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1,007 results for “single use”
Data for Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes
<p>This dataset contains the experimental data used in the figures of the paper "Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes" by E. Hyyppä, A. Vepsäläinen, ..., and J. Heinsoo published in PRX Quantum 5, 030353 (2024): https://doi.org/10.1103/PRXQuantum.5.030353.</p> <p>The data is stored mostly as csv-files, the contents of which are explained in the readme-files. Each subfolder corresponds to one figure of the paper and also contains a Jupyter Notebook for plotting the data. The subfolders S1-S10 correspond to the supplementary figures, i.e., figures 6-15 in the Appendix of the paper.</p> <p>Furthermore, we provide a Jupyter notebook in the folder Code_to_plot_FAST_and_HD_DRAG_pulses/ that provides Python functions for evaluating and plotting the proposed FAST DRAG and HD DRAG pulses in time domain and frequency domain. Please cite our paper if you use the Python code for your published research.</p> <p>The notebooks have been tested using the following Python package versions<br>Python 3.11<br>scipy 1.14.1<br>numpy 2.1.0<br>matplotlib 3.9.2</p>
Single-pulsar search for eccentric SMBHBs using NANOGrav 12.5-year data of PSR J1909--3744: Posterior samples
<p>This repository contains posterior samples for a Bayesian single-pulsar search for nanohertz gravitational waves originating from eccentric supermassive binaries, done using the NANOGrav 12.5-year dataset for PSR J1909-3744. The analysis is presented in Susobhanan 2023 [https://arxiv.org/abs/2210.11454].</p>
Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior
<p>The datasets used in the paper "Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior". A detailed description of these datasets is available at https://github.com/jaydu1/VITAE/tree/master/data.</p>
Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens
<p>This repo contains Seurat objects, differential expression analysis results, and pathway gene lists for the manuscript "Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens"<br>List of files:</p> <p>1. Seurat_object_IFNB_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-beta pathway<br>2. Seurat_object_IFNG_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-gamma pathway<br>3. Seurat_object_TNFA_Perturb_seq.rds: Seurat object of the Perturb-seq data for TNF-alpha pathway<br>4. Seurat_object_TGFB1_Perturb_seq.rds: Seurat object of the Perturb-seq data for TGF-beta1 pathway<br>5. Seurat_object_INS_Perturb_seq.rds: Seurat object of the Perturb-seq data for insulin pathway<br>6. Pathway_genelist.rds: The pathway gene lists from MultiCCA analysis<br>7. Pathway_Exclusive_genelist.rds: The pathway exclusive gene lists generated from Pathway_genelist.rds<br>8. HClust_Pathway_celltype_specific_genelist.rds: The cell-line specific pathway gene lists from hierarchical clustering analysis independently done on each cell line<br>9. DE_results_all_pathway.zip: The DE test results for all the regulators, cell lines, and pathways (from Mixscale weighted DE test.)<br>10. Bulk_RNAseq_Seurat_object_IFNG_and_TGFB_stim.rds: Seurat object for the bulk RNA-seq data for interferon-gamma and TGF-beta stimulation experiments<br>11. Parse_Guide_Capture_Protocol.pdf: The guide RNA capture protocol developed for Parse Evercode Whole Transcriptome kit</p>
Synthetic cryo electron microscopy single particle images containing biomolecular complexes with continuous conformational variability used for validating DeepHEMNMA method and validation results
