cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2b5bjd
title: CCDC 2150083: Experimental Crystal Structure Determination

description: Related Article: Frederick P. Malan|2022|Chem. Sel.|7|e202201432|doi:10.1002/slct.202201432
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.cc2b5bkf
title: CCDC 2150084: Experimental Crystal Structure Determination

description: Related Article: Frederick P. Malan|2022|Chem. Sel.|7|e202201432|doi:10.1002/slct.202201432
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.cc2b5blg
title: CCDC 2150085: Experimental Crystal Structure Determination

description: Related Article: Frederick P. Malan|2022|Chem. Sel.|7|e202201432|doi:10.1002/slct.202201432
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.cc2b5bmh
title: CCDC 2150086: Experimental Crystal Structure Determination

description: Related Article: Frederick P. Malan|2022|Chem. Sel.|7|e202201432|doi:10.1002/slct.202201432
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.cc2b5bnj
title: CCDC 2150087: Experimental Crystal Structure Determination

description: Related Article: Frederick P. Malan|2022|Chem. Sel.|7|e202201432|doi:10.1002/slct.202201432
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.cc2b5bpk
title: CCDC 2150088: Experimental Crystal Structure Determination

description: Related Article: Frederick P. Malan|2022|Chem. Sel.|7|e202201432|doi:10.1002/slct.202201432
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.cc2b5bql
title: CCDC 2150089: Experimental Crystal Structure Determination

description: Related Article: Frederick P. Malan|2022|Chem. Sel.|7|e202201432|doi:10.1002/slct.202201432
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.cc2bql4t
title: CCDC 2166656: Experimental Crystal Structure Determination

description: Related Article: Siyu Liu, Jiayu Liu, Haotian Guo, Hanhan Chen, Lin Sun, Pengtao Ma, Jingping Wang, Jingyang Niu|2022|Inorg.Chem.|61|12678|doi:10.1021/acs.inorgchem.2c01618
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.cc2bql5v
title: CCDC 2166657: Experimental Crystal Structure Determination

description: Related Article: Siyu Liu, Jiayu Liu, Haotian Guo, Hanhan Chen, Lin Sun, Pengtao Ma, Jingping Wang, Jingyang Niu|2022|Inorg.Chem.|61|12678|doi:10.1021/acs.inorgchem.2c01618
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.cc2bql6w
title: CCDC 2166658: Experimental Crystal Structure Determination

description: Related Article: Siyu Liu, Jiayu Liu, Haotian Guo, Hanhan Chen, Lin Sun, Pengtao Ma, Jingping Wang, Jingyang Niu|2022|Inorg.Chem.|61|12678|doi:10.1021/acs.inorgchem.2c01618
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: 10.34740/kaggle/dsv/4097681
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.20521944
title: FPGA-based closed-loop data acquisition system demonstration data

description: Data that demonstrates the FPGA-based data acquisition system described in the manuscript: "FPGA-Based Data Acquisition System for Closed-Loop Experiments"
DOI: 10.5281/zenodo.7012793
title: The Dataset of Quantifying Alignment Deviations for Uniaxial Material Mechanical Testing via Automated Machine Learning

description: The dataset consists of 4 alignment deviations of the uniaxial testing machine as well as 12 strain measurement points on cruciform specimens. A deep learning model is trained on the dataset to quantify 4 alignment deviations using 12 strain values on a thin plate specimen. The design of experiments includes Optimal Latin Hypercube, numerical modelling of Finite Element Methods. Using the Optimal Latin Hypercube, 12496 distinct groups of DOE simulation tests are constructed. Under the boundary conditions of 4 distinct deviations, 12 strain values at the required location on the cruciform specimen are obtained using Python scripts. The nine CSV files correspond to the nine analysis steps. The only difference among the nine analysis steps is the pretension force acting on RP1. Each CSV file contains 24 columns of data, and the corresponding contents of each column of data are as follows: Columns 1-6 are the freedoms of RP1 reference point, which are U1, U2, U3, ur1, UR2 and UR3 respectively; Columns 7-12 are the freedoms of RP2 reference points, which are U1, U2, U3, ur1, UR2 and UR3 respectively; Columns 13-24 are the strain values of the last 12 strain measurements of the thin plate rectangular specimen。
DOI: 10.5281/zenodo.7012792
title: The Dataset of Quantifying Alignment Deviations for Uniaxial Material Mechanical Testing via Automated Machine Learning

