Research Dataset Catalog and ShareScore Ratings | ScienceDex

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MCR LTER: Data from Duvall, Rosman and Hench, in review. Representation of coral reef roughness using obstacle and surface-based approaches, submitted to JGR: Oceans

This archive contains natural coral reef topography data from the northern coast of Mo’orea, French Polynesia. These data were used to compute reef roughness density using obstacle- and surface-based estimates and models, and to compare the two approaches for representing reef topography. Primary support for this product came from the National Science Foundation Physical Oceanography program (OCE-1435530 and OCE-1435133), and as well as Duke University and the University of North Carolina at Chapel Hill. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2019). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.

openCC (other)Nov 2019View details →

zenodo44/100

Students' perceived obstacles with Forced Online Distance Learning during the CoVID-19 outbreak and their preferences to continue with the introduced teaching methods after the reopening of the University of Maribor [Project documentation]

The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej &Scaron;orgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the first project with the working title: The Side Effects of Forced Online Distance Education (FODE). The aim of the second study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university students to the new situation. The project documentation provided for the Forced Online Distance Learning (FODL) consists of: <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics,</li> <li>and SPSS dataset.</li> </ul>

opencc-by-4.0Apr 2021View details →

zenodo44/100

Experimental data for validation of a variational RANS level III flow model: water waves over an array of obstacles and Ogee weir flows

Experimental dataset for the validation of a variational RANS level III flow model. The experimental data correspond to experiments on unsteady of water waves over an array of obstacles and steady curved flows over an Ogee weir. The experiments were conducted at the Hydraulics Laboratory at the Univeristy of Córdoba.

opencc-by-4.0Sep 2024View details →

zenodo40/100

Dataset for manuscript 'CeyeHao: AI-driven microfluidic flow programming with hierarchically assembled obstacles in microchannel and receptive-field-augmented neural network'

This dataset contains: 1. The dataset used to train the models related to the manuscript 'CeyeHao: AI-driven microfluidic flow programming using hierarchically assembled obstacles in microchannel with receptive-field-augmented neural network'. 2. A checkpoint of trained 'CEyeNet' proposed in the manuscript. 3. Example microchannels designed in the manuscript to produce semantic flow profiles This dataset is intended for research and academic purpose. Detailed description please refer to the enclosed ReadMe.txt.

opencc-by-4.0Aug 2024View details →

zenodo40/100

Benchmark for energy efficient obstacle detection on head mounted wearable for the vision impaired

Here we present a novel benchmark dataset with the associated challenge, that is to detect obstacles based on head-mounted sensors and lightweight wearable devices to assist Blind and Visually Impaired individuals (BVIs) navigate in indoor environments.  The challenge encompasses three objectives: (1) as accurately as possible to detect the obstacles on the pathway that likely lead to a collision; (2) as durably as possible on a given amount of battery power for the detection algorithm or model to run; (3) as reliably as possible to compensate natural head turns so nearby objects would not trigger false alarms.  The data provided in the benchmark are collected from the following head mounted sensors: (i) nine low-cost ultrasonic sensors; (ii) one high-end ultrasonic sensor with a larger detection range but higher power consumption; (iii) a 9-Degrees of Freedom (DOF) Inertial Measurement Unit (IMU).  The resulting dataset consists of more than 188,000 unique sequences obtained from multiple subjects walking in three different indoor scenarios.  This benchmark is to facilitate and encourage accurate yet fast obstacle detection solutions that can really benefit BVIs.

openmit-licenseJun 2023View details →

zenodo40/100

Microspines in tropical climbing plants: a small-scale fix for life in an obstacle course

