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6,467 results for “process”
Processed dataset used for developing tsunami inundation emulators(Part-II Test Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed datasets covering testing datasets covering tsunami inputs and outputs (for parameters of offshore waveforms, local deformation fields and inundation depths) used in the evaluation of machine learning emulator discussed in the preprint article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effect with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <br> <div>The post-processed (numpy) files for the two test locations of Catania(CT) and Siracusa(SR) are provided in compressed gzip files(.gz) typically stored in data/processed:</div> <strong>-test_dZ.tar.gz</strong> (local deformation files)</div> <div><strong>-test_d.tar.gz </strong>(inundation depth files)</div> <div><strong>-test_t.tar.gz</strong> (offshore waveform files)</div> <p>The filename follow nomenclature as below</p> <p><strong>d_CT_0.dat, dZ_CT_0.dat, dflat_CT_0.dat, dZflat_CT_0.dat, t_CT_0.dat,lat_lon_idx_CT_892.npy</strong> where the file names represent <strong>{parameter}_{site}_{size}</strong></p> <ol> <li>The first var represent parameter of the file. <ul> <li>d - 2 dimensional file for inundation depth (<strong>events </strong>x <strong>m </strong>x <strong>n</strong>)</li> <li>dZ - 2 dimensional file for local deformation( <strong>events </strong>x <strong>m</strong> x <strong>n</strong>)</li> <li>dflat - 1 dimensional flat file for inundation ( <strong>events </strong>x <strong>locations</strong>)</li> <li>dZflat - 1 dimensional flat file for local deformation( <strong>events </strong>x <strong>locations</strong>)</li> <li>t - 3 dimension offshore waveform for (<strong>events </strong>x <strong>gauges </strong>x <strong>timesteps</strong>)</li> <li>lat_lon_idx - index file for matching lat long coordinate with location indices(<strong>locations x lat x lon</strong>)</li> </ul> </li> <li>The second var represents site of the file. <ul> <li>CT - Catania</li> <li>SR - Siracusa</li> </ul> </li> <li>The third var represents number of events in the file or the selection size. <ul> <li>0,1,2,3 - represent the approx 50000 test events divided into 4 splits</li> </ul> </li> </ol> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div>
Processed dataset used for developing tsunami inundation emulators(Part-I Training Dataset and Model Weights)
<p>This dataset contains some of the processed training datasets covering tsunami inputs and outputs parameters (for offshore waveforms, local deformation fields and inundation depths used in developing a machine learning emulator discussed in the preprint article -<strong> Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects</strong> with project repo - <a href="https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.">https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</a></p> <p>####Code####</p> <p>Source code from the above github project : <strong>ML4SicilyTsunami-NPJ2024.tar.gz</strong></p> <p>The post-processed numpy dat files for the two test location of <strong>Catania(CT) and Siracusa(SR)</strong> are provided for each variable retrieved from data/processed folder of the project as compressed gzip files(.gz):</p> <p>####Model Checkpoints#### <br>ML Model checkpoints from all training experiments.<br><strong>- outSR.tar.gz (Siracusa)</strong><br><strong>- outCT.tar.gz (Catania)</strong></p> <p>####Training Datasets####<br><strong>- t.tar.gz: </strong>Offshore waveforms for gauge sites in a 3D numpy file<br><strong>- dZ.tar.gz: </strong>Local deformation fields in 2D numpy files or reduced 1D flat files on flooded grids<br><strong>- d.tar.gz: </strong>Maximum inundation depth in 2D numpy files or reduced 1D flat files</p> <p>####Flood Mask####<br>- Identifies floodable and non-floodable areas in the domain.