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1,163 results for “demonstration”
Stuck in the mud: experimental taphonomy and computed tomography demonstrate the critical role of sediment in three-dimensional carcass stabilization during early fossil diagenesis - TIFF stack data
Open the record for dataset details and reuse information.
Ranking ecological contingencies from high-order factorial data demonstrate tidy control of biodiversity from facilitation cascades in estuaries on the South Island of New Zealand
Open the record for dataset details and reuse information.
Scatter plot diagram to demonstrate correlation between relative telomere length (RTL) and age.
<p><strong>Figure S1. </strong>Scatter plot diagram to demonstrate correlation between relative telomere length (RTL) and age.</p>
Demonstration of Scientific Workflow Reproducibility with Hyperflow Workflow Management System
<p>This package contains the Experiment Digital Object that allows to reproduce the workflow execution experiment.</p> <p>The content of the package:<br> - Information about the experimental workflow (below).<br> - Experiment execution traces in the form of a dataframe (csv file) with description of the format (below).<br> - Visualization of the execution (png file).<br> - Python script for analysis of the execution trace (generates the visualization). <br> - Instructions describing how to reproduce the experiment (below).</p>
Scatter plot diagram to demonstrate correlation between relative telomere length (RTL) and age.
<p><strong>Figure S1. </strong>Scatter plot diagram to demonstrate correlation between relative telomere length (RTL) and age.</p>
MOOSE Structural Mechanics FEM Demonstration
<p>Demonstration of <a href="https://www.mooseframework.org/">MOOSE</a> finite element workflow for a simple structural mechanics problem in Windows 10 using Windows Subsystem Linux (WSL). Targeted for Windows-based MOOSE users who wish to simply use existing functionality of MOOSE -- not focused on developing / contributing to MOOSE. Datafiles to replicate are provided.</p>
Sonography display demonstrating intra-operative ultrasound imaging guidance for the localization of the foreign body (dental implant) in the soft tissues of the floor of the mouth via navigation with a spinal needle.
<p>This video demonstrates the intraoperative navigation system with using sonography to localize foreign bodies in the soft tissues of the floor of the mouth with the help of a spinal needle</p>
Clean Sky 2. Active Cockpit Ground Demonstrator
<p>Promotional video describing Active Cockpit Ground Demonstrator developed under Clean Sky 2 initiative in Getafe (spain) by airbus DS. The video comprises the aircraft cokpit simulator developed by Airbus DS and ASCENT as well as the WL reduction technologies to be integrted into it (developed by Airbus DS and REACTOR)</p> <p>This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 807097. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union.</p> <p>The results, opinions, conclusions, etc. presented in this work are those of the author(s) only and do not necessarily represent the position of the JU; the JU is not responsible for any use made of the information contained herein.</p>
Datasets for assessment of damage in flat panel and final demonstrator with UoI's sensor and PPI-LT approach
<p>Datasets acquired during experimental assessment of damage in the composite flat panel and final demonstrator by the team of the University of Ioannina, in the context of project CompInnova: An Advanced Methodology for the Inspection and Quantification of Damage on Aerospace Composites and Metals using an Innovative Approach (H2020 FETOPEN, Grant Agreement No. 665238). The PPI-LT approach and dedicated IRT sensor developed within CompInnova were used for recording the data. Data are thermograms in image format (jpg, png).<br> <br> The data were used in deliverables D7.2 & D.7.3.</p> <p> </p>
Data from: Experimental demonstration of the mechanism of steady-state microbunching
<p>A mechanism called steady-state microbunching (SSMB) has been proposed to generate high-repetition, high-power coherent radiation in an electron storage ring. Such a novel phton source will provide unprecedented opportunities for accelerator photon science and technological applications. In this paper, we report the first demonstration of the mechanism of SSMB. This demonstration represents a crucial step towards the implementation of an SSMB-based high-repetition, high-power photon source.</p>
Data from: A demonstration of unsupervised machine learning in species delimitation
One major challenge to delimiting species with genetic data is successfully differentiating population structure from species-level divergence, an issue exacerbated in taxa inhabiting naturally fragmented habitats. Many fields of science are now using machine learning, and in evolutionary biology supervised machine learning has recently been used to infer species boundaries. These supervised methods require training data with associated labels. Conversely, unsupervised machine learning (UML) uses inherent data structure and does not require user-specified training labels, potentially providing more objectivity in species delimitation. Here we demonstrate the utility of three UML approaches (random forests, variational autoencoders, t-distributed stochastic neighbor embedding) for species delimitation in an arachnid taxon with high population genetic structure (Opiliones, Laniatores, Metanonychus). We find that UML approaches successfully cluster samples according to species-level divergences and not high levels of population structure, while model-based validation methods severely over-split putative species. UML offers intuitive data visualization in two-dimensional space, the ability to accommodate various data types, and has potential in many areas of systematic and evolutionary biology. We argue that machine learning methods are ideally suited for species delimitation and may perform well in many natural systems and across taxa with diverse biological characteristics.
