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6,467 results for “process”
Experimental data used in the article entitled "Electron Microscopy Study of Structural Defects Formed in Additively Manufactured AlSi10Mg Alloy Processed by Equal Channel Angular Pressing"
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Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia: scRNA-seq and CyTOF processed datasets
<p><strong>This data was generated as part of the Tumor Profiler study. If you use it in your research, please cite:</strong></p> <p>Wegmann, R., Bonilla, X., Casanova, R. <em>et al.</em> Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia. <em>Nat Commun</em> 15, 9402 (2024). https://doi.org/10.1038/s41467-024-53535-4</p> <p><strong>Derived data - scRNA-seq</strong></p> <p>This is an R data set (.RDS) containing a SingleCellExperiment object with the following slots:</p> <div> <ul> <li>Assays: <ul> <li>counts: raw counts</li> </ul> </li> </ul> </div> <div> <ul> <li>colData: Cell-level metadata <ul> <li> barcodes: The cell barcode</li> <li> fractionMT: Fraction mitochondrial genes per cell</li> <li> n_umi: Total number of UMIs per cell</li> <li> n_gene: Total number of genes per cell</li> <li> log_umi: log10 total number of UMIs per cell</li> <li> g2m_score: Cell cycle phase score for G2M</li> <li>s_score: Cell cycle phase score for S</li> <li>cycle_phase: predicted cell cycle phase</li> <li>celltype_major_full_ct_name: Major cell type full name</li> <li>celltype_major: Major cell type short name</li> <li>celltype_final_full_ct_name: Cell subtype full name</li> <li>celltype_final: Cell subtype short name </li> <li>sample_id </li> </ul> </li> </ul> </div> <div> <ul> <li>rowData: Gene-level metadata <ul> <li>gene_ids</li> <li>gene_names</li> </ul> </li> </ul> </div> <p><strong>Derived data - CyTOF</strong></p> <p>This is an R data set (.RDS) containing a SingleCellExperiment object with the following slots:</p> <ul> <li>Assays:<br> <ul> <li>counts_raw: signal intensity based on CyTOF dual counts</li> <li>exprs_raw: arcsinh transformed raw counts (cofactor 5)</li> <li>counts: batch corrected raw counts (linear scaling based on a quantile)</li> <li>exprs: arcsin transformed counts (cofactor 5)</li> <li>scaled: 0-1 normalized exprs (clipped to the 99.95th percentile)</li> </ul> </li> <li>colData (cell metadata) <ul> <li>bc_id: barcode of the sample during staining </li> <li>run: CyTOF experiment batch, named after the first sample of the batch</li> <li>type: Sample type (blood or bone marrow)</li> <li>sample_id: TuPro sample ID</li> <li>pred_id: Predicted cell type [char]</li> <li>pred_n: Predicted cell type [integer]</li> </ul> </li> <li>rowData (marker metadata) <ul> <li>channel_name: Name and isotopic mass of the metal ion corresponding to this marker</li> <li>marker_name: Protein name</li> <li>channel_group, channel_group_integer: Biological processes the channel identifies, e.g. specific cell type, signalling, cell death</li> <li>tsne_channel: Logical - use this channel for dimensionality reduction?</li> <li>channel_order: Define the order of channels for plotting</li> <li>cluster_channel: Logical - use this channel for clustering?</li> </ul> </li> </ul>
Molecular dynamics simulation: The process of phosphate transfer from fructose-1,6-bisphosphate to phosphoglycerate mutase 1
<p><span>Molecular dynamics (MD) simulations were performed to investigate the dynamic process of the binding and phosphorylation of PGAM1 by FBP. </span></p>
Simulation output of the reference setup in "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses"
<p>This supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses" contains the simulation output of the 20 reference runs. Additional data is available upon request from the corresponding author.</p>
DS6_mT_EEG frequency tagging to track the processing of vibrotactile contrasts.v5.0
<p>EEG frequency spectra, behavioral responses, trial matrices, and Hilbert-transformed banpass-filtered A (standards) and B (oddballs) white noise snippets. See README file for description of experimental conditions and variables.</p>
Acoustic Emission Dataset for Multi-Laser LPBF Systems: Supporting DUAL DISCO Signal Processing Technique