<br><strong> - zero_mask_SR_961.npy.gz</strong><br><strong> - zero_mask_CT_892.npy.gz</strong></p> <p>####Flood Indices####<br>Links between 2D and 1D numpy files:<br>- Indices connecting 2D (events x m x n) numpy files to flattened 1D (events x m) numpy files:<br><strong> - zero_indices_SR_961.npy.gz</strong><br><strong> - zero_indices_CT_892.npy.gz</strong><br>- Indices connecting latitude-longitude coordinates to 1D (events x m) numpy files:<br><strong> - lat_lon_idx_CT_892.npy.gz</strong><br><strong> - lat_lon_idx_SR_961.npy.gz</strong></p> <p>The filenames follow nomenclature representing <strong>{parameter}_{site}_{size}</strong></p> <ol> <li>The first var represents the parameter of the file. <ul> <li>d - 2 dimensional file for inundation depth (<strong>events </strong>x <strong>m </strong>x <strong>n</strong>)</li> <li>dZ - 2 dimensional file for local deformation( <strong>events </strong>x <strong>m</strong> x <strong>n</strong>)</li> <li>dflat - 1 dimensional flat file for inundation ( <strong>events </strong>x <strong>locations</strong>)</li> <li>dZflat - 1 dimensional flat file for local deformation( <strong>events </strong>x <strong>locations</strong>)</li> <li>t - 3 dimension offshore waveform for (<strong>events </strong>x <strong>gauges </strong>x <strong>timesteps</strong>)</li> <li>lat_lon_idx - index file for matching lat long coordinate with location indices(<strong>locations x lat x lon</strong>)</li> </ul> </li> <li>The second var represents site of the file. <ul> <li>CT - Catania</li> <li>SR - Siracusa</li> </ul> </li> <li>The third var represents number of events in the file or the selection size.<br> <ul> <li>892 - represents the training events for the file, of 4 different sizes for Catania and Siracusa each</li> </ul> </li> </ol> <div>More information on the attached readme.</div>
Advances in Leukemia Detection and Classification: A Systematic Review of AI and Image Processing Techniques
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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>
Multi-modality medical image dataset for medical image processing in Python lesson
<p>This dataset contains a collection of medical imaging files for use in the <a href="https://github.com/esciencecenter-digital-skills/medical-image-processing">"Medical Image Processing with Python" lesson</a>, developed by the <a href="https://www.esciencecenter.nl/">Netherlands eScience Center</a>. </p> <p>The dataset includes:</p> <ol> <li>SimpleITK compatible files: MRI T1 and CT scans (<em>training_001_mr_T1.mha, training_001_ct.mha</em>), digital X-ray (<em>digital_xray.dcm</em> in DICOM format), neuroimaging data (<em>A1_grayT1.nrrd, A1_grayT2.nrrd</em>). Data have been downloaded from <a href="https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/00_Setup.html">here</a>. </li> <li>MRI data: a T2-weighted image (<em>OBJECT_phantom_T2W_TSE_Cor_14_1.nii</em> in NIfTI-1 format). Data have been downloaded from <a href="../records/6467772">here</a>. </li> <li>Example images for the machine learning lesson: chest X-rays (<em>rotatechest.png, other_op.png</em>), cardiomegaly example (<em>cardiomegaly_cc0.png</em>).</li> <li>Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from <a href="https://doi.org/10.1109/TNS.1974.6499235">the Schepp Logan phantom</a> and from the <a href="https://fastmri.med.nyu.edu/">NYU FastMRI dataset</a></li> <li>Data for the anonymization exercises: ultrasound (<em>identifiable_us.jpg</em>) dowloaded from <a href="https://www.flickr.com/photos/jcarter/2461223727">here</a>, and DICOM data (<em>our_sample_dicom.dcm</em>) shared for this course specifically by a colleague</li> <li>Histopathology data: histopathology slide images from <a href="https://openslide.org/">openslide</a> library samples in the freely distributable test data </li> </ol> <p>These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.</p>
Polarimetric Radar Signatures of Lightning Initiation Processes Imaged with VHF Broadband Interferometer
<p>This spreadsheet contains the details of the lightning events analyzed in this study, including the date, time, INTF mapping information, NLDN-reported information, and the positions and location errors referred from the combined analysis of the INTF and NLDN reports. </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>
