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127 results for “Setup”
Experimental data on the effects of an azimuthal mean flow on the (thermo)acoustic modes in the annular electroacoustic feedback setup at TU Berlin
<p>Experimental data obtained in the presence of an azimuthal mean flow on the acoustic/thermoacoustic response in the annular electroacoustic feedback setup at TU Berlin. This dataset was used for the published article<br> S. C. Humbert, J. P. Moeck, A. Orchini, C. O. Paschereit, "Effect of an Azimuthal Mean Flow on the Structure and Stability of Thermoacoustic Modes in an Annular Combustor Model With Electroacoustic Feedback", J. Eng. Gas Turbines Power. June 2021, 143(6): 061026. Experimental data as well as Matlab scripts to use them are provided. Useful information is contained in "readme" files.</p>
MD setup data for ansamer amanitin derivatives and precursors for atroposelective synthesis
<p>Setup files to reproduce the MD simulations for the amanitin derivatives <strong>4a </strong>and <strong>4b</strong> as well as the precursors <strong>3b</strong> and <strong>3c. </strong>The MD simulations were part of the study:</p> <p>G. Yao, S. Kosol, M. T. Wenz, E. Irran, B. G. Keller, O. Trapp, R. D. Süssmuth, <em>ChemRxiv</em> 2022, DOI 10.26434/chemrxiv-2022-ll8lq.</p> <p>For further instructions on the files, please refer to '0_README'.</p>
Figure 2. Experimental wireless sensors setup in greenhouse-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>Figure 2 illustrates how the sensor<br> nodes were deployed to the greenhouse block. The idea of the vertical deployment was to get a<br> better understanding of the microclimate layers which typically exist in the greenhouse, and to<br> figure out what kind of differences occur in the climate between lower and upper flora.</p>
Glaide.jl input data for the Aletsch setup
<p>This repository provides the source data one cannot automatically download needed to generate the input data for the Aletsch glacier setup in the Glaide.jl model.</p> <p>The repository contains following 4 datasets:</p> <table> <tbody> <tr> <td> <p><strong>aletsch_fix.dat</strong></p> </td> <td> <p>The surface mass balance (SMB) data is provided in the form of annual mass balance per elevation band. The data set covers time period from 1914 to 2022. We extract and use the SMB data for 2016-2017 hydrological year.</p> <p><em>Source: GLAMOS (2023). Swiss Glacier Mass Balance, release 2023, Glacier Monitoring Switzerland, <a href="https://doi.org/10.18750/massbalance.2023.r2023" target="_blank" rel="noopener">https://doi.org/10.18750/massbalance.2023.r2023</a>.</em></p> </td> </tr> <tr> <td> <p><strong>aletsch2009.asc</strong></p> <p><strong>aletsch2017.asc</strong></p> </td> <td> <p>The surface elevation dataset from <a href="https://www.swisstopo.admin.ch/en/height-model-swissalti3d" target="_blank" rel="noopener">swissALTI3D</a> (swisstopo) provides data to replace the missing points in the bedrock dataset for the years 2009 and 2017. </p> <p><em>Source: swissALTI3D - Das hoch aufgelöste Terrainmodell der Schweiz (2022), Swiss Federal Office of Topography swisstopo, <a href="https://backend.swisstopo.admin.ch/fileservice/sdweb-docs-prod-swisstopoch-files/files/2023/11/14/6d40e558-c3df-483a-bd88-99ab93b88f16.pdf" target="_blank" rel="noopener">https://backend.swisstopo.admin.ch/fileservice/sdweb-docs-prod-swisstopoch-files/files/2023/11/14/6d40e558-c3df-483a-bd88-99ab93b88f16.pdf</a>.</em></p> </td> </tr> <tr> <td> <p><strong>ALPES_wFLAG_wKT_ANNUALv2016-2021.nc</strong></p> </td> <td> <p>Annual glacier surface flow velocity product from Sentinel-2 data for the European Alps.</p> <p><em>Source: Rabatel, A., Ducasse, E., Millan, R., Mouginot, J. (2023). Annual glacier surface flow velocity product from Sentinel-2 data for the European Alps, <a href="https://doi.org/10.57745/XHQ7TL" target="_blank" rel="noopener">https://doi.org/10.57745/XHQ7TL</a>, Recherche Data Gouv, V1.</em></p> </td> </tr> </tbody> </table> <p> </p>
SeisSol model setup input files and supplement videos for the 3D dynamic rupture models of Wirp et al. 2024
<p>Data required to run the dynamic rupture models presented in Wirp, S. A., Gabriel, A.-A., Ulrich, T., Lorito, S. (2024). The README.txt file contains detailed information about the data and data format.</p>
Raw and processed data, gating strategy, and photographs of instrumental setup of NAVETTA
<p>Supplementary Information and Raw Data for Weiss et al., Comp Struct Biotechn J: Nanosci Adv Mat, 2024</p> <p>1. xls sheet of raw and processed data for all figures</p> <p>2.-3. gating strategies for flow cytometry experiments</p> <p>4.-8. photographs of instrumental setups</p>
Ocean model simulations in cold-water coral ecosystems off the coasts of Angola and Namibia in the Southeast Atlantic: Setup, boundary conditions and model results.
