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6,467 results for “process”
Processed dataset for InstanceDiff
<p>For accessing this dataset, please contact to 202221044432@mail.scut.edu.cn</p>
Data and code for Decoding dynamic landslide hazard processes for a massive refugee camp (KTP) in Bangladesh
<p>The codes have been implemented using R 4.4.0. Landslide priority zonation using Monte Carlo simulation is implemented in Google Colab.</p> <p>A Dynamic Landslide Hazard Assessment has been conducted using a Generalized Additive Model (GAM). The results of the GAM are also compared with standard machine learning algorithms (MLs): NNET, RF, LDA, xgBoost, and SVM.</p> <p>The code is jointly developed by Dewan Haque and Ritu Roy, with collaboration from many others. The GAM code is an update from the study published by Zhice, F. (2023), <a href="https://doi.org/10.5281/zenodo.10395153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10395153</a>, adapted to apply it across settings. The ML code has been developed from scratch.</p> <p>The required data from intensive fieldwork and satellite image analysis is uploaded here to reproduce the results. Additionally, R Markdown files are provided.</p> <p>The ReadMe file here, as well as on GitHub, will be useful for further instructions.</p> <p>GitHub Link: https://github.com/Dewan-cpu/Decoding-Landslide-Hazard-Assessment</p>
The processed clean data of 16S rRNA V4 amplicon sequecnces for the six stage of phenolic microbiome domestication
Open the record for dataset details and reuse information.
TABLE 1 in Skeletal reconstruction of fossil vertebrates as a process of hypothesis testing and a source of anatomical and palaeobiological inferences
<p>TABLE 1. — Ratio of the length of phalanx I-1 to the length of metatarsal I in several ceratopsids. Length measurements were made from images with metatarsal I and phalanx I-1 in the same focal plane, using ImageJ (Schneider <i>et al.</i> 2012). The median ratio was used to determine that the expected length of phalanx I-1 for UALVP 42 was approximately 88.45% the length of phalanx I-1 in UALVP 16248. The digital model was scaled accordingly for the reconstruction.</p><table><thead><tr><th><b>Specimen</b></th><th><b>Taxon</b></th><th><b>Ratio</b></th></tr></thead><tbody><tr><th>AMNH 5351 cast (right foot)</th><td><i>Centrosaurus apertus</i> (Lambe, 1905)</td><td>1.021</td></tr><tr><th>CMN 8547</th><td>Indeterminate chasmosaurine</td><td>0.986</td></tr><tr><th>TMP 2002.076.0001</th><td>Indeterminate pachyrinosaurin</td><td>0.893</td></tr><tr><th>CMN 41357</th><td><i>Vagaceratops irvinensis</i> (Holmes Holmes, Forster, Ryan & Shepherd, 2001)</td><td>0.885</td></tr><tr><th>TMP 1989.097.0001</th><td><i>Styracosaurus albertensis</i> Lambe, 1913</td><td>0.883</td></tr><tr><th>AMNH 5351 cast (left foot)</th><td><i>Centrosaurus apertus</i></td><td>0.859</td></tr><tr><th>Median</th><td>–</td><td>0.889</td></tr></tbody></table>
Comprehensive Ethereum Execution Data for Object-Centric Process Mining of Decentralized Applications (DApps)
<p>The dataset pertains to the collection and analysis of blockchain execution data, particularly from Ethereum-based Decentralized Applications (DApps). This data includes transactions, transaction receipts, and detailed transaction traces, documenting the execution steps performed by the Ethereum Virtual Machine (EVM). Such traces are essential for understanding the interaction between smart contracts and accounts, including Contract Accounts (CAs) and Externally Owned Accounts (EOAs).</p> <p>A blockchain is an append-only ledger that chronologically records data in blocks. Each block contains transactions that signify state transitions, and transaction receipts that provide a hashed result of these transitions to ensure uniform results across different executions. The dataset includes a classification of Ethereum accounts, detailing the functions and interactions between EOAs and CAs, where CAs deploy and execute smart contract code.</p> <p>The dataset captures the granular operational data of blockchain transactions, such as function calls, contract creations, and log entries generated by smart contracts. These details are crucial for creating object-centric event logs, aiding in process mining and analysis to bridge the gap between theoretical process models and actual execution.</p> <p>Contract creations and function calls are fundamental components of the dataset. The former documents the deployment of smart contracts, including the mechanics of contract updates and additions through various design patterns. Function calls between accounts are also extensively logged, providing insights into the flow of Ethereum's native token, Ether, and other transactional data within the blockchain.</p> <p>Delegated calls and log entries represent more specialized interactions within Ethereum, where delegated calls allow contracts to use code from other contracts to manipulate their own state, supporting upgradeable contract designs. Log entries, specified within smart contract code, facilitate the communication of contract execution details to external systems.