cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6778336
title: Full dataset of several mechanical tests on an S355 steel sheet as reference data for digital representations

description: The dataset provided in this repository comprises data obtained from a series of characterization tests performed to a sheet of typical S355 (material number: 1.0577) structural steel (designation of steel according to DIN EN 10025-2:2019). The tests include methods for the determination of mechanical properties such as, e.g., tensile test, Charpy test and sonic resonance test. This dataset is intended to be extended by the inclusion of data obtained from further test methods. Therefore, the entire dataset (concept DOI) comprises several parts (versions), each of which is addressed by a unique version DOI. The data were generated in the frame of the digitization project Innovationplatform Material<em>Digital </em>(PMD) which, amongst other activities, aims to store data in a semantically and machine understandable way. Therefore, data structuring and data formats are focused in addition to aspects in the field of material science and engineering (MSE). Hence, this data is supposed to provide reference data as basis for experimental data inclusion, conversion and structuring (data management and processing) that leads to semantical expressivity as well as for MSE experts being generally interested in the material properties and knowledge.
DOI: 10.5281/zenodo.6778335
title: Full dataset of several mechanical tests on an S355 steel sheet as reference data for digital representations

description: The dataset provided in this repository comprises data obtained from a series of characterization tests performed to a sheet of typical S355 (material number: 1.0577) structural steel (designation of steel according to DIN EN 10025-2:2019). The tests include methods for the determination of mechanical properties such as, e.g., tensile test, Charpy test and sonic resonance test. This dataset is intended to be extended by the inclusion of data obtained from further test methods. Therefore, the entire dataset (concept DOI) comprises several parts (versions), each of which is addressed by a unique version DOI. The data were generated in the frame of the digitization project Innovationplatform Material<em>Digital </em>(PMD) which, amongst other activities, aims to store data in a semantically and machine understandable way. Therefore, data structuring and data formats are focused in addition to aspects in the field of material science and engineering (MSE). Hence, this data is supposed to provide reference data as basis for experimental data inclusion, conversion and structuring (data management and processing) that leads to semantical expressivity as well as for MSE experts being generally interested in the material properties and knowledge.
DOI: 10.6084/m9.figshare.20653575
title: Data for: Integrating phylogenetics with intron positions illuminates the origin of the complex spliceosome

description: Fasta files of the LECA spliceosomal OGs, alignments used for tree inference, inferred phylogenetic tree files, alignments used for intron analysis and mapped intron files.
DOI: 10.6084/m9.figshare.20653575.v1
title: Data for: Integrating phylogenetics with intron positions illuminates the origin of the complex spliceosome

description: Fasta files of the LECA spliceosomal OGs, alignments used for tree inference, inferred phylogenetic tree files, alignments used for intron analysis and mapped intron files.
DOI: 10.5281/zenodo.6686932
title: Loki: Hardening Code Obfuscation Against Automated Attacks

description: Artifact containing binaries and result data produced during our evaluation. Check out the code on Github: https://github.com/RUB-SysSec/loki
DOI: 10.5281/zenodo.6686931
title: Loki: Hardening Code Obfuscation Against Automated Attacks

description: Artifact containing binaries and result data produced during our evaluation. Check out the code on Github: https://github.com/RUB-SysSec/loki
DOI: 10.21227/gda4-8d15
title: Assembly Gestures  of the decelerator shaft parts

description: The human hand is composed of distal phalanges, middle phalanges, proximal phalanges, and metacarpals except for the thumb which lacks the middle phalanges. A depth image sensor (Leap Motion) is used to extract the 3D gesture model parameters which includes finger joint coordinates of fingertip coordinates, distal finger joints, proximal finger joints, and finger root joint on the five fingers, the length of fingers, palm point coordinates, palm normal vector, and palm orientation (the direction from the palm to the fingers).static operation gestures include reaching, holding, grasping 1, grasping 2, grasping 3, grasping 4, and pinching
DOI: 10.21350/kk8estxo
title: Data: The contribution of alternative habitats for conservation of plant species associated with thretened semi-natural grasslands

description: The contribution of alternative habitats for conservation of plant species associated with thretened semi-natural grasslands
DOI: 10.5281/zenodo.6343988
title: A database of MMS bow shock crossings compiled using machine learning

