cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14252176
title: Additional file 2 of Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability

description: Additional file 2: Supplementary Table S1. Information on genomic features and markers. Supplementary Table S2. Properties of genetic-variant hotspots and hotspot clusters. Supplementary Table S3. Properties of selection hotspots. Supplementary Table S4. List of cluster-containing genes. Supplementary Table S5. Estimated ages of retrotransposons. Supplementary Table S6. Disease-related variants in GV hotspots and clusters. Supplementary Table S7. Overlap of genetic-variant hotspots or clusters with recombination hotspots. Supplementary Table S8. Autosomal sequences (in base pairs) in different genomic zones and replication phases that overlap with hotspots or clusters. Supplementary Table S9. Enrichment and selection status of hotspots in seven immune system gene loci. Supplementary Table S10. Genomic-feature distributions in genomic regions belonging to different replication-phase segments among 500-kb Genic, Proximal and Distal zones. Supplementary Table S11. Functional annotation of Distal-zone genes.
DOI: 10.6084/m9.figshare.14252176.v1
title: Additional file 2 of Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability

description: Additional file 2: Supplementary Table S1. Information on genomic features and markers. Supplementary Table S2. Properties of genetic-variant hotspots and hotspot clusters. Supplementary Table S3. Properties of selection hotspots. Supplementary Table S4. List of cluster-containing genes. Supplementary Table S5. Estimated ages of retrotransposons. Supplementary Table S6. Disease-related variants in GV hotspots and clusters. Supplementary Table S7. Overlap of genetic-variant hotspots or clusters with recombination hotspots. Supplementary Table S8. Autosomal sequences (in base pairs) in different genomic zones and replication phases that overlap with hotspots or clusters. Supplementary Table S9. Enrichment and selection status of hotspots in seven immune system gene loci. Supplementary Table S10. Genomic-feature distributions in genomic regions belonging to different replication-phase segments among 500-kb Genic, Proximal and Distal zones. Supplementary Table S11. Functional annotation of Distal-zone genes.
DOI: 10.6084/m9.figshare.14252179
title: Additional file 3 of Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability

description: Additional file 3: Supplementary Dataset S1. Information of genetic-variant hotspots, hotspot clusters, selection hotspots and replication-phase segments. Genomic coordinates are given in one-based format in human genome assembly hg19.
DOI: 10.6084/m9.figshare.14252179.v1
title: Additional file 3 of Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability

description: Additional file 3: Supplementary Dataset S1. Information of genetic-variant hotspots, hotspot clusters, selection hotspots and replication-phase segments. Genomic coordinates are given in one-based format in human genome assembly hg19.
DOI: 10.6084/m9.figshare.14252182
title: Additional file 1 of EQ-5D self-reported health in Barbados and Jamaica with EQ-5D-5L population norms for the English-speaking Caribbean

description: Additional file 1: Microsoft Excel .xlsx file showing the mean EQ VAS and index values by age group and gender for each country and for the Caribbean combination.
DOI: 10.6084/m9.figshare.14252185
title: Additional file 2 of EQ-5D self-reported health in Barbados and Jamaica with EQ-5D-5L population norms for the English-speaking Caribbean

description: Additional file 2: Microsoft Excel .xlsx file showing the mean EQ VAS values, EQ-5D-5L index values and ceiling effects by demographic group for each country and the Caribbean combination.
DOI: 10.6084/m9.figshare.14252185.v1
title: Additional file 2 of EQ-5D self-reported health in Barbados and Jamaica with EQ-5D-5L population norms for the English-speaking Caribbean

description: Additional file 2: Microsoft Excel .xlsx file showing the mean EQ VAS values, EQ-5D-5L index values and ceiling effects by demographic group for each country and the Caribbean combination.
DOI: 10.6084/m9.figshare.14252188
title: Additional file 3 of EQ-5D self-reported health in Barbados and Jamaica with EQ-5D-5L population norms for the English-speaking Caribbean

