Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
427
datasets available to search
ShareScore release 0.7.1
Dataset results
427 results for “modularity”
Simultaneous integration and modularity underlie the exceptional body shape diversification of characiform fishes
<p class="MsoNormal">Evolutionary biology has long striven to understand why some lineages diversify exceptionally while others do not. Most studies have focused on how extrinsic factors can promote differences in diversification dynamics, but a clade's intrinsic modularity and integration can also catalyze or restrict its evolution. Here, we integrate geometric morphometrics, phylogenetic comparative methods and visualizations of covariance to infer the presence of distinct modules in the body plan of Characiformes, an ecomorphologically diverse fish radiation. Strong covariances reveal a cranial module, and more subtle patterns support a statistically significant subdivision of the postcranium into anterior (precaudal) and posterior (caudal) modules. We uncover substantial covariation among cranial and postcranial landmarks, indicating body-wide evolutionary integration as lineages transition between compressiform and fusiform body shapes. A novel method of matrix subdivision reveals that within- and among-module covariation contributes substantially to the overall eigenstructure of characiform morphospace, and that both phenomena led to biologically important divergence among characiform lineages. Functional integration between the cranium and post-cranial skeleton appears to have allowed lineages to optimize the aspect ratio of their bodies for locomotion, while the capacity for independent change in the head, body and tail likely eased adaptation to diverse dietary and hydrological regimes. These results reinforce a growing consensus that modularity and integration synergize to promote diversification. </p>
Data from: Whole-body variational modularity in the zebrafish: An inside-out story of a model species
<p><span>Actinopterygians are the most diversified clade of extant vertebrates. Their impressive morphological disparity bears witness to tremendous ecological diversity. Modularity, the organization of biological systems into quasi-independent anatomical/morphological units, is thought to increase evolvability of organisms and facilitate morphological diversification. Our study aims to quantify patterns of variational modularity in a model actinopterygian, the zebrafish (</span><em>Danio rerio</em>), using 3D-geometric morphometrics on osteological structures isolated from micro-CT scans. 72 landmarks were digitised along cranial and postcranial ossified regions of 30 adult zebrafishes. Two methods were used to test modularity hypotheses, the covariance ratio and the distance matrix approach. We find strong support for two modules, one comprised of paired fins and the other comprised of median fins, that are best explained by functional properties of subcarangiform swimming. While the skull is tightly integrated with the rest of the body, its intrinsic integration is relatively weak supporting previous findings that the fish skull is a modular structure. Our results provide additional support for the recognition of similar hypotheses of modularity identified based on external morphology in various teleosts, and at least two variational modules are proposed. Thus, our results hint at the possibility that internal and external modularity patterns may be congruent.</p>
Modular Differential Evolution: Supplementary Material
