cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4134144
title: Dataset for Recurrent Rossby wave packets modulate the persistence of dry and wet spells across the globe

description: This dataset is used in the following study: "Recurrent Rossby wave packets modulate the persistence of dry and wet spells across the globe." Dataset includes: Dry and wet spells for the Northern and the Southern Hemisphere respectively. The output of the statistical model for each season (MJJASO/NDJFMA) and for each spell type (dry/wet) for both the hemispheres (NH/SH). File naming used belongs to mainly two categories: one for naming spell file, and second for naming the output file from the statistical model. An example from each category is explained below. The rest of the files follow the same naming style. <em>Spell file</em>;<strong> NH_1.0mm_dry_spells_all_months_gap_1_days_no_spell2_check.nc</strong>: Northern Hemisphere 1.0mm threshold dry spell for all months with a gap of 1 day <em>Statisical model output file</em>; <strong>NH_weibull_MJJASO_drythresh_1_min_spell_count_40_time_steps_min_spell_length_5D_1D_N_1980_2016.nc</strong>: Northern Hemisphere Weibull model output for MJJASO using dry threshold of 1.0 mm with a minimum spell count of 40 time-steps and a minimum spell length of 5 days for the period 1980-2016 This dataset alone is sufficient for reproducing the analysis presented in the study. Open source tools like Python, R, etc can be used to read '.nc' file type. Additional code help in the form of Jupyter notebooks reproducing figures made from this dataset can be viewed here on GitHub.
DOI: 10.6084/m9.figshare.13168253.v2
title: Reliabilty of body composition techniques.xlsx

description: Raw data set of body composition precision error (n=90, 18-85 years) for same- and between-day reliabilty testing.
DOI: 10.6084/m9.figshare.13168253.v1
title: Reliabilty of body composition techniques.xlsx

description: Raw data set of body composition precision error (n=90, 18-85 years) for same- and between-day reliabilty testing.
DOI: 10.5281/zenodo.4159413
title: Detection of fusion transcripts and their genomic breakpoints from RNA sequencing data - Table S03 - All detected SVs.xlsx

description: Large concatenated results table on all samples of the Dr. Disco study.
DOI: 10.5517/ccdc.csd.cc25jj2b
title: CCDC 2011871: Experimental Crystal Structure Determination

description: Related Article: Scott G. Stewart,  Gwion J. Harfoot,  Kenneth J. McRae,  Yinglai Teng,  Li-Juan Yu,  Bo Chen,  Roberto Cammi,  Michelle L. Coote,  Martin G Banwell,  Anthony C. Willis|2020|J.Org.Chem.|85|13080|doi:10.1021/acs.joc.0c01767
DOI: 10.5517/ccdc.csd.cc25jj4d
title: CCDC 2011873: Experimental Crystal Structure Determination

description: Related Article: Scott G. Stewart,  Gwion J. Harfoot,  Kenneth J. McRae,  Yinglai Teng,  Li-Juan Yu,  Bo Chen,  Roberto Cammi,  Michelle L. Coote,  Martin G Banwell,  Anthony C. Willis|2020|J.Org.Chem.|85|13080|doi:10.1021/acs.joc.0c01767
DOI: 10.5281/zenodo.4159415
title: 1D and 2D NMR spectra of coffee from 27 countries

description: 715 green coffee samples were gathered by Almacafé S.A. (Bogotá, Colombia) between 2012 and 2014 from 27 different countries and analyzed at the Nuclear Magnetic Resonance (NMR) laboratory at Universidad del Valle (Cali, Colombia). Over 1000 methanolic coffee extracts were prepared and a total of 4563 spectra were acquired in fully automatic mode using a 400MHz NMR spectrometer (Bruker Biospin, Germany). The dataset spans the variance that could be expected for an industrial application of origin monitoring, including samples from different harvest time, collected over several years and processed by at least two distinct operators. The resulting 1D and 2D spectra can be used to develop and evaluate feature extraction methods, multivariate algorithms and automation monitoring techniques, as a dataset for teaching, or as a reference for new studies on similar samples and approach.
DOI: 10.5281/zenodo.4126116
title: 1D and 2D NMR spectra of coffee from 27 countries