<p>This dataset contains acoustic emission (AE) signals collected during experiments with multi-laser Laser Powder Bed Fusion (LPBF) systems. The data supports the research presented in the paper titled "DUAL DISCO: A Novel Approach to Acoustic Emission Monitoring in Multi-Laser LPBF Systems." The dataset is designed to facilitate the development and validation of advanced signal processing techniques, specifically the DUAL DISCO method, which aims to disentangle and analyze AE signals from simultaneous laser operations.</p> <p><strong>Contents:</strong></p> <ul> <li> <p><strong>Raw_data.zip:</strong> This file contains the AE signals used for training. The data was recorded using two condenser microphones positioned around the LPBF build plate, capturing signals from both sequential and simultaneous laser operations across various melting regimes (conduction and keyhole modes).</p> </li> <li> <p><strong>Raw_data_test.zip:</strong> This file includes the AE signals used for testing, recorded under different experimental conditions to evaluate the generalization capabilities of signal processing algorithms.</p> </li> <li> <p><strong>params.xlsx:</strong> This spreadsheet provides the ground truth labels for the training data, indicating the melting regime (conduction or keyhole) for each signal in Raw_data. </p> </li> <li> <p><strong>params_test.xlsx:</strong> This spreadsheet contains the ground truth labels for the test data, similarly indicating the melting regime for each signal in Raw_data_test.</p> </li> </ul> <p><strong>Applications:</strong></p> <p>This dataset is intended for researchers and practitioners in the field of additive manufacturing and signal processing. It can be used to:</p> <ul> <li>Develop and test new algorithms for AE signal processing in multi-laser LPBF systems.</li> <li>Explore the acoustic characteristics of different melting regimes.</li> <li>Enhance the understanding of process monitoring techniques in additive manufacturing.</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>The dataset was collected using the AddUp FormUp 350 machine and is part of a research project supported by the Bern Economic Development Agency. Special thanks to Thomas Rytz for technical support.</p>
SST data from JAXA for "Importance of Strains in Kinetic Energy Conversion for Submesoscale Processes from an Anisotropic Perspective"
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MATLAB Code for "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"
<p>This MATLAB code is part of the study titled <em>"Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"</em>, which has been accepted for publication in the <em>Journal of Imaging (MDPI)</em>. The code supports image processing, feature extraction, and deep learning model training (including LSTM and RexNet) to classify pediatric patients as anemic or non-anemic based on palm, conjunctival, and fingernail images. Full study details are available in this paper:</p> <p>Berghout T. Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients. <em>Journal of Imaging</em>. 2024; 10(10):245. <a href="https://doi.org/10.3390/jimaging10100245">https://doi.org/10.3390/jimaging10100245 </a></p> <p>The datsets use in this work are:</p> <p>Asare, J. W., Appiahene, P. & Donkoh, E. (2022). Anemia Detection using Palpable Palm Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/ccr8cm22vz.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2023). CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana. Mendeley Data. https://doi.org/10.17632/m53vz6b7fx.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2020). Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/2xx4j3kjg2.1</p>
Data used in the paper Sensitivity of wintertime Arctic black carbon to removal processes and regional Alaskan sources by Ioannidis et al. 2024
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Processed MERFISH datasets for brain aging (coronal, sagittal) and rejuvenation (exercise, partial reprogramming)
