cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2dpbdv
title: CCDC 2225037: Experimental Crystal Structure Determination

description: Related Article: I Wayan Dasna|2022|CSD Communication|||
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.cc2dp8ds
title: CCDC 2224975: Experimental Crystal Structure Determination

description: Related Article: Eric Anthony, Axel Klein|2022|CSD Communication|||
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.cc2dpkk7
title: CCDC 2225259: Experimental Crystal Structure Determination

description: Related Article: Wei-Ching Chen, Rekha Bai, Wan-Lin Cheng, Chun-Yu Peng, Daggula Mallikarjuna Reddy, Satpal Singh Badsara, Chin-Fa Lee|2023|Org.Biomol.Chem.|21|3002|doi:10.1039/D3OB00279A
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.cc2dpqys
title: CCDC 2225426: Experimental Crystal Structure Determination

description: Related Article: Michael Ricca, Thomas Fellowes|2022|CSD Communication|||
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.cc2dq5y8
title: CCDC 2225860: Experimental Crystal Structure Determination

description: Related Article: Chao-Na An|2022|CSD Communication|||
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.cc2dq8fv
title: CCDC 2225937: Experimental Crystal Structure Determination

description: Related Article: Xue Zhang|2022|CSD Communication|||
description: Related Article: Xue Zhang, Dong-xue Qiu, Wen-ting Qiu, He-ran Wang, Zu-wei Zhao, Hai-feng Yu, Guang-bo Che|2023|Tetrahedron|134|133305|doi:10.1016/j.tet.2023.133305
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.cc2dq4ny
title: CCDC 2225820: Experimental Crystal Structure Determination

description: Related Article: Toi Kobayashi, Naoya Kumagai|2022|CSD Communication|||
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.cc2dq7bq
title: CCDC 2225903: Experimental Crystal Structure Determination

description: Related Article: Jumei Tian|2022|CSD Communication|||
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.cc2dqfwg
title: CCDC 2226106: Experimental Crystal Structure Determination

description: Related Article: xi yang|2022|CSD Communication|||
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: 10.25919/2jrc-1497
title: IN2015_C02 Great Australian Bight Bathymetry 10m - 210m Multi-resolution AusSeabed products

description: This layer group describes multibeam echosounder data collected on RV Investigator voyage IN2015_C02 titled "GAB deep-water pelagic and benthic ecosystem study". The voyage took place between November 30 to December 22, 2015, departing from Port Lincoln (SA) and arriving in Fremantle (WA). The purpose of this voyage was to characterise the deep-water pelagic and benthic community structure and identify key ecological processes in the central and eastern Great Australian Bight (GAB). It formed part of the GAB Research Program that aims to describe the key elements of the GAB marine ecosystem. This dataset is published with the permission of CSIRO. Not to be used for navigational purposes. The dataset contains bathymetry grids of 10m to 210m resolution of the Great Australian Bight, produced from the processed EM122 and EM710 bathymetry data.
DOI: 10.25919/r4dw-5318
title: IN2015_E01 Southern Ocean Bathymetry 10m - 210m Multi-resolution AusSeabed products

description: This layer group describes multibeam echosounder data collected on RV Investigator voyage IN2015_E01, titled "Cold Water Trial Voyage (including atmospherics)". The voyage took place between January 29 and February 18, 2015 departing from Hobart (TAS) and arriving in Hobart (TAS). The purpose of the voyage was to test Investigator equipment to 65° south. EM122 and EM710 were calibrated in transit in Storm Bay and subsequently used to acquire seafloor bathymetry and backscatter information from Hobart to Antarctic waters. This dataset is published with the permission of CSIRO. Not to be used for navigational purposes. The dataset contains bathymetry grids of 10m to 210m resolution of the Southern Ocean, produced from the processed EM122 and EM710 bathymetry data.
DOI: 10.6084/m9.figshare.22308463
title: (WIT2023) Mapeamento Detalhado WIT - Fatores de Atração, Permanência e Evasão