This dataset is associated to the original research article "Microspines in tropical climbing plants: a small-scale fix for life in an obstacle course" published in the Journal of Experimental Botany.  <table> <tbody> <tr> <td>Variable</td> <td>Description</td> <td>Type of variable</td> <td>Units</td> </tr> <tr> <td>sample</td> <td>Sample identifiier</td> <td>Nominal Variable</td> <td> </td> </tr> <tr> <td>sp_code</td> <td>Species identifier</td> <td>Nominal Variable</td> <td> </td> </tr> <tr> <td>sp</td> <td>Species latin name</td> <td>Nominal Variable</td> <td> </td> </tr> <tr> <td>direction</td> <td>Friction test direction</td> <td>Categorical variable</td> <td> </td> </tr> <tr> <td>static_force</td> <td>Force needed to induce motion of the stem segment</td> <td>Continuous variable</td> <td>mN</td> </tr> <tr> <td>sliding_force</td> <td>Force needed to maintain the stem segment in motion</td> <td>Continuous variable</td> <td>mN</td> </tr> <tr> <td>sliding_dist</td> <td>Distance spanned by the stem segment during test</td> <td>Continuous variable</td> <td>mm</td> </tr> <tr> <td>nPeaks</td> <td>Number of force peaks detected during test</td> <td>Discrete variable</td> <td>mN</td> </tr> <tr> <td>peak_frequ</td> <td>Peak frequency during sliding (NPeaks/sliding_dist)</td> <td>Continuous variable</td> <td>peaks.mm-1</td> </tr> <tr> <td>diam</td> <td>Diameter of the tested segment</td> <td>Continuous variable</td> <td>mm</td> </tr> </tbody> </table>

opencc-by-4.0May 2022View details →

zenodo40/100

Figure 8. Potential at every point; it is highest in the obstacles and lowest at the goal-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

The numerical potential field path planner is guaranteed to produce a path even if the start or goal is placed in an obstacle. If there is no possible way to get from the start to the goal without passing through an obstacle then the path planner will generate a path through the obstacle, although if there is any alternative then the path will do that instead. For this reason, it is important to make sure that there is some possible path, although there are ways around this restriction such as returning an error if the potential at the start point is too high. The path is found by moving to the neighboring square with the lowest potential, starting at any point in the space and stopping when the goal is reached.

opencc-by-4.0Nov 2011View details →

zenodo40/100

Figure 7. Obstacle force (repulsive potential) and goal force obstacle force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Since the motion trajectory of UAV is divided into several median points that the UAV should reach them one by one in a sequence the output obtained after the execution of AI will be a set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through the obstacles to reach the destination. The routine used for this purpose is the potential field method (also an alternative new method is in progress which models the UAV motion through opponents same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field of this system of charges a path will be suggested for the UAV.

opencc-by-4.0Nov 2011View details →

zenodo40/100

UAV Railroad Images for Segmentation and Obstacle Detection

UAV-RSOD dataset contains five different types of obstacles including person, boulder, barrel, branch, jerry can, and iron rod. This dataset supports the development and evaluation of computer vision models for railroad-related applications, such as autonomous train navigation, obstacle detection in railroad, railroad extraction and safety monitoring.  The DJI Phantom 4 PRO UAV Drone was used to take all the videos and images in the Indian Railways, Tiruchirapalli city of Tamilnadu, India. Version 1: 315 Images with Labelling, Masking for Segmentation Process and without any Data Augmentation techniques applied Version 2: 2002 Annotated Images with Data Augmentation techniques applied (Rotate, Flip, etc) for Obstacle Detection

opencc-by-4.0Jun 2024View details →

zenodo40/100

Figure 10.a. Simulation's display: The characters "\__/" represents the front part of the car that should pass through the string "....==..=.==..==" representing the range of obstacles-Designing a Growing Functional Modules "Artificial Brain"

Once performed the design and configuration, the controller and the local application are executed by pressing the corresponding button. In case of a local simulation, two terminal windows are generated. The first one, localized on the left side of the screen (figure 10.a), displays the behavior of the simulation; the second one, localized on the right side of the screen (figure 10.b), displays the behavior of the controller. Both application run concurrently and their respective contents allow the user to observe and monitor the control session. The last text line of figure 10.b displays the set of commands at cycle 201.

opencc-by-4.0Jan 2018View details →

zenodo40/100

BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 25: The fifth obstacle with 100 robots after passing all robots

The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance.