Find research datasets worth reusing

Datasets for the paper "ReSplit: Improving the Structure of Jupyter Notebooks by Re-Splitting Their Cells"

In this archive, you can find all the data used in the paper "ReSplit: Improving the Structure of Jupyter Notebooks by Re-Splitting Their Cells". sklearn_full_cells.csv is the dataset from the paper of Pimentel et al. filtered with only Data Science notebooks. complete.csv is the dataset obtained after the full run of ReSplit on the dataset: both merging and splitting. split.csv is the dataset obtained after running only the splitting part of our dataset. merged.csv is the dataset obtained after running only the merging part of our dataset. duplicates_id.csv contains the IDs of the duplicate notebooks for deduplication. changes.csv contains the IDs of the datasets, as well as their length before and after running ReSplit. survey.csv is the table with the results of the survey. In the dataset CSVs, each line is a cell that has a unique identifier and an identifier of the corresonding notebook.

Additional data for manuscript "Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data"

Additional data for manuscript "Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data". Additional mitochondrial gene sequences for Danio rerio, Homo sapiens, and Mus musculus.

opencc-zeroDec 2021View details →

dryad36/100

KerNet, 3D fluorescence and segmentation datasets of keratin networks from MDCK, HaCaT, and RPE cells

Mechanobiology requires precise quantitative spatial information on processes taking place in specific 3D microenvironments. Connecting microscopical, molecular, biochemical and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps is particular challenging for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. Here we present raw data and processed segmented data of fluorescence-tagged keratin intermediate filaments from MDCK, HaCaT and RPE epithelial cells. The 30 datasets contain unprocessed airyscan superresolution 3D image data and numerical network data of the 3D arrangement of the keratin intermediate filament cytoskeleton. Information is provided on network organization at the subcellular level, including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature and orientation. The datasets contain data in exchange format for 3d software packages. Comparison of the resulting network parameters helps to identify similarities and differences of keratin network organization in epithelial cell types. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested.

White Blood Cells with cytoplasm and surrounding red blood cells

This dataset is taken from doi:10.17632/w7cvnmn4c5.1 and is used for segmentation of irregular shaped white blood cells in 2D. We also provide dataset for cytoplasm and we trained StarDist and UNET models to be applied in the VollSeg setting on this dataset.

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: 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. 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. Data : 1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: 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. 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.   Data: 1. Immgen_cell_types.cls : Microarray Phase 1 expression 2. Immgen_norm_exp_data.gct : Microarray Phase 1 class

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: 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. 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. Data: cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: 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. 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. Data:  MDAlab_cDC1_maturation.tar : Docker image used for the analysis

2014 EPA National Emissions Inventory allocated to the grid cells of InMAP Source-Receptor Matrix

This dataset is the 2014 EPA National Emissions Inventory (NEI) v1 allocated to the individual grid cells of InMAP Source-Receptor Matrix (<a href="https://zenodo.org/record/2589760#.Yds79GjMI2w">ISRM</a>). The source types is classified by EPA Source Classification Codes (SCCs). The dataset includes emissions of both primary and secondary PM2.5. Secondary PM2.5 includes four precursors: NOx, SOx, NH3, and VOC. The detailed description of emission processing is in <a href="https://doi.org/10.1073/pnas.1818859116">Tessum et al. (2019</a>). Each shapefile in the dataset is in the format of input file of <a href="http://spatialmodel.com/inmap/">InMAP</a>/ISRM, which includes the emission amounts of five pollutants (Primary PM2.5, NOx, SOx, NH3, and VOC), stack information (height, diameter, temperature, and velocity), and SCCs. The unit of emissions is \(\mu g/s\). (If these emissions are used directly with the ISRM, the resulting outputs will be concentrations, in units of \(\mu g/m^3\).)

