cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2f1yl1
title: CCDC 2236203: Experimental Crystal Structure Determination

description: Related Article: Junki Ochi, Takumi Yanagihara, Kazuo Tanaka, Yoshiki Chujo|2023|Phys.Chem.Chem.Phys.(PCCP)|25|11839|doi:10.1039/D3CP00334E
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: 10.15139/s3/l6qohu
title: Allen Wilson - Replication and Extension (Engelhardt et al., 2021)

description: Replication and Extension data for Engelhardt et al., 2021
DOI: 10.22002/sk6em-th949
title: DAS peak amplitude data for "Earthquake magnitude with DAS: a transferable data-based scaling relation"

description: This repository holds the measured peak strain rate datasets from multiple DAS arrays: Ridgecrest and Long Valley studied in the manuscript "Earthquake magnitude with DAS: a transferable data-based scaling relation." The datasets are all in .csv format and the meaning of columns is in column_info.txt 
Some Python scripts are also provided for results reproduction. The full code repository is available on https://github.com/yinjiuxun/das_strain_scaling.
DOI: 10.34740/kaggle/dsv/5552984
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.5517/ccdc.csd.cc2f61g5
title: CCDC 2240136: Experimental Crystal Structure Determination

description: Related Article: Raman Kavitha Preethi, Sathananthan Kannadasan, Ponnusamy Shanmugam|2023|Tetrahedron Lett.|120|154448|doi:10.1016/j.tetlet.2023.154448
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.cc2cjlvc
title: CCDC 2190703: Experimental Crystal Structure Determination

description: Related Article: Ryotaro Haku, Kazuhiko Takatori, Naoto Suzuki, Makoto Ono, Varin Titapiwatanakun, Toshiro Fukami|2023|CrystEngComm|25|2523|doi:10.1039/D3CE00181D
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: 10.25493/h0jj-0yd
title: F-TRACT: a probabilistic atlas of anatomo-functional connectivity of the human brain (F-TRACT_P_11_v2210)

description: These data come from a cohort of adult patients suffering from pharmaco-resistant focal epilepsies who in the course of preparation to a brain surgery were implanted with intracerebral stereoencephalographic (SEEG) electrodes. The protocol involved brain stimulation at a single site and a simultaneous recording with all remaining contacts on a number of electrodes. Beyond medical aim, performing a number of such stimulations in a single patient allows to infer partial information about their brain anatomo-functional connectivity. What is more, aggregating data from a large cohort of such patients, thus from implantations at different brain regions, allows to derive probabilistic connectivity of the whole human brain. This was achieved by the Functional Tractography project ((F-TRACT)[https://f-tract.eu]) and the results are shared in this dataset. The aggregated data are projected onto a number of parcellations. They describe probabilistic connectivity and connectivity features (such as signal propagation delay) in a context of two parcels: the stimulated and the recorded one. A map for one feature in one parcellation is expressed as a square matrix that can be asymmetric. These matrices were derived from signal analysis of responses to stimulation on an individual level and then aggregation of results on the group level. They can be used to inform about connectivity strength and latency of signal propagation between different areas of the human brain. The naming convention for the atlas versions is the following: "F-TRACT\_P\_XX\_vYYMM" "F-TRACT" and "P" are the common across versions. "F-TRACT" stands for "functional tractography" and "P" stands for "parcellation-based". "XX" varies across alternative versions of a major atlas release and refers to different quality assessments of the same source data as binary code. "vYYMM" varies only across major atlas releases where "YYMM" refers to the year and month of the release. Note that major atlas releases can differ in the source data and/or in the selection of the applied parcellations.
DOI: 10.25493/snfn-bnz
title: F-TRACT: a probabilistic atlas of anatomo-functional connectivity of the human brain (F-TRACT_P_01_v2210)

