Find research datasets worth reusing

Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation"

Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation". All nuclei passed QC and were corrected for background noise using cellBender. Files are in R objects saved in RDS (R Data Serialization) format. This repo contains one Seurat v4 object and one gene-by-cell raw RNA count matrix in sparse matrix format (dgCMatrix).

Rayleigh-Bénard flow in closed cubical cell

The repository contains simulation snapshots from a long-term trajectory of turbulent Rayleigh-Bénard flows in a cubical closed cell at two Rayleigh numbers.

Data associated with "Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones"

These data include all tropical cyclone tornado reports from 2013–2020, as part of all data from 1995–2022, included in the Storm Prediction Center (SPC) Tropical Cyclone TORnado database (TCTOR; Edwards and Mosier, 2022) used in the following publication: Schenkel, B., K. Calhoun, T. Sandmael, M. Ake, Z. Fruits, B. Kassel, and I. Schick, 2023: Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones. J. Geophys. Res.: Atmospheres, accepted. Each specific tropical cyclone tornado record has been extracted from the broader SPC tornado database, for all Atlantic and Gulf of Mexico tropical cyclones impacting the continental United States from 1995–2022. The tornado records were analyzed individually to determine their presence within the circulation envelope of either a classified or remnant tropical cyclone, without regard to fixed radii from tropical cyclone center, inland extent, temporal cutoffs before or after landfall, or other such arbitrary thresholds that may either exclude tropical cyclone events or include non-tropical cyclone tornadoes unnecessarily. These data will not be updated regularly. Citation for SPC TCTOR dataset: Edwards, R., & Mosier, R. M. (2022). Over a quarter century of TCTOR: Tropical cyclone tornadoes in the WSR-88D era [Dataset]. In Proc., 30th conf. on severe local storms (p. 171). Santa Fe, NM.

Reads-per-UMI tables across single-cell RNA sequencing protocols

Data analyzed in <a href="https://www.biorxiv.org/content/10.1101/2023.08.02.551637v1">Lause, Ziegenhain et al. (2023)</a>. Code to obtain these tables from public data sources is available on <a href="https://github.com/berenslab/read-normalization">github</a>.   Each row in the table is a UMI-tag detected in a certain cell (column RG) attached to a molecule from a specific gene (column GE) with a certain barcode (column UB). Column N gives the number of times the UMI was detected for that gene and cell. Data sources and protocols are given with the respective file names below. Johnsson2022_Smartseq3_PE.hd1.txt.gz: Mouse fibroblasts profiled with Smart-seq3 paired-end; accession E-MTAB-10148, sample plate2, <a href="https://doi.org/10.1038/s41588-022-01014-1">Paper</a> Hagemann-Jensen2020_Smartseq3_SE.hd1.txt.gz: Mouse fibroblasts profiled with Smart-seq3 single-end; accession E-MTAB-8735, sample Smartseq3.Fibroblasts.smFISH <a href="https://doi.org/10.1038/s41587-020-0497-0">Paper</a> Hagemann-Jensen2022_Smartseq3xpress.hd1.txt.gz: HEK293 cells profiled with Smart-seq3Xpress; accession E-MTAB-11467. <a href="https://www.biorxiv.org/content/10.1101/2021.07.10.451889v1">Paper</a> Ziegenhain2017.hd1.txt.gz: Mouse embryonic stem cells profiled by CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq; GEO accession GSE75790 <a href="https://doi.org/10.1016/j.molcel.2017.01.023">Paper</a>

PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes

Supplementary Data for "PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes"

Dataset for publication "Influence of precursor morphology and cathode processing on performance and cycle life of sodium-zinc chloride (Na-ZnCl2) battery cells"

High-temperature sodium-metal battery; sodium-metal halide battery (ZEBRA); molten-salt battery; zinc battery for stationary energy storage; alkali metal anode. Datasets used in the above manuscript.

