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Dataset results
15 results for “single-unit”
Single-unit and multi-unit peer-to-peer transactions in a microgrid (uGIM dataset)
<p>uGIM is a microgrid intelligent management platform that can represent individual end-users using a multi-agent approach. This dataset has data regarding two weeks (May 13<sup>th</sup> to May 18<sup>th</sup>, 2019, and September 30<sup>th</sup> to October 6<sup>th</sup>, 2019) where auction-based peer-to-peer transactions were performed in a real microgrid.</p> <p>The data was collected by uGIM agents and auctions were executed every hour. The hour-ahead auctions are performed by the sellers in a fully distributed approach. To create this dataset two NanoPi M1 Plus (with 1.2 GHz quad-core CPUs and 1 GB of RAM running the Ubuntu 16.04.6 LTS operating system), and three Raspberry Pi Model B+ (with 1.4 GHz 64-bit quad-core CPUs and GB of RAM running the Raspberry Pi OS operating system) were used.</p> <p>uGIM related publications:<br> - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. <a href="http://doi.org/10.1016/j.measurement.2019.107427">https://doi.org/10.1016/j.measurement.2019.107427</a><br> - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. <a href="http://doi.org/10.1109/ACCESS.2020.2985254">https://doi.org/10.1109/ACCESS.2020.2985254</a><br> - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. <a href="http://doi.org/10.14201/gredos.144238">https://doi.org/10.14201/gredos.144238</a><br> - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. <a href="http://doi.org/10.1016/j.jclepro.2019.118154">https://doi.org/10.1016/j.jclepro.2019.118154</a></p> <p> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p><br>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Single-unit auditory nerve fibre responses of young-adult and aging gerbils
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Simultaneous single-unit recording from the basal amygdala and dorsal hippocampus
<p>Animals seeking survival needs must be able to assess different locations of threats in their habitat. However, the neural integration of spatial and risk information essential for guiding goal-directed behavior remains poorly understood. Thus, we investigated simultaneous activities of fear-responsive basal amygdala (BA) and place-responsive dorsal hippocampus (dHPC) neurons as rats left the safe nest to search for food in an exposed space and encountered a simulated 'predator.' In this realistic situation, BA cells increased their firing rates and dHPC place cells decreased their spatial stability near the threat. Importantly, only those dHPC cells synchronized with the predator-responsive BA cells remapped significantly as a function of escalating risk location. Moreover, optogenetic stimulation of BA neurons was sufficient to cause spatial avoidance behavior and disrupt place fields. These results suggest a dynamic interaction of BA's fear signaling cells and dHPC's spatial coding cells as animals traverse safe-danger areas of their environment.</p>
LFP and single-unit data published in Olafsdottir, Carpenter and Barry (2016) Nature Neuroscience 19: 792-794
<p><strong>OVERVIEW</strong></p> <p>All data are recorded using tetrodes - eight in MEC (deep layers) and eight in hippocampus - and the DACQ system from Axona Ltd</p> <p>For all recording files (except R2142), tetrodes 1-8 are in MEC (for R2142 it's the other way around)</p> <p>Filenames indicate the recording date, animal ID and whether the animal was running on the Z-track ('track1'), resting ('sleepPOST'), or foraging in the open field ('Training') - e.g. 20151201_R2337_track1 indicates that this file is from animal R2337 running on the Z-track on 1st December 2015</p> <p>Rest recordings were recorded immediately after animals were exposed to the track; foraging was carried out in a 1m square arena</p> <p>All files ending with .cut have spike sorted data - e.g. 20151127_R2337_track1_11.cut, contains the spike sorted data for tetrode 11; files ending with .pos contain position data (sampled at 50Hz); files ending with .egf contain LFP data (sampled at 4.8kHz); files ending with .set contain the header information; files ending with a number contain tetrode data - e.g. '20151127_R2337_track1.1' contains tetrode data from tetrode 1; and some folder have .clu files (the output of KlustaKwik), but these should be ignored as they are superseded by the .cut files</p> <p><br> <strong>RAT SPECIFIC NOTES</strong></p> <p>R2142</p> <p>>>2014-08-06</p> <p>Screening, Track1 and SleepPost present</p> <p> </p> <p>R2192</p> <p>>>2104-09-17</p> <p>Track1 and SleepPost sessions present</p> <p>>>2014-10-01</p> <p>Note this animal has a typo in the file name for the track session, indicating the wrong date - i.e. 20140110 should be 20141001, as this recording was made on 1st October 2014 NOT 10th January 2014. Note that this digit switch applies to all files for that session (i.e. track) but not to the other files from that day (i.e. sleepPost)</p> <p> </p> <p>R2217</p> <p>>>2014-12-13</p> <p>Screening, Track1 and SleepPost present</p> <p>>>2014-12-18</p> <p>Screening, Track1 and SleepPost present</p> <p><br> R2335</p> <p>>>2015-10-26</p> <p>Screening, Track1 and SleepPost present<br> </p> <p>R2336</p> <p>>>2015-11-01</p> <p>Track1 and SleepPost present</p> <p>>>2015-11-04</p> <p>Track1 and SleepPost present</p> <p> </p> <p>R2337</p> <p>>>2015-11-27</p> <p>Track1 and SleepPost present</p> <p>>>2015-12-01</p> <p>Track1 and SleepPost present</p>
SMaRT Blood: Single-unit Versus Multiple-unit Packed Red Blood Cell Transfusion in Non-acute Postpartum Anemia
ClinicalTrials.gov study NCT03419780. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Simultaneous single-unit recording from the basal amygdala and dorsal hippocampus
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Data from: Dynamics of mesoscale brain network during decision-making learning revealed by chronic, large-scale single-unit recording
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High-density single-unit human cortical recordings using the Neuropixels probe
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Carcea et al., 2021 single-unit recordings during co-housing
<p>These are raw data from single-unit recordings during co-housing experiments, in female mouse (virgin) PVN. These recordings are associated with the Carcea et al., 2021 manuscript. </p>
Dataset Effects of repeated ultrasonic instrumentation on single-unit crowns
<p>Dataset of the restoration quality assessments</p>
Automatic Sound Management 3.0 in a Single-unit Audio Processor
ClinicalTrials.gov study NCT04918654. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Treatment of Hematologic Malignancies With Single-Unit or Double-Unit Cord Blood Transplantation
ClinicalTrials.gov study NCT00328237. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of Single-unit Implant-supported Prostheses Survival Using CoCr Prosthetic Abutments: Prospective Observational Study
ClinicalTrials.gov study NCT05649085. IPD Sharing: NO. Countries: 1. Publications: 0.
Incisors Single-Unit Rehabilitation With Narrow GM Implants
ClinicalTrials.gov study NCT05260892. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Splinted or Non-splinted Single-unit Crowns on Marginal Bone-level Alterations Around Implants
ClinicalTrials.gov study NCT02880891. IPD Sharing: NO. 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.