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9,882 results for “Scale”
Dataset for On the Relevance of Static Cells for Fast Scale-Up of New Redox Flow Battery Chemistries
<p>Dataset for the results shown in the publications "On the Relevance of Static Cells for Fast Scale-Up of New Redox Flow Battery Chemistries"</p>
A Functional Response in Resource Selection Links Multi-Scale Responses of a Large Carnivore to Human Mortality Risk
<p>This repository contains code and data to reproduce results from the manuscript 'A Functional Response in Resource Selection Links Multi-Scale Responses of a Large Carnivore to Human Mortality Risk'. </p>
Best Worst Scale under uncertainty
<p><span>Dataset from a questionnaire that included a Best Worst Scaling task as follows “Imagine you are in your favourite wine shop to buy a bottle of wine to drink at home with family and/or friends. You will then be shown a series of tables with 6 attributes or characteristics of the wine in each table. Please select the characteristic that would influence you the most and the characteristic that would influence you the least when considering the purchase of a bottle of wine in the shop. Some attributes or characteristics shown may not be common but please consider all the characteristics as if they were possible”. The Excel data contains two worksheets associated with the BWS experiments (control and treatment with follow up question on preference uncertainty) and socio-demographic characteristics (gender, income age, education level). </span></p>
Simulation cases of a lab-scale wet-operated stirred media mill using coupled CFD-DEM
<p>Simulation cases described in the article, "Coupled CFD-DEM simulation of pin-type wet stirred media mills using immersed boundary approach and hydrodynamic lubrication force", DOI: <a href="https://doi.org/10.1016/j.powtec.2024.120060" rel="nofollow">https://doi.org/10.1016/j.powtec.2024.120060</a></p> <p><strong>Pre-requisites:</strong> LIGGGHTS, OpenFOAM-6, cfdemCoupling, and their corresponding dependencies, Python (>3.6)</p> <p>*The versions of simulation softwares used in the simulation cases are taken from Institute for Particle Technology's (iPAT) GitLab repository: <a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a></p> <p>To run the simulations in this repository, one should first install the pre-requisites i.e., LIGGGHTS, OpenFOAM-6 and cfdemCoupling. The repositiries can be found at Institute for Particle Technology's GitLab (<a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a>) if not, they shall be requested.</p> <p>Running the simulations in the repositories includes, generation of the cases in "Base_Cases_Init", using the "generateCases.py" file (Python3), then run the "variables_Modify.py" file. Running of the "jobfile_Modify.py" and "jrun.py", sequentially, will submit the simulations to a HPC cluster. After the successful run of these simulations, the cases in the folders "Base_Cases_Stable" and "Base_Cases_Stable_Lubrication" can be launched in the same manner as described above, i.e., sequentially running "generateCases.py", "variables_Modify.py", "jobfile_Modify.py" and "jrun.py" (one needs to check if the corresponding restart files are existing in the Base_Cases_Stable*/Base_Case_Stable/Restart folder, which are generated from the "Base_Cases_Init" runs). Following this, the cases in "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" can be run using the same method as described above (one needs to check if the corresponding restart files are existing in the Base_Cases_Run*/Base_Case_Run/Restart folder, which are generated from the "Base_Cases_Stable" runs). After successfully running of the simulations the python file "generateAndRunPostFiles.py", in each of the corresponding "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" folders should be run.