cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7358744
title: Post-remediation evaluation of contaminated site using geophysical methods: soilpits

description: Discription of soilpits. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”
DOI: 10.5519/qd.a43wzoov
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (361 records)
DOI: 10.5517/ccdc.csd.cc29w5ft
title: CCDC 2141276: Experimental Crystal Structure Determination

description: Related Article: Yue-Xiao Wu, Shi-Ya Wu, Zhi-Bing Dong|2022|J.Org.Chem.|87|15350|doi:10.1021/acs.joc.2c01924
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.cc2bqfwd
title: CCDC 2166524: Experimental Crystal Structure Determination

description: Related Article: Hui Xu, Ming-Jun Li, Hong Chen, Fei-Hong Huang, Qi-Yue Zhu, Guan-Wu Wang, Ze Zhang|2022|Org.Lett.|24|8406|doi:10.1021/acs.orglett.2c03537
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.cc2bqfzh
title: CCDC 2166527: Experimental Crystal Structure Determination

description: Related Article: Hui Xu, Ming-Jun Li, Hong Chen, Fei-Hong Huang, Qi-Yue Zhu, Guan-Wu Wang, Ze Zhang|2022|Org.Lett.|24|8406|doi:10.1021/acs.orglett.2c03537
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.cc2bqqd6
title: CCDC 2166788: Experimental Crystal Structure Determination

description: Related Article: Hunter J. Windsor, William Lewis, Suzanne M. Neville, Samuel G. Duyker, Deanna M. D’Alessandro, Cameron J. Kepert|2022|Chem.Commun.|58|13127|doi:10.1039/D2CC03780G
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.cc2bqqf7
title: CCDC 2166789: Experimental Crystal Structure Determination

description: Related Article: Hunter J. Windsor, William Lewis, Suzanne M. Neville, Samuel G. Duyker, Deanna M. D’Alessandro, Cameron J. Kepert|2022|Chem.Commun.|58|13127|doi:10.1039/D2CC03780G
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.cc2c1mvx
title: CCDC 2176319: Experimental Crystal Structure Determination

description: Related Article: Daniel N. Huh, Ross F. Koby, Zoe E. Stuart, Rachel J. Dunscomb, Nathan D. Schley, Ian A. Tonks|2022|Chemical Science|13|13330|doi:10.1039/D2SC04368H
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: 10.17909/6wy6-nq36
title: HST NUV exposures from Paper VII: MgII Wilson-Bappu Effect

description: These are NUV data sets from STIS and GHRS, of late-type stars within 1 kpc, covering the wavelengths around 2800 A, to capture the chromospheric MgII h and k lines.
DOI: 10.6084/m9.figshare.21206990
title: epoch data after pre-processing

description: The epoch data are a fieldtrip format structure, all epochs (ended with 'WrdOn') are aligned with fixation onset to a given word (as time 0), with a length of one second ([-0.5 0.5]s). Epochs were named as the combination of the acquisition date, subject code, and data type. Epochs that ended with 'BL_Cross' were the baseline period before the presentation of the sentences. This filed 'trialinfo' is the information about each trial, the header of all columns is as followings: 1- sentence_id: sentence number for this epoch 2- word_loc: the location of the current word in a sentence 3- loc2targ:location distance between the current word and target word; loc2targ for pre-target, target, post-target are -1, 0, and 1 4- saccade2this_duration: saccade duration toward this word 5- fixation_on_MEG: MEG trigger for fixation onset to this word 6- fixation_duration 7- NextOrder: next word location minus the current word location; negative value indicates saccade backward to the previous words 8- FirstPassFix: whether this fixation is the first for this word or not 9- PreviousOrder: previous word location minus the current word location; negative value indicates saccade forward to the next words 10- SentenceCondition: the current word is in a sentence with incongruent or congruent target word; 11 -- incongruent, 2 -- congruent 11- PupilSize: averaged pupil size during this fixation
DOI: 10.6084/m9.figshare.21206990.v3
title: epoch data after pre-processing