<p>The dataset contains all essential data for the setup of high-resolution local area model implementations using the ROMS-AGRIF model version 3.1 in two cold-water coral regions off the coasts of Angola and Namibia in the Southeast Atlantic. The data include computational grids, initialization fields (temperature, salinity), and boundary conditions (temperature, salinity, currents, and sea surface height) for each model area. It also includes model output, which has been used in different studies of the local oceanography of the region .</p> <p>Initialization, forcing and output data for each ROMS-AGRIF model implementation are provided in two compressed archive data files:</p> <ul> <li>Angola Margin: Angola_Model_Setup1.7z</li> <li>Namibia Margin: Namibia_Model_Setup1.7z</li> </ul> <p>The data set description is provided in the file:</p> <ul> <li>DataSet_Description_Angola_Namibia_Model.pdf</li> </ul> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
MITgcm model setup and output for "Impact of subglacial freshwater discharge on Pine Island Ice Shelf"
<p>Here, it contains the results of the regional PIG simulation. Model grid is lat-lon similar to Nakayama et al., 2019 roughly 200 m in the region. See Nakayama et al., submitted to GRL for detail. </p> <p>For complete model output (10*Qsg and 50*Qsg cases), please access NASA data (Registration is required).<br> https://ecco.jpl.nasa.gov/drive/files/ECCO2/High_res_PIG/PIG_only_200m </p> <p>Contents can be downloaded easily using wget (see link below). <br> https://ecco-group.org/docs/wget_download_multiple_files_and_directories.pdf</p> <p><strong>(Contents)</strong><br> code.zip (code to run this simulation)<br> input.zip (input file required for this simulation)<br> latlon_run23.zip (CTRL)<br> latlon_run24.zip (Qsg case)<br> latlon_run25.zip (2*Qsg case)<br> <br> <strong>(How to build and run)</strong><br> mkdir build<br> ./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas -mpi -mods ../code/<br> make depend<br> make -j 16<br> cd ..</p> <p>mkdir test<br> cd test<br> ln -sf ../input/* .<br> ln -sf /nobackup/hzhang1/forcing/era_xx_it50/ .<br> cp ../build/mitgcm_uv .<br> qsub latlon_run23.sh</p>
Cow drinking monitoring tests with ultrasonic sensor setup Aug 12th and Sep 20th, 2022
<p>We tested a device that could potentially be developed for use in large scale drinking monitoring of individual animals. Our setup consisted of two plastic cylindrical containers having the inner diameter of 275 millimeters. On top of the other cylinder were placed two ultrasonic sensors that measured the distance to the water level in the container. The containers were connected with a hose (inner diameter 25 millimeters) so that the water level on both containers was always the same. Faucets shown on the image were completely open during the experiments that took place on August 12th and September 20th, 2022. Cows were able to freely access and drink from the second container which had the height of 100 centimeters in the first experiment and reduced height of 80 centimeters in the second experiment. </p> <p>Two ultrasonic sensors were used and they were JSN-SR04T-2.0 ja Maxbotix MB7389. Sensors were connected to circuit board V2 ESP8266 Development Board (CH341) from which measurements were collected to a PC programmatically at about 0.3 second intervals. Both sensors had the resolution of 1 millimeter which corresponds to 0.6 liters in the used containers.</p> <p>Image of the setup and its installation during the experiment are shown in the photographs. There was a small leakage in the container as is seen in the data (slow and constant decrease of water level). In the first experiment, the leakage was compensated by continuous flow of water after 17:30. On other times the containers were filled manually.</p> <p>Videos are taken with no particular plan but, instead, when something interesting was seen. Timestamp in the filename refers to the starting time of the video in format YYYYMMDD_HHMMSSsss.</p> <p>All times refer to Finnish time UTC+3.</p> <p>Data and videas are shared with the following license: Creative Commons ByAttribution (CC-BY).</p>
Model setup and output for 'Tidal conversion and dissipation at steep topography in a channel poleward of the critical latitude'