Impacts of dry deposition processes with resolved dust particle sizes on simulating the Martian Dust
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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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Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - Artefact - PEVA
<p><strong>Summary</strong><br>This artifact accompanies the PEVA submission "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes". It contains the implementation (<code>switss-multi</code>) of the presented techniques, that is, the computation of certificates, witnessing subsystems and schedulers for multi-objective queries in MDPs. Further, the artifact contains the PRISM models, PRISM properties and scripts bundled in a Docker image for completely reproducing the experimental results presented in Section 6. Additionally, it also contains the original raw experimental data presented in Section 6 and the corresponding analysis scripts. Lastly, we provide a documentation of our implementation <code>switss-multi</code> and describe how to use our tool via its command-line and programmatically via its Python interface.</p> <p><strong>Relation to paper</strong><br>This artifact can be used to reproduce all the experimental results (including examples) presented in the paper, that is:<br>- The toy examples presented in Example 12, Example 14, Example 22 and Example 34<br>- Table 3 in Section 6<br>- Table 4 in Section 6<br>- Table 5 in Section 6<br>- Table 8 in Section 6<br>- Figure 9 in Section 6<br>- Figure 10 in Section 6<br>- Figure 11 in Section 6</p> <p><strong>Structure</strong><br>This artifact consists of the following files and folders:<br>- <code>data</code>: Contains original raw experimental data presented in Section 6. Additionally, the log files and scripts for summarizing the raw experimental data are provided.<br>- <code>switss-multi/experiments</code>: Contains the PRISM models, PRISM properties (queries) and scripts for running the experiments.<br>- <code>switss-multi</code>: The source code of the implementation of our presented techniques.<br>- <code>switss-multi-docs</code>: A documentation of the Python API of <code>switss-multi</code>.<br>- <code>peva-docker-image.tar.gz</code>: The compressed Docker image, with the installed implementation (<code>switss-multi</code>), PRISM models, PRISM properties and the scripts for running the experiments and analysing the raw experimental data. Moreover, it contains a copy of the <code>data</code> folder, in case you want to run the analysis scripts on the original data.<br>- <code>docker-results</code>: An empty folder that will be populated with results when running the experiments and analysis with the provided Docker image.<br>- <code>LICENSE</code>: The license of this artifact (MIT license).<br>- <code>GUROBI-EULA</code>: The end-user license agreement of Gurobi (also see https://pypi.org/project/gurobipy/).<br>- <code>GPL-3.0</code>: The GPL 3.0 license. It is included because our dependency Storm (https://www.stormchecker.org) is licensed under it.</p>
A Dataset for Business Process Deviance Mining with Sequential and Declarative Patterns
<p>This repository contains the data for running the experiments in the journal paper "Business Process Deviance Mining with Sequential and Declarative Patterns". The relative source code can be found at this <a href="https://github.com/ivanDonadello/Deviance_mining_sequential_declarative_patterns" target="_blank" rel="noopener">GitHub repository</a>.</p>
Impact of Super Absorbent Polymer Treatment on Drying Process of Layered Soils
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Differentially Private Release of Event Logs for Process Mining
<p>The anonymized event logs of the experiments in the paper "Differentially Private Release of Event Logs for Process Mining"</p>
Table S1 Full factorial combination of laser process parameters and experimental results.