<p>This repository contains the code and data for reproducibility of the paper 'Modular Differential Evolution'. </p> <p>The following files are included:</p> <p>- Modde.zip: A zip-file of the version of the modular differential evolution code used throughout the paper. This is a static version of the github repository (not linked now to preserve anonymity). This can be installed using pip; installation is needed for the data collection script</p> <p>- Irace_files: A zip-file containing all relevant settings and code for running irace to tune on BBOB, which can be processed into the set of elites (irace_elites_modde.csv).</p> <p>- *_runner: Python files which runs each elite and each common + single module DE on the required instances. Resulting IOH-files are included in the 'raw_data' zipfile</p> <p>- R files: code which takes the raw IOH data and turns it into the relevant csv-files which are used to create the figures</p> <p>- Visualization*: notebooks which are used to generate the figures from the papers</p>
Modular nanomagnet design for spin qubits confined in a linear chain
<p>supporting data</p>
Small Modular Reactor Neutronics Challenge Problem Input Files for OpenMC
<p>OpenMC is part of the small modular reactor (SMR) simulation subproject "ExaSMR" in the broader Exascale Computing Project (ECP), which is a US DOE project. The goal of the ExaSMR project is to enable extreme-fidelity simulation of SMRs, which have been a recent focus of engineering development. These reactors are of great interest due to their ability to economically provide consistent baseload power while avoiding many of the downsides of traditional (large) reactor designs.</p> <p>Part of the ExaSMR project was to define a representative challenge problem. The problem defined in this input dataset to OpenMC is a realistic SMR geometry that represents the NuScale reactor design recently licensed by the US Nuclear Regulatory Commission. The SMR problem is also defined with the goal of depletion calculations in mind, where hundreds of nuclides are present in the reactor fuel, and isoptic content lists are maintained for hundreds of thousands of unique fuel burnup regions throughout the reactor core. While the SMR problem is defined in-depth in Reference [1], we give an overview of additional key input parameters below that illustrate the difficulty of the simulation:<br> </p> <table> <thead> <tr> <th scope="col">Parameter</th> <th scope="col">Value</th> </tr> </thead> <tbody> <tr> <td>Number of unique materials</td> <td>195,368</td> </tr> <tr> <td>Number of unique fuel materials</td> <td>195,360</td> </tr> <tr> <td>Number of depletion tally regions</td> <td>195,360</td> </tr> <tr> <td>Number of nuclides in each fuel region</td> <td>244</td> </tr> <tr> <td>Number of nuclides in simulation</td> <td>281</td> </tr> </tbody> </table> <p>Overall, the ExaSMR challenge problem forms an ideal basis for performance analysis due to its relevance to the nuclear industry and its prominence as a high performance computing "hero problem" as part of ECP. For these reasons, we will use the SMR problem as the main basis of our performance analysis and scaling challenges.</p> <p>The model is also available in python format at: <a href="https://github.com/mit-crpg/ecp-benchmarks/tree/fom-model">https://github.com/mit-crpg/ecp-benchmarks/tree/fom-model</a></p> <p>References:</p> <p>[1] Kord Smith. 2017. NuScale Small Modular Reactor (SMR) Progression Problems for the ExaSMR Project. Milestone Report WBS 1.2.1.08 ECP-SE-08-43. Exascale Computing Project.</p>
Free Lunch in Evolutionary Embodied Computation in Modular Robotics
<p>We demonstrate, based on anecdotal experimental results, that physical constraints (e.g., in physics-based simulations of evolutionary robotics) can significantly increase the diversity of results obtained by evolutionary computation methods.</p>
Cactus height increases the modularity of a plant-frugivore network in the Caatinga dry forest
