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1,506 results for “objects”
THE EMERGENCE OF THE ASPECT OF LINGUOPRAGMATICS, THE STAGES OF DEVELOPMENT AND OBJECT OF STUDY
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Seurat object input file - Galaxy Training Material
<p>A Seurat input file for conversion to AnnData.</p>
Figure 1 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 1 Conceptual model of a Knowledge Object (KO) containing a payload, machine-actionable service and deployment specifications, metadata and a unique persistent identifier. We are exploring aligning our conceptual model with emerging best practices for FAIR Digital Objects. Derived from Wittenburg et al's Digital Objects as Drivers towards Convergence in Data Infrastructures (Wittenburg et al. 2019).
Figure 3 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 3 This figure illustrates the dual nature of Knowledge Objects: knowledge-as-resource and knowledge-as-service. A KO can be curated and maintained in a repository, pass metadata to a knowledge graph or deployed into applications. Different to other digital objects, the methods to deploy the KO to applications via custom or generic runtimes called by microservices are built into the KO.
Figure 2 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 2 (L) Sample KO as viewed from the KGrid Library, from which the KO can be implemented in a hosted runtime environment or downloaded. (R) Sample output results from deploying the KO.
OBJECTIVE INDICATORS OF NONCOMPLIANCE WITH SAFETY REGULATIONS FOR VEHICLE MOVEMENT OR UTILIZATION
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Figure 5 from: Sambaraju P (2022) Use of Worksheet events in Excel to save solver objective cell value from each iteration. Research Ideas and Outcomes 8: e79006. https://doi.org/10.3897/rio.8.e79006
Figure 5 Plot showing the values of b1, b2 (top) and SSE (bottom) for each iteration during the solver optimization.
The SoccerSum Dataset for Automated Detection, Segmentation, and Tracking of Objects on the Soccer Pitch
<p>The SoccerSum dataset was curated by capturing and annotating soccer videos from the Norwegian Eliteserien league. This collection spans three years, covering 2021, 2022, and 2023. It comprises 750 frames from 41 unique sequences, with 4 to 40 frames per sequence, carefully chosen to represent a diverse selection of scenarios encountered in professional soccer games.</p>
Learning to Grasp Unknown Objects in Domestic Environments with GP-net+
<p>This record includes data for the paper "Learning to Grasp Unknown Objects in Domestic Environments", currently under review.<br><br><strong>Simulation environment with pre-trained GP-net+ model</strong><br><br>The paper presents a simulation environment for grasping objects in domestic environments. The presented objects and furniture units, as well as a pre-trained GP-net+ model can be found in the "gpnetplus_simulation_data.zip" file. After this zip file is downloaded, it can be unpacked it into the <a href="https://github.com/AuCoRoboticsMU/GP-netplus" target="_blank" rel="noopener">GP-net+ directory</a>. It includes all necessary data to use the simulation environment, for example, for testing GP-net+ or other grasping models in simulated domestic environments.</p> <p> </p> <p><strong>ROS model</strong></p> <p>The paper additionally presents an <a href="https://github.com/AuCoRoboticsMU/GP-netplus-ros" target="_blank" rel="noopener">ROS package</a> that can be deployed for grasping unknown objects in domestic environments with simulated or real robots. We make a ROS-compatbile model of GP-net+ available in the "ros_gpnet_plus.zip" file, which can be used with the ROS package.</p> <p> </p> <p><strong>Training dataset</strong></p> <p>We used the simulation environment in our paper to generate a training dataset and train GP-net+. This training dataset is included in this record and can be used to replicate our results or train modifications of GP-net+.</p> <p>To improve handling of the training dataset (total size 25GB+), we split the dataset into several .zip files, named val.zip (validation data) and train_[0-6].zip (training data). Download all files individually and extract them into a single folder. Combine all files train_[0-6].zip directory into a single directory called 'train', for example, by using the 'move_train_data.sh' script provided.<br><br>The final structure for the dataset should look similar to this:<br><br>gpnet_data</p> <p>|-- val</p> <p> |-- depth_image_0000000.npz</p> <p> |-- depth_image_0000001.npz</p> <p> ...</p> <p> |--segmask_image_0052346.npz</p> <p>|-- train</p> <p> |-- depth_image_0000000.npz</p> <p> |-- depth_image_0000001.npz</p> <p> ...</p> <p> |-- segmask_image_0602506.npz</p> <p> |-- segmask_image_0602507.npz</p> <p><br><br><br>For generation of the training and simulation data, the following mesh databases have been used:<br><br>B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar,"Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set," IEEE Robotics and Automation Magazine, vol. 22, no. 3, pp. 36–52, 2015<br><br>A. Singh, J. Sha, K. S. Narayan, T. Achim, and P. Abbeel, "BigBIRD: A large-scale 3D database of object instances," 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 509–516, 2014.<br><br>A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu, "ShapeNet: An Information-Rich 3D Model Repository," Tech. Rep. arXiv:1512.03012 [cs.GR], Stanford University — Princeton University — Toyota Technological Institute at Chicago, 2015.</p> <p>D. Morrison, P. Corke, and J. Leitner, "EGAD! An Evolved Grasping Analysis Dataset for Diversity and Reproducibility in Robotic Manipulation," IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4368–4375, 2020</p>
Results of visual servoing architecture of mobile manipulators for precise industrial operations on moving objects
<p>Results obtained during the experiment of screw fastening on moving objects using a mobile manipulator.</p>
Human Developing Retina Atlas (Intermediate Data Object)
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Supplementary material 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
ChickenEgg-SCNM
Figure 3 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 3 Choropleth map for production of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model.