Dataset related to article "Amnion epithelial cells are an effective source of factor H and prevent kidney complement deposition in factor H deficient mice"

File excel with raw data of parameters (column) for any single animal (line) of each experimental group

Input data for Differential NicheNet analysis performed in the liver atlas paper Guilliams et al., Cell 2022

Input data for Differential NicheNet analysis performed in the liver atlas paper Guilliams et al., Cell 2022 See https://github.com/saeyslab/NicheNet_LiverCellAtlas and https://www.sciencedirect.com/science/article/pii/S0092867421014811

Non-centrosomal microtubules at kinetochores promote rapid chromosome biorientation during mitosis in human cells

Kinetochore tracking data associated with  "Non-centrosomal microtubules at kinetochores promote rapid chromosome biorientation during mitosis in human cells" by Renda, Miles, et al., Current Biology, 2022. Contact Alexey Khodjakov at alexey.khodjakov@health.ny.gov for any questions or processing routines. Each file contains coordinates of centrioles and kinetochores in a single cell.  Key to variables: ao -- time point corresponding to anaphase onset cell_id -- comprises three parts: cell type_treatment_number (NaN when only first 15 min of prometaphase were tracked). centrioles -- coordinates of centrioles. Two pages correspond to the two centrosomes, first three columns are X-Y-Z of the mother and the second three columns are X-Y-Z of the daughter centrioles kinetochores -- coordinates of kinetochores. Each page corresponds to a chromosome. X-Y-Z coordinates of sister kinetochores are in columns 1-3 and 4-6. neb -- time point corresponding to nuclear envelope breakdown. total_chrs -- number of chromosomes in the cell tracked -- number of tracks obtained in the cell

Coloc summary results for "Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets"

Text files containing coloc results between MS GWAS loci and CD4 T and B cell cis-eQTLs from DICE. These results accompany the paper "Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets"

opencc-by-4.0Jun 2022View details →

GeoMx DSP dataset of "Integrated multi-omics reveals cellular and molecular interactions governing the invasive niche of basal cell carcinoma"

This data set is linked to the paper "Integrated multi-omics reveals cellular and molecular interactions governing the invasive niche of basal cell carcinoma". It contains datasets and images from GeoMx DSP analysis.

Tracking Glioblastoma-astrocytoma cells imaged in brightfield with TrackMate-Cellpose

Glioblastoma-astrocytoma U373 cells migrating on a polyacrylamide gel. This dataset is used in a tutorial on using TrackMate and its cellpose integration to track such cells in brightfield, using a custom cellpose model (included in the dataset). See here for details: <a href="https://imagej.net/plugins/trackmate/trackmate-cellpose">https://imagej.net/plugins/trackmate/trackmate-cellpose</a>

opencc-by-4.0Oct 2021View details →

Annotated images from yeast cell lifespans - Testset- DetecDiv (id01)

This dataset has been generated by manual annotation from timelapse images of yeast cells dividing using the DetecDiv software (see below). It contains ~ 35 000 images from 50 cell lifespans (each lifespan is made of between 700 and 1000 images). Each image is classified between 6 classes: "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog", according to the subfolder of the image. Besides, this folder also contains a .mat file containing 50 timeseries of classes corresponding to the lifespan of the 250 cells. It is related to the trained network <a href="https://doi.org/10.5281/zenodo.5553862">doi.org/10.5281/zenodo.5553862</a> from the software DetecDiv: <a href="https://github.com/gcharvin/DetecDiv">github.com/gcharvin/DetecDiv</a> <a href="https://biorxiv.org/content/10.1101/2021.10.05.463175v1">biorxiv.org/content/10.1101/2021.10.05.463175v1</a>   Data type: 3D microscopy images (3 stacks brightfield) (.tif) + annotation (.mat) Microscopy data type: Brightfield images with 3 stacks Imaging: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS Cell type: Budding yeast wild type cell (BY4742) File format: .tif (16-bit RGB, 1 color per z-stack) + .mat Image size: 60x60x1 (Pixel size: x,y: 325 nm, 3*z: 3*1325 nm)   Author(s): Théo, ASPERT Contact email: theo.aspert@gmail.com Affiliation: IGBMC, Université de Strasbourg Funding bodies: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.

Tracking breast cancer cells migrating collectively and imaged in fluorescence with TrackMate-Cellpose

Breast cancer cells migrating collectively. This dataset is used in a tutorial on using TrackMate and its cellpose integration to track such cells. See here for details: <a href="https://imagej.net/plugins/trackmate/trackmate-cellpose">https://imagej.net/plugins/trackmate/trackmate-cellpose</a>

Self-adjuvanting Nanovaccines Boost Lung-resident CD4+ T Cell Immune Responses in BCG-primed mice