description: The dataset consists of 4 alignment deviations of the uniaxial testing machine as well as 12 strain measurement points on cruciform specimens. A deep learning model is trained on the dataset to quantify 4 alignment deviations using 12 strain values on a thin plate specimen. The design of experiments includes Optimal Latin Hypercube, numerical modelling of Finite Element Methods. Using the Optimal Latin Hypercube, 12496 distinct groups of DOE simulation tests are constructed. Under the boundary conditions of 4 distinct deviations, 12 strain values at the required location on the cruciform specimen are obtained using Python scripts. The nine CSV files correspond to the nine analysis steps. The only difference among the nine analysis steps is the pretension force acting on RP1. Each CSV file contains 24 columns of data, and the corresponding contents of each column of data are as follows: Columns 1-6 are the freedoms of RP1 reference point, which are U1, U2, U3, ur1, UR2 and UR3 respectively; Columns 7-12 are the freedoms of RP2 reference points, which are U1, U2, U3, ur1, UR2 and UR3 respectively; Columns 13-24 are the strain values of the last 12 strain measurements of the thin plate rectangular specimen。
DOI: 10.5281/zenodo.7012638
title: Merging triaxial halos (simulation_icsGalacticus_massRatio0.100_orbitAngle0.000_cP09.0_cS12.0_pP1.0_qP1.0_pS1.0_qS1.0)

description: Gadget-format simulation data for a simulation of a merging pair of halos from the paper by Menker & Benson (2022; https://arxiv.org/abs/2205.02955), for model simulation_icsGalacticus_massRatio0.100_orbitAngle0.000_cP09.0_cS12.0_pP1.0_qP1.0_pS1.0_qS1.0
DOI: 10.5281/zenodo.7012637
title: Merging triaxial halos (simulation_icsGalacticus_massRatio0.100_orbitAngle0.000_cP09.0_cS12.0_pP1.0_qP1.0_pS1.0_qS1.0)

description: Gadget-format simulation data for a simulation of a merging pair of halos from the paper by Menker & Benson (2022; https://arxiv.org/abs/2205.02955), for model simulation_icsGalacticus_massRatio0.100_orbitAngle0.000_cP09.0_cS12.0_pP1.0_qP1.0_pS1.0_qS1.0
DOI: 10.6084/m9.figshare.20522001
title: erythroxylum_oxycarpum_spades_01.fasta.gz

description: ......
DOI: 10.6084/m9.figshare.20522001.v1
title: erythroxylum_oxycarpum_spades_01.fasta.gz

description: ......
DOI: 10.5281/zenodo.7012829
title: 707 code for dissertation

description: 707 code and data for dissertation
DOI: 10.5281/zenodo.7012828
title: 707 code for dissertation

description: 707 code and data for dissertation
DOI: 10.6084/m9.figshare.20522022
title: erythroxylum_minutifolium_decontaminated_gx.fasta.gz

description: ......
DOI: 10.6084/m9.figshare.20522022.v1
title: erythroxylum_minutifolium_decontaminated_gx.fasta.gz

description: ......
DOI: 10.6084/m9.figshare.20522112
title: Data F1000 (1)

description: Nanocrystal usnic acid: Formulation, physicochemical characterization, and its effect on MMP-13 and collagen 2 expression in rabbit with experimental osteoarthritis
DOI: 10.6084/m9.figshare.20522112.v1
title: Data F1000 (1)

description: Nanocrystal usnic acid: Formulation, physicochemical characterization, and its effect on MMP-13 and collagen 2 expression in rabbit with experimental osteoarthritis
DOI: 10.5281/zenodo.7012904
title: TwiBot22: Towards Graph-Based Twitter Bot Detection

description: Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. State-of-the-art bot detection methods generally leverage the graph structure of the Twitter network, and they exhibit promising performance when confronting novel Twitter bots that traditional methods fail to detect. However, very few of the existing Twitter bot detection datasets are graph-based, and even these few graph-based datasets suffer from <strong>limited dataset scale</strong>, <strong>incomplete graph structure</strong>, as well as <strong>low annotation quality</strong>. In fact, the lack of a large-scale graph-based Twitter bot detection benchmark that addresses these issues has seriously hindered the development and evaluation of novel graph-based bot detection approaches. In this paper, we propose <strong>TwiBot-22</strong>, a comprehensive graph-based Twitter bot detection benchmark that presents the largest dataset to date, provides diversified entities and relations on the Twitter network, and has considerably better annotation quality than existing datasets. In addition, we re-implement 35 representative Twitter bot detection baselines and evaluate them on 9 datasets, including TwiBot-22, to promote a fair comparison of model performance and a holistic understanding of research progress. To facilitate further research, we consolidate all implemented codes and datasets into <strong>the TwiBot-22 evaluation framework</strong>, where researchers could consistently evaluate new models and datasets. Due to the limitation at Zenodo, you can access the whole dataset and more information on this website.
DOI: 10.5281/zenodo.7012903
title: TwiBot22: Towards Graph-Based Twitter Bot Detection

description: Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. State-of-the-art bot detection methods generally leverage the graph structure of the Twitter network, and they exhibit promising performance when confronting novel Twitter bots that traditional methods fail to detect. However, very few of the existing Twitter bot detection datasets are graph-based, and even these few graph-based datasets suffer from <strong>limited dataset scale</strong>, <strong>incomplete graph structure</strong>, as well as <strong>low annotation quality</strong>. In fact, the lack of a large-scale graph-based Twitter bot detection benchmark that addresses these issues has seriously hindered the development and evaluation of novel graph-based bot detection approaches. In this paper, we propose <strong>TwiBot-22</strong>, a comprehensive graph-based Twitter bot detection benchmark that presents the largest dataset to date, provides diversified entities and relations on the Twitter network, and has considerably better annotation quality than existing datasets. In addition, we re-implement 35 representative Twitter bot detection baselines and evaluate them on 9 datasets, including TwiBot-22, to promote a fair comparison of model performance and a holistic understanding of research progress. To facilitate further research, we consolidate all implemented codes and datasets into <strong>the TwiBot-22 evaluation framework</strong>, where researchers could consistently evaluate new models and datasets. Due to the limitation at Zenodo, you can access the whole dataset and more information on this website.
DOI: 10.34740/kaggle/dsv/4098086
title: 3d segmentation preprocessed data with falsePos
DOI: 10.5281/zenodo.7012914
title: Exploring the conformers of an organic adsorbate on a metal cluster with Bayesian optimization

description: [1] Lincan Fang, Xiaomi Guo, Milica Todorovic, Patrick Rinke, Xi Chen. Exploring the conformers of an organic adsorbate on a metal cluster with Bayesian optimization This dataset shows 1000 sampling points in the configuration space of the cysteine ligand binding with the S site of Au25S18 cluster. For Au25S18 cluster, there are two inequivalent S sites (Au-S-Au-S-Au-S-Au arrow top and side). Here, the binding S site is on "arrow side", we named it "system B". The sampling method is the Bayesian Optimization Structure Search (BOSS), each sampling point consists of structure features and DFT structure energy. The structure features are five dihedral angles of cysteine ligand, d1 (Au-S-C1-C2), d2 (S-C1-C2-N), d3 (C1-C2-N-H), d4 (C1-C2-C3-O1), and d5 (C2-C3-O1-H). The energy was calculated by FHI-aims with PBE functional, tier 2 setting with many-body dispersion corrections. Due to the confined configuration space of this system, we use the energy transformation method (see in manuscript) to tackle one sampling structure that cannot be simulated by DFT or has a huge high DFT energy. For additional details on BOSS search please refer to [1]. The stable local minimum structures from BOSS can be found in NOMAD: https://dx.doi.org/10.17172/NOMAD/2022.08.20-1
description: Kelohongantie 12C 29
DOI: 10.5281/zenodo.7012913
title: Exploring the conformers of an organic adsorbate on a metal cluster with Bayesian optimization

description: [1] Lincan Fang, Xiaomi Guo, Milica Todorovic, Patrick Rinke, Xi Chen. Exploring the conformers of an organic adsorbate on a metal cluster with Bayesian optimization This dataset shows 1000 sampling points in the configuration space of the cysteine ligand binding with the S site of Au25S18 cluster. For Au25S18 cluster, there are two inequivalent S sites (Au-S-Au-S-Au-S-Au arrow top and side). Here, the binding S site is on "arrow side", we named it "system B". The sampling method is the Bayesian Optimization Structure Search (BOSS), each sampling point consists of structure features and DFT structure energy. The structure features are five dihedral angles of cysteine ligand, d1 (Au-S-C1-C2), d2 (S-C1-C2-N), d3 (C1-C2-N-H), d4 (C1-C2-C3-O1), and d5 (C2-C3-O1-H). The energy was calculated by FHI-aims with PBE functional, tier 2 setting with many-body dispersion corrections. Due to the confined configuration space of this system, we use the energy transformation method (see in manuscript) to tackle one sampling structure that cannot be simulated by DFT or has a huge high DFT energy. For additional details on BOSS search please refer to [1]. The stable local minimum structures from BOSS can be found in NOMAD: https://dx.doi.org/10.17172/NOMAD/2022.08.20-1
description: Kelohongantie 12C 29
DOI: 10.34943/0273da54-51aa-4208-8f0b-24d4ef35f1b5
title: Conception Bay Power Supply Deployed 2021-02-08

