cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21171562
title: Fossil pollen of Parkia R.Br. (Fabaceae) pollen from the Miocene of western Amazonia

description: Table S1. Raw measurements and morphological traits of fossil Parkiidites (P. marileae Leite et al. 2021 and P. microreticulatus Guinet et Salard-Cheboldaeff 1975) and of extant species of Parkia. <br> Figure S1. Boxplots showing the main morphological characters from extant Parkia species, compared to the same characters from two fossils (Parkiidites marileae Leite et al. 2021; and Parkiidites microreticulatus Guinet et Salard-Cheboldaeff 1975). For detailed data see Table S1. <br> Figure S2. Species distribution modelling for extant species of Parkia assigned as nearest living relative (NLR) of Parkiidites marileae Leite et al. 2021. Color legend is probability of occurrence. Outline of the Amazon biome based on WWF Ecoregions (Olson et al. 2001). <br> Figure S3. Species distribution modelling for extant species of Parkia that do not belong to the NLR (nearest living relative) of Parkiidites marileae Leite et al. 2021. Color legend is probability of occurrence. Outline of the Amazon biome based on WWF Ecoregions (Olson et al. 2001). <br>
DOI: 10.5281/zenodo.4905536
title: Dataset: On the Properties of Next Generation Wireless Backhaul

description: This dataset contains all the data used in the Paper: "On the Properties of Next Generation Wireless Backhaul" sent to Transaction of Network Science and Engineering. It is divided into three main archives: The first archive, called <strong>geodata.zip</strong>, contains the Data Surface Model (DSM) and the topographical maps used to generate the intervisibility graphs. These maps have been aggregated from different sources and they are all released under a CC-BY-SA 4.0 license. It is divided into two folders: 'dsm' and 'topo_maps': 'dsm' contains the Data Surface Model (DSM) of the nine areas used in our research. Each area is saved in a separate .tif file and can be used directly from the tool. The projection system is EPSG:3003. 'topo_mapsì contains the PostGIS dump of the tables containing the topographical maps of the cities. In order to use them, you have to import those in PostGIS and connect our tool to the PostGIS DB.<br> The CTR files (technical region maps) are divided by region, while the openstreetmap file (osm.tar) contains the whole Italian peninsula. All the geographical data are projected in the reference system EPSG:3003. The CTR of Campania is unavailable due to licensing incompatibilities. The second archive, called <strong>visibility_graphs.zip</strong>, contains the visibility graph we have generated using our tool. It is released as a CC-BY-SA 4.0 license. It is divided into folders, one for each area of the analysis. Each folder contains two files: best_p.csv : contains the ids of the nodes associated with their coordinates on a cartesian projection. The projection is EPSG:3003 intervisibility.adj : contains the intervisibility graph represented as an adjacency matrix. It can be read by common libraries such as networkx or igraph. The intervisibility has been calculated 2m above the building. The third folder, called <strong>network_topologies.zip</strong>, contains the network topologies we have computed using a State-of-the-Art algorithm from Polese et Al. The folder is divided into two folders: '30gNB' and '60gNB', corresponding to the density of base stations for square km. It s further divided in 9 folders (one for each area), then divided on the basis of the model we use for the visibility graph. You can find more information about these models in section V of the paper. In this last folder we have two sets of 50 files, which are the intervisibility graph of that set of Base Stations and the topology produced. This dataset has been released under a CC-BY-SA 4.0 license. All the results can be replicated using our code which will be released with an open-source license as soon as the article is published.
DOI: 10.5281/zenodo.4905535
title: Dataset: On the Properties of Next Generation Wireless Backhaul