</p> <p> </p> <p> </p> <p> </p> <p><strong>Description:</strong> This repository provides the simulation cases to generate and run the simulation cases of the "stirred media mill" (MiniCeR). The simulations are setup to couple the CFD and DEM via two-way coupling and the corresponding files in the "Run" folder contain the post-processing scripts to extract the "collision/stress energies" and assemble them into a "collision/stress energy distribution". The simulations are setup in three stages, namely, "Init", "Stable", and "Run". The combinations of operating settings can be easily modified and the respective cases can be generated using the python scripts in the corresponding repositories. The scripts to run the simulations on the HPC-cluster systems are also added.</p> <p><strong><em>a. Init:</em></strong> This stage is to initialize the system with the particles. Three insertion faces are used to generate and insert the required number of particles (calculated according to their size and filling degree) into the system. The "base case" folder contains the necessary DEM scripts of the case setup and the required CAD (geometry) files. The python script "generateCases.py" generates the requested simulation cases according to the specified operating settings. It uses the help of "MakeCases.sh". The "variables_Modify.py" file modifies the variables in the generated folders of the simulation cases to alter the operation setting values. The "jobfile_Modify.py", and the "jrun.py" are used to modify the cluster job files and run the submit the simulation jobs onto the cluster, respectively.</p> <p><strong><em>b. Stable:</em></strong> This is the first stage couples the CFD and DEM. The restart files generated in the "Init" stage are used to start the coupling and run for a specified time. It follows the similar system as init, i.e., to generate the cases and modify the variables, but with additional generation and modifications in the CFD folder i.e., the mesh generation, etc. The simulations are launched in the same way as described above and the corresponding restart files are extracted.</p> <p><strong><em>c. Run:</em></strong> This second stage of the coupling of CFD and DEM launches the srabilized system and extracts the collision energies and stores them in ".txt" files which are postprocessed later to assemble the stress energy distribution. The post-processing to extract the stress energy distribution is done using the "Stress_Energy_Calculation.py" and "generateAndRunPostFiles.py", which generate corresponding folders of post-processing in each of the corresponding case folders.</p>
Data and code to reproduce analyses in Heinken et al, "A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites"
<p>This datasets archives the GitHub version found at https://github.com/ThieleLab/CodeBase to reproduce simulations for the article Heinken et al, "A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites", Cell Systems, in press.</p>
Continuous-Flow Synthesis of BiVO4 Nanoparticles: From laboratory scale to practical systems
<p>#Data set of "Continuous-Flow Synthesis of BiVO4 Nanoparticles: From laboratory scale to practical systems"</p> <p>---</p> <p>## GENERAL INFORMATION</p> <p>1. Data set title: "Continuous-Flow Synthesis of BiVO4 Nanoparticles: From laboratory scale to practical systems"</p> <p>2.Authorship: <br> Name: Christian Robles<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0003-1166-2950</p> <p> Name: Laura Montañés<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-1076-013X <br> <br> Name: Camilo A. Mesa<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-8450-2563</p> <p> Name:Diego Iglesias <br> Institution: institute of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-4030-5816</p> <p> Name: Helena Rabelo<br> Institution: Catalan Institute of Nanoscience and Nanotechnology (ICN2) and BIST, Campus UAB, Ballaterra, 08193, Barcelona, Spain<br> ORCID: 0000-0002-7773-3929</p> <p> Name: Maria Chiara Spadaro<br> Institution: Catalan Institute of Nanoscience and Nanotechnology (ICN2) and BIST, Campus UAB, Ballaterra, 08193, Barcelona, Spain. Department SIMAU, Marche Polytechnic University, 60131, Ancona, Italy.