description: These data come from a cohort of adult patients suffering from pharmaco-resistant focal epilepsies who in the course of preparation to a brain surgery were implanted with intracerebral stereoencephalographic (SEEG) electrodes. The protocol involved brain stimulation at a single site and a simultaneous recording with all remaining contacts on a number of electrodes. Beyond medical aim, performing a number of such stimulations in a single patient allows to infer partial information about their brain anatomo-functional connectivity. What is more, aggregating data from a large cohort of such patients, thus from implantations at different brain regions, allows to derive probabilistic connectivity of the whole human brain. This was achieved by the Functional Tractography project ((F-TRACT)[https://f-tract.eu]) and the results are shared in this dataset. The aggregated data are projected onto a number of parcellations. They describe probabilistic connectivity and connectivity features (such as signal propagation delay) in a context of two parcels: the stimulated and the recorded one. A map for one feature in one parcellation is expressed as a square matrix that can be asymmetric. These matrices were derived from signal analysis of responses to stimulation on an individual level and then aggregation of results on the group level. They can be used to inform about connectivity strength and latency of signal propagation between different areas of the human brain. The naming convention for the atlas versions is the following: "F-TRACT\_P\_XX\_vYYMM" "F-TRACT" and "P" are the common across versions. "F-TRACT" stands for "functional tractography" and "P" stands for "parcellation-based". "XX" varies across alternative versions of a major atlas release and refers to different quality assessments of the same source data as binary code. "vYYMM" varies only across major atlas releases where "YYMM" refers to the year and month of the release. Note that major atlas releases can differ in the source data and/or in the selection of the applied parcellations.
DOI: 10.25493/q882-qxn
title: F-TRACT: a probabilistic atlas of anatomo-functional connectivity of the human brain (F-TRACT_P_00_v2210)

description: These data come from a cohort of adult patients suffering from pharmaco-resistant focal epilepsies who in the course of preparation to a brain surgery were implanted with intracerebral stereoencephalographic (SEEG) electrodes. The protocol involved brain stimulation at a single site and a simultaneous recording with all remaining contacts on a number of electrodes. Beyond medical aim, performing a number of such stimulations in a single patient allows to infer partial information about their brain anatomo-functional connectivity. What is more, aggregating data from a large cohort of such patients, thus from implantations at different brain regions, allows to derive probabilistic connectivity of the whole human brain. This was achieved by the Functional Tractography project ((F-TRACT)[https://f-tract.eu]) and the results are shared in this dataset. The aggregated data are projected onto a number of parcellations. They describe probabilistic connectivity and connectivity features (such as signal propagation delay) in a context of two parcels: the stimulated and the recorded one. A map for one feature in one parcellation is expressed as a square matrix that can be asymmetric. These matrices were derived from signal analysis of responses to stimulation on an individual level and then aggregation of results on the group level. They can be used to inform about connectivity strength and latency of signal propagation between different areas of the human brain. The naming convention for the atlas versions is the following: "F-TRACT\_P\_XX\_vYYMM" "F-TRACT" and "P" are the common across versions. "F-TRACT" stands for "functional tractography" and "P" stands for "parcellation-based". "XX" varies across alternative versions of a major atlas release and refers to different quality assessments of the same source data as binary code. "vYYMM" varies only across major atlas releases where "YYMM" refers to the year and month of the release. Note that major atlas releases can differ in the source data and/or in the selection of the applied parcellations.
DOI: 10.5281/zenodo.7259531
title: gmd-2022-246

description: Supplementary material for <em>"Reconstructing tephra fall deposits via ensemble-based data assimilation techniques"</em> by Mingari et al. (2022). GMD-2022-246
DOI: 10.5281/zenodo.7259530
title: gmd-2022-246

description: Supplementary material for <em>"Reconstructing tephra fall deposits via ensemble-based data assimilation techniques"</em> by Mingari et al. (2022). GMD-2022-246
DOI: 10.5281/zenodo.7877915
title: gmd-2022-246

description: Supplementary material for <em>"Reconstructing tephra fall deposits via ensemble-based data assimilation techniques"</em> by Mingari et al. (2022). GMD-2022-246
DOI: 10.15139/s3/s6axu2
title: Replication Data for: Cole Harvey- Can Courts in Nondemocracies Deter Election Fraud? De Jure Judicial Independence, Political Competition and Election Integrity

description: This is a replication file of Cole Harvey's piece on "Can Courts in Nondemocracies Deter Election Fraud "
DOI: 10.5281/zenodo.7875822
title: Simulation data -- Electron-scale current sheet for southward IMF KHI event (Part 2)

