cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/t3wtty9rzv.3
title: Crystal chemistry of ferric smectites and implications for Martian clay detection

description: We synthesized several series of Fe/Mg smectites, each with a graded composition and crystallinity. We studied the relationship between the layer ordering of these smectites and near-infrared (NIR) wavelength and absorption depth by analyzing the crystallinity index (V/P) from X-ray diffraction and the M-OH absorption and its second-order derivative spectra from NIR spectra at around 2390 nm. Using this analysis, we proposed a semi-quantitative method for evaluating the crystallinity orders of Martian smectites based on CRISM NIR spectra.
Table 1 outlines the experimental conditions for synthesizing Fe/Mg smectites. Figure 1 displays X-ray diffraction (XRD) patterns of the synthesized smectites with varying reaction times and Fe/Mg ratios. Meanwhile, Figure 2 illustrates the near-infrared (NIR) spectra of the synthesized nontronites with differing Fe/Mg ratios and reaction times, and Figure 3 shows the Deriv2 NIR spectra of those depicted in Figure 2. For statistical purposes, Figure 4 shows the position of the M−OH combination bands of smectites with different Fe/Mg ratios. Figure 5 displays a visualization of the smectite crystallinity index (V/P), and Table 2 presents the corresponding crystallinity indices of the smectites based on the XRD results. The correlation between the Deriv2 ratio of the NIR spectra and the crystallinity indices (V/P) of the smectites can be found in Figure 6. Moreover, Figure 7 compares the XRD patterns of a natural nontronite with that of a synthesized saponite. Figures 8a and 8b illustrate the NIR spectra and the Deriv2 NIR spectra of the natural nontronite and the synthesized saponite respectively. Lastly, Figure 9 depicts the NIR spectra of the smectites found on Mars (CRISM) and their analogs on Earth, with various V/P indices.
DOI: 10.17632/sfbppnjy7r.1
title: Foam Stabilization by Surfactant/SiO2 Composite Nanofluids

description: This research deals with the potential of aggregates of surfactant and SiO2 nanoparticles as foam stabilizers for practical applications. The effects of different chain lengths and concentrations of the cationic surfactant C_nTAB on the performance of C_nTAB–SiO2 nanofluids are examined to gain a comprehensive understanding of their ability to stabilize foam. The results indicate enhanced foam stability in the presence of SiO2 nanoparticles. These findings help to better understand foam stabilization and its potential in various industrial applications such as enhanced oil recovery and foam-based separation processes.
DOI: 10.17632/xdw4cgnvhm.3
title: URiver-X version1

description: The current dataset contains the matlab program of URiver-X version 1 as well as related example file
DOI: 10.17632/xdw4cgnvhm.1
title: URiver-X version1

description: The current dataset contains the matlab program of URiver-X version 1 as well as related example file
DOI: 10.17632/xdw4cgnvhm.2
title: URiver-X version1

description: The current dataset contains the matlab program of URiver-X version 1 as well as related example file
DOI: 10.17632/xdw4cgnvhm.4
title: URiver-X version1

description: The current dataset contains the matlab program of URiver-X version 1 as well as related example file
DOI: 10.5281/zenodo.8297980
title: Developing circularity, renewability and efficiency indicators for sustainable resource management : propanol production as a showcase

description: The data used for the exergy calculations in the associated article.
DOI: 10.5281/zenodo.8297981
title: Developing circularity, renewability and efficiency indicators for sustainable resource management : propanol production as a showcase

description: The data used for the exergy calculations in the associated article.
DOI: 10.5281/zenodo.6855794
title: Data from: Electroencephalography Responses to Simplified Visual Signals Reveal Explain Differences in Speech-in-Noise Comprehension

