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21 results for “relative error”
Data of Relative errors in derived multi-wavelength intensive aerosol optical proberties
<p>Measurement Data of "Relative errors in derived multi-wavelength intensive aerosol optical<br> properties using cavity attenuated phase shift single-scattering<br> albedo monitors, a nephelometer, and tricolour<br> absorption photometer measurements"</p> <p> </p>
Error Related Potentials from Gaze-Based Typesetting
<p>The recording protocol relied on a standard gaze-based keyboard paradigm that was implemented by an eye-tracker attached to a PC monitor. The gazing information, in the form of a densely sampled sequence of x-y coordinates corresponding to the eye trace on the screen, was registered simultaneously with the participant’s brainwaves. The purpose of this experiment was to provide data where patterns in the physiological activity, of either brain or eyes, could be associated with the case of a typo (due to either the inaccuracy of the eye-tracker or a human mistake).</p>
Error Related Potential at the start of the gait with a lower limb exoskeleton
<h2>Description</h2> <p>This dataset contains EEG signals from experiments designed to evoke Error Related Potentials (ErrP) at the onset of gait using a Brain-Computer Interface (BCI) to control a lower limb exoskeleton. The ErrP is elicited using three different stimuli: Tactile, Visual, and VisuoTactile.</p> <p>During the experiment, participants remain stationary and engage in two mental tasks: Relax (R) and Motor Imagery (I) of walking to activate the exoskeleton. These tasks can be executed correctly (RC, IC) or incorrectly (RE, IE). For example, during RC (Relax Correct), the subject maintains an idle state, whereas during RE (Relax Error), the exoskeleton activates unexpectedly. Conversely, in IC, the subject imagines the sensation of starting to walk in their muscles, and the exoskeleton activates, but during IE, the exoskeleton does not move despite the motor imagery. When the exoskeleton activates before starting to walk, the stimulus remains active for 2 seconds to alert the subject about the impending movement. Therefore, ErrP is elicited by the stimuli in RE and can be compared with the absence of ErrP in IC, where the stimulus activates but should not evoke an error.</p> <p>Each subject participates in three sessions, one for each stimulus, consisting of 12 trials. In each trial, 10 mental tasks are performed, 5 Relax and 5 Imagination, interleaved. Since the subject is never in control of the system, tasks are correctly performed 70% of the time (RC, IC), while the remaining 30% are incorrect (RE, IE). In an exception, subject R01_VisuoTactile performed 7 trials of 20 mental tasks each, 10 of each type. However, due to the extended duration of the trials and resulting fatigue, they were later split for subsequent sessions.</p> <p> </p> <h2>Data information</h2> <p>A trial consists of a Matlab structure that stores all information related to the trial experiment. </p> <ul> <li><em>data_EEG</em>: Original EEG signals recorded with a sampling rate of 250Hz, where each row is a channel (1-28 EEG, 29-32 EOG, 33-35 inertial electrodes).</li> <li><em>data_preprocessed_EEG</em>: Matrix that contains the preprocessed signals for each channel. Rows 1-35 are the original signals and then, the preprocessed signals in blocks of 35. Find the indexes of each filter in <em>session.conf.info.preprocessingSteps.ListPreprocessingSteps</em>.</li> <li><em>trigger_EEG</em>: Information related to signal quality and missing data while recording. </li> <li><em>data_EXO</em>: Exoskeleton recorded data with a sampling rate of 250Hz.</li> <li><em>data_preprocessed_EXO: </em>The same data recorded by the exoskeleton in <em>data_EXO</em>, since it does not require the application of any filter.</li> <li><em>trigger_EXO</em>: Empty vector. </li> <li><em>data_Actuators</em>: Arduino response when activates (1) and deactivates (-1) the feedback. </li> <li><em>data_preprocessed_Actuators: </em>The same Arduino resposes recorded in <em>data_Actuators</em>, because it does not require any filter application. </li> <li><em>trigger_Actuators</em>: Empty vector. </li> <li><em>task_EEG</em>: Vector that associates a task to each signal sample.</li> <li><em>task_index_EEG</em>: Zero vector with negative peaks at the samples indicating the start of a task. Each peak decrements by one unit with each task. </li> <li><em>task_order_EEG</em>: Vector that increments a unit with each task change. </li> <li><em>event_EEG</em>: Vector of commands to activate (1) and deactivate (-1) the feedback in Arduino. </li> <li><em>conf</em>: Configuration employed for data acquisition and preprocessing. <ul> <li><em>acquisition</em>: User and signals acquisition information. <ul> <li><em>user_code</em>: User code name.</li> <li><em>feedback</em>: Trial in openloop (User do not have control of the system).</li> <li><em>feedbackErrP</em>: Feedback type employed during the trial.</li> <li><em>readfile</em>: Path to read files after its acquisition.</li> <li><em>saveSession_Script</em>: Script used to save the recorded data.</li> <li><em>writeResults</em>: Path to save the recorded data.</li> <li><em>device</em>: List of connected devices during the trial and their related information, such as name, sampling rate, connection order, etc. </li> <li><em>task</em>: Information about tasks occurring during the trial. <ul> <li><em>task_list</em>: Decodes tasks numbers. The first number is the global task/mental activity, the second one is the physiological state of the user, and the third one indicates the task version (preparation or basic task).</li> <li><em>sequence_tasks</em>: List of tasks in order of execution.</li> <li><em>sequence_times</em>: List with the duration of each task in the sequence.</li> </ul> </li> <li><em>deviceOutput</em>: List of devices that receive commands to execute orders, such as the exoskeleton for walking and stopping and the VibroLed for turning feeedback on and off.</li> <li><em>eye_index</em>: Indexes of EOG electrodes.</li> <li><em>EEG_index</em>: Indexes of EEG electrodes.</li> <li><em>inertial_index</em>: Indexes of inertial electrodes.</li> <li><em>file_name</em>: Trial name.</li> <li><em>num_epochs</em>: Number of epochs within a trial. An epoch is the half of sampling rate (250Hz), this means that an epoch has a duration of 0.5s and 125 samples. </li> </ul> </li> <li><em>preadjustment</em>: Empty list. </li> <li><em>preprocessing</em>: Information of the preprocessing filters, parameters and order of application.</li> <li><em>processing</em>: Not necessary for this analysis. </li> <li><em>static</em>: Information used internally by the architecture for its correct operation.</li> <li><em>info</em>: Important information about filters, their order and indexes in <em>data_processed_EEG</em>.</li> </ul> </li> <li><em>times</em>: Struct with information of the devices synchronization and preprocessing times.</li> <li><em>times_processing</em>: Processing duration times. </li> </ul>
Data for: Assortative mating in an ecological context: Effects of mate choice errors and relative species abundance on the frequency and asymmetry of hybridization
<p><span>The frequency and asymmetry of mixed-species mating set the initial stage for the ecological and evolutionary implications of hybridization. How such patterns of mixed-species mating, in turn, are influenced by the combination of mate choice errors and relative species abundance remain largely unknown. We develop a mathematical model that generates predictions for how relative species abundances and mate choice errors affect hybridization patterns. When mate choice errors are small (<5%) the highest frequency of hybridization occurs when one of the hybridizing species is at low abundance, but when mate choice errors are high (>5%) the highest hybridization frequency occurs when species occur in equal proportions. Furthermore, females of the less abundant species are overrepresented in mixed-species matings. We compare our theoretical predictions with empirical data on naturally hybridizing Ficedula flycatchers and find that hybridization is highest when the two species occur in equal abundance, implying rather high mate choice errors. We discuss ecological and evolutionary implications of our findings and encourage future work on hybrid zone dynamics that take demographic aspects, such as relative species abundance, into account.</span></p>
New Ideas for Brain Modelling 4-Figure 4. LHS relates to neuron binding ensemble mass, with central column activated. RHS relates to hierarchy, with a direct mapping. The two red lines show where the ensemble is missing and so it needs to be learned. The blue lines show extra neurons from the hierarchy back to the ensemble, but can be removed as error. The other paired black squares represent where the patterns match and can oscillate together.
<p>This paper continues the research that considers a new cognitive model based strongly on the human brain, last updated in Greer (2016). In particular, it considers figure 4 of that paper (Figure below) and how it might be useful in practice. The paper also describes some new methods in the areas of image processing and behaviour simulation. The image processing introduces a most classical form of pattern cross-referencing, while the behaviour equations used feedback for a memory-type of cross-referencing. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with ‘full linking’, if you like. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared with. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its current state and course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. Sensory input may be static and binary, but cross- references result in variable comparisons that make the input more dynamic. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.</p>
Relative Random Errors in the Convective Atmospheric Boundary Layer Estimated by the Relaxed Filtering Method from Large Eddy Simulations
<p>Data supporting the paper "How representative are uncrewed aircraft system measurements of the convective boundary layer?" by Brian R. Greene, Leia M. Otterstatter, and Scott T. Salesky, submitted to Geophysical Research Letters in 2024. Data are postprocessed from large-eddy simulations of the convective atmospheric boundary layer that are used to produce the figures within the paper. Details on the production of these files are included in the supplementary informatin of this paper.</p>
Data for: Assortative mating in an ecological context: Effects of mate choice errors and relative species abundance on the frequency and asymmetry of hybridization
Open the record for dataset details and reuse information.
Computerized Intervention Targeting the Error-Related Negativity and Balance N1 in Anxious Children
ClinicalTrials.gov study NCT05503017. IPD Sharing: YES. Countries: 1. Publications: 0.
Error Related Potential in motion to stop the gait with a lower limb exoskeleton
<h2>Description</h2> <p>This dataset contains EEG signals from experiments designed to evoke Error-Related Potentials (ErrP) in motion, during gait using a Brain-Computer Interface (BCI) to control a lower limb exoskeleton. The ErrP is elicited using Tactile stimuli that activates while walking and, once it deactivates, the exoskeleton stops.</p> <p>The experiment consists in a circuit with color marks on the floor, with 4 regions: 2 Gait Regions, where the subject has to keep walking, and 2 Stop Regions, where the subject tries to stop the exoskeleton using motor imagination. At the beginning of each repetition, the exoskeleton activates automatically and, while walking, they must perform two mental tasks: Relax (R), at Gair Regions to keep walking, and Motor Imagery (I) of stop, at Stop Regions to deactivate the exoskeleton and stop. These tasks can be executed correctly (RC, IC) or incorrectly (RE, IE). Since it is an open-loop experiment and the subject is never in control of the system, tasks are correctly performed 70% of the time (RC, IC), while the remaining 30% are incorrect (RE, IE). </p> <p>For instance, one the subject enters in the Gait Region and maintains an idle state while walking to continue with the gait. In RC (Relax Correct) they cross the region without any stop, but in RE (Relax Error) the feedback activates and the exoskeleton erroneously stops. Conversely, in IC (Imagination Correct, the subject imagines the sensation of stop walking in their muscles, and the feeedback activates and the exoskeleton stops the gait, but during IE (Imagination Error), they walk through the region and the exoskeleton does not stop despite the motor imagery. Therefore, ErrP is elicited by the stimuli in RE and can be compared with the absence of ErrP (NoErrP) in IC, where the stimulus activates but should not evoke an error.</p> <p>Each subject participates in three sessions, consisting of 20 trials. In each trial, 4 mental tasks are performed, 2 Relax and 2 Imagination, interleaved. Thus, in each session, a total of 12 ErrP and 28 NoErrP signals are recorded in the dataset. Except subject R06, who only participated in 2 sessions. </p> <p> </p> <h2>Data information</h2> <p>A trial consists of a Matlab structure that stores all information related to the trial experiment. </p> <ul> <li><em>data_EEG</em>: Original EEG signals recorded with a sampling rate of 250Hz, where each row is a channel (1-28 EEG, 29-32 EOG, 33-35 inertial electrodes).</li> <li><em>data_preprocessed_EEG</em>: Matrix that contains the preprocessed signals for each channel. Rows 1-35 are the original signals and then, the preprocessed signals in blocks of 35. Find the indexes of each filter in <em>session.conf.info.preprocessingSteps.ListPreprocessingSteps</em>.</li> <li><em>trigger_EEG</em>: Information related to signal quality and missing data while recording. </li> <li><em>data_EXO</em>: Exoskeleton recorded data with a sampling rate of 250Hz.</li> <li><em>data_preprocessed_EXO: </em>The same data recorded by the exoskeleton in <em>data_EXO</em>, since it does not require the application of any filter.</li> <li><em>trigger_EXO</em>: Empty vector. </li> <li><em>data_Actuators</em>: Arduino response when activates (1) and deactivates (-1) the feedback. </li> <li><em>data_preprocessed_Actuators: </em>The same Arduino resposes recorded in <em>data_Actuators</em>, because it does not require any filter application. </li> <li><em>trigger_Actuators</em>: Empty vector. </li> <li><em>task_EEG</em>: Vector that associates a task to each signal sample.</li> <li><em>task_index_EEG</em>: Zero vector with negative peaks at the samples indicating the start of a task. Each peak decrements by one unit with each task. </li> <li><em>task_order_EEG</em>: Vector that increments a unit with each task change. </li> <li><em>event_EEG</em>: Vector of commands to activate (1) and deactivate (-1) the feedback in Arduino. </li> <li><em>conf</em>: Configuration employed for data acquisition and preprocessing. <ul> <li><em>acquisition</em>: User and signals acquisition information. <ul> <li><em>user_code</em>: User code name.</li> <li><em>feedback</em>: Trial in openloop (User do not have control of the system).</li> <li><em>feedbackErrP</em>: Feedback type employed during the trial.</li> <li><em>readfile</em>: Path to read files after its acquisition.</li> <li><em>saveSession_Script</em>: Script used to save the recorded data.