Influence of protein corona on cytotoxicity of metal oxide nanoparticles against human keratinocyte cell line (HaCaT)

The model identified, among the factors determining the cytotoxic properties of metal oxide nanoparticles against HaCaT cell lines, a number of variables related to the processes occurring on the surface of nanoparticles in a biological medium, including the ability to form protein corona. The selected descriptors describe both the electronic structure of the metal oxides that are the components of the nanoparticles, i.e. the ionization potential (IP_ActivM_SM_#1, IP_ActivM_SM_#2) and the initial nanoforms, i.e. the particle size (Primary size) and the percentage content of the metal oxide which is the main component of the nanoparticle (Purity_#1) ; characterize nanoparticles in the medium, i.e. the isoelectric point (PZZP_#2), stability (Stability), potential for dissolution (Dissolution), generation of reactive oxygen species (ROS production) and protein adsorption (Protein adsorption). The listed descriptors reflect the features that are discussed in the literature as potentially related to the toxic effect of nanoparticles.

Predictive nano-QSAR modeling of the cytotoxicity using epithelial cells obtained from Chinese hamster ovary (CHO-K1 cell line) for hybrid TiO2-based nanomaterials

Results obtained from developed model indicated that the cytotoxicity of hybrid TiO2-based nanomaterials is related to additive electronegativity (χmix) of studied nanomaterials that are indirectly related to the electron generation and ROS formation. ROS production is the most common toxicity cause as discussed in the literature in the case of nanoparticles. The high efficiency of surface modified TiO2-based semiconductors can be attributed to the involvement of TiO2 band gap (Eg) excitation and absence of noble metals at the TiO2 surface. It can be expected that noble metals (i.e. Pd/Pt) may trap holes (h+), at the same time photo-generated electrons can be then transferred from the valence band to the conduction band of TiO2 and to its surface where redox processes were initiated. Thus, observed reduction of the electron–hole pair recombination influences the reactive oxygen species (ROS) formation and the photocatalytic redox process initiation. Since the electronegativity was positively correlated with the cytotoxicity it can be expected that some ions are released from the TiO2 surface easier than others.

Generation of beta-like cell subtypes from differentiated human induced pluripotent stem cells in 3D spheroids

This repository contains single-cell RNA-sequencing data files (raw FASTQ files generated from Illumina HiSeq sequencing) related to the article entitled "Generation of beta-like cell subtypes from differentiated human induced pluripotent stem cells in 3D spheroids" by Lisa Morisseau et al. (2023) published in the Molecular Omics journal (DOI: 10.1039/d3mo00050h).

ERK activity in migrating MDA-MB-231 cells (clover-ERK-KTR + sir-DNA)

MDA-MB-231 or U2OS cells stably expressing clover-ERK-KTR were seeded on fibronectin-coated (1 µg /ml) Ibidi 8-well slides (Ibidi) 1 day before imaging. Four hours before imaging, the medium was supplemented with 250 nM sir-DNA (Cytoskeleton) and 25 mM HEPES (Sigma). Cells were then imaged live (37 °C, 5% CO2) using a Nikon Eclipse Ti2-E microscope (Nikon) equipped with an sCMOS Orca Flash4.0 camera (Hamamatsu) and controlled by the NIS-Elements software (Nikon, v 5.11.01). MDA-MB-231 cells were imaged using a 20× Nikon CFI Plan Apo Lambda objective (NA 0.75), either 1 frame per minute for 2 hours or 1 frame every 5 minutes for 17 hours. In these experiments, a camera binning of 2 × 2 was used.  This dataset consists of 20 videos.

Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression

Supplemental Data 1 is single-cell response to rapamycin count data first sequenced in this work and deposited in GEO with accession GSE242556. It is a 173348 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Gene', 'Replicate', 'Pool', and 'Experiment') are cell-specific metadata. Supplemental Data 2 is bulk response to rapamycin count data first sequenced in this work. It is a 33 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Oligo', 'Time', 'Replicate', and 'Sample_barcode') are sample-specific metadata. Supplemental Data 3 is single-cell count data published as GSE125162 and re-analyzed with the pipeline used for single-cell quantification in this work. It is a 65068 rows × 5850 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 7 columns ('Condition', 'Sample', 'Genotype_Group', 'Genotype_Individual', 'Genotype', 'Replicate', 'Cell_Barcode') are cell-specific metadata. Supplemental Data 4 is the four deep learning models trained in this work. It is a TAR.GZ file containing the final biophysical transcription/decay model, the pre-trained decay model, the velocity prediction model, and the count prediction model. Each model file is an h5 file containing a pytorch model that can be loaded with supirfactor\_dynamical.read(). Supplemental Data 5 is the prior knowledge network used to constrain the models for TF interpretability. It is a 1574 rows × 204 columns [Genes x TFs] TSV.GZ file where the first row is a header with TF names, the first column is an index of gene names, and TF-gene interactions are indicated by non-zero values in the matrix. There are 2799 TF-gene interactions. Supplemental Table 6 is the oligonucleotide sequences used in this work. It is a TSV file with a header row. Supplemental Table 7 is the yeast strains used in this work. It is a TSV file with a header row. Supplemental Table 8 is gene metadata used in this work (e.g. Ribosomal Protein gene labels, etc). It is a TSV file with a header row. Supplemental Table 9 is FY4/5 growth curve data generated in this work. It is a 20 rows × 7 columns TSV file where the first row is a header with replicate IDs, the first column is an index of times in minutes, and values are cell densities in YPD culture, in units of 10$^6$ cells / mL. Supplemental Data 10 is a TAR.GZ file containing the yeast SacCer3 genome, modified to add UTR sequences, that was used to generate transcripts for kallisto pseudoalignment in this work.

