cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc26697z
title: CCDC 2031840: Experimental Crystal Structure Determination

description: Related Article: Shu-kai Liu, Wen-Ching Chen, Glenn P. A. Yap, Tiow-Gan Ong|2020|Organometallics|39|4395|doi:10.1021/acs.organomet.0c00618
DOI: 10.5517/ccdc.csd.cc266980
title: CCDC 2031841: Experimental Crystal Structure Determination

description: Related Article: Shu-kai Liu, Wen-Ching Chen, Glenn P. A. Yap, Tiow-Gan Ong|2020|Organometallics|39|4395|doi:10.1021/acs.organomet.0c00618
DOI: 10.5517/ccdc.csd.cc266991
title: CCDC 2031842: Experimental Crystal Structure Determination

description: Related Article: Shu-kai Liu, Wen-Ching Chen, Glenn P. A. Yap, Tiow-Gan Ong|2020|Organometallics|39|4395|doi:10.1021/acs.organomet.0c00618
DOI: 10.5517/ccdc.csd.cc1q4589
title: CCDC 1582930: Experimental Crystal Structure Determination

description: Related Article: Yu.S. Marfin, S.D. Usoltsev, A.V. Kazak, O.S. Vodyanova, N.E. Novikova, I.A. Verin, E.V. Rumyantsev, I. V. Kholodkov, D.A. Merkusheva|2020|CSD Communication|||
description: Related Article: Yu. S. Marfin, S. D. Usoltsev, A. V. Kazak, O. S. Vodyanova, N. E. Novikova, I. A. Verin, E. V. Rumyantsev, I. V. Kholodkov, D. A. Merkushev|2020|New J.Chem.|44|19046|doi:10.1039/D0NJ02855J
DOI: 10.5517/ccdc.csd.cc25nzd7
title: CCDC 2016159: Experimental Crystal Structure Determination

description: Related Article: Guangyang Xu, Ying Shao, Shengbiao Tang, Qun Chen, Jiangtao Sun|2020|Org.Lett.|22|9303|doi:10.1021/acs.orglett.0c03533
DOI: 10.5517/ccdc.csd.cc25nzc6
title: CCDC 2016158: Experimental Crystal Structure Determination

description: Related Article: Guangyang Xu, Ying Shao, Shengbiao Tang, Qun Chen, Jiangtao Sun|2020|Org.Lett.|22|9303|doi:10.1021/acs.orglett.0c03533
DOI: 10.5517/ccdc.csd.cc26g876
title: CCDC 2039497: Experimental Crystal Structure Determination

description: Related Article: Guangyang Xu, Ying Shao, Shengbiao Tang, Qun Chen, Jiangtao Sun|2020|Org.Lett.|22|9303|doi:10.1021/acs.orglett.0c03533
DOI: 10.5517/ccdc.csd.cc266xn0
title: CCDC 2032442: Experimental Crystal Structure Determination

description: Related Article: Xingchen Yan, Luo Ge, Marta Castiñeira Reis, Syuzanna R. Harutyunyan|2020|J.Am.Chem.Soc.|142|20247|doi:10.1021/jacs.0c09974
DOI: 10.5517/ccdc.csd.cc25x56h
title: CCDC 2023066: Experimental Crystal Structure Determination

description: Related Article: Yingwei Li, Michael J. Cowan, Meng Zhou, Tian-Yi Luo, Yongbo Song, He Wang, Nathaniel L. Rosi, Giannis Mpourmpakis, Rongchao Jin|2020|J.Am.Chem.Soc.|142|20426|doi:10.1021/jacs.0c09110
DOI: 10.5517/ccdc.csd.cc26ph1g
title: CCDC 2046435: Experimental Crystal Structure Determination

description: Related Article: Yingwei Li, Michael J. Cowan, Meng Zhou, Tian-Yi Luo, Yongbo Song, He Wang, Nathaniel L. Rosi, Giannis Mpourmpakis, Rongchao Jin|2020|J.Am.Chem.Soc.|142|20426|doi:10.1021/jacs.0c09110
DOI: 10.5258/soton/d1669
title: Dataset for "High power 739 nm VECSELs for future Yb+ ion cooling"

