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Abbazia Valtolla - 3D-P - VR navata
Ricostruzione 3D del sito archeologico dell'Abbazia di Valtolla in comune di Morfasso (Piacenza). Esperienza immersiva attravero un visore VR da indossare, con centro nella navata Source: Objaverse 1.0 / Sketchfab
Dataset for article: Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88
<p>Dataset for article:</p> <p>Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88</p>
dataset for article: Ortega-Roldan, B., Rodriguez-Ruiz, S., Perakakis, P., Fernandez-Santaella, M.C., Vila, J. (2014). The emotional and attentional impact of exposure to one’s own body in bulimia nervosa: A physiological view. PLoS ONE 9(7): e102595
<p>dataset for article: Ortega-Roldan, B., Rodriguez-Ruiz, S., Perakakis, P., Fernandez-Santaella, M.C., Vila, J. (2014). The emotional and attentional impact of exposure to one’s own body in bulimia nervosa: A physiological view. PLoS ONE 9(7): e102595</p>
Protein stable isotope fingerprinting (P-SIF): Multidimensional protein chromatography coupled to stable isotope-ratio mass spectrometry
<p>Carbon stable isotope ratios (δ<sup>13</sup>C) for protein fractions extracted from a mixture of cultured cells of <em>Allochromatium vinosum </em>DSM 180 and <em>Synechocystis</em> sp. PCC6803, as well as from extracts of each pure culture.</p> <p>Citation: Mohr W, Tang T, Sattin SR, Bovee RJ, Pearson A. (2014) Protein stable isotope fingerprinting (P-SIF): Multidimensional protein chromatography coupled to stable isotope-ratio mass spectrometry. Analytical Chemistry 86, 8514-8520.</p> <p>Contact: Ann Pearson (pearson@eps.harvard.edu)</p>
Protein stable isotope fingerprinting (P-SIF): Chocolate Pots (Yellowstone) biomass
<p>Stable carbon isotope data for proteins extracted from a Yellowstone microbial mat, Chocolate Pots hot springs.</p>
Protein stable isotope fingerprinting (P-SIF): Mahoney Lake (Canada) 7-m biomass
<p>Carbon isotope data for proteins extracted from a biomass pellet from a sulfide-oxidizing phototroph layer (7m); Mahoney Lake, British Columbia, Canada.</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>
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>
Dataset for article: Luque-Casado, A., Perakakis, P., Ciria, L.F., Sanabria, D. (2016). Transient autonomic responses during sustained attention in high and low fit young adults. Sci. Rep. 6, 27556
<p>Dataset for article:</p> <p>Luque-Casado, A., Perakakis, P., Ciria, L.F., Sanabria, D. (2016). Transient autonomic responses during sustained attention in high and low fit young adults. Sci. Rep. 6, 27556</p>
FIGURES 29 – 30. Pneuminion distribution maps. — 29. P. punctatum. — 30. P in A revision of the South African endemic water beetle genus Pneuminion Perkins (Coleoptera: Hydraenidae)
FIGURES 29 – 30. Pneuminion distribution maps. — 29. P. punctatum. — 30. P. natalensis.
FIGURE 12. Male P. nigra spec. nov, 22. V. 2016 in New species of Protosticta Selys from Vietnam with a key to the males of the P. curiosa group (Odonata: Platystictidae)
FIGURE 12. Male P. nigra spec. nov, 22. V. 2016, Tam Dao National Park, Vinh Phuc Province.
FIGURE 10. Female P. linnaei, 15. V. 2016 in New species of Protosticta Selys from Vietnam with a key to the males of the P. curiosa group (Odonata: Platystictidae)
FIGURE 10. Female P. linnaei, 15. V. 2016, Quang Nam Province.
FIGURE 4. Male P. proboscis spec. nov., 18. V. 2016 in New species of Protosticta Selys from Vietnam with a key to the males of the P. curiosa group (Odonata: Platystictidae)
FIGURE 4. Male P. proboscis spec. nov., 18. V. 2016, Lam Dong Province, paratype before capture.
