cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/s5sx-qg83
title: Curated Plant and Invertebrate Data for Bushfire Modelling

description: This data asset contains observations of individual plants and animals (“occurrences”) sourced from the Atlas of Living Australia. Data on vascular plants are based on the following paper: Godfree et al. (2021) Implications of the 2019-2020 Megafires for the Biogeography and Conservation of Australian Vegetation. Nature Communications 12: 1023 https://doi.org/10.1038/s41467-021-21266-5 Data on invertebrates are from the following report: Marsh et al. (2021) Threatened species hub report: Assessment of the impacts of the 2019-20 wildfires on southern and eastern Australia on invertebrate species. NESP Threatened Species Recovery Hub Project 8.3.1 Final report, Brisbane. Both studies were performed to understand the impacts of the Australian “Black Summer” (2019-20) fires on the taxonomic group in question. Consequently, this aggregated dataset is designed to support off-the-shelf bushfire impact modelling, and to provide useful context for associated biodiversity conservation work. A total of 896 species of vascular plants and 44,146 invertebrate species. The combined data asset was produced by the Science & Decision Support Team at the Atlas of living Australia (ALA) in collaboration with the authors of the original data sets. This was done as part of an Australian Research Data Commons (ARDC) bushfire data challenges project. The data contained in this deposition has been cleaned by researchers and taxonomists to ensure it is of high quality. Therefore, compared to data returned by the ALA, the unique record ID’s may return a different species or location than is denoted in this data set: these records have potentially been changed by the authors of the data to reflect what they believe is correct.
DOI: 10.5281/zenodo.7513781
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2014 (185–361)

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 FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2014 (185–361). <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2014 (001–177)</strong> and <em><strong>click here</strong> to download the MUSES FVC produ</em>ct <strong>in 2015 (001–177)</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2014 (185–361) Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 100 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7513780
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2014 (185–361)

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 FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2014 (185–361). <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2014 (001–177)</strong> and <em><strong>click here</strong> to download the MUSES FVC produ</em>ct <strong>in 2015 (001–177)</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2014 (185–361) Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 100 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.3886/e183906
title: No Wheat Crisis: Trade Liberalization and Transportation Innovation in Quebec during the 1830s and 1840s

description: This data is a copy of the data used in the project titled "No Wheat Crisis: Trade Liberalization and Transportation Innovation in Quebec during the 1830s and 1840s." The data combines several publicly available datasets in a useable format.
DOI: 10.3886/e183906v1
title: No Wheat Crisis: Trade Liberalization and Transportation Innovation in Quebec during the 1830s and 1840s

description: This data is a copy of the data used in the project titled "No Wheat Crisis: Trade Liberalization and Transportation Innovation in Quebec during the 1830s and 1840s." The data combines several publicly available datasets in a useable format.
DOI: 10.6084/m9.figshare.21853215
title: Supplementary Data.pdf

description: Supplementary data for palaeoseismic investigation near Indo-Nepal border.
DOI: 10.6084/m9.figshare.21853215.v1
title: Supplementary Data.pdf

description: Supplementary data for palaeoseismic investigation near Indo-Nepal border.
DOI: 10.5281/zenodo.7487999
title: Data underlying publication: Ambient precipitation determines the sensitivity of soil respiration to precipitation treatments in a marsh

description: This dataset contains ambient climate data, soil three-parameter data, vegetation biomass data, soil respiration and its autotrophic and heterotrophic components collected from 2016-2021 in a field precipitation changes experiment established at the Yellow River Delta Ecological Experiment Station of the Chinese Academy of Sciences. This study aims to investigate whether ambient climate affects the sensitivity of soil respiration to long-term precipitation treatments and the underlying mechanisms. Briefly, the relationship between net radiation, air temperature and annual precipitation in the ambient climate and the sensitivity of soil respiration to precipitation treatments was investigated to explore if the ambient climate affects the sensitivity of soil respiration to long-term precipitation treatments. The mechanism underlying the above results was also explored through the relationship between the sensitivity of soil and vegetation factors and the sensitivity of soil respiration to precipitation treatments. For the process of data collection: first, soil temperature was collected using soil sensors. Soil moisture and soil conductivity data. Secondly, aboveground biomass and belowground biomass data of vegetation were collected using the harvesting method. Finally, soil respiration and its autotrophic and heterotrophic components data were collected using LI-8100 and LI-7810 infrared gas analyzers (Li-Cor, Inc., Lincoln, NE, USA).
DOI: 10.5281/zenodo.7487998
title: Data underlying publication: Ambient precipitation determines the sensitivity of soil respiration to precipitation treatments in a marsh