description: This is the dataset for "High power 739 nm VECSELs for future Yb+ ion cooling", authored by Jonathan Woods, Hermann Kahle, Alan Gray, Jake Daykin, Anne Tropper, Corin Gawith, Mircea Guina, and Vasilis Apostolopoulos in Applied Optics, DOI: 10.1364/AO.409970 Dataset contains raw data and instructions of recreating figures in the publication. The figures and process in required detail for recreation are described in the readme file. The figures are as follows: Fig. 2 Optical spectra Fig. 3 Efficiency curves Fig. 4 Spatial mode profile and fitting Fig. 5 RF spectra
DOI: 10.18434/mds2-2326
title: Blind Calibration of Phase Drift in Millimeter-Wave Channel Sounders
DOI: 10.6084/m9.figshare.13522826
title: Phylogenetic trees of Trimezieae (Iridaceae) based on combined and partitions of nrITS and CP dataset

description: Trees obtained from phylogenetic analysis based on Bayesian inference with combinad and partitions of nrITS and CP dataset obtained from Lovo et al. (2018), used for ancestral state reconstruction..
DOI: 10.6084/m9.figshare.13522826.v1
title: Phylogenetic trees of Trimezieae (Iridaceae) based on combined and partitions of nrITS and CP dataset

description: Trees obtained from phylogenetic analysis based on Bayesian inference with combinad and partitions of nrITS and CP dataset obtained from Lovo et al. (2018), used for ancestral state reconstruction..
DOI: 10.5281/zenodo.4386965
title: SiLeNe data -- a Sinitic Lexical Network

description: This dataset is the semi-raw data used to build the Sinitic Lexical Network, browsable at https://silene.magistry.fr/ It is a compilation of various sources of lexical description of different languages for which sinograms are a traditional script. These sources were released as Open Data by different third parties and are combined here for convenience in cross-lingual linguistic studies. This work is a graph-based extention of a work started with Fabienne Marc on Mandarin Chinese in the early 2000's. It has since then shifted to a multilingual perspective on the Chinese script, with a larger team of young researchers. For the sake of simplicity, the format is a single CSV file where each row describes the reading of a sinogram in a specific word in a specific language. Its design was dictated by our need for the work on the book «À L'Écoute des Sinogrammes» (ALES), but it can be converted to build the SiLeNe graph.
DOI: 10.6084/m9.figshare.13522883
title: Alignments of cp and nrITS dataset for Trimezieae (Iridacaeae)

description: Alignment of nrITS and CP dataset for Trimezieae (Iridacaeae) obtained from Lovo et al. (2018)
DOI: 10.6084/m9.figshare.13522883.v1
title: Alignments of cp and nrITS dataset for Trimezieae (Iridacaeae)

description: Alignment of nrITS and CP dataset for Trimezieae (Iridacaeae) obtained from Lovo et al. (2018)
DOI: 10.5281/zenodo.4310183
title: A disease-specific functional connectome of Parkinson's disease patients.

description: The contribution of the presented work is a data record featuring a disease-specific dataset of resting state functional magnetic resonance imaging (rs-fMRI) acquisitions of 75 Parkinson's disease (PD) patients prior to deep brain stimulation (DBS) implantation - the Tor-PD connectome. Specifically, the dataset comprises 77 matrices (in the folder entitled 'vol'), each containing blood-oxygen-level-dependent-signal (BOLD) signal values of every voxel in 77 corresponding rs-fMRI acquisitions in standard MNI152 NLIN 2009b space. BOLD signal time-series matrices are given as .mat files and are viewable in MATLAB. In addition to the matrices, we have also provided a mask in NIfTI-1 format corresponding to voxels in standard space where a BOLD signal could be calculated in &gt;80% of the 77 matrices (NaN mask). The data records derived from this work can be obtained through Zenodo (https://zenodo.org/) and Lead DBS (http://lead-dbs.org/). Crucially, the format of choice can be directly used without further conversion or preprocessing steps with openly available software (http://lead-dbs.org/).
DOI: 10.5281/zenodo.4310182
title: A disease-specific functional connectome of Parkinson's disease patients.