description: We use a machine learning approach to automatically identify shock crossings from the Magnetospheric Multiscale (MMS) spacecraft. We compile a database of 2797 crossings including various spacecraft related and shock related parameters for each event. Furthermore, for each event we provide an overview plot containing key parameters of the shock crossing. A Technical report detailing the content of the database can be found at the DOI: http://dx.doi.org/10.1029/2022JA030454
DOI: 10.5281/zenodo.6343989
title: A database of MMS bow shock crossings compiled using machine learning

description: We use a machine learning approach to automatically identify shock crossings from the Magnetospheric Multiscale (MMS) spacecraft. We compile a database of 2797 crossings including various spacecraft related and shock related parameters for each event. Furthermore, for each event we provide an overview plot containing key parameters of the shock crossing. A Technical report detailing the content of the database can be found at the DOI: http://dx.doi.org/10.1029/2022JA030454
DOI: 10.5281/zenodo.7024893
title: nNPipe: A neural network pipeline for automated analysis of morphologically diverse catalyst systems - Resources

description: This dataset comprises of resources required to replicate the results described in "<em>nNPipe</em>: A neural network pipeline for automated analysis of morphologically diverse catalyst systems". <em>nNPipe </em>is a deep learning based method in which two deep convolutional neural networks are used for the automated analysis of 2048x2048 HRTEM images. The file contains:<br> - Relevant experimental images as well as ground truth for Pd/C and Au/Ge systems.<br> - A workflow file explaining the nNPipe workflow.<br> - Mathematica 12.1 code for the generation of computational models.<br> - MATLAB code for HRTEM multislice simulations using MULTEM, as well as code required to form respective training datasets.<br> - Weights and files required for training the YOLOv5x module.<br> - Weights and files required for training the SegNet module.<br> - Mathematica 12.1 code required for reconstruction of 2048x2048 binary segmented maps of HRTEM images.
DOI: 10.5517/ccdc.csd.cc1p06gc
title: CCDC 1549332: Experimental Crystal Structure Determination

description: Related Article: Ulrich Flörke, Christine Nagel, Gerald Henkel|2017|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: 10.5281/zenodo.7024892
title: nNPipe: A neural network pipeline for automated analysis of morphologically diverse catalyst systems - Resources

description: This dataset comprises of resources required to replicate the results described in "<em>nNPipe</em>: A neural network pipeline for automated analysis of morphologically diverse catalyst systems". <em>nNPipe </em>is a deep learning based method in which two deep convolutional neural networks are used for the automated analysis of 2048x2048 HRTEM images. The file contains:<br> - Relevant experimental images as well as ground truth for Pd/C and Au/Ge systems.<br> - A workflow file explaining the nNPipe workflow.<br> - Mathematica 12.1 code for the generation of computational models.<br> - MATLAB code for HRTEM multislice simulations using MULTEM, as well as code required to form respective training datasets.<br> - Weights and files required for training the YOLOv5x module.<br> - Weights and files required for training the SegNet module.<br> - Mathematica 12.1 code required for reconstruction of 2048x2048 binary segmented maps of HRTEM images.
DOI: 10.5517/ccdc.csd.cc1pbkdz
title: CCDC 1559281: Experimental Crystal Structure Determination

description: Related Article: Totan Ghosh, Natalia Fridman, Monica Kosa, Galia Maayan|2018|Angew.Chem.,Int.Ed.|57|7703|doi:10.1002/anie.201800583
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.cc1pbxfc
title: CCDC 1559623: Experimental Crystal Structure Determination

description: Related Article: Erick Ramírez, Esau Martínez, Simón Hernández-Ortega, Marcos Flores-Alamo, Ivan Castillo|2018|Inorg.Chim.Acta|481|181|doi:10.1016/j.ica.2017.08.039
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.cc21mlkv
title: CCDC 1895667: Experimental Crystal Structure Determination

description: Related Article: Mani Kaushal, Tarlok S. Lobana, Lovedeep Nim, Ritu Bala, Daljit S. Arora, Isabel Garcia-Santos, Courtney E. Duff, Jerry P. Jasinski|2019|New J.Chem.|43|11727|doi:10.1039/C9NJ01459D
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.cc25p8w1
title: CCDC 2016453: Experimental Crystal Structure Determination