description: Additional file 3: Microsoft Excel .xlsx file showing the frequencies of reporting problems at each level on each dimension for each country and for the Caribbean combination.
DOI: 10.6084/m9.figshare.14252182.v1
title: Additional file 1 of EQ-5D self-reported health in Barbados and Jamaica with EQ-5D-5L population norms for the English-speaking Caribbean

description: Additional file 1: Microsoft Excel .xlsx file showing the mean EQ VAS and index values by age group and gender for each country and for the Caribbean combination.
DOI: 10.6084/m9.figshare.14252191
title: Additional file 4 of EQ-5D self-reported health in Barbados and Jamaica with EQ-5D-5L population norms for the English-speaking Caribbean

description: Additional file 4: Microsoft Excel .xlsx file showing generalized linear model of the effects of demographic variables on EQ VAS values with controls for age and gender.
DOI: 10.6084/m9.figshare.14252188.v1
title: Additional file 3 of EQ-5D self-reported health in Barbados and Jamaica with EQ-5D-5L population norms for the English-speaking Caribbean

description: Additional file 3: Microsoft Excel .xlsx file showing the frequencies of reporting problems at each level on each dimension for each country and for the Caribbean combination.
DOI: 10.6084/m9.figshare.14252191.v1
title: Additional file 4 of EQ-5D self-reported health in Barbados and Jamaica with EQ-5D-5L population norms for the English-speaking Caribbean

description: Additional file 4: Microsoft Excel .xlsx file showing generalized linear model of the effects of demographic variables on EQ VAS values with controls for age and gender.
DOI: 10.6084/m9.figshare.14252230
title: Additional file 1 of The iron-sulfur scaffold protein HCF101 unveils the complexity of organellar evolution in SAR, Haptista and Cryptista

description: Additional file 1: Table S1. List of retrieved Nbp35-like protein sequences that were used for cellular localization predictions.
DOI: 10.6084/m9.figshare.14252230.v1
title: Additional file 1 of The iron-sulfur scaffold protein HCF101 unveils the complexity of organellar evolution in SAR, Haptista and Cryptista

description: Additional file 1: Table S1. List of retrieved Nbp35-like protein sequences that were used for cellular localization predictions.
DOI: 10.6084/m9.figshare.14252233
title: Additional file 2 of The iron-sulfur scaffold protein HCF101 unveils the complexity of organellar evolution in SAR, Haptista and Cryptista

description: Additional file 2: Table S2. Predictions of domain structure in Nbp35 homologs included in phylogenetic analysis (Fig. 4).
DOI: 10.6084/m9.figshare.14252233.v1
title: Additional file 2 of The iron-sulfur scaffold protein HCF101 unveils the complexity of organellar evolution in SAR, Haptista and Cryptista

description: Additional file 2: Table S2. Predictions of domain structure in Nbp35 homologs included in phylogenetic analysis (Fig. 4).
DOI: 10.5281/zenodo.4624589
title: Appendix 2 Raw data for CD013156

description: Raw data for the Cochrane review "Lifestyle modifications for non-alcohol related fatty liver disease: a network meta-analysis" (CD013156)
DOI: 10.21227/d9wa-0294
title: EEG PAIN

description: The EEG pain dataset was collected from 15 subjects using Cold pressor test (CPT), and EEG signals were recorded using the Emotiv EPOC Flex Cap (32 channels) with 32 electrodes placed over the scalp at predetermined locations based on the International Electroencephalographic Society (10-10), with a frequency of 128 Hz.
DOI: 10.6084/m9.figshare.14251679
title: Determine urban building types based on deep-learning classification of points of interest

description: data and coding
DOI: 10.6084/m9.figshare.14251679.v1
title: Determine urban building types based on deep-learning classification of points of interest

description: data and coding
DOI: 10.6084/m9.figshare.14252609
title: simulation_10%_R1

description: simulation_10%_R1.fastq
DOI: 10.6084/m9.figshare.14252609.v1
title: simulation_10%_R1

description: simulation_10%_R1.fastq
DOI: 10.25919/12v3-2d33
title: Parkes observations for project PX500 semester 2020OCTS_13

