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309 results for “swarm”
Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot
<p>This video is a supporting material of the paper "Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot". The paper presents a plant root behavior-based approach to defining the control architecture of a plant-root-inspired robot, which is composed of three root-agents for nutrient uptake and one shoot-agent for nutrient redistribution. By taking inspiration and extracting key principles from the uptake of nutrient, movements and communication strategies adopted by plant roots, we developed an uptake–kinetics feedback control for the robotic roots. Exploiting the proposed control, each root is able to regulate the growth direction, towards the nutrients that are most needed, and to adjust nutrient uptake, by decreasing the absorption rate of the most plentiful one. Results from computer simulations and implementation of the proposed control on the robotic platform, Plantoid, demonstrate an emergent swarming behavior aimed at optimizing the internal equilibrium among nutrients through the self-organization of the roots. Plant wellness is improved by dynamically adjusting nutrients priorities only according to local information without the need of a centralized unit delegated for wellness monitoring and task allocation among the agents. Thus, the root-agents can ideally and autonomously grow at the best speed, exploiting nutrient distribution and improving performance, in terms of exploration capabilities and exploitation of resources, with respect to the tropism-inspired control previously proposed by the same authors.</p> <p>The supplementary video (Supplementary Video S1) shows how each agent independently moves according to their internal state and local perception, and the immediate response of the uptake–kinetics mechanism that, as soon as the missing nutrient is inserted in the environment, leads to a decreasing of the imbalance of nutrients in the whole plant.</p>
Swarm-C Neutral Density Data for Large-Scale Wave (LSW) Structures
Open the record for dataset details and reuse information.
3-D Crustal Vp, Vs, Vp/Vs models around the Huoshan earthquake swarm (霍山震群区三维地壳速度与纵横波速度比模型)
<p>3-D Crustal Vp, Vs, Vp/Vs models around the Huoshan earthquake swarm with a grid spacing of 0.33deg*0.33deg.</p> <p>The data format follows "longitude, latitude, depth, Vp, Vp average, Vp perturbation, Vs, Vs average, Vs perturbation".</p> <p>霍山震群区三维地壳速度与纵横波速度比模型,网格间距为0.33°×0.33°。</p> <p>数据格式如下:“经度 纬度 深度 P波绝对速度 P波平均速度 P波速度扰动 S波绝对速度 S波平均速度 S波速度扰动 纵横波速度比”。</p> <p> </p>
Vehicle trajectories collected from a swarm of drones in a Swiss city
<p>This is an open dataset of naturalistic vehicle trajectories that have been collected by two drones flying over the city of Pully, Switzerland. This detailed dataset includes 30 datapoints per second for each vehicle, covering three types: Car, Bus, and Truck.</p> <p><strong>How are the files organized?</strong></p> <p>The name of the file follows the format: DX_TIMEDAYZ_LOCATION</p> <ul> <li>DX - indicates the drone number, either Drone 1 or Drone 2</li> <li>TIMEDAYZ - indicates whether the dataset is collected during morning (AM), noon (NOON) or afternoon (PM) and then Z for the number of session.</li> <li>LOCATION - indicates the location that each drone was flying. As Drone 1 was only in 1 location, this value changes only for Drone 2.</li> </ul> <p><strong>How are the .csv files organized?</strong></p> <div>For each .csv file the following apply:</div> <div> <ul> <li>each row represents one datapoint</li> <li>the first column includes the unique track_id (per file)</li> <li>the second column includes the type of the specific vehicle</li> <li>the third and fourth column includes the longitude and latitude</li> <li>the fifth column includes the local time in ISO 8601 format</li> </ul> </div>
Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".