description: The data from a VPIC simulation of the electron-scale current sheet at a KH vortex for the southward IMF. Simulation is performed using the VPIC code (githab.com/lanl/vpic). This part includes data for the in-plane magnetic field (ay) and the location of the virtual spacecraft (loc).
DOI: 10.5281/zenodo.7875821
title: Simulation data -- Electron-scale current sheet for southward IMF KHI event (Part 2)

description: The data from a VPIC simulation of the electron-scale current sheet at a KH vortex for the southward IMF. Simulation is performed using the VPIC code (githab.com/lanl/vpic). This part includes data for the in-plane magnetic field (ay) and the location of the virtual spacecraft (loc).
DOI: 10.5439/1968884
title: Snow-substrate interface temperature and air temperature for the SALVO 2019 Campaign

description: The springtime, surface-albedo transition in the Alaskan Arctic and the forcings that determine the duration and nature of that transition are the focus of our Snow ALbedo eVOlution (SALVO I & II) campaign. The SALVO team are assessing the “whys” and “how longs” of the stages of the spring melt during which albedo values drop from 0.8 to 0.1, the largest and most significant change of the year. The near-shore location of the ARM NSA observatory makes this an ideal place to investigate these melt stages.
DOI: 10.5281/zenodo.7861880
title: AccessFixer: Enhancing GUI Accessibility for Low Vision Users with R-GCN model

description: Here is the relevant dataset and open-source code for the article titled "AccessFixer: Enhancing GUI Accessibility for Low Vision Users with RGCN-based Method" <strong>Introduction</strong> In this work, we designed and implemented a tool, named <strong>AccessFixer</strong>, capable of fixing GUI accessibility issues in terms of small size, narrow interval, and low color contrast. It can ensure the consistency of GUI visual effects in the repair process, and will not produce inconsistent components. <strong>Functions</strong> <strong>Convert the GUIs screenshots to GUI-graphs. </strong>AccessFixer converts the GUIs into their corresponding GUI-graphs by parsing the XML file (in Graph_Presentation()), and meanwhile, our tool could remove all edges that connected the problematic nodes detected by Google Accessibility Scanner. <strong>Pre-trained model based on Relational-Graph Convolutional Neural Network (R-GCN). </strong>Along with Capture_Signal(model), users can obtain the spectral signals of predicted links. Encoder and Decoder are responsible for generating new edges and scoring edges. <strong>AccessFixer</strong> builds a mapping relationship between signal values and attribute values of GUI components in Attribute_Mapp(). Based on this mapping, it is possible to input a GUI to be repaired and generate repair strategies for its various components. <strong>Environment</strong> This tool can be run on an Android emulator based on Android 11.0 on a typical development machine, using Windows 11 with 2.4GHz core i7 CPU and 16 GB memory. The pre-trained RGCN model is run in PyCharm 4.5.4 with the packages of tensorflow, GraphConvolution, pandas, time, numpy, argparse, optimization, Parameter, and Module. <strong>Installation</strong> Using pip install package-name to install the required packages. Configure GCN model training in the same directory. Click the run button in PyCharm or use the command run-train.sh [configuration] <strong>Frequently Asked Questions</strong> Unable to install packages using pip Answer: It could be the version of the pip install command. Detailed solution could be found in https://stackoverflow.com/questions/17869101/unable-to-install-pygame-using-pip/74229901#74229901 Some GUIs fail to build GUI-graphs Answer: To create GII-graphs, the user is required to provide the GUI screenshot and the layout file parsed by UIAutomator. <strong>Contact Information</strong> If you have any questions about this tool, you can contact the author of this work at zmxalakay@126.com <strong>Copyright</strong> All copyright of the tool is owned by the author of the paper.
DOI: 10.5281/zenodo.7861881
title: AccessFixer: Enhancing GUI Accessibility for Low Vision Users with R-GCN model