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.11577/1771456
title: Facet-dependent strain determination in electrochemically synthetized platinum model catalytic nanoparticles

description: Please check the README file for more information about the dataset.
DOI: 10.5281/zenodo.4624742
title: Supplementary code and data of the paper Evaluating network embedding techniques' performances in software bug prediction.

description: Supplementary code and data of the paper <strong><em>Evaluating network embedding techniques' performances in software bug prediction</em>.</strong> This repo can also be used as a complementary material for our previous paper: Qu, Yu, Ting Liu, Jianlei Chi, Yangxu Jin, Di Cui, Ancheng He, and Qinghua Zheng. "node2defect: using network embedding to improve software defect prediction." In 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 844-849. IEEE, 2018. https://dl.acm.org/doi/abs/10.1145/3238147.3240469 <strong>Steps to Run this dataset:</strong> <strong>First: Generating Class Dependency Network</strong> In each subdirectory, we have already included the corresponding Class Dependency Network (CDN) (classgraph.dot). If you want to generate your own CDN, you can use the Understand Perl Script file -- Class-Graph.pl, after installing the Understand tool, by using the commend: <pre><code class="language-bash">uperl Class-Graph.pl %YourOwnProjectDirectory%</code></pre> <strong>Second: Generating the input file for network embedding algorithms</strong> After generating the CDN, we can use the Driver.py to generate the input file for network embedding algorithms. <pre><code class="language-bash">python Driver.py</code></pre> After executing Driver.py, we can get the "edgelist" file in each directory. Then, for instance, we can use the ProNE implementation to generate the embedding file (classgraph.emd): <pre><code class="language-bash">python proNE.py -graph edgelist -emb1 classgraph.emd -emb2 classgraph-2.emd -dimension 32 -step 10 -theta 0.5 -mu 0.2</code></pre> <strong>Third: Run the experiment</strong> After generating the classgraph.emd file, we can run the experiment by executing Node2Defect-Final-CrossValidation.py: <pre><code class="language-bash">python Node2Defect-Final-CrossValidation.py</code></pre> The overall results are listed in the files like "All-Popt-%s-%s-%s.csv" <strong>Requirements:</strong> python==3.7<br> scipy==1.5.2<br> networkx==2.5<br> scikit-learn==0.23.2<br> numpy==1.19.1<br> pandas==1.1.2 We have used the following implementations of different network embedding algorithms, please refer to their repos for their dependencies: OpenNE<br> ProNE<br> Walklets
DOI: 10.5281/zenodo.4624748
title: Statistical analysis of hal-03170325

description: This is the data set provided by Sovacool et al. on their recent paper "Differences in carbon emissions reduction between countries pursuing renewable electricity versus nuclear power". We have replied to this paper here, and our statistical analysis can be found herein.
DOI: 10.5281/zenodo.4624747
title: Statistical analysis of hal-03170325

description: This is the data set provided by Sovacool et al. on their recent paper "Differences in carbon emissions reduction between countries pursuing renewable electricity versus nuclear power". We have replied to this paper here, and our statistical analysis can be found herein.
DOI: 10.5281/zenodo.4624751
title: Supplementary code and data of the paper Evaluating network embedding techniques' performances in software bug prediction.