The "readcount_genename.xls" file and "fpkm_genename.xls" file contain gene expression data for genes (gene list is detailed in "gene.xls") in the panel. All data were obtained from bone marrow derived dendritic cells from WT C57BL/6 mice treated with KFE8 nanofibers or mock. All RNA sequencing and subsequent processing was performed by Novogene Corporation. The remaining .xls files contain the differential expression analysis for KFE8 nanofiber treatment at four hours compared with mock (NF4vsC4) or KFE8 nanofiber treatment at sixteen hours compared with mock (NF16vsC16). The "pathway analysis Rcode.R" file contains the code used to generate Reactome pathway analysis for both time points.  The "NF16 volc Rcode.R" file contains the code used to generate a volcano plot for the 16 hour time point.

opencc-by-4.0Dec 2020View details →

Engineering of Fully Humanized and Vascularized 3D Bone Marrow Niches Sustaining Undifferentiated Human Cord Blood Hematopoietic Stem and Progenitor Cells

Data underlying the figures in the publication “Engineering of fully humanized and vascularized 3D bone marrow niches sustaining undifferentiated human cord blood hematopoietic stem and progenitor cells”, published in J Tissue Eng., 2021, 12, 2041731421044855. DOI:10.1177/20417314211044855   Table of Contents: 1. P12-03_TP01_Suppernatant: Enzyme-linked immunosorbent assay of VEGF concentration in vascularized and not vascularized BM niches. 2. P12_WholeNiche: qPCR data of vascular and osteogenic markers in vascularized and not vascularized BM niches. 3. FACs-Overview-SN_relativ: FACS data showing haematopoietic cell populations in the supernatant after HSC cocoulture in vascularized and not vascularized BM niches. 4. FACs-Overview-niche_Relativ: FACS data showing haematopoietic cell populations in the niche after HSC cocoulture in vascularized and not vascularized BM niches. 5. CFU: Colony formation statistics after HSC cocoulture from vascularized and not vascularized BM niches. 6. Cell cycle: Cell cycle statistics of HSPCs after HSC cocoulture from vascularized and not vascularized BM niches. 7. P12_03_AfterHSPC-vs-BeforeHSPCs: qPCR data of vascular and osteogenic markers in vascularized and not vascularized BM niches after and before HSC cocoulture. 8. Scheme: Vector graphic of experimental setup. 9. Images engineered niches: Folder containing images of engineered niches.

Single Cell Phenotypic Profiling to Identify a Set of Immune Cell Protein Biomarkers for Relapsed and Refractory Diffuse Large B Cell Lymphoma: A Single-Center Study

Diffuse large B-cell lymphoma (DLBCL) is the most common invasive type of non-Hodgkin lymphoma. Cell-of-origin (COO) classification is related to patients’ prognoses. Primary drug resistance in treatment for DLBCL has been observed. The specific serum biomarkers in these patients who suffer from relapsed and refractory (R/R)-DLBCL remains unclear. In the current study, using single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF), we determined and verified immune cell biomarkers at the mRNA and protein levels in single-cell resolution from 18 diagnostic peripheral blood mononuclear cell (PBMC) specimens collected from patients with R/R DLBCL. As controls, five PBMC specimens from healthy volunteers were obtained. We identified a panel of 35 surface marker genes for the features of R/R DLBCL unique cell cluster by scRNA-seq of eight R/R DLBCL patient samples and validated its efficiency in an external cohort consisting of 10 R/R DLBCL patients by CyTOF. The cell clustering and dimension reduction were compared among R/R DLBCL samples in CyTOF Space with COO as well as the C-MYC expression designation. Immune cells from each patient occupied unique regions in the 32-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Significant heterogeneity observed in subgroups was mainly attributed to individual differences among samples and not to expression differences in a single, homogeneous immune cell subpopulation. The marker panel showed reliability in labeling R/R DLBCL without any influence from COO stratification and C-MYC expression designation. Furthermore, we compared all the markers between R/R DLBCL and normal samples. A total of 12 biomarkers were significantly overexpressed in R/R DLBCL relative to the normal samples. Therefore, we further optimized the diagnostic biomarker panel of R/R DLBCL comprising CD82, CD55, CD36, CD63, CD59, IKZF1, CD69, CD163, CD14, CD226, CD84, and CD31. In summary, we developed a novel set of biomarkers for the diagnoses of patients with R/R DLBCL. Detections procedures at single-cell resolution provide precise biomarkers which may substantially overcome intertumoral and intratumoral heterogeneity among primary samples. The findings confirmed that each case was unique and may comprise multiple, genetically distinct subclones. Here we uploaded the dataset of CyTOF for external validation. For more detailed information, please contact Dr zheng (zenki_zheng@163.com)