description: Aqui encontram-se os dados completos e detalhados do Mapeamento Sistemático da Literatura realizado dos artigos disponíveis nos Anais do <em>Women in Information Technology</em> (WIT), dos anos de 2016 a 2022. Os dados referem-se aos fatores que motivam a atração, permanência e o abandono de mulheres nas áreas da Computação, objetivo do trabalho submetido ao WIT 2023, intitulado "Fatores de Atração, Evasão e Permanência de Mulheres nas Áreas da Computação".
DOI: 10.6084/m9.figshare.22308463.v10
title: (WIT2023) Mapeamento Detalhado WIT - Fatores de Atração, Permanência e Evasão

description: Aqui encontram-se os dados completos e detalhados do Mapeamento Sistemático da Literatura realizado dos artigos disponíveis nos Anais do <em>Women in Information Technology</em> (WIT), dos anos de 2016 a 2022. Os dados referem-se aos fatores que motivam a atração, permanência e o abandono de mulheres nas áreas da Computação, objetivo do trabalho submetido ao WIT 2023, intitulado "Fatores de Atração, Evasão e Permanência de Mulheres nas Áreas da Computação".
DOI: 10.6084/m9.figshare.22308463.v9
title: (WIT2023) Mapeamento Detalhado WIT - Fatores de Atração, Permanência e Evasão

description: Aqui encontram-se os dados completos e detalhados do Mapeamento Sistemático da Literatura realizado dos artigos disponíveis nos Anais do <em>Women in Information Technology</em> (WIT), dos anos de 2016 a 2022. Os dados referem-se aos fatores que motivam a atração, permanência e o abandono de mulheres nas áreas da Computação, objetivo do trabalho submetido ao WIT 2023, intitulado "Fatores de Atração, Evasão e Permanência de Mulheres nas Áreas da Computação".
DOI: 10.5281/zenodo.7803857
title: geukensia_demissa_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7803856
title: geukensia_demissa_spades_01.fasta.gz

description: .
DOI: 10.15151/esrf-es-1112990112
title: Structural Biology at EMBL Grenoble, IBS-CIBB and IAB

description: This proposal clusters crystallography projects at the EMBL Grenoble, IBS and IAB working on different topics with a focus on nucleic acid-protein complexes and host-pathogen interactions. The proposers have long-standing experience in the use, as well as the development of beam lines for macromolecular crystallography and have participated in successive BAG applications over the years, mostly at the ESRF but also at other synchrotrons (i.e. Soleil, Petra II, Diamond, SLS)
DOI: 10.24431/rw1k7di
title: Gulf Watch Ocean Acidification Monitoring, year 2020 to year 2023. Samples collected from the science cruises conducted by the Prince William Sound Science Center and the Katchemak Bay National Estuarine Research Reserve. Samples were processed at the Alutiiq Pride Marine Center's Ocean Acidification Research Laboratory.

description: These data are part of the Gulf Watch Alaska (GWA) Non-Program Project initative, Pelagic Component of the Exxon Valdez Oil Spill Trustee Council, project number 22200127. Gulf Watch Alaska is the long-term ecosystem monitoring program of the Exxon Valdez Oil Spill Trustee Council for the marine ecosystem affected by the 1989 oil spill. The Chugach Regional Resources Commission (CRRC) operates the Alutiiq Pride Marine Institute (APMI), and its Ocean Acidification Research Laboratory (OAR Lab), in Seward Alaska. This project incorporates dissolved inorganic carbon (DIC) sampling for the purposes of quantifying ocean acidification into the Gulf Watch Program currently funded by Exxon Valdez Oil Spill Trustee Council (EVOSTC). The Gulf Watch program, through its routine sampling, has added the collection of DIC samples to several of its sampling sites. This has added to the current data sets from these locations and offers a broader understanding of ocean acidification in Prince William Sound and Lower Cook Inlet. The Prince William Sound Science Center (PWSSC) and the Kachemak Bay National Estuarine Research Reserve (KBNERR) are current partners in the Gulf Watch program and routinely conduct marine (vessel-based) sampling transects on a time series. The cost to sample and process DIC samples ($34,323 per year) is the only additional cost to the PWSSC and KBNERR existing programs and would go directly to CRRC.
DOI: 10.24431/rw1k7dj
title: Gulf Watch Ocean Acidification Monitoring, year 2020 to year 2022. Samples collected from the science cruises conducted by the Prince William Sound Science Center and the Katchemak Bay National Estuarine Research Reserve. Samples were processed at the Alutiiq Pride Marine Center's Ocean Acidification Research Laboratory.