<p>This archive contains a synthetic dataset used for validating DeepHEMNMA method and the validation results. DeepHEMNMA is a deep learning extension of HEMNMA approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron (cryo-EM) microscopy single particle images. We provide a training set of 20,000 images and an inference set of 50,000 images. The training images were used (1) to estimate the conformational and rigid-body parameters with HEMNMA and (2) to train the neural network using the parameters previously estimated with HEMNMA (the file with the HEMNMA-estimated parameters is provided). The inference images were used to infer the parameters with the trained neural network. Also, we provide (1) the input PDB structure, its normal modes, and the conformational and rigid-body parameters used to synthesize the 20,000 training images (ground-truth parameters) and (2) the conformational and rigid-body parameters inferred from the set of 50,000 inference images.</p> <p>The DeepHEMNMA method and the method for synthesizing images have been fully described in the following article: "Hamitouche I and Jonic S (2022), DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 9, 965645. <a href="https://doi.org/10.3389/fmolb.2022.965645">https://doi.org/10.3389/fmolb.2022.965645</a> (in press)". Additionally, this article describes a test of DeepHEMNMA using one experimental cryo-EM dataset (available in EMPIAR database under the accession code EMPIAR-10016). </p>
Optimizing the Shelling Process of InP/ZnS Quantum Dots Using a Single-Source Shell Precursor: Implications for Lighting and Display Applications
<p>This is the data supporting the manuscript "Optimizing the Shelling Process of InP/ZnS Quantum Dots Using a Single-Source Shell Precursor: Implications for Lighting and Display Applications".</p> <p>Abstract</p> <p>InP/ZnS core/shell quantum dots (QDs), recognized as highly promising heavy-metal-free emitters, are increasingly utilized in lighting and display applications. Their synthesis in a tubular flow reactor enables production in a highly efficient, scalable, and reproducible manner, particularly when combined with a single-source shell precursor, such as zinc diethyldithiocarbamate (Zn(S2CNEt2)2). However, the photoluminescence quantum yield (PLQY) of QDs synthesized with this route remains significantly lower compared to those synthesized in batch reactors involving multiple steps for the shell growth. Our study identifies the formation of absorbing, yet non-emissive ZnS nanoparticles during the ZnS shell formation process as a main contributing factor to this discrepancy. By varying the shelling conditions, especially the shelling reaction temperature and InP core concentration, we investigated the formation of pure ZnS nanoparticles and their impact on the optical properties, particularly PLQY, of the resultant InP/ZnS QDs through UV-vis absorption, steady-state and time-resolved photoluminescence (PL) spectroscopy, scanning transmission electron microscopy (STEM) and analytical ultracentrifugation (AUC) measurements. Our results suggest that process conditions, such as lower shelling temperatures or reduced InP core concentrations (resulting in a lower external surface area), encourage the homogeneous nucleation of ZnS. This reduces the availability of shell precursors necessary for an effective passivation of the InP core surfaces, ultimately resulting in lower PLQYs. These findings explain the origin of persistently underperformed PLQY of InP/ZnS QDs synthesized from this synthesis route and suggest further optimization strategies to improve their emission for lighting and display applications.</p> <p>The data are sorted per techniques used for characterization. Information about the measurement details can be found in README files attached to each technique folder.</p>
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2nd Web-Delphi process to HTA stakeholders, organized in a single panel
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2<sup>nd</sup> Web-Delphi process to HTA stakeholders, organized in a single panel (all stakeholder groups in a single panel, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</p>
Synthetic dataset used for validating MDSPACE method for analyzing continuous conformational variability of biomolecules in cryo-EM single particle images
<p>Synthetic dataset used for validating MDSPACE method for analyzing continuous conformational variability of biomolecules in cryo-EM single particle images. A README file with the contents of the dataset is included. </p>
Supplementary datasets: sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data (Ng et al.)