description: Supplementary code and data of the paper <strong><em>Evaluating network embedding techniques' performances in software bug prediction</em>.</strong> This repo can also be used as a complementary material for our previous paper: Qu, Yu, Ting Liu, Jianlei Chi, Yangxu Jin, Di Cui, Ancheng He, and Qinghua Zheng. "node2defect: using network embedding to improve software defect prediction." In 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 844-849. IEEE, 2018. https://dl.acm.org/doi/abs/10.1145/3238147.3240469 <strong>Steps to Run this dataset:</strong> <strong>First: Generating Class Dependency Network</strong> In each subdirectory, we have already included the corresponding Class Dependency Network (CDN) (classgraph.dot). If you want to generate your own CDN, you can use the Understand Perl Script file -- Class-Graph.pl, after installing the Understand tool, by using the commend: <pre><code class="language-bash">uperl Class-Graph.pl %YourOwnProjectDirectory%</code></pre> <strong>Second: Generating the input file for network embedding algorithms</strong> After generating the CDN, we can use the Driver.py to generate the input file for network embedding algorithms. <pre><code class="language-bash">python Driver.py</code></pre> After executing Driver.py, we can get the "edgelist" file in each directory. Then, for instance, we can use the ProNE implementation to generate the embedding file (classgraph.emd): <pre><code class="language-bash">python proNE.py -graph edgelist -emb1 classgraph.emd -emb2 classgraph-2.emd -dimension 32 -step 10 -theta 0.5 -mu 0.2</code></pre> <strong>Third: Run the experiment</strong> After generating the classgraph.emd file, we can run the experiment by executing Node2Defect-Final-CrossValidation.py: <pre><code class="language-bash">python Node2Defect-Final-CrossValidation.py</code></pre> The overall results are listed in the files like "All-Popt-%s-%s-%s.csv" <strong>Requirements:</strong> python==3.7<br> scipy==1.5.2<br> networkx==2.5<br> scikit-learn==0.23.2<br> numpy==1.19.1<br> pandas==1.1.2 We have used the following implementations of different network embedding algorithms, please refer to their repos for their dependencies: OpenNE<br> ProNE<br> Walklets
DOI: 10.5281/zenodo.4624741
title: Supplementary code and data of the paper Evaluating network embedding techniques' performances in software bug prediction.

description: Supplementary code and data of the paper <strong><em>Evaluating network embedding techniques' performances in software bug prediction</em>.</strong> This repo can also be used as a complementary material for our previous paper: Qu, Yu, Ting Liu, Jianlei Chi, Yangxu Jin, Di Cui, Ancheng He, and Qinghua Zheng. "node2defect: using network embedding to improve software defect prediction." In 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 844-849. IEEE, 2018. https://dl.acm.org/doi/abs/10.1145/3238147.3240469 <strong>Steps to Run this dataset:</strong> <strong>First: Generating Class Dependency Network</strong> In each subdirectory, we have already included the corresponding Class Dependency Network (CDN) (classgraph.dot). If you want to generate your own CDN, you can use the Understand Perl Script file -- Class-Graph.pl, after installing the Understand tool, by using the commend: <pre><code class="language-bash">uperl Class-Graph.pl %YourOwnProjectDirectory%</code></pre> <strong>Second: Generating the input file for network embedding algorithms</strong> After generating the CDN, we can use the Driver.py to generate the input file for network embedding algorithms. <pre><code class="language-bash">python Driver.py</code></pre> After executing Driver.py, we can get the "edgelist" file in each directory. Then, for instance, we can use the ProNE implementation to generate the embedding file (classgraph.emd): <pre><code class="language-bash">python proNE.py -graph edgelist -emb1 classgraph.emd -emb2 classgraph-2.emd -dimension 32 -step 10 -theta 0.5 -mu 0.2</code></pre> <strong>Third: Run the experiment</strong> After generating the classgraph.emd file, we can run the experiment by executing Node2Defect-Final-CrossValidation.py: <pre><code class="language-bash">python Node2Defect-Final-CrossValidation.py</code></pre> The overall results are listed in the files like "All-Popt-%s-%s-%s.csv" <strong>Requirements:</strong> python==3.7<br> scipy==1.5.2<br> networkx==2.5<br> scikit-learn==0.23.2<br> numpy==1.19.1<br> pandas==1.1.2 We have used the following implementations of different network embedding algorithms, please refer to their repos for their dependencies: OpenNE<br> ProNE<br> Walklets
DOI: 10.5281/zenodo.4621868
title: K.388 Hĭn Kˀôn Pillar : Transcription, translation and analysis

description: K.388 Hĭn Kˀôn Pillar : : Transcription, translation and analysis. <br> Inscriptions -- Thailand.<br> Inscriptions, Thai -- Thailand.<br> Inscriptions.
DOI: 10.5281/zenodo.4621867
title: K.388 Hĭn Kˀôn Pillar : Transcription, translation and analysis