<br> ORCID: 0000-0002-6540-0377<br> <br> Name: Jordi Arbiol<br> Institution: Catalan Institute of Nanoscience and Nanotechnology (ICN2) and BIST, Campus UAB, Ballaterra, 08193, Barcelona, Spain ICREA, 08010, Barcelona, Spain<br> ORCID: 0000-0002-0695-1726</p> <p> Name: Jesús Redondo<br> Institution: Department of Polymers and Advanced Materials, Centro de Física de Materiales CFM (UPV/EHU-CSIC), University of the Basque Country UPV/EHU, 20018, San Sebastián Spain. Department of Surface and Plasma Science, Faculty of Mathematics and Physics, Charles University, 180 00 Prague 8, Czech Republic<br> ORCID: 0000-0002-8147-689X</p> <p> Name: F.Schiller<br> Institution: Department of Polymers and Advanced Materials, Centro de Física de Materiales CFM (UPV/EHU-CSIC), University of the Basque Country UPV/EHU, 20018, San Sebastián Spain. Donostia International Physics Center DIPC, 20018 San Sebastián, Spain<br> ORCID: 0000-0003-1727-3542</p> <p> Name:Sara Barja<br> Institution: Department of Polymers and Advanced Materials, Centro de Física de Materiales CFM (UPV/EHU-CSIC), University of the Basque Country UPV/EHU, 20018, San Sebastián Spain. Donostia International Physics Center DIPC, 20018 San Sebastián, Spain. IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain</p> <p> Name: Beatriz Julián-López<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0003-1019-776X</p> <p> Name: Ana Gutiérrez-Blanco<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0001-9412-2321<br> Email: <angutier@uji.es></p> <p> Name: Victor Sans<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0001-7045-5244<br> Email: <sans@uji.es></p> <p> Name: Sixto Giménez<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-4522-3174<br> Email: <sjulia@uji.es></p> <p>## FILE DESCRIPTION<br>----------------<br>### Figure 1<br>-Fig1b.txt : Distribution hystogram of the BiVO4 NPs after the synthetic procedure.</p> <p>### Figure 3 (All measurements were carried out in 0.1 M potassium phosphate buffer (pH 7), containing 0.1 M Na2SO3 as hole scavenger.)<br>-Fig3a.txt : LSVs under chopped illumination conditions, scan rate, 10 mV·s-1, simulated solar light (AM 1.5 G, 100 mW·cm-2), back illumination.<br>-Fig3b.txt : Chronoamperometries at 1.23 V vs. RHE.<br>-Fig3c.txt : IPCE measurements and integrated photocurrent at 1.23 V vs. RHE</p> <p>### Figure 4<br>-Fig4c.txt : LSV of the measured photoanode (the electrolyte consisted on 0.5 M KPi at a constant pH of 7 and 0.5 M Na2SO3 as hole scavenger) with back illumination configuration.</p> <p>### Figure S4<br>-FigS4a.txt : Absorbance spectra of the different BiVO4 colloidal suspension alliquots taken for reproducibility studies.Seven consecutive synthetic runs were carried out and an aliquot was taken from each run, always under identical conditions regarding the collection time of the sample (30 minutes after starting the reaction).<br>-FigS4b.txt : Transmittance spectra of the different BiVO4 colloidal suspension alliquots taken for reproducibility studies.Seven consecutive synthetic runs were carried out and an aliquot was taken from each run, always under identical conditions regarding the collection time of the sample (30 minutes after starting the reaction).<br>-FigS4c.txt : Absorbance mean + error or the different BiVO4 colloidal suspension alliquots taken for reproducibility studies.<br>-FigS4d.txt : Transmittance at the selcted wavelength (400 nm) for the different BiVO4 colloidal suspension alliquots taken for reproducibility studies.</p> <p>## Figure S6<br>-FigS6.txt : Thermogravimetric analysis (TGA) curve of the BiVO4 NPs from room temperature to 500 ºC.</p> <p>### Figure S7<br>-FigS7.txt : XRD patterns of the FTO/BiVO4 photoanodes before and after thermal annealing. </p> <p>### Figure S8<br>-FigS8a.txt : Raman spectra of the FTO/BiVO4/Al2O3 based films before and after annealing. <br>-FigS8b.txt : FT-IR spectra of the FTO/BiVO4/Al2O3 based films before and after annealing. </p> <p>### Figure S9<br>-FigS9a.txt : Absorptance spectra of the FTO/BiVO4/Al2O3 samples before and after the annealing process.<br>-FigS9b.txt : Tauc plot of the FTO/BiVO4/Al2O3 samples before and after the annealing process. </p> <p>### Figure S11<br>-Fig11a.txt : LSV of the FTO/BiVO4/Al2O3 films in 0.1 M potassium phosphate buffer (pH 7) containing 0.1 M Na2SO3 as hole scavenger under AM 1.5 G, 100 mW·cm-2 back side illumination (scan rate, 10 mV·s-1) for the deposition reproducibility studies.<br>-Fig11b takes the same data contained in Fig11a.txt</p> <p>### Figure S12 (Measurements in 0.1 M phosphate buffer (pH 7) containing 0.1 M Na2SO3 as hole scavenger under AM 1.5 G, 100 mW·cm-2 back side illumination.)<br>-FigS12a.txt : Consecutive LSVs (LSV1, LSV2 LSV3) and LSV after 2 hours testing for the FTO/BiVO4 (area 5 cm2).<br>-FigS12b.txt : Consecutive LSVs (LSV1, LSV2 LSV3) and LSV after 2 hours testing for the FTO/BiVO4/Al2O3 films (area 5 cm2) showing the enhanced stability after deposition of the Al2O3 layer.</p> <p> </p>