description: The TDK Lambda 600VDC Power Supply 20H8549AB was deployed on 2021-02-08 at Conception Bay. Conception Bay is located west of the city St. John's, Newfoundland. This device is a Power Supply. Power supplies provide power to associated devices. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.5281/zenodo.7008297
title: U.S. cities increasingly integrate justice into climate planning and create policy tools for climate justice (Diezmartínez & Short Gianotti, 2022) - Data and code

description: This repository contains datasets and coding corresponding to the journal article titled "U.S. cities increasingly integrate justice into climate planning and create policy tools for climate justice". We include: DataRegressionAnalysis.csv CSV file with data used for regression analysis. This file can be used directly to run R code provided in this repository. QualitativeCodingResults.nvp NVivo project with all results for the qualitative coding of urban climate action plans. This file also contains all climate action plans analyzed in this research. QualitativeCodingResults_Summary.xlsx Excel file with a results summary for the qualitative coding of urban climate action plans. RegressionAnalysis.Rmd Rmd file with R code used for regression analysis. RegressionAnalysis_KnitOutput.html Knit output from R code with regression analysis results, html format. RegressionAnalysis_KnitOutput.pdf Knit output from R code with regression analysis results, PDF format.
DOI: 10.34740/kaggle/dsv/4098195
title: Mahanagar Yatayat Dataset
DOI: 10.5281/zenodo.7012824
title: Merging triaxial halos (simulation_icsAgama_massRatio0.100_orbitAngle0.393_cP10.0_cS10.0_pP1.0_qP1.0_pS1.0_qS1.0)

description: Gadget-format simulation data for a simulation of a merging pair of halos from the paper by Menker & Benson (2022; https://arxiv.org/abs/2205.02955), for model simulation_icsAgama_massRatio0.100_orbitAngle0.393_cP10.0_cS10.0_pP1.0_qP1.0_pS1.0_qS1.0
DOI: 10.5281/zenodo.7012823
title: Merging triaxial halos (simulation_icsAgama_massRatio0.100_orbitAngle0.393_cP10.0_cS10.0_pP1.0_qP1.0_pS1.0_qS1.0)

description: Gadget-format simulation data for a simulation of a merging pair of halos from the paper by Menker & Benson (2022; https://arxiv.org/abs/2205.02955), for model simulation_icsAgama_massRatio0.100_orbitAngle0.393_cP10.0_cS10.0_pP1.0_qP1.0_pS1.0_qS1.0
DOI: 10.7284/142217
title: Multibeam Echosounder data collected during research cruise NBP1305 using a Kongsberg EM122 instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141878
title: GNSS (Global Navigation Satellite System) data collected during research cruise NBP1404 using a Furuno GP-330B instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141872
title: Gravimeter data collected during research cruise NBP1404 using a Bell BGM-3 instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141880
title: Gyrocompass data collected during research cruise NBP1404 using a Yokogawa CMZ700B instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141870
title: INS (Inertial Navigation System) data collected during research cruise NBP1404 using a Kongsberg Seapath 200 instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141874
title: Meteorological Station data collected during research cruise NBP1404 using a USAP RVDAS Metstation instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141876
title: SSV (Sound Speed Velocimeter) data collected during research cruise NBP1404 using a AML SV2000 instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142248
title: Multibeam Echosounder data collected during research cruise NBP1511 using a Kongsberg EM122 instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142250
title: Multibeam Echosounder data collected during research cruise NBP1609 using a Kongsberg EM122 instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5281/zenodo.4286169
title: Data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'

description: This is the data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'. Both algorithm and dataset described in this publication can be found in this folder. Please cite 'Global dataset of thermohaline staircases obtained from Argo floats and Ice-Tethered Profilers' when using this data set (doi: 10.5194/essd-2020-197). The newest/most updated version of the code can be found on GitHub: https://github.com/cvanderboog/Staircase-detection-algorithm.
DOI: 10.5281/zenodo.4286170
title: Data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'

description: This is the data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'. Both algorithm and dataset described in this publication can be found in this folder. Please cite 'Global dataset of thermohaline staircases obtained from Argo floats and Ice-Tethered Profilers' when using this data set (doi: 10.5194/essd-2020-197).