</p> <p>To handle the diverse and dynamic nature of blockchain data, the dataset employs the Object-Centric Event Log (OCEL) format. This format accommodates multiple object types in a single log, addressing issues such as event divergence and convergence, typical of traditional single-case logs. The latest version, OCEL 2.0, supports documenting dynamic object roles and relationships, improving the fidelity of logs in capturing blockchain operations.</p> <p>In summary, the dataset is structured to support a comprehensive analysis of blockchain behaviors, particularly focusing on Ethereum DApps. It is tailored to assist researchers and practitioners in understanding and analyzing the decentralized execution of smart contracts and the associated data flows within the blockchain environment.</p>
Dataset of the publication: Sublimable complexes with spin switching: Chemical design, processing as thin films and integration in graphene-based devices
<p>Dataset of the publication: Sublimable complexes with spin switching: Chemical design, processing as thin films and integration in graphene-based devices</p> <p>DOI: 10.1039/d2tc04120k</p> <p>M. Guevara-Edo, F. J. Valverde-Muñoz, R. Cordoba, M. C. Muñoz, J. Herrero-Martín, J. A. Real, E. Coronado</p> <p>J. Mater. Chem. C, 11, 24, 8107-8120 (2023)</p>
Literature review of the enablers and barriers to stakeholder and citizen engagement in climate change adaptation process (as part of Adaptation AGORA project)
<p>This dataset is the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This database was used to conduct a literature review of the enablers and barriers to stakeholder and citizen engagement in climate change adaptation process. It contains 123 papers retrived from Web of Science Databse in June 2023. <span>We used a keyword search to identify and select articles that fell within the scope of our research, with each article containing at least one keyword related to climate change adaptation solutions, climate change, co-production, citizen and stakeholder involvement and factors (enablers and barriers). </span></p> <p><span>We divided the coding framework into four main sections:</span></p> <ul> <li> <p><span>Section 1 collected basic information about the paper (i.e., date, journal, authors, type of study and methods for data collection). </span></p> </li> <li> <p><span>Section 2 sought to better understand the adaptation initiatives treated in the paper. Here, we analysed 5 variables (the adaptation solutions type, sectors, benefits, scale, and location). </span></p> </li> <li> <p><span>Section 3 collected characteristics of the climate change adaptation co-production process, including the definition of co-production, the type of the co-production process, its outputs, and the methods used to engage stakeholders. </span></p> </li> <li> <p><span>Section 4 described the factors that enable or hinder the co-production process and their influence on different aspects of the process. After naming and defining each driver, we recorded the main type of factor, its impact, origin, and spatial and temporal scale of influence; the stakeholders who were responsible for and influenced by the factor, and the impacts on the various steps and outcomes of the co-production process.</span></p> <span> </span></li> </ul>
Dataset of the publication: Design and processing as ultrathin films of a sublimable Iron(II) spin crossover material exhibiting efficient and fast light-induced spin transition
<p>Dataset of the publication: Design and processing as ultrathin films of a sublimable Iron(II) spin crossover material exhibiting efficient and fast light-induced spin transition</p> <p>DOI: 10.1021/acs.chemmater.3c01704</p> <p>M. Gavara-Edo, F. J. Valverde-Muñoz, M. C. Muñoz, S. Elidrissi, F. Marques-Moros, J. Herrero-Martín, K. Znovjyak, M. Seredyuk, J. A. Real, E. Coronado <br><br>Chem. Mater., 35, 22, 9591-9602 (2023)</p>
Exploring Gaussian processes for short-term forecasting in offshore energy systems: Supplementary material
<p>Two supplementary videos are provided. The first video analyses the performance of wave excitation force forecasting across different horizons in a noise-free case. The second video examines the impact of noise on the forecast. Both videos include results from a Gaussian-based forecaster, an AR forecaster, and show the uncertainty bounds provided by the Gaussian forecaster. The variable analysed and forecasted in these videos is the wave excitation force.</p>
METHODS OF TRAINING AND ADAPTATION OF AI AGENTS IN COMPLEX PROCESS CONTROL SYSTEMS
