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1,481 results for “data processing”
[Data] Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
<div> <div> <div> <p>Different Laser Powder Bed Fusion (LPBF) process spaces were deliberately introduced by employing two distinct 316L stainless steel powder distributions (with particle sizes >45 μm and < 45 μm) and processing them with two sets of laser parameters, resulting in the creation of four datasets [D1, D2, D3, and D4]. These datasets encompass LoF pores, conduction mode, and keyhole formations, each associated with three LPBF regimes denoted as D1, D2, D3, and D4. The experiments utilized a Sisma MYSINT 100 commercial LPBF printer and an airborne AE sensor system with a flat frequency response ranging from 0 to 150 kHz. Validation of the ground truths for the three laser regimes across the four datasets, representing distinct process spaces, was accomplished through the confirmation of cross-sectional images. In the course of fabricating a cube using a powder bed and laser, data acquisition from an AE sensor was triggered when the optical intensity reached a threshold of 0.5 V for each scan length. The photodiode trigger gain was adjusted to saturate at 5 V, and the ensuing continuous-time window, where the optical signal remained at 5 V for 12.5 ms, was calculated and segmented to generate the dataset. Irrespective of the specific regime (Lack of Fusion, Conduction, and Keyhole) or the cube being fabricated (with two powder distributions), the signals obtained during this process were then segmented into a 12.5 ms window comprising 5000 data points. To eliminate any noise, an offline application of a low-pass Butterworth filter with a 150 kHz cut-off frequency was employed, aligned with the frequency response specification of the AE sensor. Each dataset has two files against it [raw/groundtruth label].</p> </div> </div> </div>
Supplementary data to: Large rock and ice avalanches frequently produce cascading processes in High Mountain Asia
<p>This data, focusing on large rock and/or ice avalanche events with severe consequences in High Mountain Asia (HMA), <span>provide a valuable first step toward improved understanding of the frequency, scope, and societal impact of such hazards across HMA</span> (linked to a journal article: Large rock and ice avalanches frequently produce cascading processes in High Mountain Asia, published in Geomorphology in 2024).</p>
Soil respiration processed data at Palazzelli CREA_OFA citrus farm
<p>Soil respiration processed data at Palazzelli CREA_OFA citrus farm</p>
Original research data for the paper "Additive Manufacturing of Polymeric Gradient Index Optics via Grayscale Digital Light Processing Vat Photopolymerization Technology"
<p>[CMOS camera data]</p> <p>[conversion maps]</p> <p>[cure kinetics]</p> <p>[GRIN profiles arbitrary]</p> <p>[MonoPrinter grayscale power density]</p> <p>[MonoPrinter print files]</p> <p>[pictures of arbitrary GRINs]</p> <p>[predicted printing param matrices]</p> <p>[refractive index vs conversion]</p> <p>[working curve]</p>
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
<p>Hydrological data measured in Lompolonjängänoja (LJO) catchment and used in Nousu et al.</p> <p> </p> <p>ET_fluxes.csv<br>- Eddy-covariance based, daily evapotranspiration (ET) fluxes [mm/d] at Kenttärova (NFOR) and Lompolojänkkä (NWET) stations</p> <p>GW_levels.csv<br>- Observed groundwater levels [m] relative to the ground surface measured around the LJO catchment</p> <p>Q_runoff.csv<br>- Observed specific discharge [mm/d] at the LJO catchment outlet</p> <p>THETA_kenttarova.csv<br>- Automatically measured soil moisture (i.e. volumetric water content [m3/m3]) around Kenttärova stations</p> <p>THETA_spatial.csv<br>- Manually measured soil moisture (i.e. volumetric water content [m3/m3]) around the LJO catchment</p>
Data for: Passive Daytime Cooling Foils for Everyone: A Scalable Lamination Process Based on Upcycling Aluminum-Coated Chips Bags
<p>Raw data of the measurements presented in the affiliated publication. The data are organized in folders corresponding to the respective characterization techniques. The employed techniques comprise: optical spectroscopy, indoor and field testing of the passive cooling performance, and scanning electron microscopy imaging.</p>
Uncoupling of Behavioral and Metabolic Twenty-Four-Hour Rhythms in Reindeer (Current Biology, Meier et al. 2024): Pre-Processed metabolomics data
<p>Pre-processed metabolomics data (peak picking, peak alignment, integration and annotation using XCMS) obtained from untargeted UPLC-MS measurements of reindeer blood plasma.</p>
Supplementary data: Predicting grid frequency short-term dynamics with Gaussian processes and sequence modeling
<p>This repository contains data and result files for the paper "Predicting grid frequency short-term dynamics with Gaussian processes and sequence modelling". The code to generate the models and reproduce the results of the comparative study in the above paper is available on this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository</a></p> <p><strong>Supplementary data</strong>:</p> <p>- The <strong>trained_models</strong> folder contains the results of the trained models.</p> <p>- The folder <strong>data</strong> contains data needed for for the comparative study for the year 2019 in the paper above. This data set (except knn_point_predictions.npy) is generated with the code in this <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">github repository</a>. knn_point_predictions.npy is generated with the code in this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository </a>.</p>
