cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12629861
title: Thermal comfort indices derived from ERA-Interim reanalysis for Northern Eurasia

description: <b>Authors: </b>Mikhail Varentsov, Pavel Konstantinov, Natalia Shartova <b>Description: </b>The data set provides a historical reconstruction of the set of indices that represent the human thermal comfort or discomfort in outdoor environment, derived from ERA-Interim reanalysis with 0.75 ° spatial resolution on 3-hourly intervals for the period of 1979-2018, for the territory of Northern Eurasia (10°W – 170 °W, 40 °N - 80 °N). It contains five different thermal comfort indices: · Physiologically-Equivalent Temperature (PET), · Universal Thermal Climate Index (UTCI), · Heat Index (UTCI), · Humidex (HUM), · Wind Chill Temperature (WCT) The calculation of PET and UTCI indices was performed in RayMan software. The meteorological variables from the ERA-Interim reanalysis are also included. The data is separated into 40 files, that corresponds to 20x20 °cells. <br>
DOI: 10.6084/m9.figshare.12629861.v1
title: Thermal comfort indices derived from ERA-Interim reanalysis for Northern Eurasia

description: <b>Authors: </b>Mikhail Varentsov, Pavel Konstantinov, Natalia Shartova <b>Description: </b>The data set provides a historical reconstruction of the set of indices that represent the human thermal comfort or discomfort in outdoor environment, derived from ERA-Interim reanalysis with 0.75 ° spatial resolution on 3-hourly intervals for the period of 1979-2018, for the territory of Northern Eurasia (10°W – 170 °W, 40 °N - 80 °N). It contains five different thermal comfort indices: · Physiologically-Equivalent Temperature (PET), · Universal Thermal Climate Index (UTCI), · Heat Index (UTCI), · Humidex (HUM), · Wind Chill Temperature (WCT) The calculation of PET and UTCI indices was performed in RayMan software. The meteorological variables from the ERA-Interim reanalysis are also included. The data is separated into 40 files, that corresponds to 20x20 °cells. <br>
DOI: 10.6084/m9.figshare.21220066
title: Additional file 2 of Facilitators and barriers to COVID-19 vaccination among healthcare workers and the general population in Guinea

description: Additional file 2. Dataset supporting the conclusions of this article.
DOI: 10.6084/m9.figshare.21220066.v1
title: Additional file 2 of Facilitators and barriers to COVID-19 vaccination among healthcare workers and the general population in Guinea

description: Additional file 2. Dataset supporting the conclusions of this article.
DOI: 10.6084/m9.figshare.21220069
title: Additional file 3 of Facilitators and barriers to COVID-19 vaccination among healthcare workers and the general population in Guinea

description: Additional file 3. Dataset supporting the conclusions of this article.
DOI: 10.6084/m9.figshare.21220069.v1
title: Additional file 3 of Facilitators and barriers to COVID-19 vaccination among healthcare workers and the general population in Guinea

description: Additional file 3. Dataset supporting the conclusions of this article.
DOI: 10.6084/m9.figshare.21220072.v1
title: Additional file 4 of Facilitators and barriers to COVID-19 vaccination among healthcare workers and the general population in Guinea

description: Additional file 4. Dataset supporting the conclusions of this article.
DOI: 10.6084/m9.figshare.21220075
title: Additional file 5 of Facilitators and barriers to COVID-19 vaccination among healthcare workers and the general population in Guinea

description: Additional file 5. Dataset supporting the conclusions of this article.
DOI: 10.6084/m9.figshare.21220075.v1
title: Additional file 5 of Facilitators and barriers to COVID-19 vaccination among healthcare workers and the general population in Guinea

description: Additional file 5. Dataset supporting the conclusions of this article.
DOI: 10.6084/m9.figshare.21220072
title: Additional file 4 of Facilitators and barriers to COVID-19 vaccination among healthcare workers and the general population in Guinea

