cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13145/bacdive165544.20221219.7.1
title: <i>Sphingomonas</i> <i>parapaucimobilis</i> Yabuuchi <i>et al.</i> 1990

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive165545.20220920.7
title: <i>Sphingomonas</i> <i>paucimobilis</i> (Holmes <i>et al.</i> 1977) Yabuuchi <i>et al.</i> 1990

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive165545.20221219.7.1
title: <i>Sphingomonas</i> <i>paucimobilis</i> (Holmes <i>et al.</i> 1977) Yabuuchi <i>et al.</i> 1990

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive165546.20220920.7
title: <i>Sphingomonas</i> <i>paucimobilis</i> (Holmes <i>et al.</i> 1977) Yabuuchi <i>et al.</i> 1990

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive165546.20221219.7.1
title: <i>Sphingomonas</i> <i>paucimobilis</i> (Holmes <i>et al.</i> 1977) Yabuuchi <i>et al.</i> 1990

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive165547.20220920.7
title: <i>Sphingomonas</i> <i>parapaucimobilis</i> Yabuuchi <i>et al.</i> 1990

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive165547.20221219.7.1
title: <i>Sphingomonas</i> <i>parapaucimobilis</i> Yabuuchi <i>et al.</i> 1990

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.5523/bris.3dbgv3yfqsocq249pro82kazil
title: Long-Term Analysis of a Psychoeducational Course on University Students’ Mental Well-Being

description: We have previously found that the psychoeducational 'Science of Happiness' course has a beneficial effect on participant well-being (Hood et al, 2021; Hobbs et al, 2022). In this study, we examine whether these benefits are sustained 1-2 years post course.
DOI: 10.26180/22590316.v2
title: The PCOS Phenotype in Unselected Populations Study (P-PUP)

description: This project consists of a data repository that will be used to define normal ranges and diagnostic cut-offs for all PCOS diagnostic features using individual participant data (IPD) from unselected populations around the world. It includes data of over 10,000 participants within the age range of 15 to 80 years from various PCOS research collaborating centres in China, USA, Italy, South Korea, Iran, Turkey, Russia and Nigeria. With evolving and new evidence, more collaborations are anticipated.
DOI: 10.26180/22590316.v1
title: The PCOS Phenotype in Unselected Populations Study (P-PUP)

description: This project consists of a data repository that will be used to define normal ranges and diagnostic cut-offs for all PCOS diagnostic features using individual participant data (IPD) from unselected populations around the world. It includes data of over 10,000 participants within the age range of 15 to 80 years from various PCOS research collaborating centres in China, USA, Italy, South Korea, Iran, Turkey, Russia and Nigeria. With evolving and new evidence, more collaborations are anticipated.
DOI: 10.5281/zenodo.7880588
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2018

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 LAI product at 1km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2018. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2017</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2019</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2018 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7880587
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2018

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 LAI product at 1km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2018. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2017</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2019</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2018 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.6084/m9.figshare.21716945.v1
title: Raw Data of BiSb

description: Raw Data of BiSb
DOI: 10.6084/m9.figshare.21716945
title: Raw Data of BiSb

description: Raw Data of BiSb
DOI: 10.6084/m9.figshare.19656744
title: Peucedanum praeruptorum genome assembly data

description: Peucedanum praeruptorum genome assembly data
DOI: 10.5281/zenodo.7880335
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2018

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 500m spatial resolution and monthly 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 500m 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 2018. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2017</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2019</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2018 Spatial Resolution: 500m Temporal Resolution: 1 month 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 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.7880334
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2018

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 500m spatial resolution and monthly 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 500m 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 2018. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2017</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2019</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2018 Spatial Resolution: 500m Temporal Resolution: 1 month 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 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: 10.34740/kaggle/dsv/5569615
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.26180/21391629
title: ASPREE Whole Genome Sequencing Dataset

description: Garvan Institute of Medical Research launched the Medical Genome Reference Bank, the world’s largest publicly available ‘genome bank’ specifically of healthy older people, an initiative of Garvan’s Kinghorn Centre for Clinical Genomics and NSW Health. Because it includes only the genomes of individuals over the age of 70 who have no history of major disease (cardiovascular disease, neurodegenerative disease or cancer), the Reference Bank is expected to be relatively free of genetic variants associated with disease – making it a powerful filter, or ‘control’, for accelerating genomic discovery in medical research. In addition, it will aid in the diagnosis of genetic disease and may shed light on mechanisms of healthy aging. <br> To make data requests, you may undertake an approval process by contacting Paul Lacaze on Paul.Lacaze@monash.edu
DOI: 10.26180/21097654
title: ASPREE Genome-wide SNP Genotyping Dataset

