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23 results for “triggered events”
Dataset for the manuscript "Event-triggered STED imaging"
<p>Dataset that supports the implementation of the event-triggered STED method and support the findings in the manuscript: "Event-triggered STED imaging" (Jonatan Alvelid, Martina Damenti, Chiara Sgattoni, Ilaria Testa, preprint: https://doi.org/10.1101/2021.10.26.465907). The data files are organized according to the various experiments performed for characterization or application of the method. References to the specific figures in the manuscript that uses the different data is provided in the info file.</p> <p>The scripts provided at https://github.com/jonatanalvelid/etSTEDanalysis (https://doi.org/10.5281/zenodo.6469723) have been used for image and data handling, and image analysis of the here provided dataset.</p>
Variation in Detected Adverse Events using Trigger Tools: A Systematic Review and Meta-Analysis
<p>Raw data sets for the meta-analysis.</p> <p>Data collection file with all the information extracted from the included studies.</p> <p>QAT file with the information from the quality assessment tool (QAT) for all included studies.</p> <p>ReadMe with information on data sets and updates.</p> <p>Codebooks for both data sets.</p>
The Meltwater Pulse1A Triggered an Extreme Cooling Event: Evidence From Southern China. Meltwater Pulse Cooling Event (MCE). Winter temperature data during the last deglacial of Huguangyan Maar lake, Surface water temperature and seasonal diatom assemblage data of Huguangyan and Yunlong Lake.
<p>Here we present results of The lake averaged monthly mean surface water temperature over the period from September 2013 to August 2015 from Yunlong Tianchi Lake(YL)(25°52.2′N, 99°16.8′E, altitude: 2551 m a.s.l), southwestern China. The dataset include sediment trap main diatom percentages over the period from September 2013 to August 2015 from YL. Lake water temperature profiles at different depths (1, 3, 6, 9, 11, 13, 16 m) from November 2008 to May 2009 in Huguang Maar Lake (HML)(21°9′N, 110°17′E), Southern China. AMS radiocarbon dates of plant remains and bulk sediment samples for Huguangyan Maar Lake over the last ~17 cal ka BP. The main diatom assemblage percentages (%) from 17 to 10 cal ka BP at Huguangyan Maar Lake. Diatom-based reconstruction of winter temperature (WT) from 17 to 10 cal ka BP at Huguangyan Maar Lake.</p>
Triggers and consequences of landslide-induced tsunamis – reconstruction of the Taan Fiord 2015 tsunami event
<p>Models codes are provided for some examples of the adopted modeling approaches.</p> <p>STL. files for the solid bodies are available in the zip file.</p> <p>A bathymetric map of the Taan Fiord on October 2015 and related contour shape file are provided.</p> <p>Values of the solid-fluid mixture density for different initial close packing volume fractions are shown in the table.</p> <p>A table summarizes the available model approaches, implemented in FLow 3D, useful in reproducing a Landslide-induced tsunami</p>
OGD triggered Zn2+ rises and Ca2+ deregulation events in CA1 and CA3 hippocampal pyramidal neurons of MT-III and ZnT3 knockout mice.
<p>The posted data provide the control characterization of the oxygen glucose deprivation (OGD) triggered occurrences of cytosolic Zn<sup>2+</sup> rises and terminal Ca<sup>2+</sup> deregulation events in individual CA1 and CA3 pyramidal neurons in acute hippocampal slices of the MT-III knockout mice ( <strong>004649 - 129S7-Mt3<sup>tm1Rpa</sup>/J</strong>, Jackson Laboratory) and ZnT3 knockout mice ( <strong>005064 -</strong> <strong>B6;129-Slc30a3<sup>tm1Rpa</sup>/J</strong>, Jackson Laboratory).</p>
Ugric vocabulary (appendix to Grünthal et al. 2022: Drastic demographic events triggered the Uralic spread)
<p>Ugric cognates (Appendix to the paper Grünthal, R., Heyd, V., Holopainen, S., Janhunen, J., Khanina, O., Miestamo, M., Nichols, J., Saarikivi, J. & Sinnemäki, K. 2022: Drastic demographic events triggered the Uralic spread. – Diachronica. https://doi.org/10.1075/dia.20038.gru)</p> <p> </p> <p>Words found only in Hungarian and Khanty and/or Mansi.</p> <p> </p> <p>Further work on Ugric etymologies (with updates to the information on this table) will be published on https://sanat.csc.fi/wiki/Hungarian_Historical_Phonology</p>
Indo-Iranian loans in Uralic (Appendix to Grünthal et al. 2022: Drastic demographic events triggered the Uralic spread)
<p>Early Indo-Iranian loanwords in Uralic and their distribution (assembled from Holopainen, Sampsa 2019: Indo-Iranian borrowings in Uralic. PhD thesis, University of Helsinki).</p>
Proto-Uralic cognates (Appendix to Grünthal et al. 2022: Drastic demographic events triggered the Uralic spread)
<p>A selection of Uralic cognates and their distribution (Appendix to the paper Grünthal, R., Heyd, V., Holopainen, S., Janhunen, J., Khanina, O., Miestamo, M., Nichols, J., Saarikivi, J. & Sinnemäki, K. 2022: Drastic demographic events triggered the Uralic spread. – Diachronica. https://doi.org/10.1075/dia.20038.gru)</p>
Cooperative circumnavigation with event-triggered bearing measurements
<p>This demonstration shows a team of Aerial Robotic Workers (ARW)s performing a circumnavigation mission. The ARWs circumnavigate a virtual target whose position is progressively estimated by means of event-triggered bearing measurements.</p> <p>The control objective is that the ARWs circumnavigate the target at a desired speed, while forming a regular polygon around the target.</p> <p>Each ARW maintains a running estimation of the position of the target, which is updated every time a new bearing measurement is taken. New bearing measurements are triggered with a recursive law that guarantees that the estimated position converges to the real position of the virtual target.</p> <p>Each ARW intermittently monitors the relative position of the ARW that precedes it in the circumnavigation, and adjusts its circumnavigation speed according to such relative position. This control logic allows the ARW to converge to a regular polygon around the target.</p> <p>The control algorithm is implemented on a ROS architecture where the controller of each ARW corresponds to a different ROS node.</p>
Event Marker Ingested To Trigger Event Recorder 3.0 Psychiatry Study
ClinicalTrials.gov study NCT01804257. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Formation and transport of fluid mud triggered by typhoon events in front of the subaqueous Changjiang Delta
<p>1_SSC_calibration.xlsx is used for producing figure 2.</p> <p>2_Wind_Wave.xlsx is used for producing figure 3.</p> <p>3_SSC_contour_and_conducitivity.xlsx is used for producing figure 4.</p> <p>4_SSC_Current_Profile.xlsx is used for producing figure 5.</p> <p>5_Grain_Size.xlsx is used for producing figure 6.</p> <p>6_Current_And_Fluid_Mud_Dynamics.xlsx is used for producing figures 1, 4 and 7 and tables 1 and 2.</p> <p>7_Sediment_Flux.xlsx is used for producing figure 8.</p>
Datasets for triggered events
<p>datasets for aseismic and seismic events dynamically triggered by the 2023 Turkey earthquake sequence including InSAR observations, geodetic model, detected seismicity catalogs and seismic stations</p>
Supplements for dynamically triggered events in the Caucasian Region
<p>the repository contains seismic data and related products (Table S1 and S2) used in studying dynamically triggered events in Caucasian Region. </p>
Triggering Events, Opinion Leader Networks, and Framing Strategies of Climate Change on Chinese Social Media
Open the record for dataset details and reuse information.
Benchmarking data on worker reactions to triggering events
<p>1. Real-world Benchmarking Data</p> <p>The objective of this task was to determine if Virtual Reality-based captured behavioral data on responses to notifications are similar to what is expected in real-world settings. For this purpose, a real-world bench mark experiment was designed to capture participant response times to wearable watch alarms triggered upon simulated traffic near the mobile work zone on the experiment site in an urban setting. The proposed scope of data collection of the real-world study included the external environmental factors (e.g., site accessibility, weather). The key parameters of research are defined as reaction time to received alarms and the heart rate measures. Table 1 provides the list of parameters that were controlled and measured during the experiments.</p> <table align="center"> <caption>Table 1. Key parameters measured and tracked during real-world experiments</caption> <tbody> <tr> <td> </td> <td>Variable name</td> <td>Descriptions</td> </tr> <tr> <td>Key parameters captured</td> <td>Reaction time</td> <td>The time that one takes from getting the haptic or sound alarm from a wearable alarm device, herein referring to the apple watch, to the point when the participant gives a response by stopping the alarm by pressing on the screen of the smartwatch</td> </tr> <tr> <td> </td> <td>Inter-beat interval (IBI, heart rate)</td> <td>The time interval between individual beats of the heart; the data is measured by using E4 application provided by Empatica</td> </tr> <tr> <td>External factors tracked</td> <td>Ambient noise</td> <td>The level of ambient