<p>The article presents a study of modern methods of training and adaptation of artificial agents used in managing complex processes, which are characterized by a high level of uncertainty and the need for prompt response to changes. Key methodological approaches such as machine learning and neuroevolution are discussed. These approaches allow AI agents to accumulate knowledge about the behavior of systems continuously, analyze external changes, and adjust the management strategy depending on environmental conditions, which significantly increases their ability to predict and prevent possible failures in management.</p> <p>In the course of the study, models were considered that allow automating the execution of complex, multitasking processes, minimizing human intervention, and reducing the likelihood of errors. In addition, the presented methods provide high flexibility and scalability of systems, which is especially important in industrial and technological industries, where stability and reliability are critical. The results showed that AI agents with adaptive learning capabilities can increase operational efficiency while reducing costs and optimizing resource use. The conclusion highlights the prospects of using artificial intelligence to build highly autonomous control systems capable of responding to dynamic challenges, which opens up new horizons for automation and intellectual support in industrial production, logistics, and other key areas.</p> <p>Thus, the article makes a significant contribution to understanding the role of AI in management modernization, offering practical recommendations on the implementation of intelligent agents in real-world scenarios to increase productivity and sustainability.</p>
Dataset and software for processing of hyperspectral images of different CDW materials
<h2>Overview</h2> <p>The provided scripts are designed to process hyperspectral images of construction and demolition waste (CDW) materials, extract relevant features, and train a machine-learning model for material classification. The scripts perform the following tasks:</p> <ol> <li><strong>Feature Extraction</strong>: Extract spectral features from hyperspectral data.</li> <li><strong>Background Removal and Subset Extraction</strong>: Remove backgrounds from images and extract subsets for analysis.</li> <li><strong>Data Visualization</strong>: Generate plots to visualize the extracted features and reflectance curves.</li> <li><strong>Machine Learning Model Training</strong>: Using the extracted features, train and evaluate a multilayer perceptron (MLP) classifier.</li> </ol> <h2>Prerequisites</h2> <p>Before running the scripts, ensure that you have the following:</p> <ul> <li><strong>Python 3.x</strong> installed on your system.</li> <li>Required Python packages: <ul> <li><code>numpy</code></li> <li><code>matplotlib</code></li> <li><code>scipy</code></li> <li><code>pandas</code></li> <li><code>scikit-learn</code></li> <li><code>seaborn</code></li> <li><code>rembg</code> (for background removal)</li> <li><code>Pillow</code> (PIL)</li> </ul> </li> <li><strong>Hyperspectral data files</strong> in <code>.mat</code> format containing calibrated hyperspectral cubes and wavelength information.</li> <li>A directory structure to organize input and output files as described in each script.</li> </ul> <h2>Scripts Description</h2> <h3>1. <code>hyperspectral_features_v2.py</code></h3> <h4><strong>Purpose</strong></h4> <p>This script processes individual hyperspectral image files to extract spectral features from a central subset of the image. It generates RGB images from the hyperspectral data, plots the mean reflectance spectra, and outputs a LaTeX-formatted table containing the extracted features.</p> <h4><strong>Functionality</strong></h4> <ul> <li><strong>Loading Data</strong>: Reads <code>.mat</code> files containing hyperspectral data from a specified input directory.</li> <li><strong>Feature Calculation</strong>: <ul> <li>Calculates mean reflectance within a central window of the image.</li> <li>Extracts spectral features such as peak wavelength and area under the reflectance curve.</li> <li>Records reflectance values at selected wavelengths, including standard RGB channels and additional wavelengths.</li> </ul> </li> <li><strong>RGB Image Generation</strong>: Creates RGB images using specific wavelengths corresponding to the red, green, and blue channels.</li> <li><strong>Spectra Plotting</strong>: Plots the mean reflectance spectra for each sample.</li> <li><strong>LaTeX Table Generation</strong>: Produces a LaTeX-formatted table of the extracted features for inclusion in a report or paper.</li> </ul> <h4><strong>Usage Instructions</strong></h4> <ol> <li> <p><strong>Prepare Input Data</strong>:</p> <ul> <li>Place your <code>.mat</code> files containing the hyperspectral data in the appropriate input directory (e.g., <code>input/mortar</code>).</li> </ul> </li> <li> <p><strong>Run the Script</strong>:</p> <ul> <li>Modify the <code>materials</code> list at the end of the script to include the materials you want to process (e.g., <code>materials = ['mortar']</code>).</li> <li>Execute the script: <div> <div>bash</div> <div> <div> <div> </div> </div> </div> </div> </li> </ul> </li> </ol>
Data release: Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes
<p>The data required to reproduce the analyses of "Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes" (<a href="https://arxiv.org/abs/2404.03166" target="_blank" rel="noopener">arxiv:2404.03166</a>). The main inference code can be found at <a href="https://github.com/AnaryaRay1/gppop/tree/spin-dev" target="_blank" rel="noopener">https://github.com/AnaryaRay1/gppop/tree/spin-dev </a> (commit: <a href="https://github.com/AnaryaRay1/gppop/commit/ee5ffc421e2c96eeed15a0e0d3839da42b982842">ee5ffc</a>). To reproduce the analyses, follow the instructions at <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts">https://github.com/AnaryaRay1/bbh-subpopulations-scripts</a> (commit <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts/commit/de88f931d8c1a2cb31ad2fa9d6fdf9a5a00a3c3b">de88f93</a>). Frozen versions of these repositories that were used to generate all the results are available as part of this data release, in the files "gppop_spin_dev_ee5ffc421.tar.gz" and "bbh-subpopulations-scripts_de88f931.tar.gz" respectively.</p>
Data set for the study "Assessing the lifetime of anthropogenic CO2 and its sensitivity to different carbon cycle processes"
<p>This repository contains the data necessary to reproduce the results of the paper: <br>"Assessing the lifetime of anthropogenic CO<sub>2</sub> and its sensitivity to different carbon cycle processes" <br><a href="https://doi.org/10.5194/bg-22-2767-2025" target="_blank" rel="noopener">https://doi.org/10.5194/bg-22-2767-2025</a></p> <h3><strong>Data organization:</strong></h3> <p>The Zenodo upload is organized as the following inside of <code>results.zip</code>:</p> <ul> <li>Data analysis and figure generation are given by "*.pynb" and "*.m" files<br><br></li> <li>Data files as NetCDF output are organized with the following structure inside of <code>data</code>:<br><br> <ul> <li><strong>Experiment</strong>: <code>REF</code>, <code>noLAND</code>, <code>noWEATH</code>, <code>ECS2</code>, <code>ECS4</code>, <code>intCH4</code>, <code>PATH1</code>, <code>PATH2</code>, and <code>PULSE</code><br><br> <ul> <li><strong>Emissions scenario</strong>: <code>0_gtc</code>, <code>500_gtc</code>, <code>1000_gtc</code>, <code>2000_gtc</code>, <code>3000_gtc</code>, <code>4000_gtc</code>, and <code>5000_gtc</code><br><br> <ul> <li><strong>Component</strong>: atmosphere (<code>atm</code>), land (<code>lnd</code>), ocean (<code>ocn</code>), biogeochemistry (<code>bgc</code>), and the carbon cycle (<code>co2</code>)<br> <ul> <li>Note: for <code>intCH4</code>, there is another file concerning methane (<code>ch4</code>)</li> <li>Note: surface ocean pH and surface ocean DIC were not part of the standard output in the original CLIMBER-X model. Instead, these variables were calculated during post-processing using 2D spatial data. Since the 2D data was only output every 1 kyr, the first millennium of data was missing. To address this, we re-ran the experiments with surface ocean pH and DIC included in the output for the first 1 kyr. This is why there are additional individual files for pH, DIC, and the Revelle factor (see "fig5_7_8_9.ipynb" for further details).<br><br></li> </ul> </li> <li><strong>File type</strong>: for each component, files are divided into timeseries (<code>*_ts.nc</code>) or 2D data with a 1 kyr output frequency (<code>*.nc</code>)<br> <ul> <li>Note: due to size constraints of the Zenodo repository, only some 2D spatial data presented in the publication (for the <code>REF</code> experiment) is available. However, this is not an exhaustive dataset. For inquiries regarding additional data, please contact the corresponding author to explore potential availability.</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Processed data and trained models for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf">https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</a></p> <p>Link to the Arxiv article: <a href="https://arxiv.org/abs/2402.17062">https://arxiv.org/abs/2402.17062</a></p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.</div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/11668766/draft/files/ckpts.zip/content" target="_blank" rel="noopener noreferrer">ckpts.zip</a> - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)</div> <div>├── <a href="../api/records/11668766/draft/files/annotations.zip/content" target="_blank" rel="noopener noreferrer">annotations.zip</a> - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.</div> <div>├── <a href="../api/records/11668766/draft/files/simple_ycb_models.zip/content" target="_blank" rel="noopener noreferrer">simple_ycb_models.zip</a> - Contains the preprocessed YCB objects for batched evaluation.</div> <div>├── <a href="../api/records/11668766/draft/files/test.zip/content" target="_blank" rel="noopener noreferrer">test.zip</a> - Contains the processed SDF files for DexYCB test set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_render_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_render_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set and rendering set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: <a href="https://github.com/amathislab/HOISDF">https://github.com/amathislab/HOISDF</a></div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Country-ocean-moisture-flows-reconciled-with-ERA5-reanalysis obtained processing Lagrangian moisture connections