<p>This dataset is experimental results from a research topic of An improved grey wolf optimizer for process optimization of pulsed laser cutting of electrical steel sheet in different environments.</p>
Data for "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes"
<p>This is sampling data of "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes".</p> <p>'qv_nn_in' for the large scale specific humidity, [kg/kg]<br> 'T_nn_in' for the large scale temperature, [K]<br> 'dqvls_nn_in' for the large scale moisture advection, [kg/kg/s]<br> 'dTls_nn_in' for the large scale moisture advection, [K/s]<br> 'qtend_check' for the moistening rate by CRM, [kg/kg/s]<br> 'stend_check' for the heating rate by CRM, [K/s]<br> 'SOLS' for direct shorwave solar radiation down to surface, [W/m2]<br> 'SOLSD' for diffusive shortwave solar radiation down to surface, [W/m2]<br> 'SOLL' for direct near infrared solar radiation down to surface, [W/m2]<br> 'SOLLD' for diffusive near infrared solar radiation down to surface, [W/m2]<br> 'SOLIN' for insolation at model top, [W/m2]<br> 'FSNS' for net shortwave radiation at model surface, [W/m2]<br> 'FSNT' for net shortwave radiation at model top, [W/m2]<br> 'FLNS' for net longwave radiation at model surface, [W/m2]<br> 'FLNT' for net longwave radiation at model top, [W/m2]<br> 'SPPS' for surface pressure, [Pa]</p> <p>To download the full dataset of the SPCAM simulation in 1998. Please click the dropbox link: https://www.dropbox.com/s/p841v1tw00rokdy/SPCAM_VAR_1998.tar.gz?dl=0</p>
Sensitive, Efficient and Portable Analysis of Molecular Exchange Processes by Hyperpolarized Ultrafast NMR
<p>Dataset for manuscript:</p> <p><a href="https://doi.org/10.1002/anie.202203957"><strong>Sensitive, Efficient and Portable Analysis of Molecular Exchange Processes by Hyperpolarized Ultrafast NMR </strong></a></p> <p> </p> <p>The uploaded archive contains:</p> <p>1. File Analysis.ipynb: Jupyter notebook with analysis and figure generation presented in the article<br> 2. UF-DEXSY experiment with mixing time 10ms: DEXSY/25<br> 3. UF-DEXSY experiment with mixing time 30ms: DEXSY/26<br> 4. UF-DEXSY experiment with mixing time 100ms: DEXSY/28<br> 5. Reference CPMG: CPMG/16<br> 6. Coil profile with yeast sample: UFProfile/4<br> 7. Coil profile with dopped water sample: UFProfile/101</p> <p> </p>
Evidence for ecological processes driving speciation among endemic lizards of Madagascar
<p><span><span><span><span><span><span><span><span><span><span><span>Although genetic patterns produced by population isolation during speciation are well documented, the biogeographic and ecological processes that trigger speciation remain poorly understood. Alternative hypotheses for the biogeography and ecology of speciation include geographic isolation combined with niche conservation (soft allopatry), or parapatric distribution on an environmental gradient with niche divergence (ecological speciation). Here we utilize species' distributions, environmental data and two null models (Random Translation and Rotation, RTR, and the Background Similarity Test, BST) to test these alternative hypotheses among 28 sister pairs of micro-endemic lizards in Madagascar. Our results demonstrate strong bimodal peaks along a niche divergence-conservation spectrum, with at least 25 out of 28 sister pairs exhibiting either niche conservation or divergence, and the remaining pairs showing weak ecological signals. Yet despite these significant results, we do not find strong associations of niche conservation with allopatric distributions, or niche divergence with parapatric distributions. Our findings thus provide strong evidence of a role for ecological processes driving speciation, rather than the classic expectation of speciation through geographic isolation, but demonstrate that the link between ecological speciation and parapatry is complex and requires further analysis of a broader taxonomic sample to fully resolve.</span></span></span></span></span></span></span></span></span></span></span></p>
Examining the relationship between processing fluency and memory for source information
<p>Familiarity-based processes such as processing fluency can influence memory judgements in tests of item recognition. Many conventional accounts of source memory assume minimal influence of familiarity on source memory, but recent work has suggested that source memory judgements are affected when test stimuli are processed with greater fluency as a result of priming. The present experiments investigated the relationship between fluency and the accuracy of source memory decisions. Participants studied words presented with different source attributes. During test, they identified words that gradually clarified on screen through progressive de-masking, made old/new and source memory judgements, and reported confidence ratings for those words. Response times (RTs) recorded from the item identification task formed the basis of a fluency measure, and identification RTs were compared across categories of item recognition, source accuracy, and confidence. Identification RTs were faster in trials with correct retrieval of source information compared to trials for which source could not be accurately retrieved. These findings are consistent with the assumption that familiarity-based processes can contribute to source memory judgements.</p>
Table S1 Full factorial combination of laser process parameters and experimental results.
<p>This dataset is experimental results from a research topic of An improved grey wolf optimizer for process optimization of laser cutting of electrical steel sheet in different environments.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.