Data and analysis process for Microbiome processing of organic nitrogen input supports growth and cyanotoxin production of Microcystis aeruginosa cultures
<p>Data and analysis process for manuscript titled "Microbiome processing of organic nitrogen input supports the growth and cyanotoxin production of Microcystis aeruginosa cultures"</p>
Processed Data for Ring Current Oxygen Ion ANN Model
<p>Processed data, including:</p> <p>the coordinate of Van Allen Probe B in SM coordinates.</p> <p>Geomagnetic indices (Sym-H, SME and F10.7).</p> <p>log10 O+ fluxes, here the unit of 38 keV and 52 keV O+ (measured by HOPE) is s-1 cm-2 ster-1 keV-1, the unit of other energy channels (measured by RBSPICE) is s-1 cm-2 MeV-1.</p>
Source Data for the publication "Sub-100-fs energy transfer in coenzyme NADH is a coherent process assisted by a charge-transfer state"
<p>Molecular Structures for solvated NADH. </p> <p>The folder "QMMM_OPTIMIZED_STRUCTS" contains the pdb files of the six representatives for the three conformational clusters obtained after REMD used in the Supplementary Information.</p> <p>The folder "SOLVENT_ENSEMBLE_AROUND_FIXED_SOLUTE" conatins AMBER RESTART files for 200 solvent configurations around two cluster reps displayed in Figure 2 of main manuscript. </p> <p>The folder "PARAMETERS_FOR_MLMCTDH" contains the input file, operator file and parameters for ML-MCTDH dynamics for the structures shown in Main Manuscript and Supplementary. </p>
Experimental Data - Physicochemical Properties of 20 Ionic Liquids Prepared by the Carbonate-Based IL (CBILS) Process
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Single-neutron transfer on 47K(d.p): Processed data of experiment e793s
<p>This is the calibrated, sorted and processed data of experiment e793s (<a title="E793S_19 - Proton-neutron interactions across the N = 28 shell closure via 47K(d,p) 48K, and implications for the most neutron-rich phosphorus" href="doi.org/10.26143/GANIL-2021-E793S_19" target="_blank" rel="noopener">10.26143/GANIL-2021-E793S_19</a>), wherein the single-neutron transfer reaction 47K(d,p) was performed in inverse kinematics using a radioactive isotope beam. This data file formatted as a TTree, readable using CERN's ROOT Data Analysis Framework.</p> <p>The analytical code to process this data is also available (<a title="Single-neutron transfer on 47K(d.p): nptool framework for the analysis of e793s" href="doi.org/10.5281/zenodo.13748333" target="_blank" rel="noopener">10.5281/zenodo.13748333</a>).</p>
processed scRNA-seq data for Neuwirth & Malzl et al. 2024
<p>This dataset contains the analysed scRNA-seq data used in Neuwirth & Malzl et al. 2024 as AnnData objects. You can find the code used to produce and analyse this data on <a href="https://github.com/menchelab/Neuwirth_Malzl_et_al_2024">GitHub</a>. All data was preprocessed using cellranger v6.0.1 with GRCh38 3.0.0 as reference.</p> <h3><strong>Psoriasis and Sarcoidosis PBMC data</strong></h3> <p><strong><em>pbmc.scps.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered data of Psoriasis and Sarcoidosis patient blood as well as healthy controls (Psoriasis data was generated within this study; Sarcoidosis data was reprocessed from 10.1016/j.immuni.2023.01.014)<br><strong><em>tcells.pbmc.scps.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered T cell subset of Psoriasis and Sarcoidosis PBMC data</p> <h3><strong>Atopic dermatitis skin data</strong></h3> <p><strong><em>tissue.ad.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered data of healthy and atopic dermatitis patient skin (reprocessed from 10.1126/science.aba6500)<br><strong><em>tcells.tissue.ad.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered T cell subset of atopic dermatitis data</p> <h3><strong>Psoriasis and Sarcoidosis skin data</strong></h3> <p><strong><em>tissue.scps.integrated.annotated.h5ad</em></strong>: contains scVI-integrated and celltypist-annotated data from Psoriasis and Sarcoidosis patient skin as well as healthy controls (Psoriasis data was reprocessed from 10.1126/science.aba6500; Sarcoidosis data was reprocessed from 10.1016/j.immuni.2023.01.014)<br><strong><em>tcells.tissue.scps.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered T cell subset of Psoriasis and Sarcoidosis skin data<br><strong><em>tregs.tissue.scps.integrated.annotated.h5ad</em></strong>: contains scVI-integrated and SAT1 status annotated regulatory T cell subset of Psoriasis and Sarcoidosis skin data<br><em><strong>tregs.tissue.scps.integrated.milo.h5ad</strong></em>: basically same as above but with cell neighborhood overrepresentation analysis on top</p> <h3><strong>IBD colon data</strong></h3> <p><strong><em>tissue.uc.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered data of Crohn's disease and ulcerative colitis patients as well as healthy controls (reprocessed from 10.1126/sciimmunol.abb4432)<br><strong><em>tcells.tissue.uc.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered T cell subset of IBD data</p> <h3><strong>Raw and unfiltered data</strong></h3> <p><strong><em>inflammatory_disease.h5ad</em></strong>: contains the raw, unfiltered and unprocessed data of all the files above (i.e. combined cellranger output) and is the source data file of all analyses in this study. If you just want the untouched data this is what you want to use.</p>
Data For Process Spoel Data
<p>Data for article: 'Floating large wood dynamics during a flood: insights from a drone survey and machine learning'</p> <p>Github: https://github.com/janbertoo/process_spoel_data</p>
Processed data files - Muysers et al.