description: 715 green coffee samples were gathered by Almacafé S.A. (Bogotá, Colombia) between 2012 and 2014 from 27 different countries and analyzed at the Nuclear Magnetic Resonance (NMR) laboratory at Universidad del Valle (Cali, Colombia). Over 1000 methanolic coffee extracts were prepared and a total of 4563 spectra were acquired in fully automatic mode using a 400MHz NMR spectrometer (Bruker Biospin, Germany). The dataset spans the variance that could be expected for an industrial application of origin monitoring, including samples from different harvest time, collected over several years and processed by at least two distinct operators. The resulting 1D and 2D spectra can be used to develop and evaluate feature extraction methods, multivariate algorithms and automation monitoring techniques, as a dataset for teaching, or as a reference for new studies on similar samples and approach.
DOI: 10.5281/zenodo.4081726
title: Virulence mismatches in index hosts shape the outcomes of cross-species transmission

description: Supplemental data and code for the paper <em>Virulence mismatches in index hosts shape the outcomes of cross-species transmission</em>. Dataset 1 is an R Shiny app allowing the estimation of rabies disease progression parameters for all observed combinations of virus source (reservoir) and recipient species, including within-species inoculations. Dataset 2 contains the original data and the analysis code used in this study. See the README files in each dataset folder for further information and usage instructions.
description: Changelog - v1.0: initial upload for peer review | v2.0: minor code changes, added documentation and data references, improved reproducibility
DOI: 10.5281/zenodo.4159436
title: Role of FFT grid in paired refinement

description: This archive contains runs of PAIREF - a tool for macromolecular crystallographers performing the paired refinement protocol. All refinements were carried out using the same FFT grid. Program website: https://pairef.fjfi.cvut.cz/ Results of five data sets presented in the publication "Role of FFT grid in paired refinement" by M. Maly, K. Diederichs, J. Dohnalek, L Svecova, P. Kolenko (2020) in Proceedings of the 9th Student Scientific Conference on Solid State Physics and Physics of Materials are included.
DOI: 10.5281/zenodo.4159435
title: Role of FFT grid in paired refinement

description: This archive contains runs of PAIREF - a tool for macromolecular crystallographers performing the paired refinement protocol. All refinements were carried out using the same FFT grid. Program website: https://pairef.fjfi.cvut.cz/ Results of five data sets presented in the publication "Role of FFT grid in paired refinement" by M. Maly, K. Diederichs, J. Dohnalek, L Svecova, P. Kolenko (2020) in Proceedings of the 9th Student Scientific Conference on Solid State Physics and Physics of Materials are included.
DOI: 10.25592/uhhfdm.764
title: Clay Tablet (KUG 4) from the University Library Giessen (3D model)

description: ID: KUG 4<br> Object type: tablet<br> Material: clay<br> Measurements: 2,8 x 3,5 x 0,7 cm<br> Provenience: Kanesh (mod. Kültepe)<br> Period/Date: Old Assyrian<br> Language: Akkadian<br> Genre: legal<br> Museum/Collection: Universitätsbibliothek, University of Giessen, Germany<br> CDLI: P283861 The research for this 3D object was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2176 ‘Understanding Written Artefacts: Material, Interaction and Transmission in Manuscript Cultures’, project no. 390893796. The research was conducted within the scope of the Centre for the Study of Manuscript Cultures (CSMC) at Universität Hamburg.
DOI: 10.25592/uhhfdm.763
title: Clay Tablet (KUG 4) from the University Library Giessen (3D model)