description: This dataset contains ambient climate data, soil three-parameter data, vegetation biomass data, soil respiration and its autotrophic and heterotrophic components collected from 2016-2021 in a field precipitation changes experiment established at the Yellow River Delta Ecological Experiment Station of the Chinese Academy of Sciences. This study aims to investigate whether ambient climate affects the sensitivity of soil respiration to long-term precipitation treatments and the underlying mechanisms. Briefly, the relationship between net radiation, air temperature and annual precipitation in the ambient climate and the sensitivity of soil respiration to precipitation treatments was investigated to explore if the ambient climate affects the sensitivity of soil respiration to long-term precipitation treatments. The mechanism underlying the above results was also explored through the relationship between the sensitivity of soil and vegetation factors and the sensitivity of soil respiration to precipitation treatments. For the process of data collection: first, soil temperature was collected using soil sensors. Soil moisture and soil conductivity data. Secondly, aboveground biomass and belowground biomass data of vegetation were collected using the harvesting method. Finally, soil respiration and its autotrophic and heterotrophic components data were collected using LI-8100 and LI-7810 infrared gas analyzers (Li-Cor, Inc., Lincoln, NE, USA).
DOI: 10.6084/m9.figshare.21853239
title: The data of four cruises

description: This file is the data about four cruises in the Changjiang River and East China Sea.
DOI: 10.6084/m9.figshare.21853239.v2
title: The data of four cruises

description: This file is the data about four cruises in the Changjiang River and East China Sea.
DOI: 10.6084/m9.figshare.21853239.v1
title: The data of four cruises

description: This file is the data about four cruises in the Changjiang River and East China Sea.
DOI: 10.25919/2q8p-p757
title: Tree Reconstructions fom Pointclouds Scanned in Pullenvale QLD

description: Tree Data for the paper Tree Reconstruction using Topology Optimisation This repository contains the data presented in the results tables of the paper. The folders reflect each of the 21 cases and each folder contains the following files: 1. A ray cloud (.ply) file representing the registered lidar data and coloured according to surface normal. This format is compatible with the RayCloudTools library https://github.com/csiro-robotics/raycloudtools and its format is detailed in the link's README.md. Points within 30 cm of the ground have been painted black as a flag to not apply a load at these points. The alpha value is set to zero on all unbounded rays, and these are also rendered as black. 2. A mesh file (_mesh.ply) representing the ground surface. This was generated using the rayextract terrain function within the RayCloudTools library's experimental branch. 3. The output BSG (_trees.txt) file. This is a simple comma-delimited description of the cylindrical segments of each tree, with a space separating each segment and a new line separating each tree. It is best to view this directly with word wrap off. 4. A mesh file (_treemesh.ply) of the BSG for easy viewing. This file approximates each cylinder with a triangular mesh. 5-7. Three images showing the topology optimisation process and outputs prior to BSG generation: An isometric illustration of the optimiser output at each iteration (.ply), a 2D image of the output from front and side view with a high density threshold (_views_thin.png), and the same image with a low threshold (_views_fat.png) All .ply files can be viewed in packages such as Meshlab or Cloudcompare. The ray cloud rays can be viewed in Meshlab by enabling View Vertex Normals, and adjusting the overall length in its options settings. All results are shown for the alpha=1 default branch angle.
DOI: 10.5281/zenodo.7519172
title: 照片综合

description: 照片
DOI: 10.5281/zenodo.7519171
title: 照片综合

description: 照片
DOI: 10.6084/m9.figshare.21853638
title: Data for 'Isoprene production and its driving factors in the Northwest Pacific Ocean'

description: Data for '<strong>Isoprene production and its driving factors in the Northwest Pacific Ocean</strong>'
DOI: 10.6084/m9.figshare.21853638.v1
title: Data for 'Isoprene production and its driving factors in the Northwest Pacific Ocean'

description: Data for '<strong>Isoprene production and its driving factors in the Northwest Pacific Ocean</strong>'
DOI: 10.5281/zenodo.7519243
title: Contour Comparison Example Data