Buried Interface Engineering Enables Efficient and 1,960-hour Isos-L-2i Stable Inverted Perovskite Solar Cells

High-performance perovskite solar cells (PSCs) typically require interfacial passivation, yet this is challenging for the buried interface, owing to the dissolution of passivation agents during the deposition of perovskites. Here, we overcome this limitation with in-situ buried interface passivation – achieved via directly adding a cyanoacrylic acid-based molecular additive, namely BT-T, into the perovskite precursor solution. Classical and ab-initio molecular dynamics simulations reveal that BT-T spontaneously may self-assemble at the buried interface during the formation of the perovskite layer on a nickel oxide hole transporting layer. The preferential buried interface passivation results in facilitated hole transfer and suppressed charge recombination. In addition, residual BT-T molecules in the perovskite layer enhance its stability and homogeneity. We report a power-conversion efficiency (PCE) of 23.48% for 1.0 cm2 inverted-structure PSCs. The encapsulated PSC retains 95.4% of its initial PCE following 1,960-hour maximum power point tracking under continuous light illumination at 65°C (i.e., ISOS-L-2I protocol). Our demonstration of operating-stable PSCs under accelerated ageing conditions represents a step closer to the commercialization of this emerging technology.

Multivariate analysis of FcR-mediated NK cell functions identifies unique clustering among humans and rhesus macaques - dataset

Dataset from Tuyishime M, Spreng RL, et al. Multivariate analysis of FcR-mediated NK cell functions identifies unique clustering among humans and rhesus macaques. Frontiers in Immunology 2023 doi: 10.3389/fimmu.2023.1260377

Data from: MET receptor activation by stromal cells serves as promising target in melanoma brain metastases

Supplementary tables to our recent manuscript: MET receptor activation by stromal cells serves as promising target in melanoma brain metastases

Confocal microscopy images of Escherichia coli cells treated with various antibiotics (LB, exponential phase)

This dataset and CARE model is part of the publication "Transertion and cell geometry organize the Escherichia coli nucleoid during rapid growth".It contains all CLSM images that were used for the publication, as well as the single-cell regions of interest for analyses.Cells were grown to exponential phase in LB Lennox and antibiotics were added for 0-60 min. Cultures were then chemically fixed, immobilized and stained for DNA (DAPI) and membrane (Nile Red). The strain (NO34) expresses a MreBsw-sfGFP fusion protein from the native chromosomal locus. It was a kind gift from Zemer Gitai (<a href="http://doi:10.1016/j.bpj.2016.07.017">Ouzounov et al., 2016</a>).More information can be found in the publication.

Dataset for study "Band gap engineering by cationic substitution in Sn(Zr1-xTix)Se3 alloy for bottom sub-cell application in solar cells"

This dataset contains raw and processed data that were used to for the study entilted "Band gap engineering by cationic substitution in Sn(Zr1-xTix)Se3 alloy for bottom sub-cell application in solar cells".

Hepatitis D infection induces IFN-β-mediated NK cell activation and TRAIL-dependent cytotoxicity

The dataset contains bulk RNA-seq read count matrices of NK cells from healthy donors after 48 h of co-culture with hepatitis D virus infected and non-infected HepG2-hNTCP cells. The samples are described in the table RNA-seq_samples.xls that is included with the count matrices. Further details are given in the publication associated with this dataset (Groth et al., Front Immunol, 2023, https://doi.org/10.3389/fimmu.2023.1287367).