<p>Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".</p>
Help wanted identifying hams in Swarm-E (e-POP) RRI data
<p>In the spirit of making data from the <a href="https://epop.phys.ucalgary.ca/rri/">Radio Receiver Instrument (RRI)</a> onboard <a href="http://www.esa.int/Our_Activities/Observing_the_Earth/Swarm/Swarm_trio_becomes_a_quartet">Swarm-E</a> (formally known as <a href="https://epop.phys.ucalgary.ca/">e-POP</a>) more accessible to the ham radio community via the Ham Radio Science Citizen Investigation (<a href="http://hamsci.org/about-hamsci">HamSCI</a>), we have converted RRI's data into a ".raw" format so that it can be ingested into open source software such as <a href="http://gqrx.dk/">Gqrx</a> or <a href="https://www.gnuradio.org/">GNU Radio</a>. We have done this for all RRI data related to the 2015, 2017, and 2018 ARRL Field Days.</p> <p>We encourage everyone to help us identify hams in RRI's signal. You can use the Gqrx tool discussed here, or you can use your own technique. If you decode a ham's call sign, if you would like to share your technique, or if you have any comments or suggestion <a href="mailto:perry@phys.ucalgary.ca?subject=HamSCI%3A%20RRI%20ARRL%20%22raw%22%20data%20dump">contact us</a> and let us know! </p> <p><strong>Data Rules of the Road</strong></p> <p>Please acknowledge the HamSCI project and Gareth Perry when using data from this archive in presentations and publications.</p> <p><strong>Swarm-E RRI</strong></p> <p>Swarm-E RRI is a digital radio receiver with 4 3-m monopole antennas. In most cases, the monopoles are electronically configured into a crossed-diople configuration. In this configuration, RRI records I/Q samples for the two dipoles. RRI has a sampling rate of 62500.33933 Hz, and a ~40 kHz bandpass, and can be tuned to anywhere between 10 Hz and 18 MHz. More information on Swarm-E RRI can be found in the <a href="https://link.springer.com/article/10.1007/s11214-014-0130-y">Swarm-E RRI instrument paper</a> or Gareth Perry's recent <a href="http://hamsci.org/article/field-day-cassiope-2015-results-published-radio-science">Radio Science article</a>.</p> <p><strong>Data Format</strong></p> <p>Each data file contains raw 32 bit complex I/Q samples for a given RRI dipole at a given frequency. The samples are interleaved, e.g., IQIQIQIQ... The data files do not contain any metadata. Any information regarding the time, frequency, and corresponding RRI dipole is in the file name. </p> <p><strong>Filename Format</strong></p> <p>The filename format gives information about the time and data of the recording, the tuned frequency, and which of RRI's dipoles the recording corresponds too. For example, <em>gqrx_20150628_011614_3525000_62500_RRI_Dipole1 </em>contains data recorded on Dipole 1, starting at 01:16:14 UT on June 28, 2015, at 3525000 Hz (3.525 MHz), at a sampling rate of 62500 Hz (RRI's 62500.33933 Hz sampling rate).</p> <p><strong>Gqrx</strong></p> <p>We have opted to convert the data into the .raw format so that it can be ingested into <a href="http://gqrx.dk/">Gqrx</a>. There are other ways of analyzing RRI's data; this is just one way which we felt was as easy first step. We are open to posting about other techniques on the HamSCI site as well. To help get started with Gqrx, we have developed a <a href="http://hamsci.org/resource/how-play-rri-raw-iq-file-gqrx"><em>How to play an RRI raw IQ file on Gqrx</em></a> page.</p>
Fig. 2 in Metapolybia araujoi, a new species of swarming social wasp from the Brazilian Amazon rainforest (Vespidae: Polistinae)
Fig. 2. Labels found attached to the specimen.
Figure 8 in Contribution and acclimatization of the swarming tropical copepod Dioithona oculata (Farran, 1913) in a Mediterranean coastal ecosystem
Figure 8. Interannual variations of proportional ratio in female, male, and copepodits in autumn.
Figure 2 in Contribution and acclimatization of the swarming tropical copepod Dioithona oculata (Farran, 1913) in a Mediterranean coastal ecosystem
Figure 2. Seasonal changes of the sea water temperature and salinity values in the study area.
Figure 1 in Contribution and acclimatization of the swarming tropical copepod Dioithona oculata (Farran, 1913) in a Mediterranean coastal ecosystem
Figure 1. Sampling stations.
Figure 5 in Contribution and acclimatization of the swarming tropical copepod Dioithona oculata (Farran, 1913) in a Mediterranean coastal ecosystem
Figure 5. Annual mean abundance of D. oculata in autumn.