BDFoodSent: A Large-Scale Sentiment-Labeled Restaurant Review Dataset from Bangladesh
<p>BDFoodReview is a large-scale dataset containing 334,119 restaurant reviews collected from "Foodpanda Bangladesh". The dataset includes customer reviews in mixed languages (Bangla, English, and Banglish), translated into English, along with their corresponding ratings and sentiment labels.</p> <p> </p> <h3>Dataset Statistics</h3> <p>Total Reviews: 334,119</p> <p>Features/Columns: 19</p> <p> </p> <h3>Potential Applications</h3> <p>Sentiment Analysis</p> <p>Restaurant Review Classification</p> <p>Customer Satisfaction Analysis</p> <p>Opinion Mining</p> <p>Natural Language Processing Research</p> <p>Food Service Industry Analysis</p>
Not all Heterogeneity is Equal: Length Scale of Frictional Property Variation as a control on Subduction Megathrust Sliding Behavior
<p>The folder SSS_Results.zip contains the following files:</p> <p>1) A .txt for each simulation that contains (1st column) simulated timestep in seconds; (2nd column) maximum slip velocity at each timestep in m/s; and (3rd column) maximum slip velocity taken from the center of the velocity-strengthening blocks in m/s at each time step.</p> <p>2) A .mat file for each simulation that contains the same output as the .txt file, and in addition a MATLAB structure 'p' that serves as an input file to run each simulation using RSFaultZ, found at <a href="https://github.com/rmskarbek/RSFaultZ">https://github.com/rmskarbek/RSFaultZ</a></p> <p>3) PartialStabilityData.mat - results from linear stability analysis.</p> <p>4) ReducedArchiveData.m, SSS_Fig3.m, SSS_Fig4.m - Three MATLAB scripts that will automatically generate figures 3 and 4 in the related paper. </p> <p>NOTE: SSS_Fig3.m uses a perceptually uniform color map that requires an additional MATLAB package: <a href="https://www.mathworks.com/matlabcentral/fileexchange/68546-crameri-perceptually-uniform-scientific-colormaps">https://www.mathworks.com/matlabcentral/fileexchange/68546-crameri-perceptually-uniform-scientific-colormaps</a></p> <p>For any additional information please do not hesitate to contact the corresponding author Rob Skarbek at rskarbek@psi.edu.</p> <p>Additional MATLAB scripts that will reproduce the simulations themselves can be found at <a href="https://github.com/rmskarbek/RSFaultZ/tree/main/examples/blocks">https://github.com/rmskarbek/RSFaultZ/tree/main/examples/blocks</a></p>
Evaluating country-scale irrigation demand through parsimonious agro-hydrological modeling
<p>WaterCROPv2 is an advanced agro-hydrological model designed to estimate irrigation<br>water demand at a national scale by effectively balancing hydrological accuracy with manageable data<br>requirements. Building on the original WaterCROPv1 model, WaterCROPv2 incorporates several<br>significant enhancements, including hourly computation, rainwater canopy interception, soil-dependent5<br>leakage dynamics, and daily evapotranspiration trends based on localized meteorological data.</p> <p>WaterCROPv2 was used to assess mean irrigation water demand for maize from 2005<br>to 2015, illustrating its potential as a decision-support tool for policymakers. </p>
Tree census data associated to 'Large-scale informative priors to better predict the local occurrence rate of a rare tree-related microhabitat'