description: Here is the relevant dataset and open-source code for the article titled "AccessFixer: Enhancing GUI Accessibility for Low Vision Users with RGCN-based Method" <strong>Introduction</strong> In this work, we designed and implemented a tool, named <strong>AccessFixer</strong>, capable of fixing GUI accessibility issues in terms of small size, narrow interval, and low color contrast. It can ensure the consistency of GUI visual effects in the repair process, and will not produce inconsistent components. <strong>Functions</strong> <strong>Convert the GUIs screenshots to GUI-graphs. </strong>AccessFixer converts the GUIs into their corresponding GUI-graphs by parsing the XML file (in Graph_Presentation()), and meanwhile, our tool could remove all edges that connected the problematic nodes detected by Google Accessibility Scanner. <strong>Pre-trained model based on Relational-Graph Convolutional Neural Network (R-GCN). </strong>Along with Capture_Signal(model), users can obtain the spectral signals of predicted links. Encoder and Decoder are responsible for generating new edges and scoring edges. <strong>AccessFixer</strong> builds a mapping relationship between signal values and attribute values of GUI components in Attribute_Mapp(). Based on this mapping, it is possible to input a GUI to be repaired and generate repair strategies for its various components. <strong>Environment</strong> This tool can be run on an Android emulator based on Android 11.0 on a typical development machine, using Windows 11 with 2.4GHz core i7 CPU and 16 GB memory. The pre-trained RGCN model is run in PyCharm 4.5.4 with the packages of tensorflow, GraphConvolution, pandas, time, numpy, argparse, optimization, Parameter, and Module. <strong>Installation</strong> Using pip install package-name to install the required packages. Configure GCN model training in the same directory. Click the run button in PyCharm or use the command run-train.sh [configuration] <strong>Frequently Asked Questions</strong> Unable to install packages using pip Answer: It could be the version of the pip install command. Detailed solution could be found in https://stackoverflow.com/questions/17869101/unable-to-install-pygame-using-pip/74229901#74229901 Some GUIs fail to build GUI-graphs Answer: To create GII-graphs, the user is required to provide the GUI screenshot and the layout file parsed by UIAutomator. <strong>Contact Information</strong> If you have any questions about this tool, you can contact the author of this work at zmxalakay@126.com <strong>Copyright</strong> All copyright of the tool is owned by the author of the paper.
DOI: 10.5281/zenodo.3532214
title: ikala dataset

description: The iKala dataset comprises of 252 30-second excerpts sampled from 206 iKala songs (plus 100 hidden excerpts reserved for MIREX). The music accompaniment and the singing voice are recorded at the left and right channels respectively and can be found under the <em>Wavfile</em> directory. In addition, the human-labeled pitch contours and timestamped lyrics can be found under <em>PitchLabel</em> and <em>Lyrics</em> respectively.
DOI: 10.5281/zenodo.3532213
title: ikala dataset

description: The iKala dataset comprises of 252 30-second excerpts sampled from 206 iKala songs (plus 100 hidden excerpts reserved for MIREX). The music accompaniment and the singing voice are recorded at the left and right channels respectively and can be found under the <em>Wavfile</em> directory. In addition, the human-labeled pitch contours and timestamped lyrics can be found under <em>PitchLabel</em> and <em>Lyrics</em> respectively.
DOI: 10.34740/kaggle/dsv/5553505
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.5281/zenodo.3531980
title: artist 20 the public datatset from LabROSA

description: Music Artist Identification: artist20 the public datatset from LabROSA
DOI: 10.5281/zenodo.3531790
title: artist 20 the public datatset from LabROSA

description: Music Artist Identification: artist20 the public datatset from LabROSA
DOI: 10.15139/s3/vdley8
title: Replication Data for: POLI 784 Replication and Extension

description: Replication and extension of Kaslovsky "Senators at Home: Local Attentiveness and Policy Representation in Congress."
DOI: 10.34740/kaggle/dsv/5548954
title: ML Methods/Algorithms  Dataset
DOI: 10.21227/02g6-0924
title: Relationship between the braking force and speed 

description: A kind of superconductive coil was designed as the linear eddy current brake for the Electric Multiple Units (EMU）. 2D and 3D simulation were carried out on the designed coils. The 2D simulation results showed that the generated braking force could reach 10kN when the train speed was between 28m/s and 110m/s. The simulation results indicated that the reduced gap between the coil and the railway, the increased pole distance, and the increased coil quantity would bring more braking force. The relationship curve between speed and braking force was also investigated during the 2D simulation. The speed of 28m/s and 110m/s was applied with the 3D simulation based on the 2D results. Both the magnetic inducting intensity and the current 180A could meet the requirements of the chosen superconducting wire.
DOI: 10.6084/m9.figshare.22687216
title: Processed Aquaculture Data