description: Additional file 4. Dataset supporting the conclusions of this article.
DOI: 10.6084/m9.figshare.21220067
title: Additional file 1 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 1: Table S1. Statistics of previous and VGP assemblies.
DOI: 10.6084/m9.figshare.21220067.v1
title: Additional file 1 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 1: Table S1. Statistics of previous and VGP assemblies.
DOI: 10.6084/m9.figshare.21220073
title: Additional file 3 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 3: Table S2. Mis-annotations caused by false duplications in both previous and VGP assemblies.
DOI: 10.6084/m9.figshare.21220073.v1
title: Additional file 3 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 3: Table S2. Mis-annotations caused by false duplications in both previous and VGP assemblies.
DOI: 10.6084/m9.figshare.21220076
title: Additional file 4 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 4: Table S3. False duplication of V1R family genes in the previous platypus assembly.
DOI: 10.6084/m9.figshare.21220076.v1
title: Additional file 4 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 4: Table S3. False duplication of V1R family genes in the previous platypus assembly.
DOI: 10.6084/m9.figshare.21220079
title: Additional file 5 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 5: Table S4. False duplications on transposable elements in previous assemblies.
DOI: 10.6084/m9.figshare.21220079.v1
title: Additional file 5 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 5: Table S4. False duplications on transposable elements in previous assemblies.
DOI: 10.6084/m9.figshare.21220082
title: Additional file 6 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 6: Table S5. Gene ontology enrichment analysis for the false gene gains, false chimeric gains and false exon gains in previous assemblies.
DOI: 10.6084/m9.figshare.21220082.v1
title: Additional file 6 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 6: Table S5. Gene ontology enrichment analysis for the false gene gains, false chimeric gains and false exon gains in previous assemblies.
DOI: 10.6084/m9.figshare.21220085
title: Additional file 7 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 7: Table S6. Reduction of false duplications in the reassembled bTaeGut1.4 zebra finch genome with the VGP v1.7 pipeline.
DOI: 10.6084/m9.figshare.21220085.v1
title: Additional file 7 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 7: Table S6. Reduction of false duplications in the reassembled bTaeGut1.4 zebra finch genome with the VGP v1.7 pipeline.
DOI: 10.6084/m9.figshare.21220088
title: Additional file 8 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 8: Table S7. Proportion of k-mer duplication measured for each assembly strategy.
DOI: 10.6084/m9.figshare.21220088.v1
title: Additional file 8 of Widespread false gene gains caused by duplication errors in genome assemblies

description: Additional File 8: Table S7. Proportion of k-mer duplication measured for each assembly strategy.
DOI: 10.6084/m9.figshare.21220093
title: Additional file 1 of The applicability domain of EPI Suite™ for screening phytotoxins for potential to contaminate source water for drinking

description: Additional file 1. Euclidean distances and classification as in- or out-of-domain of BIOWIN5 for toxins in the TPPT database.
DOI: 10.6084/m9.figshare.21220093.v1
title: Additional file 1 of The applicability domain of EPI Suite™ for screening phytotoxins for potential to contaminate source water for drinking

description: Additional file 1. Euclidean distances and classification as in- or out-of-domain of BIOWIN5 for toxins in the TPPT database.
DOI: 10.6084/m9.figshare.21220096
title: Additional file 2 of The applicability domain of EPI Suite™ for screening phytotoxins for potential to contaminate source water for drinking

description: Additional file 2. Euclidean distances and classification as in- or out-of-domain of KOWWIN for toxins in the TPPT database.
DOI: 10.6084/m9.figshare.21220096.v1
title: Additional file 2 of The applicability domain of EPI Suite™ for screening phytotoxins for potential to contaminate source water for drinking

description: Additional file 2. Euclidean distances and classification as in- or out-of-domain of KOWWIN for toxins in the TPPT database.
DOI: 10.6084/m9.figshare.21220135
title: Additional file 1 of Genomic distribution and context dependent functionality of novel WRKY transcription factor binding sites

description: Additional file 1: Table S1. The number and position of the 16 WT-boxes NGACTTTN in the whole genome determined with the “TAIR10 Whole Genome Sequence Database”.
DOI: 10.6084/m9.figshare.21220135.v1
title: Additional file 1 of Genomic distribution and context dependent functionality of novel WRKY transcription factor binding sites

description: Additional file 1: Table S1. The number and position of the 16 WT-boxes NGACTTTN in the whole genome determined with the “TAIR10 Whole Genome Sequence Database”.
DOI: 10.6084/m9.figshare.21220138
title: Additional file 2 of Genomic distribution and context dependent functionality of novel WRKY transcription factor binding sites

description: Additional file 2: Table S2. The number and position of the 16 WT-boxes NGACTTTN 500 bp upstream of all genes determined with the “Araport11 Loci Upstream Seq – 500 bp (DNA)” sequence database.
DOI: 10.6084/m9.figshare.21220138.v1
title: Additional file 2 of Genomic distribution and context dependent functionality of novel WRKY transcription factor binding sites

description: Additional file 2: Table S2. The number and position of the 16 WT-boxes NGACTTTN 500 bp upstream of all genes determined with the “Araport11 Loci Upstream Seq – 500 bp (DNA)” sequence database.
DOI: 10.6084/m9.figshare.21220141
title: Additional file 3 of Genomic distribution and context dependent functionality of novel WRKY transcription factor binding sites

description: Additional file 3: Table S3. A list of 4553 nuclear genes harbouring a WT-box NGACTTTN but not a W-box TTGACY within their 500 bp upstream region.
DOI: 10.6084/m9.figshare.21220141.v1
title: Additional file 3 of Genomic distribution and context dependent functionality of novel WRKY transcription factor binding sites

