cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6651638
title: Source data for the paper A laser-assisted chlorination process for reversible writing of doping patterns in graphene

description: Source Data supporting the plots within the paper "A laser-assisted chlorination process for reversible writing of doping patterns in graphene." Including Source Data for Figures 1-4 in the main text, and Supplementary Figures S1, S3-12, S14, S17.
DOI: 10.5281/zenodo.6651635
title: data set for ariyoshi2022jgr

description: 1. This is the source code and simulation results of Ariyoshi (2022 JGR).<br> Date: June 11th 2022 2. source and model of Ariyoshi (2022 JGR) 3. version: 69cses4 69cses4: source code (*.f, *.mk)<br> Note that the source code is customized to the Earth Simulator of JAMSTEC which needs special libraries for the FFT operations.<br> Methodology is described in Ariyoshi et al. (2012) Ariyoshi, et al.(2012). Migration process of very low-frequency events based on a chain-reaction model and its application to the detection of preseismic slip for megathrust earthquakes. Earth Planets Space, 64(8), 693-702. https://doi.org/10.5047/eps.2010.09.003 <br> 4. common: green's function file (*.grn) and input model (simu3dscalar.dat) 5. model:<br> 71s02r110dd200a230b215L199ob : model A<br> 7is02r110dd200a230b100L199ob : model B<br> 7is02r110dd200a230b215L199ob : model C<br> 7ih02r110dd200a230b100L199ob : model D *.ctl : control file<br> *.o?? : input file<br> *.ini : initial condition file<br> *_hg028.dat & *_v_vel.dat : datafile of Figure 5 6. Please use refer Ariyoshi (2022 JGR) if you use this code with any modifications.
DOI: 10.5281/zenodo.6651634
title: data set for ariyoshi2022jgr

description: 1. This is the source code and simulation results of Ariyoshi (2022 JGR).<br> Date: June 11th 2022 2. source and model of Ariyoshi (2022 JGR) 3. version: 69cses4 69cses4: source code (*.f, *.mk)<br> Note that the source code is customized to the Earth Simulator of JAMSTEC which needs special libraries for the FFT operations.<br> Methodology is described in Ariyoshi et al. (2012) Ariyoshi, et al.(2012). Migration process of very low-frequency events based on a chain-reaction model and its application to the detection of preseismic slip for megathrust earthquakes. Earth Planets Space, 64(8), 693-702. https://doi.org/10.5047/eps.2010.09.003 <br> 4. common: green's function file (*.grn) and input model (simu3dscalar.dat) 5. model:<br> 71s02r110dd200a230b215L199ob : model A<br> 7is02r110dd200a230b100L199ob : model B<br> 7is02r110dd200a230b215L199ob : model C<br> 7ih02r110dd200a230b100L199ob : model D *.ctl : control file<br> *.o?? : input file<br> *.ini : initial condition file<br> *_hg028.dat & *_v_vel.dat : datafile of Figure 5 6. Please use refer Ariyoshi (2022 JGR) if you use this code with any modifications.
DOI: 10.34740/kaggle/dsv/3812896
title: YouTube Daily
DOI: 10.5281/zenodo.6651688
title: Source data for the paper A laser-assisted chlorination process for reversible writing of doping patterns in graphene

description: Source Data supporting the plots within the paper "A laser-assisted chlorination process for reversible writing of doping patterns in graphene." Including Source Data for Figures 1-4 in the main text, and Supplementary Figures S1, S3-12, S14, S17.
DOI: 10.5281/zenodo.6651687
title: Source data for the paper A laser-assisted chlorination process for reversible writing of doping patterns in graphene

description: Source Data supporting the plots within the paper "A laser-assisted chlorination process for reversible writing of doping patterns in graphene." Including Source Data for Figures 1-4 in the main text, and Supplementary Figures S1, S3-12, S14, S17.
DOI: 10.5523/bris.2s0fn0bc89omq2kj2rol7iolwt
title: Wood et al 2014 GEB connectivity model output
title: Modeling dispersal and connectivity of broadcast spawning corals at the global scale

description: Connectivity model output and Habitat cells from Wood, S., Paris, C.B., Ridgwell, A., Hendy, E.J. (2014) Modeling dispersal and connectivity of broadcast spawning corals at the global scale Global Ecology and Biogeography, 23(1), 1-11 https://onlinelibrary.wiley.com/doi/full/10.1111/geb.12101
DOI: 10.6084/m9.figshare.20079286
title: Additional file 10 of Genome-wide translation control analysis of developing human neurons

description: Additional file 10: Table S3. Neuronal subcellular compartment data.
DOI: 10.6084/m9.figshare.20079289
title: Additional file 11 of Genome-wide translation control analysis of developing human neurons

description: Additional file 11: Table S4. Translation efficiency of the translation-regulated genes found in our dataset in comparison with published data with cells in different stages of differentiation.
DOI: 10.6084/m9.figshare.20079286.v1
title: Additional file 10 of Genome-wide translation control analysis of developing human neurons

description: Additional file 10: Table S3. Neuronal subcellular compartment data.
DOI: 10.6084/m9.figshare.20079289.v1
title: Additional file 11 of Genome-wide translation control analysis of developing human neurons

description: Additional file 11: Table S4. Translation efficiency of the translation-regulated genes found in our dataset in comparison with published data with cells in different stages of differentiation.
DOI: 10.6084/m9.figshare.20079313
title: Additional file 8 of Genome-wide translation control analysis of developing human neurons

description: Additional file 8: Table S1. Sequencing overview and gene expression data.
DOI: 10.6084/m9.figshare.20079316
title: Additional file 9 of Genome-wide translation control analysis of developing human neurons

description: Additional file 9: Table S2. Gene Ontology analysis data.
DOI: 10.6084/m9.figshare.20079313.v1
title: Additional file 8 of Genome-wide translation control analysis of developing human neurons

description: Additional file 8: Table S1. Sequencing overview and gene expression data.
DOI: 10.6084/m9.figshare.20079316.v1
title: Additional file 9 of Genome-wide translation control analysis of developing human neurons

description: Additional file 9: Table S2. Gene Ontology analysis data.
DOI: 10.6084/m9.figshare.20078933.v3
title: Glucose Level Affects the Regulation of MgaSpn on Virulence and Adaptability of Streptococcus pneumoniae

description: <em>Streptococcus pneumoniae</em> can regulate virulence gene expression by sensing environmental changes, which is the key to pathogenicity. Carbohydrate is a significant environmental factor affecting the growth and virulence of bacteria. Here, we found that glucose level could affect the content of phosphocholine, which was mediated by Mga<em>Spn</em>. Mga<em>Spn</em> belongs to a virulence regulator (PCVR) family containing phosphotransferase system (PTS) regulatory domains (PRDs). PTS can phosphorylate Mga<em>Spn</em> to varying degrees according to the availability of glucose, thereby altering its virulence regulation. In addition, Transcriptome analysis of wild-type D39s between low and high sugar concentrations revealed that Mga<em>Spn</em> was also involved in the regulation of carbon metabolism inhibition (CCR) and translation process, which made <em>S.pneumoniae</em> still have strong competitiveness in fluctuating environments. In contrast, Mga<em>Spn</em> is closely related to virulence, carbohydrate metabolism, and environmental adaptability of <em>Streptococcus pneumoniae</em>.
DOI: 10.5519/qd.fyw4jcvc
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.6651735
title: Supplementary Materials

description: Supplementary Materials of Genome-wide characterization of lncRNAs and mRNAs in muscles with differential meat quality
DOI: 10.5281/zenodo.6651734
title: Supplementary Materials

description: Supplementary Materials of Genome-wide characterization of lncRNAs and mRNAs in muscles with differential meat quality
DOI: 10.5281/zenodo.6651749
title: MAVEN 40k dependency data

description: This is the dependency data used in the "Analyzing the Criticality of Apache Maven Packages Through a Temporal<br> Dependency Graph" paper, with the following structure: <pre><code>{"pkgs":[{ "name": "react", "versions": { "1.00": { "timestamp": "06-05-2022T10:00:01", "dependencies": { "name": "^1.0.2" } } } }, ... ]}</code></pre>
DOI: 10.5281/zenodo.6651752
title: Dataset from "Identifying zebrafish segmentation phenotype features using multiple instance learning"

description: The dataset was used to produce the results from the "Identifying zebrafish segmentation phenotype features using multiple instance learning" manuscript. It contains images of zebrafish embryos obtained by in situ hybridization that were used to train and evaluate the performance of different neural network-based image classifiers. Images were split into 4 classes: unmodified (WT) zebrafish embryo, and 3 more phenotypes reflecting different segmentation clock defects.<br> Folder 'data' contains 3 different directories: 'training', the set used for training of the classifiers, 'validation', the set used for evaluation of the performance of the classifiers, with 20 images from each class, and 'fish_part_labels' that contains the annotation of zebrafish embryo parts (head, trunk, tail, yolk, and yolk extension) of the images from the 'validation' set.
DOI: 10.5281/zenodo.6645291
title: Dataset from "Identifying zebrafish segmentation phenotype features using multiple instance learning"

description: The dataset was used to produce the results from the "Identifying zebrafish segmentation phenotype features using multiple instance learning" manuscript. It contains images of zebrafish embryos obtained by in situ hybridization that were used to train and evaluate the performance of different neural network-based image classifiers. Images were split into 4 classes: unmodified (WT) zebrafish embryo, and 3 more phenotypes reflecting different segmentation clock defects.<br> Folder 'data' contains 3 different directories: 'training', the set used for training of the classifiers, 'validation', the set used for evaluation of the performance of the classifiers, with 20 images from each class, and 'fish_part_labels' that contains the annotation of zebrafish embryo parts (head, trunk, tail, yolk, and yolk extension) of the images from the 'validation' set.
DOI: 10.5281/zenodo.6651793
title: SciExpeM - 10.5281/zenodo.6651793

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651792
title: SciExpeM - 10.5281/zenodo.6651793

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651803
title: SciExpeM - 10.5281/zenodo.6651803

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651802
title: SciExpeM - 10.5281/zenodo.6651803

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651805
title: SciExpeM - 10.5281/zenodo.6651805

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651804
title: SciExpeM - 10.5281/zenodo.6651805

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651807
title: SciExpeM - 10.5281/zenodo.6651807

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651806
title: SciExpeM - 10.5281/zenodo.6651807

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651809
title: SciExpeM - 10.5281/zenodo.6651809

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651808
title: SciExpeM - 10.5281/zenodo.6651809

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651811
title: SciExpeM - 10.5281/zenodo.6651811

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651810
title: SciExpeM - 10.5281/zenodo.6651811

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651813
title: SciExpeM - 10.5281/zenodo.6651813

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651812
title: SciExpeM - 10.5281/zenodo.6651813

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651815
title: SciExpeM - 10.5281/zenodo.6651815

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651814
title: SciExpeM - 10.5281/zenodo.6651815

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651817
title: SciExpeM - 10.5281/zenodo.6651817

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651816
title: SciExpeM - 10.5281/zenodo.6651817

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651821
title: SciExpeM - 10.5281/zenodo.6651821

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651820
title: SciExpeM - 10.5281/zenodo.6651821

description: Xie, Yongliang; Wang, Jinhua; Zhang, Meng; Gong, Jing; Jin, Wu; Huang, Zuohua - Energy &Amp; Fuels, 2013, (27), 6231-6237
DOI: 10.5281/zenodo.6651823
title: SciExpeM - 10.5281/zenodo.6651823

description: Han, Ji-Woong; Lee, Chang-Eon; Kum, Sung-Min; Hwang, Yong-Soo - Energy &Amp; Fuels, 2007, (21), 3202-3207
DOI: 10.5281/zenodo.6651822
title: SciExpeM - 10.5281/zenodo.6651823

description: Han, Ji-Woong; Lee, Chang-Eon; Kum, Sung-Min; Hwang, Yong-Soo - Energy &Amp; Fuels, 2007, (21), 3202-3207
DOI: 10.5281/zenodo.6651825
title: SciExpeM - 10.5281/zenodo.6651825