description: K.388 Hĭn Kˀôn Pillar : : Transcription, translation and analysis. <br> Inscriptions -- Thailand.<br> Inscriptions, Thai -- Thailand.<br> Inscriptions.
DOI: 10.21227/p0t9-qb69
title: January-22-Tang-Wenyuan.csv

description: January-22-Tang-Wenyuan.csv
DOI: 10.6084/m9.figshare.14252639
title: Raw Data - Acute effects of cigarette smoke

description: Increasing evidence suggests that cigarette smoke exposure could induce VCAM-1 (enhance pro-atherogenicproperty), and a decrease of e-NOS level (anti-atherogenic depletioon). Thus, cigarette smoke may represent a significant risk factor for atherosclerosis by increasing aortic IMT. This evidence is discussed herein.
DOI: 10.34740/kaggle/dsv/2001911
title: evaluating-participant-responses-toVR-using-RL
DOI: 10.34740/kaggle/dsv/2041005
title: evaluating-participant-responses-toVR-using-RL
DOI: 10.34740/kaggle/dsv/2041053
title: LUFlow Network Intrusion Detection Data Set
DOI: 10.6084/m9.figshare.14252663
title: CMSO_CFAR_NN.zip

description: Constant False Alarm Rate (CFAR) via NN<br>
DOI: 10.5281/zenodo.4556803
title: QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species Brassica oleracea var. villosa [Main code]

description: This is the code supplement for my computational analysis for the manuscript: "QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species <em>Brassica oleracea </em>var<em>. villosa".</em> The main code is availabe in separate html-files. For more information see the preprint [10.21203/rs.3.rs-309600/v1]. <strong>Work in progress!</strong>
DOI: 10.5517/ccdc.csd.cc2694d2
title: CCDC 2034573: Experimental Crystal Structure Determination

description: Related Article: Jingwen Liu, Xue Han, Yantong Lu, Shuo Wang, Dian Zhao, Chunxia Li|2021|Inorg.Chem.|60|4133|doi:10.1021/acs.inorgchem.1c00310
DOI: 10.5517/ccdc.csd.cc2694rf
title: CCDC 2034584: Experimental Crystal Structure Determination

description: Related Article: Jingwen Liu, Xue Han, Yantong Lu, Shuo Wang, Dian Zhao, Chunxia Li|2021|Inorg.Chem.|60|4133|doi:10.1021/acs.inorgchem.1c00310
DOI: 10.5517/ccdc.csd.cc26jg5d
title: CCDC 2041603: Experimental Crystal Structure Determination

description: Related Article: Kehui Han, Yanyan Wang, Pei Zhao, Xiaohui You, Jun Wang, Yanchun Guo, Yufen Zhao, Shuxia Cao|2021|J.Org.Chem.|86|4512|doi:10.1021/acs.joc.0c02953
DOI: 10.5517/ccdc.csd.cc26jfsz
title: CCDC 2041591: Experimental Crystal Structure Determination

description: Related Article: Kehui Han, Yanyan Wang, Pei Zhao, Xiaohui You, Jun Wang, Yanchun Guo, Yufen Zhao, Shuxia Cao|2021|J.Org.Chem.|86|4512|doi:10.1021/acs.joc.0c02953
DOI: 10.5517/ccdc.csd.cc26jg7g
title: CCDC 2041605: Experimental Crystal Structure Determination

description: Related Article: Kehui Han, Yanyan Wang, Pei Zhao, Xiaohui You, Jun Wang, Yanchun Guo, Yufen Zhao, Shuxia Cao|2021|J.Org.Chem.|86|4512|doi:10.1021/acs.joc.0c02953
DOI: 10.5517/ccdc.csd.cc26knm2
title: CCDC 2042764: Experimental Crystal Structure Determination

