cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7850810
title: Data Repo for "Momentum-resolved spin-conserving two-triplon bound state and continuum in a cuprate ladder"

description: Data repo for the paper "Momentum-resolved spin-conserving two-triplon bound state and continuum in a cuprate ladder"
DOI: 10.5281/zenodo.7850809
title: Data Repo for "Momentum-resolved spin-conserving two-triplon bound state and continuum in a cuprate ladder"

description: Data repo for the paper "Momentum-resolved spin-conserving two-triplon bound state and continuum in a cuprate ladder"
DOI: 10.5281/zenodo.7896738
title: Dataset of the Distress Screening Scale for the article: Emotional distress, coping, and social support: A network analysis of risk and resources in persons with cancer

description: Dataset of the Distress Screening System comprises 52 items that relate to six psycho-social domains of human functioning: depression, anxiety, social support, coping efficacy, satisfaction with care, and functional status
DOI: 10.57745/qas9ne
title: Samarch_export - Copie.tab

description: :unav
DOI: 10.6084/m9.figshare.22761890
title: RAW DATA OF "Mediating Immunosuppressive Functions: A New Perspective on the Complex Immunological Properties of SEMA4D in the Tumor Microenvironment"

description: All raw data involved in "Mediating Immunosuppressive Functions: A New Perspective on the Complex Immunological Properties of SEMA4D in the Tumor Microenvironment", DOI: 10.3389/fonc.2023.1171926 have been uploaded.
DOI: 10.6084/m9.figshare.22761890.v1
title: RAW DATA OF "Mediating Immunosuppressive Functions: A New Perspective on the Complex Immunological Properties of SEMA4D in the Tumor Microenvironment"

description: All raw data involved in "Mediating Immunosuppressive Functions: A New Perspective on the Complex Immunological Properties of SEMA4D in the Tumor Microenvironment", DOI: 10.3389/fonc.2023.1171926 have been uploaded.
DOI: 10.25810/2eb5-8x48
title: Photo-expansion microscopy enables super-resolution imaging of cells embedded in 3D hydrogels
DOI: 10.21427/a9sn-jy06
title: FEM EEM Dataset: Optimising Retrofitted Insulation for Irish Residential Building Walls
DOI: 10.5066/p9xg2w7c
title: Three-Dimensional Chronostratigraphic Model and supporting well data for the Los Angeles Coastal Plain, Los Angeles and Orange Counties, California

