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1,481 results for “processed data”
Data for article of validation of a two-stage process for polyhydroxyalkanoates production
<p>The information contained in this data section corresponds to the article titled "Microrespirometric validation of a two-stage process for polyhydroxyalkanoates production from peanut oil and propionate with Cupriavidus necator," published in The Open Chemical Engineering Journal. Three Excel files are attached as data, providing information on the figures related to the growth kinetics and the results of the respirometric experiments.</p>
(Processed data) How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study: a descriptive study
<p>Processed data used in the article 'How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study', from the project Comscienciaeduspain (FCT-20-15761), executed with the collaboration of the Spanish Foundation for Science and Technology – Ministry of Science and Innovation.</p>
1-km high resolution model outputs using the WRF and WRF-Hydro model Raw data from the manuscipt "Process-based Atmosphere-Hydrology-Malaria Modeling: Performance for Spatio-temporal Malaria Transmission Dynamics in Sub-Saharan Africa "
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Health and Demographic Surveillance Systems (HDSS) site regions of Nouna in Burkina Faso. Model results are used for investigating the influence of surface hydrology representation, environmental and climate-sensitive driver factors on malaria incidence.<br>The experiments use the following model configuration: 1km horizontal resolution with 200*200 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Karlsruhe Steinbuch Centre for Computing (SCC) Horeka.</p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrf-hydro_pr_2000-2020_d02-1km.nc" provides Precipitation,<br>n mm/day"wrf-hydro_tas_2000-2020_d02-1km.nc" provides mean temperature in Celsius, "wrf-hydro_tasmax_2000-2020_d02-1km.nc" provides maximum temperature in Celsius, "wrf-hydro_tasmin_2000-2020_d02-1km.nc" provides minmum temperature in Celsius, "wrf-hydro_dtr_2000-2020_d02-1km.nc" provides diurnal temperature ranges in Celius, "wrf-hydro_rh_2000-2020_d02-1km.nc" provides relative humudity in % and "wrf-hydro_sw_2000-2020_d02-1km.nc" provides the surface hydrology.</p>
Data for JGR-Atmospheres Paper: Stratospheric Hydration Processes in Tropopause-Overshooting Convection Revealed by Tracer-Tracer Correlations from the DCOTSS Field Campaign
<p>Airborne 1-second data merger of observations from the NASA Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign. This merger includes subjective feature identifications analyzed in the paper referenced in the title. </p>
Post‐processed data and analysis codes for the research "Significant reduction of potential exposure to extreme marine heatwaves by achieving carbon neutrality"
<p>[Earth's Future] Oh et al. "Significant reduction of potential exposure to extreme marine heatwaves by achieving carbon neutrality"</p> <p>1. Information for Raw datasets<br>- The data of eight global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) can be accessed at https://esgf-node.llnl.gov/search/cmip6/, <br> and can also be accessed in Eyring et al. (2016). <br>- The NOAA OISST high resolution dataset can be obtained in Reynolds et al. (2007) or via https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html. <br>- The five ocean mask dataset can be obtained from https://reccap2-ocean.github.io/regions/. </p> <p>2. Information for Software<br>- The raw data in this study were analyzed using Fortran 90, R version 4.0.3, and Grads version 2.2.1.<br>- The Fortran 90 can be accessed at https://www.intel.com/content/www/us/en/developer/articles/tool/oneapi-standalone-components.html#fortran. <br>- The R version 4.0.3 is available from https://cran.r-project.org/bin/windows/base/old/4.0.3/. <br>- The Grads version 2.2.1 can be downloaded from http://cola.gmu.edu/grads/downloads.php.</p> <p>3. Information for Post-Processed data and Codes used in this work.<br>Please find each folder and the relevant post-processed dataset and codes.</p>
Processed Datasets - Imputation in Well Log Data: A Benchmark
<p>Imputation of well log data is a common task in the field. However a quick review of the literature reveals a lack of padronization when evaluating methods for the problem. The goal of the benchmark is to introduce a standard evaluation protocol to any imputation method for well log data. </p> <p>In the proposed benchmark, three public datasets are used:</p> <ul> <li><strong>Geolink:</strong> The Geolink Dataset is another public dataset of wells in the Norwegian offshore. The data is provided by the company of the same name, <a href="https://www.geolink-s2.com/" target="_blank" rel="noopener">GEOLINK</a> and follows the NOLD 2.0 license. <br>This dataset contains a total of 223 wells. It also has lithology labels for the wells with a total of 36 lithology classes. [<a href="https://drive.google.com/drive/folders/1EgDN57LDuvlZAwr5-eHWB5CTJ7K9HpDP" target="_blank" rel="noopener">download original</a>]</li> <li><strong>Taranaki Basin:</strong> The Taranaki Basin Dataset is a curated set of wells and a convenient option for experimentation especially due to it is ease of accessibility and use.<br>This collection, under the CDLA-Sharing-1.0 license, contains well logs extracted from the <a href="https://geodata.nzpam.govt.nz/" target="_blank" rel="noopener">New Zealand Petroleum & Minerals Online Exploration Database</a> and <a href="http://pet.gns.cri.nz/" target="_blank" rel="noopener">Petlab</a>.