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1,548 results for “Trajectories”
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y9 & Y10
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 2004 (Y9) and 2005 (Y10).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y7 & Y8
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 2002 (Y7) and 2003 (Y8).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y11 & Y12
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 2006 (Y11) and 2007 (Y12).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y3 & Y4
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 1998 (Y3) and 1999 (Y4).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y11 & Y12
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 2006 (Y11) and 2007 (Y12).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 1000m Y1 & Y2
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 1000m particles releases for the years 1996 (Y1) and 1997 (Y2).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 1000m Y13 & Y14
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 1000m particles releases for the years 2008 (Y13) and 2009 (Y14).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 1000m Y15 & Y16
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 1000m particles releases for the years 2010 (Y15) and 2011 (Y16).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 1000m Y9 & Y10
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 1000m particles releases for the years 2004 (Y9) and 2005 (Y10).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 1000m Y3 & Y4
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 1000m particles releases for the years 1998 (Y3) and 1999 (Y4).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 1000m Y7 & Y8
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 1000m particles releases for the years 2002 (Y7) and 2003 (Y8).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 1000m Y19 & Y20
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 1000m particles releases for the years 2014 (Y19) and 2015 (Y20).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 1000m Y17 & Y18
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 1000m particles releases for the years 2012 (Y17) and 2013 (Y18).</p>
The ECS-FVCOM results and also the input variables of FMGDM, the initial particles, satellite pictures, and trajectories data
<p>The data for the manuscript: A Lagrangian-based Floating Macro<br> -algal Growth and Drift Model (FMGDM v1.0): application to the Yellow Sea green tides. <br> -----------------------------------------------------------------------------<br> Updata: 2021/03/18</p> <p>-----------------------------------------------------------------------------<br> Part 1: The ECS-FVCOM results and also the input variables of FMGDM<br> Part 2: The initial particles position imformation (lat, lon, depth)<br> Part 3: The satellite pictures of green tides in YS, 2014 and 2015</p> <p>Part 4: Drifters trajectories dataset (lat, lon)</p> <p> </p> <p>=============================================</p> <p>Version 5 updata: 2021/12/20</p> <p>Note: Modified and added some missing variables ('omega') in Part1,</p> <p> the results of ECS-FVCOM</p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data : </strong></p> <p>1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis<br> 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation<br> 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s</p> <p> </p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p> </p> <p><strong>Data:</strong></p> <p>1. Immgen_cell_types.cls : Microarray Phase 1 expression</p> <p>2. Immgen_norm_exp_data.gct : Microarray Phase 1 class</p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis</p> <p><br> </p> <p> </p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary:</strong> Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>MDAlab_cDC1_maturation.tar : Docker image used for the analysis</p>
Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection
<p><strong>scRNA-seq data</strong></p> <p>Data were processed using cellranger v 4.0.0 with the refdata-gex-GRCh38-2020-A reference.</p> <p><em>h5files.zip</em>: contains all h5-Files of raw feature-barcode counts (e.g. 20094_0001_A_B_raw_feature_bc_matrix.new.h5 )</p> <p><em>raw_feature_bc_matrices.zip</em>: contains the <em>same data</em> as h5files.zip, but also in mtx-format.</p> <p>covid_object_ncomms<em>.RDS</em>: contains the Seurat file with which all analyses were conducted.</p> <p><em>samples2condition.df</em>: text file containing sample to condition information</p> <p><strong>Bulk RNA-seq</strong></p> <p><em>covid_bulk.zip</em> contains the count matrices extracted from the zUMIs runs for the bulk cohort.</p> <p><em>nasal_swabs.zip</em> contains the count matrices extracted from the zUMIs run for the nasal swab cohort.</p> <p>The extracted count matrices were then used with the bulk analysis scripts provided with the source code.</p> <p><strong>Source Code</strong></p> <p>All <strong>source code</strong> for the publication is available from: <a href="https://github.com/mjoppich/covidSC">https://github.com/mjoppich/covidSC</a> or from tagged releases: <a href="https://github.com/mjoppich/covidSC/releases/tag/ncomms">https://github.com/mjoppich/covidSC/releases/tag/ncomms</a></p> <p>When using any of these data, please cite:<br> <br> Pekayvaz et al., Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection, Nature Communications 2022</p>
QM/MM nonadiabatic trajectories for cytosine in argon, benzene and water
<p>The dataset includes all trajectories for the nonadiabatic (QM/MM) molecular dynamics simulations of cytosine in argon, benzene and water. Each tar.gz directory contains two directories: INPUT and RESULTS. In this latter one has the 50 trajectories for cytosine in argon and benzene and the 75 trajectories for cytosine in water. In the files named dyn.out one has the atomic coordinates, velocities and energies for each time step.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.