Figure 2 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 2 Choropleth map for total consumption of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model .
Figure 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 1 Simplified schematic representation of the food supply chain network in Germany illustrating actors (indicated by boxes) and transport processes (indicated by arrows) according to the dynamic freight flow model from Balster and Friedrich (2019). *Regarding Warehouse and Store: the respective 28 brands are implemented as aggregated and as individual actors in the "egg supply chain network model".
Supplemental materials for: Implementation and evaluation of an objective structured clinical examination (OSCE) tool for neurology post-graduate trainees in Lusaka, Zambia
<p>Introduction: Despite the high burden of neurologic disease in Zambia, a low-middle income sub-Saharan African country of 18 million people, there are currently no Zambian neurologists. We established the first neurology residency program in Zambia at the University of Zambia School of Medicine and the University Teaching Hospital in Lusaka. We evaluated the feasibility and effectiveness of a modified Objective Structured Clinical Examination (OSCE) to assess clinical skills. </p> <p>Program Description: The neurology training program began in October 2018, and its 3 participants completed the OSCE exercise in February 2019. The exercise involved using a smartphone to videotape trainees performing a physical examination and oral presentation in the neurology clinic. Trainees and neurology faculty reviewed the videos independently and assessed performance using a standardized rubric. The faculty and trainee then met in person and discussed the activity and scoring rubric. </p> <p>Assessment & Outcomes: Three trainees completed pre- and post-OSCE surveys where they rated their confidence in elements of the history and neurological examination and provided feedback about the OSCE exercise. One faculty member reviewed each video and provided feedback to each trainee. Trainees' average self-confidence scores improved from the pre- to post-OSCE survey in every category (pre-OSCE: mean score 6.84, range 4.8-7.8, SD 0.92; post-OSCE: mean score 7.9, range 5.67-9.33, SD 0.86). Qualitative responses showed trainees found the OSCE helpful, routinely applied feedback, and would appreciate repeating OSCEs every few months. </p> <p>Lessons Learned: This study demonstrated that OSCEs can be modified and successfully implemented in a resource-limited neurology post-graduate training program and result in improved self-confidence among trainees. Important modifications to the OSCE involved using smartphones to videotape the OSCE's and a real patient encounter rather than a standardized patient. Because there are many neurology patients and few providers in Zambia, embedding the experience within a busy clinic day was practical, directly applicable, and at a lower opportunity cost. </p> <p>Future Directions: Future work should expand use of OSCEs both within the Zambian neurology residency program and non-neurology training programs. Including additional video reviewers could add to the validity of clinical skills assessment. Videos created could also be used for remote mentorship and teaching purposes.</p>
Dataset Package for the paper "Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments"
<p><br> #Dataset Package for the paper "Automated Test Cases Prioritization for Self-driving Cars in Virtual Environments"</p> <p><br> Description of the content:</p> <p><br> 1) "1_datasets" folder contains all the datasets concerning the test scenarios considered in the study. <br> <br> - AsFault supports two SDC software as test subjects while generating test cases, which we use to generate our test suites.<br> - BeamNG.AI. <br> - DeepDriving<br> - STRUCTURE: We report test subjects considered and organized with sub-folders for each of the supported two SDC software (i.e., Driving AIs):<br> - fullroad<br> - BeamNG_AI<br> - Driver_AI<br> 2) "2_SDC-source-code" contains all code and scripts composing the MATLAB/R prototypical implementation of SDC-pririotizer: </p> <p> - STRUCTURE:<br> - runSearch.m<br> - runGreedy.m<br> - plotResults.m<br> - permutationMutation.m<br> - mainGreedy.m<br> - permutationCrossover.m<br> - main.m<br> - initialPopulation.m<br> - fitness.m<br> - featureSelection.m<br> - faultDetectionRatePlots.m<br> - faultDetection.m<br> - (folder) r-script<br> - (folder) python<br> <br> 3) "3_study-data" folder contains all the main experimental data concerning the study conducted, RQs and all generated figures and main row-data reported in section results<br> <br> - results.csv<br> - greedy_results.csv<br> - (folder) tables<br> - (folder) figures<br> - (folder) DriverAI<br> - (folder) BeamNG_RF_1_5<br> - (folder) BeamNG_RF_1 </p>
Dataset of the Floating Objects Detection Notebook
<p>This dataset contains the data used in the notebook "Detecting floating objects using Deep Learning and Sentinel-2 imagery", published in the ocean modelling section of The Environmental Data Science Book.</p>
Class-aware Sounding Objects Localization via Audiovisual Correspondence
<p>Videos for "Class-aware Sounding Objects Localization via Audiovisual Correspondence"</p>
Class-aware Sounding Objects Localization via Audiovisual Correspondence_mp4
<p>Class-aware Sounding Objects Localization via Audiovisual Correspondence</p>
ScienceDex guides
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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.