cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5385611
title: Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

description: <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. <strong>Data: </strong> MDAlab_cDC1_maturation.tar : Docker image used for the analysis
DOI: 10.5281/zenodo.5385610
title: Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

description: <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. <strong>Data: </strong> MDAlab_cDC1_maturation.tar : Docker image used for the analysis
DOI: 10.6084/m9.figshare.19697413.v2
title: Other data from "Notch-dependent and -independent transcription are modulated by tissue movements at gastrulation"

description: Other live imaging data from "Levels of Notch-regulated transcription are modulated by tissue movements at gastrulation": - raw MS2 Live imaging movies: 58 '.lif' files captured in a Leica SP8 confocal. Each is a 1-2h movie of a developing fly embryo in the genetic condition described in the file name. The metadata file contains the information linking each movie with parameters used for image analysis and identifier of the genetic condition ("nickname" column). - data obtained from image analysis: 57 '.mat' files obtained from MATLAB after image analysis for each movie. README contains information of the data structure and all variables stored in each.
DOI: 10.5281/zenodo.5512569
title: Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

description: <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. <strong>Data : </strong> 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
DOI: 10.5281/zenodo.5512568
title: Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

description: <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. <strong>Data : </strong> 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
DOI: 10.5281/zenodo.5511970
title: Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

description: <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. <strong>Data:</strong> 1. Immgen_cell_types.cls : Microarray Phase 1 expression 2. Immgen_norm_exp_data.gct : Microarray Phase 1 class
DOI: 10.5281/zenodo.5511969
title: Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

description: <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. <strong>Data:</strong> 1. Immgen_cell_types.cls : Microarray Phase 1 expression 2. Immgen_norm_exp_data.gct : Microarray Phase 1 class
DOI: 10.5517/ccdc.csd.cc25xypr
title: CCDC 2023825: Experimental Crystal Structure Determination

description: Related Article: Fan Gao, Wanru Xiao, Shujun Li, Bing Yu|2022|ACS Applied Materials and Interfaces|14|19278|doi:10.1021/acsami.1c23753
DOI: 10.25824/redu/zev58a
title: Zeta potential and hydrodynamic diameter.docx
DOI: 10.25824/redu/sq0ocp
title: Zeta potential and hydrodynamic diameter of pure gellan gum (G) solution, pure lactoferrin (L) solution and lactoferrin-gellan nanoparticles

description: Zeta potential and hydrodynamic diameter of pure gellan gum (G) solution, pure lactoferrin (L) solution and nanoparticles composed by different proportions of lactoferrin and gellan gum.
DOI: 10.5517/ccdc.csd.cc2b4s4f
title: CCDC 2149544: Experimental Crystal Structure Determination

description: Related Article: Alexander V. Artem'ev, Andrey Yu. Baranov, Irina Yu. Bagryanskaya|2022|Inorg.Chem.Commun.|140|109478|doi:10.1016/j.inoche.2022.109478
DOI: 10.5517/ccdc.csd.cc2b4s5g
title: CCDC 2149545: Experimental Crystal Structure Determination

description: Related Article: Alexander V. Artem'ev, Andrey Yu. Baranov, Irina Yu. Bagryanskaya|2022|Inorg.Chem.Commun.|140|109478|doi:10.1016/j.inoche.2022.109478
DOI: 10.5517/ccdc.csd.cc2b53zm
title: CCDC 2149880: Experimental Crystal Structure Determination

description: Related Article: Ju Young Kang, Suho Kim, Junghyea Moon, Eunjae Chung, Jaeyoung Kim, So Young Kyung, Hyung Sik Kim, Neeraj Kumar Mishra, In Su Kim|2022|ACS Omega|7|14712|doi:10.1021/acsomega.1c07363
DOI: 10.5517/ccdc.csd.cc2bdhcm
title: CCDC 2156960: Experimental Crystal Structure Determination

description: Related Article: Kazuishi Fukuda, Takuma Harada, Nobuharu Iwasawa, Jun Takaya|2022|Dalton Trans.|51|7035|doi:10.1039/D2DT01222G
DOI: 10.5517/ccdc.csd.cc2bh4cc
title: CCDC 2159502: Experimental Crystal Structure Determination

