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111,247 results for “cells”
RxRx1-Wilds Cell-Level Dataset
<p>Cell-level dataset of RxRx1-Wilds, for the paper "<em>Incorporating Knowledge of Plates in Batch Normalization Improves Generalization of Deep Learning for Microscopy Images</em><strong>" </strong>by Alex Lin and Alex Lu.</p> <p>This dataset contains cell crops of the publicly available RxRx1-Wilds, accessed at https://wilds.stanford.edu/datasets/. </p>
Cycling Data of 64 Cells manufactured by AutoBASS
<p>This is the complete dataset for cells made for the publication of <a href="https://doi.org/10.1039/D2DD00046F">https://doi.org/10.1039/D2DD00046F</a> </p> <p>Robotic cell assembly to accelerate battery research, <em><strong>Digital Discovery</strong></em>, 2022, Advance Article by Zhang et al.</p> <p>This dataset goes beyond the inital cycles and tested, wherever possibile, up to 500 cycles at a constant 1C/1D rate.</p> <p>The details of the robot that made the cells can be found at https://github.com/Helge-Stein-Group/AutoBASS and the details of the cycling procedure and chemistry can be found in the article.</p>
Sampling time-dependent artifacts in single-cell genomics studies: scRNA-seq data
<p>Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.</p> <p>This repository contains the expression matrices and Seurat objects associated with the scRNA-seq data of the manuscript: "Sampling time-dependent artifacts in single-cell genomics studies" published in Genome Biology in 2020. The purpose of this repo is to share processed files and metadata for immediate access and reproducibility. The code to analyze it is thoroughly documented at the associated Github repository (https://github.com/massonix/sampling_artifacts).</p>
Highly-multiplexed mass cytometry screen of human bone marrow hematopoietic stem and progenitor cells
<p>In contrast to the rich single-cell transcriptomic and epigenetic data, the corresponding protein level information of human hematopoietic stem and progenitor cell (HSPC) populations is still missing. We used a highly-multiplexed single-cell screen to quantify the protein expression of 353 surface molecules and 79 functional intracellular molecules (TFs, chromatin regulators, and metabolic enzymes) with mass cytometry. In doing this, we created a core panel with probes against functional protein molecules associated with specific lineage potentials to better illuminate the differentiation potentials of the progenitors. In total, we analyzed 556,226 CD34+ bone marrow HSPCs across three individuals. Our analysis identified ten distinct clusters among HSPCs by unsupervised method and defined their unique proteomic composition. We compare our data-driven populations to the canonical HSPC cell types identified by cell surface proteins and observe discrepancies, especially in the lympho-myeloid axis. Overall, we supply a quantified summary of the proteomes of human HSPCs and create a framework to redefine progenitor populations with unique functional states along hematopoiesis. </p>
Interplay between Li and Na amid co-doped solution-processed Cu2ZnSn(S,Se)4 absorbers for solar cells
<p>Alkali doping and alloying are well-known strategies to improve the performance of Cu<sub>2</sub>ZnSn(S,Se)<sub>4</sub> (CZTSSe) absorber based thin film solar cells. The effects of individual light alkali elements such as Li and Na have been thoroughly investigated, with both dopants resulting in significant improvements in performance of CZTSSe solar cells. Here, the combined effects of Li and Na are investigated in a so-called co-doping approach to capture the benefits from both elements simultaneously. In order to do that, various concentrations of Li and Na between 0.0 M and 0.5 M are added to the solution used for spin coating of the precursor layer. After annealing under Se-enriched atmosphere, the two alkali elements displayed mutual dependency of in terms of their concentrations in the CZTSSe absorber layer. Furthermore, both, Li and Na showed signs of forming alloys with the CZTSSe phase. The efficiencies of the best Li-Na co-doped solar cells are above 10%, slightly above the Li baseline thanks to increased open-circuit-voltage and short-circuit current. A non-negligible Na incorporation was observed even in nominally Na-free devices, likely from indirect contamination from the SLG substrate. Further work is needed to better understand Na-poor compositions and draw conclusions relevant to Na-free substrates.</p>
Diagnostic algorithm for surgical management of limbal stem cell deficiency
<p>Supplementary material (tables) for the article entitled "Diagnostic algorithm for surgical management of limbal stem cell deficiency".</p>
Dataset: Literature review on cyberlaw and cell phone
<p>Merupakan Dataset dari Literature review tentang hukum cyber dan cell phone</p>
Activation patterns of afferent synapses on layer 5 tufted pyramidal cells in a biologically detailed simulation