The Carbon Data Explorer: Demonstration Video
<p>This video demonstrates usage of the Carbon Data Explorer's web client. The source code for the Carbon Data Explorer can be found on Github (https://github.com/MichiganTechResearchInstitute/CarbonDataExplorer). More details are available on the project web site (http://spatial.mtri.org/flux/).</p>
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>
Quality Analyzer Demonstration
<p>A demonstration video of the Quality Analyzer. It shows the basic functionality of the tool.</p>
Quality Assisted Editor Demonstration
<p>This video shows the basic functionality of the Quality Assisted Editor</p>
Lulu - a software simulator for P colonies. Use case scenarios and demonstration videos
<p>The videos show three different examples of using the Lulu P colony simulator.</p> <p>The Lulu P colony simulator is available under an open-source MIT license at https://github.com/andrei91ro/lulu_pcol_sim. All of the secondary applications, including Lulu_Kilobot are available (also under open-source licenses) at https://github.com/andrei91ro.</p> <p>The first two videos present the simulator running addition (+1) and subtraction (-1). In these two examples, the simulator is ran in a step by step mode in order to clearly visualize the results of running each simulation step. For this reason the total simulation time reported at the end of the simulation is in the order of minutes.</p> <p>The average (of five runs) simulation time for a normal (non-interactive) simulation is 0.0021050 seconds for the addition and 0.0047492 seconds for the subtraction examples.</p> <p>The third example (lulu_kilobot_30_steps) presents the simulator running a more complex P colony that controls a Kilobot robot simulated in V-REP. This P colony is based on the subtraction P colony in the sense that each move the robot makes is marked by the removal of an f object from the environment.</p> <p>The input file used in the addition example (lulu_sim_ag_increment):</p> <p>pi = {<br /> A = {l_p};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, l_p};<br /> B = {AG_1};<br /> AG_1 = ({e, e}; < e->f, e<->l_p >, < l_p->e, f<->e >);<br /> }</p> <p>The input file used in the subtraction example (lulu_sim_ag_decrement):</p> <p>pi = {<br /> A = {l_m, l_p, l_z};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, l_m};<br /> B = {AG_1};<br /> AG_1 = ({e, e};<br /> < e->e, e<->l_m >,<br /> < l_m->l_p, e<->f/e<->e >,<br /> < f->e, l_p<->e >,<br /> < l_p->l_z, e<->e >,<br /> < e->e, l_z<->e > );<br /> }</p> <p>The input file used in the Kilobot example (lulu_kilobot_30_steps):</p> <p>pi = {<br /> A = {l_m, m_0, m_S, m_L, m_R, c_R, c_G, c_B};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, l_m};<br /> B = {AG_command, AG_motion};<br /> AG_command = ({e, e};<br /> < e->e, e<->l_m >,<br /> < l_m->m_S, e<->f/e<->e >,<br /> < f->e, m_S<->e >,<br /> < m_S->m_0, e<->e >,<br /> < e->e, m_0<->e > );</p> <p> AG_motion = ({e, e};<br /> < e->l_m, e<->m_S >,<br /> < m_S->e, l_m<->e/e->e ><br /> < e->e, e<->m_0 >,<br /> < m_0->e, e<->m_0/e->e >);<br /> }</p> <p> </p>
Demonstration videos on using P swarms for deployment tasks in swarm robotics.
<p>In this video we present the capability of the Lulu P colony / P swarn simulator to control a Kilobot robot that is a member of a swarm using a P colony based controller. The swarm application presented here is the dispersion from neighbouring robots, until a certain distance from all neighbours is reached.</p> <p>In the first video entitled kilombo_disperse_1000.avi, we simulated the interactions between 1000 Kilobots using the Kilombo simulator. The i5-4240 CPU used in this test allowed the simulation of 1000 robots, each one controlled by an individual P colony at a peak speed of 29 x real world speed.<br /> <br /> The source code of the P colony used for the dispersion algorithm is also included. This P colony is defined in the input file format accepted by the Lulu P colony / P swarm simulator.</p>
Video Demonstration of an Interactive Visual Analytics Approach to Literature
<p>In this video we are demonstrating our tool prototype for close and distant reading of Victorian novels. The system leverages the Transcendental Cascades Approach to construct networks of character co-occurrence in novels and provides a variety of visualisations. The application area is literature studies as well as university education in the humanities.</p>
Dataset for cgeniepy demonstration
<p>Example data for the paper: "cgeniepy: A Python package for analysing cGENIE Earth System Model output"</p> <p>This file includes</p> <p>(1) the LGM model output from CESM</p> <p>(2) the LGM model output from HadCM3</p> <p>(3) the LGM 13C data compilation from Peterson 2014 Geophysical Research Letter</p> <p>(4) GLODAPv2 temperature data</p> <p>The accuracy behind these data is not guaranteed because this is only for demonstration purpose.</p> <p> </p>
Using Old Laboratory Equipment with Modern Web-of-Things Standards: a Smart Laboratory with LabThings Retro. Supplementary material - Video demonstration
<p>Supplementary Material for 'Using Old Laboratory Equipment with Modern Web-of-Things Standards: a Smart Laboratory with LabThings Retro'. </p><p>Video demonstration for LabThings Retro. </p><p>This video has no sound.</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.