description: Contents and Folder Structure: <strong>EEG Experiment</strong> <strong>EEG_stimuli</strong>: these are the videos that were presented to participants in the EEG experiment, and the code that generates them from the original corpus (link) <strong>data_2020&gt;split_trials</strong>: contains the raw EEG data starting 1.995s before each trial and ending 1.995s after each trial with naming convention subXx_VV_YY_N.fif where X or Xx is the subject number, YY is the modality condition (AV for audiovisual and V0 for video only), N is the trial number (between 0 and 4 inclusive), and VV is the video condition (1e for the envelope dot, 1m is the mismatched dot, 4v is the cartoon, bw is the edge detection and nh is the natural condition). <strong>unprocessed&gt;raw</strong>: contains the unprocessed raw EEG data <strong>processed&gt;Fs-200&gt;BP-1-80-ASR-INTP-AVR</strong>: contains the pre-processed raw EEG data: the output of <em>run_preprocessing.m</em> <strong>processed&gt;Fs-200&gt;BP-1-80-ASR-INTP-AVR-ICr</strong>: contains the pre-processed raw EEG data after ICA cleaning: the output of <em>run_reject_ICs.m</em> <strong>stim&gt;stim_dwnspl</strong>: contains the aligned 200Hz envelopes of the presented speech used as features for the time-lagged models <strong>EEG_analysis_code </strong>[note: please extract the contents of this folder to match paths] <strong>2_ICA_filt:</strong> this folder contains the MATLAB code that performs the pre-processing of the EEG data, including filtering, downsampling, ICA cleaning etc. The main functions are: <em>run_preprocessing.m: downsampling, filtering, ASR cleaning</em> <em>run_reject_ICs.m: ICLabel ICA cleaning</em> <strong>3_analysis:</strong> this is the Python code that performs the TRF and backward modelling on the EEG data. The main functions are: <em>multisensory_bw.py</em>: backwards model <em>multisensory_fw.py</em>: forwards model <strong>Behavioural Experiment</strong> <strong>behavioural</strong> <strong>0_dataset</strong>: these are the videos that were presented to participants in the behavioural experiment, and the code that generates them from the original corpus (AV GRID corpus) <strong>3_analysis</strong>: behavioural data analysis script main function: <em>data_grid_v3.py</em> <strong>behavioural_data&gt;data_grid</strong>: behavioural results
DOI: 10.5281/zenodo.6855795
title: Data from: Electroencephalography Responses to Simplified Visual Signals Reveal Explain Differences in Speech-in-Noise Comprehension

description: Contents and Folder Structure: <strong>EEG Experiment</strong> <strong>EEG_stimuli</strong>: these are the videos that were presented to participants in the EEG experiment, and the code that generates them from the original corpus (link) <strong>data_2020&gt;split_trials</strong>: contains the raw EEG data starting 1.995s before each trial and ending 1.995s after each trial with naming convention subXx_VV_YY_N.fif where X or Xx is the subject number, YY is the modality condition (AV for audiovisual and V0 for video only), N is the trial number (between 0 and 4 inclusive), and VV is the video condition (1e for the envelope dot, 1m is the mismatched dot, 4v is the cartoon, bw is the edge detection and nh is the natural condition). <strong>unprocessed&gt;raw</strong>: contains the unprocessed raw EEG data <strong>processed&gt;Fs-200&gt;BP-1-80-ASR-INTP-AVR</strong>: contains the pre-processed raw EEG data: the output of <em>run_preprocessing.m</em> <strong>processed&gt;Fs-200&gt;BP-1-80-ASR-INTP-AVR-ICr</strong>: contains the pre-processed raw EEG data after ICA cleaning: the output of <em>run_reject_ICs.m</em> <strong>stim&gt;stim_dwnspl</strong>: contains the aligned 200Hz envelopes of the presented speech used as features for the time-lagged models <strong>EEG_analysis_code </strong>[note: please extract the contents of this folder to match paths] <strong>2_ICA_filt:</strong> this folder contains the MATLAB code that performs the pre-processing of the EEG data, including filtering, downsampling, ICA cleaning etc. The main functions are: <em>run_preprocessing.m: downsampling, filtering, ASR cleaning</em> <em>run_reject_ICs.m: ICLabel ICA cleaning</em> <strong>3_analysis:</strong> this is the Python code that performs the TRF and backward modelling on the EEG data. The main functions are: <em>multisensory_bw.py</em>: backwards model <em>multisensory_fw.py</em>: forwards model <strong>Behavioural Experiment</strong> <strong>behavioural</strong> <strong>0_dataset</strong>: these are the videos that were presented to participants in the behavioural experiment, and the code that generates them from the original corpus (AV GRID corpus) <strong>3_analysis</strong>: behavioural data analysis script main function: <em>data_grid_v3.py</em> <strong>behavioural_data&gt;data_grid</strong>: behavioural results
DOI: 10.5281/zenodo.8298239
title: Data from: Electroencephalography Responses to Simplified Visual Signals Reveal Explain Differences in Speech-in-Noise Comprehension

