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Dataset results
6 results for “virtual plant”
Virtual Storage Plant (VSP) final demonstration of voltage control
<p>This the dataset of final demostration of using distributed control with consensus algorithm for voltage regulation using a virtual storage plant. The dataset contains raw data from 1 experiment for 1 HLU5-UC3 scenarios: TC_5.03.</p> <p>The experiments were conducted at the University of Zagreb's Smart Grid Laboratory (SGLab).</p>
Virtual Storage Plant (VSP) final demonstration of congestion management
<p>This the dataset of final demostration of using distributed control with consensus algorithm for congestion management using a virtual storage plant. The dataset contains raw data from 1 experiment for 1 HLU5-UC4 scenarios: TC_5.04.</p> <p>The experiments were conducted at the University of Zagreb's Smart Grid Laboratory (SGLab).</p>
Virtual Storage Plant (VSP) preliminary demonstration of Load Frequency Control (LFC)
<p>This the dataset of preliminary demostration of using distributed control with consensus algorithm for utilizing a virtual storage plant for automatic load frequency control. The dataset contains raw data from 3 experiments (6 units in a VSP) for 5 scenarios: TC_5.02.01, TC_5.02.02, TC_5.02.03, TC_5.02.04, TC_5.02.05.</p> <p>The experiments were conducted at the University of Zagreb's Smart Grid Laboratory (SGLab).</p>
Virtual Storage Plant (VSP) preliminary demonstration of active power control
<p>This the dataset of preliminary demostration of using distributed control with consensus algorithm for dispatching a virtual storage plant. The dataset contains raw data from 4 experiments (3, 4, 5 and 6 units in a VSP) for 5 scenarios: TC_5.02.01, TC_5.02.02, TC_5.02.03, TC_5.02.04, TC_5.02.05.</p> <p>The experiments were conducted at the University of Zagreb's Smart Grid Laboratory (SGLab).</p> <p> </p>
Data segmentation and analysis: developing algorithms to virtually dissect plants
<p><strong>The following video describes how biological data analysis and image segmentation is conducted at different scales and informs the ROMI data pipeline. Funded by EU Grant 773875.</strong></p> <p><em>Videos are available in:</em></p> <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> <p><strong>Video script:</strong></p> <p>(LEGRAND)<strong> </strong>I’m an engineer in a biological data analysis. So what I'm doing is to create tools and create a code helping biologists to go from these images they acquire to this labeled image from which we extract cellular features, and from these features we do statistical analysis. Okay so on this board we describe the pipeline we are trying to set up for this image analysis so we're starting with potentially five dimensional images so you have the XYZ spatial Dimension then you have channels and potentially time. So with these images they go to a reader this reader creates a specific data structure so for example if you have a multiple observation of the same object from different angle what you would like to do is to reconstruct and fuse together this multiple angle and then that gives you one big image that you may want to filter and from from these images you can then perform segmentation. For example nuclei detection or cellular segmentation for example this is a pull out transport pump being able to quantify how many pumps you have gives you an idea of the flow of protein or hormones so from that you will be able to build up models and to try and make a realistic model of flower development or phyllotaxis. If you talk about the flower arrangement around the the stem.<br> <br> So compared to my original work the ROMI project is for me we represent a change in scale so we're moving from the cellular scale or to tissular scale, to where we try to understand how flower or leaves are arranged around the stem to use a macroscopic scale where you see the plant in full. And you're trying to observe and also quantify also how flowers or leaves are arranged around the stem.<br> <br> (HÉTROY-WHEELER) So I'm a bit at the end of the pipeline so as input we take a 3D Point cloud which is a sample of the plant represented in a virtual way so we've got a points which each of them has three 3D coordinates. And only from the set of points we try to infer the geometry of the plant and from this geometry so basically to recover the shape of the plant we try then to segment the plant into its organs so the leaves the stems and in between stems and leaves the petals. So the idea is that only from geometry and maybe some colour information or other information which we try to use as less information as possible we would be able to detect the organs of the plants and then do some computation for example simple computation like computing the number of leaves but also more advanced computation like for example trying to guess what is the area of the leaves or the angles between the different stems and so on things that are useful for a biologist and also for people in Agronomy or agriculture.</p>
A Virtual Reality-Based Horticultural Planting Program for Elderly With Subthreshold Depression
ClinicalTrials.gov study NCT07275034. IPD Sharing: YES. Countries: 1. Publications: 0.
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.