description: The contribution of the presented work is a data record featuring a disease-specific dataset of resting state functional magnetic resonance imaging (rs-fMRI) acquisitions of 75 Parkinson's disease (PD) patients prior to deep brain stimulation (DBS) implantation - the Tor-PD connectome. Specifically, the dataset comprises 77 matrices (in the folder entitled 'vol'), each containing blood-oxygen-level-dependent-signal (BOLD) signal values of every voxel in 77 corresponding rs-fMRI acquisitions in standard MNI152 NLIN 2009b space. BOLD signal time-series matrices are given as .mat files and are viewable in MATLAB. In addition to the matrices, we have also provided a mask in NIfTI-1 format corresponding to voxels in standard space where a BOLD signal could be calculated in &gt;80% of the 77 matrices (NaN mask). The data records derived from this work can be obtained through Zenodo (https://zenodo.org/) and Lead DBS (http://lead-dbs.org/). Crucially, the format of choice can be directly used without further conversion or preprocessing steps with openly available software (http://lead-dbs.org/).
DOI: 10.5281/zenodo.4420101
title: Shoreline position, wave height and wave period datasets at Grand Popo (Benin) and Nha Trang (Vietnam)

description: Shoreline position, wave height and wave period datasets at Narrabeen (Australia), Grand Popo (Benin) and Nha Trang (Vietnam) (.mat files)
DOI: 10.5281/zenodo.3669125
title: Shoreline position, wave height and wave period datasets at Grand Popo (Benin) and Nha Trang (Vietnam)

description: Shoreline position, wave height and wave period datasets at Narrabeen (Australia), Grand Popo (Benin) and Nha Trang (Vietnam) (.mat files)
DOI: 10.18434/mds2-2346
title: GAA-GAA Coexistence in the CBRS Band: Performance Evaluation of Approach 3
DOI: 10.6073/pasta/93999a64cc4650828f633e2ab5b237fa
title: Stream temperature and discharge measured each summer for Oksrukuyik Creek at Dalton Road crossing, Arctic LTER, Toolik Field Station, Alaska, 1989-2019

description: Oksrukuyik Creek stage height and calculated discharge for the summer of 1989 to present. Stream temperature and discharge measured each summer for several streams in the Toolik area. Stream height is converted into stream discharge based on a rating curve calculated from manual discharge measurements throughout the season. The principal investigator in charge of the temperature and discharge measurements is Dr. Breck Bowden. Note: This file combines the previous individual yearly files.
DOI: 10.5281/zenodo.4420087
title: WP1 Task 1.2 Metadata Crop Preliminary Collections

description: Dataset includes information on variety names, origin, year of release or collection/acquisition date, original and/or maintenance breeder, holding instiute, accession code and selection traits of the ECOBREED preliminary collections of wheat (Triticum aestivum), potato (Solanum tuberosum, S. phureja), soybean (Glycine max) and buckwheat (Fagopyrum esculentum, F. tataricum).
DOI: 10.5281/zenodo.4420086
title: WP1 Task 1.2 Metadata Crop Preliminary Collections

description: Dataset includes information on variety names, origin, year of release or collection/acquisition date, original and/or maintenance breeder, holding instiute, accession code and selection traits of the ECOBREED preliminary collections of wheat (Triticum aestivum), potato (Solanum tuberosum, S. phureja), soybean (Glycine max) and buckwheat (Fagopyrum esculentum, F. tataricum).
DOI: 10.6084/m9.figshare.13522685
title: Trait matrix of floral display in Trimezieae (Iridaceae)

description: Trait matrix with species and coding for the different flower morphologies in Trimezieae (Iridaceae)
DOI: 10.6084/m9.figshare.13522685.v1
title: Trait matrix of floral display in Trimezieae (Iridaceae)