description: The epoch data are a fieldtrip format structure, all epochs (ended with 'WrdOn') are aligned with fixation onset to a given word (as time 0), with a length of one second ([-0.5 0.5]s). Epochs were named as the combination of the acquisition date, subject code, and data type. Epochs that ended with 'BL_Cross' were the baseline period before the presentation of the sentences. This filed 'trialinfo' is the information about each trial, the header of all columns is as followings: 1- sentence_id: sentence number for this epoch 2- word_loc: the location of the current word in a sentence 3- loc2targ:location distance between the current word and target word; loc2targ for pre-target, target, post-target are -1, 0, and 1 4- saccade2this_duration: saccade duration toward this word 5- fixation_on_MEG: MEG trigger for fixation onset to this word 6- fixation_duration 7- NextOrder: next word location minus the current word location; negative value indicates saccade backward to the previous words 8- FirstPassFix: whether this fixation is the first for this word or not 9- PreviousOrder: previous word location minus the current word location; negative value indicates saccade forward to the next words 10- SentenceCondition: the current word is in a sentence with incongruent or congruent target word; 11 -- incongruent, 2 -- congruent 11- PupilSize: averaged pupil size during this fixation
DOI: 10.6084/m9.figshare.21618858.v1
title: Automatically Classifying Kano Model Factors in App Reviews

description: This is the replication package that accompanies the paper <br> <strong>Automatically Classifying Kano Model Factors in App Reviews</strong> by <em>Michelle Binder, Annika Vogt, Adrian Bajraktari, and Andreas Vogelsang</em> <br> The package contains the following folders <strong>code</strong>: This folder contains Jupyter notebooks for all tested classifiers and the necessary preprocessing <strong>datasets</strong>: This folder contains the used dataset (Statik and Brunotte) including the Kano labels that we put manually. Additionally, we also provide the five downsampled datasets that we used to evaluate the classifiers. <strong>results</strong>: This folder contains a table with the collected results when the classifiers where trained on the Statik datasets and tested on the Brunotte dataset. To run the code, you need Python3 including standard libraries (`pandas`, `numpy`, `matplotlib`, `sklearn`). In addition, you need to install the 'simpletransformers` library (e.g., via `pip`)
DOI: 10.34740/kaggle/dsv/4579641
title: Country Data
DOI: 10.57967/hf/0133
title: riddlesense_plusplus
DOI: 10.6084/m9.figshare.21618870
title: Questionnaire on digital literacy competencies.pdf

description: The complete questionnaire on digital literacy competencies among youngsters in Spain. <br>
DOI: 10.5281/zenodo.7358638
title: Classifying the generation and formation channels of dynamically-formed gravitational-wave events

description: This dataset contains all the simulations of dynamically-formed binaries performed with the software rapster, together with trained machine-learning classification models from (Antonelli, Kritos et al, in prep.), see the public codes online. All items starting with "mergers_*" are simulations of clusters and they follow the structure reported in the documentation of rapster. The simulations differ in the choice of the hyperparameters for the distribution of the cluster mass, half-mass radius and initial spin distribution for the binaries. All items starting from "RFClassifier_*" are machine-learning classification models that use a Random Forest Classifier and that are trained with the simulations above. The models ending with "*_gen" predict the generation of the black holes, those with "*_form" predict their formation channels.
DOI: 10.5281/zenodo.7358637
title: Classifying the generation and formation channels of dynamically-formed gravitational-wave events

description: This dataset contains all the simulations of dynamically-formed binaries performed with the software rapster, together with trained machine-learning classification models from (Antonelli, Kritos et al, in prep.), see the public codes online. All items starting with "mergers_*" are simulations of clusters and they follow the structure reported in the documentation of rapster. The simulations differ in the choice of the hyperparameters for the distribution of the cluster mass, half-mass radius and initial spin distribution for the binaries. All items starting from "RFClassifier_*" are machine-learning classification models that use a Random Forest Classifier and that are trained with the simulations above. The models ending with "*_gen" predict the generation of the black holes, those with "*_form" predict their formation channels.
DOI: 10.6084/m9.figshare.21379104.v1
title: A data set about digital literacy competencies among youngsters (16-18) in Spain

description: Young people need skills and competencies to make the most of the benefits of the Internet and digital media. This is especially relevant to bridge the socioeconomic and political and cultural participation gap. Public policies should prioritize and support innovation and the acquisition of youth work and development methods in the digital context. This goal should be developed from social education to support youth in their role as active and critical citizens. Thus, the project in which this dataset frames explores how certain uses of the Internet and social media allow young people to position themselves and stand as political actors, actors involved in social, cultural and economic political life and proposes.
DOI: 10.17608/k6.auckland.21618879
title: Cumulative propagule pressure exerted by escaped pet parrots

