cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22601792
title: Additional file 12 of Single-cell transcriptomics highlights immunological dysregulations of monocytes in the pathobiology of COPD

description: Additional file 12: Dataset 10. Lists for genes expressed in endothelial cells which are strongly correlated with genes from macrophages (Spearman correlation coefficient ≥ 0.8 or ≤ -0.8), related to Fig. 6H. Lists for genes expressed in macrophages which are strongly correlated with the 8 dysregulated sphingolipid metabolic enzyme genes in macrophages (Spearman correlation coefficient ≥ 0.8 or ≤ -0.8), related to Fig. 6H.
DOI: 10.6084/m9.figshare.22601759
title: Additional file 1 of Single-cell transcriptomics highlights immunological dysregulations of monocytes in the pathobiology of COPD

description: Additional file 1. Supplementary Methods.
DOI: 10.6084/m9.figshare.22601780
title: Additional file 8 of Single-cell transcriptomics highlights immunological dysregulations of monocytes in the pathobiology of COPD

description: Additional file 8: Dataset 6. Gene lists for all co-expression gene modules of AT2s detected by WGCNA analysis shown in Fig. 4F.
DOI: 10.6084/m9.figshare.22601780.v1
title: Additional file 8 of Single-cell transcriptomics highlights immunological dysregulations of monocytes in the pathobiology of COPD

description: Additional file 8: Dataset 6. Gene lists for all co-expression gene modules of AT2s detected by WGCNA analysis shown in Fig. 4F.
DOI: 10.6084/m9.figshare.22601795.v1
title: Additional file 13 of Single-cell transcriptomics highlights immunological dysregulations of monocytes in the pathobiology of COPD

description: Additional file 13: Dataset 11. Demographics and clinical data of the 9 patients from Adams’ study integrated by us for validation, related to Fig. S3P, S3Q and Fig. 7.
DOI: 10.6084/m9.figshare.22601762.v1
title: Additional file 2 of Single-cell transcriptomics highlights immunological dysregulations of monocytes in the pathobiology of COPD

description: Additional file 2: Figures S1–S6.
DOI: 10.6084/m9.figshare.22601765.v1
title: Additional file 3 of Single-cell transcriptomics highlights immunological dysregulations of monocytes in the pathobiology of COPD

description: Additional file 3: Dataset 1. Demographics and clinical data of the 9 patients included in this study.
DOI: 10.6084/m9.figshare.22601795
title: Additional file 13 of Single-cell transcriptomics highlights immunological dysregulations of monocytes in the pathobiology of COPD

description: Additional file 13: Dataset 11. Demographics and clinical data of the 9 patients from Adams’ study integrated by us for validation, related to Fig. S3P, S3Q and Fig. 7.
DOI: 10.57745/m2bhsb
title: N-parvum_metadata.tab

description: :unav
DOI: 10.6084/m9.figshare.21551118
title: Additional file 6 of Bromodomain-containing factor GTE4 regulates Arabidopsis immune response

description: Additional file 6: Table S4. List of differentiallyexpressed genes in gte4 under Pst DC3000 condition.
DOI: 10.6084/m9.figshare.22599611
title: Additional file 2 of A new thinking: deciphering the aberrance and clinical implication of copper-death signatures in clear cell renal cell carcinoma

description: Additional file 2: Table S1. List of copper-induced cell death-related genes. Table S2. Clinicopathological features of different subtypes in ccRCC.
DOI: 10.6084/m9.figshare.21551127
title: Additional file 9 of Bromodomain-containing factor GTE4 regulates Arabidopsis immune response

description: Additional file 9: Table S5. List of genes with GTE4enrichment and co-upregulated in gte4under both conditions.
DOI: 10.6084/m9.figshare.22599617.v1
title: Additional file 4 of A new thinking: deciphering the aberrance and clinical implication of copper-death signatures in clear cell renal cell carcinoma

