cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5x05xp3c
title: MassIVE MSV000091931 - Development of a deep proteomic pipeline for recalcitrant olive leaf tissue
title: Proteomic analysis is a powerful tool to unravel the complexity of plant cellular processes that underpin the regulation of plant immunity. A major challenge is the improvement of the detectable fraction of the crop proteome that is still markedly lower compared to other omics, such as next generation sequencing technologies. This is due in part to the occurrence of large amounts of secondary compounds, which co-precipitate with proteins and severely interfere with the analysis. Olive leaf tissue is notoriously recalcitrant to common protein extraction methods due to high levels of interfering compounds, hence hampering deep proteomic investigations. The interest in the chemical composition of olive leaves has increased with the scope to re-evaluate this agricultural waste byproduct as their extracts are enriched in diverse bioactive compounds. Many of these secondary metabolites are involved in the defense systems along with their biosynthesis enzymes, whose activity is usually cultivar- and stimuli-dependent. Despite olive leaf proteomics providing important insights into the defense pathways as well as health diagnostic biomarkers, it has received much less attention compared to oil, drupes, seed and pollen tissues. Our study aims to overcome these hurdles and expand the application of deep proteomic analyses to olive leaves. We developed a complete proteomic pipeline, from sample preparation to LC-HRMS and data analyses, allowing the first comparative proteomic study among three Italian olive cultivars, i.e., Leccino, Ogliarola and Coratina, known to exhibit different susceptibility to Xylella fastidiosa infections, and enabling the detection of 1.922 proteins. Olive proteomic research is expected to become an essential part of integrated omics approaches; thus, our study is a significant contribution, paving the way to unravel the molecular complexity underlying the genotype-dependent immune response to stress.
DOI: 10.6084/m9.figshare.22814648
title: Dataset for CCR study

description: Data set with 103 countries <br>
DOI: 10.5281/zenodo.7930286
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2009

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 1 km 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 1 km 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 2009. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2008</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2010</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2009 Spatial Resolution: 1 km 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.7930285
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2009

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 1 km 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 1 km 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 2009. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2008</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2010</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2009 Spatial Resolution: 1 km 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/5673126
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.6084/m9.figshare.22814774
title: Ambulatory training.csv

description: Data of training
DOI: 10.6084/m9.figshare.22814774.v1
title: Ambulatory training.csv

description: Data of training
DOI: 10.5281/zenodo.7931581
title: North Atlantic vertical velocity estimates from OGCM geostrophic meridional velocities

description: Annual vertical velocity (w_GLVB) estimates from OGCM geostrophic velocities within the North Atlantic ocean during the 1960-2015 period. The w fields are computed following the methodology described in <em>Cortés-Morales and Lazar, submitted.</em> ---------------------------------------------------------------------------------------- Geographical coverage: North Atlantic Ocean [107ºW-25ºE/12ºS-75ºN] Grid and horizontal spatial resolution: 1/4º (ORCA025 grid) Vertical levels: 58 levels from sigma level 21 to 28.05 kg m<sup>-3</sup> Temporal resolution: Annual (1960-2015) ----------------------------------------------------------------------------------------- Variables: nav_lon (2D) nav_lon (2D) isop_level (1D) time (1D) w_GLVB (4D)
DOI: 10.5281/zenodo.7931580
title: North Atlantic vertical velocity estimates from OGCM geostrophic meridional velocities

description: Annual vertical velocity (w_GLVB) estimates from OGCM geostrophic velocities within the North Atlantic ocean during the 1960-2015 period. The w fields are computed following the methodology described in <em>Cortés-Morales and Lazar, submitted.</em> ---------------------------------------------------------------------------------------- Geographical coverage: North Atlantic Ocean [107ºW-25ºE/12ºS-75ºN] Grid and horizontal spatial resolution: 1/4º (ORCA025 grid) Vertical levels: 58 levels from sigma level 21 to 28.05 kg m<sup>-3</sup> Temporal resolution: Annual (1960-2015) ----------------------------------------------------------------------------------------- Variables: nav_lon (2D) nav_lon (2D) isop_level (1D) time (1D) w_GLVB (4D)
DOI: 10.25582/data.2022-05.2515727/1930525
title: Data for EMSL Project 51568 from May 2022
DOI: 10.25582/data.2022-05.2515726/1933100
title: Data for EMSL Project 51266 from May 2022
DOI: 10.6084/m9.figshare.22814816
title: Supplementary materials.xlsx