description: Additional file 3: Table S3. A list of 4553 nuclear genes harbouring a WT-box NGACTTTN but not a W-box TTGACY within their 500 bp upstream region.
DOI: 10.25440/smu.21218471
title: Data and code for: Topic-aware Heterogeneous Graph Neural Network for Link Prediction

description: This record contains the data and code for CIKM 2021 paper “Topic-aware Heterogeneous Graph Neural Network for Link Prediction”. <br> Heterogeneous graphs (HGs), consisting of multiple types of nodes and links, can characterize a variety of real-world complex systems. Recently, heterogeneous graph neural networks (HGNNs), as a powerful graph embedding method to aggregate heterogeneous structure and attribute information, has earned a lot of attention. Despite the ability of HGNNs in capturing rich semantics which reveal different aspects of nodes, they still stay at a coarse-grained level which simply exploits structural characteristics. In fact, rich unstructured text content of nodes also carries latent but more fine-grained semantics arising from multi-facet topic-aware factors, which fundamentally manifest why nodes of different types would connect and form a specific heterogeneous structure. However, little effort has been devoted to factorizing them.In this paper, we propose a Topic-aware Heterogeneous Graph Neural Network, named THGNN, to hierarchically mine topic-aware semantics for learning multi-facet node representations for link prediction in HGs. Specifically, our model mainly applies an alternating two-step aggregation mechanism including intra-metapath decomposition and inter-metapath mergence, which can distinctively aggregate rich heterogeneous information according to the inferential topic-aware factors and preserve hierarchical semantics. Furthermore, a topic prior guidance module is also designed to keep the quality of multi-facet topic-aware embeddings relying on the global knowledge from unstructured text content in HGs. It helps to simultaneously improve both performance and interpretability. Experimental results on three real-world HGs demonstrate that our proposed model can effectively outperform the state-of-the-art methods in the link prediction task, and show the potential interpretability of learnt multi-facet topic-aware representations.
DOI: 10.25440/smu.21218471.v1
title: Data and code for: Topic-aware Heterogeneous Graph Neural Network for Link Prediction

description: This record contains the data and code for CIKM 2021 paper “Topic-aware Heterogeneous Graph Neural Network for Link Prediction”. <br> Heterogeneous graphs (HGs), consisting of multiple types of nodes and links, can characterize a variety of real-world complex systems. Recently, heterogeneous graph neural networks (HGNNs), as a powerful graph embedding method to aggregate heterogeneous structure and attribute information, has earned a lot of attention. Despite the ability of HGNNs in capturing rich semantics which reveal different aspects of nodes, they still stay at a coarse-grained level which simply exploits structural characteristics. In fact, rich unstructured text content of nodes also carries latent but more fine-grained semantics arising from multi-facet topic-aware factors, which fundamentally manifest why nodes of different types would connect and form a specific heterogeneous structure. However, little effort has been devoted to factorizing them.In this paper, we propose a Topic-aware Heterogeneous Graph Neural Network, named THGNN, to hierarchically mine topic-aware semantics for learning multi-facet node representations for link prediction in HGs. Specifically, our model mainly applies an alternating two-step aggregation mechanism including intra-metapath decomposition and inter-metapath mergence, which can distinctively aggregate rich heterogeneous information according to the inferential topic-aware factors and preserve hierarchical semantics. Furthermore, a topic prior guidance module is also designed to keep the quality of multi-facet topic-aware embeddings relying on the global knowledge from unstructured text content in HGs. It helps to simultaneously improve both performance and interpretability. Experimental results on three real-world HGs demonstrate that our proposed model can effectively outperform the state-of-the-art methods in the link prediction task, and show the potential interpretability of learnt multi-facet topic-aware representations.
DOI: 10.25502/jzhj-m192/d
title: Don’t panic, maize plants can compensate significant damage by the Fall Armyworm and recover under optimum crop production conditions

description: Assessing how maize plants damage at different injury levels can recover and reproduce.
DOI: 10.5281/zenodo.7118299
title: Reversible transformations between the non-porous phases of a flexible coordination network enabled by transient porosity

description: Raw data archive for the manuscript titled "Reversible transformations between the non-porous phases of a flexible coordination network enabled by transient porosity"
DOI: 10.5281/zenodo.7118298
title: Reversible transformations between the non-porous phases of a flexible coordination network enabled by transient porosity

description: Raw data archive for the manuscript titled "Reversible transformations between the non-porous phases of a flexible coordination network enabled by transient porosity"
DOI: 10.5517/ccdc.csd.cc2b7lgn
title: CCDC 2152251: Experimental Crystal Structure Determination