description: Related Article: Nicole W. G. Smits, Bas van Dijk, Iris de Bruin, Samantha L. T. Groeneveld, Maxime A. Siegler, Dennis G. H. Hetterscheid|2020|Inorg.Chem.|59|16398|doi:10.1021/acs.inorgchem.0c02204
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.cc24073j
title: CCDC 1966426: Experimental Crystal Structure Determination

description: Related Article: Luca Rigamonti, Francesco Reginato, Erika Ferrari, Laura Pigani, Lara Gigli, Nicola Demitri, Pavel Kopel, Barbora Tesarova, Zbynek Heger|2020|CSD Communication|||
description: Related Article: Luca Rigamonti, Francesco Reginato, Erika Ferrari, Laura Pigani, Lara Gigli, Nicola Demitri, Pavel Kopel, Barbora Tesarova, Zbynek Heger|2020|Dalton Trans.|49|14626|doi:10.1039/D0DT03038D
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.cc2cq7jw
title: CCDC 2196118: Experimental Crystal Structure Determination

description: Related Article: B.D. Newell, C.D. McMillen, J.P. Lee|2022|IUCrData|7|x220804|doi:10.1107/S2414314622008045
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: 10.34740/kaggle/dsv/4124571
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.4498364
title: The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

description: <strong>Overview</strong> Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. The COUGHVID dataset provides over 20,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses. Furthermore, experienced pulmonologists labeled more than 2,000 recordings to diagnose medical abnormalities present in the coughs, thereby contributing one of the largest expert-labeled cough datasets in existence that can be used for a plethora of cough audio classification tasks. As a result, the COUGHVID dataset contributes a wealth of cough recordings for training ML models to address the world’s most urgent health crises. <strong>Private Set and Testing Protocol</strong> Researchers interested in testing their models on the private test dataset should contact us at coughvid@epfl.ch, briefly explaining the type of validation they wish to make, and their obtained results obtained through cross-validation with the public data. Then, access to the unlabeled recordings will be provided, and the researchers should send the predictions of their models on these recordings. Finally, the performance metrics of the predictions will be sent to the researchers. The private testing data is not included in any file within our Zenodo record, and it can only be accessed by contacting the COUGHVID team at the aforementioned e-mail address.
description: For more information about the data collection, pre-processing, validation, and data structure, please refer to the following publication: https://www.nature.com/articles/s41597-021-00937-4 The cough pre-processing and feature extraction code is available from the following c4science repository: https://c4science.ch/diffusion/10770/
DOI: 10.5281/zenodo.4048311
title: The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

description: <strong>Overview</strong> Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. The COUGHVID dataset provides over 30,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses. Furthermore, experienced pulmonologists labeled more than 2,000 recordings to diagnose medical abnormalities present in the coughs, thereby contributing one of the largest expert-labeled cough datasets in existence that can be used for a plethora of cough audio classification tasks. As a result, the COUGHVID dataset contributes a wealth of cough recordings for training ML models to address the world’s most urgent health crises. <strong>Private Set and Testing Protocol</strong> Researchers interested in testing their models on the private test dataset should contact us at coughvid@epfl.ch, briefly explaining the type of validation they wish to make, and their obtained results obtained through cross-validation with the public data. Then, access to the unlabeled recordings will be provided, and the researchers should send the predictions of their models on these recordings. Finally, the performance metrics of the predictions will be sent to the researchers. The private testing data is not included in any file within our Zenodo record, and it can only be accessed by contacting the COUGHVID team at the aforementioned e-mail address. <strong>New Semi-Supervised Labeling</strong> The third version of the COUGHVID dataset contains thousands of additional recordings obtained through October 2021. Additionally, the recordings containing coughs were re-labeled according to a semi-supervised learning algorithm that combined the user labels with those of the expert physicians, which were modeled using ML and expanded on the previously unlabeled data. These labels can be found in the "status_SSL" column of the "metadata_compiled.csv" file.
description: For more information about the data collection, pre-processing, validation, and data structure, please refer to the following publication: https://www.nature.com/articles/s41597-021-00937-4 The cough pre-processing and feature extraction code is available from the following c4science repository: https://c4science.ch/diffusion/10770/
DOI: 10.5281/zenodo.7024894
title: The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