<p>This repository contains two excel spreadsheets providing data about the trees studied in Cottais et al. study (doi : 10.1101/2024.11.28.625900):</p> <ul> <li>df_TreM.xlsx has 1462 lines (headers not included); each line corresponds to one oak tree sampled in 2021 survey of Cottais et al. study. Fields are: <ul> <li>Plot_Id : the id of the sampling plot where the tree is located</li> <li>Tree_Id : a unique id of the tree used in the BloBiForM project</li> <li>Species_Latin_Name : the latin name of the tree species ('Quercus_sp' for all trees given than only oaks are reported here)</li> <li>DBH_cm : the diameter at breast height of the tree in cm</li> <li>TreM : whether the tree harbours a basal rot hole (1) or not (0)</li> <li>Life_Stage : whether the tree is a living tree or a snag</li> <li>Last_Logging_Year : the year of the last logging event in the stand where the tree is located</li> <li>conv : whether the last logging event occured less that 120 years before 2024 (conv=0; the stand is now classic high forest) or is more ancient (conv=1, the stand is being converted to high forest through sprout thinning)</li> </ul> </li> <li>DMH_2022.xlsx has 1190 lines (headers not included); each line corresponds to one tree sampled in 2022 survey of Cottais et al. study. Fields are: <ul> <li>Plot_Id: the id of the sampling plot where the tree is located</li> <li>Species_Latin_Name: the latin name of the tree species, obtained as a translation from french using the dictionnary in page 2; oaks are not the only species reported, but only oaks are used in Cottais et al. study;</li> <li>Species_French_Name : the french name of the tree species;</li> <li>DBH_cm: the diameter at breast height of the tree in cm</li> <li>TreM: whether the tree harbours a basal rot hole (1) or not (0)</li> <li>Tree_Id: when the tree belongs to the cohort of the BloBiForM project, the tree id is reported; left empty otherwise</li> <li>Nb_brin: number of stems on the tree stump;</li> <li>Commentaire: any comments during fieldwork</li> </ul> </li> </ul>
SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I)
<p><strong>This page only provides the ground-level image dataset. </strong></p> <ul> <li><strong>For the drone-view image dataset, please visit <a href="https://zenodo.org/records/13955516" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)</em></a>.</strong></li> <li><strong>For the point clouds, please visit <em><a href="https://zenodo.org/records/14228467" target="_blank" rel="noopener">SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III)</a>.</em></strong></li> </ul> <p>The dataset contains ground-level RGB images, depth maps, semantic segmentation labels, and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a <code>color_palette.xlsx</code> file, which contains the RGB_id and corresponding RGB values.</p> <p>All files follow the naming convention: <code>{central_tree_id}_{timestamp}</code>, where <code>{central_tree_id}</code> represents the ID of the tree centered in the image, which is typically in a prominent position, and <code>timestamp</code> indicates the time when the data was collected.</p> <p>Specifically, each 7z file includes the following folders:</p> <ul> <li> <p><strong>rgb</strong>: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: <code>Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36</code>.</p> </li> <li> <p><strong>depth_pfm</strong>: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection.</p> </li> <li> <p><strong>semantic_segmentation</strong>: This folder contains grayscale images representing semantic segmentation labels, where 1 indicates tree trunks and 0 represents other elements.</p> </li> <li> <p><strong>instance_segmentation</strong>: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps <code>tree_id</code> to <code>RGB_id</code>. The <code>tree_id</code> can be used to look up detailed information about each tree in <code>obj_info_final.xlsx</code>, while the <code>RGB_id</code> can be matched to the corresponding RGB values in <code>color_palette.xlsx</code>. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image.</p> </li> <li> <p><strong>obj_info_final.xlsx</strong>: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm).</p> </li> <li> <p><strong>landscape_info.txt</strong>: This file contains the ground location information within the scene, sampled every 0.5 meters.