description: Trait matrix with species and coding for the different flower morphologies in Trimezieae (Iridaceae)
DOI: 10.6084/m9.figshare.13522649
title: Maximum Clade Credibility Tree of Trimezieae (Iridaceae)

description: Maximum Credibility Tree of Trimezieae (Iridaceae) based on combined nrITS + chloroplast combined dataset
DOI: 10.6084/m9.figshare.13522649.v1
title: Maximum Clade Credibility Tree of Trimezieae (Iridaceae)

description: Maximum Credibility Tree of Trimezieae (Iridaceae) based on combined nrITS + chloroplast combined dataset
DOI: 10.5281/zenodo.4419526
title: Ultrasonic guided-waves in quasi-isotropic and cross-ply composite beams using a finite element model

description: Ultrasonic guided-wave data taken from numerical evaluations of a finite element model (FEM) considering two composite beam structures and applying three noise levels in signal-to-noise ratio (SNR): Quasi-isotropic carbon fiber composite beam (dimensions: 1m x 4mm x 2mm) with a [+45/-45/90/0]_s layup and a transverse crack (1mm deep at 0.40m from excitation) in it and the following noise levels (the file directories are specified in parentheses): Noisefree (/Quasi-Isotropic_Crack/Noisefree/MeasurementCrack_NF.mat) SNR 10 dB (/Quasi-Isotropic_Crack/SNR_10dB/MeasurementCrack_10dB.mat) SNR 5 dB (/Quasi-Isotropic_Crack/SNR_05dB/MeasurementCrack_5dB.mat) Cross-ply carbon fiber composite beam (dimensions: 1m x 3mm x 2mm) with [0_{2}/90]_s stacking sequence and a delamination (5mm long at 0.45m from excitation) in it and the following noise levels: Noisefree (/Cross-Ply_Delam/Noisefree/MeasurementDelam_NF.mat) SNR 10 dB (/Cross-Ply_Delam/SNR_10dB/MeasurementDelam_10dB.mat) SNR 5 dB (/Cross-Ply_Delam/SNR_05dB/MeasurementDelam_5dB.mat) FEM notes: solid elements (i.e. C3D8R in Abaqus) with the following dimensions: 0.5mm thick, 0.5mm width, and 0.2mm long. Damage modes are modeled by node duplication. Ultrasonic tests data: 6-cycle Hanning windowed sinusoid centered at 100 kHz of frequency. Excitation signal at one end of the beam in the vertical direction. Sensor situated at 0.2m from the point of excitation and measuring for 720 \mu s. First antisymmetric mode (A0) is excited.
DOI: 10.5281/zenodo.4419525
title: Ultrasonic guided-waves in quasi-isotropic and cross-ply composite beams using a finite element model

description: Ultrasonic guided-wave data taken from numerical evaluations of a finite element model (FEM) considering two composite beam structures and applying three noise levels in signal-to-noise ratio (SNR): Quasi-isotropic carbon fiber composite beam (dimensions: 1m x 4mm x 2mm) with a [+45/-45/90/0]_s layup and a transverse crack (1mm deep at 0.40m from excitation) in it and the following noise levels (the file directories are specified in parentheses): Noisefree (/Quasi-Isotropic_Crack/Noisefree/MeasurementCrack_NF.mat) SNR 10 dB (/Quasi-Isotropic_Crack/SNR_10dB/MeasurementCrack_10dB.mat) SNR 5 dB (/Quasi-Isotropic_Crack/SNR_05dB/MeasurementCrack_5dB.mat) Cross-ply carbon fiber composite beam (dimensions: 1m x 3mm x 2mm) with [0_{2}/90]_s stacking sequence and a delamination (5mm long at 0.45m from excitation) in it and the following noise levels: Noisefree (/Cross-Ply_Delam/Noisefree/MeasurementDelam_NF.mat) SNR 10 dB (/Cross-Ply_Delam/SNR_10dB/MeasurementDelam_10dB.mat) SNR 5 dB (/Cross-Ply_Delam/SNR_05dB/MeasurementDelam_5dB.mat) FEM notes: solid elements (i.e. C3D8R in Abaqus) with the following dimensions: 0.5mm thick, 0.5mm width, and 0.2mm long. Damage modes are modeled by node duplication. Ultrasonic tests data: 6-cycle Hanning windowed sinusoid centered at 100 kHz of frequency. Excitation signal at one end of the beam in the vertical direction. Sensor situated at 0.2m from the point of excitation and measuring for 720 \mu s. First antisymmetric mode (A0) is excited.
DOI: 10.25345/c5wv3b
title: MassIVE MSV000086662 - The Campylobacter jejuni CiaD effector co-opts the host cell protein IQGAP1 to promote cell entry
title: Proteomic data from pulldown performed with the recombinant Campylobacter CiaD-GST fusion was expressed in E. coli BL21(DE3) on a lysate of human cultured INT 407 cells. One non-specific binding control was performed on INT 407 cell lysates and two-pulldowns were realized with the recombinant protein. Samples were digested with trypsin, then analyzed by LC-MS/MS. Data was searched with MS-GF+ using PNNL's DMS processing pipeline.
DOI: 10.5063/f1fj2f6t
title: Sea otter foraging analysis using stable isotopes