<br>There are a total of 407 wells, of which 289 are onshore and 118 are offshore exploration and production wells. [<a href="https://developer.ibm.com/exchanges/data/all/taranaki-basin-curated-well-logs/" target="_blank" rel="noopener">download original</a>]</li> <li><strong>Teapot Dome:</strong> The Teapot Dome dataset is provided by the Rocky Mountain Oilfield Testing Center (RMOTC) and the US Department of Energy.<br>It contains different types of data related to the Teapot Dome oil field, such as 2D and 3D seismic data, well logs, and GIS data. The data is licensed under the Creative Commons 4.0 license. <br>In total, the dataset has 1,179 wells with available logs. The number of available logs varies across wells. There are only 91 wells with the gamma ray, bulk density, and neutron porosity logs, while only three wells have the complete basic suite. [<a href="http://s3.amazonaws.com/open.source.geoscience/open_data/teapot/rmotc.tar" target="_blank" rel="noopener">direct download</a>]</li> </ul> <p>Here you can download all three datasets already preprocessed to be used with our implementation, found <a href="https://github.com/uai-ufmg/well-log-imputation" target="_blank" rel="noopener">here</a>.</p> <p> </p> <h3>File Description:</h3> <p>There are six files for each fold partition for each dataset.</p> <ul> <li><code><em>datasetname_fold_k_well_log_metadata_train.json </em></code>: JSON file with general information of the slices of <strong>training </strong>partition of the fold <strong>k</strong>. Contains total number of slices and the number of slices per well.<em> </em></li> <li><em><code>datasetname_fold_k_well_log_metadata_val.json</code> </em>: JSON file with general information of the slices of <strong>validation </strong>partition of the fold <strong>k</strong>. Contains total number of slices and the number of slices per well. </li> <li><em><code>datasetname_fold_k_well_log_slices_train.npy</code>: </em>.npy (numpy) file ready to be loaded with the slices for <strong>training </strong>of the fold <strong>k </strong>already processed. When loaded<em> </em>should have shape of<em> (total_slices, 256, number_of_logs)</em></li> <li><em><code>datasetname_fold_k_well_log_slices_val.npy</code> </em>: .npy (numpy) file ready to be loaded with the slices for <strong>validation </strong>of the fold <strong>k </strong>already processed.</li> <li><em><code>datasetname_fold_k_well_log_slices_meta_train.json</code> : </em>JSON file with the slices info for all slices in the <strong>training </strong>partition of the fold <strong>k</strong>. For each slice, 7 data points are provided, the last four are discarded (it would contain other information that was not used). The first three are in order the: origin well name, the starting position in that well, and the end position of the slice in that well.</li> <li><em><code>datasetname_fold_k_well_log_slices_meta_val.json</code> </em>: JSON file with the slices info for all slices in the <strong>validation </strong>partition of the fold <strong>k</strong>.</li> </ul>
Solution-processed PbS quantum dot infrared laser with room-temperature tuneable emission in the optical telecommunications window - Open Data
<p>This is a supplementary upload attached to the paper titled "Solution-processed PbS quantum dot infrared laser with room-temperature tuneable emission in the optical telecommunications window" 10.1038/s41566-021-00878-9.</p> <p><strong>Figures</strong></p> <p>All figure data from the publication can be obtained from the original MATLAB .fig files. If one does not have access to MATLAB the figures can be opened using the open source software GNU Octave.</p> <p><strong>FDFD Simulation</strong></p> <p>Also in the upload is the original matlab code used to perform the simulations presented in the paper.</p> <p>"FDFD_2D_Ez_Hz_DFB_laser_UPLOAD" - Variable gain FDFD solver is uploaded as .mat and .pdf files.</p> <p>To run the code the functions "Dgen" and "gen2xDFB" are required and the .mat files containing the refractive indices "PbS1520" and "Al2O3".</p> <p>Parameters to vary can be found in the "DASHBOARD" section of the code. The uploaded code solves for the out-of-plane electric field (Ez Mode).</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.
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>
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>
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>
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>
Processed data and models in support of manuscript "Deciphering the state of the lower crust and upper mantle with multi-physics inversion"
<p>Data and model files in original format used in the manuscript "Deciphering the state of the lower crust and upper mantle with multi-physics inversion". These files are accompanied by a set of python scripts to reproduce several of the figures in the Manuscript. Please refer to the Manuscript and the included files for further information on data origin and how to use the scripts. A link will be added upon acceptance.</p>
Supplementary Data for "Sequencing the Pandemic: Rapid and High-Throughput Processing and Analysis of COVID-19 Clinical Samples for 21st Century Public Health"
<p>Supplementary material for F1000 methods manuscript. Includes raw sequencing metrics for two COVID sequencing methodologies, as well as a complete cost breakdown for each methodology.</p>
Compositional discovery of architecture-aware and sound process models from event logs of multi-agent systems: experimental data.