description: These data are part of the Gulf Watch Alaska (GWA) Non-Program Project initative, Pelagic Component of the Exxon Valdez Oil Spill Trustee Council, project number 22200127. Gulf Watch Alaska is the long-term ecosystem monitoring program of the Exxon Valdez Oil Spill Trustee Council for the marine ecosystem affected by the 1989 oil spill. The Chugach Regional Resources Commission (CRRC) operates the Alutiiq Pride Marine Institute (APMI), and its Ocean Acidification Research Laboratory (OAR Lab), in Seward Alaska. This project incorporates dissolved inorganic carbon (DIC) sampling for the purposes of quantifying ocean acidification into the Gulf Watch Program currently funded by Exxon Valdez Oil Spill Trustee Council (EVOSTC). The Gulf Watch program, through its routine sampling, has added the collection of DIC samples to several of its sampling sites. This has added to the current data sets from these locations and offers a broader understanding of ocean acidification in Prince William Sound and Lower Cook Inlet. The Prince William Sound Science Center (PWSSC) and the Kachemak Bay National Estuarine Research Reserve (KBNERR) are current partners in the Gulf Watch program and routinely conduct marine (vessel-based) sampling transects on a time series. The cost to sample and process DIC samples ($34,323 per year) is the only additional cost to the PWSSC and KBNERR existing programs and would go directly to CRRC.
DOI: 10.5281/zenodo.7796593
title: Molecular gas and star formation in nearby starburst galaxy mergers

description: This compressed file contains a python script, fits images and numpy table produce the animations in the paper. To make animations, run the script `make_final_animation.py` in `scripts/` directory. The script is run in python 3.7. After running the script, the generated html animations will be stored in `figures/` directory.
DOI: 10.5281/zenodo.7796592
title: Molecular gas and star formation in nearby starburst galaxy mergers

description: This compressed file contains a python script, fits images and numpy table produce the animations in the paper. To make animations, run the script `make_final_animation.py` in `scripts/` directory. The script is run in python 3.7. After running the script, the generated html animations will be stored in `figures/` directory.
DOI: 10.5281/zenodo.7577033
title: A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?

description: Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials.<br> In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram.<br> We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties.<br> This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions.<br> An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties.<br> These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process.<br> The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.
description: We thank Maria Muniz, Athanassios Panagiotopoulos, and Vinicius Cruzeiro for stimulating discussions at the early stage of this research. This research was supported by the Air Force Office of Scientific Research under award FA9550-20-1-0351 and used computational resources of the Department of Defense High Performance Computing Modernization Program (HPCMP) as well as the Triton Shared Computing Cluster (TSCC) at the San Diego Supercomputer Center (SDSC). This work was supported in part by the UC Southern California Hub, with funding from the UC National Laboratories division of the University of California Office of the President.
DOI: 10.5281/zenodo.7577034
title: A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?

description: Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials.<br> In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram.<br> We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties.<br> This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions.<br> An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties.<br> These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process.<br> The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.
description: We thank Maria Muniz, Athanassios Panagiotopoulos, and Vinicius Cruzeiro for stimulating discussions at the early stage of this research. This research was supported by the Air Force Office of Scientific Research under award FA9550-20-1-0351 and used computational resources of the Department of Defense High Performance Computing Modernization Program (HPCMP) as well as the Triton Shared Computing Cluster (TSCC) at the San Diego Supercomputer Center (SDSC). This work was supported in part by the UC Southern California Hub, with funding from the UC National Laboratories division of the University of California Office of the President.
DOI: 10.5281/zenodo.7798475
title: MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 8-Day Global 500m SIN Grid in 2016

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FAPAR product at 500m spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of PAR through the entire canopy (Xiao <em>et al</em>., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous. This dataset is the MUSES FAPAR product in 2016. <em>Please <strong>click here</strong> to download the MUSES FAPAR product <strong>in 2015</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES FAPAR product <strong>in 2017</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2016 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. <em>Remote Sensing of Environment</em>, 171,105-117, 2015. Xiao Zhiqiang, <em>et al</em>., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 56, 5509-5524, 2018. Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. <em>Remote Sensing</em>, 14, 3304, 2022. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).