description: Additional file 4: Table S4. List of 138 kinds of small-molecule drugs that could be used as potential drugs for ccRCC.
DOI: 10.5281/zenodo.10628832
title: Metadata - Pharmacological Evidence of the Important Roles of CCR1 and CCR3 and Their Endogenous Ligands CCL2/7/8 in Hypersensitivity Based on a Murine Model of Neuropathic Pain
DOI: 10.5281/zenodo.10628833
title: Metadata - Pharmacological Evidence of the Important Roles of CCR1 and CCR3 and Their Endogenous Ligands CCL2/7/8 in Hypersensitivity Based on a Murine Model of Neuropathic Pain
DOI: 10.5281/zenodo.10628734
title: Crop Health Monitoring Dataset: Disease Classification in Maize, Tea, Tomatoes, Apples, and Beans

description: The Multi-Crop Disease Classification Dataset provides a diverse collection of plant health data across five critical crops: maize, tea, tomatoes, apples, and beans. It serves as a valuable resource for developing and evaluating machine learning models aimed at accurately classifying common diseases affecting these crops, alongside a category representing healthy plants.

Key Features:

The dataset meticulously documents and labels two prevalent diseases for each crop, alongside a category representing healthy plants. To ensure inclusivity, a range of diseases commonly observed in agricultural settings is represented, offering a comprehensive perspective on plant health challenges. Efforts have been made to maintain a balanced distribution of samples across disease classes and crops to enhance model training efficacy and generalization.

Captured using low-cost cameras, the dataset includes images showcasing various stages of disease progression, particularly for crops where visual symptoms are indicative of disease presence. These images are complemented by textual descriptions detailing observable symptoms, enriching the dataset with both visual and textual analysis capabilities. Each data instance is labeled with the corresponding crop type, disease class, and health status, facilitating supervised learning tasks such as disease classification and health monitoring.

Potential Applications:

The dataset is well-suited for training and evaluating classification models, including convolutional neural networks (CNNs), decision trees, and support vector machines (SVMs), for automated disease diagnosis and plant health monitoring. Furthermore, it enables the development of intelligent agricultural systems capable of early disease detection, targeted intervention, and optimized crop management practices. Researchers, academia, and agricultural professionals can utilize the dataset to study disease patterns, devise mitigation strategies, and enhance agricultural productivity and sustainability.
DOI: 10.5281/zenodo.10628735
title: Crop Health Monitoring Dataset: Disease Classification in Maize, Tea, Tomatoes, Apples, and Beans

description: The Multi-Crop Disease Classification Dataset provides a diverse collection of plant health data across five critical crops: maize, tea, tomatoes, apples, and beans. It serves as a valuable resource for developing and evaluating machine learning models aimed at accurately classifying common diseases affecting these crops, alongside a category representing healthy plants.

Key Features:

The dataset meticulously documents and labels two prevalent diseases for each crop, alongside a category representing healthy plants. To ensure inclusivity, a range of diseases commonly observed in agricultural settings is represented, offering a comprehensive perspective on plant health challenges. Efforts have been made to maintain a balanced distribution of samples across disease classes and crops to enhance model training efficacy and generalization.

Captured using low-cost cameras, the dataset includes images showcasing various stages of disease progression, particularly for crops where visual symptoms are indicative of disease presence. These images are complemented by textual descriptions detailing observable symptoms, enriching the dataset with both visual and textual analysis capabilities. Each data instance is labeled with the corresponding crop type, disease class, and health status, facilitating supervised learning tasks such as disease classification and health monitoring.

Potential Applications:

The dataset is well-suited for training and evaluating classification models, including convolutional neural networks (CNNs), decision trees, and support vector machines (SVMs), for automated disease diagnosis and plant health monitoring. Furthermore, it enables the development of intelligent agricultural systems capable of early disease detection, targeted intervention, and optimized crop management practices. Researchers, academia, and agricultural professionals can utilize the dataset to study disease patterns, devise mitigation strategies, and enhance agricultural productivity and sustainability.
DOI: 10.57745/2g3spe
title: B-dothidea_metadata.tab

description: :unav
DOI: 10.57745/zn6vvt
title: N-parvum_SSR_genotypes.tab

description: :unav
DOI: 10.6084/m9.figshare.22599614
title: Additional file 3 of A new thinking: deciphering the aberrance and clinical implication of copper-death signatures in clear cell renal cell carcinoma