<p>The dataset "Reconciled global atmospheric moisture flows between countries/oceans and subcontinents" presents tracked volumes of precipitation and evaporation reconciled with reanalysis data, closing the annual hydrological balance, and provides robust estimates of terrestrial moisture recycling and net moisture flows to support global water governance analysis.</p> <p>This repository is supplement to a study by De Petrillo & Fahrländer et al. (2025), which describes the development of the reconciliation framework, includes a perfromance analysis of the method and shows an exemplary case study on the published data. </p> <p>The atmospheric moisture flows are sourced from the UTrack atmospheric moisture flow dataset by Tuinenburg et al. (2020a) (dataset access: Tuinenburg et al., 2020b) and reconciled with ERA5 precipitation and evaporation data (Hersbach et al., 2020) on the mean annual basis in the period 2008-2017, by means of a post-processing framework, based on the Iterative Proportional Fitting (IPF) algorithm.</p> <p>NOTE: The final dataset is available in form of bilateral matrices (country/ocean and subcontinent/ocean) and in form of direct flows (flow edges). Supporting material to read the dataset is in the folder "List" . Processed ERA5 data (where the precipitation-evaporation annual balance is met) and input data to generate the figures are also available.</p> <p>References:</p> <p>De Petrillo, E., Fahrländer, S., Tuninetti, M., Andersen, L.S., Monaco, L., Ridolfi, L., Laio, F. (2025). Reconciling tracked atmospheric moisture flows to close the global freshwater cycle.<em> </em><em>Commun Earth Environ <strong>6</strong>, 347 (2025). </em><a href="https://doi.org/10.1038/s43247-025-02289-y">https://doi.org/10.1038/s43247-025-02289-y</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., & Staal, A. (2020a). High-resolution global atmospheric moisture connections from evaporation to precipitation. <em>Earth System Science Data</em>, <em>12</em>(4), 3177–3188. <a href="https://doi.org/10.5194/essd-12-3177-2020">https://doi.org/10.5194/essd-12-3177-2020</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., Staal, A. (2020b): Global evaporation to precipitation flows obtained with Lagrangian atmospheric moisture tracking. PANGAEA, <a href="https://github.com/ObbeTuinenburg/UTrack_global_database">https://doi.pangaea.de/10.1594/PANGAEA.912710</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 global reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em>, <em>146</em>(730), 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p> <p> </p>
Analysis materials for "Defininig a Knowledge Graph Development Process through a Systematic Review"
<p>This depository stores the analysis materials for the article "Defining a Knowledge Graph Development Process through a Systematic Review". It includes:</p> <ul> <li><strong>Analysis of KG development process - Articles.csv </strong>- a table of summary of the articles included in the systematic review.</li> <li><strong>Analysis of KG development process - Tasks by level (count).csv</strong> - a table of counting the frequency of the tasks in the knowledge graph development process.</li> <li><strong>Analysis of KG development process - Tasks by level count (synonyms) (1).csv </strong>- a table of counting the frequency of the tasks in the knowledge graph development process after its been adjusted to synonyms.</li> <li><strong>Knowledge graph development processes - </strong>a folder of process figures from the articles that have been included in the systematic review.</li> </ul>
OHS data provided by Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application
<p>Images from Chinese Orbita Hyperspectral Satellites (OHS) provided by <em>the Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application</em> are shared. All the images have been radiometric calibrated and atmospheric corrected by the author.</p> <p>Paper: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, "Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution," <em>IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)</em>, 2021.</p> <p>More information about the author can be found at https://jianghe96.github.io/</p> <p>If this dataset is helpful please cite as:</p> <pre>@article{he2021spectral, title={Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author={He, Jiang and Li, Jie and Yuan, Qiangqiang and Shen, Huanfeng and Zhang, Liangpei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, }</pre>
Seismic source location with a match field processing approach during the RESOLVE dense seismic array experiment on the Glacier d'Argentiere