description: Data (reported lost bird listings and species reference table) and R code for simulated cumulative propagule pressure for manuscript: Journal of Applied Ecology
DOI: 10.5281/zenodo.7358840
title: Comparison of Large Eddy Simulations against measurements from the Lillgrund offshore wind farm - Manuscript data

description: Time averaged power and farm inflow velocity for the manuscript "Comparison of Large Eddy Simulations against measurements from the Lillgrund offshore wind farm" for publication in the wind energy science journal. Data is uploaded for the 5 simulation cases covered. 'Power' files contain average power production for 48 turbines. First row corresponds to LES data, second row corresponds to SCADA data from the Lillgrund wind farm. 'Velocity' files contain inflow mean velocity measurements at the 72 range gate locations. First row corresponds to LES inflow data, second row corresponds to LIDAR inflow data from the Lillgrund wind farm.
DOI: 10.5517/ccdc.csd.cc2c9281
title: CCDC 2183462: Experimental Crystal Structure Determination

description: Related Article: Daniel N. Huh, Ross F. Koby, Zoe E. Stuart, Rachel J. Dunscomb, Nathan D. Schley, Ian A. Tonks|2022|Chemical Science|13|13330|doi:10.1039/D2SC04368H
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.cc2c92f6
title: CCDC 2183467: Experimental Crystal Structure Determination

description: Related Article: Daniel N. Huh, Ross F. Koby, Zoe E. Stuart, Rachel J. Dunscomb, Nathan D. Schley, Ian A. Tonks|2022|Chemical Science|13|13330|doi:10.1039/D2SC04368H
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.cc2c9xh3
title: CCDC 2184275: Experimental Crystal Structure Determination

description: Related Article: Jinming Fan, Yulin Xu, Nengquan Li, Jingsheng Miao, Changjiang Zhou, Tengxiao Liu, Minrong Zhu, Xiaojun Yin|2022|J.Mater.Chem.C|10|17059|doi:10.1039/D2TC03933H
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: 10.6084/m9.figshare.21618888
title: A data set about digital literacy competencies among youngsters (16-18) in Spain

description: Young people need skills and competencies to make the most of the benefits of the Internet and digital media. This is especially relevant to bridge the socioeconomic and political and cultural participation gap. Public policies should prioritize and support innovation and the acquisition of youth work and development methods in the digital context. This goal should be developed from social education to support youth in their role as active and critical citizens. Thus, the project in which this dataset frames explores how certain uses of the Internet and social media allow young people to position themselves and stand as political actors, actors involved in social, cultural and economic political life and proposes.
DOI: 10.5517/ccdc.csd.cc2cvg0q
title: CCDC 2200163: Experimental Crystal Structure Determination

description: Related Article: Efraín Polo-Cuadrado, Cristian Rojas-Peña, Karen Acosta-Quiroga, Lorena Camargo-Ayala, Iván Brito, Jonathan Cisterna, Félix Moncada, Jorge Trilleras, Yeray A. Rodríguez-Núñez, Margarita Gutierrez|2022|RSC Advances|12|33032|doi:10.1039/D2RA05532E
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.cc2cvg1r
title: CCDC 2200164: Experimental Crystal Structure Determination

description: Related Article: Efraín Polo-Cuadrado, Cristian Rojas-Peña, Karen Acosta-Quiroga, Lorena Camargo-Ayala, Iván Brito, Jonathan Cisterna, Félix Moncada, Jorge Trilleras, Yeray A. Rodríguez-Núñez, Margarita Gutierrez|2022|RSC Advances|12|33032|doi:10.1039/D2RA05532E
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.cc2cvg2s
title: CCDC 2200165: Experimental Crystal Structure Determination

description: Related Article: Efraín Polo-Cuadrado, Cristian Rojas-Peña, Karen Acosta-Quiroga, Lorena Camargo-Ayala, Iván Brito, Jonathan Cisterna, Félix Moncada, Jorge Trilleras, Yeray A. Rodríguez-Núñez, Margarita Gutierrez|2022|RSC Advances|12|33032|doi:10.1039/D2RA05532E
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.cc2cvg3t
title: CCDC 2200166: Experimental Crystal Structure Determination