description: These are the Global Times Editorials list and the keywords list for generating concordance lines. The data is only for peer review.
DOI: 10.6084/m9.figshare.22814816.v1
title: Supplementary materials.xlsx

description: These are the Global Times Editorials list and the keywords list for generating concordance lines. The data is only for peer review.
DOI: 10.6084/m9.figshare.22579582
title: test.csv

description: test
DOI: 10.6084/m9.figshare.22579582.v2
title: test.csv

description: test
DOI: 10.6084/m9.figshare.22579582.v1
title: individual CMM emissions.csv

description: individual coal mine methane emission
DOI: 10.6084/m9.figshare.22814819
title: Data of <strong>"The Effect of High Calorie Formula on Weight, Height Increment, IGF-1 and TLC</strong> <strong>in Growth Faltering Children"</strong>

description: Growth faltering in children aged 1-2 years is a common nutritional problem in developing countries that leads to undernutrition, especially stunting, a worldwide burden that refers to children with current weight or height or weight gain rate and height below the expected value compared to similar children at the same age, sex, and ethnicity. IGF-I is closely associated with weight and nutritional intake, especially milk and animal protein as amino acids sources. IGF-1 is affected by undernutrition, followed by thyroid and growth hormone (GH). Growth faltering affects immune functions and can develop into undernutrition with complicated consequences with menial change or even an increase in lymphocyte count. Total lymphocyte count (TLC) has been used as one prognostic variable to sense malnutrition, as its decrease can be correlated with malnutrition. This study investigates the effect of using high-calorie formula as an intervention on weight, height increment, IGF-1 and TLC in children with growth faltering or undernutrition.
DOI: 10.6084/m9.figshare.22814834
title: Culture-negative acute cystitis

description: This data was used to write the paper "Shotgun metagenomic sequencing of urine from patients having acute-cystitis-like symptoms with negative urine culture".
DOI: 10.6084/m9.figshare.22814834.v1
title: Culture-negative acute cystitis

description: This data was used to write the paper "Shotgun metagenomic sequencing of urine from patients having acute-cystitis-like symptoms with negative urine culture".
DOI: 10.6084/m9.figshare.22814822
title: ASV.table

description: Microbial diversity in four glacial cryoconites on the Tibetan Plateau. (Tanggula Glacier, Palong No.4 Glacier, Qiangyong Glacier, Jiemayangzong Glacier).
DOI: 10.6084/m9.figshare.22814822.v2
title: ASV.table

description: Microbial diversity in four glacial cryoconites on the Tibetan Plateau. (Tanggula Glacier, Palong No.4 Glacier, Qiangyong Glacier, Jiemayangzong Glacier).
DOI: 10.6084/m9.figshare.22814822.v1
title: ASV.xlsx

description: Microbial diversity in four glacial cryoconites on the Tibetan Plateau. (Tanggula Glacier, Palong No.4 Glacier, Qiangyong Glacier, Jiemayangzong Glacier).
DOI: 10.21227/rc3z-fk71
title: Single-Cell RNA Sequencing Identifies Extracellular Matrix Gene Expression by Pancreatic Circulating Tumor Cells