description: ID: KUG 4<br> Object type: tablet<br> Material: clay<br> Measurements: 2,8 x 3,5 x 0,7 cm<br> Provenience: Kanesh (mod. Kültepe)<br> Period/Date: Old Assyrian<br> Language: Akkadian<br> Genre: legal<br> Museum/Collection: Universitätsbibliothek, University of Giessen, Germany<br> CDLI: P283861 The research for this 3D object was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2176 ‘Understanding Written Artefacts: Material, Interaction and Transmission in Manuscript Cultures’, project no. 390893796. The research was conducted within the scope of the Centre for the Study of Manuscript Cultures (CSMC) at Universität Hamburg.
DOI: 10.14278/rodare.526
title: Data for: Spectral X-ray Computed Micro Tomography: 3-dimensional chemical imaging

description: The files show the data we used for the publication.
DOI: 10.17045/sthlmuni.13143158
title: Open data: Are new gender-neutral pronouns difficult to process in reading? The case of hen in Swedish

description: The raw data, preprocessing script, preprocessed data file, and the main analyses for the project "Are new gender-neutral pronouns difficult to process in reading? The case of <i>hen</i> in Swedish". <br>The psychopy data set includes measures of key press responses to comprehension questions recorded during the experiment. It also includes information on in what order the stimuli were displayed to the participant, and how much time they spent looking at the stimulus until they pressed a key to move on to the next stimulus.<br>The questionnaire data set includes background information collected through the online questionnaire designing webpage Qualtrics (https://qualtrics.com) after the eye-tracking experiment. Data was collected between Sept 25th 2017 and March 8th 2018.<br>The eye-tracking data set includes measures of reading behaviors such as fixation time on stimuli including gendered and gender-neutral pronouns. <br>Instrument- or software-specific information needed to run the experiment and interpret the data:<br>Psychopy (Peirce et al., 2019) for running the experiment (https://www.psychopy.org/). Python for opening and managing the related .py files (https://www.python.org).<br><br>BeGaze (SensoMotoric Instruments, 2014) for opening and managing the .idf files. Requires software license. These files were created using SMI's software iView for recording eye movements with the eye-tracker.[https://gazeintelligence.com/smi-software-download]<br>Rstudio used with R (R Core Team, 2017): https://rstudio.com/products/rstudio/ <br>More information can be found in the README files, and on our OSF page.
DOI: 10.5281/zenodo.4117556
title: Mapping GPIIb/IIIa on murine blood platelets with expansion microscopy

description: <strong>Mapping GPIIb/IIIa on murine blood platelets with expansion microscopy</strong> We visualize the membrane architecture of platelets by mapping platelet integrin GPIIb/IIIa by super-resolution fluorescence. Our approach combines colocalization and expansion microscopy and successfully interrogates highly abundant receptors and their interactions where conventional methods fail in such dense 3D scenarios. Here we provide a s<em>election of typical images</em> of receptor distributions on unexpanded and expanded murine platelets and the <em>code</em> for simulation of platelet receptor distributions including a <em>user manual.</em> <strong>Sample images: </strong> Unexpanded, 4x expanded and 10x expanded resting murine platelets on glycine coated coverslips were imaged on a SP5 confocal microscope (detailed methods can be found in the manuscript). There are three different labeling combinations: Case I: directly immunolabeled with monoclonal antibodies antibodies carrying Alexa Fluor 488 (MWReg30, green channel) or Alexa Fluor 594 (JON6, magenta channel) can bind simultaneously on the same GPIIb/IIIa receptor -&gt; high colocalization Case II: directly immunolabeled with a monoclonal antibody targeting GPIIb/IIIa (MWReg30) carrying either Alexa Fluor 594 or Alexa Fluor 488) -&gt; low colocalizaiton Case III: directly immunolabeld with monoclonal antibodies carrying Alexa Fluor 488 targeting GPIIb/IIIa (JON6) and carrying Alexa Fluor 594 targeting GPIX (p0p6) -&gt; low colocalization The provided sample images are .ims (Imaris) files contain the raw data, chromatic aberration corrected data & deconvolved data as consecutive time points, as well as the cropped image stack of a single platelet with the masked data used for colocalization analysis. <strong>MatLab Scipts: 3D-Image simulation of Platelet Receptor Distributions</strong> The provided MatLab scripts allow to create 2-color, 3D image stacks based on simulated platelet receptor distributions and the image resolution provided by confocal microscopy and sample expansion. Refer to the user manual for a detailed description of how to set up and run the simulation.
DOI: 10.5281/zenodo.4159392
title: The sharing of research raw data in journals indexed in the Reproductive Biology JCR category