<p><strong>Data supplement to Hughes and Klymak 2019</strong></p> <p>Model input and output in a reduced form associated with the following paper:</p> <p><strong>Tidal conversion and dissipation at steep topography in a channel poleward of the critical latitude<br></strong><em>Journal of Physical Oceanography.</em> <a href="http://dx.doi.org/10.1175/JPO-D-18-0132.1">doi:10.1175/JPO-D-18-0132.1</a></p> <p><strong>Inputs</strong></p> <p>As described in Table 1 of the associated paper, there are three main sets of simulations. The input files for these sets are contained in their respective directories (`vary_width`, `vary_forcing`, and `vary_freq`). The python script that creates all of the necessary files is `gendata.py`. A fourth directory is titled `baroclinic_terms` and includes that simulation in which <em>u'</em> and <em>p'</em> are output at high temporal resolution.</p> <p>A key point regarding the input files is that for the vary width and vary forcing cases, a single simulation involves multiple channels. This lets me compile a single executable `mitgcmuv` with a Nx × Ny grid of 600 × 1280, which I divide up into the necessary number of channels by putting vertical walls in appropriate places. For the vary width cases, the 'narrow' simulations are all channels from 0.2 to 32 km and the 'wide' simulations are all wider channels. Once the simulation has run, I use netcdf tools (`ncks`) to extract the individual channels using the scripts in the `extract_scripts` directory.</p> <p>Most of the files in the `code` directories will be familiar to anyone that uses the MITgcm. An exception is the `energy_diagnostics_fill.F` (and `diagnostics_main_init.F` and `do_statevars_diags.F`, which have minor additions). The original, from `https://github.com/jklymak/MITgcmcode`, was modified slightly to suit this project.</p> <p><strong>Outputs</strong></p> <p>The results directory contains five subdirectories to be described in turn.</p> <p>Notes that in all cases, energy terms in the netCDF files do not include a factor of ρ. This was added in at the plotting stage.</p> <p>All simulations used Checkpoint67b and were run on Graham: https://docs.computecanada.ca/wiki/Graham.</p> <p><strong>vary_width</strong></p> <p>The majority of the files are of the form `obstacle_FFF_YY.nc` where `FFF` is $1000 ω/f$ and `YY` is the channel width in kilometres. These files contain the tidally averaged, depth-integrated energy diagnostics for the seventh tidal cycle at all points (<em>x, y</em>) within the energy control volume.</p> <p>There are also three files entitled `tophat_995_YY.nc`, which contain fields of <em>U</em>, <em>V</em>, and <em>T</em> (which gives density with α = 0.0002) at two levels. These fields are used as examples for weakly and strongly responding channels.</p> <p><br><strong>vary_forcing</strong></p> <p>These files are of the form `forcing_UU.nc` where `UU` is the deep-water tidal current amplitude <em>U_</em>0 in cm/s. They contain the same energy terms as for the vary width simulations.</p> <p><strong>vary_freq</strong></p> <p>These files are of the form `freq_FFF_fields.nc` and contain fields of <em>U</em>, <em>V</em>, and <em>T</em> at two levels. Energy terms are not included because the vary frequency simulations were only run to get estimates of the along-ridge wavelength.</p> <p><strong>baroclinic_terms</strong></p> <p>The single file within this directory contains <em>u'</em> and <em>p'</em> at a single <em>x</em> position every five minutes for four tidal cycles.</p> <p><strong>gaussian_26</strong></p> <p>This directory, named for its obstacle and width, contains <em>U</em>, <em>V</em>, and <em>T</em> at every grid point for a snapshot in time and another file with the corresponding snapshots of all energy terms.</p>
The Bolton experiment: experimental setup