description: This data release contains a digital geospatial dataset generated by the U.S. Geological Survey under a cooperative agreement with the Water Replenishment District of Southern California to characterize the three-dimensional hydrogeology of the Los Angeles Coastal Plain. The geospatial dataset covers about 580 square miles of the largest coastal plain of southern California and contains the unit tops and extents of the three-dimensional chronostratigraphic model (3D CM) and supporting well data. The 3D CM was constructed in EarthVision using methods described in Development of a Groundwater-Simulation Model in the Los Angeles Coastal Plain, Los Angeles County, California; Chapter B (Ponti and Martin, 2021). Specifically, the geospatial database contains (1) a shapefile (point feature class) for the altitude of the top of each chronostratigraphic unit where it exists on a spatial grid in the 3D CM; and (2) a shapefile (point feature class) of wells used to construct the 3D CM including their corresponding name, location, source of well data, and depths of stratigraphic picks.
DOI: 10.5522/04/22601260
title: Dawnn benchmarking dataset: Heart cells processing and label simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the benchmarking dataset based on single-cell RNAseq of heart cells. <br> FILES: Input data Dataset from: "Integrated multi-omic characterization of congenital heart disease". Nature 608 pp. 181-191 (2022). <strong>heart_barcodes.tsv.gz</strong> Cell barcode list <strong>heart_genes.tsv.gz </strong>Gene list <strong>heart_expression_matrix.mtx.gz</strong> Cell-by-gene expression matrix Data processing code <strong>process_heart_cells.R</strong> Generates benchmarking dataset from input data. (Reads <em>heart_barcodes.tsv.gz</em>, <em>heart_genes.tsv.gz</em>, and <em>heart_expression_matrix.mtx.gz</em>; Runs the standard Seurat pipeline; Saves the resulting Seurat dataset as <em>heart_tissue_cells.RDS</em> and the resulting cell labels as <em>benchmark_dataset_heart_data_type_labels.csv</em>) Resulting datasets <strong>heart_tissue_cells.RDS</strong> Seurat dataset generated by <em>process_heart_cells.R</em>. <strong>benchmark_dataset_heart_data_type_labels.csv </strong>Cell labels generated by <em>process_heart_cells.R</em>.
DOI: 10.5522/04/22601260.v1
title: Dawnn benchmarking dataset: Heart cells processing and label simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the benchmarking dataset based on single-cell RNAseq of heart cells. <br> FILES: Input data Dataset from: "Integrated multi-omic characterization of congenital heart disease". Nature 608 pp. 181-191 (2022). <strong>heart_barcodes.tsv.gz</strong> Cell barcode list <strong>heart_genes.tsv.gz </strong>Gene list <strong>heart_expression_matrix.mtx.gz</strong> Cell-by-gene expression matrix Data processing code <strong>process_heart_cells.R</strong> Generates benchmarking dataset from input data. (Reads <em>heart_barcodes.tsv.gz</em>, <em>heart_genes.tsv.gz</em>, and <em>heart_expression_matrix.mtx.gz</em>; Runs the standard Seurat pipeline; Saves the resulting Seurat dataset as <em>heart_tissue_cells.RDS</em> and the resulting cell labels as <em>benchmark_dataset_heart_data_type_labels.csv</em>) Resulting datasets <strong>heart_tissue_cells.RDS</strong> Seurat dataset generated by <em>process_heart_cells.R</em>. <strong>benchmark_dataset_heart_data_type_labels.csv </strong>Cell labels generated by <em>process_heart_cells.R</em>.
DOI: 10.5522/04/22634200
title: Dawnn training set simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the code and resulting dataset from the training set generation procedure. <br> FILES: <strong>autogen4_code.R</strong> R code to generate training set (250,000 independent random walks are simulated, with each random walk constituting an instance within the training set). Saves output to<em><strong> </strong></em><em>labels_df.csv</em>. <strong>labels_df.csv</strong> Training dataset generated by <em>autogen4_code.R</em> (Each row corresponds to a training instance, with the first column containing the simulated probability of the cell at the centre of a simulated trajectory having been drawn from <em>Condition_1</em>, and the remaining columns containing the labels corresponding to its 1000 neighbouring cells, with labels drawn according to the random walks simualted in <em>autogen4_code.R</em>).
DOI: 10.5522/04/22634200.v1
title: Dawnn training set simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the code and resulting dataset from the training set generation procedure. <br> FILES: <strong>autogen4_code.R</strong> R code to generate training set (250,000 independent random walks are simulated, with each random walk constituting an instance within the training set). Saves output to<em><strong> </strong></em><em>labels_df.csv</em>. <strong>labels_df.csv</strong> Training dataset generated by <em>autogen4_code.R</em> (Each row corresponds to a training instance, with the first column containing the simulated probability of the cell at the centre of a simulated trajectory having been drawn from <em>Condition_1</em>, and the remaining columns containing the labels corresponding to its 1000 neighbouring cells, with labels drawn according to the random walks simualted in <em>autogen4_code.R</em>).