description: Related Article: Vincent Boquet, Ali Nasrallah, Alejandro L. Dana, Erwan Brunard, Pablo H. Di Chenna, Fernando J. Duran, Pascal Retailleau, Benjamin Darses, Marie Sircoglou, Philippe Dauban|2022|J.Am.Chem.Soc.|144|17156|doi:10.1021/jacs.2c07337
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.cc2c09g5
title: CCDC 2175036: Experimental Crystal Structure Determination

description: Related Article: Mingyang Li, Jiawei Lin, Na Wang, Kunjie Liu, Liubing Fan, Zhongnan Guo, Wenxia Yuan, Jing Zhao, Quanlin Liu|2022|Inorg.Chem.|61|15016|doi:10.1021/acs.inorgchem.2c01900
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.cc2c8nlx
title: CCDC 2183069: Experimental Crystal Structure Determination

description: Related Article: Mark Stitch, Rayhaan Z. Boota, Alannah S. Chalkley, Tony D. Keene, Jeremy C. Simpson, Paul A. Scattergood, Paul I. P. Elliott, Susan J Quinn|2022|Inorg.Chem.|61|14947|doi:10.1021/acs.inorgchem.2c01231
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.cc2c9qv8
title: CCDC 2184100: Experimental Crystal Structure Determination

description: Related Article: Collin H. Witt, K. A. Woerpel|2022|Org.Lett.|24|6722|doi:10.1021/acs.orglett.2c02445
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.cc2c9qw9
title: CCDC 2184101: Experimental Crystal Structure Determination

description: Related Article: Collin H. Witt, K. A. Woerpel|2022|Org.Lett.|24|6722|doi:10.1021/acs.orglett.2c02445
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.cc2c9qxb
title: CCDC 2184102: Experimental Crystal Structure Determination

description: Related Article: Collin H. Witt, K. A. Woerpel|2022|Org.Lett.|24|6722|doi:10.1021/acs.orglett.2c02445
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.cc2c09h6
title: CCDC 2175037: Experimental Crystal Structure Determination

description: Related Article: Mingyang Li, Jiawei Lin, Na Wang, Kunjie Liu, Liubing Fan, Zhongnan Guo, Wenxia Yuan, Jing Zhao, Quanlin Liu|2022|Inorg.Chem.|61|15016|doi:10.1021/acs.inorgchem.2c01900
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.21220010
title: Phase coherence values and analysis results for 19 persons

description: The task of matching the tapping to the auditory stimulus rhythm was performed, and the displacement between the auditory stimulus and the tapping onset time was determined. Normalized relative phase was calculated based on the displacement, and phase coherence values were calculated. The phase coherence of the original data and surrogate data for 19 subjects in each condition was calculated. The mean and standard deviation were calculated for each. T-tests and effect sizes were calculated based on those results.
DOI: 10.6084/m9.figshare.21220010.v1
title: Phase coherence values and analysis results for 19 persons

description: The task of matching the tapping to the auditory stimulus rhythm was performed, and the displacement between the auditory stimulus and the tapping onset time was determined. Normalized relative phase was calculated based on the displacement, and phase coherence values were calculated. The phase coherence of the original data and surrogate data for 19 subjects in each condition was calculated. The mean and standard deviation were calculated for each. T-tests and effect sizes were calculated based on those results.
DOI: 10.5281/zenodo.7117518
title: 4-fluoro dataset for 2022 LCLS smSFX workshop

description: This is the dataset for the 2022 LCLS user meeting workshop, "Applications for small-molecule Serial Femtosecond Diffraction in Materials Science and Chemistry" For full details, please refer to the full set of data processing instructions here:<br> https://docs.google.com/document/d/1UNz3zTATIak5UkVGIli0W3HjHZr9orBiWp4nt12AzOQ Reminder: 4-fluoro is an unpublished dataset. For use outside of the workshop, please contact the workshop organizers. The dataset contains these folders: calibration: calibration constants needed for indexing r0215.expt: refined geometry file for the JF4M detector psana_border_hot_shift4.mask: untrusted pixel mask spotfinding: r0165_strong.expt, r0165_strong.refl: spotfinding results for run 165 4fluoro_peaks.txt: a list of 20 peaks harvested from the spotfinder powder pattern. ten_cbfs: 10 cbf files of 4-fluoro, created from LCLS experiment LY65, run 164. indexing: working folder for indexing the 10 cbfs. Contains params_1.phil file, the indexing parameters for cctbx.small_cell_process merging: data folders, one per run, with integrated.expt and integrated.refl files mark1.phil and mark0.phil: phil parameters for merging refinement: 4fluoro_final.res 4fluoro.hkl 4fluoro_solved1.res 4fluoro_solved2.res 4fluoro_start.ins, input and output files for solving and refining the 4fluoro structure
description: {"references": ["Related paper: Schriber EA, Paley DW, et. al. (2022): Chemical crystallography by serial femtosecond X-ray diffraction. Nature 601, 360-365"]}