<p>This repository contains data files from the manuscript Ng et al. "sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data".</p> <p><strong>Directories</strong></p> <p>Please untar the sciCSR-data-files.tar.gz archive.</p> <p><em><strong>Folder "Simulated_data"</strong></em></p> <ul> <li>'simulated_IGHC_reads' folder: containing list of simulated data (FASTQ sequence files and aligned BAM files) to test the accuracy of commonly used RNA-seq aligners (STAR, HISAT2) to distinguish sterile and productive heavy-chain transcripts. The code to generate these data is in the repository https://github.com/Fraternalilab/sciCSR-analysis.</li> <li>'simulated_transitions.RData': .RData file containing list of Seurat objects of simulated datasets of different number of cells, to test the robustness of sciCSR-inferred transitions across different dataset sizes.</li> </ul> <p><em><strong>Folder "Seurat_objects"</strong></em></p> <ul> <li>'human_Bcells_atlas_IGHC_NMF_rank.rds': Nonnegative matrix factorization (NMF) results to derive isotype signatures from the human B cell atlas (see below).</li> <li>'mouse_Bcells_atlas_IGHC_NMF_rank.rds': NMF results to derive isotype signatures from the mouse B cell atlas (see below)</li> <li>'Human_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'human B cell atlas' (i.e. merging data from Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) and King et al (https://doi.org/10.1101/2020.04.28.054775))</li> <li>'mouse_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'mouse B cell atlas' (i.e. merging data from Mathew et al (https://doi.org/10.1016/j.celrep.2021.109286) and Luo et al (https://doi.org/10.1186/s13578-022-00795-6))</li> <li>'Stewart_HumanPeripheral_Bcells_IGHC.rds': Seurat object containing cells from the Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) peripheral blood B cell atlas.</li> <li>* 'King_HumanTonsil_Bcells_IGHC.rds': Seurat object containing cells from the King et al. (https://doi.org/10.1101/2020.04.28.054775) human tonsilar B cell atlas.</li> <li>'Kim_Covid_Bcells_IGHC.rds': Seurat object containing cells from the Kim et al. (https://doi.org/10.1038/s41586-022-04527-1) time-course scRNA-seq data on human B cell response to SARS-CoV-2 vaccine.</li> <li>'Gomez_AID_VDJ_IGHC.rds': Seurat object containing cells from the Gómez-Escolar et al. (https://doi.org/10.15252/embr.202255000) Aicda mouse knockout scRNA-seq data.</li> <li>'Hong_IL23_Bcells_IGHC.rds': Seurat object containing cells from the Hong et al. (https://doi.org/10.4049/jimmunol.2000280) Il23 p19 mouse knockout scRNA-seq data.</li> <li>'scIFNg.rds': Seurat object containing scRNA-seq data of time-course in vitro culture of B cells stimulated with interferon gamma generated in this work.</li> </ul>
Joint embedding of vertebrate brain single-cell RNA-Seq using sequence or structure
<p>Embeddings of single-cell RNA-Seq data from three adult vertebrate brain datasets into Orthogroup feature space or Structural cluster feature space. Orthogroups were generated using OrthoFinder v5.5.0; Structural clusters were assigned by using FoldSeek to cluster AlphaFold-v4 structural predictions.<br> <br> The three datasets used as the basis for these embeddings were:</p> <ul> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3768152">"Brain8"</a> from the <a href="https://www.frontiersin.org/articles/10.3389/fcell.2021.743421/full">Jiang et al. 2021</a> zebrafish cell atlas (files beginning with GSM3768152)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2906405">"Brain1"</a> from the <a href="https://www.sciencedirect.com/science/article/pii/S0092867418301168#sec4">Han et al. 2018</a> mouse cell atlas (files beginning with GSM2906405)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM6214268">"Xenopus_brain_COL65"</a> from the <a href="https://www.nature.com/articles/s41467-022-31949-2">Liao et al. 2022</a> Xenopus laevis adult cell atlas (files beginning with GSM6214268)</li> </ul> <p>For each dataset, we also generated a standardized cell type annotation file based on the author's originally provided cell type annotation data. The first column is the cell barcode for that species and the second column is the original study's cell type annotation for that cell.</p> <p>For the Xenopus brain data, we removed around ~18k cells that were not annotated in the original data to simplify data analyses - these are reflected in the files with the "subsampled" suffix. Subsampled versions of the data are also available for the joint embedding space (prefixed with "DrerMmusXlae").</p> <p>For the final datasets used in our analyses, we also provide features x cell matrices as .h5ad files for smaller file sizes and faster loading using Scanpy. </p> <p>For visualizing our UMAP plots of our top200 embedding space, we provide ".tsv" files with a variety of metrics and the x and y positions of each cell in the UMAP. See "DrerMmusXlae_adultbrain_FoldSeek_plotlydata.tsv" and "DrerMmusXlae_adultbrain_OrthoFinder_plotlydata.tsv"</p> <p>These data are part of the Arcadia Science Pub titled <a href="https://doi.org/10.57844/arcadia-vw5e-2670">"Comparing gene expression across species based on protein structure instead of sequence"</a>.</p>