description: This dataset contains all the data used in the Paper: "On the Properties of Next Generation Wireless Backhaul" sent to Transaction of Network Science and Engineering. It is divided into three main archives: The first archive, called <strong>geodata.zip</strong>, contains the Data Surface Model (DSM) and the topographical maps used to generate the intervisibility graphs. These maps have been aggregated from different sources and they are all released under a CC-BY-SA 4.0 license. It is divided into two folders: 'dsm' and 'topo_maps': 'dsm' contains the Data Surface Model (DSM) of the nine areas used in our research. Each area is saved in a separate .tif file and can be used directly from the tool. The projection system is EPSG:3003. 'topo_mapsì contains the PostGIS dump of the tables containing the topographical maps of the cities. In order to use them, you have to import those in PostGIS and connect our tool to the PostGIS DB.<br> The CTR files (technical region maps) are divided by region, while the openstreetmap file (osm.tar) contains the whole Italian peninsula. All the geographical data are projected in the reference system EPSG:3003. The CTR of Campania is unavailable due to licensing incompatibilities. The second archive, called <strong>visibility_graphs.zip</strong>, contains the visibility graph we have generated using our tool. It is released as a CC-BY-SA 4.0 license. It is divided into folders, one for each area of the analysis. Each folder contains two files: best_p.csv : contains the ids of the nodes associated with their coordinates on a cartesian projection. The projection is EPSG:3003 intervisibility.adj : contains the intervisibility graph represented as an adjacency matrix. It can be read by common libraries such as networkx or igraph. The intervisibility has been calculated 2m above the building. The third folder, called <strong>network_topologies.zip</strong>, contains the network topologies we have computed using a State-of-the-Art algorithm from Polese et Al. The folder is divided into two folders: '30gNB' and '60gNB', corresponding to the density of base stations for square km. It s further divided in 9 folders (one for each area), then divided on the basis of the model we use for the visibility graph. You can find more information about these models in section V of the paper. In this last folder we have two sets of 50 files, which are the intervisibility graph of that set of Base Stations and the topology produced. This dataset has been released under a CC-BY-SA 4.0 license. All the results can be replicated using our code which will be released with an open-source license as soon as the article is published.
DOI: 10.5281/zenodo.7074406
title: TRUSTONOMY - Pilot 2 (Univ. Eiffel) Dataset

description: The following dataset contains information collected during the project TRUSTONOMY: Building Acceptance and Trust in Autonomous Mobility (H2020, MG 3.3 Grant no. 815003, 2018-2022). It presents the results of Pilot 2 conducted by Gustave Eiffel University in Versailles, France. The experiment was conducted on a passenger car simulator (automation level L4) and focuses on the Automated Decision-Support pillar. The dataset contains the results of 51 participants in the experiment, which consisted in a series of 18 runs where the driver needs to take control of the car after an initial section in autonomous mode. Each of the run takes place in one of 6 locations and uses one of 6 scenarios. The trajectory, speed and automation mode data can be found in the 18 csv files. The attached pdf file describes the different scenarios and locations used in the experiment, the labels used in the csv files, as well as some trajectory data for each location.
DOI: 10.5281/zenodo.7074405
title: TRUSTONOMY - Pilot 2 (Univ. Eiffel) Dataset

description: The following dataset contains information collected during the project TRUSTONOMY: Building Acceptance and Trust in Autonomous Mobility (H2020, MG 3.3 Grant no. 815003, 2018-2022). It presents the results of Pilot 2 conducted by Gustave Eiffel University in Versailles, France. The experiment was conducted on a passenger car simulator (automation level L4) and focuses on the Automated Decision-Support pillar. The dataset contains the results of 51 participants in the experiment, which consisted in a series of 18 runs where the driver needs to take control of the car after an initial section in autonomous mode. Each of the run takes place in one of 6 locations and uses one of 6 scenarios. The trajectory, speed and automation mode data can be found in the 18 csv files. The attached pdf file describes the different scenarios and locations used in the experiment, the labels used in the csv files, as well as some trajectory data for each location.
DOI: 10.5281/zenodo.7097148
title: D3 annotation with CSO Classifier