description: Lightly processed data from the study: https://github.com/hiro-toju/EelMicrobiome128/
DOI: 10.6084/m9.figshare.22687216.v1
title: Processed Aquaculture Data

description: Lightly processed data from the study: https://github.com/hiro-toju/EelMicrobiome128/
DOI: 10.5517/ccdc.csd.cc2fc577
title: CCDC 2245058: Experimental Crystal Structure Determination

description: Related Article: Yunzhe Zhou, Zhonghua Deng, Zhenyu Ji, Ziqing Zhang, Cheng Chen, Mingyan Wu|2023|New J.Chem.|47|7486|doi:10.1039/D3NJ01012K
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.21586371.v1
title: behavioral and erp data

description: behavioral and ERP data
DOI: 10.6084/m9.figshare.21586371.v2
title: behavioral and erp data

description: behavioral and ERP data
DOI: 10.6084/m9.figshare.22720291
title: Plant physiological data

description: Our dataset gathers data collected from a time-course experiment, with four treatments: 1) control trees; 2) trees infected with <em>P. cinnamomi</em>; 3) trees inoculated with the BCA; 4) trees infected with <em>P. cinnamomi </em>and inoculated with the BCA. During the infection process, we measured plant morphological and physiological variables and collected rhizosphere soil samples for bacterial and fungal amplicon sequencing, bacterial RNA-seq and metabolomic analyses.
DOI: 10.6084/m9.figshare.22721164
title: Substrate data

description: Our dataset gathers data collected from a time-course experiment, with four treatments: 1) control trees; 2) trees infected with <em>P. cinnamomi</em>; 3) trees inoculated with the BCA; 4) trees infected with <em>P. cinnamomi </em>and inoculated with the BCA. During the infection process, we measured plant morphological and physiological variables and collected rhizosphere soil samples for bacterial and fungal amplicon sequencing, bacterial RNA-seq and metabolomic analyses.
DOI: 10.6084/m9.figshare.22721176
title: 16S amplicon sequences (Sequence Read Archive submission template)

description: Our dataset gathers data collected from a time-course experiment, with four treatments: 1) control trees; 2) trees infected with <em>P. cinnamomi</em>; 3) trees inoculated with the BCA; 4) trees infected with <em>P. cinnamomi </em>and inoculated with the BCA. During the infection process, we measured plant morphological and physiological variables and collected rhizosphere soil samples for bacterial and fungal amplicon sequencing, bacterial RNA-seq and metabolomic analyses.
DOI: 10.6084/m9.figshare.22721182
title: UHPLC-HRMS metabolomic data (positive mode)

description: Our dataset gathers data collected from a time-course experiment, with four treatments: 1) control trees; 2) trees infected with <em>P. cinnamomi</em>; 3) trees inoculated with the BCA; 4) trees infected with <em>P. cinnamomi </em>and inoculated with the BCA. During the infection process, we measured plant morphological and physiological variables and collected rhizosphere soil samples for bacterial and fungal amplicon sequencing, bacterial RNA-seq and metabolomic analyses.
DOI: 10.6084/m9.figshare.22721182.v1
title: UHPLC-HRMS metabolomic data (positive mode)

description: Our dataset gathers data collected from a time-course experiment, with four treatments: 1) control trees; 2) trees infected with <em>P. cinnamomi</em>; 3) trees inoculated with the BCA; 4) trees infected with <em>P. cinnamomi </em>and inoculated with the BCA. During the infection process, we measured plant morphological and physiological variables and collected rhizosphere soil samples for bacterial and fungal amplicon sequencing, bacterial RNA-seq and metabolomic analyses.
DOI: 10.6084/m9.figshare.22721116
title: data

description: N2 and P3 in smoking object and social cues for smokers and non-smokers
DOI: 10.6084/m9.figshare.22721116.v1
title: data

description: N2 and P3 in smoking object and social cues for smokers and non-smokers
DOI: 10.34740/kaggle/dsv/5553828
title: Michelin Guide Restaurants