description: ASPirin in Reducing Events in the Elderly (ASPREE) is a clinical trial and longitudinal study of healthy ageing, involving 16,703 Australians aged over 70 years and 2,411 Americans aged over 65 enrolled. The primary ASPREE trial has received &gt;$60M in NIH funding since 2012. Each ASPREE participants’ health is tracked longitudinally through extensive phenotyping and collection of clinical outcome data. The ASPREE Healthy Ageing Biobank is an associated biorepository of blood, saliva and urine samples from &gt;15,000 ASPREE participants, 10,000 of whom have now provided a matched follow-up 3-year sample. Biospecimens are consented for genetic and biomarker studies, enabling ASPREE to conduct molecular epidemiology and healthy ageing research. ASPREE facilitates over 12 sub-studies funded through NHMRC. Genome wide association analysis has been undertaken in multiple areas: All-cause and vascular dementia Stroke Genetic modifiers for Alzheimer's disease Poygenic resilence scores capture genetic effect for Alzheimers disease Genome wide association analysis for Alzheimers Disease https://bioplatforms.com/projects/aspree-framework-initiative/ To make data requests, you may undertake an approval process by contacting Paul Lacaze on Paul.Lacaze@monash.edu <br> <br>
DOI: 10.34740/kaggle/dsv/5569683
title: Polish-Ukrainian translations from Tatoeba
DOI: 10.26180/20296554
title: Chapter 2. Genomic variants associated with modulating risk of anti-seizure medication induced Steven-Johnson syndrome/Toxic epidermal necrolysis.

description: This contains the data used for the genomic variant analysis. The final ASM plink file (.bim, .bed, .log, .fam) Text file to obtain perform the sub analysis All association files (.assoc) Zoomlocus code. R code for generating the QQ and manhattan pltos Race text file <br>
DOI: 10.5281/zenodo.7880907
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2017

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 LAI product at 1km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2017. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2016</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2018</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2017 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7880906
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2017

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 LAI product at 1km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2017. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2016</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2018</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2017 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7882217
title: Data set: genetic variants related to SARS-CoV-2 viremia

description: Data set generated to evaluate the association between SNPs from genes related to SARS-CoV-2 pathogenesis and the presence of viremia.
DOI: 10.5281/zenodo.7882216
title: Data set: genetic variants related to SARS-CoV-2 viremia

description: Data set generated to evaluate the association between SNPs from genes related to SARS-CoV-2 pathogenesis and the presence of viremia.
DOI: 10.25959/nj1h-8312
title: Geospatial data for tectonic model development

description: This dataset consists of various raster and gpkg feature classes which formed most of the data used to guide construction of tectonic elements and GPlates model discussed in chapters 3 and 4 of the thesis. Most of these datasets are from freely-available government sources. Those which were mentioned in the thesis but not included in this data package are predominantly sourced from supplementary data packages in other research studies. A README file is contained with more information.
DOI: 10.25959/s43s-2r98
title: REVISED May 2023: Lachlan Orogen GPlates model August 2021

description: REVISED as of May 2023. Model data and GPlates project file for Cambrian-Devonian reconstruction of the Lachlan Orogen integrated into a global model (Wright et al., 2013 - doi:10.5194/bg-10-1529-2013). All spatial datasets are provided in .gpml format and rotation files in .grot format.
DOI: 10.25959/bb58-0977
title: Animations for GPlates model of the Lachlan Orogen

description: A series of videos showing animations captured directly from the GPlates visualisation interface, with annotations added at key times. Animations begin at the start-time indicated by the first number of the filename in Ma, and end at the second number in Ma. They start playing forwards in time and then play backwards in the second half. An additional animation is also provided, depicting the Tasmanian Orocline model with figures from Cayley (2015).
DOI: 10.6084/m9.figshare.22724066
title: Profitable Production Problem (PRP) SAT benchmarks

description: Profitable Production Problem (PRP) SAT benchmarks for SAT Competition-2023.
DOI: 10.6084/m9.figshare.22724066.v1
title: Profitable Production Problem (PRP) SAT benchmarks

description: Profitable Production Problem (PRP) SAT benchmarks for SAT Competition-2023.
DOI: 10.25914/pptj-g181
title: FourCastNet Data, Predictions and Models