<p>Pre-processed 1P data for the extraction of functional cell types during task learning.</p>
Code and Data for manuscript "Floral phenotypic divergence and genomic insights in an Ophrys orchid: Unraveling early speciation processes"
<p>Floral phenotypic divergence and genomic insights in an Ophrys orchid: Unraveling early speciation</p> <p>--------</p> <p>This repository contains all the R code used in the manuscript:</p> <p>* Title: "Floral phenotypic divergence and genomic insights in an Ophrys orchid: Unraveling early speciation"</p> <p>* Authors: Anais Gibert, Schatz Bertrand, Buscail Roselyn, Dominique Nguyen, Baguette Michel, Bartes Nicolas and Joris Bertrand</p> <p>* Year of publication: 2024</p> <p>* doi: https://doi.org/10.1101/2024.03.21.586062</p> <p> </p> <p>Synopsis of the study</p> <p>--------</p> <ul> <li> <p>Adaptive radiation in <em>Ophrys</em> orchids leads to complex floral phenotypes that vary in scent, color and shape.</p> </li> <li> <p>Using a novel pipeline to quantify these phenotypes, we investigated trait divergence at early stages of speciation in six populations of <em>Ophrys aveyronensis</em> experiencing recent allopatry. By integrating different genetic/genomic techniques, we investigated: (i) variation and integration of floral components (scent, color and shape), (ii) phenotypes and genomic regions under divergent selection, and (iii) the genomic bases of trait variation.</p> </li> <li> <p>We identified a large genomic island of divergence, associated with phenotypic variation in particular in floral odor. We detected potential divergent selection on macular color, while convergent selection was suspected on floral morphology and for several volatile olfactive compounds. We also identify candidate genes involved in anthocyanin and in steroid biosynthesis pathways associated with standing genetic variation in color and odor.</p> </li> <li> <p>This study sheds light on early differentiation in <em>Ophrys</em>, revealing patterns that often become invisible over time, i.e., the geographic mosaic of traits under selection and the early appearance of strong genomic divergence. It also supports a crucial genomic region for future investigation and highlights the value of a multifaceted approach in unraveling speciation within taxa with large genomes.</p> </li> </ul> <p> </p> <p>Running the code</p> <p>--------</p> <p>Here we present the data and code for carrying out the analyses, as well as the figures and tables from the article and the supplementary material. Once you have installed the necessary packages, run the commands in 'analysis_share.R'. This script uses several functions available in the '/R' directory.</p> <p>Figures and tables are produced in a 'manuscript/figures' and 'manuscript/tables' directory. <br>The `/data' directory contains the data used in the analyses (data/input or data/output), but also the resulting datasets produced by the code (data/RData/).</p> <p> </p>
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations: Data and Visualization Notebooks
<p>The data, jupyter notebooks, and saved model weights for the "Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations" Hu et al. (2025) arxiv preprint: <a href="https://arxiv.org/abs/2407.00124">arXiv:2407.00124</a>. This updated version contains more analysis notebooks together with related data/model.</p>
Processed gene and clinical data
<p>The processed cancer dataset mentioned in the paper "Cox-Sage: Enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis," which is currently under review in Briefings in Bioinformatics. The gene expression data and clinical data of seven cancer types downloaded from TCGA (https://portal.gdc.cancer.gov/) were processed to retain only protein-coding genes. A patient similarity graph was constructed based on the similarity of clinical data. The data for each type of cancer consists of a `gene_expression.csv`, a `clinical.csv`, and an `adj_list.pkl`. In addition, the `prognostic_genes.zip` file contains the hazards contour plot of all prognostic genes identified in the study. And the `all_benchmarks_prediction_results.zip` file contains the hazards prediction results of all benchmarks that being reproduced.</p>
Raw GC-ToF-MS and processed data from individuals sampled in allopatric and contact zones and MZmine 3.9.0 and Rstudio analysis
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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.