description: Additional file 3: Table S3. Recurrent amplification and deletion regions in the two subtypes calculated by GISTIC2.0.
DOI: 10.6084/m9.figshare.22599614.v1
title: Additional file 3 of A new thinking: deciphering the aberrance and clinical implication of copper-death signatures in clear cell renal cell carcinoma

description: Additional file 3: Table S3. Recurrent amplification and deletion regions in the two subtypes calculated by GISTIC2.0.
DOI: 10.6084/m9.figshare.22599617
title: Additional file 4 of A new thinking: deciphering the aberrance and clinical implication of copper-death signatures in clear cell renal cell carcinoma

description: Additional file 4: Table S4. List of 138 kinds of small-molecule drugs that could be used as potential drugs for ccRCC.
DOI: 10.6084/m9.figshare.10022561
title: MOESM3 of Integrative analysis of genetic and epigenetic profiling of lung squamous cell carcinoma (LSCC) patients to identify smoking level relevant biomarkers

description: Additional file 3: Fig. S3. Global DNA methylation status with Hierarchical clustering (a) DNA methylation hierarchical clustering (b) Beta value distribution of global DNA methylation.
DOI: 10.6084/m9.figshare.10022561.v1
title: MOESM3 of Integrative analysis of genetic and epigenetic profiling of lung squamous cell carcinoma (LSCC) patients to identify smoking level relevant biomarkers

description: Additional file 3: Fig. S3. Global DNA methylation status with Hierarchical clustering (a) DNA methylation hierarchical clustering (b) Beta value distribution of global DNA methylation.
DOI: 10.6084/m9.figshare.14213309.v1
title: Chinese tree shrew: a permissive model for in vitro and in vivo replication of human adenovirus species B

description: Human adenovirus (HAdV) species B can cause severe acute respiratory diseases. However, the researches to combat this infection have been hampered by the lack of an animal model permissive to the virus. Here, we report <i>in vitro</i> and <i>in vivo</i> HAdV species B infections of tree shrews, the closest relative of primates. HAdV-3, -7, -14, and -55 efficiently replicated in primary cell cultures. After intranasal inoculation of tree shrews with HAdV-55, the viral replication in the oropharyngeal region remained high until day 5 post-infection and was still detected until day 12. HAdV-55 in the lung or turbinate bone tissues reached the highest levels between days 3 and 5 post-infection, which indicated viral replication in the upper and lower respiratory tracts. HAdV-55 infection caused severe interstitial pneumonia in the animal. IL-8, IL-10, IL-17A, and IFN-γ expression in the peripheral blood mononuclear cells from infected animals was up-regulated. The pre-vaccination with HAdV-55 cleared the virus faster in the respiratory tract, mitigated lung pathological changes. Finally, HAdV-55 infection was propagated among tree shrews. Our study demonstrated that the tree shrew is a permissive animal model for HAdV species B infection and may serve as a valuable platform for testing multiple anti-viral treatments.
DOI: 10.34740/kaggle/dsv/7580527
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.6084/m9.figshare.21551115.v1
title: Additional file 5 of Bromodomain-containing factor GTE4 regulates Arabidopsis immune response

description: Additional file 5: Table S3. List of differentiallyexpressed genes in gte4 under controlcondition.
DOI: 10.5281/zenodo.10555697
title: NorHand v2 / Dataset for Handwritten Text Recognition in Norwegian

description: This dataset comprises Norwegian letter and diary documents from 19th and early 20th century. It can be used to train Handwritten Text Recognition (HTR) models.
DOI: 10.5281/zenodo.10628791
title: NorHand v2 / Dataset for Handwritten Text Recognition in Norwegian

description: This dataset comprises Norwegian letter and diary documents from 19th and early 20th century. It can be used to train Handwritten Text Recognition (HTR) models.
DOI: 10.6084/m9.figshare.21551118.v1
title: Additional file 6 of Bromodomain-containing factor GTE4 regulates Arabidopsis immune response

description: Additional file 6: Table S4. List of differentiallyexpressed genes in gte4 under Pst DC3000 condition.
DOI: 10.57745/goja5s
title: B-dothidea_SSR_genotypes.tab