description: The DBLP Discovery Dataset (D3) is a newly created dataset of research papers in the field of Computer Science which can support several tasks like identifying trends in research activity, productivity, focus, bias, accessibility, and impact. This dataset stems from DBLP and integrates additional information from the full-texts. We argue that papers classified with their research topics can improve the identification of research trends. To this end, we used the CSO Classifier to annotate all the papers within D3 and we made such extension available for research purposes. More info: https://www.salatino.org/wp/annotating-d3-dataset-with-the-cso-classifier/ More info pdf: https://www.salatino.org/wp/wp-content/uploads/2022/09/Annotating-D3-dataset-with-the-CSO-Classifier.pdf
description: This version follows the version 2.0 of the D3 Dataset
description: {"references": ["Salatino, Angelo A., Francesco Osborne, Thiviyan Thanapalasingam, and Enrico Motta. \"The CSO classifier: Ontology-driven detection of research topics in scholarly articles.\" In International Conference on Theory and Practice of Digital Libraries, pp. 296-311. Springer, Cham, 2019."]}
DOI: 10.5281/zenodo.7097147
title: D3 annotation with CSO Classifier

description: The DBLP Discovery Dataset (D3) is a newly created dataset of research papers in the field of Computer Science which can support several tasks like identifying trends in research activity, productivity, focus, bias, accessibility, and impact. This dataset stems from DBLP and integrates additional information from the full-texts. We argue that papers classified with their research topics can improve the identification of research trends. To this end, we used the CSO Classifier to annotate all the papers within D3 and we made such extension available for research purposes. More info: https://www.salatino.org/wp/annotating-d3-dataset-with-the-cso-classifier/ More info pdf: https://www.salatino.org/wp/wp-content/uploads/2022/09/Annotating-D3-dataset-with-the-CSO-Classifier.pdf
description: This version follows the version 2.0 of the D3 Dataset
description: {"references": ["Salatino, Angelo A., Francesco Osborne, Thiviyan Thanapalasingam, and Enrico Motta. \"The CSO classifier: Ontology-driven detection of research topics in scholarly articles.\" In International Conference on Theory and Practice of Digital Libraries, pp. 296-311. Springer, Cham, 2019."]}
DOI: 10.34740/kaggle/dsv/4229698
title: Bengali Cyber Bullying Dataset
DOI: 10.7278/s50d-a50h-3te4
title: Aletsch Glacier subglacial water pressure and temperature data
DOI: 10.25416/ntr.21130323.v1
title: Exploring the Open Mic Guidelines & Template.pdf

description: Drawing on reflections and experience of organising and hosting two open mic events and performing at a rnage of open mic events, the guidelines and template in this document are designed to support others to create their own open mic events in their own context successfully.
DOI: 10.15784/601607
title: Physical and geochemical data from shelf sediments near the Antartic Pennisula

description: Shelf sediment samples were collected around the Antarctic Peninsular with the mega corer in 2020 (Nov. to Dec.). The sample locations and water depths are recorded in this dataset. These samples were used to collect data on organic matter (total organic carbon, total nitrogen, delta 13C (organic), delta 15N, and C to N ratios). Nutrient data (nitrate, nitrite, sulfate, and ammonia) and grain size analysis were collected on a subsample set (10).
DOI: 10.15784/601609
title: PIPERS Noble Gases

description: Discrete noble gases were collected by cold-welded copper tubes within the Amundsen and Ross Sea polynyas.
DOI: 10.5281/zenodo.5730466
title: Pre-vaccination and early B cell signatures of the antibody response to SARS-CoV-2 mRNA vaccine