description: Related Article: Alexander L. Estrada, Taveechai Wititsuwannakul, Klemenz Kromm, Frank Hampel, Michael B. Hall, John A. Gladysz|2022|Dalton Trans.|51|7305|doi:10.1039/D2DT00890D
DOI: 10.5517/ccdc.csd.cc2bh4dd
title: CCDC 2159503: Experimental Crystal Structure Determination

description: Related Article: Alexander L. Estrada, Taveechai Wititsuwannakul, Klemenz Kromm, Frank Hampel, Michael B. Hall, John A. Gladysz|2022|Dalton Trans.|51|7305|doi:10.1039/D2DT00890D
DOI: 10.5517/ccdc.csd.cc268kxz
title: CCDC 2034031: Experimental Crystal Structure Determination

description: Related Article: Zhiqiang Hao, Junhua Zhang, Xiaoying Zhang, Zhihong Ma, Zhangang Han, Jin Lin, Guo-Liang Lu|2022|Polyhedron|221|115869|doi:10.1016/j.poly.2022.115869
DOI: 10.18739/a20k26c63
title: Beaufort Gyre Climate Response Function experiments: sea ice transport and freshwater content

description: This dataset contains the variables used in the manuscript: Cornish et al., Impact of sea ice transport on Beaufort gyre liquid freshwater content (preprint: https://doi.org/10.21203/rs.3.rs-1478152/v1). The purpose of the study was to test the hypothesis that wind-driven sea ice transport into/from the Beaufort Gyre region influences the freshwater content of the gyre and its variability. To test this hypothesis, we used the results of a coordinated climate response function (CRF) experiment with four ice-ocean models, in combination with targeted experiments using a regional setup of the Massachusetts Institute of Technology general circulation model (MITgcm), in which we rotated the surface wind forcing vectors by 10 degrees, thereby changing the ageostrophic component of these winds. The datasets here provide the outputs of all the climate model experiments used in the paper. These data can be used to acquire insights into the role of sea ice transport in influencing Beaufort Gyre freshwater content. The mechanistic links are explored in the paper. The experimental procedure involved perturbing the ice-ocean models with exactly the same 10 meter (m) wind anomaly, based on particular a sea-level pressure anomaly pattern, by adding this 10 m wind perturbation to the original forcing fields (which vary from model to model). Two anomaly patterns were used: an anticyclonic pattern that strengthens the Beaufort Sea High (BGP, for Beaufort Gyre Plus), and a cyclonic pattern that weakens the Beaufort Sea High (BGM, for Beaufort Gyre Minus). The anomalies are centred on 77 North, 149 West, with a radius of influence on the order of 1,000 kilometers (km). In these datasets, we provide outputs from the control simulations of each model (CTRL), and the BGP and BGM runs. The climate response functions are extracted as the timeseries of a variable in the anomaly run (e.g. BGP) minus the equivalent timeseries in the control run. For the MITgcm, we provide two parallel sets of experiments. Firstly, the original CRF experiment, as above, which is comprised of the model runs CTRL, BGP, and BGM. Secondly, a CRF experiment with a modification to the angle of wind vectors by 10 degrees anticlockwise only over sea ice. The modification is applied to both the background winds and the perturbation winds, and yields the runs CTRL10, BGP10, and BGM10. The 10 degree angle is considered to roughly reflect the uncertainty in the cross-isobaric angle of surface wind over sea ice. Note that only one direction of rotation is required in order to probe both ice export and import scenarios, given that this angular adjustment is applied to anomalies with both senses of rotation: anticyclonic and cyclonic, and we can extract the effect of changing the sea ice dynamics (ice import and export) by comparing against the original perturbation experiments (which have no such modification to the surface winds). CRFs are calculated using the respective control and perturbation runs for each experiment.
DOI: 10.6084/m9.figshare.17029445.v3
title: Seasonal Reconstructions (Excel)

description: Excel spreadsheets of Seasonal Antarctic Sea Ice Extent Reconstructions
DOI: 10.6084/m9.figshare.17029445.v2
title: Seasonal Reconstructions (Excel)

description: Excel spreadsheets of Seasonal Antarctic Sea Ice Extent Reconstructions
DOI: 10.6084/m9.figshare.19740475
title: Simulation data

description: Simulation dataset used in the paper. The short and long-reads are present. The long-reads have been converted to FASTA format to save on space.
DOI: 10.6084/m9.figshare.19740475.v1
title: Simulation data

description: Simulation dataset used in the paper. The short and long-reads are present. The long-reads have been converted to FASTA format to save on space.