc.csd.cc25dk05
title: CCDC 2008056: Experimental Crystal Structure Determination

description: Related Article: Fuxing Pan, Lukas Guggolz, Florian Weigend, Stefanie Dehnen|2020|Angew.Chem.,Int.Ed.|59|16638|doi:10.1002/anie.202008108
DOI: 10.5517/ccdc.csd.cc25dk16
title: CCDC 2008057: Experimental Crystal Structure Determination

description: Related Article: Fuxing Pan, Lukas Guggolz, Florian Weigend, Stefanie Dehnen|2020|Angew.Chem.,Int.Ed.|59|16638|doi:10.1002/anie.202008108
DOI: 10.5517/ccdc.csd.cc25dk27
title: CCDC 2008058: Experimental Crystal Structure Determination

description: Related Article: Fuxing Pan, Lukas Guggolz, Florian Weigend, Stefanie Dehnen|2020|Angew.Chem.,Int.Ed.|59|16638|doi:10.1002/anie.202008108
DOI: 10.5517/ccdc.csd.cc25jy6w
title: CCDC 2012278: Experimental Crystal Structure Determination

description: Related Article: Fabian A. Watt, Nicole Dickmann, Roland Schoch, Stephan Hohloch|2020|Inorg.Chem.|59|13621|doi:10.1021/acs.inorgchem.0c01971
DOI: 10.5517/ccdc.csd.cc25jy8y
title: CCDC 2012280: Experimental Crystal Structure Determination

description: Related Article: Fabian A. Watt, Nicole Dickmann, Roland Schoch, Stephan Hohloch|2020|Inorg.Chem.|59|13621|doi:10.1021/acs.inorgchem.0c01971
DOI: 10.5281/zenodo.4005975
title: Mining and Classification Privacy Requirements in Issue Reports

description: A replication package for a paper entitled "Mining and Classification Privacy Requirements in Issue Reports" submitted to the 43rd International Conference on Software Engineering (ICSE 2021)
DOI: 10.5281/zenodo.4005974
title: Mining and Classification Privacy Requirements in Issue Reports

description: A replication package for a paper entitled "Mining and Classification Privacy Requirements in Issue Reports" submitted to the 43rd International Conference on Software Engineering (ICSE 2021)
DOI: 10.21227/5t0q-j802
title: Dataset for classification of handwritten and printed text in a Doctor's prescription

description: Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to erratic writing style of the individuals. Problem becomes more severe when the input image is doctor's prescription. Before feeding such image to the OCR engine, the classification of printed and handwritten texts is a necessity as doctor's prescription contains both handwritten and printed texts which are to be processed separately. Much work have been done in the domain of handwritten and printed text separation albeit work related to doctor's handwriting. This dataset consists of various localized and extracted images of handwritten and printed texts from various prescriptions of doctors.The images are categorized into 4 categories namely:HandwrittenMixedOtherPrintedThere are 11340 images in total after using augmentation techniques. Our model has achieved 99.5% accuracy in classifying the images. The publication link for the same will be added soon. To try out the gui for the same visit:https://garain.vision/Authentication/prescriptionIf you are unable to access the dataset drop a mail at avishekgarain@gmail.com.Please cite the dataset if it comes to any use.
DOI: 10.6084/m9.figshare.12927650
title: Basalt petrogenesis of the Tarim (not so large) igneous province, NW China Table 1-5

description: Table 1. Calculated basalt volumes derived from four model scenarios.Table 2.Major (wt.%) and trace (ppm) element compositions of the Keping and Yingmai basalts.Table 3. Sr and Nd isotopic composition for the Keping and the Yingmai basalts. <sup>87</sup>Sr/<sup>86</sup>Sr<sub>i</sub> and <sup>143</sup>Nd/<sup>144</sup>Nd<sub>i</sub> are calculated at 267.0 Ma for Keping basalts and 287.2 Ma for Yingmai basalts.Table 4. Zn isotopic composition for the Keping basalts, Yingmai basalts and separated clinopyroxene from sample Ym5-8.Table 5. Main chemical characteristics of the two Tarim basalt types (group 1 and 2).
DOI: 10.6084/m9.figshare.12927650.v3
title: Basalt petrogenesis of the Tarim (not so large) igneous province, NW China Table 1-5

description: Table 1. Calculated basalt volumes derived from four model scenarios.Table 2.Major (wt.%) and trace (ppm) element compositions of the Keping and Yingmai basalts.Table 3. Sr and Nd isotopic composition for the Keping and the Yingmai basalts. <sup>87</sup>Sr/<sup>86</sup>Sr<sub>i</sub> and <sup>143</sup>Nd/<sup>144</sup>Nd<sub>i</sub> are calculated at 267.0 Ma for Keping basalts and 287.2 Ma for Yingmai basalts.Table 4. Zn isotopic composition for the Keping basalts, Yingmai basalts and separated clinopyroxene from sample Ym5-8.Table 5. Main chemical characteristics of the two Tarim basalt types (group 1 and 2).
DOI: 10.6084/m9.figshare.12927650.v2
title: Basalt petrogenesis of the Tarim (not so large) igneous province, NW China Table 1-5