noise in the area is a factor potentially influencing participants’ reactions and is considered in the experiment design</td> </tr> <tr> <td> </td> <td>Temperature</td> <td>Daytime temperature recorded at each experiment</td> </tr> <tr> <td> </td> <td>Number of pedestrians on site</td> <td>Number of participants counted during the time of the experiment to record on the varying factors in the external environment in real-world settings</td> </tr> </tbody> </table> <p><br> In the experiment, each participant was asked to participate in the experiment three times. In each trial, data was recorded separately for each alarm sent to smartwatch from the administrator at triggering events (precisely, every time the remote-controlled toy car reaches the line 30 ft apart from the designated work area). Each alarm signal at each trial was recorded for all 31 participants to the experiment. Timestamps are automatically recorded in server in the events recorded in the format of Table 2: </p> <table align="center"> <caption>Table 2. Format of raw data stored in the server, starting in December 2022.</caption> <tbody> <tr> <td> </td> <td>Timestamp</td> <td>From</td> <td>Event</td> </tr> <tr> <td>0</td> <td>2022-12-08 13:37:53.101391 </td> <td>VR</td> <td>Received car approaching alert, mode=3, id=1000</td> </tr> <tr> <td>1</td> <td>2022-12-08 15:53:05.098288</td> <td>Watch</td> <td>Start Simulation</td> </tr> <tr> <td>2</td> <td>2022-12-08 15:53:07.437488 </td> <td>VR</td> <td>Received car approaching alert, mode=4, id=1004</td> </tr> <tr> <td>3</td> <td>2022-12-08 15:53:13.064067</td> <td>Watch</td> <td>Stop Simulation</td> </tr> <tr> <td>4</td> <td>2022-12-08 15:53:13.163635</td> <td>Watch</td> <td>Stop Simulation</td> </tr> <tr> <td>...</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>2417</td> <td>2023-03-03 16:17:46.166644</td> <td>Watch</td> <td>1398</td> </tr> <tr> <td>2418</td> <td>2023-03-03 16:18:00.004425</td> <td>Watch</td> <td>1398</td> </tr> <tr> <td>2419</td> <td>2023-03-03 16:18.01.272071</td> <td>Watch</td> <td>1398</td> </tr> <tr> <td>2420</td> <td>2023-03-03 16:18:07.359187</td> <td>Watch</td> <td>Stop Simulation</td> </tr> <tr> <td>2421</td> <td>2023-03-03 16:18:07.388183 </td> <td>Watch</td> <td>Stop Simulation</td> </tr> </tbody> </table> <p>Some intervals used different timestamps as benchmarks to calibrate on the vehicle speed and user response time to the alarm signals, which include the following cases: </p> <p>1) At the beginning of each trial, vehicle travels 70 ft from start point to the 30 ft apart point, when the first alarm is signaled; given this travel distance, the travel time of the first trip the toy vehicle makes is calculated by subtracting tn_alarm1_sent from tn_start.</p> <p>2) Similarly, user response times to all alarms are recorded by subtracting the timestamps when the alarm is received by participant from when the alarm is sent from the server. (tn_alarmn_sent - tn_alarmn_received)</p> <p> </p> <p>2. Supplementary Data</p> <p>Ambient noise level data were collected using a noise meter, allowing to save noise level by seconds to multiple seconds (i.e., 5, 10, 30, 60 seconds). All noise data recorded were recorded in the interval of one second using the meter.<br> The collected data was processed to match the certain timestamps collected for user response time data collected in the experiment to allow comparisons and correlation analysis to be performed later on, which include the following: 1) worker response; 2) sending of alarm signals; 3) start and stop of experiments.<br> All data points were later modified using the rolling mean function of pandas python module to replace the missing data points by moving average method. </p>
Events Exposure as a Trigger of the Clinical Manifestations of Parkinson's Disease
ClinicalTrials.gov study NCT05308485. IPD Sharing: NO. Countries: 1. Publications: 1.
Specific Respiratory Infections as Triggers of Acute Medical Events
ClinicalTrials.gov study NCT02984280. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A single miR390 targeting event is sufficient for triggering TAS3-tasiRNA biogenesis in Arabidopsis.
GEO Series GSE89345. Arabidopsis thaliana. 5 samples. Type: Expression profiling by high throughput sequencing.
Acute Cardiovascular Events Triggered by COVID-19-Related Stress
ClinicalTrials.gov study NCT04368637. IPD Sharing: NO. Countries: 1. Publications: 0.
Bleeding Events Before vs After Lowering Departmental Platelet Transfusion Trigger
ClinicalTrials.gov study NCT06187831. IPD Sharing: YES. Countries: 1. Publications: 0.
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.