description: Raw data belonged to the study of use and sharing of raw research data in the Journal Citation Reports' Reproductive Biology Category.
DOI: 10.5281/zenodo.4159391
title: The sharing of research raw data in journals indexed in the Reproductive Biology JCR category

description: Raw data belonged to the study of use and sharing of raw research data in the Journal Citation Reports' Reproductive Biology Category.
DOI: 10.21227/mgnh-3d20
title: iSignDB: A biometric signature database created using smartphone

description: iSignDB: A biometric signature database created using smartphoneSuraiya Jabin, Sumaiya Ahmad, Sarthak Mishra, and Farhana Javed ZareenDepartment of Computer Science, Jamia Millia Islamia, New Delhi-110025, IndiaIt's a database of biometric signatures recorded using sensors present in a smartphone. ​The dataset iSignDB is created to implement a novel anti-spoof biometric signature authentication for smartphone users.We named it iSignDB as we collected it using a licensed MathWorks cloud account and with iOS and Android based smartphone devices (iPhone 7 Plus and Redmi Note 7) for capturing dynamic signatures.A total of 48 subjects volunteered for data collection out of which we identified 32 users as genuine signature contributors and 16 users as fake signature contributors with skilled forgery.Data was collected in 3 different sessions separated by at least 20 days in order to capture the emotional intelligence of users.During each session, one pair of subjects, out of which one subject contributed 10 original signatures and the other contributed 5 fake signatures.For obtaining a fake signature, a subject was allowed to practice copying not only the signature image of a genuine user but also the behaviorism (e.g. number of touchpoints, style of finger movement while signing, etc.) while genuine signer signs on the touch screen of a smartphone.A total of 30 genuine and 15 fake samples were collected for each of 32 users.One sign of a user contains a sensor log captured using sensors present in the smartphone: Accelerometer, Gyroscope, Magnetometer, and GPS, etc along with images of signature as obtained by performing a sign on the touch screen of the device.We have uploaded the smartphone biometric sign database of all 32 users in this repository. The full database of 32 users is available for other researchers only after they sign and submit the terms and conditions of using it.We successfully trained 32 BiGRU models on dynamic signature dataset created with EER of 0.66% which is a significant improvement.We provide Matlab code (compatible with MATLAB 2020a licensed version) for training, testing, and calculating EER in this repository (https://github.com/suraiyajabin/iSignDB2020).iSignDB is available to other researchers only after signing its "Term of use" agreement and acknowledging it properly in their work.Nomenclature for files in the dataset iSignDB: (each sign with 5 sensor logs corresponding to Acceleration, Angular Velocity, Magnetic Field, Orientation, and Position, and image of signature u01_s3_r010_AngVel.txt : means a signature of user 1, on session 3, real signature, 10th sample’s Angular velocity sensor log u01_s1_f02_MagField.txt : means a signature of user 1, on session 1, fake signature, 2nd sample’s magnetic field sensor log u01_s1_r01_im.png : image of the genuine signature of user 1, captured on session 1, sample 1
DOI: 10.5281/zenodo.3894567
title: Decomposition Tool Validation Dataset 2

description: This dataset provides artifacts, benchmarks and models used to evaluate the utility of the optimization approach as well as the corresponding results: Base TOSCA models for generating the considered model set TOSCA models obtained through the optimization approach and the naive one Analytics of the resulting models in terms of memory, concurrency and costs
DOI: 10.5517/ccdc.csd.cc25d4tk
title: CCDC 2007678: Experimental Crystal Structure Determination