description: :unav
DOI: 10.6084/m9.figshare.21551127.v1
title: Additional file 9 of Bromodomain-containing factor GTE4 regulates Arabidopsis immune response

description: Additional file 9: Table S5. List of genes with GTE4enrichment and co-upregulated in gte4under both conditions.
DOI: 10.5281/zenodo.10623548
title: CpG sites included in Holstein Friesian signature matrix for cell type deconvolution of blood

description: These files are additional data from the paper 'Profiling the immune epigenome across global cattle breeds' (https://doi.org/10.1186/s13059-023-02964-3). The text file contains the list of CpG sites included in the Holstein Friesian signature matrix (1-based coordinates) generated using CIBERSORTx. The PDF is a heatmap showing the methylation levels of the signature CpG sites across the cell types used to build the matrix.
DOI: 10.5281/zenodo.10623547
title: Holstein Friesian signature matrix for cell type deconvolution of blood

description: These files are additional data from the paper 'Profiling the immune epigenome across global cattle breeds' (https://doi.org/10.1186/s13059-023-02964-3).

HF_SignatureMatrix_Heatmap.pdf: heatmap showing the methylation levels of the CpG sites across the cell types used to build the Holstein Friesian signature matrix.

HF_SignatureMatrix_CpGsites_PercentMeth.txt: list of CpG sites included in the Holstein Friesian signature matrix (1-based coordinates) generated using CIBERSORTx with their percentage methylation across all samples studied. The sample ID indicates the breed (HF denotes Holstein Friesian and ND denotes N'Dama), ENA animal ID (for the corresponding animal ID used in the original manuscript see Additional file 3: Table S10), and cell type (Bcell = B cell, CD4 = CD4 T cell, CD8 = CD8 T cell, gdT = γδ T cell, NK = NK cell, mono = monocyte and gran = granulocyte).

CibersortInput_HF_PercentMeth_Reference.txt: The reference sample file uploaded to Cibersortx to generate the Holstein Friesian signature matrix.

CibersortInput_Phenotypes.txt: The phenotype classes file uploaded to Cibersortx to define the Holstein Friesian reference sample file.
DOI: 10.5281/zenodo.10628740
title: Holstein Friesian signature matrix for cell type deconvolution of blood

description: These files are additional data from the paper 'Profiling the immune epigenome across global cattle breeds' (https://doi.org/10.1186/s13059-023-02964-3).

HF_SignatureMatrix_Heatmap.pdf: heatmap showing the methylation levels of the CpG sites across the cell types used to build the Holstein Friesian signature matrix.

HF_SignatureMatrix_CpGsites_PercentMeth.txt: list of CpG sites included in the Holstein Friesian signature matrix (1-based coordinates) generated using CIBERSORTx with their percentage methylation across all samples studied. The sample ID indicates the breed (HF denotes Holstein Friesian and ND denotes N'Dama), ENA animal ID (for the corresponding animal ID used in the original manuscript see Additional file 3: Table S10), and cell type (Bcell = B cell, CD4 = CD4 T cell, CD8 = CD8 T cell, gdT = γδ T cell, NK = NK cell, mono = monocyte and gran = granulocyte).

CibersortInput_HF_PercentMeth_Reference.txt: The reference sample file uploaded to Cibersortx to generate the Holstein Friesian signature matrix.

CibersortInput_Phenotypes.txt: The phenotype classes file uploaded to Cibersortx to define the Holstein Friesian reference sample file.
DOI: 10.6084/m9.figshare.22599775.v1
title: Additional file 1 of A simple method to isolate structurally and chemically intact brain vascular basement membrane for neural regeneration following traumatic brain injury

description: Additional file 1.
DOI: 10.5281/zenodo.10628867
title: Distribution of 'gewinkt' and 'gewunken' in German newspapers

description: This dataset contains aggregated data of the distribution of the two word forms 'gewinkt' and 'gewunken' as past participles of 'winken' in German newspapers between 1950–2019. The data were taken from Deutsches Referenzkorpus (DeReKo-2023-I), Archive "W-ohneWikipedia-öffentlich - alle öffentlichen Korpora des Archivs W (mit Neuakquisitionen, ohne Wikipedia)".
DOI: 10.5281/zenodo.10628868
title: Distribution of 'gewinkt' and 'gewunken' in German newspapers