An Experimental Test of the Use of Microwave Attenuation to improve Rainfall Estimates in Urban Areas, and hence to Enhance Flood Warning. Data was collected from microwave and radar links and a number of telemetered tipping bucket raingauges. The adjacent relief map of the catchment area, shows the position of the links (white lines) together with the locations of the rainguages (blue dots). View larger image Four links were set up, three dual-frequency and one single-frequency. The shorter links to the west cover Bolton's town centre. The link to the east is a link to the UK Met Office Hameldon Hill C-band weather radar. Not only does this pass along the valley of the River Irwell, but, being radial from the radar, enables estimates of the effects of attenuation on radar data to be estimated. Data were also available from a number of telemetered tipping bucket raingauges most of which belong to North West Water and the Environment Agency. The Met Office also installed two automatic weather stations (marked by red crosses).
Phantom imaging data and analysis macros for the article "Monochromatic computed tomography using laboratory-scale setup"
<p>The raw and processed data and analysis macros of the article <em>A.-P.</em> <em>Honkanen et S. J. Huotari, Monochromatic computed tomography using laboratory-scale setup, Scientific Reports (2023), doi:<a href="http://doi.org/10.1038/s41598-023-27409-6">10.1038/s41598-023-27409-6</a></em></p> <p>The data set consists of the raw and reconstructed computed tomography projection data taken of an PMMA phantom embedded with three different chemical species of selenium taken with a monochromatic X-ray imaging setup based on a laboratory-scale Johann-type crystal X-ray spectrometer. In addition to the imaging data, the set contains also the Jupyter Notebooks used to process and analyse the data. The details of the instrument and the analysis are presented in the article.</p> <p>The dataset is licensed under Creative Commons Attribution 4.0 International License <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Model setup code and input for internal tide-eddy simulation
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>The dataset includes the setup code and input files for simulations of internal tide-eddy interactions using MITgcm. Due to the large size of the model output data, it is not included but can be made available upon request at <a target="_new">yangwangow@gmail.com</a>. If you have any questions about using or testing these files, please feel free to reach out. Cheers!</p> </div> </div> </div> </div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Lipid membrane simulations with flat-bottom and double-bilayer setups, part 2/2
<p>To cite: Biriukov, D. and Javanainen, M. Efficient Simulations of Solvent Asymmetry Across Lipid Membranes Using Flat-Bottom Restraints. J. Chem. Theory Comput. 2023, 19 (18), 6332–6341. DOI: <a href="https://doi.org/10.1021/acs.jctc.3c00614">10.1021/acs.jctc.3c00614</a></p> <p>Gromacs molecular dynamics simulations to compare membrane and solvent properties from lipid membrane simulations with flat-bottom and double-bilayer setups. CHARMM36 force field was used except for simulations with peptides, where a prosECCo model was used [Nencini et al., Biophys. J. 121, 157a (2022)]</p> <p>This dataset contains only double-bilayer simulations. The flat-bottom simulations together with all topologies and mdp files can be found in part 1 : DOI: <a href="https://zenodo.org/record/7973838">10.5281/zenodo.7973838</a></p> <p>Abbreviations in the names of simulation files:</p> <ul> <li>"fb" - simulations with a flat-bottom setup</li> <li>"2m" - simulations with two lipid membranes, i.e., a double-bilayer setup</li> <li>"popc" - membrane is modeled as a POPC lipid bilayer</li> <li>"mix" - a realistic membrane with various lipids is modeled, resembling the composition from [Lorent et al., Nat. Methods 16, 644–652 (2020)]</li> <li>"nak" - only sodium and potassium cations, together with chloride anions, are present in the system</li> <li>"ext" - as "nak", but also calcium and magnesium cations are added</li> <li>"r9" - as "nak" but also R9 (nona-arginine) peptides are added on both sides of the membrane</li> <li>"r9k" - as "nak" but also R9 (nona-arginine) peptides are added on the extracellular side of the membrane</li> <li>"one" - ions are present only on one side of a lipid membrane</li> <li>"freecl" - flat-bottom simulations but without restraints on chloride anions</li> <li>"s" - simulations were performed using the scaled-charge prosECCo75 force field based on CHARMM [Nencini et al., Biophys. J. 121, 157a (2022)]</li> <li>"restr" - restraint .gro file with ionic/peptide <em>z</em> coordinates set to zero</li> </ul>