description: Some test data extracted from the TCIA used for some examples to demonstrate how to compute contour comparison metrics. WARNING: This is sample/dummy data, only intended for demonstration purposes. This data is part of the LCTSC dataset provided by The Cancer Imaging Archive (TCIA) and is released under the Creative Commons Attribution 3.0 Unported License. Citations & Data Usage Policy Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 3.0 Unported License under which it has been published. Attribution should include references to the following citations: Dataset Citation Yang, Jinzhong; Sharp, Greg; Veeraraghavan, Harini ; van Elmpt, Wouter ; Dekker, Andre; Lustberg, Tim; Gooding, Mark. (2017). Data from Lung CT Segmentation Challenge. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2017.3r3fvz08 Publication Citation Yang, J. , Veeraraghavan, H. , Armato, S. G., Farahani, K. , Kirby, J. S., Kalpathy‐Kramer, J. , van Elmpt, W. , Dekker, A. , Han, X. , Feng, X. , Aljabar, P. , Oliveira, B. , van der Heyden, B. , Zamdborg, L. , Lam, D. , Gooding, M. and Sharp, G. C. (2018), Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017. Med. Phys.. . doi: 10.1002/mp.13141 TCIA Citation Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)
DOI: 10.5281/zenodo.7519242
title: Contour Comparison Example Data

description: Some test data extracted from the TCIA used for some examples to demonstrate how to compute contour comparison metrics. WARNING: This is sample/dummy data, only intended for demonstration purposes. This data is part of the LCTSC dataset provided by The Cancer Imaging Archive (TCIA) and is released under the Creative Commons Attribution 3.0 Unported License. Citations & Data Usage Policy Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 3.0 Unported License under which it has been published. Attribution should include references to the following citations: Dataset Citation Yang, Jinzhong; Sharp, Greg; Veeraraghavan, Harini ; van Elmpt, Wouter ; Dekker, Andre; Lustberg, Tim; Gooding, Mark. (2017). Data from Lung CT Segmentation Challenge. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2017.3r3fvz08 Publication Citation Yang, J. , Veeraraghavan, H. , Armato, S. G., Farahani, K. , Kirby, J. S., Kalpathy‐Kramer, J. , van Elmpt, W. , Dekker, A. , Han, X. , Feng, X. , Aljabar, P. , Oliveira, B. , van der Heyden, B. , Zamdborg, L. , Lam, D. , Gooding, M. and Sharp, G. C. (2018), Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017. Med. Phys.. . doi: 10.1002/mp.13141 TCIA Citation Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)
DOI: 10.26036/cnp0003539
title: Comparative analysis of soil microbial bacterial colonies of two type of characteristic gingers

description: Ginger is grown worldwide, especially in China, where it has been cultivated for thousands of years, and it is an important cash crop. However, comparative studies on soil microbial communities of characteristic ginger from different regions, especially after flooding, are scarce. In this study, we comprehensively compared the bacterial communities of ginger soils from four different sites.
DOI: 10.6084/m9.figshare.21853782
title: FigS2 CA，EPS,ECP.xlsx

description: Polysaccharide proteins and enzymatic activities of mineralization-inducing ability of two bacteria
DOI: 10.6084/m9.figshare.21853782.v1
title: FigS2 CA，EPS,ECP.xlsx

description: Polysaccharide proteins and enzymatic activities of mineralization-inducing ability of two bacteria
DOI: 10.6084/m9.figshare.21853695
title: Fig2 XRD.xlsx

description: XRD patterns of mineralization inducing ability of different bacteria
DOI: 10.6084/m9.figshare.21853854.v1
title: FigS3 AZ-pH.xlsx

description: pH after inhibition of enzyme activity <br>
DOI: 10.6084/m9.figshare.21853572
title: Fig1 pH, Ca，dry weight.xlsx

description: Physicochemical indicators related to the ability of different bacteria to induce mineralization
DOI: 10.6084/m9.figshare.21853572.v1
title: Fig1 pH, Ca，dry weight.xlsx

description: Physicochemical indicators related to the ability of different bacteria to induce mineralization
DOI: 10.6084/m9.figshare.21853854
title: FigS3 AZ-pH.xlsx

description: pH after inhibition of enzyme activity <br>
DOI: 10.6084/m9.figshare.21853947
title: FigS4 FTIR,Carbonate， TG-DTG.xlsx

description: TG and FTIR of two bacterial induced mineralization
DOI: 10.6084/m9.figshare.21853947.v1
title: FigS4 FTIR,Carbonate， TG-DTG.xlsx