<p class="MsoNormal"><span>Cacti fruits are key resources to many frugivorous animals in Neotropical arid and semiarid regions. However, most studies have focused on a particular animal group or cacti species, but few have explored the overall interactions of such species at the community level. Here we monitored frugivory on five cacti species using camera traps that sampled diurnal and nocturnal interactions. We investigated the structure of interactions with bird, mammal, and reptile frugivores in the Brazilian Caatinga dry forest. We hypothesized that the height of cacti limit interactions with different types of frugivores, which would result in highly structured and modular interaction networks. In 2,929 camera-days, we recorded 23 vertebrate species feeding on cacti fruits, including seven new records, all determined to be primary seed dispersers. As predicted, the cacti-frugivore network was modular and non-nested, with the two shortest cacti species grouped in a module dominated by interactions with reptiles and non-flying mammals. The tallest cacti species were dominated by frugivory interactions with birds and had comparatively less interaction diversity than shorter cacti species. Our results support the contention that cacti are keystone species in semiarid ecosystems where they produce small-seeded fleshy fruits year-round.</span></p>
Data for: A Modular Double Electrode Flow Cell with Exchangeable Generator and Detector Electrodes
<p>Raw data and processed data shown in figures of the publication titled:</p> <p>"A Modular Double Electrode Flow Cell with Exchangeable Generator and Detector Electrodes"</p> <p>DOI: <a href="https://doi.org/10.1002/celc.202300126">10.1002/celc.202300126</a></p> <p>by</p> <p>Frederik J. Stender<sup>[a]</sup>, Keisuke Obata<sup>[b]</sup>, Max Baumung<sup>[a,c]</sup>, Fatwa F. Abdi<sup>[b]</sup>, Marcel Risch<sup>[a,c]</sup></p> <p>[a] Frederik Johannes Stender, Max Baumung, Dr. Marcel Risch<br> Institut für Material Physik<br> Georg-August-Universität Göttingen<br> Friedrich-Hund-Platz 1, 37085 Göttingen<br> E-mail: mrisch@material.physik.uni-goettingen.de</p> <p>[b] Dr. Keisuke Obata, Dr. Fatwa Firdaus Abdi<br> Institut für Solare Brennstoffe<br> Helmholtz-Zentrum Berlin für Materialien und Energie GmbH<br> Hahn-Meitner-Platz 1, 14109 Berlin</p> <p>[c] Dr. Marcel Risch<br> Nachwuchsgruppe Gestaltung des Sauerstoffentwicklungsmechanismus<br> Helmholtz-Zentrum Berlin für Materialien und Energie GmbH<br> Hahn-Meitner-Platz 1, 14109 Berlin<br> E-mail: marcel.risch@helmholtz-berlin.de</p>
ROS 2 Bags acquired from AgRob Modular-e during the IV SCORPION Integration (Aymavilles, Italy)
<p>ROS 2 Bags acquired with the AgRob Modular-e robot during the IV SCORPION Integration in Cave des Onze Communes vineyard (Aymavilles, Italy), between 13 and 15 June 2023.<br><br>Main topics available include:<br>- Robot odometry (/odom) [nav_msgs/msg/Odometry]<br>- Redshift UM7 IMU data (/eut_sensors/imu/data) [sensor_msgs/msg/Imu]<br>- RS-LIDAR 3D scans (/rslidar_points) [sensor_msgs/msg/PointCloud2]<br>- Livox MID-70 scans in proprietary format (/livox/lidar) [livox_ros_driver/msg/CustomMsg]<br>- U-Blox ZED-F9P base receiver GNSS coordinates (/base/fix) [sensor_msgs/msg/NavSatFix]<br>- U-Blox ZED-F9P heading data from base-rover moving baseline setup (/rover/navheading) [sensor_msgs/msg/Imu]<br><br>Datasets description:<br><br>2023-06-13T161127Z - Robot navigating across two crop rows, closing a loop, with RS-LIDAR, Livox MID-70 and IMU data available.<br>2023-06-13T170852Z - Same as previous, but with GNSS data from SCORPION receiver (no data from U-Blox)<br>2023-06-13T175518Z - Robot navigating across three crop rows, forming an 8. No IMU or odometry data available.<br>2023-06-14T174658Z - Robot navigating along five crop rows with Livox data, base+rover GNSS, IMU and wheel odometry.