Figure 8. - Metasternal process (in the circle) and aedeagi of Dicronocephalusadamsidrumonti and Dicronocephalusadamsiadamsi. A, B, C, D Dicronocephalusadamsidrumonti (Tibet) E, F, G, H Dicronocephalusadamsidrumonti (Sichuan) I, J, K, L Dicronocephalusadamsiadamsi (South Korea) M, N, O, P Dicronocephalusadamsiadamsi (North Korea) Q, R, S, T Dicronocephalusadamsiadamsi (Dandong, China).
Figure 8. - Metasternal process (in the circle) and aedeagi of Dicronocephalusadamsidrumonti and Dicronocephalusadamsiadamsi. A, B, C, D Dicronocephalusadamsidrumonti (Tibet) E, F, G, H Dicronocephalusadamsidrumonti (Sichuan) I, J, K, L Dicronocephalusadamsiadamsi (South Korea) M, N, O, P Dicronocephalusadamsiadamsi (North Korea) Q, R, S, T Dicronocephalusadamsiadamsi (Dandong, China).
P. aeruginosa expression compendium for the PAO1 KEGG pathways case study in PathCORE.
<p>The normalized P. aeruginosa (PAO1) gene expression compendium uses datasets from ArrayExpress. Each gene expression vector is linearly normalized to the range between 0 and 1.</p> <p>The script used to download the PAO1 datasets and process all samples into a single expression compendium is available here: https://bitbucket.org/greenelab/eadage/src/d8a98586e0f448a5b7d497f5516cc4e241635361/data_collection/data_collection.sh</p>
Phytochemical Analysis and Antimicrobial Studies of Leaves and Roots of P. angulata
<p><strong>Quantifying the bioactive compounds present in plant parts (leaves and roots). This study aimed to perform comprehensive qualitative phytochemical analysis and antimicrobial analysis of extract of leaves and roots of p.angulata. phytochemicals test where carried out on the extracts to detect the presence of phytochemicals such as phenols, flavonoids, saponins, alkaloids, tannins and terpenoids etc. Different chemical tests were employed to confirm the presence of these compounds. The color change indicates the presence of specific phytochemicals. The results obtained from the qualitative analysis revealed the presence of phenols, flavonoids, saponins, alkaloids, tannins, terpenoids, phlobatannins, anthraquinones, glycosides and steroids in both the leaves and roots part of the plant. However the results of the antimicrobial test obtained from the extracts of both leaves and roots extracts of </strong><i><strong>Physalis angulata</strong></i><strong> revealed that both the crude extracts of the leaves and roots inhibited Anti-microbial activities against </strong><i><strong>Bacillus species,</strong></i><strong> </strong><i><strong>Styphylococcus aureus</strong></i><strong>, Plasmodium parasites and Streptococcus species except the leaves extract of acetone which does not act against </strong><i><strong>Styphylococcus aureus</strong></i><strong>. The facts that the plant was active against laboratory isolates are also an indication that the plant parts has medicinal values and Potential therapeutic or pharmacological properties. The findings of this analysis can be further utilized for developing drugs, functional foods, or dietary supplements with specific phytochemical.</strong></p>
Thermal and dynamo evolution of the lunar core based on transport properties of Fe-S-P alloys
<p>These data are our original measured resistivity data and calculation data. </p>
Performance Measurement Dataset of the HPC Benchmarks FASTEST, Kripke, LULESH, MiniFE, Quicksilver, and RELeARN for Scalability Studies with Extra-P
<p>This dataset contains performance measurements of the HPC benchmarks FASTEST, Kripke, LULESH, MiniFE, Quicksilver, and RELeARN intended to be used for scalability studies with Extra-P (https://github.com/extra-p/extrap). The datasets contains measurements of various application configurations considering several model parameters, e.g., the number of MPI ranks and the input problem size, using weak scaling for each benchmark.</p>
Fig. 1 in Zur Kenntnis der Scirtidae des Iran mit Beschreibung je einer neuen Art aus den Gattungen Cyphon P , 1799 und Elodes L , 1796 (Coleoptera) (175. Beitrag zur Kenntnis der Scirtidae)
Fig. 1: Cyphon pareuoplus n. sp., Habitus, dorsal.
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.