<p>The data set is based on a biologically detailed simulation of a circuit of cortical neurons. The circuit was activated by thalamo-cortical inputs every 1 s for 500 ms. We report for a number of exemplary tufted pyramidal cells in layer 5 the pattern activation of their afferent synapses.</p> <p>Specifically, we report for all afferent excitatory synapses the pairwise path distances along the dendrite / soma (note that the soma was simplified to a point for the purpose of calculating path distances, but not during the simulation), and the times of activation of each of these synapses.</p> <p>The simulation is based on the model described in <a href="https://www.biorxiv.org/content/10.1101/2022.08.11.503144v1">this preprint</a>. The model can also be found <a href="https://zenodo.org/record/6906785">here on Zenodo</a>. For more details on the simulation, contact the author.</p> <p>For more details about the format of the data, refer to the included jupyter notebook.</p>
Data from: eDNA-stimulated cell dispersion from Caulobacter crescentus biofilms upon oxygen limitation is dependent on a toxin-antitoxin system
<p><span>In their natural environment, most bacteria preferentially live as complex surface-attached multicellular colonies called biofilms. Biofilms begin with a few cells adhering to a surface, where they multiply to form a mature colony. When conditions deteriorate, cells can leave the biofilm. This dispersion is thought to be an important process that modifies the overall biofilm architecture and that promotes colonization of new environments. In <em>Caulobacter crescentus</em> biofilms, extracellular DNA (eDNA) is released upon cell death and prevents newborn cells from joining the established biofilm. Thus, eDNA promotes the dispersal of newborn cells and the subsequent colonization of new environments. These observations suggest that eDNA is a cue for sensing detrimental environmental conditions in the biofilm. Here we show that </span><span>the toxin-antitoxin system (TAS) <em>ParDE<sub>4</sub></em> stimulates cell death in areas of a biofilm with decreased O<sub>2</sub> availability. In conditions where O<sub>2</sub> availability is low, eDNA concentration is correlated with cell death. Cell dispersal away from biofilms is decreased when <em>parDE<sub>4</sub></em> is deleted, probably due to the lower local eDNA concentration. Expression of <em>parDE<sub>4</sub></em> is positively regulated by O<sub>2</sub> and the expression of this operon is decreased in biofilms where O<sub>2</sub> availability is low. Thus, a programmed cell death mechanism using an O<sub>2</sub>-regulated TAS stimulates dispersal away from areas of a biofilm with decreased O<sub>2</sub> availability and favors colonization of a new, more hospitable environment. </span></p>
single-cell RNAseq data (data set 20) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset20) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from breast cancer samples downloaded from the GEO website (GSE180286)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <p> </p>
single-cell RNAseq data (data set 18) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset18) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from Liver cancer set 1 samples downloaded from the GEO website (GSE125449)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 17) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset17 was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from PBMC metastatic MCC samples downloaded from the GEO website (GSE117988)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 12) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset12) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor10 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 16) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset16) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CD4 T-cells in PACA samples downloaded from the GEO website (GSE156728)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <p> </p>
single-cell RNAseq data (data set 11) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset11) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor9 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 14) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset14) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor12 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 9) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset9) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor7 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 8) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset8) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor6 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 7) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset7) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor5 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 19) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset19) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from Liver cancer set 2 samples downloaded from the GEO website (GSE125449)<strong>. </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
ScienceDex guides
Understand access before you commit
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.