cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fzvs2
title: CCDC 2263985: Experimental Crystal Structure Determination

description: Related Article: Yusheng Chen, Anastasia Kuvayskaya, Maren Pink, Alan Sellinger, Amar H. Flood|2024|Chemical Science|15|389|doi:10.1039/D3SC03685E
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: 10.25901/ywjv-2v76
title: Fogg Dam Flux Data Release 2023_v2
DOI: 10.34740/kaggle/dsv/7334070
title: WallStreetBets 2022
DOI: 10.5281/zenodo.10452763
title: Sherlock & Midway RSS-related data files backup
DOI: 10.5281/zenodo.10452764
title: Sherlock & Midway RSS-related data files backup
DOI: 10.25901/kvx6-m534
title: Howard Springs Flux Data Release 2023_v2
DOI: 10.25901/68tc-d142
title: Litchfield Flux Data Release 2023_v2
DOI: 10.25901/s0n1-gt91
title: Longreach Mitchell Grass Rangeland Flux Data Release 2023_v2
DOI: 10.6084/m9.figshare.24847161.v1
title: Macrophyte methane flux data compilation

description: Data compilation of plant-associated methane fluxes.Search inquiry for Web of Science consisted of the following keywords: plant* or macrophyte* and aquatic or freshwater, and methane (asterisks where used to consider singular and plural), mediated, and methane. The combination of keywords resulted in the following formula: "TS=((plant* OR macrophyte*) AND (aquatic OR freshwater) AND methane)"TS= (plant* AND mediated AND methane), TS meant that the search was done in the title, keywords, and the abstract of research papers.
DOI: 10.6084/m9.figshare.24939480
title: XRD Milled and Quenched samples. EDS spectrum.

description: XRD of mechanical alloying and quenched samples. Energy-Dispersive X-ray Spectroscopy (EDS) spectra from various spots.
DOI: 10.25901/x1sr-dn91
title: Loxton Flux Data Release 2023_v2
DOI: 10.6084/m9.figshare.24939474
title: SPAIS1.7_Analysis_Share.xlsx

description: Geospatial Location Embedding (GLE) helps a Large Language Model (LLM) assimilate and analyze spatial data. GLE emergence in Geospatial Artificial Intelligence (GeoAI) is precipitated by the need for deeper geospatial awareness and the success of LLMs in extracting deep meaning in Generative AI. We searched Google Scholar, Science Direct, and arXiv for papers on geospatial location embedding and LLM and reviewed articles focused on gaining deeper spatial "‘knowing"’. We screened 304 titles, 30 abstracts, and 18 full-text papers that reveal four GLE themes - Entity Location Embedding (ELE), Document Location Embedding (DLE), Sequence Location Embedding (SLE), and Token Location Embedding (TLE). Synthesis is narrative, including a dialogic conversation between "‘Space"’ and "‘LLM.."’ GLEs themes reveal deeper spatial understanding comes via geospatial superimposing and show opportunities to advance in spatial modalities and generalized reasoning. Ultimately, GLEs signal the need for a <i>Spatial Foundation/Language Model</i>  (SLM) that embeds spatial knowing within the model architecture. The SLM framework advances Spatial Artificial Intelligence Systems (SPAIS) by establishing a Spatial Vector Space (SVS), mapping to physical space. SVS uniquely imbues an AI-capable space with a representation of actual space, paving the way for AI native geo storage, analysis, and multi-modality as the basis for SPAIS.
DOI: 10.6084/m9.figshare.24939474.v1
title: SPAIS1.7_Analysis_Share.xlsx