DOI: 10.5522/04/22619851
title: Dawnn benchmarking dataset: Simulated branching trajectories processing and label simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the benchmarking dataset based on simulated branching trajectories. <br> FILES: Data processing code <strong>adapted_traj_sim_milo_paper.R</strong> Lightly adapted code from Dann <em>et al.</em> to simulate single-cell RNAseq datasets that form branching trajectories . <strong>generate_test_data_branching_traj_sim_milo_paper.R</strong> R code to assign simulated labels to datatsets generated from <em>adapted_traj_sim_milo_paper.R</em>. Seurat objects saved as <em>cells_sim_branching_traj_gex_seed_*.rds</em>. Simulated labels saved as <em>benchmark_dataset_sim_branching_traj.csv</em>. Resulting datasets <strong>cells_sim_branching_traj_gex_seed_*.rds</strong> Seurat objects generated by <em>generate_test_data_branching_traj_sim_milo_paper.R</em>. <strong>benchmark_dataset_sim_branching_traj</strong><strong>.csv</strong> Cell labels generated by <em>generate_test_data_branching_traj_sim_milo_paper.R</em>.
DOI: 10.5522/04/22619851.v1
title: Dawnn benchmarking dataset: Simulated branching trajectories processing and label simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the benchmarking dataset based on simulated branching trajectories. <br> FILES: Data processing code <strong>adapted_traj_sim_milo_paper.R</strong> Lightly adapted code from Dann <em>et al.</em> to simulate single-cell RNAseq datasets that form branching trajectories . <strong>generate_test_data_branching_traj_sim_milo_paper.R</strong> R code to assign simulated labels to datatsets generated from <em>adapted_traj_sim_milo_paper.R</em>. Seurat objects saved as <em>cells_sim_branching_traj_gex_seed_*.rds</em>. Simulated labels saved as <em>benchmark_dataset_sim_branching_traj.csv</em>. Resulting datasets <strong>cells_sim_branching_traj_gex_seed_*.rds</strong> Seurat objects generated by <em>generate_test_data_branching_traj_sim_milo_paper.R</em>. <strong>benchmark_dataset_sim_branching_traj</strong><strong>.csv</strong> Cell labels generated by <em>generate_test_data_branching_traj_sim_milo_paper.R</em>.
DOI: 10.5522/04/22616611
title: Dawnn benchmarking dataset: Simulated linear trajectories processing and label simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the benchmarking dataset based on simulated linear trajectories. <br> FILES: Data processing code <strong>adapted_traj_sim_milo_paper.R</strong> Lightly adapted code from Dann <em>et al.</em> to simulate single-cell RNAseq datasets that form linear trajectories . <strong>generate_test_data_linear_traj_sim_milo_paper.R</strong> R code to assign simulated labels to datatsets generated from <em>adapted_traj_sim_milo_paper.R</em>. Seurat objects saved as <em>cells_sim_linear_traj_gex_seed_*.rds</em>. Simulated labels saved as <em>benchmark_dataset_sim_linear_traj.csv</em>. Resulting datasets <strong>cells_sim_linear_traj_gex_seed_*.rds</strong> Seurat objects generated by <em>generate_test_data_linear_traj_sim_milo_paper.R</em>. <strong>benchmark_dataset_sim_linear_traj.csv</strong> Cell labels generated by <em>generate_test_data_linear_traj_sim_milo_paper.R</em>.
DOI: 10.5522/04/22616611.v1
title: Dawnn benchmarking dataset: Simulated linear trajectories processing and label simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the benchmarking dataset based on simulated linear trajectories. <br> FILES: Data processing code <strong>adapted_traj_sim_milo_paper.R</strong> Lightly adapted code from Dann <em>et al.</em> to simulate single-cell RNAseq datasets that form linear trajectories . <strong>generate_test_data_linear_traj_sim_milo_paper.R</strong> R code to assign simulated labels to datatsets generated from <em>adapted_traj_sim_milo_paper.R</em>. Seurat objects saved as <em>cells_sim_linear_traj_gex_seed_*.rds</em>. Simulated labels saved as <em>benchmark_dataset_sim_linear_traj.csv</em>. Resulting datasets <strong>cells_sim_linear_traj_gex_seed_*.rds</strong> Seurat objects generated by <em>generate_test_data_linear_traj_sim_milo_paper.R</em>. <strong>benchmark_dataset_sim_linear_traj.csv</strong> Cell labels generated by <em>generate_test_data_linear_traj_sim_milo_paper.R</em>.
DOI: 10.5522/04/22614004
title: Dawnn benchmarking dataset: Mouse embryo cells processing and label simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the benchmarking dataset based on single-cell RNAseq of mouse emrbyo cells. <br> FILES: Input data Dataset from: "A single-cell molecular map of mouse gastrulation and early organogenesis". Nature 566, pp490–495 (2019). The input data is loaded from the <em>MouseGastrulationData</em> R package. We upload here the RDS file generated by loading the dataset in <em>process_mouse_cells.R</em> in case the R package becomes unavailable <strong>MouseGastrulationData_loaded_dataset.RDS</strong> Dataset loaded from <em>MouseGastrulationData</em> R package in <em>process_mouse_cells.R</em> (in call to <em>EmbryoAtlasData</em> function). Data processing code <strong>process_mouse_cells.R</strong> Generates benchmarking dataset from input data. (Loads input data; Runs the standard single-cell RNAseq pipeline). Follows Dann <em>et al.