Lipid membrane simulations with flat-bottom and double-bilayer setups, part 1/2
<p>To cite: Biriukov, D. and Javanainen, M. Efficient Simulations of Solvent Asymmetry Across Lipid Membranes Using Flat-Bottom Restraints. J. Chem. Theory Comput. 2023, 19 (18), 6332–6341. DOI: <a href="https://doi.org/10.1021/acs.jctc.3c00614">10.1021/acs.jctc.3c00614</a></p> <p>Gromacs molecular dynamics simulations to compare membrane and solvent properties from lipid membrane simulations with flat-bottom and double-bilayer setups. CHARMM36 force field was used except for simulations with peptides, where a prosECCo model was used [Nencini et al., Biophys. J. 121, 157a (2022)]</p> <p>This dataset contains all the topologies and flat-bottom simulation files. The double-bilayer simulation files can be found in part 2: DOI: <a href="https://zenodo.org/record/7974633">10.5281/zenodo.7974633</a></p> <p>Abbreviations in the names of simulation files:</p> <ul> <li>"fb" - simulations with a flat-bottom setup</li> <li>"2m" - simulations with two lipid membranes, i.e., a double-bilayer setup</li> <li>"popc" - membrane is modeled as a POPC lipid bilayer</li> <li>"mix" - a realistic membrane with various lipids is modeled, resembling the composition from [Lorent et al., Nat. Methods 16, 644–652 (2020)]</li> <li>"nak" - only sodium and potassium cations, together with chloride anions, are present in the system</li> <li>"ext" - as "nak", but also calcium and magnesium cations are added</li> <li>"r9" - as "nak" but also R9 (nona-arginine) peptides are added on both sides of the membrane</li> <li>"r9k" - as "nak" but also R9 (nona-arginine) peptides are added on the extracellular side of the membrane</li> <li>"one" - ions are present only on one side of a lipid membrane</li> <li>"freecl" - flat-bottom simulations but without restraints on chloride anions</li> <li>"s" - simulations were performed using the scaled-charge prosECCo75 force field based on CHARMM [Nencini et al., Biophys. J. 121, 157a (2022)]</li> <li>"restr" - restraint .gro file with ionic/peptide <em>z</em> coordinates set to zero</li> </ul> <p> </p>
SBC LTER: Regional Oceanic Modeling System (ROMS) Setup Files, Code, and Lagrangian Model Setup Files
This data contains all the necessary code, grid, forcing, initial, and boundary condition files for running the UCLA version of the Regional Oceanic Modeling System (ROMS) for the Santa Barbara Channel nested solution set that is analyzed in Dauhajre and McWilliams (2019): Nearshore Lagrangian Connectivity: Resolution Sensitivity and Submesoscale Influence. Along with this abstract and separate methods, a brief readme file (README_ROMS) contains other relevant details for running the simulations. The ROMS files in this data correspond to a one-way grid-nesting with the following horizontal resolutions: dx=1km, 300m, 100m, 36m that correspond to R1km, R300m, R100m, and R36m in the publication. In the data directories here, these grids have the following prefixes for all relevant files (grids, forcing, initial condition, and boundary conditions): “usw1” (dx=1km), “usw2” (dx=300m), “usw3” (dx=100m), and “usw4sbc” (dx=36m). The ROMS code is in fortran and requires compilation, with compilation instructions in the directory /src_ROMS_UCLA_2018 in the file “compile.sh”. All grid, forcing, initial, and boundary condition files are in netcdf form. The grid files are in the directory “grids”. Note that the R1km grid has 2 files: usw1_grd.nc and usw1_grd_samp.nc. The latter is a smaller version of the grid that focuses on the Santa Barbara Channel that can be used for the offline Lagrangian model for faster computation (which requires an analogous sub-sampling of the ROMS output). For running ROMS, usw1_grd.nc needs to be used as it corresponds to the entire domain. The atmospheric forcing, derived from a Weather Research and Forecasting model at dx=6km resolution is given in the directory “forcing_files”; all atmospheric forcing files are interpolated to the relevant ROMS grid and formatted to be used as inputs in ROMS. Each grid contains a file corresponding to precipitation (e.g., usw1_prec.nc), radiation (e.g., usw1_rad.nc), atmospheric temperature and specific humidity (
Dataset/Development of a novel calorimetry setup based on metallic paramagnetic temperature sensors