description: Geospatial Location Embedding (GLE) helps a Large Language Model (LLM) assimilate and analyze spatial data. GLE emergence in Geospatial Artificial Intelligence (GeoAI) is precipitated by the need for deeper geospatial awareness and the success of LLMs in extracting deep meaning in Generative AI. We searched Google Scholar, Science Direct, and arXiv for papers on geospatial location embedding and LLM and reviewed articles focused on gaining deeper spatial "‘knowing"’. We screened 304 titles, 30 abstracts, and 18 full-text papers that reveal four GLE themes - Entity Location Embedding (ELE), Document Location Embedding (DLE), Sequence Location Embedding (SLE), and Token Location Embedding (TLE). Synthesis is narrative, including a dialogic conversation between "‘Space"’ and "‘LLM.."’ GLEs themes reveal deeper spatial understanding comes via geospatial superimposing and show opportunities to advance in spatial modalities and generalized reasoning. Ultimately, GLEs signal the need for a <i>Spatial Foundation/Language Model</i>  (SLM) that embeds spatial knowing within the model architecture. The SLM framework advances Spatial Artificial Intelligence Systems (SPAIS) by establishing a Spatial Vector Space (SVS), mapping to physical space. SVS uniquely imbues an AI-capable space with a representation of actual space, paving the way for AI native geo storage, analysis, and multi-modality as the basis for SPAIS.
DOI: 10.6084/m9.figshare.24159564.v2
title: SnakeTraits 1.0: a species-level database of life history and ecology of snakes

description: Understanding biodiversity from a functional perspective is important for both evolutionary and ecological studies. Snakes, a unique and diverse group of terrestrial vertebrates with more than 4000 species, are an important part of ecosystems. However, information on the functional traits of snakes is scattered throughout an extensive primary literature. With the exception of a few traits like body size, most traits remain relatively inaccessible to researchers interested in studying the ecology and evolution of snakes on a large scale. In this study, we present a global species-level functional traits database for snakes. This database follows the most recently taxonomic scheme and covers 4040 species. We compiled this database based on 1289 scientific literature sources, including peer-reviewed papers, books, online databases, natural history records and field guides. We collected information on traits including body size, relative tail length, diel activity pattern, colour, microhabitats, reproductive mode, clutch size, diet, venomous and infrared sensing ability. Detailed information on these traits and the coding system for categorical traits can be found in the supporting materials. This database is the most comprehensive on snakes as far as we know. As our objective was to build a living dataset, we will update the database annually by adding new species and modified the data following the taxonomic changes. We hope that this database will facilitate macroecological and macroevolutionary studies of this interesting and important group.
DOI: 10.25345/c5513v650
title: MassIVE MSV000093761 - Screening ginger component against 46 human nuclear receptors
title: 30 ug of ginger ethanol extract (1% DMSO) was spiked into an E. coli lysate containing 1 to 10 uM of a His-tagged NR LBD fusion protein. Following incubation and Nickel affinity purification, the samples were analyzed by untargeted LC-MS to identify masses enriched by the NR pulldown. Parallel pull-downs were conducted using an NR LBD fusion protein without the addition of the ginger extract as control.
DOI: 10.25901/q0w8-0r89
title: Otway Flux Data Release 2023_v2
DOI: 10.25901/k8y6-cb16
title: Red Dirt Melon Farm Flux Data Release 2023_v2
DOI: 10.25901/x3za-h871
title: Ridgefield Flux Data Release 2023_v2
DOI: 10.25901/zbpm-4n12
title: Riggs Creek Flux Data Release 2023_v2
DOI: 10.5281/zenodo.10457047
title: Prioritizing forestation in China through incorporating biogeochemical and local biogeophysical effects

description: A database of plot-scale forest type and forest biomass was constructed by integrating four existing databases: the Forest Carbon Database (ForC) (Anderson-Teixeira et al., 2018), the Forest Observation System (FOS) (Schepaschenko D et al., 2019), a fieldwork database established by Zhu et al. (2017), and datasets collected by Ye et al. (2021). These four existing databases provide information on geographic location (latitude and longitude), dominant species or forest types, stand age, and aboveground or belowground biomass or biomass carbon stock. It should be noted that sites in the ForC dataset may consist of multiple different plots. In this case, (1) if the plots shared both the same forest type and the same stand age, the reported observations were averaged; (2) otherwise, if they shared the same forest type but had different stand ages, then they were treated as different entries; (3) if their forest types were different, then, again, they were treated as different entries. Following this procedure, to ensure the consistency of the constructed database in this study, forest types were classified into coniferous, broad-leaved, and mixed forests based on the information on dominant species and forest types contained in these four databases. In addition, biomass density (in Mg ha-1) was converted to biomass carbon density (in Mg C ha-1) using the conversion factor of 0.5 (Petersson et al., 2012). The above processing generated a database containing information on geographic location, forest types, stand age, and aboveground or belowground biomass carbon density, consisting of 3411 observations.