description: Related Article: Wangyang Ma, Iskander Douair, Laurent Maron, Qing Ye|2020|Chem.-Eur.J.|16|13573|doi:10.1002/chem.202003611
DOI: 10.5517/ccdc.csd.cc25d4vl
title: CCDC 2007679: Experimental Crystal Structure Determination

description: Related Article: Wangyang Ma, Iskander Douair, Laurent Maron, Qing Ye|2020|Chem.-Eur.J.|16|13573|doi:10.1002/chem.202003611
DOI: 10.5517/ccdc.csd.cc25d4xn
title: CCDC 2007681: Experimental Crystal Structure Determination

description: Related Article: Wangyang Ma, Iskander Douair, Laurent Maron, Qing Ye|2020|Chem.-Eur.J.|16|13573|doi:10.1002/chem.202003611
DOI: 10.5517/ccdc.csd.cc25jj5f
title: CCDC 2011874: Experimental Crystal Structure Determination

description: Related Article: Scott G. Stewart,  Gwion J. Harfoot,  Kenneth J. McRae,  Yinglai Teng,  Li-Juan Yu,  Bo Chen,  Roberto Cammi,  Michelle L. Coote,  Martin G Banwell,  Anthony C. Willis|2020|J.Org.Chem.|85|13080|doi:10.1021/acs.joc.0c01767
DOI: 10.5517/ccdc.csd.cc25jj6g
title: CCDC 2011875: Experimental Crystal Structure Determination

description: Related Article: Scott G. Stewart,  Gwion J. Harfoot,  Kenneth J. McRae,  Yinglai Teng,  Li-Juan Yu,  Bo Chen,  Roberto Cammi,  Michelle L. Coote,  Martin G Banwell,  Anthony C. Willis|2020|J.Org.Chem.|85|13080|doi:10.1021/acs.joc.0c01767
DOI: 10.5517/ccdc.csd.cc25jj7h
title: CCDC 2011876: Experimental Crystal Structure Determination

description: Related Article: Scott G. Stewart,  Gwion J. Harfoot,  Kenneth J. McRae,  Yinglai Teng,  Li-Juan Yu,  Bo Chen,  Roberto Cammi,  Michelle L. Coote,  Martin G Banwell,  Anthony C. Willis|2020|J.Org.Chem.|85|13080|doi:10.1021/acs.joc.0c01767
DOI: 10.5517/ccdc.csd.cc25jj8j
title: CCDC 2011877: Experimental Crystal Structure Determination

description: Related Article: Scott G. Stewart,  Gwion J. Harfoot,  Kenneth J. McRae,  Yinglai Teng,  Li-Juan Yu,  Bo Chen,  Roberto Cammi,  Michelle L. Coote,  Martin G Banwell,  Anthony C. Willis|2020|J.Org.Chem.|85|13080|doi:10.1021/acs.joc.0c01767
DOI: 10.5517/ccdc.csd.cc25jj9k
title: CCDC 2011878: Experimental Crystal Structure Determination

description: Related Article: Scott G. Stewart,  Gwion J. Harfoot,  Kenneth J. McRae,  Yinglai Teng,  Li-Juan Yu,  Bo Chen,  Roberto Cammi,  Michelle L. Coote,  Martin G Banwell,  Anthony C. Willis|2020|J.Org.Chem.|85|13080|doi:10.1021/acs.joc.0c01767
DOI: 10.5517/ccdc.csd.cc23shw9
title: CCDC 1959971: Experimental Crystal Structure Determination

description: Related Article: Yanhui Chen,  Weiwei Lv,  Dan Ba,  Si Wen,  Guolin Cheng|2020|J.Org.Chem.|85|13280|doi:10.1021/acs.joc.0c01695
DOI: 10.5281/zenodo.4159586
title: Durada del cicle vegetatiu de la vinya a la D.O. Empordà (Alt Empordà) a llarg del segle XXI als escenaris de canvi climàtic RCP 8.5 i RCP4.5 (format SHP)