Single Particle Investigation of Triolein Digestion using Optical Manipulation, Polarized Video Microscopy, and SAXS
<p>Hypothesis: Understanding how soft colloids, such as food emulsion droplets, transform based on their environment is critical for various applications, including drug and nutrient delivery and biotechnology. However, the mechanisms behind colloidal transformations within individual oil droplets still need to be better understood.</p> <p>Experiments: This study employs optical micromanipulation with microfluidics and polarized optical video microscopy to investigate the pancreatic lipase- and pH-triggered colloidal transformations in a single triolein droplet. Small-angle X-ray scattering (SAXS) provides complementary statistical insights and allows for detailed structural assignment.</p> <p>Findings: Optical video microscopy recorded the transformation of individual triolein emulsion droplets, with the smooth surface of these spherical particles becoming rough and the entire volume eventually being affected. The polarized microscopy revealed the coexistence of at least two distinct structures in a single particle during digestion, with their ratio and distribution altered by pH. The SAXS analysis assigned the optical anisotropy to emulsified inverse hexagonal- and multilamellar phases, coexisting with isotropic structures such as the micellar cubic phase. These results can help understand the liquid-liquid crystalline phase transformations inside an emulsion droplet and guide the design of advanced food emulsions.</p>
Supplementary Materials for "Simultaneous single-shot radiographic imaging using a laser-driven x-ray and proton micro-source"
<p>Simulation Data Repository, please read the contained README file in the contained simulation/ directory.</p> <p>This directory contains a copy of the used PIConGPU source code, version 0.5.0-dev-60ad9eb85 and analysis scripts.</p> <p>The PIConGPU source code is archived including its complete git history (git version 2.17.1) in source/picongpu.tar.gz with the input parameter template inside in share/picongpu/examples/Wneedle .</p> <p>Generally, PIConGPU source code is available via <a href="https://doi.org/10.5281/zenodo.591746">https://doi.org/10.5281/zenodo.591746</a> with its public git repository being maintained on <a href="https://github.com/ComputationalRadiationPhysics/picongpu">https://github.com/ComputationalRadiationPhysics/picongpu</a> .</p> <p>The two simulations’ exact input is modified accordingly in the directory input/ inside: 2D_a0-45_Z-10_ppc-20_002_light.tar.gz (p-polarized; along X) 2D_a0-45_Z-10_ppc-20_003_light.tar.gz (s-polarized; along Z).</p> <p>“Heavy” simulation data (checkpoints in simOutput/checkpoints/, full-resolution field and particle output in simOutput/bp/ ) has been stripped from this archive and are archived on NERSC’s HPSS tape archive.</p> <p>Analysis scripts are provided as Jupyter notebooks (DensityPlot_polX.ipynb and DensityPlot_polZ.ipynb) and depend on the following software:</p> <p>- adios 1.13.1 python bindings with enabled c-blosc transformations<br> - numpy 1.17.1<br> - matplotlib 3.1.1<br> - PIConGPU post-processing helper modules located in each simulation root directory under “input/lib/python/”<br> <br> The detailed conda environment can be found in the README.</p>
Data of "Single-Photon Distillation via a Photonic Parity Measurement Using Cavity QED"
<p>Data published in "<em>Single-Photon Distillation via a Photonic Parity Measurement Using Cavity QED</em>"</p> <p>Phys. Rev. Lett. <strong>122</strong>, 133603</p>
A survey of the sorghum transcriptome using single-molecule long reads
<p>Alternative splicing and alternative polyadenylation (APA) of pre-mRNAs greatly contribute to transcriptome diversity, coding capacity of a genome and gene regulatory mechanisms in eukaryotes. Second-generation sequencing technologies have been extensively used to analyze transcriptomes. However, a major limitation of short-read data is that it is difficult to accurately predict full-length splice isoforms. Here we sequenced the sorghum transcriptome using Pacific Biosciences single molecule real time long-read isoform sequencing and developed a pipeline called TAPIS (Transcriptome Analysis Pipeline for Isoform Sequencing) to identify full-length splice isoforms and APA sites. Our analysis reveals transcriptome-wide full-length isoforms at an unprecedented scale with over 11,000 novel splice isoforms. Additionally, we uncover APA of ~11,000 expressed genes and more than 2,100 novel genes. These results greatly enhance sorghum gene annotations and aid in studying gene regulation in this important bioenergy crop. The TAPIS pipeline will serve as a useful tool to analyze Iso-Seq data from any organism.</p>