description: TG and FTIR of two bacterial induced mineralization
DOI: 10.5281/zenodo.7519249
title: 学习硕士

description: 学习
DOI: 10.5281/zenodo.7519248
title: 学习硕士

description: 学习
DOI: 10.26180/5044225.v2
title: Deaths registered in Tasmania, 1900-1930

description: Flat file. Complete transcription of deaths registered in Tasmania, 1900-1930, for the fellowship project "Epidemics, mortality and longevity", funded by the Australian Research Council. <br>Hard-copy data provided by the Tasmanian Registry of Births, Deaths and Marriages. Not for general release.
DOI: 10.4225/03/5927b0645e4f9
title: Deaths registered in Tasmania, 1900-1930

description: Flat file. Complete transcription of deaths registered in Tasmania, 1900-1930, for the fellowship project "Epidemics, mortality and longevity", funded by the Australian Research Council. <br>Hard-copy data provided by the Tasmanian Registry of Births, Deaths and Marriages. Not for general release.
DOI: 10.5281/zenodo.7519274
title: National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-temporal Multi-modality datasets

description: Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extract the training samples from the agricultural census information. We focus on Japan and demonstrate how agricultural census data in 2015 can map different crop types for the entire country. Due to the lack of Sentinel-2 datasets in 2015, this study utilized Sentinel-1 and Landsat-8 collected across Japan and combined observations into composites for different prefecture periods (monthly, bimonthly, seasonal). Recent deep learning techniques have been investigated the performance of the samples from agricultural census information.<br> Finally, we obtain nine crop types on a countrywide scale (around 31 million parcels) and compare our results to those obtained from agricultural census testing samples as well as those obtained from recent land cover products in Japan. The generated map accurately represents the distribution of crop types across Japan and achieves an overall accuracy of 87% for nine classes in 47 prefectures.
description: Class numbers 1–9 represented rice, wheat, millet, potato, beans, industrial crops, vegetables, flowers, and others. Agricultural parcels are gathered by hand from high-resolution remote sensing images (0.5 m) by MAFF (https://www.maff.go.jp/j/tokei/porigon/hudeporidl.html). The final results are produced by the random forest with the Sentinel-1/Landsat-8 images (from Google Earth Engine) and the agricultural census information from MAFF.
DOI: 10.5281/zenodo.7519273
title: National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-temporal Multi-modality datasets

description: Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extract the training samples from the agricultural census information. We focus on Japan and demonstrate how agricultural census data in 2015 can map different crop types for the entire country. Due to the lack of Sentinel-2 datasets in 2015, this study utilized Sentinel-1 and Landsat-8 collected across Japan and combined observations into composites for different prefecture periods (monthly, bimonthly, seasonal). Recent deep learning techniques have been investigated the performance of the samples from agricultural census information.<br> Finally, we obtain nine crop types on a countrywide scale (around 31 million parcels) and compare our results to those obtained from agricultural census testing samples as well as those obtained from recent land cover products in Japan. The generated map accurately represents the distribution of crop types across Japan and achieves an overall accuracy of 87% for nine classes in 47 prefectures.
description: Class numbers 1–9 represented rice, wheat, millet, potato, beans, industrial crops, vegetables, flowers, and others. Agricultural parcels are gathered by hand from high-resolution remote sensing images (0.5 m) by MAFF (https://www.maff.go.jp/j/tokei/porigon/hudeporidl.html). The final results are produced by the random forest with the Sentinel-1/Landsat-8 images (from Google Earth Engine) and the agricultural census information from MAFF.
DOI: 10.6084/m9.figshare.21780400
title: Finite element models of the double-spirals used in the article. from Double-spiral: a bioinspired pre-programmable compliant joint with multiple degrees of freedom

description: 2D and 3D finite element models of the double-spirals developed in this study for numerical simulations and 3D printing, respectively.
DOI: 10.6084/m9.figshare.21780400.v1
title: Finite element models of the double-spirals used in the article. from Double-spiral: a bioinspired pre-programmable compliant joint with multiple degrees of freedom

description: 2D and 3D finite element models of the double-spirals developed in this study for numerical simulations and 3D printing, respectively.
DOI: 10.6084/m9.figshare.21747053
title: Table S2: from Single-cell transcriptome sequencing reveals <i>Wolbachia</i>-mediated modification in early stages of <i>Drosophila</i> spermatogenesis

description: All the primers used for PCR and QPCR in the manuscript.