description: Arxius del shapefile (format SHP de Esri) amb la distribució espacial del valor mitjà de la durada del cicle vegetatiu (dies) de la vinya a la D.O. Empordà (Alt Empordà) als escenaris de canvi climàtic RCP 8.5 i RCP4.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090 i el període Control (1972-2005). A més, un arxiu Word amb les metadades del shapefile.
DOI: 10.5281/zenodo.4159592
title: Cloud-native Deploy-ability: An Analysis of Required Features of Deployment Technologies to Deploy Arbitrary Cloud-native Applications

description: Deployment modeling example used in the paper.
DOI: 10.5281/zenodo.4159591
title: Cloud-native Deploy-ability: An Analysis of Required Features of Deployment Technologies to Deploy Arbitrary Cloud-native Applications

description: Deployment modeling example used in the paper.
DOI: 10.5281/zenodo.4159607
title: Calendar Dates and Days November 2020

description: Days against each calendar date in November 2020
DOI: 10.5281/zenodo.4159606
title: Calendar Dates and Days November 2020

description: Days against each calendar date in November 2020
DOI: 10.5281/zenodo.4159449
title: Supporting datasets

description: Datasets supporting forcing
DOI: 10.21227/vfpw-k153
title: iSignDB: A biometric signature database created using smartphone

description: iSignDB: A biometric signature database created using smartphoneSuraiya Jabin, Sumaiya Ahmad, Sarthak Mishra, and Farhana Javed ZareenDepartment of Computer Science, Jamia Millia Islamia, New Delhi-110025, IndiaIt's a database of biometric signatures recorded using sensors present in a smartphone. ​The dataset iSignDB is created to implement a novel anti-spoof biometric signature authentication for smartphone users.We named it iSignDB as we collected it using a licensed MathWorks cloud account and with iOS and Android based smartphone devices (iPhone 7 Plus and Redmi Note 7) for capturing dynamic signatures.A total of 48 subjects volunteered for data collection out of which we identified 32 users as genuine signature contributors and 16 users as fake signature contributors with skilled forgery.Data was collected in 3 different sessions separated by at least 20 days in order to capture the emotional intelligence of users.During each session, one pair of subjects, out of which one subject contributed 10 original signatures and the other contributed 5 fake signatures.For obtaining a fake signature, a subject was allowed to practice copying not only the signature image of a genuine user but also the behaviorism (e.g. number of touchpoints, style of finger movement while signing, etc.) while genuine signer signs on the touch screen of a smartphone.A total of 30 genuine and 15 fake samples were collected for each of 32 users.One sign of a user contains a sensor log captured using sensors present in the smartphone: Accelerometer, Gyroscope, Magnetometer, and GPS, etc along with images of signature as obtained by performing a sign on the touch screen of the device.We have uploaded the smartphone biometric sign database of all 32 users in this repository. The full database of 32 users is available for other researchers only after they sign and submit the terms and conditions of using it.We successfully trained 32 BiGRU models on dynamic signature dataset created with EER of 0.66% which is a significant improvement.We provide Matlab code (compatible with MATLAB 2020a licensed version) for training, testing, and calculating EER in this repository (https://github.com/suraiyajabin/iSignDB2020).iSignDB is available to other researchers only after signing its "Term of use" agreement and acknowledging it properly in their work.Nomenclature for files in the dataset iSignDB: (each sign with 5 sensor logs corresponding to Acceleration, Angular Velocity, Magnetic Field, Orientation, and Position, and image of signature u01_s3_r010_AngVel.txt : means a signature of user 1, on session 3, real signature, 10th sample’s Angular velocity sensor log u01_s1_f02_MagField.txt : means a signature of user 1, on session 1, fake signature, 2nd sample’s magnetic field sensor log u01_s1_r01_im.png : image of the genuine signature of user 1, captured on session 1, sample 1