description: The data presented in Code repository for Kardava, L., Rachmaninoff, N., Lau, W. W., Buckner, C. M., Trihemasava, K., Blazkova, J., ... & Moir, S. (2022). Early human B cell signatures of the primary antibody response to mRNA vaccination. Proceedings of the National Academy of Sciences, 119(28), e2204607119.https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119 are made available here. All code to reproduce the figures can be found here: https://github.com/niaid/COVID_Vaccine_Bcells all_subjects_cd19_positive_and_keys.zip contains a CSV file of all CD19+ cells with flowSOM clusters shown. Accompanying files allow for matching of timepoint and subject information. All_subjects_FCS_files_deidentified.zip contains the raw fcs files and is organized by timepoint and subject.
DOI: 10.5281/zenodo.5730465
title: Pre-vaccination and early B cell signatures of the antibody response to SARS-CoV-2 mRNA vaccine

description: The data presented in Code repository for Kardava, L., Rachmaninoff, N., Lau, W. W., Buckner, C. M., Trihemasava, K., Blazkova, J., ... & Moir, S. (2022). Early human B cell signatures of the primary antibody response to mRNA vaccination. Proceedings of the National Academy of Sciences, 119(28), e2204607119.https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119 are made available here. All code to reproduce the figures can be found here: https://github.com/niaid/COVID_Vaccine_Bcells all_subjects_cd19_positive_and_keys.zip contains a CSV file of all CD19+ cells with flowSOM clusters shown. Accompanying files allow for matching of timepoint and subject information. All_subjects_FCS_files_deidentified.zip contains the raw fcs files and is organized by timepoint and subject.
DOI: 10.5517/ccdc.csd.cc28zl0w
title: CCDC 2114758: Experimental Crystal Structure Determination

description: Related Article: Alexey N. Bilyachenko, Evgenii I. Gutsul, Victor N. Khrustalev, Grigorii S. Astakhov, Anna Y. Zueva, Yan V. Zubavichus, Marina V. Kirillova, Lidia S. Shul’pina, Nikolay S. Ikonnikov, Pavel V. Dorovatovskii, Elena S. Shubina, Alexander M. Kirillov, Georgiy B. Shul’pin|2022|Inorg.Chem.|61|14800|doi:10.1021/acs.inorgchem.2c02217
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.cc2bky95
title: CCDC 2162197: Experimental Crystal Structure Determination

description: Related Article: Ya-Fang Ye, Hui-Ying Zhang, Feng Li, Wan-Wan Yang, Ben-Pu Luo, Yan-Bo Wang|2022|J.Org.Chem.|87|12132|doi:10.1021/acs.joc.2c01282
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.cc2bkyb6
title: CCDC 2162198: Experimental Crystal Structure Determination

description: Related Article: Ya-Fang Ye, Hui-Ying Zhang, Feng Li, Wan-Wan Yang, Ben-Pu Luo, Yan-Bo Wang|2022|J.Org.Chem.|87|12132|doi:10.1021/acs.joc.2c01282
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.cc2bkyc7
title: CCDC 2162199: Experimental Crystal Structure Determination

description: Related Article: Ya-Fang Ye, Hui-Ying Zhang, Feng Li, Wan-Wan Yang, Ben-Pu Luo, Yan-Bo Wang|2022|J.Org.Chem.|87|12132|doi:10.1021/acs.joc.2c01282
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.cc2bkyd8
title: CCDC 2162200: Experimental Crystal Structure Determination

description: Related Article: Ya-Fang Ye, Hui-Ying Zhang, Feng Li, Wan-Wan Yang, Ben-Pu Luo, Yan-Bo Wang|2022|J.Org.Chem.|87|12132|doi:10.1021/acs.joc.2c01282
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.cc2bs005
title: CCDC 2168016: Experimental Crystal Structure Determination

description: Related Article: Sambasivarao Kotha, Naveen Kumar Gupta, Saima Ansari|2022|RSC Advances|12|25154|doi:10.1039/D2RA04872H
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.cc2bs016
title: CCDC 2168017: Experimental Crystal Structure Determination

description: Related Article: Sambasivarao Kotha, Naveen Kumar Gupta, Saima Ansari|2022|RSC Advances|12|25154|doi:10.1039/D2RA04872H
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.cc2bs58l
title: CCDC 2168179: Experimental Crystal Structure Determination