description: Contents and Folder Structure: <strong>EEG Experiment</strong> <strong>EEG_stimuli</strong>: these are the videos that were presented to participants in the EEG experiment, and the code that generates them from the original corpus (link) <strong>data_2020&gt;split_trials</strong>: contains the raw EEG data starting 1.995s before each trial and ending 1.995s after each trial with naming convention subXx_VV_YY_N.fif where X or Xx is the subject number, YY is the modality condition (AV for audiovisual and V0 for video only), N is the trial number (between 0 and 4 inclusive), and VV is the video condition (1e for the envelope dot, 1m is the mismatched dot, 4v is the cartoon, bw is the edge detection and nh is the natural condition). <strong>unprocessed&gt;raw</strong>: contains the unprocessed raw EEG data <strong>processed&gt;Fs-200&gt;BP-1-80-ASR-INTP-AVR</strong>: contains the pre-processed raw EEG data: the output of <em>run_preprocessing.m</em> <strong>processed&gt;Fs-200&gt;BP-1-80-ASR-INTP-AVR-ICr</strong>: contains the pre-processed raw EEG data after ICA cleaning: the output of <em>run_reject_ICs.m</em> <strong>stim&gt;stim_dwnspl</strong>: contains the aligned 200Hz envelopes of the presented speech used as features for the time-lagged models <strong>EEG_analysis_code </strong>[note: please extract the contents of this folder to match paths] <strong>2_ICA_filt:</strong> this folder contains the MATLAB code that performs the pre-processing of the EEG data, including filtering, downsampling, ICA cleaning etc. The main functions are: <em>run_preprocessing.m: downsampling, filtering, ASR cleaning</em> <em>run_reject_ICs.m: ICLabel ICA cleaning</em> <strong>3_analysis:</strong> this is the Python code that performs the TRF and backward modelling on the EEG data. The main functions are: <em>multisensory_bw.py</em>: backwards model <em>multisensory_fw.py</em>: forwards model <strong>Behavioural Experiment</strong> <strong>behavioural</strong> <strong>0_dataset</strong>: these are the videos that were presented to participants in the behavioural experiment, and the code that generates them from the original corpus (AV GRID corpus) <strong>3_analysis</strong>: behavioural data analysis script main function: <em>data_grid_v3.py</em> <strong>behavioural_data&gt;data_grid</strong>: behavioural results
DOI: 10.34770/gkye-wc69
title: Feasibility study of a high spatial and time resolution beam emission spectroscopy diagnostic for localized density fluctuation measurements in Lithium Tokamak eXperiment-β (LTX-β)

description: Datasets for the plots used in the aforesaid publication
DOI: 10.5281/zenodo.8298052
title: Stream discharge and groundwater level data for the USVI

description: .csv files containing 1) annual maximum stream discharge and corresponding cumulative precipitation data for three U.S. Virgin Islands (USVI) streams (one on St. Croix, St. Thomas, St. John) and 2) annual average groundwater levels for three monitoring wells in the USVI (one on St. Croix, St. Thomas, St. John). All of the data were collected by the U.S. Geological Survey and are available in government reports and online databases.
description: {"references": ["Colon-Dieppa, E., Diaz, P.L., Garcia., R. (1985). Water resources data, Puerto Rico and the U.S. Virgin Islands, water year 1985. Water Data Report PR-85-1. https://doi.org/10.3133/wdrPR851", "Curtis, R. E., Guzman-Rios, S., & Diaz, P. L. (1984). Water resources data, Puerto Rico and the U.S. Virgin Islands, water year 1984. Water Data Report PR-84-1. https://doi.org/10.3133/wdrPR841", "Robinson, T. M. (1973). Water records of U.S. Virgin Islands 1962-69. U.S. Geological Survey. https://doi.org/10.3133/70240216"]}
DOI: 10.5281/zenodo.8298062
title: In-shop Clothes Retrieval