</li> <li><em>writeResults</em>: Path to save the recorded data.</li> <li><em>device</em>: List of connected devices during the trial and their related information, such as name, sampling rate, connection order, etc. </li> <li><em>task</em>: Information about tasks occurring during the trial. <ul> <li><em>task_list</em>: Decodes tasks numbers. The first number is the global task/mental activity, the second one is the physiological state of the user, and the third one indicates the task version (preparation or basic task).</li> <li><em>sequence_tasks</em>: List of tasks in order of execution.</li> <li><em>sequence_times</em>: List with the duration of each task in the sequence.</li> </ul> </li> <li><em>deviceOutput</em>: List of devices that receive commands to execute orders, such as the exoskeleton for walking and stopping and the VibroLed for turning feeedback on and off.</li> <li><em>eye_index</em>: Indexes of EOG electrodes.</li> <li><em>EEG_index</em>: Indexes of EEG electrodes.</li> <li><em>inertial_index</em>: Indexes of inertial electrodes.</li> <li><em>file_name</em>: Trial name.</li> <li><em>num_epochs</em>: Number of epochs within a trial. An epoch is the half of sampling rate (250Hz), this means that an epoch has a duration of 0.5s and 125 samples. </li> </ul> </li> <li><em>preadjustment</em>: Empty list. </li> <li><em>preprocessing</em>: Information of the preprocessing filters, parameters and order of application.</li> <li><em>processing</em>: Not necessary for this analysis. </li> <li><em>static</em>: Information used internally by the architecture for its correct operation.</li> <li><em>info</em>: Important information about filters, their order and indexes in <em>data_processed_EEG</em>.</li> </ul> </li> <li><em>times</em>: Struct with information of the devices synchronization and preprocessing times.</li> <li><em>times_processing</em>: Processing duration times. </li> </ul>
Relative error
<p>Relative error E<sub>rel</sub> for the runs of <strong>multi-solve</strong> and <strong>multi-factorization</strong> having the best execution times and for both of the solver couplings MUMPS/HMAT and MUMPS/SPIDO. Parallel runs using 24 threads on single <em>miriel</em> node.</p>
Fig. 7. The relative errors between the experimental and calculated 13C in Highly oxygenated isoryanodane diterpenoids from the leaves of Cinnamomum cassia and their immunomodulatory activities
Fig. 7. The relative errors between the experimental and calculated 13C NMR chemical shifts of 5 and 6.
Endometrioma Per se Versus Treatment Related Reduction in Ovarian Reserve (ERROR-2 Trial)
ClinicalTrials.gov study NCT03620838. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Immune Regulation in Patients With Common Variable Immunodeficiency and Related Inborn Errors of Immunity (IEI)
ClinicalTrials.gov study NCT00001244. IPD Sharing: NO. Countries: 1. Publications: 3.
Study of Inborn Errors of Cholesterol Synthesis and Related Disorders
ClinicalTrials.gov study NCT00046202. IPD Sharing: NO. Countries: 1. Publications: 3.
Surgery Before Embryo Transfer in ERROR (Endometrioma Related Reduction in Ovarian Reserve)
ClinicalTrials.gov study NCT04233983. IPD Sharing: NO. Countries: 1. Publications: 2.
Preventing Drug Errors Related to Caregiver Interruptions
ClinicalTrials.gov study NCT03062852. IPD Sharing: NO. Countries: 1. Publications: 6.
Relative xylanase activities [%] obtained from fermentation by using C. phragmiteti in LB medium containing 1 w/w% of dry matter waste paper as a function of fermentation time. Average values of relative xylanase activity are presented with error bars representing standard deviations of tripli-cates.
Open the record for dataset details and reuse information.
Code and data for manuscript: Does ankle push-off correct for errors in anterior-posterior foot placement relative to center-of-mass states?
<p>Data and code documentation for paper: “Does ankle push-off correct for errors in anterior-posterior foot placement relative to center-of-mass states?” All code in the current study can be accessed via the link (https://drive.google.com/drive/folders/1AnL9ajMk_Q3Gwzv_OrOp7A8DNwS8lniC?usp=sharing). The code was written by Jian Jin, Sjoerd Bruijn and Moira van Leeuwen. The data was from (van Leeuwen et al., 2020)and can be accessed through the link (https://doi.org/https://doi.org/10.5281/zenodo.4229851).</p>
Attention Training to Modify Error-related Negativity and Risk for Anxiety in Adolescence
ClinicalTrials.gov study NCT03176004. IPD Sharing: YES. Countries: 1. Publications: 0.
Detection of Error Related Potentials in Stroke Patients
ClinicalTrials.gov study NCT03149224. IPD Sharing: Not stated. 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.