<p>This repository contains the experimental data used for the evaluation of the compositional approach to the discovery of process models from event logs of multi-agent systems, where agents interact according to specific patterns of synchronous and asynchronous interactions.</p> <p>According to the experiment plan, there is the folder for each interface pattern containing:</p> <ol> <li>The reference model (Petri net encoded in PNML-file)</li> <li>The event log obtained by simulating the behavior of the reference model (XES-file)</li> <li>The model discovered directly from the generated event log (Petri net encoded in PNML-file)</li> <li>The model discovered by composing the agent model w.r.t. the interface pattern (Petri net encoded in PNML-file)</li> </ol>
All data of the manuscript "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process" submitted to Geophysical Research Letters
<p>The data supports the manuscript entitled "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process”. Microsoft Notepad can open the *.txt files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at the first fork of positive or negative leader channels. A normal video player software can open Movies S1.avi, and it shows the entire development process of IC1 discharge.</p> <p>The data can be used freely for scientific purposes with the appropriate citation.</p>
Data of Self-Weight Consolidation Process of Water-Saturated Deltas on Mars and Earth
<p><strong>Data of the paper "Self-Weight Consolidation Process of Water-Saturated Deltas on Mars and Earth".</strong> This dataset includes five tables. <strong>Table S1</strong> is the original data of the measurements of the moisture content <em>w</em><sub>0</sub>, <strong>Table S2</strong> is the original data of the pycnometer test, which was conducted to obtain the specific gravity <em>G</em><sub>s</sub> of our samples, <strong>Table S3</strong> is the original data of the consolidation experiments, <strong>Table S4</strong> is the original data of the permeability experiments, and <strong>Table S</strong><strong>5</strong> is the martian global delta relief obtained by us based on MOLA data, which is used as the maximum thickness of a delta.</p> <p><strong>Table S1.</strong> The original data of the measurements of the moisture content <em>w</em><sub>0</sub>. The initial void ratio is calculated by equation (1). <em>A'</em> is the inner area of the consolidation container.</p> <p><strong>Table S2.</strong> The original data of the pycnometer test, which was conducted to obtain the specific gravity <em>G</em><sub>s</sub> of our samples. The specific gravity <em>G</em><sub>s </sub>can be derived from <em>m</em><sub>d</sub><em>G</em><sub>wT</sub> /(<em>m</em><sub>bw+</sub><em>m</em><sub>d+</sub><em>m</em><sub>bws</sub>), where <em>m</em><sub>d </sub>is the samples’ dry mass, <em>m</em><sub>bw </sub>is the total mass of the pycnometer and water,<em> m</em><sub>bws </sub>is the total mass of the pycnometer, water and samples, and <em>G</em><sub>wT</sub> is the specific gravity of pure water at<em> T </em>℃.</p> <p><strong>Table S3.</strong> The original data of consolidation experiments of our samples. The void ratio is calculated by equation (2).</p> <p><strong>Table S4. </strong>The original data of permeability experiments of our samples. The hydraulic conductivity <em>K </em>was calculated by equations (3) and (4). The inner area of the consolidation container is 30 cm<sup>2</sup>, the cross-sectional area<em> a' </em>of the water pipe is 0.89286 cm<sup>2</sup>, and the seepage path length <em>L</em> equals the sample initial height <em>h</em><sub>0</sub> minus the accumulated height <em>Σ</em>Δ<em>h</em><sub>i</sub>. <em>t</em>1 and <em>t</em>2<sub> </sub>are the first and the second test results of time-taken for water dropping from <em>H</em><sub>1</sub> to <em>H</em><sub>2</sub>, respectively. <em>t</em> is the average of <em>t</em>1 and <em>t</em>2. <em>T</em><em>’</em> is the temperature during the experiments. Note: we only test the <em>T</em><em>’</em> of the third group and here we used the average of <em>T’ </em>(=12.5℃) to represent the temperature of all three parallel groups during the experiments. It’s acceptable because <em>T’</em> varies slightly throughout the experiments, whose fluctuations hardly affect the order of magnitude of the hydraulic conductivity <em>K</em>. The seepage velocity <em>v</em>=<em>Q</em>/<em>A’t</em>, in which <em>Q</em> is the volume of water that seeps out of the samples.</p> <p><strong>Table S5.</strong> The delta relief of a delta. The locations of martian deltas are based on the database of Wilson et al. (2021)</p> <p> </p> <p> </p>
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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.