Results of 16000 simulations in "Voronoids: Fast and Ordered Swarm Coordinated Motion"
<p>The (CSV) table DataSet contains the aggregate results of the 16000 following simulation cases (one per line), defined by the first ten columns:</p> <p>A) ID: # case identifier</p> <p>B) n=10,20,40,80,160 # number of robots</p> <p>C) S2M=0,0.5 # Stall-to-Maximum, 0 for differential-drive and quadrotor-like robots, 0.5 for fUAV-like robots</p> <p>D) PF=0.25,0.5,0.75,1 # Prudence Factor, max. velocity is PF*r/s (+/- 10%), where r is the communication factor, and s is one second</p> <p>E) OF=1,2 # Overpopulation Factor</p> <p>F) T=0.25,0.5,0.75,1 # high-level control period (+/- 10%)</p> <p>G) FF=1 # Feed-Forward (True or False)</p> <p>H) CC=1,3 # Connectivity Control: 0=None,1=NN,2=MST,3=IMRG,4=Voronoi</p> <p>I) scn=1,2,3,4,5 # Scenario</p> <p>J) rands=0,1,2,3,4 # random seed</p> <p> </p> <p>Two auxiliary columns are added:</p> <p>K) r # communication radius (the arena axis are [-16,16,-9,9]) - physically, r=100 m, for instance, so this parameter defines the space scale of the simulation</p> <p>L) PFxT # Prudence Factor multiplied by control period, fraction of r that, at max. velocity, the robot advances without high-level control</p> <p> </p> <p>From each simulation case, the following aggreagate results, or indices, are obtained:</p> <p> </p> <p>The principal Indices of Quality, given in the following four columns:</p> <p>M) IQA # IQ Area coverage: time-average ratio of uncovered area</p> <p>N) IQN # IQ Nearest-neighbor distance uniformity: time-average of average difference of distances to nearest neighbor wrt. average distance, divided by r</p> <p>O) IQC # IQ collisions: ratio of robots lost by collision</p> <p>P) IQX # IQ connectedness: ratio of robots lost by disconnection</p> <p> </p> <p>Additional indices are given in the following four columns:</p> <p>Q) minD # minimum distance from any robot to anything else during the whole simulation, divided by r</p> <p>R) IQV # IQ Velocity: average advance velocity of robots, divided by max. velocity (approx. PF*r/s)</p> <p>S) SpenTime # time to complete the mission, including 3 s for initial deployment</p> <p>T) SimDuration # time to complete the simulation (in a single core of our cluster)</p>
Catalog of Earthquake Swarms in the Middle America Subduction Zone (2001-2024)
<p>This dataset contains the catalog of earthquake swarms in the Middle America subduction zone from January 2001 to March 2024.</p>
Ground deformation map for the 2011 Hawthorne seismic swarm (Nevada, USA)
<p>This repository contains eight interferograms from RADARSAT-2 and ENVISAT satellites acquired over the March-September, 2011 Hawthorne seismic swarm, Nevada (USA).</p> <p>Five interferograms were obtained by processing the Canadian Space Agency RADARSAT-2 SAR data along one ascending track (incidence angle 35<sup>◦</sup> and heading angle 350<sup>◦</sup>) with GAMMA software.</p> <ul> <li>20110322_HH_20110415_HH.adf.unw.grd</li> <li>20110226_HH_20110415_HH.adf.unw.grd</li> <li>20110322_HH_20110720_HH.adf.unw.grd</li> <li>20110415_HH_20110626_HH.adf.unw.grd</li> <li>20110315_HH_20110526_HH.adf.unw.grd</li> </ul> <p>Three interferograms were obtained by processing the European Space Agency ENVISAT SAR data along one descending track (incidence angle 35<sup>◦</sup> and heading angle -166<sup>◦</sup>) with DORIS software and ISCE software.</p> <ul> <li>20110320_20110618.lld.grd</li> <li>20110419_20110618.lld.grd</li> <li>20110718_20110916_filt_topophase.unw.geo</li> </ul> <p>These interferogram were generated for figures in: Jiang, Y., Samsonov, S. V., and González, P. J. (2021). "Aseismic fault slip nucleation during a shallow normal-faulting seismic swarm constrained using a physically-informed geodetic inversion method. " JGR: Solid Earth.</p>
Integrated automatic design process for robot swarms
<p>Demonstration of the Integrated automatic process for robot swarms in three missions: Aggregation, Foraging, and Migration.<br> The dataset contains the following:<br> 1. SML (Swarm Mission Language) related files: <br> - specification files used to create missions,<br> - generated files to be used by an optimization method <br> 2. AUTOMODE related files:<br> - the log files running AUTOMODE - an optimization method that generates control software for different missions<br> - generated control software <br> 3. Demonstration:<br> - snapshots and videos of running the missions on real robots</p>
Data for the research article: "Detecting Seismo-ionospheric Anomalies Possibly Associated with the 2019 Ridgecrest (California) Earthquakes by GNSS, CSES and Swarm Observations"
<p>The zip archives contain 10 .mat files (MATLAB readable). Each .mat file can be loaded into the MATLAB workspace using the <em>load</em> command.</p>
Data from: Secure and secret cooperation in robot swarms
