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Dataset results
55 results for “learning objects”
Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses
<p>Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.</p>
BIM Learning Objects
<p>BIM Learning Objects.</p>
Raw dataset for "Multi-Objective Bayesian Active Learning for MeV-ultrafast electron diffraction"
<p>this dataset contains raw data collected at the SLAC MeV-UED facility, the data was saved in .npy format. The name of each file starts with a number referring to the time stamp when it was recorded.</p> <p>“xxxxxxxxxx_Andor1.npy” contains the beam images recorded at the diffraction detector plane associated with the q-resolution</p> <p>“xxxxxxxxxx_qm.npy” contains the beam images recorded at the sample plane associated with the spot size</p> <p>“xxxxxxxxxx_scalars.npy” contains the machine settings and readouts from the EPICs system, scalar names are listed in “scalars.txt”</p> <p>“xxxxxxxxxx_vcc.npy” contains the images recorded at a virtual cathode camera</p> <p>“xxxxxxxxxx_THzon_img.npy” contains the THz streaked beam images associated with the temporal length</p> <p>“xxxxxxxxxx_THzoff_img.npy” contains the unstreaked beam images for subtracting intrinsic broadening without THz pulses</p>
Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization
<p>This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article <em>Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization</em> by Zijun Deng and Lev Sarkisov.</p>
Intermediate data objects from running the machine learning code for Shen et al, Nature, 2018
<p>These are the RData objects of the processed cfMeDIP data that were used to run the machine learning analyses in "Sensitive tumour detection and classification using plasma cell-free DNA methylomes", Nature, 2018. This archive also includes the models we generated, and training and validation data matrices that people can use to fit new models and evaluate performance. These objects are to be used with the scripts and markdown at doi: 10.5281/zenodo.1242697 . The ReadMe contains descriptions. </p>
Hydrograph and Recession Flows Predictions Simulations using Deep Learning: Watershed Uniqueness and Objective Functions
<p>This resource contains the results presented in the paper titled 'Hydrograph and Recession Flows Simulations using Deep Learning: Watershed Uniqueness and Objective Functions' by Abhinav Gupta and Sean McKenna.<span><span><br></span></span></p> <p>'</p>
Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery in the framework of precision agriculture
<p>Supplementary material of the paper</p>
Object detection for graphical user interface: old fashioned or deep learning or a combination? - Model&Datasets
<p>This repo contains the datasets, trained models, and data splitting in ESEC/FSE 2020 "Object detection for graphical user interface: old fashioned or deep learning or a combination?" paper.</p>
Feedback Given by an Automatic and Objective System in Simulated Colonoscopy Increase Learning and Time Spent Practicing
ClinicalTrials.gov study NCT03248453. IPD Sharing: NO. Countries: 1. Publications: 8.
Efficacy of Near-Assisted Learning (NAL) in Improving Students' Objective Structured Clinical Examination (OSCE) Grades
ClinicalTrials.gov study NCT04177979. IPD Sharing: NO. Countries: 1. Publications: 13.
Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses
Open the record for dataset details and reuse information.
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>
Assessing Student Learning of Spinal Mobilization With Real-time Objective Feedback
ClinicalTrials.gov study NCT05445622. IPD Sharing: NO. Countries: 1. Publications: 0.
The Application of BOPPPS（Bridge-in,Objective,Pre-assessment,Participatory Learning,Post-assessment,Summary） Model in the Ward Rounds of Nurses' Standardized Training
ClinicalTrials.gov study NCT06194370. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Deep Learning for Real Time 3D Multi-Object Detection, Localization, and Tracking : Application to Smart Mobility
<p>In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account real-time constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detector-based on YOLOV3 [1] which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 [2,3] for monocular vision and MADNEt [4] for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in real-time condition. Object tracking is necessary in order to mitigate the risks of collisions. Unlike, traditional tracking approaches which requires target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT [5] approach for 3D object tracking. We introduce an extended Kalman filter [6] to better estimate the position of objects. Extensive experiments carried out on KITTI dataset [7] prove that our proposal outperforms state-of-the-art approches. </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.