<br>2023-06-15T110725Z - Robot navigating along four crop rows (forming loops), with RS-LIDAR data, base+rover GNSS, IMU and wheel odometry. Dataset interrupted in the middle of last row when returning to base.<br>2023-06-15T110725Z - Robot navigating along three crop rows (forming an 8), with RS-LIDAR data, base+rover GNSS, IMU and wheel odometry.<br><br>The measured transformations (estimated) between robot and sensor frames are found below (format: [x y z yaw pitch roll]). Please note these are not recorded with the datasets and must be included as static transforms using the tf2_ros package.</p><p>"base_footprint" to "imu_link": ["-0.05", "0", "0.759", "0", "0", "0"]<br>"imu_link" to "rslidar": ["0", "0", "-0.05464", "3.14159", "0", "0"]<br>"imu_link" to "livox_frame": ["0.0698", "0", "-0.14464", "0", "0", "0"]<br>"imu_link" to "gps_base": ["-0.025", "0", "0.27396", "3.14159", "0.0", "0"]<br>"gps_base" to "gps_rover": ["0.7", "0", "0", "0", "0", "0"]</p>
Data from: Lower jaw modularity in the African Clawed Frog (Xenopus laevis) and Fire Salamander (Salamandra salamandra gigliolii)
<p>Modularity describes the degree to which the components of complex phenotypes vary semi-autonomously due to developmental, genetic, and functional correlations. This is a key feature underlying the potential for evolvability, as it can allow individual components to respond to different selective pressures semi-independently. The vertebrate lower jaw has become a model anatomical system for understanding modularity, but to date, most of this work has focused on the mandible of mammals and other amniotes. In contrast, modularity in the mandible of lissamphibians has been less well-studied. Here, we used geometric morphometrics to quantify the static (intraspecific) modularity patterns in <em>Xenopus laevis</em> and <em>Salamandra salamandra gigliolii.</em> We tested developmental and functional hypotheses of modularity and demonstrate that both species exhibit significant modularity. Functional modularity was supported in <em>Xenopus</em>, yet the lack of definitive support for both the developmental and functional hypotheses in <em>Salamandra</em> suggests influences on modularity are much more complex. Allometry has a small yet significant impact on lower jaw shape in both taxa and sex has a significant effect on shape in <em>Xenopus</em>. The high modularity seen in both species mimics the results of other studies on the amphibian cranium, suggesting that modularity is a ubiquitous feature of the tetrapod jaw.</p>
All sensors from AgRob Modular E recorded in Vineyard at Quinta do Seixo (Tabuaço - Portugal)
<p>There are three bag files here recording all topics of the Robot AgRob Modular-E. The bags have no Image data, but they have 3D LIDAR and RTK GNSS. All the bags were recorded at Quinta do Seixo (Tabuaço, Portugal) in 3 distinct locations of the steep slope vineyard:<br> <br> rosbag2_2023_07_27-19_23_35 - Recorded in this area: 41.16631864403413, -7.555086219643922</p> <p>rosbag2_2023_07_28-12_52_03 - Recorded in this area: 41.166966,-7.552726</p> <p>rosbag2_2023_07_28-16_30_46 - Recorded in this area: 41.166814,-7.552784</p> <p> </p> <p>Here are the topics off the rosbag files:<br> <br> Topic information: Topic: /rover/aidalm | Type: ublox_msgs/msg/AidALM<br> Topic: /rover/monhw | Type: ublox_msgs/msg/MonHW<br> Topic: /rover/navpvt | Type: ublox_msgs/msg/NavPVT<br> Topic: /rover/navheading | Type: sensor_msgs/msg/Imu<br> Topic: /rover/diagnostics | Type: diagnostic_msgs/msg/DiagnosticArray<br> Topic: /rslidar_scan | Type: sensor_msgs/msg/LaserScan<br> Topic: /teleop/cmd_vel | Type: geometry_msgs/msg/Twist<br> Topic: /robot_electrical_data | Type: itrci_hardware/msg/RobotElectricalData<br> Topic: /mowit_scissors_motor_driver_node/duty_cycle_ref | Type: std_msgs/msg/Int32<br> Topic: /motors_outputs | Type: itrci_hardware/msg/MotorsArrayOutput<br> Topic: /rover/fix | Type: sensor_msgs/msg/NavSatFix<br> Topic: /localization_stars_link/gnss_heading | Type: visualization_msgs/msg/Marker<br> Topic: /imu/data_raw | Type: sensor_msgs/msg/Imu<br> Topic: /parametric_trajectories_editor_node/update | Type: visualization_msgs/msg/InteractiveMarkerUpdate<br> Topic: /rover/navrelposned | Type: ublox_msgs/msg/NavRELPOSNED9<br> Topic: /rover/navclock | Type: ublox_msgs/msg/NavCLOCK<br> Topic: /spraying/cmd_vel | Type: geometry_msgs/msg/Twist<br> Topic: /joy | Type: sensor_msgs/msg/Joy<br> Topic: /navigation_current_state | Type: std_msgs/msg/String<br> Topic: /base/navstatus | Type: ublox_msgs/msg/NavSTATUS<br> Topic: /tf | Type: tf2_msgs/msg/TFMessage<br> Topic: /parametric_trajectories_control_node/Point1 | Type: visualization_msgs/msg/Marker<br> Topic: /base/rxmrtcm | Type: ublox_msgs/msg/RxmRTCM<br> Topic: /path_it | Type: std_msgs/msg/Float64<br> Topic: /parametric_trajectories_control_node/Point2 | Type: visualization_msgs/msg/Marker<br> Topic: /base/monhw | Type: ublox_msgs/msg/MonHW<br> Topic: /motors_speed_ref | Type: itrci_hardware/msg/MotorsArrayInput<br> Topic: /base/navstate | Type: ublox_msgs/msg/NavSAT<br> Topic: /control_status | Type: itrci_nav/msg/ParametricTrajectoriesControlStatus<br> Topic: /sprayer_prescription | Type: std_msgs/msg/String<br> Topic: /parameter_events | Type: rcl_interfaces/msg/ParameterEvent<br> Topic: /PathWithEndOffset | Type: itrci_nav/msg/ParametricPathSetWithEndOffset<br> Topic: /followme_current_state | Type: std_msgs/msg/String<br> Topic: /base/navsvin | Type: ublox_msgs/msg/NavSVIN<br> Topic: /base/navrelposned | Type: ublox_msgs/msg/NavRELPOSNED9<br> Topic: /rosout | Type: rcl_interfaces/msg/Log<br> Topic: /base/aideph | Type: ublox_msgs/msg/AidEPH<br> Topic: /BackLaserScanObstacles | Type: sensor_msgs/msg/LaserScan<br> Topic: /path_num | Type: std_msgs/msg/Float64<br> Topic: /base/diagnostics | Type: diagnostic_msgs/msg/DiagnosticArray<br> Topic: /rover/rxmrawx | Type: ublox_msgs/msg/RxmRAWX<br> Topic: /base/fix_velocity | Type: geometry_msgs/msg/TwistWithCovarianceStamped<br> Topic: /rover/rxmrtcm | Type: ublox_msgs/msg/RxmRTCM<br> Topic: /rover/fix_velocity | Type: geometry_msgs/msg/TwistWithCovarianceStamped<br> Topic: /base/navclock | Type: ublox_msgs/msg/NavCLOCK<br> Topic: /ControlPathData | Type: geometry_msgs/msg/PoseStamped<br> Topic: /base/navheading | Type: sensor_msgs/msg/Imu<br> Topic: /path_percent_done | Type: std_msgs/msg/Float64<br> Topic: /base/fix | Type: sensor_msgs/msg/NavSatFix<br> Topic: /TrajectoryControlCommand | Type: std_msgs/msg/String<br> Topic: /motion_current_state | Type: std_msgs/msg/String<br> Topic: /cmd_vel | Type: geometry_msgs/msg/Twist<br> Topic: /parametric_trajectories_control_node/Patch | Type: visualization_msgs/msg/MarkerArray<br> Topic: /position_error | Type: std_msgs/msg/Float64<br> Topic: /debug/TimeLoadNSec | Type: std_msgs/msg/Float64<br> Topic: /laser_security_area | Type: visualization_msgs/msg/Marker<br> Topic: /base/navpvt | Type: ublox_msgs/msg/NavPVT<br> Topic: /pose_goal | Type: geometry_msgs/msg/PoseStamped<br> Topic: /safety_current_state | Type: std_msgs/msg/String<br> Topic: /rover/navsvin | Type: ublox_msgs/msg/NavSVIN<br> Topic: /rover/navstatus | Type: ublox_msgs/msg/NavSTATUS<br> Topic: /Detections | Type: vision_msgs/msg/Detection2DArray<br> Topic: /ActualPath | Type: itrci_nav/msg/ParametricPathSet<br> Topic: /splines/cmd_vel | Type: geometry_msgs/msg/Twist<br> Topic: /orientation_error | Type: std_msgs/msg/Float64<br> Topic: /tf_static | Type: tf2_msgs/msg/TFMessage<br> Topic: /PathNoStackUp2 | Type: itrci_nav/msg/ParametricPathSet2<br> Topic: /localization_stars_link/fix_pos | Type: geometry_msgs/msg/PointStamped<br> Topic: /TrajectoryControlStatus | Type: std_msgs/msg/String<br> Topic: /base/rxmrawx | Type: ublox_msgs/msg/RxmRAWX<br> Topic: /PathNoStackUp | Type: itrci_nav/msg/ParametricPathSet<br> Topic: /joy/set_feedback | Type: sensor_msgs/msg/JoyFeedback<br> Topic: /position_goal | Type: geometry_msgs/msg/PoseStamped<br> Topic: /rover/aideph | Type: ublox_msgs/msg/AidEPH<br> Topic: /FrontLaserScanObstacles | Type: sensor_msgs/msg/LaserScan<br> Topic: /rover/navstate | Type: ublox_msgs/msg/NavSAT<br> Topic: /parametric_trajectories_editor_node/feedback | Type: visualization_msgs/msg/InteractiveMarkerFeedback<br> Topic: /imu/rpy | Type: geometry_msgs/msg/Vector3Stamped<br> Topic: /base/aidalm | Type: ublox_msgs/msg/AidALM<br> Topic: /Path | Type: itrci_nav/msg/ParametricPathSet<br> Topic: /LaserScanObstacles | Type: sensor_msgs/msg/LaserScan<br> Topic: /localization_stars_link/gnss_pose | Type: geometry_msgs/msg/PoseWithCovarianceStamped<br> Topic: /ControlPathDataFeedForward | Type: geometry_msgs/msg/PoseStamped<br> Topic: /localization_stars_link/nav_pos_rel | Type: geometry_msgs/msg/PointStamped<br> Topic: /safety_system_flag | Type: std_msgs/msg/Int16<br> Topic: /odom | Type: nav_msgs/msg/Odometry<br> Topic: /rslidar_points | Type: sensor_msgs/msg/PointCloud2<br> Topic: /debug/TimeCycleSec | Type: std_msgs/msg/Float64<br> Topic: /localization_stars_link/magnetometer_heading | Type: visualization_msgs/msg/Marker<br> </p>
Bags Recorded with MowIt AgRob Modular E Robot in vineyard at Terras Gauda
<p>Bags Recorded @Terra Gauda Vineyard in Spain (41.93816100127097, -8.791314972313891) with all the sensors available in the robot:</p> <p> </p> <ol> <li>Topic: /teleop/cmd_vel | Type: geometry_msgs/msg/Twist</li> <li>Topic: /robot_electrical_data | Type: itrci_hardware/msg/RobotElectricalData</li> <li>Topic: /safety_system_flag | Type: std_msgs/msg/Int16</li> <li>Topic: /clock | Type: rosgraph_msgs/msg/Clock</li> <li>Topic: /map_server/transition_event | Type: lifecycle_msgs/msg/TransitionEvent</li> <li>Topic: /navigation_current_state | Type: std_msgs/msg/String</li> <li>Topic: /spraying/cmd_vel | Type: geometry_msgs/msg/Twist</li> <li>Topic: /joy | Type: sensor_msgs/msg/Joy</li> <li>Topic: /rover/navclock | Type: ublox_msgs/msg/NavCLOCK</li> <li>Topic: /laser_security_area | Type: visualization_msgs/msg/Marker</li> <li>Topic: /debug/TimeLoadNSec | Type: std_msgs/msg/Float64</li> <li>Topic: /PathWithEndOffset | Type: itrci_nav/msg/ParametricPathSetWithEndOffset</li> <li>Topic: /Detections | Type: vision_msgs/msg/Detection2DArray</li> <li>Topic: /rover/navsvin | Type: ublox_msgs/msg/NavSVIN</li> <li>Topic: /ActualPath | Type: itrci_nav/msg/ParametricPathSet</li> <li>Topic: /rover/navstatus | Type: ublox_msgs/msg/NavSTATUS</li> <li>Topic: /splines/cmd_vel | Type: geometry_msgs/msg/Twist</li> <li>Topic: /base/navpvt | Type: ublox_msgs/msg/NavPVT</li> <li>Topic: /pose_goal | Type: geometry_msgs/msg/PoseStamped</li> <li>Topic: /base/navsvin | Type: ublox_msgs/msg/NavSVIN</li> <li>Topic: /imu/data_raw | Type: sensor_msgs/msg/Imu</li> <li>Topic: /orientation_error | Type: std_msgs/msg/Float64</li> <li>Topic: /Path | Type: itrci_nav/msg/ParametricPathSet</li> <li>Topic: /base/aidalm | Type: ublox_msgs/msg/AidALM</li> <li>Topic: /imu/rpy | Type: geometry_msgs/msg/Vector3Stamped</li> <li>Topic: /FrontLaserScanObstacles | Type: sensor_msgs/msg/LaserScan</li> <li>Topic: /rover/aideph | Type: ublox_msgs/msg/AidEPH</li> <li>Topic: /base/diagnostics | Type: diagnostic_msgs/msg/DiagnosticArray</li> <li>Topic: /BackLaserScanObstacles | Type: sensor_msgs/msg/LaserScan</li> <li>Topic: /base/aideph | Type: ublox_msgs/msg/AidEPH</li> <li>Topic: /LaserScanObstacles | Type: sensor_msgs/msg/LaserScan</li> <li>Topic: /localization_stars_link/gnss_pose | Type: geometry_msgs/msg/PoseWithCovarianceStamped</li> <li>Topic: /cmd_vel | Type: geometry_msgs/msg/Twist</li> <li>Topic: /parametric_trajectories_control_node/Patch | Type: visualization_msgs/msg/MarkerArray</li> <li>Topic: /path_percent_done | Type: std_msgs/msg/Float64</li> <li>Topic: /rslidar_scan | Type: sensor_msgs/msg/LaserScan</li> <li>Topic: /odom | Type: nav_msgs/msg/Odometry</li> <li>Topic: /goal_pose | Type: geometry_msgs/msg/PoseStamped</li> <li>Topic: /path_it | Type: std_msgs/msg/Float64</li> <li>Topic: /sprayer_prescription | Type: std_msgs/msg/String</li> <li>Topic: /control_status | Type: itrci_nav/msg/ParametricTrajectoriesControlStatus</li> <li>Topic: /rslidar_points | Type: sensor_msgs/msg/PointCloud2</li> <li>Topic: /base/rxmrawx | Type: ublox_msgs/msg/RxmRAWX</li> <li>Topic: /TrajectoryControlStatus | Type: std_msgs/msg/String</li> <li>Topic: /PathNoStackUp | Type: itrci_nav/msg/ParametricPathSet</li> <li>Topic: /localization_stars_link/fix_pos | Type: geometry_msgs/msg/PointStamped</li> <li>Topic: /rover/navrelposned | Type: ublox_msgs/msg/NavRELPOSNED9</li> <li>Topic: /parametric_trajectories_editor_node/update | Type: visualization_msgs/msg/InteractiveMarkerUpdate</li> <li>Topic: /base/navstatus | Type: ublox_msgs/msg/NavSTATUS</li> <li>Topic: /parameter_events | Type: rcl_interfaces/msg/ParameterEvent</li> <li>Topic: /PathNoStackUp2 | Type: itrci_nav/msg/ParametricPathSet2</li> <li>Topic: /tf_static | Type: tf2_msgs/msg/TFMessage</li> <li>Topic: /motion_current_state | Type: std_msgs/msg/String</li> <li>Topic: /base/fix | Type: sensor_msgs/msg/NavSatFix</li> <li>Topic: /TrajectoryControlCommand | Type: std_msgs/msg/String</li> <li>Topic: /clicked_point | Type: geometry_msgs/msg/PointStamped</li> <li>Topic: /motors_speed_ref | Type: itrci_hardware/msg/MotorsArrayInput</li> <li>Topic: /base/navstate | Type: ublox_msgs/msg/NavSAT</li> <li>Topic: /base/monhw | Type: ublox_msgs/msg/MonHW</li> <li>Topic: /rosout | Type: rcl_interfaces/msg/Log</li> <li>Topic: /base/navrelposned | Type: ublox_msgs/msg/NavRELPOSNED9</li> <li>Topic: /ControlPathDataFeedForward | Type: geometry_msgs/msg/PoseStamped</li> <li>Topic: /localization_stars_link/nav_pos_rel | Type: geometry_msgs/msg/PointStamped</li> <li>Topic: /battery_current | Type: std_msgs/msg/Int32</li> <li>Topic: /localization_stars_link/gnss_heading | Type: visualization_msgs/msg/Marker</li> <li>Topic: /rover/fix | Type: sensor_msgs/msg/NavSatFix</li> <li>Topic: /initialpose | Type: geometry_msgs/msg/PoseWithCovarianceStamped</li> <li>Topic: /debug/TimeCycleSec | Type: std_msgs/msg/Float64</li> <li>Topic: /localization_stars_link/magnetometer_heading | Type: visualization_msgs/msg/Marker</li> <li>Topic: /map/prescription | Type: nav_msgs/msg/OccupancyGrid</li> <li>Topic: /localization_stars_link/magnetometer_heading_array | Type: visualization_msgs/msg/MarkerArray</li> <li>Topic: /joy/set_feedback | Type: sensor_msgs/msg/JoyFeedback</li> <li>Topic: /position_goal | Type: geometry_msgs/msg/PoseStamped</li> <li>Topic: /motors_outputs | Type: itrci_hardware/msg/MotorsArrayOutput</li> <li>Topic: /mowit_scissors_motor_driver_node/duty_cycle_ref | Type: std_msgs/msg/Int32</li> <li>Topic: /localization_stars_link/gnss_heading_array | Type: visualization_msgs/msg/MarkerArray</li> <li>Topic: /rover/rxmrawx | Type: ublox_msgs/msg/RxmRAWX</li> <li>Topic: /base/fix_velocity | Type: geometry_msgs/msg/TwistWithCovarianceStamped</li> <li>Topic: /rover/navstate | Type: ublox_msgs/msg/NavSAT</li> <li>Topic: /parametric_trajectories_editor_node/feedback | Type: visualization_msgs/msg/InteractiveMarkerFeedback</li> <li>Topic: /tf | Type: tf2_msgs/msg/TFMessage</li> <li>Topic: /parametric_trajectories_control_node/Point1 | Type: visualization_msgs/msg/Marker</li> <li>Topic: /base/rxmrtcm | Type: ublox_msgs/msg/RxmRTCM</li> <li>Topic: /parametric_trajectories_control_node/Point2 | Type: visualization_msgs/msg/Marker</li> <li>Topic: /rover/fix_velocity | Type: geometry_msgs/msg/TwistWithCovarianceStamped</li> <li>Topic: /rover/rxmrtcm | Type: ublox_msgs/msg/RxmRTCM</li> <li>Topic: /ControlPathData | Type: geometry_msgs/msg/PoseStamped</li> <li>Topic: /base/navclock | Type: ublox_msgs/msg/NavCLOCK</li> <li>Topic: /battery_voltage | Type: std_msgs/msg/Int32</li> <li>Topic: /safety_current_state | Type: std_msgs/msg/String</li> <li>Topic: /raw_can | Type: std_msgs/msg/String</li> <li>Topic: /rover/navpvt | Type: ublox_msgs/msg/NavPVT</li> <li>Topic: /rover/monhw | Type: ublox_msgs/msg/MonHW</li> <li>Topic: /path_num | Type: std_msgs/msg/Float64</li> <li>Topic: /rover/aidalm | Type: ublox_msgs/msg/AidALM</li> <li>Topic: /rover/navheading | Type: sensor_msgs/msg/Imu</li> <li>Topic: /position_error | Type: std_msgs/msg/Float64</li> <li>Topic: /rover/diagnostics | Type: diagnostic_msgs/msg/DiagnosticArray</li> <li>Topic: /base/navheading | Type: sensor_msgs/msg/Imu</li> </ol> <p><br> <br> </p>
Dataset: Modular Impedance Matrix Method for Transient Modeling in Pipe Network Systems
<p>Unsteady flow is an important engineering problem in urban pipe network systems, requiring pressure and flow rate management analyses and reliable drinking water quality maintenance. Efficiently solving the hyperbolic partial differential equation and integrating it with various boundary conditions under the complex layout scenarios of pipe networks is a challenging issue for pipeline modelers. Frequency-domain modeling with a time-domain response was developed as an alternative to the traditional method of characteristics. However, this solution requires a substantial array size for large pipe network systems, significantly affecting applicability in field pipe network systems. This study proposes an innovative transient analysis method, the modular impedance matrix method, to solve the most labor- and cost-intensive computational issues affecting the unsteady flow analysis of large, complicated pipe networks. This method was applied to a field pipe network system and its performance compared to existing approaches. The algorithm of the proposed method fundamentally solved the computational problems associated with other methods, and its modular scheme allowed feasible integration with an analytical formulation that can be tailored to the modeler's preferences. The modular impedance matrix method's strength can be amplified according to the size and complexity of the pipe network system owing to its unique complementary validation capability. </p>
Modular Walls - Steampunk Style Low Poly FBX
Modular Walls Low Poly fbx bricks, doors, window, wood planks, bend 3x Walls 2x Doors 2x Windows 2x Curved Windows 2x Floors 2x Roofs 2x Pillars Source: Objaverse 1.0 / Sketchfab
Modular Study to Evaluate CT7001 Alone in Cancer Patients With Advanced Malignancies
ClinicalTrials.gov study NCT03363893. IPD Sharing: NO. Countries: 2. Publications: 2.
Patient-centered Modular CBT for CPTSD: A Randomised Controlled Pilot Study
ClinicalTrials.gov study NCT05259592. IPD Sharing: NO. Countries: 1. Publications: 3.
Effectiveness of the EMPOWER™ Modular Pacing System and EMBLEM™ Subcutaneous ICD to Communicate Antitachycardia Pacing
ClinicalTrials.gov study NCT04798768. IPD Sharing: NO. Countries: 9. Publications: 3.
Feasibility Study of a Modular Control to Range System in Type 1 Diabetes Mellitus
ClinicalTrials.gov study NCT01418703. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Lower jaw modularity in the African Clawed Frog (Xenopus laevis) and Fire Salamander (Salamandra salamandra gigliolii)
Open the record for dataset details and reuse information.
Data from: The role of social attraction and social avoidance in shaping modular networks
Open the record for dataset details and reuse information.
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.