<p>This deposit contains the data set we used in our paper ‘<em>Dynamic imaging of glacier structures at high-resolution using source localization with a dense seismic array</em>’. The paper is in review for GRL and a preprint can be found here: <a href="http://dx.doi.org/10.1002/essoar.10507953.1">10.1002/essoar.10507953.1</a>.</p> <p>The dataset present here contains 34 files named ‘<strong>beam_15423_jd***.h5</strong>’. These files correspond to the output of the matched field processing for each day. They are in .h5 format and we provide a matlab code (<strong>read_MFP_data.m</strong>) to read these files. These files can be read with any other language since they are in . h5.</p> <p>In linux you can use <strong>h5dump –A filename.h5</strong> and you can see the content of each files.</p> <p> </p> <p>More information on how the MFP process is conducted can be found in on the <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>dedicated to this aspect or on our paper. The whole procedure and associated codes is provided on the <a href="http://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/</a>.</p> <p>We also deliver with this deposit one day of seismic data <strong><a href="https://zenodo.org/api/files/873ccbe8-814d-4202-90ae-e115aab1942d/ZO_2018_121.h5?versionId=c59d2014-a6e1-43c5-96a6-91ee9a6b89ce">ZO_2018_121.h5 </a></strong>that can be used to test our MFP process. The data corresponds to the signal measured for 24 hours at each of the 98 sensors with a sampling rate of 500 Hz. More information on these seimsic signals can be found on our <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>and the whole seimsic dataset can be found here <a href="https://seismology.resif.fr/networks/#/ZO__2018">https://seismology.resif.fr/networks/#/ZO__2018</a>. Detailed for downloading the dataset should be search on our website.</p> <p> </p> <p>Other dataset linked to this project are:</p> <ul> <li>Nanni, Ugo, Gimbert, Florent, Roux, Phillipe, & Lecointre, Albanne. (2020). DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations." [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4024660">https://doi.org/10.5281/zenodo.4024660 </a></li> <li>Nanni, Gimbert, Roux, Helmstetter, Garambois, Lecointre, Walpersdorf, Jourdain, Langlais, Laarman, Lindner, Sergenat, Vincent, & Walter. (2020). DATA of the RESOLVE Project (https://resolve.osug.fr/) [Data set]. In Seismological Research Letters (Version v0). Zenodo. <a href="https://doi.org/10.5281/zenodo.3971815">https://doi.org/10.5281/zenodo.3971815 </a></li> </ul> <p>This dataset is also linked to two other study:</p> <p><em>Observing the subglacial hydrology network and its dynamics with a dense seismic array: </em></p> <p><a href="https://doi.org/10.1073/pnas.2023757118">https://doi.org/10.1073/pnas.2023757118</a></p> <p><em>A Multi‐Physics Experiment with a Temporary Dense Seismic Array on the Argentière Glacier, French Alps: The RESOLVE Project</em></p> <p><a href="https://doi.org/10.1785/0220200280">https://doi.org/10.1785/0220200280</a></p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Do not hesitate to contact us if you would like to try this approach an another dataset.</p>
Processed Data of "Selective laser melting of a Fe-Si-Cr-B-C-based complex-shaped amorphous soft-magnetic electric motor rotor with record dimensions"
<p>This data set includes the processed data of the pubblication. ABSTRACT: A record large amorphous rotor bearing an intricate 3D-geometry is produced through additive manufacturing via selecting laser melting using a powder of a traditional bulk metallic glass-forming composition of the Fe-Si-Cr-B-C system. Not only does this technique overcome the technical limitations characteristic of casting processes for amorphous alloys, but the possibility to print complex 3D geometries is expected to greatly facilitate the channeling of the magnetic flux, when such component is used as a rotor in an electric machine. The as-built part is characterized in comparison to the powder material as well as as-spun ribbons using a wide range of complementary techniques, including synchrotron x-ray diffraction, calorimetry, electron microscopy as well as room temperature ferromagnetic and hardness testing. The built part has extraordinarily high values of hardness (877 HV) and remarkable high magnetic susceptibility (9.17). This latter feature leads to a better magnetic response in the presence of an external magnetic field evidenced by a faster approach to saturation. The coercivity is small (0.51 kA/M) and the magnetic saturation relatively high (1.29 T). In addition, a large anisotropic effect on the magnetization reaction in connection with the partial crystallization in the melt pool areas is investigated experimentally.</p>
How to Build an Image Processing Pipeline for Automating Multiparameter Histocytometry Analysis
<p>Image files for evaluation of an upcoming Current Protocols submission, as well as associated reference files.</p>
ScienceDex guides
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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.