<p>The importance of swarm robotics systems in both academic research and real-world applications is steadily increasing. However, to reach widespread adoption, new models that ensure the secure cooperation of large groups of robots need to be developed. This work introduces a method to encapsulate cooperative robotic missions in an authenticated data structure known as Merkle tree. With this method, operators can provide the "blueprint" of the swarm's mission without disclosing its raw data. In other words, data verification can be separated from data itself. We propose a system where robots in a swarm, to cooperate towards mission completion, have to "prove'' their integrity to their peers by exchanging cryptographic proofs. We show the implications of this approach for two different swarm robotics missions: foraging and maze formation. In both missions, swarm robots were able to cooperate and carry out sequential tasks without having explicit knowledge about the mission's high-level objectives. The results presented in this work demonstrate the feasibility of using Merkle trees as a cooperation mechanism for swarm robotics systems in both simulation and real-robot experiments, which has implications for future decentralized robotics applications where security plays a crucial role. This dataset includes all experimental data generated for this paper. </p>
The soundscape of swarming: Proof of concept for a non-invasive acoustic species identification of swarming Myotis bats
<p>Bats emit echolocation calls to orientate in their predominantly dark environment. Recording of species-specific calls can facilitate species identification, especially when mist-netting is not feasible. However, some taxa, such as Myotis bats are hard to distinguish acoustically. In crowded situations where calls of many individuals overlap the subtle differences between species are additionally attenuated. Here we sought to non-invasively study the phenology of <em>Myotis</em> bats during autumn swarming at a prominent hibernaculum. To do so we recorded sequences of overlapping echolocation calls (N=564) during nights of high swarming activity and extracted spectral parameters (peak frequency, start frequency, spectral centroid) and Linear Frequency Cepstral Coefficients (LFCCs) which additionally encompass the timbre (vocal 'colour') of calls. We used this parameter combination in a stepwise discriminant function analysis (DFA) to classify the call sequences to species level. A set of previously identified call sequences of single flying <em>Myotis</em> <em>daubentonii</em> and <em>Myotis</em> <em>nattereri</em>, the most common species at our study site, functioned as a training set for the DFA. 90.2% of the call sequences could be assigned to either <em>M</em>. <em>daubentonii</em> or <em>M</em>. <em>nattereri</em>, indicating the predominantly swarming species at the time of recording. We verified our results by correctly classifying a second set of previously identified call sequences with an accuracy of 100%. In addition, our acoustic species classification corresponds well to the existing knowledge on swarming phenology at the hibernaculum. Moreover, we successfully classified call sequences from a different hibernaculum to species level and verified our classification results by capturing swarming bats while we recorded them. Our findings provide the basis for a new non-invasive acoustic monitoring technique that analyses "swarming soundscapes" by combining classical acoustic parameters and LFCCs, instead of analysing single calls. Our approach for species identification is especially beneficial in situations with multiple calling individuals, such as autumn swarming.</p>
Predictive Search Model of Flocking for Quadcopter Swarm in the Presence of Static and Dynamic Obstacles
<p>The folder includes experimental data for the paper titled "Predictive Search Model of Flocking for Quadcopter Swarm in the Presence of Static and Dynamic Obstacles".</p> <p>In the paper, we present a Predictive Search Model (PSM) for flocking with Heading and Speed Shared (HSS) and Heading and Speed Unshared (HSU) prediction methods. We compare the performance of PSM with Potential Field Model (PFM) in the presence of static and dynamic obstacles in simulation. Also, we validate the performance of PSM with a quadcopter swarm indoors.</p> <p>The 'simulation experiments' folder includes simulation experiment data and MATLAB scripts that can simulate the experiments and provide plots for analysis.</p> <p>The 'quadcopter experiments' folder includes quadcopter experiment data and MATLAB scripts that can simulate the experiments and provide plots for analysis.</p>
Radio-Source Tracker: Autonomous Attitude Determination on a Radio Interferometric Swarm: supplementary dataset
<p>This repository contains the data files used to generate the figures presented in the following article:<br> Rouillé, E. et al. (2023) "Radio-Source Tracker: Autonomous Attitude Determination on a Radio Interferometric Swarm", submitted to Radio Science.<br> (Pre-print Arxiv, TBD)</p> <p>The data are the output of various runs of the simulation pipeline.<br> Simulations were run for various interferometer configurations, frequencies, noise levels and random draws.<br> This pipeline and its related package can be found online at the following address:<br> https://gitlab.obspm.fr/erouille/noire_simulation/</p>
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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.