Matrix multiplication software and results bundle for paper "Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library" for P^3MA submission
<p>This is the archive containing the matrix multiplication software and the results of the publication "<em>Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library</em>" submitted to the P^3MA workshop 2017.</p> <p><strong>The archive has the following content:</strong></p> <ul> <li>Source code for the (tiled) matrix multiplication in "src": <ul> <li>regular version in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-compatible-alpaka-0-1-0</li> <li>Commit: a63ba4810d6bfcca62c68dd57408af15028e78a3</li> </ul> </li> <li>forked version for XL in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-xl-workaround</li> <li>Commit: 1fee028eccb8cf7b677e8071233e08aa9f81846a</li> </ul> </li> </ul> </li> <li>The compiled binaries and the results of the tuning and scaling runs are in "runs" in sub folders for each type of run and architectures.</li> </ul>
High-power intracavity single-cycle THz pulse generation using thin lithium niobate
<p>This dataset is accompanying the paper "High-power intracavity single-cycle THz pulse generation using thin lithium niobate"<br><br><strong>Autocorrelation.txt:</strong> second harmonic generation noncollinear autocorrelation trace data. (measurement device: Femtochrome FR-103XL)</p><p><strong>Spectrum.txt:</strong> optical spectrum (measurement device: APE wavescan)</p><p><strong>RF_1Mspan.txt:</strong> radio frequency spectrum with 1 MHz span (measurement device: ROHDE & SCHWARZ FPC1000)</p><p><strong>RF_1Gspan.txt:</strong> radio frequency spectrum with 1 GHz span (measurement device: ROHDE & SCHWARZ FPC1000)</p><p><strong>EOS_THz_raw.h5: </strong>electro-optic sampling raw data of the THz measurement in HDF-5 format (measurement device: ROHDE & SCHWARZ RTM3004)</p><p><strong>EOS_noise_raw.h5: </strong>electro-optic sampling raw data of the noise measurement in HDF-5 format (measurement device: ROHDE & SCHWARZ RTM3004)</p><p><strong>THz_time.csv:</strong> processed electro-optic sampling data of the THz measurement in time</p><p><strong>THz_freq.csv:</strong> processed electro-optic sampling data of the THz measurement in frequency</p><p><strong>Dark_time.csv:</strong> processed electro-optic sampling data of the noise measurement in time</p><p><strong>Dark_freq.csv:</strong> processed electro-optic sampling data of the noise measurement in frequency</p>
Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China"
<p>Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China".</p><p>Transformations among the parameters of the moment tensor model refer to the code package from Tape and Tape (https://github.com/carltape/mtbeach/; https://github.com/carltape/surfacevel2strain; Tape and Tape, 2009, 2012, 2013, 2015).</p><p>Tape, C., P. Muse, M. Simons, D. Dong, and F. Webb (2009). Multiscale estimation of GPS velocity fields, Geophys. J. Int. 179, no.2, 945-971, doi: 10.1111/j.1365-246X.2009.04337.x.</p><p>Tape, W., and C. Tape (2012). A geometric setting for moment tensors, Geophys. J. Int. 190, no. 1, 476–498, doi: 10.1111/j.1365-246X.2012.05491.x.</p><p>Tape, W., and C. Tape (2013). The classical model for moment tensors, Geophys. J. Int. 195, no. 3, 1701–1720, doi: 10.1093/gji/ggt302.</p><p>Tape, W., and C. Tape (2015). A uniform parametrization of moment tensors, Geophys. J. Int. 202, no. 3, 2074–2081, doi: 10.1093/gji/ggv262.</p>
An Approach Based on an Increased Bandpass for Enabling the Use of Internal Standards in Single Particle ICP-MS: Application to AuNPs Characterization
<p>This dataset contains the raw data corresponding to the figures of the publication https://doi.org/10.3390/nano13121838</p>
Multi-cell type deconvolution using a probabilistic model for single-molecule DNA methylation haplotypes
<p>Files required to run deconvolution with CelFIE-ISH and Epistate, in U250 regions from Loyfer et al. 2023, in both "pat" and "epiread" formats. </p>
Datasets for CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning
<p>This repository contains datasets used in the project CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning. This project aims at learning cell annotations for missing cell labels via NMF and recursive k-Means clustering.</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.