<p>This repository contains several AnnData ".h5ad" objects containing the processed MERFISH data and metadata for the datasets generated in our study:</p> <blockquote> <p>"aging_coronal.h5ad" - the aging coronal section dataset</p> <p>"aging_sagittal.h5ad" - the aging sagittal section dataset</p> <p>"exercise.h5ad" - exercise dataset</p> <p>"reprogramming.h5ad" - partial reprogramming dataset</p> </blockquote> <p> </p> <p>Key metadata are saved as observation annotations in each object, and include:</p> <blockquote> <p>"mouse_id" - identifier for mouse from which the cell was derived</p> <p>"slide_id" - identifier for MERSCOPE slide</p> <p>"batch" - A and B denote different experimental batches for MERFISH</p> <p>"age" - mouse age in months</p> <p>"cohort" - experimental condition / group</p> <p>"celltype" - cell type annotation</p> <p>"region" - brain region annotation</p> <p>"subregion" - brain subregion annotation (will be more variabile/noisy than region)</p> </blockquote>
Dataset for the plots in paper: 'A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation' in 'Journal of Computational Physics'
<p>The .xlsx file contains the data used for the plots Fig. 3, 4, 7, 8 and 9 in the paper 'Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation'.</p>
Audio processing and editing videos
<p>List of audio processing and editing videos</p> <p>Videos are in Spanish with test questions during reproduction</p>
NB-FOXR2 processed data
<p>The sample names in this dataset match supplementary tables 1 (human) and 9 (mouse).</p> <p>The tarball contains 2 directories: human and mouse. Each of these contains directories specifying data types.</p> <p>See the README.md for a description of all the files.</p>
DUGseis processing example with needed data files
<p>The DUGseis software is designed for seismic data processing and developed for monitoring seismicity during hydraulic stimulations in the Bedretto Undergound Laboratory. Here, we provide a python run-file and the associated configuration file (.yaml). In the configuration file, paths to station xml files (.xml) and waveform files (.h5) are defined. As an example and to test the software, a small subset of 7 minute waveform data of two acquisition systems (system 03 ending on _03 and system 04 ending on _04) and the needed station xml files are available.</p> <p> </p> <p><span> </span></p>
Corpus Nummorum - Natural Language Processing Dataset
<p>This Natural Language Processing (NLP) dataset contains a part of the MySQL <a href="https://www.corpus-nummorum.eu/">Corpus Nummorum (CN)</a> database. It covers Greek and Roman coins from ancient Thrace, Moesia Inferior, Troad and Mysia.</p> <p>The dataset contains 7,900 coin descriptions (or designs) created by the members of the CN project. Most of them are actual coin designs which can be linked through our relational database to the matching CN coins, types and their images. However, some of them (about 450) were only created for the training of the NLP model. </p> <p>There are nine different MySQL tables: </p> <ol> <li>data_coins: contains the data of all coins in the CN database</li> <li>data_coins_images: contains data of all images in the CN database</li> <li>data_coins_imagesets: contains the image pairs for the CN coins</li> <li>data_designs: contains every coin description in German, English and Bulgarian</li> <li>data_types: contains the data of alle coin types the Cn database</li> <li>nlp_hierarchy: contains the classes and subclasses of all entity categories </li> <li>nlp_list_entities: contains the data of all nlp entities in the CN database</li> <li>nlp_relation_extraction_en_v2: contains the annotations for the training of our NLP model</li> <li>nlp_training_designs: contains the coin designs used for training our NLP model</li> </ol> <p>Only tables 8 and 9 are important for NLP training, as they contain the descriptions and the corresponding annotations. The other tables (data_...) make it possible to link the coin descriptions with the various coins and types in the CN database. It is therefore also possible to provide the CN image data sets with the appropriate descriptions (<a href="../records/10033993">CN - Coin Image Dataset</a> and <a href="../records/13748799">CN - Object Detection Coin Dataset</a>). The other NLP tables provide information about the entities and relations in the descriptions and are used to create the RDF data for the <a href="https://nomisma.org/datasets">nomisma.org</a> portal. The tables of the relational CN database can be related via the various ID columns using foreign keys.