<p>Dataset related to the publication "Development of a novel calorimetry setup based on metallic paramagnetic temperature sensors" submitted to Review of Scientific Instruments.</p>
Dataset for publication "Setup and Characterisation of Reference Current-to-Voltage Transformers for Wideband Current Transformers Calibration up to 2 kA"
<p>This is dataset related to paper published in AMPS 2019:</p> <p>Y. Chen, E. Mohns, H. Badura, P. Räther and M. Luiso: Setup and Characterisation of Reference Current-to-Voltage Transformers for Wideband Current Transformers Calibration up to 2 kA, <a href="https://doi.org/10.7795/EMPIR.17NRM01.CA.20190411">https://doi.org/10.7795/EMPIR.17NRM01.CA.20190411</a></p> <p>Excel file contains the data for Figures 7, 8, 9 and 10.</p>
Shrek Retold Shot 46 Scene Setup
Textures Fingerprints 004 https://www.poliigon.com/texture/fingerprints-004 Smear: https://cc0textures.com/view?id=Smear007 Smudges: https://www.poliigon.com/texture/smudges-large-001 Source: Objaverse 1.0 / Sketchfab
P colonies and P swarms for controlling robot swarms. Experimental setups and demonstration videos
<p>These eleven videos present different experimental scenarios used to test the flexibility and functioning of the LULU P colony/P swarm simulator and of the associated application Lulu_Kilobot for controlling robot swarms. Both applications will be published on Github under an open-source license. The input P colony (input) file, swarm configuration (config) file and V-REP scene (.ttt) are available for each video in the associated .zip archive.</p> <p>The LULU simulator was included as a Python module in Lulu_Kilobot in order to test robot controllers based on P colonies, XP colonies, and P swarms for swarms of up to 10 Kilobot robots.</p> <p>In the following sections, we present a small description for each of the eleven attached videos.</p> <p>-----------------------------------------------------------------------------------------------<br /> 1_clone_10_circle</p> <p>This video demonstrates the use of the robot cloning function of the vrep_bridge script in order to create 9 distinct copies of the source robot and distribute them on a circle around the source robot. The copies are so positioned by a distribution function that can be adapted to other forms. This cloning function allows one to generate large swarms of robots with ease.</p> <p>-----------------------------------------------------------------------------------------------<br /> 2_one_pcolony_for_three_kilobots</p> <p>In this video, we simulate a simple P minus colony using Lulu_Kilobot, on three different robots. At each subtraction, the robots move one step forward. At the beginning of the clip one can see the Robot - P colony association table, where each robot has a distinct copy of the original P colony.</p> <p>-----------------------------------------------------------------------------------------------<br /> 3_pswarm_5_robots_3_colonies</p> <p>This video demonstrates the flexibility offered by the config file of Lulu_Kilobot. From the config file we explicitly specify that the first two robots should use the go straight P colony. For the other colonies, we specify the number of robots that should be assigned, go left = 1 and go right = 2.</p> <p>From the Robot - P colony association table, one can see that the first robot that is assigned a P colony uses the original P colony while the others use an independent copy of the P colony.</p> <p>-----------------------------------------------------------------------------------------------<br /> 4_pswarm_2_robots_avoid_collision</p> <p>In this experiment, we test the msg_distance agent from the input module, by continuously checking the distance from another robot.</p> <p>If the distance is short, then we stop the movement and otherwise continue to subtract f objects from the environment and move forward.</p> <p>Each of the two robots has a different P colony that was designed to check the distance from the other robot (robot_0 checks the distance from robot_1).</p> <p>-----------------------------------------------------------------------------------------------<br /> 5_pswarm_2_robots_xp_colonies_15_steps</p> <p>In this video we employ the exteroceptive communication rules (denoted by <=>) in order to synchronize the movement of two robots. The first robot moves forward 15 steps and after it stops, it signals the second robot to start moving. At this signal, the second robot starts to turn left 15 steps.