description: Table 1. Calculated basalt volumes derived from four model scenarios.Table 2.Major (wt.%) and trace (ppm) element compositions of the Keping and Yingmai basalts.Table 3. Sr and Nd isotopic composition for the Keping and the Yingmai basalts. <sup>87</sup>Sr/<sup>86</sup>Sr<sub>i</sub> and <sup>143</sup>Nd/<sup>144</sup>Nd<sub>i</sub> are calculated at 267.0 Ma for Keping basalts and 287.2 Ma for Yingmai basalts.Table 4. Zn isotopic composition for the Keping basalts, Yingmai basalts and separated clinopyroxene from sample Ym5-8.Table 5. Main chemical characteristics of the two Tarim basalt types (group 1 and 2).
DOI: 10.6084/m9.figshare.12927680
title: Basalt petrogenesis of the Tarim (not so large) igneous province, NW China Table S4 and S5

description: Calculation details of non-modal batch melting modeling of peridotite with different proportions of garnet in Fig. 7b and mixing model of melt derived from garnet peridotite and carbonated eclogite in Fig. 10.
DOI: 10.6084/m9.figshare.12927680.v3
title: Basalt petrogenesis of the Tarim (not so large) igneous province, NW China Table S4 and S5

description: Calculation details of non-modal batch melting modeling of peridotite with different proportions of garnet in Fig. 7b and mixing model of melt derived from garnet peridotite and carbonated eclogite in Fig. 10.
DOI: 10.6084/m9.figshare.12927680.v2
title: Basalt petrogenesis of the Tarim (not so large) igneous province, NW China Table S4 and S5

description: Calculation details of non-modal batch melting modeling of peridotite with different proportions of garnet in Fig. 7b and mixing model of melt derived from garnet peridotite and carbonated eclogite in Fig. 10.
DOI: 10.5281/zenodo.4063823
title: Triaxial test of Bunter Sandstone

description: This data set contains the data of a triaxial test of Bunter Sandstone. The sandstone was consolidated and fully saturated. The aim was to measure dilatancy and three circumferential extensiometer chains were installed for this purpose.
DOI: 10.5281/zenodo.4063822
title: Triaxial test of Bunter Sandstone

description: This data set contains the data of a triaxial test of Bunter Sandstone. The sandstone was consolidated and fully saturated. The aim was to measure dilatancy and three circumferential extensiometer chains were installed for this purpose.
DOI: 10.5281/zenodo.4061461
title: Research data supporting for Application of electron tomography of dislocations in beam-sensitive quartz to the determination of strain components

description: This archives contains all the raw data (micrographs) supporting the publication
DOI: 10.6084/m9.figshare.13049489.v1
title: Table S2 from The ammonite septum is not an adaptation to deep water: re-evaluating a centuries-old idea

description: Stress data extracted from the line probe for all models with a 0.5 mm thick shell
DOI: 10.6084/m9.figshare.13049489
title: Table S2 from The ammonite septum is not an adaptation to deep water: re-evaluating a centuries-old idea

description: Stress data extracted from the line probe for all models with a 0.5 mm thick shell
DOI: 10.6084/m9.figshare.13049492.v1
title: Table S1 from The ammonite septum is not an adaptation to deep water: re-evaluating a centuries-old idea

description: Nanoindentation data taken from a cross section through the shell wall of Nautilus pompilius
DOI: 10.6084/m9.figshare.13049492
title: Table S1 from The ammonite septum is not an adaptation to deep water: re-evaluating a centuries-old idea

description: Nanoindentation data taken from a cross section through the shell wall of Nautilus pompilius
DOI: 10.6084/m9.figshare.13049498
title: Table S3 from The ammonite septum is not an adaptation to deep water: re-evaluating a centuries-old idea

description: Stress data extracted from the line probe for all models with a 0.1 mm thick shell
DOI: 10.6084/m9.figshare.13049498.v1
title: Table S3 from The ammonite septum is not an adaptation to deep water: re-evaluating a centuries-old idea

description: Stress data extracted from the line probe for all models with a 0.1 mm thick shell
DOI: 10.6084/m9.figshare.13026845
title: Assessment of morphometric characters to delimit species of <i>Apolpium</i> Chamberlin, 1930 (Pseudoscorpiones, Olpiidae)

description: Pseudoscorpiones is a mesodiverse order of Arachnida, with more than 3600 described species in the world. Species delimitation is sometimes difficult due to the high variability of some characters, difficulty in finding informative and useful characters for species differentiation and the subjectivity of individual specialists. The aim of this work is to explore and to evaluate the role of morphometric characters in species delimitation within the genus <i>Apolpium</i> (Olpiidae). Measurements of 17 structures of 57 individuals were taken and their length/width ratios calculated. Cluster analyses, principal components analyses, discriminant function analyses and non-metric multidimensional scaling were carried out. The results showed low resolution for the number of species currently recognised in the genus. These results highlight the importance of looking for other morphological characters, and also for a more rigorous assessment of previously used characters for defining species.
DOI: 10.6084/m9.figshare.13026845.v1
title: Assessment of morphometric characters to delimit species of <i>Apolpium</i> Chamberlin, 1930 (Pseudoscorpiones, Olpiidae)

description: Pseudoscorpiones is a mesodiverse order of Arachnida, with more than 3600 described species in the world. Species delimitation is sometimes difficult due to the high variability of some characters, difficulty in finding informative and useful characters for species differentiation and the subjectivity of individual specialists. The aim of this work is to explore and to evaluate the role of morphometric characters in species delimitation within the genus <i>Apolpium</i> (Olpiidae). Measurements of 17 structures of 57 individuals were taken and their length/width ratios calculated. Cluster analyses, principal components analyses, discriminant function analyses and non-metric multidimensional scaling were carried out. The results showed low resolution for the number of species currently recognised in the genus. These results highlight the importance of looking for other morphological characters, and also for a more rigorous assessment of previously used characters for defining species.