description: Related Article: Efraín Polo-Cuadrado, Cristian Rojas-Peña, Karen Acosta-Quiroga, Lorena Camargo-Ayala, Iván Brito, Jonathan Cisterna, Félix Moncada, Jorge Trilleras, Yeray A. Rodríguez-Núñez, Margarita Gutierrez|2022|RSC Advances|12|33032|doi:10.1039/D2RA05532E
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: 10.6084/m9.figshare.21133567.v1
title: riodoce18

description: This are sequences obtained from sediment samples collected in the Rio Doce estuary.
DOI: 10.6084/m9.figshare.21133567
title: riodoce18

description: This are sequences obtained from sediment samples collected in the Rio Doce estuary.
DOI: 10.6084/m9.figshare.21586923
title: CVEjoin: A Dataset of Information-Security Vulnerability and Threat Intelligence

description: It is impossible to accurately assess the risk of exploiting vulnerabilities using a single metric, such as the Common Vulnerability Scoring System (CVSS). This approach disregards the global threat landscape and the vulnerable asset. Therefore, in addition to using traditional Vulnerability Management (VM) tools, analysts, and researchers must manually curate datasets containing threat intelligence and context-specific information about security flaws. However, this activity is non-trivial and error-prone. To aid this endeavor, we developed a fully automated tool capable of gathering data about the intrinsic characteristics of vulnerabilities presented in the National Vulnerability Database (NVD) and augmented it with information collected from multiple security feeds and social networks. Altogether, we collected data on more than 100,000 vulnerabilities that can be used for various research topics, e.g., analyzing the risk of exploiting security flaws, vulnerability severity prediction, etc. In this paper, we present a detailed description of the methodology used to create our dataset and its attributes. Additionally, we perform an exploratory analysis of the data gathered, and finally, we present an illustrative example of how analysts could use the data collected.
DOI: 10.5281/zenodo.7353512
title: GLAMI-1M Dataset Baselines

description: Weights of baseline models for "GLAMI-1M: A Multilingual Image-Text Fashion Dataset" paper. The largest multilingual image-text classification dataset GLAMI-1M is available is at https://github.com/glami/glami-1m
DOI: 10.5281/zenodo.7353511
title: GLAMI-1M Dataset Baselines

description: Weights of baseline models for "GLAMI-1M: A Multilingual Image-Text Fashion Dataset" paper. The largest multilingual image-text classification dataset GLAMI-1M is available is at https://github.com/glami/glami-1m
DOI: 10.6084/m9.figshare.21618909
title: 165 samples0812.zip

description: <strong>Cold Chain in the Frozen and Chilled Food Industry</strong>
DOI: 10.6084/m9.figshare.21618909.v1
title: 165 samples0812.zip

description: <strong>Cold Chain in the Frozen and Chilled Food Industry</strong>
DOI: 10.5281/zenodo.5148612
title: Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

description: <strong>Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation</strong> This repository contains data contributed and used in our MICCAI 2021 paper:<br> Fidon, L. et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. Our code is publicly available at<br> https://github.com/LucasFidon/fetal-brain-segmentation-partial-supervision-miccai21 The FeTA 3D MRIs and segmentations contained in this repository shall be used only for research and education purposes. <strong>How to cite:</strong> If you use the deep neural network weights please cite: Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI (2021). if you use the FeTA data (release 1) and the corrected segmentations that we contributed please cite: Payette, K., de Dumast, P., Kebiri, H. et al. An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset. Sci Data 8, 167 (2021). https://doi.org/10.1038/s41597-021-00946-3 Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI (2021). <strong>Terms of use - FeTA 3D MRIs and segmentations:</strong> This is an agreement (“Agreement”) between you the downloader (“Downloader”) and the owner of the materials (“User”) governing the use of the Fetal Tissue Annotation and Segmentation Dataset to be downloaded. I. Acceptance of this Agreement By downloading or otherwise accessing the Fetal Tissue Annotation and Segmentation Dataset, the Downloader represents his/her acceptance of the terms of this Agreement. II. Data ownership The owner of the Fetal Tissue Annotation and Segmentation Dataset is the University Children’s Hospital Zurich. III. Use of the Materials Fetal Tissue Annotation and Segmentation Dataset is used only for research and education. Any other kind of use you will lead to the recall of all datasets, stop of collaboration, and legal consequences. This Agreement represents the entire agreement between Downloader and User with respect to the downloading and use of the Materials and supersedes all prior or contemporaneous communications and proposals (whether oral, written, or electronic) between Downloader and User with respect to downloading or using the Materials.