</p> <p>This shows the utility of exteroceptive rules that allow XP colonies (P colonies with exteroceptive rules) to communicate using the global P swarm environment.</p> <p>-----------------------------------------------------------------------------------------------<br /> 6_1_pswarm_10_robots_disperse_steps_infinite_loop</p> <p>In this video we run a more complex algorithm that involves the use of the following modules: msg_distance, led_rgb, and motion.</p> <p>This video demonstrates dispersion, which is a typical self-deploying scenario in swarm robotics. The robots should position themselves away from one another, so that each robot is at least at a minimum distance from each of its neighbours.</p> <p>All decisions are taken by the command module, on the basis of the received input data from msg_distance. A new direction of motion (and color) is randomly chosen if there are other robots closer than a pre-set threshold distance.</p> <p>-----------------------------------------------------------------------------------------------<br /> 6_2_pswarm_10_robots_optimized_disperse_infinite_loop</p> <p>This video is an optimized version of 6.1 (10 robots disperse).</p> <p>The optimization consists in only exchanging data with V-REP when a new input request is detected in the input agents or likewise a new command object is detected in the output agents.</p> <p>This results in a step-less movement of the robots and reduces the time needed for a new decision to be applied resulting in a faster overall simulation time.</p> <p>-----------------------------------------------------------------------------------------------<br /> 7_pswarm_10_robots_optimized_disperse_infinite_loop_with_intruder</p> <p>In this video we use the previously presented optimized dispersion algorithm (6.2) and introduce an intruder robot into the scene in order to evaluate the influence that this intruder has over the behaviour of the swarm.</p> <p>One can see that robots that have stopped their movement, restart dispersing when the intruder robot is brought close enough. This can cause a chain reaction and ultimately cause the swarm to reposition.</p> <p>-----------------------------------------------------------------------------------------------<br /> 8_1_pswarm_10_robots_secure_disperse_fast</p> <p>In this video we test the proposed security protocol (based on entity authentification and P colony based id check) using the non-optimized dispersion algorithm.</p> <p>In this film we see that the robots ignore the intruder robot even though it is placed in the middle of the swarm. On the other hand, if we bring a swarm member robot close to another swarm member robot, these two will start to disperse normally.</p> <p>-----------------------------------------------------------------------------------------------<br /> 8_2_pswarm_10_robots_secure_disperse_infinite_loop_2</p> <p>In this video we present the optimized (see 6.2 for details) version of the secured dispersion algorithm.</p> <p>As was the case of the secured un-optimized version (8.1), in this secured version we test the influence of the intruder on the behaviour of the swarm by moving the intruder close to the center of the swarm and also moving the intruder close to the swarm after the dispersion is finished. We also note that if two member robots approach, the algorithm continues to work normally.</p> <p>-----------------------------------------------------------------------------------------------<br /> 9_pswarm_10_robots_secure_d_min_disperse_infinite_loop</p> <p>In this clip we show the effects of transparent input data processing.</p> <p>The command agent always requests the smallest distance available from the neighbour list, by using the d_min command.</p> <p>When the intruder robot is the nearest robot (the smallest value in the list) d_min will always return the intruder robot which cannot be processed because it is unknown to the swarm members. For this reason, a member robot that is in this situation will be blocked by the intruder robot.</p>
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.