description: Han, Ji-Woong; Lee, Chang-Eon; Kum, Sung-Min; Hwang, Yong-Soo - Energy &Amp; Fuels, 2007, (21), 3202-3207
DOI: 10.5281/zenodo.6651824
title: SciExpeM - 10.5281/zenodo.6651825

description: Han, Ji-Woong; Lee, Chang-Eon; Kum, Sung-Min; Hwang, Yong-Soo - Energy &Amp; Fuels, 2007, (21), 3202-3207
DOI: 10.5281/zenodo.6651827
title: SciExpeM - 10.5281/zenodo.6651827

description: Han, Ji-Woong; Lee, Chang-Eon; Kum, Sung-Min; Hwang, Yong-Soo - Energy &Amp; Fuels, 2007, (21), 3202-3207
DOI: 10.5281/zenodo.6651826
title: SciExpeM - 10.5281/zenodo.6651827

description: Han, Ji-Woong; Lee, Chang-Eon; Kum, Sung-Min; Hwang, Yong-Soo - Energy &Amp; Fuels, 2007, (21), 3202-3207
DOI: 10.5281/zenodo.6651831
title: SciExpeM - 10.5281/zenodo.6651831

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651830
title: SciExpeM - 10.5281/zenodo.6651831

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651833
title: SciExpeM - 10.5281/zenodo.6651833

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651832
title: SciExpeM - 10.5281/zenodo.6651833

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651835
title: SciExpeM - 10.5281/zenodo.6651835

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651834
title: SciExpeM - 10.5281/zenodo.6651835

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651839
title: SciExpeM - 10.5281/zenodo.6651839

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651838
title: SciExpeM - 10.5281/zenodo.6651839

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651841
title: SciExpeM - 10.5281/zenodo.6651841

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651840
title: SciExpeM - 10.5281/zenodo.6651841

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651843
title: SciExpeM - 10.5281/zenodo.6651843

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651842
title: SciExpeM - 10.5281/zenodo.6651843

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651845
title: SciExpeM - 10.5281/zenodo.6651845

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651844
title: SciExpeM - 10.5281/zenodo.6651845

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651849
title: SciExpeM - 10.5281/zenodo.6651849

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651848
title: SciExpeM - 10.5281/zenodo.6651849

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651853
title: SciExpeM - 10.5281/zenodo.6651853

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651852
title: SciExpeM - 10.5281/zenodo.6651853

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651855
title: SciExpeM - 10.5281/zenodo.6651855

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651854
title: SciExpeM - 10.5281/zenodo.6651855

description: Cai, Xiao; Wang, Jinhua, 0000-0003-1490-9111; Zhang, Weijie; Xie, Yongliang; Zhang, Meng; Huang, Zuohua - Fuel, 2016, (184), 466-473
DOI: 10.5281/zenodo.6651857
title: SciExpeM - 10.5281/zenodo.6651857

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651856
title: SciExpeM - 10.5281/zenodo.6651857

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651859
title: SciExpeM - 10.5281/zenodo.6651859

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651861
title: SciExpeM - 10.5281/zenodo.6651861

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651860
title: SciExpeM - 10.5281/zenodo.6651861

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651858
title: SciExpeM - 10.5281/zenodo.6651859

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651871
title: SciExpeM - 10.5281/zenodo.6651871

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651870
title: SciExpeM - 10.5281/zenodo.6651871

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651873
title: SciExpeM - 10.5281/zenodo.6651873

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651872
title: SciExpeM - 10.5281/zenodo.6651873

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651879
title: SciExpeM - 10.5281/zenodo.6651879

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651878
title: SciExpeM - 10.5281/zenodo.6651879

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651875
title: SciExpeM - 10.5281/zenodo.6651875

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651874
title: SciExpeM - 10.5281/zenodo.6651875

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152
DOI: 10.5281/zenodo.6651886
title: SciExpeM - 10.5281/zenodo.6651886

description: Wang, Shixing; Wang, Zhihua; He, Yong; Han, Xinlu; Sun, Zhiwei, 0000-0001-7899-9676; Zhu, Yanqun; Costa, Mario, 0000-0002-3118-2762 - Fuel, 2020, (259), 116152