</p> <p>For easier access without MySQL, we have attached two csv files with the descriptions in English and German and the annotations for the English designs. The annotations can be related to the descriptions via the Design_ID column. </p> <p>During the summer semester 2024, we held the "Data Challenge" event at our Department of Computer Science at the Goethe-University. Our students could choose between the Object Detection dataset and a Natural Language Processing dataset as their challenge. We gave the teams that decided to take part in the NLP challenge this dataset with the task of trying out their own ideas. Here are the results:</p> <ul> <li><a href="https://github.com/jasperforth/DataChallenge_LLM_REPipeline">LLM_RE Pipeline</a></li> <li><a href="https://github.com/Axolord/coin-description-embeddings">Coin description embeddings</a></li> <li><a href="https://github.com/calul0/nlp_coin_app/tree/master">NLP coin app</a></li> </ul> <p>Now we would like to invite you to try out your own ideas and models on our coin data.</p> <p>If you have any questions or suggestions, please, feel free to contact us. </p>
Radar Observations of Convective Processes Associated with Eyewall Formation during the Rapid Intensification of Typhoon Cempaka (2021)
<p>Dataset for Radar Observations of Convective Processes Associated with Eyewall Formation during the Rapid Intensification of Typhoon Cempaka (2021)</p>
Sand transport processes change in response to changing atmospheric properties at higher elevations
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Data for "Magnetic Characterization of Sediment Source-to-Sink Processes in the Bengal Fan since 45 ka"
<p>The file is the dataset for the manuscript entitled 'Magnetic Characterization of Sediment Source-to-Sink Processes in the Bengal Fan since 45 ka' by Huang et al., including age, original and smoothed low frequency magnetic susceptibility, anhysteretic remanence magnetization (ARM) data, and bulk mean grain size data of five gravity cores in the manuscript. Hysteresis loop data is also included.</p>
Inferring GCE Morphology with Gaussian Processes: Figure Data
<p>Figure data for Inferring GCE Morphology with Gaussian Processes.</p> <p>See GitHub repo for details on creating figures.</p>
Input Files and Processed Results for FreeMHD: validation and verification of the open-source, multi-domain, multi-phase solver for electrically conductive flows
<p><strong>Input Files (StartingFiles.zip) and Processed Results (FreeMHDPaperAllFigures.zip) used to make paper figures. </strong></p> <p> </p> <p>FreeMHD: validation and verification of the open-source, multi-domain, multi-phase solver for electrically conductive flows</p> <p><em>The extreme heat fluxes in the divertor region of tokamaks may require an alternative to solid plasma-facing components, for the extraction of heat and the protection of the surrounding walls. Flowing liquid metals are proposed as an alternative, but raise additional challenges that require investigation and numerical simulations. Free surface designs are desirable for plasma-facing components (PFCs), but steady flow profiles and surface stability must be ensured to limit undesirable interactions with the plasma. Previous studies have mainly used steady-state, 2D, or simplified models for internal flows and have not been able to adequately model free-surface liquid metal (LM) experiments. Therefore, FreeMHD has been recently developed as an open-source magnetohydrodynamics (MHD) solver for free-surface electrically conductive flows subject to a strong external magnetic field. The FreeMHD solver computes incompressible free-surface flows with multi-region coupling for the investigation of MHD phenomena involving fluid and solid domains. The model utilizes the finite-volume OpenFOAM framework under the low magnetic Reynolds number approximation. FreeMHD is validated using analytical solutions for the velocity profiles of closed channel flows with various Hartmann numbers and wall conductance ratios. Next, experimental measurements are then used to verify FreeMHD, through a series of cases involving dam breaking, 3D magnetic fields, and free-surface LM flows. These results demonstrate that FreeMHD is a reliable tool for the design of LM systems under free surface conditions at the reactor scale. Furthermore, it is flexible, computationally inexpensive, and can be used to solve fully 3D transient MHD flows.</em></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.