</em> Resulting dataset saved as <em>mouse_gastrulation_data_regen.RDS</em>. <strong>simulate_mouse_pc1_Rscript.R</strong> R code to simulate <em>P(Condition_1</em>)s for benchmarking. <strong>simulate_mouse_pc1_bash.sh </strong>Bash script to execute <em>simulate_mouse_pc1_Rscript.R</em>. Outputs stored in <em>benchmark_dataset_mouse_pc1s_regen.csv</em>. <strong>simulate_mouse_labels_Rscript.R</strong> R code to simulate labels for benchmarking. <strong>simulate_mouse_labels_bash.sh </strong>Bash script to execute <em>simulate_mouse_labels_Rscript.R</em>. Outputs stored in <em>benchmark_dataset_mouse.csv</em>. Resulting datasets <strong>mouse_gastrulation_data_regen.RDS</strong> Seurat dataset generated by <em>process_mouse_cells.R</em>. <strong>benchmark_dataset_mouse.csv </strong>Cell labels generated by<em> simulate_mouse_labels_bash.sh.</em> <strong>benchmark_dataset_mouse_pc1s_regen.csv</strong> <em>P(Condition_1</em>)s generated by<em> simulate_mouse_pc1_bash.sh.</em>
DOI: 10.5522/04/22614004.v1
title: Dawnn benchmarking dataset: Mouse embryo cells processing and label simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the benchmarking dataset based on single-cell RNAseq of mouse emrbyo cells. <br> FILES: Input data Dataset from: "A single-cell molecular map of mouse gastrulation and early organogenesis". Nature 566, pp490–495 (2019). The input data is loaded from the <em>MouseGastrulationData</em> R package. We upload here the RDS file generated by loading the dataset in <em>process_mouse_cells.R</em> in case the R package becomes unavailable <strong>MouseGastrulationData_loaded_dataset.RDS</strong> Dataset loaded from <em>MouseGastrulationData</em> R package in <em>process_mouse_cells.R</em> (in call to <em>EmbryoAtlasData</em> function). Data processing code <strong>process_mouse_cells.R</strong> Generates benchmarking dataset from input data. (Loads input data; Runs the standard single-cell RNAseq pipeline). Follows Dann <em>et al.</em> Resulting dataset saved as <em>mouse_gastrulation_data_regen.RDS</em>. <strong>simulate_mouse_pc1_Rscript.R</strong> R code to simulate <em>P(Condition_1</em>)s for benchmarking. <strong>simulate_mouse_pc1_bash.sh </strong>Bash script to execute <em>simulate_mouse_pc1_Rscript.R</em>. Outputs stored in <em>benchmark_dataset_mouse_pc1s_regen.csv</em>. <strong>simulate_mouse_labels_Rscript.R</strong> R code to simulate labels for benchmarking. <strong>simulate_mouse_labels_bash.sh </strong>Bash script to execute <em>simulate_mouse_labels_Rscript.R</em>. Outputs stored in <em>benchmark_dataset_mouse.csv</em>. Resulting datasets <strong>mouse_gastrulation_data_regen.RDS</strong> Seurat dataset generated by <em>process_mouse_cells.R</em>. <strong>benchmark_dataset_mouse.csv </strong>Cell labels generated by<em> simulate_mouse_labels_bash.sh.</em> <strong>benchmark_dataset_mouse_pc1s_regen.csv</strong> <em>P(Condition_1</em>)s generated by<em> simulate_mouse_pc1_bash.sh.</em>
DOI: 10.5522/04/22616590
title: Dawnn benchmarking dataset: Simulated discrete clusters processing and label simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the benchmarking dataset based on simulated discrete clusters. <br> FILES: Data processing code <strong>adapted_discrete_clusters_sim_milo_paper.R</strong> Lightly adapted code from Dann <em>et al.</em> to simulate single-cell RNAseq datasets that form discrete clusters . <strong>generate_test_data_discrete_clusters_sim_milo_paper.R</strong> R code to assign simulated labels to datatsets generated from <em>adapted_discrete_clusters_sim_milo_paper.R</em>. Seurat objects saved as <em>cells_sim_discerete_clusters_gex_seed_*.rds</em>. Simulated labels saved as <em>benchmark_dataset_sim_discrete_clusters.csv</em>. Resulting datasets <strong>cells_sim_discerete_clusters_gex_seed_*.rds</strong> Seurat objects generated by <em>generate_test_data_discrete_clusters_sim_milo_paper.R</em>. <strong>benchmark_dataset_sim_discrete_clusters.csv </strong>Cell labels generated by <em>generate_test_data_discrete_clusters_sim_milo_paper.R</em>.
DOI: 10.5522/04/22616590.v1
title: Dawnn benchmarking dataset: Simulated discrete clusters processing and label simulation

description: This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the benchmarking dataset based on simulated discrete clusters. <br> FILES: Data processing code <strong>adapted_discrete_clusters_sim_milo_paper.R</strong> Lightly adapted code from Dann <em>et al.</em> to simulate single-cell RNAseq datasets that form discrete clusters . <strong>generate_test_data_discrete_clusters_sim_milo_paper.R</strong> R code to assign simulated labels to datatsets generated from <em>adapted_discrete_clusters_sim_milo_paper.R</em>. Seurat objects saved as <em>cells_sim_discerete_clusters_gex_seed_*.rds</em>. Simulated labels saved as <em>benchmark_dataset_sim_discrete_clusters.csv</em>. Resulting datasets <strong>cells_sim_discerete_clusters_gex_seed_*.rds</strong> Seurat objects generated by <em>generate_test_data_discrete_clusters_sim_milo_paper.R</em>. <strong>benchmark_dataset_sim_discrete_clusters.csv </strong>Cell labels generated by <em>generate_test_data_discrete_clusters_sim_milo_paper.R</em>.