description: The purpose of these data was to analyze sea otter diets around Prince of Wales Island (POW). Sea otter prey was collected in three seasons (May 2018, August 2018, and February 2019) around Prince of Wales Island. Sea otter vibrissae were obtained through the USFWS sea otter tagging program, which monitors legal sea otter harvest in Alaska. Data from this effort include bulk d13C and d15N stable isotope values. These data are a part of a NOAA collaboration and were collected in part for an NSF-funded project: Apex Predators, Ecosystems, and Community Sustainability (APECS, http://apecs-ak.org/). Other datasets to support this work are also archived with KNB.
DOI: 10.25318/3310027901-eng
title: Layoffs since the start of the COVID-19 pandemic, by business characteristics

description: Percentage of businesses with layoffs since the start of the COVID-19 pandemic, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.25318/3310027901-fra
title: Mises à pied depuis le début de la pandémie de COVID-19, selon les caractéristiques de l’entreprise

description: Pourcentage d'entreprises ayant effectué des mises à pied depuis le début de la pandémie de COVID-19, selon le code du Système de classification des industries de l'Amérique du Nord (SCIAN), l'effectif de l'entreprise, le type d'entreprise, l'activité commerciale et la participation majoritaire.
DOI: 10.25318/3310028001-eng
title: Of businesses where at least one employee was laid off, percentage of workforce laid off and rehired due to COVID-19, by business characteristics

description: Of businesses or organizations where at least one employee was laid off, percentage of workforce laid off and rehired due to COVID-19, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.25318/3310028001-fra
title: Pourcentage de l’effectif mis à pied et réembauché en raison de la COVID-19 parmi les entreprises où au moins un employé a été mis à pied, selon les caractéristiques de l’entreprise

description: Pourcentage de l'effectif mis à pied et réembauché en raison de la COVID-19, parmi les entreprises où au moins un employé a été mis à pied, selon le code du Système de classification des industries de l'Amérique du Nord (SCIAN), l'effectif de l'entreprise, le type d'entreprise, l'activité commerciale et la participation majoritaire.
DOI: 10.21227/eh17-pt98
title: Obstacle Detection for Automated Guided Vehicles

description: Nowadays, produced cars are equipped with mechatronical actuators as well as with a wide range of sensors in order to realize driver assistance functions.These components could enable cars’ automation at low speeds on company premises, although autonomous driving in public traffic is still facing technical and legal challenges.For automating vehicles in an industrial environment a reliable obstacle detection system is required. State-of-the-art solution for protective devices in Automated Guided Vehicles is the distance measuring laser scanner. Since laser scanners are not basic equipment of today’s cars in contrast to monocameras mounted behind the windscreen, we develop a computer vision algorithm that is able to detect obstacles in camera images reliably. Therefore, we make use of our well-known operational design domain by teaching an anomaly detection how the vehicle path should look like.The result is an anomaly detection algorithm that consists of a pre-trained feature extractor and a shallow classifier, modelling the probability of occurrence. We record a data set of a real industrial environment and show a robust classifier after training the algorithm with images of only one run.
DOI: 10.21227/y8n3-bx27
title: Obstacle Detection for Automated Guided Vehicles