Reference



Anderson-Teixeira K, Wang M, McGarvey J, et al. ForC: A global database of forest carbon stocks and fluxes. Ecology 2018;99:1507.

Schepaschenko D, Chave J, Phillips O, et al. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass. Sci. Data 2019;6:

Zhu J, Hu H, Tao S, et al. Carbon stocks and changes of dead organic matter in China's forests. Nat Commun 2017;8:151.

Ye J, Yue C, Hu Y, et al. Spatial patterns of global-scale forest root-shoot ratio and their controlling factors. Sci Total Environ 2021;800:149251.

Petersson H, Holm S, Ståhl G, et al. Individual tree biomass equations or biomass expansion factors for assessment of carbon stock changes in living biomass – A comparative study. For. Ecol. Manage. 2012; 270: 78-84.
DOI: 10.5281/zenodo.10457048
title: Prioritizing forestation in China through incorporating biogeochemical and local biogeophysical effects

description: A database of plot-scale forest type and forest biomass was constructed by integrating four existing databases: the Forest Carbon Database (ForC) (Anderson-Teixeira et al., 2018), the Forest Observation System (FOS) (Schepaschenko D et al., 2019), a fieldwork database established by Zhu et al. (2017), and datasets collected by Ye et al. (2021). These four existing databases provide information on geographic location (latitude and longitude), dominant species or forest types, stand age, and aboveground or belowground biomass or biomass carbon stock. It should be noted that sites in the ForC dataset may consist of multiple different plots. In this case, (1) if the plots shared both the same forest type and the same stand age, the reported observations were averaged; (2) otherwise, if they shared the same forest type but had different stand ages, then they were treated as different entries; (3) if their forest types were different, then, again, they were treated as different entries. Following this procedure, to ensure the consistency of the constructed database in this study, forest types were classified into coniferous, broad-leaved, and mixed forests based on the information on dominant species and forest types contained in these four databases. In addition, biomass density (in Mg ha-1) was converted to biomass carbon density (in Mg C ha-1) using the conversion factor of 0.5 (Petersson et al., 2012). The above processing generated a database containing information on geographic location, forest types, stand age, and aboveground or belowground biomass carbon density, consisting of 3411 observations.

Reference



Anderson-Teixeira K, Wang M, McGarvey J, et al. ForC: A global database of forest carbon stocks and fluxes. Ecology 2018;99:1507.

Schepaschenko D, Chave J, Phillips O, et al. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass. Sci. Data 2019;6:

Zhu J, Hu H, Tao S, et al. Carbon stocks and changes of dead organic matter in China's forests. Nat Commun 2017;8:151.

Ye J, Yue C, Hu Y, et al. Spatial patterns of global-scale forest root-shoot ratio and their controlling factors. Sci Total Environ 2021;800:149251.

Petersson H, Holm S, Ståhl G, et al. Individual tree biomass equations or biomass expansion factors for assessment of carbon stock changes in living biomass – A comparative study. For. Ecol. Manage. 2012; 270: 78-84.
DOI: 10.5517/ccdc.csd.cc2g52k9
title: CCDC 2269000: Experimental Crystal Structure Determination

description: Related Article: Lucy A. Harwood, Ziyue Xiong, Kirsten E. Christensen, Ruiyao Wang, Luet L. Wong, Jeremy Robertson|2023|J.Am.Chem.Soc.|145|27767|doi:10.1021/jacs.3c10542
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.cc2g52lb
title: CCDC 2269001: Experimental Crystal Structure Determination

description: Related Article: Lucy A. Harwood, Ziyue Xiong, Kirsten E. Christensen, Ruiyao Wang, Luet L. Wong, Jeremy Robertson|2023|J.Am.Chem.Soc.|145|27767|doi:10.1021/jacs.3c10542
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.cc2gzsmw
title: CCDC 2293709: Experimental Crystal Structure Determination

description: Related Article: Milan Gembicky|2023|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: 10.6084/m9.figshare.24936927
title: Rational design of dynamic fiber membrane for sustainable biofouling control

description: We've innovatively addressed membrane biofouling, introducing a facile approach for prolonged use of anti-biofouling agents. This led to the creation of the dynamic fiber membrane, with a sophisticated spatial structure. This smart membrane dynamically loads and releases a biofilm inhibitor D-tyrosine, establishing a multidimensional system that surpasses current anti-biofouling effectiveness. This advancement significantly enhances membrane anti-biofouling capabilities, offering a robust and sustainable solution for water treatment.
DOI: 10.6084/m9.figshare.24936927.v2
title: Rational design of dynamic fiber membrane for sustainable biofouling control

description: We've innovatively addressed membrane biofouling, introducing a facile approach for prolonged use of anti-biofouling agents. This led to the creation of the dynamic fiber membrane, with a sophisticated spatial structure. This smart membrane dynamically loads and releases a biofilm inhibitor D-tyrosine, establishing a multidimensional system that surpasses current anti-biofouling effectiveness. This advancement significantly enhances membrane anti-biofouling capabilities, offering a robust and sustainable solution for water treatment.
DOI: 10.6084/m9.figshare.24939543
title: Data for: Asymmetric environmental selection on intraspecific body size inCollembola communities along an elevational gradient in northern Japan

description: Data for Asymmetric environmental selection on intraspecific body size inCollembola communities along an elevational gradient in northern Japan by Hishi et al.
DOI: 10.6084/m9.figshare.24939543.v1
title: Data for: Asymmetric environmental selection on intraspecific body size inCollembola communities along an elevational gradient in northern Japan

description: Data for Asymmetric environmental selection on intraspecific body size inCollembola communities along an elevational gradient in northern Japan by Hishi et al.
DOI: 10.34740/kaggle/ds/4257672
title: Infectious Municipal Waste
DOI: 10.6084/m9.figshare.24310780
title: Low-loss hybrid germanium-on-zinc selenide waveguides in the longwave infrared

description: All original data is stored in the folder named "data", and all plots in the manuscript can be generated using the file "Generate_All_Figures.m".
DOI: 10.6084/m9.figshare.24310780.v1
title: Low-loss hybrid germanium-on-zinc selenide waveguides in the longwave infrared

description: All original data is stored in the folder named "data", and all plots in the manuscript can be generated using the file "Generate_All_Figures.m".
DOI: 10.25442/hku.24939606
title: Baseline Mapping of Global WUI Areas in 2020

description: Intensifying wildfires and human settlement expansion have placed more people and infrastructure at the wildland–urban interface (WUI) areas under risk. Wildfire management and policy response are needed to protect ecosystems and residential communities; however, maps containing spatially explicit information on the distribution of WUI areas are limited to certain countries or local regions, and therefore global WUI patterns and associated wildfire exposure risk remain unclear. Here, we generated the global WUI data layers for 2020 baseline and 1985-2020 time series by incorporating fine-resolution housing and vegetation mapping. We estimated the total global WUI area to be 6.62 million km2. Time-series analysis revealed that global WUI areas experienced a substantial increase of 12.56% between 1985 and 2020. By overlapping 2001-2020 wildfire burned area maps and fine-resolution population dataset, our analysis revealed that globally, 7.07% (12.54%) of WUI areas housing 4.47 million (10.11 million) people are within a 2400-m (4800-m) buffer zone of wildfire threat. Regionally, we found that the United States, Brazil, China, India, and Australia account for the majority of WUI areas, but African countries experience higher wildfire risk. Our quantification of global WUI spatiotemporal patterns and the associated wildfire risk could support improvement of wildfire management.
DOI: 10.25442/hku.24939621
title: Time Series Mapping of Global WUI Areas from 1985 to 2020