description: 循环肿瘤细胞 （CTC） 从原发性脱落 肿瘤进入血液，介导癌症向远处器官的血源性扩散。自 定义它们的组成，我们比较了胰腺癌小鼠模型中CTCs与匹配原发肿瘤的全基因组表达谱， 使用抗性表位分离单个CTC 微流体捕获，然后是单细胞RNA 测 序。CTC 与主要 CTC 分开聚集 肿瘤和肿瘤来源的细胞系，显示低增殖特征，干细胞相关基因 Aldh1a2 富集，上皮和间充质标志物的双表型表达，以及 Igfbp5的表达，一种富集在 上皮-基质界面。鼠标以及 人胰腺CTC表现出非常高的基质衍生细胞外基质（ECM）表达 蛋白质，包括SPARC，其敲低 癌细胞抑制细胞迁移和侵袭性。CTCs对基质的异常表达 ECM基因指出它们对癌症扩散到远处的微环境信号的贡献 器官
DOI: 10.5281/zenodo.7314837
title: Efficient Real-Time Selective Genome Sequencing on Resource-Constrained Devices

description: This dataset contains the curated nanopore raw signal data in BLOW5 format used to benchmark Hardware Accelerated Read Until (HARU). This dataset was created by using the publicly available datasets: SARS-CoV-2 SP1 (1.382M reads) and NA12878 PromethION subset (500,000 reads). The tarball when extracted will have the following directory structure: haru-data<br> ├── na12878-rfc1<br> │ ├── blow5-rawsignal<br> │ │ ├── na12878_dna_0.blow5<br> │ │ ├── na12878_dna_100.blow5<br> │ │ ├── na12878_dna_101.blow5<br> │ │ ├── na12878_dna_102.blow5<br> │ │ ├── ...<br> │ └── reference<br> │ └── rfc1.fa<br> └── SARS-CoV-2-sp1<br> ├── blow5-rawsignal<br> │ ├── readgroup0<br> │ │ ├── reads_0_0.blow5<br> │ │ ├── reads_0_10.blow5<br> │ │ ├── reads_0_11.blow5<br> │ │ ├── reads_0_12.blow5<br> │ │ ├── ...<br> │ └── readgroup1<br> │ ├── reads_1_0.blow5<br> │ ├── reads_1_10.blow5<br> │ ├── reads_1_11.blow5<br> │ ├── ....<br> └── reference<br> └── nCoV-2019.reference.fasta nCoV-2019.reference.fasta is the SARS-CoV-2 MN908947.3 reference genome. rfc1.fa is the genomic region hr4:39262456-39391375 extracted from hg38 human genome.
DOI: 10.5281/zenodo.4472682
title: The turbulent flow over the BARC rectangular cylinder: a DNS study

description: This set of results stems from a database computed with a Direct Numerical simulation of the incompressible flow around a rectangular cylinder with chord-to-thickness ratio 5:1 (also known as BARC benchmark). The Reynolds number based on the cylinder thickness and the incoming velocity is set to Re=3000. We provide the two-dimensional mean field as well as the complete set of Reynolds stresses and the terms involved in their single-point budget equations. Further details can be found on an accompanying paper by Chiarini & Quadrio, Flow Turbul. Combust. 107, 875–899 (2021), available at https://doi.org/10.1007/s10494-021-00254-1. Data are provided in VTK file format, so that they can be visualized with several applications, as for example the open-source package ParaView. The file mean.vtk contains the mean flow in terms of the velocity components U and V and pressure field P in the x − y plane.<br> The files uu-Budget.vtk, vv-Budget.vtk, ww-Budget.vtk, uv-Budget.vtk, uw-Budget.vtk, vw-Budget.vtk and k-Budget.vtk contain the complete set of terms appearing in the budget equations for the components of the Reynolds stress tensor and for the turbulent kinetic energy.
DOI: 10.5281/zenodo.4472681
title: The turbulent flow over the BARC rectangular cylinder: a DNS study