</p> </li> </ul> <p>For forest datasets: birch_forest, broadleaf_forest, burned_forest, rainforest and redwood_forest, there's an additional folder called <strong>coco_annotation</strong> where we generated the COCO-format annotation files (.json) for each image.</p> <p>⚠️: 7z files that begin with "<strong>!</strong>" indicate that the RGB values in the images within the <code>instance_segmentation</code> folder cannot be found in <code>color_palette.xlsx</code>. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in <a href="https://github.com/microsoft/AirSim/issues/3423" target="_blank" rel="noopener">link1</a> and <a href="https://github.com/microsoft/AirSim/issues/1852" target="_blank" rel="noopener">link2</a>.</p>
Ultra-high scale cytometry-based cellular interaction mapping - Data repository
<p>This is the repository for datasets used in Vonficht, Jopp-Saile, Yousefian, Flore <em>et al. </em>Ultra-high scale cytometry-based cellular interaction mapping, <em>Nature Methods </em>(2025) <a href="https://doi.org/10.1038/s41592-025-02744-w" rel="nofollow">https://doi.org/10.1038/s41592-025-02744-w</a>. Associated analysis code can be found at <a href="https://github.com/agSHaas/ultra-high-scale-cytometry-based-cellular-interaction-mapping">https://github.com/agSHaas/ultra-high-scale-cytometry-based-cellular-interaction-mapping</a>, and the repository for the accompanying R package is hosted at <a href="https://github.com/agSHaas/PICtR">https://github.com/agSHaas/PICtR</a>. </p>
SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III)
<p><strong>This page only provides point clouds. </strong></p> <ul> <li><strong>For the ground-level image dataset, please visit <a href="https://zenodo.org/records/13570934" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I)</em></a>.</strong></li> <li><strong>For the drone-view image dataset, please visit <a href="https://zenodo.org/records/13955516" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)</em></a>.</strong></li> </ul> <p>This dataset contains point clouds collected from different virtual forest scenes. Data from each scene is stored in a separate .7z file, along with a <code>point_cloud_color_palette.txt</code>file, which contains the Tree_id and corresponding RGB values.</p> <p>Specifically, each 7z file includes the following folders:</p> <ul> <li> <p><strong>tree</strong>: This folder contains the point cloud data of every single tree within the forest scene. Each tree is stored separately in a <code>.ply</code> file including both location and color infomation. For performance reasons, the maximum number of point clouds for each tree is limited to 10,000.</p> </li> <li> <p><strong>ground</strong>: This folder contains a <code>landscape.ply</code> describing the ground information. The color of the point cloud is set to [0,0,0].</p> </li> </ul> <p><strong>The unit of the point cloud is meters (m).</strong></p>
Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona
<p>These are supplementary data for the paper "Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona". They are:</p> <ul> <li>Python code to forecast Kp index</li> <li><span>Code to construct a nerual network model</span></li> </ul>
Nonlinear sound-sheet microscopy: imaging opaque organs at the capillary and cellular scale
<p>This dataset accompanies the article entitled: "Nonlinear sound-sheet microscopy: imaging opaque organs at the capillary and cellular scale".</p> <div> <h3><strong>Wells with E. Coli (Wells_EColi.zip)</strong></h3> <p><strong>Codes are hosted <a title="NSSM github repo" href="https://github.com/MarescaRenaudLabs/NSSM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong></p> </div> <p>Representative dataset for paper figure 2D,E. The file <code>RFData_planes_figure_2E.mat</code> contains the RFData and the required parameters to reconstruct two orthogonal sound sheets, in SSM and NSSM mode. The file <code>demo_reconstruct_orthogonal_NSSM_images.m</code> shows how to reconstruct the sound sheet data.</p> <p>The file <code>beamformed_volume_figure_2E.mat</code> contains beamformed SSM and NSSM volumes as displayed in figure 2G. The file <code>demo_navigate_3D_NSSM_data.m</code> can be used to view the volumes.