description: In-shop Clothes Retrieval
DOI: 10.5281/zenodo.8298061
title: In-shop Clothes Retrieval

description: In-shop Clothes Retrieval
DOI: 10.5281/zenodo.8298236
title: Quantification of the global and regional impacts of gas flaring on human health via spatial differentiation

description: Supplementary material for the associated publication.
DOI: 10.5281/zenodo.8298235
title: Quantification of the global and regional impacts of gas flaring on human health via spatial differentiation

description: Supplementary material for the associated publication.
DOI: 10.34770/k547-cq20
title: Laboratory study of the failed torus mechanism in arched, line-tied, magnetic flux ropes

description: Coronal mass ejections (CMEs) are some of the most energetic and violent events in our solar system. The prediction and understanding of CMEs is of particular importance due to the impact that they can have on Earth-based satellite systems, and in extreme cases, ground-based electronics. CMEs often occur when long-lived magnetic flux ropes (MFRs) anchored to the solar surface destabilize and erupt away from the Sun. One potential cause for these eruptions is an ideal magnetohydrodynamic (MHD) instability such as the kink or torus instability. Previous experiments on the Magnetic Reconnection eXperiment (MRX) revealed a class of MFRs that were torus-unstable but kink-stable, which failed to erupt. These “failed-tori” went through a process similar to Taylor relaxation where the toroidal current was redistributed before the eruption ultimately failed. We have investigated this behavior through additional diagnostics that measure the current distribution at the foot points and the energy distribution before and after an event. These measurements indicate that ideal MHD effects are sufficient to explain the energy distribution changes during failed torus events. This excludes Taylor relaxation as a possible mechanism of current redistribution during an event. A new model that only requires non-ideal effects in a thin layer above the electrodes is presented to explain the observed phenomena. This work broadens our understanding of the stability of MFRs and the mechanism behind the failed torus through the improved prediction of the torus instability and through new diagnostics to measure the energy inventory and current profile at the foot points.
DOI: 10.5281/zenodo.7386479
title: agroBRIDGES EIP-AGRI Practice Abstracts

description: The present dataset is a collection of the practice abstracts developed in the framework of the agroBRIDGES project, using the EIP-AGRI common format. The aim is to share project outputs so other actors of the agri-food sector can leverage on them and to see the main added value and benefits met by the end-users if the generated knowledge is implemented. The Practice Abstracts are also published on the EIP-AGRI website and available at this <strong>link. </strong> The current (first) version of the dataset contains the set of the first 12 Practice Abstracts generated to share the project's knowledge and insights gained from co-creation, data collection, communication and other activities aimed at promoting the development of Short Food Supply Chains, while motivating the local network of agri-food actors in the process (multi-actor approach). At least one Practice Abstract has been generated per Beacon Region (i.e. focal region of the agroBRIDGES project). <strong>This version of the dataset contains one Practice Abstract per country using the standard EIP-AGRI format in .xlsx.</strong> The final (second) version will be enhanced with Practice Abstracts describing the development and improvement of the (12) agroBRIDGES tools and the business model taxonomy developed for Short Food Supply Chains.
DOI: 10.5517/ccdc.csd.cc25lr0k
title: CCDC 2014008: Experimental Crystal Structure Determination

description: Related Article: Hong-Liang Jia, Bing-Xue Han, Gang Li|2023|J.Solid State Chem.|327|124268|doi:10.1016/j.jssc.2023.124268
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.cc2dccw1
title: CCDC 2215473: Experimental Crystal Structure Determination

description: Related Article: Tariq M. Bhatti, Akshai Kumar, Ashish Parihar, Hellan K. Moncy, Thomas J. Emge, Kate M. Waldie, Faraj Hasanayn, Alan S. Goldman|2023|J.Am.Chem.Soc.|145|18296|doi:10.1021/jacs.3c03376
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.cc2dccx2
title: CCDC 2215474: Experimental Crystal Structure Determination