description: Nowadays, produced cars are equipped with mechatronical actuators as well as with a wide range of sensors in order to realize driver assistance functions.These components could enable cars’ automation at low speeds on company premises, although autonomous driving in public traffic is still facing technical and legal challenges.For automating vehicles in an industrial environment a reliable obstacle detection system is required. State-of-the-art solution for protective devices in Automated Guided Vehicles is the distance measuring laser scanner. Since laser scanners are not basic equipment of today’s cars in contrast to monocameras mounted behind the windscreen, we develop a computer vision algorithm that is able to detect obstacles in camera images reliably. Therefore, we make use of our well-known operational design domain by teaching an anomaly detection how the vehicle path should look like.The result is an anomaly detection algorithm that consists of a pre-trained feature extractor and a shallow classifier, modelling the probability of occurrence. We record a data set of a real industrial environment and show a robust classifier after training the algorithm with images of only one run.
DOI: 10.25318/3310028101-eng
title: Business or organization revenue from August 2020 compared with August 2019, by business characteristics

description: Change in reported revenue of the business or organization from August 2020 compared with August 2019, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.25505/fiz.icsd.cc26yhf3
title: CSD 2054135: Experimental Crystal Structure Determination

description: Related Article: Yamanaka, T., Komatsu, Y., Nomori, H.|2021|ICSD Communication|||
DOI: 10.25318/3310028101-fra
title: Revenu de l’entreprise ou de l’organisme en août 2020 par rapport à août 2019, selon les caractéristiques de l’entreprise

description: Variation du revenu déclaré par l'entreprise ou l'organisme en août 2020 par rapport à août 2019, selon le code du Système de classification des industries de l'Amérique du Nord (SCIAN), l'effectif de l'entreprise, le type d'entreprise, l'activité commerciale et la participation majoritaire.
DOI: 10.25318/3310028201-eng
title: Business or organization expenses from August 2020 compared with August 2019, by business characteristics

description: Change in reported expenses of the business or organization from August 2020 compared with August 2019, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.25318/3310028201-fra
title: Dépenses de l’entreprise ou de l’organisme en août 2020 par rapport à août 2019, selon les caractéristiques de l’entreprise

description: Variation des dépenses déclarées par l'entreprise ou l'organisme en août 2020 par rapport à août 2019, selon le code du Système de classification des industries de l'Amérique du Nord (SCIAN), l'effectif de l'entreprise, le type d'entreprise, l'activité commerciale et la participation majoritaire.
DOI: 10.25318/3310028301-eng
title: COVID-19 impacts on various expenditures, by business characteristics

description: COVID-19 impacts on various expenditures made by the business or organization, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.25318/3310028301-fra
title: Répercussions de la COVID-19 sur les diverses dépenses, selon les caractéristiques de l’entreprise

description: Répercussions de la COVID-19 sur les diverses dépenses effectuées par l'entreprise ou l'organisme, selon le code du Système de classification des industries de l'Amérique du Nord (SCIAN), l'effectif de l'entreprise, le type d'entreprise, l'activité commerciale et la participation majoritaire.
DOI: 10.25318/3310028401-eng
title: Approved funding or credit due to COVID-19, by business characteristics

description: Approved funding or credit sources for businesses or organizations due to COVID-19, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.25318/3310028401-fra
title: Financement ou crédit approuvé en raison de la COVID-19, selon les caractéristiques de l’entreprise

description: Sources de financement ou de crédit approuvées pour les entreprises ou les organismes en raison de la COVID-19, selon le code du Système de classification des industries de l'Amérique du Nord (SCIAN), l'effectif de l'entreprise, le type d'entreprise, l'activité commerciale et la participation majoritaire.