</p> <div> <h3><strong>mARG expression in orthopic tumors (mArg_tumors.zip)</strong></h3> <p><strong>Codes are hosted <a title="NSSM github repo" href="https://github.com/MarescaRenaudLabs/NSSM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong></p> </div> <p>Representative dataset for paper figure 3B. The file <code>RFData_planes_figure_3B.mat</code> contains the RFData and the required parameters to reconstruct two orthogonal sound sheets, in SSM and NSSM mode. The file <code>demo_reconstruct_orthogonal_NSSM_images.m</code> shows how to reconstruct the sound sheet data.</p> <p>The file <code>beamformed_volume_figure_3B.mat</code> contains beamformed SSM and NSSM volumes as displayed in figure 3C. The file <code>demo_navigate_3D_NSSM_data.m</code> can be used to view the volumes.</p> <h3><strong>Nonlinear Soundsheet Localization Microscopy (NSSLM_zenodo_archive.zip)</strong></h3> <p><strong>Codes are hosted <a title="NSSLM github repo" href="https://github.com/MarescaRenaudLabs/NSSLM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong></p> <p>A zip compressed dataset containing beamformed images and post-processed trajectories for Nonlinear Soundsheet Localization Microscopy.</p> <ul> <li>The files <code>ImgNSSM_00x.mat</code> are 4D data arrays of size (157x160x2x2100). They hold 2 soundsheets repeated for 2100 frames at 1000Hz (more details are provided in the Material and Methods section of accompanying papers). Use this dataset to run script <code>processNSSLM.m</code></li> <li>The file <code>Params.mat</code> is a file containing the <code>ULM</code> structure. This comprises the fields necessary to execute ULM codes from the <a href="https://github.com/AChavignon/PALA" target="_blank" rel="noopener">PALA toolbox</a></li> <li>A folder named Trajectories. This contains 50 files. Each contains the trajectories obtained from NSSLM processing of the entire sequence for soundsheet indexed 1 in the 4D matrix. Use this dataset to run script <code>renderingNSSLM.m</code></li> </ul> <p>Each of these datasets is to be used in conjunction with the different example scripts given on <strong><a title="NSSLM github repo" href="https://github.com/MarescaRenaudLabs/NSSLM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong>.</p> <h3>Codes hosted on github</h3> <p>For accompanying codes, please refer to:</p> <ul> <li>https://github.com/MarescaRenaudLabs/NSSM/releases/tag/v1.0.0</li> <li>https://github.com/MarescaRenaudLabs/NSSLM/releases/tag/v1.0.0</li> </ul>
Analysis data for location- and scale-invariant power transformations
<p>This repository contains various files and folders related to the machine learning experiments in a forthcoming manuscript on location- and scale-invariant power transformations.</p>
CodePori: Large Scale System for Autonomous Software Development by Using Multi-Agents
<p>This dataset accompanies the paper <strong>"CodePori: A Large-Scale System for Autonomous Software Development Using Multi-Agents."</strong> The dataset is recorded in an MS Excel file, which contains the following sheets, with a brief description of each provided below:</p> <ol> <li> <p><strong>Selected Projects:</strong> Contains the descriptions of the 20 selected projects along with the GitHub URL for each.</p> </li> <li> <p><strong>Modifications:</strong> Contains details of the modifications made to the projects to ensure successful execution.</p> </li> <li> <p><strong>Outputs:</strong> Contains the output for each project.</p> </li> <li> <p><strong>Failed Projects:</strong> Contains data on the projects that failed.</p> </li> </ol>
Unveiling the link between Phytoplankton Molecular Physiology and Biogeochemical Cycling via Genome-Scale Modeling
<p>Data used for the manuscript "Unveiling the link between Phytoplankton Molecular Physiology and Biogeochemical Cycling via Genome-Scale Modeling"</p>
Seismicity patterns and multi-scale imaging of Krafla (N-E) Iceland with local earthquake tomography: Raw event waveforms for all events used in the inversion and manual picks for temporary network