description: This dataset contains aggregated data of the distribution of the two word forms 'gewinkt' and 'gewunken' as past participles of 'winken' in German newspapers between 1950–2019. The data were taken from Deutsches Referenzkorpus (DeReKo-2023-I), Archive "W-ohneWikipedia-öffentlich - alle öffentlichen Korpora des Archivs W (mit Neuakquisitionen, ohne Wikipedia)".
DOI: 10.5285/b435d6b4-3628-43cc-aa24-6175ed5eef16
title: Input parameters for ab initio molecular dynamics simulations of water at non-ambient pressure and temperature using the CP2K code

description: This deposit consists of a readme file, which describes the file 'simulationinput.in'. This is a simple text file that contains the information necessary to run any of the ab initio molecular dynamics computer simulations described in the paper that links to this deposit, using the CP2K software package. CP2K is open source.
Paper in press: Mineral–water reactions in Earth’s mantle: predictions from Born theory and ab initio molecular dynamics, Fowler, S. J. and Sherman, D. M. and Brodholt, J. P. and Sherman, D. M. Geochimica et Cosmochimica Acta.
DOI: 10.6084/m9.figshare.7397666
title: Additional file 2: of Dynamic transcriptomes of resistant and susceptible peach lines after infestation by green peach aphids (Myzus persicae SĂźlzer) reveal defence responses controlled by the Rm3 locus

description: Gene expression in peach leaves after aphid infestation. (XLSX 11389 kb)
DOI: 10.6084/m9.figshare.14211329
title: Additional file 6 of Transcriptomes analysis reveals novel insight into the molecular mechanisms of somatic embryogenesis in Hevea brasiliensis

description: Additional file 6: Table S3. FPKM and annotation of histone modifications related genes.
DOI: 10.6084/m9.figshare.19980412
title: Additional file 8 of Systematic analysis and expression profiles of TCP gene family in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn.) revealed the potential function of FtTCP15 and FtTCP18 in response to abiotic stress

description: Additional file 8: Supplementaryfile 8: Table S7. The primers used forqRT-PCR of FtTCPs in this study.
DOI: 10.15468/dl.gs7bhw
title: Occurrence Download

description: A dataset containing 601 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Robinia pseudoacacia L."
 ]
}

 The dataset includes 601 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021371-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.2642279
title: Binary black-hole simulation SXS:BBH:0540

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2626024
title: Binary black-hole simulation SXS:BBH:0540

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2642278
title: Binary black-hole simulation SXS:BBH:0458

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2625935
title: Binary black-hole simulation SXS:BBH:0458

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2642277
title: Binary black-hole simulation SXS:BBH:0334

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.1214424
title: Binary black-hole simulation SXS:BBH:0285

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638783
title: Binary black-hole simulation SXS:BBH:0243

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638780
title: Binary black-hole simulation SXS:BBH:0255

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638772
title: Binary black-hole simulation SXS:BBH:0297

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638771
title: Binary black-hole simulation SXS:BBH:0268

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638770
title: Binary black-hole simulation SXS:BBH:0256

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638746
title: Binary black-hole simulation SXS:BBH:0295

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638743
title: Binary black-hole simulation SXS:BBH:0301

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638742
title: Binary black-hole simulation SXS:BBH:0279

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638741
title: Binary black-hole simulation SXS:BBH:0214

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638740
title: Binary black-hole simulation SXS:BBH:0277

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638739
title: Binary black-hole simulation SXS:BBH:0212

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638730
title: Binary black-hole simulation SXS:BBH:0215

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638729
title: Binary black-hole simulation SXS:BBH:0216

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638728
title: Binary black-hole simulation SXS:BBH:0227

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638727
title: Binary black-hole simulation SXS:BBH:0275

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638724
title: Binary black-hole simulation SXS:BBH:0302

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638723
title: Binary black-hole simulation SXS:BBH:0218

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638793
title: Binary black-hole simulation SXS:BBH:0254

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2638792
title: Binary black-hole simulation SXS:BBH:0258

description: Simulation of a black-hole binary system evolved by the SpEC code.