description: Intensifying wildfires and human settlement expansion have placed more people and infrastructure at the wildland–urban interface (WUI) areas under risk. Wildfire management and policy response are needed to protect ecosystems and residential communities; however, maps containing spatially explicit information on the distribution of WUI areas are limited to certain countries or local regions, and therefore global WUI patterns and associated wildfire exposure risk remain unclear. Here, we generated the global WUI data layers for 2020 baseline and 1985-2020 time series by incorporating fine-resolution housing and vegetation mapping. We estimated the total global WUI area to be 6.62 million km2. Time-series analysis revealed that global WUI areas experienced a substantial increase of 12.56% between 1985 and 2020. By overlapping 2001-2020 wildfire burned area maps and fine-resolution population dataset, our analysis revealed that globally, 7.07% (12.54%) of WUI areas housing 4.47 million (10.11 million) people are within a 2400-m (4800-m) buffer zone of wildfire threat. Regionally, we found that the United States, Brazil, China, India, and Australia account for the majority of WUI areas, but African countries experience higher wildfire risk. Our quantification of global WUI spatiotemporal patterns and the associated wildfire risk could support improvement of wildfire management.
DOI: 10.25442/hku.24760632
title: Supporting data for "Electrochemical catalysis for zinc-air battery and hydrogen production"

description: The research starts with material design and fabrication. The fabrication methodology includes ion-exchange reactions, electrochemical deposition, etching, and high-temperature pyrolysis. The materials are metal oxides, hydroxides, carbides, and phosphorus. The morphology of catalysts under scanning electron microscopy (SEM) and transmission electron microscopy (TEM) was examined. Other techniques, such as X-ray photoelectron spectroscopy (XPS) and X-ray diffractometer (XRD), were also used to identify the surface chemical properties and crystalline planes. CH electrochemical workstation equipped with the Model 600E series helps to perform electrochemical tests, such as linear sweep voltammetry (LSV) and cyclic voltammetry(CV) curves.
DOI: 10.25442/hku.24760632.v1
title: Supporting data for "Electrochemical catalysis for zinc-air battery and hydrogen production"

description: The research starts with material design and fabrication. The fabrication methodology includes ion-exchange reactions, electrochemical deposition, etching, and high-temperature pyrolysis. The materials are metal oxides, hydroxides, carbides, and phosphorus. The morphology of catalysts under scanning electron microscopy (SEM) and transmission electron microscopy (TEM) was examined. Other techniques, such as X-ray photoelectron spectroscopy (XPS) and X-ray diffractometer (XRD), were also used to identify the surface chemical properties and crystalline planes. CH electrochemical workstation equipped with the Model 600E series helps to perform electrochemical tests, such as linear sweep voltammetry (LSV) and cyclic voltammetry(CV) curves.
DOI: 10.25442/hku.24760695
title: Supporting data for In-Situ Investigation of Cellular Level Biomarkers Using Atomic Force Microscopy(AFM) Based Nano Robot

description: Data set for the dissertation entitled with "In-Situ Investigation of Cellular Level Biomarkers Using Atomic Force Microscopy(AFM) Based Nano Robot". The dataset includes the support data for fundamental techniques of AFM based nanorobot, and the AFM based nanorobotic system with enhancement such as cellular auscultation system; augmentation manipulation system to investigate the cellular behavior in depth and accuracy. The dataset also contains the files for the practical applications from single molecule level to the embryo level in clinical level.
DOI: 10.25442/hku.24760695.v1
title: Supporting data for In-Situ Investigation of Cellular Level Biomarkers Using Atomic Force Microscopy(AFM) Based Nano Robot

description: Data set for the dissertation entitled with "In-Situ Investigation of Cellular Level Biomarkers Using Atomic Force Microscopy(AFM) Based Nano Robot". The dataset includes the support data for fundamental techniques of AFM based nanorobot, and the AFM based nanorobotic system with enhancement such as cellular auscultation system; augmentation manipulation system to investigate the cellular behavior in depth and accuracy. The dataset also contains the files for the practical applications from single molecule level to the embryo level in clinical level.
DOI: 10.25442/hku.24794517
title: Supporting data for the thesis "<b>Transcriptional Plasticity and Environmental Change in Marine Fishes</b>"

description: This dissertation focuses on transcriptional plasticity and its regulation in marine fishes in response to changes in their environment. Several factors that influence plastic responses such as environmental stability, parental experiences, and the presence of genetic variation for plasticity are studied. Additionally, this thesis also explores molecular processes underlying the mutualistic relationship between the anemone and anemonefish.