<p>This Data and Software were used in the submitted paper "Seismicity patterns and multi-scale imaging at Krafla (N-E Iceland) wih local earthquake tomography" by Glück et al.<br>The data and software provided here are used to compute the velocity models with TomoTV.<br>The raw data (.mseed format) can be visualised with the Python package Pyrocko/Snuffler, which was also used for the arrival time picking.<br>For the temporary network the manual picks are provided along with the code to prepare the manual picks as the input files for a localisation with NonLinLoc by weighting and quality checking the data. This resulting localsitations and the weighted traveltimes are then used for the LET.<br>The same workflow was used for the picks from the permanent network.</p> <p>Data:<br>- Raw data (\WaveformsPermanentStations): 7s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/ for the years 2021 and 2022.<br>- Raw data (\WaveformsNodes): 5s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/2022 recorded with the temporary network of 98 temporary nodes in June and July 2022.<br>- Pickfile (ManualPicks_100Nodes_Kafla2022.txt): Manual picks of the events listed in the ISOR catalogue for the evenst recorded with the temporary network.<br>- Station file (Station_file.txt): The station file includes the coordinates (Lat, Lon, Elevation) of the permanent stations (StationID starting with K...) and of the temporary nodes (StationID starting with N...).</p> <p>Software (Hyp_format.py):<br>- Weighting: The picks are weighted according to their Signal-to-Noise ratio (described in more detail in Section 2.3 in the main text of the paper)<br>- Writing the inputfile for NonLinLoc (with the selecting the mode option "PorS" in line 118), including all picks, also for those stations where not both phases were picked. The file "endfile.txt" is needed to write the picks to the NonLinLoc input format.<br>- Quality check of the picks: Computing a modified Wadati diagram from the traveltime differences of P and S phases for all the events available (with the selecting the mode option "PandS" in line 118)<br>- Python packages needed: numpy, scipy, matplotlib, pandas, obspy</p>
Trophic resource partitioning drives fine-scale coexistence in cryptic bat species
Understanding the processes that enable species coexistence has important implications for assessing how ecological systems will respond to global change. Morphology and functional similarity increase the potential for competition, and therefore, co-occurring morphologically similar but genetically unique species are a good model system for testing coexistence mechanisms. We used DNA metabarcoding and High Throughput Sequencing to characterise for the first time the trophic ecology of two recently-described cryptic bat species with parapatric ranges, Myotis escalerai and Myotis crypticus. We collected faecal samples from allopatric and sympatric regions and from syntopic and allotopic locations within the sympatric region to describe the diets both taxonomically and functionally and compare prey consumption with prey availability. The two bat species had highly similar diets characterised by high arthropod diversity, particularly Lepidoptera, Diptera and Araneae, and a high proportion of prey that is not volant at night, which points to extensive use of gleaning. Diet overlap at the prey-item level was lower in syntopic populations, supporting trophic shift under fine-scale co-occurrence. Furthermore, the diet of M. escalerai had a marginally lower proportion of not nocturnally volant prey in syntopic populations, suggesting that the shift in diet may be driven by a change in foraging mode. Our findings suggest that fine-scale coexistence mechanisms can have implications for maintaining broad-scale diversity patterns. This study highlights the importance of including both allopatric and sympatric populations and choosing meaningful spatial scales for detecting ecological patterns. We conclude that a combination of high taxonomic resolution with a functional approach helps identify patterns of niche shift.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.