description: Related Article: Polly L. Arnold, Connor J. V. Halliday, Laura Puig-Urrea, Gary S. Nichol|2021|Inorg.Chem.|60|4162|doi:10.1021/acs.inorgchem.1c00327
DOI: 10.5517/ccdc.csd.cc26knn3
title: CCDC 2042765: Experimental Crystal Structure Determination

description: Related Article: Polly L. Arnold, Connor J. V. Halliday, Laura Puig-Urrea, Gary S. Nichol|2021|Inorg.Chem.|60|4162|doi:10.1021/acs.inorgchem.1c00327
DOI: 10.5517/ccdc.csd.cc26knp4
title: CCDC 2042766: Experimental Crystal Structure Determination

description: Related Article: Polly L. Arnold, Connor J. V. Halliday, Laura Puig-Urrea, Gary S. Nichol|2021|Inorg.Chem.|60|4162|doi:10.1021/acs.inorgchem.1c00327
DOI: 10.5517/ccdc.csd.cc26knq5
title: CCDC 2042767: Experimental Crystal Structure Determination

description: Related Article: Polly L. Arnold, Connor J. V. Halliday, Laura Puig-Urrea, Gary S. Nichol|2021|Inorg.Chem.|60|4162|doi:10.1021/acs.inorgchem.1c00327
DOI: 10.5517/ccdc.csd.cc26knr6
title: CCDC 2042768: Experimental Crystal Structure Determination

description: Related Article: Polly L. Arnold, Connor J. V. Halliday, Laura Puig-Urrea, Gary S. Nichol|2021|Inorg.Chem.|60|4162|doi:10.1021/acs.inorgchem.1c00327
DOI: 10.5517/ccdc.csd.cc26kns7
title: CCDC 2042769: Experimental Crystal Structure Determination

description: Related Article: Polly L. Arnold, Connor J. V. Halliday, Laura Puig-Urrea, Gary S. Nichol|2021|Inorg.Chem.|60|4162|doi:10.1021/acs.inorgchem.1c00327
DOI: 10.5517/ccdc.csd.cc26knt8
title: CCDC 2042770: Experimental Crystal Structure Determination

description: Related Article: Polly L. Arnold, Connor J. V. Halliday, Laura Puig-Urrea, Gary S. Nichol|2021|Inorg.Chem.|60|4162|doi:10.1021/acs.inorgchem.1c00327
DOI: 10.5517/ccdc.csd.cc26knv9
title: CCDC 2042771: Experimental Crystal Structure Determination

description: Related Article: Polly L. Arnold, Connor J. V. Halliday, Laura Puig-Urrea, Gary S. Nichol|2021|Inorg.Chem.|60|4162|doi:10.1021/acs.inorgchem.1c00327
DOI: 10.5517/ccdc.csd.cc26knwb
title: CCDC 2042772: Experimental Crystal Structure Determination

description: Related Article: Polly L. Arnold, Connor J. V. Halliday, Laura Puig-Urrea, Gary S. Nichol|2021|Inorg.Chem.|60|4162|doi:10.1021/acs.inorgchem.1c00327
DOI: 10.5517/ccdc.csd.cc26mfq0
title: CCDC 2044472: Experimental Crystal Structure Determination

description: Related Article: Teng Yuan|2020|CSD Communication|||
description: Related Article: Teng Yuan, Qi Tang, Chuan Shan, Xiaohan Ye, Jin Wang, Pengyi Zhao, Lukasz Wojtas, Nicholas Hadler, Hao Chen, Xiaodong Shi|2021|J.Am.Chem.Soc.|143|4074|doi:10.1021/jacs.1c01811
DOI: 10.5517/ccdc.csd.cc26mfs2
title: CCDC 2044474: Experimental Crystal Structure Determination

description: Related Article: Teng Yuan|2020|CSD Communication|||
description: Related Article: Teng Yuan, Qi Tang, Chuan Shan, Xiaohan Ye, Jin Wang, Pengyi Zhao, Lukasz Wojtas, Nicholas Hadler, Hao Chen, Xiaodong Shi|2021|J.Am.Chem.Soc.|143|4074|doi:10.1021/jacs.1c01811