description: This set of results stems from a database computed with a Direct Numerical simulation of the incompressible flow around a rectangular cylinder with chord-to-thickness ratio 5:1 (also known as BARC benchmark). The Reynolds number based on the cylinder thickness and the incoming velocity is set to Re=3000. We provide the two-dimensional mean field as well as the complete set of Reynolds stresses and the terms involved in their single-point budget equations. Further details can be found on an accompanying paper by Chiarini & Quadrio, Flow Turbul. Combust. 107, 875–899 (2021), available at https://doi.org/10.1007/s10494-021-00254-1. Data are provided in VTK file format, so that they can be visualized with several applications, as for example the open-source package ParaView. The file mean.vtk contains the mean flow in terms of the velocity components U and V and pressure field P in the x − y plane.<br> The files uu-Budget.vtk, vv-Budget.vtk, ww-Budget.vtk, uv-Budget.vtk, uw-Budget.vtk, vw-Budget.vtk and k-Budget.vtk contain the complete set of terms appearing in the budget equations for the components of the Reynolds stress tensor and for the turbulent kinetic energy.
DOI: 10.5281/zenodo.7927852
title: Forest Types Show Divergent Biophysical Responses After Fire: Challenges to Ecological Modeling

description: Datasets and scripts (MATLAB and R) used to document patterns of post-fire biophysical dynamics in seven forest types and 21 Level III ecoregions of the western United States.
DOI: 10.5281/zenodo.7927851
title: Forest Types Show Divergent Biophysical Responses After Fire: Challenges to Ecological Modeling

description: Datasets and scripts (MATLAB and R) used to document patterns of post-fire biophysical dynamics in seven forest types and 21 Level III ecoregions of the western United States.
DOI: 10.6084/m9.figshare.22815018
title: Additional file 1 of The VGNC: expanding standardized vertebrate gene nomenclature

description: Additional file 1: Table S1. Summary of cytochrome P450 genes approved in VGNC.
DOI: 10.6084/m9.figshare.22815018.v1
title: Additional file 1 of The VGNC: expanding standardized vertebrate gene nomenclature

description: Additional file 1: Table S1. Summary of cytochrome P450 genes approved in VGNC.
DOI: 10.6084/m9.figshare.22815021
title: Additional file 2 of The VGNC: expanding standardized vertebrate gene nomenclature

description: Additional file 2: Table S2. Comparison of gene symbols for curated human-mouse orthologs.
DOI: 10.6084/m9.figshare.22815021.v1
title: Additional file 2 of The VGNC: expanding standardized vertebrate gene nomenclature

description: Additional file 2: Table S2. Comparison of gene symbols for curated human-mouse orthologs.
DOI: 10.6084/m9.figshare.22815024
title: Additional file 3 of The VGNC: expanding standardized vertebrate gene nomenclature

description: Additional file 3: Table S3. HGNC genes without VGNC approved orthologs as of January 2022.
DOI: 10.6084/m9.figshare.22815024.v1
title: Additional file 3 of The VGNC: expanding standardized vertebrate gene nomenclature

description: Additional file 3: Table S3. HGNC genes without VGNC approved orthologs as of January 2022.
DOI: 10.6084/m9.figshare.22815027
title: Additional file 4 of The VGNC: expanding standardized vertebrate gene nomenclature

description: Additional file 4: Table S4. VGNC approved orthologs of human unitary pseudogenes.
DOI: 10.6084/m9.figshare.22815027.v1
title: Additional file 4 of The VGNC: expanding standardized vertebrate gene nomenclature

description: Additional file 4: Table S4. VGNC approved orthologs of human unitary pseudogenes.
DOI: 10.6084/m9.figshare.22815072
title: Additional file 6 of Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning

description: Additional file 6: Table S4. Detailed DFS risk scores and related variables.
DOI: 10.6084/m9.figshare.22815075
title: Additional file 7 of Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning

description: Additional file 7: Table S5. Detailed OS risk scores and related variables.
DOI: 10.6084/m9.figshare.22815075.v1
title: Additional file 7 of Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning

description: Additional file 7: Table S5. Detailed OS risk scores and related variables.
DOI: 10.6084/m9.figshare.22815078
title: Additional file 8 of Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning

description: Additional file 8: Table S6. Detailed DFS risk scores and related factors of patients younger than 46 years old.
DOI: 10.6084/m9.figshare.22815072.v1
title: Additional file 6 of Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning

description: Additional file 6: Table S4. Detailed DFS risk scores and related variables.
DOI: 10.6084/m9.figshare.22815078.v1
title: Additional file 8 of Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning

description: Additional file 8: Table S6. Detailed DFS risk scores and related factors of patients younger than 46 years old.
DOI: 10.6084/m9.figshare.22815090
title: Additional file 2 of Genomic insights into cryptic cycles of microbial hydrocarbon production and degradation in contiguous freshwater and marine microbiomes

description: Additional file 2: Supplementary Dataset 1. Detail of the genes (KO) identified in the MAGs involved in hydrocarbon production and degradation.
DOI: 10.6084/m9.figshare.22815090.v1
title: Additional file 2 of Genomic insights into cryptic cycles of microbial hydrocarbon production and degradation in contiguous freshwater and marine microbiomes

description: Additional file 2: Supplementary Dataset 1. Detail of the genes (KO) identified in the MAGs involved in hydrocarbon production and degradation.
DOI: 10.6084/m9.figshare.22815096
title: Additional file 2 of UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

description: Additional file 2: Table S9. All the raw in-house data were both manually analyzed and through UmetaFlow for method validation.
DOI: 10.6084/m9.figshare.22815096.v1
title: Additional file 2 of UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

description: Additional file 2: Table S9. All the raw in-house data were both manually analyzed and through UmetaFlow for method validation.
DOI: 10.6084/m9.figshare.22815099
title: Additional file 3 of UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

description: Additional file 3: Table S10. Feature detection, structural and formula predictions for commercial standards germicidins A and B, kanamycin, tetracycline hydrochloride, thiostreptone, globomycin, ampicillin and apramycin.
DOI: 10.6084/m9.figshare.22815099.v1
title: Additional file 3 of UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

description: Additional file 3: Table S10. Feature detection, structural and formula predictions for commercial standards germicidins A and B, kanamycin, tetracycline hydrochloride, thiostreptone, globomycin, ampicillin and apramycin.
DOI: 10.6084/m9.figshare.22815102
title: Additional file 4 of UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

description: Additional file 4: Table S11. Feature detection, structural and formula predictions for kirromycin and desferrioxamine B from extracts of Streptomyces collinus Tü 365 and epemicins A and B from extracts of Kutzneria sp. CA-103260.
DOI: 10.6084/m9.figshare.22815102.v1
title: Additional file 4 of UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

description: Additional file 4: Table S11. Feature detection, structural and formula predictions for kirromycin and desferrioxamine B from extracts of Streptomyces collinus Tü 365 and epemicins A and B from extracts of Kutzneria sp. CA-103260.
DOI: 10.6084/m9.figshare.22815122
title: Additional file 1 of Comparison of Illumina and Oxford Nanopore Technology for genome analysis of Francisella tularensis, Bacillus anthracis, and Brucella suis

description: Additional file 1: Table S1. Quality measures of raw sequencing data.
DOI: 10.6084/m9.figshare.22815122.v1
title: Additional file 1 of Comparison of Illumina and Oxford Nanopore Technology for genome analysis of Francisella tularensis, Bacillus anthracis, and Brucella suis

description: Additional file 1: Table S1. Quality measures of raw sequencing data.
DOI: 10.6084/m9.figshare.22815125
title: Additional file 2 of Comparison of Illumina and Oxford Nanopore Technology for genome analysis of Francisella tularensis, Bacillus anthracis, and Brucella suis

description: Additional file 2: Table S2. Quality measures of assembled genomes.
DOI: 10.6084/m9.figshare.22815125.v1
title: Additional file 2 of Comparison of Illumina and Oxford Nanopore Technology for genome analysis of Francisella tularensis, Bacillus anthracis, and Brucella suis

description: Additional file 2: Table S2. Quality measures of assembled genomes.