description: <strong>Overview</strong> Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. The COUGHVID dataset provides over 30,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses. Furthermore, experienced pulmonologists labeled more than 2,000 recordings to diagnose medical abnormalities present in the coughs, thereby contributing one of the largest expert-labeled cough datasets in existence that can be used for a plethora of cough audio classification tasks. As a result, the COUGHVID dataset contributes a wealth of cough recordings for training ML models to address the world’s most urgent health crises. <strong>Private Set and Testing Protocol</strong> Researchers interested in testing their models on the private test dataset should contact us at coughvid@epfl.ch, briefly explaining the type of validation they wish to make, and their obtained results obtained through cross-validation with the public data. Then, access to the unlabeled recordings will be provided, and the researchers should send the predictions of their models on these recordings. Finally, the performance metrics of the predictions will be sent to the researchers. The private testing data is not included in any file within our Zenodo record, and it can only be accessed by contacting the COUGHVID team at the aforementioned e-mail address. <strong>New Semi-Supervised Labeling</strong> The third version of the COUGHVID dataset contains thousands of additional recordings obtained through October 2021. Additionally, the recordings containing coughs were re-labeled according to a semi-supervised learning algorithm that combined the user labels with those of the expert physicians, which were modeled using ML and expanded on the previously unlabeled data. These labels can be found in the "status_SSL" column of the "metadata_compiled.csv" file.
description: For more information about the data collection, pre-processing, validation, and data structure, please refer to the following publication: https://www.nature.com/articles/s41597-021-00937-4 The cough pre-processing and feature extraction code is available from the following c4science repository: https://c4science.ch/diffusion/10770/
DOI: 10.25574/14805
title: Chandra X-ray Observatory ObsId 14805

description: Enigmatic X-ray Emission from a High Magnetic Field Pulsar
DOI: 10.25574/14806
title: Chandra X-ray Observatory ObsId 14806

description: Is the CCO associated with SNR G350.1-0.3 an anti-magnetar?
DOI: 10.25574/14884
title: Chandra X-ray Observatory ObsId 14884

description: An asymmetric X-ray nebula around the magnetar SGR1806-20?
DOI: 10.5281/zenodo.7025233
title: Downsampled Calorimeter Shower Images to 8 Pixels

description: Energy depositions from single particle showers in ECAL calorimeters of the CLIC detector. The data set is preprocessed and down-sampled to 8 pixels along the longitudinal shower direction. It contains a training and a test set with around 1000 image samples. It is applied for simplified quantum image generation simulations. The original data set can be found here: https://doi.org/10.5281/zenodo.3603122
description: {"references": ["https://doi.org/10.5281/zenodo.3603122"]}
DOI: 10.5281/zenodo.7025232
title: Downsampled Calorimeter Shower Images to 8 Pixels

description: Energy depositions from single particle showers in ECAL calorimeters of the CLIC detector. The data set is preprocessed and down-sampled to 8 pixels along the longitudinal shower direction. It contains a training and a test set with around 1000 image samples. It is applied for simplified quantum image generation simulations. The original data set can be found here: https://doi.org/10.5281/zenodo.3603122
description: {"references": ["https://doi.org/10.5281/zenodo.3603122"]}
DOI: 10.6084/m9.figshare.20560821.v1
title: Data and code for comparing genetic analyses in the CC, CC-RIX, and DO

description: This project includes data and code used to generate results for manuscript comparing heritability estimation and QTL mapping power in related mouse multiparental populations: Collaborative Cross (CC), their F1s (CC-RIX), and Diversity Outbred (DO) population.
DOI: 10.20372/aau_rdr/3jyfaw/keoh0p
title: Derib_Stata_PhD_Prjoect_Survey_Data.tab

description: :unav
DOI: 10.20372/aau_rdr/3jyfaw/rmzbmc
title: Derib_PhD Project_Case_Study.hpr7

description: :unav
DOI: 10.20372/aau_rdr/3jyfaw
title: Relating Pastoralists Culture, marketing and Food Security

description: The data are about case transitions and survey on pastoralists culture, livestock marketing and food security