description: Related Article: Tariq M. Bhatti, Akshai Kumar, Ashish Parihar, Hellan K. Moncy, Thomas J. Emge, Kate M. Waldie, Faraj Hasanayn, Alan S. Goldman|2023|J.Am.Chem.Soc.|145|18296|doi:10.1021/jacs.3c03376
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.24050628
title: Raw_data.xlsx

description: All of the raw data to support the publication entitled "Regulation of Natural Killer Cell TGF-b and AhR Signaling Pathways Via the Intestinal Microbiota is Critical for Host Defense Against Alcohol-Associated Bacterial Pneumonia."
DOI: 10.6084/m9.figshare.24050628.v1
title: Raw_data.xlsx

description: All of the raw data to support the publication entitled "Regulation of Natural Killer Cell TGF-b and AhR Signaling Pathways Via the Intestinal Microbiota is Critical for Host Defense Against Alcohol-Associated Bacterial Pneumonia."
DOI: 10.5517/ccdc.csd.cc2f1tp0
title: CCDC 2236082: Experimental Crystal Structure Determination

description: Related Article: Baoshan Hou, Haiyan Zheng, Kunhao Zhang, Qi Wu, Chao Qin, Chunyi Sun, Qinhe Pan, Zhenhui Kang, Xinlong Wang, Zhongmin Su|2023|Chemical Science|14|8962|doi:10.1039/D3SC02924G
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.cc2f1tq1
title: CCDC 2236083: Experimental Crystal Structure Determination

description: Related Article: Baoshan Hou, Haiyan Zheng, Kunhao Zhang, Qi Wu, Chao Qin, Chunyi Sun, Qinhe Pan, Zhenhui Kang, Xinlong Wang, Zhongmin Su|2023|Chemical Science|14|8962|doi:10.1039/D3SC02924G
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.cc2f1tr2
title: CCDC 2236084: Experimental Crystal Structure Determination

description: Related Article: Baoshan Hou, Haiyan Zheng, Kunhao Zhang, Qi Wu, Chao Qin, Chunyi Sun, Qinhe Pan, Zhenhui Kang, Xinlong Wang, Zhongmin Su|2023|Chemical Science|14|8962|doi:10.1039/D3SC02924G
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.cc2f1ts3
title: CCDC 2236085: Experimental Crystal Structure Determination

description: Related Article: Baoshan Hou, Haiyan Zheng, Kunhao Zhang, Qi Wu, Chao Qin, Chunyi Sun, Qinhe Pan, Zhenhui Kang, Xinlong Wang, Zhongmin Su|2023|Chemical Science|14|8962|doi:10.1039/D3SC02924G
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.cc2f1tt4
title: CCDC 2236086: Experimental Crystal Structure Determination

description: Related Article: Baoshan Hou, Haiyan Zheng, Kunhao Zhang, Qi Wu, Chao Qin, Chunyi Sun, Qinhe Pan, Zhenhui Kang, Xinlong Wang, Zhongmin Su|2023|Chemical Science|14|8962|doi:10.1039/D3SC02924G
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.cc2f1tv5
title: CCDC 2236087: Experimental Crystal Structure Determination

description: Related Article: Baoshan Hou, Haiyan Zheng, Kunhao Zhang, Qi Wu, Chao Qin, Chunyi Sun, Qinhe Pan, Zhenhui Kang, Xinlong Wang, Zhongmin Su|2023|Chemical Science|14|8962|doi:10.1039/D3SC02924G
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.cc2fm72d
title: CCDC 2252803: Experimental Crystal Structure Determination

description: Related Article: Jelena M. Aksić, Marija S. Genčić, Niko S. Radulović, Marina V. Dimitrijević, Zorica Z. Stojanović-Radić, Tatjana Ilic Tomic, Marko V. Rodić|2023|Bioorg.Chem.|139|106708|doi:10.1016/j.bioorg.2023.106708
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.