description: Related Article: Yongshen Zheng, Xiao Han, Puxin Cheng, Xiaodi Jia, Jialiang Xu, Xian-He Bu|2022|J.Am.Chem.Soc.|144|16471|doi:10.1021/jacs.2c05063
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.cc2bs59m
title: CCDC 2168180: Experimental Crystal Structure Determination

description: Related Article: Yongshen Zheng, Xiao Han, Puxin Cheng, Xiaodi Jia, Jialiang Xu, Xian-He Bu|2022|J.Am.Chem.Soc.|144|16471|doi:10.1021/jacs.2c05063
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.cc2bs5bn
title: CCDC 2168181: Experimental Crystal Structure Determination

description: Related Article: Yongshen Zheng, Xiao Han, Puxin Cheng, Xiaodi Jia, Jialiang Xu, Xian-He Bu|2022|J.Am.Chem.Soc.|144|16471|doi:10.1021/jacs.2c05063
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.cc2bs5cp
title: CCDC 2168182: Experimental Crystal Structure Determination

description: Related Article: Yongshen Zheng, Xiao Han, Puxin Cheng, Xiaodi Jia, Jialiang Xu, Xian-He Bu|2022|J.Am.Chem.Soc.|144|16471|doi:10.1021/jacs.2c05063
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: 10.5281/zenodo.7036103
title: Deducing Subnanometer Cluster Size and Shape Distributions of Heterogeneous Supported Catalysts

description: Dataset for "Deducing Subnanometer Cluster Size and Shape Distributions of Heterogeneous Supported Catalysts".
DOI: 10.5281/zenodo.7036102
title: Deducing Subnanometer Cluster Size and Shape Distributions of Heterogeneous Supported Catalysts

description: Dataset for "Deducing Subnanometer Cluster Size and Shape Distributions of Heterogeneous Supported Catalysts".
DOI: 10.6084/m9.figshare.21171781
title: Protein Alignments of Rbp and Chaperones for Interspecies Killing Manuscript

description: Protein alignments for receptor binding proteins and tailocins across four Pseudomonas syringae strains. Alignments generated by Geneious.
DOI: 10.6084/m9.figshare.21171781.v1
title: Protein Alignments of Rbp and Chaperones for Interspecies Killing Manuscript

description: Protein alignments for receptor binding proteins and tailocins across four Pseudomonas syringae strains. Alignments generated by Geneious.
DOI: 10.5281/zenodo.7079268
title: Supplementary data for article "Reduced B-cell antigenicity of Omicron lowers host serologic response"

description: This repository contains five supplementary data files for the research article "<strong>Reduced B-cell antigenicity of Omicron lowers host serologic response</strong>". For more information, please refer to the article preprint https://doi.org/10.1101/2022.02.15.480546 and upcoming article at Cell Reports. <strong>Table_hCoV229E.xlsx:</strong> list of hCoV229E RBD sequences, with associated isolate and collection date identifiers, and ScanNet antigenicity score for reproducing <strong>Figure 3A</strong>. Aligned sequences and templates are also provided. <strong>table_artificial_variants.csv</strong>: List of artificial RBD sequences generated by an evolutionary-based sequence generative model for reproducing <strong>Figure 3B</strong>, <strong>Supplementary Figure S6H</strong>. <strong>MSA_RBD.fasta:</strong> Multiple Sequence Alignment of RBD sequences and sample weights used for training the sequence generative model used in <strong>Figure 3B</strong>, <strong>Supplementary Figures S2C, S6</strong>. <strong>table_antibody_hit_rate_RBD.csv</strong>: Empirical epitope distribution for the RBD, as determined from the Protein Data Bank and raw data for <strong>Supplementary Figure S1</strong>). <strong>RBD_virtual_DMS.xlsx</strong>: virtual Deep Mutational Scan performed with ScanNet and the sequence generative model shown in <strong>Supplementary Figure S2</strong>.