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Dataset results
77 results for “Predictive processing”
Increased Protein Processing Gene Signature in HDACi-resistant cells predicts response to proteasome inhibitors.
GEO Series GSE58421. Homo sapiens. 8 samples. Type: Expression profiling by array.
Data from Schmitt et al. 2018: Preattentive and Predictive Processing of Visual Motion
<p><strong>Dataset associated with the following publication:</strong></p> <p>Schmitt C, Klingenhoefer S, Bremmer F. Preattentive and Predictive Processing of Visual Motion. <em>Sci Rep</em>. 2018;8(1):12399. Published 2018 Aug 17. doi:10.1038/s41598-018-30832-9</p> <p><strong>Description of dataset:</strong></p> <p>The dataset contains preprocessed EEG data recorded from the 15 electrodes (Cz, Fz, FCz, FC1, FC2, F3, F4, FC5, FC6, P3, P4, P7, P8, PO3, PO4) used for analysis in our paper. Data containing eye movements, blinks or a movement response of the participants were removed. As a reference the average signal of the mastoid electrodes TP9 and TP10 was used.</p> <p>In a first preporcessing step data were low pass filtered with a cut-off frequency of 70 Hz and additionally a Notch filter at 50 Hz was applied. In a second step data were aligned to the (re)appearance of the moving target next to the central occluder and cut into 850 ms long epochs ranging from 200 ms before this (re)appearance to 650 ms after this time point. The average signal from a 50 ms long time window starting 50 ms before the (re)appearance was used for a baseline correction of each epoch before epochs were averaged separately for subjects and conditions as a last preprocessing step. </p> <p>Each uploaded file contains data of all 8 participants and all 15 electrodes separately for the four different conditions: 1) target movement to the right in complete trajectory trails (data_movedirR_complete_trajectory.mat); 2) target movement to the left in complete trajectory trails (data_movedirL_complete_trajectory.mat); 3) target movement to the right in half trajectory trails (data_movedirR_half_trajectory.mat); 4) target movement to the left in half trajectory trails (data_movedirL_half_trajectory.mat).</p> <p>Each file contains 30 matrices for the half trajectory conditions and 60 matrices for the complete trajectory conditions. Each matrix presents the data recorded at one electrode, for one type of trial (standard "AllS", deviant "AllD" or half trajectory "AllH") and one attention condition (attention to the fixation target, central "1" and attention to the moving target, peripheral "2"). Example: "P3_AllD_1"</p> <p>Each matrix consists of 8 lines representing the 8 participants. Data for the relevant condition was averaged for each participant and is presented in a separate line. The matrices consist of 850 columns representing the length of the presented recording time of 850 ms. Data from 200 ms before to 650 ms after the (re)appearance of the moving target is presented. The uploaded matrices contain values in µV.</p>
Data from: EEG theta dynamics within frontal and parietal cortices for error processing during reaching movements in a prism adaptation study altering visuo-motor predictive planning
Modulation of frontal midline theta (fmθ) is observed during error commission, but little is known about the role of theta oscillations in correcting motor behaviours. We investigate EEG activity of healthy partipants executing a reaching task under variable degrees of prism-induced visuo-motor distortion and visual occlusion of the initial arm trajectory. This task introduces directional errors of different magnitudes. The discrepancy between predicted and actual movement directions (i.e. the error), at the time when visual feedback (hand appearance) became available, elicits a signal that triggers on-line movement correction. Analysis were performed on 25 EEG channels. For each participant, the median value of the angular error of all reaching trials was used to partition the EEG epochs into high- and low-error conditions. We computed event-related spectral perturbations (ERSP) time-locked either to visual feedback or to the onset of movement correction. ERSP time-locked to the onset of visual feedback showed that fmθ increased in the high- but not in the low-error condition with an approximate time lag of 200 ms. Moreover, when single epochs were sorted by the degree of motor error, fmθ started to increase when a certain level of error was exceeded and, then, scaled with error magnitude. When ERSP were time-locked to the onset of movement correction, the fmθ increase anticipated this event with an approximate time lead of 50 ms. During successive trials, an error reduction was observed which was associated with indices of adaptations (i.e., aftereffects) suggesting the need to explore if theta oscillations may facilitate learning. To our knowledge this is the first study where the EEG signal recorded during reaching movements was time-locked to the onset of the error visual feedback. This allowed us to conclude that theta oscillations putatively generated by anterior cingulate cortex activation are implicated in error processing in semi-naturalistic motor behaviours.
Reward is not Reward: Differential Impacts of Primary and Secondary Rewards on Expectation, Outcome, and Prediction Error in the Human Brain's Reward Processing Regions
<p>Raw data accompanying the following article:</p><p>Ulrich, M., Rüger, A., Durner, V., Grön, G., & Graf, H. (2023). Reward is not reward: Differential impacts of primary and secondary rewards on expectation, outcome, and prediction error in the human brain's reward processing regions. <i>Neuroimage</i>, 283, 120440. doi:10.1016/j.neuroimage.2023.120440</p>
The Use of a US-measured Styloid Process of the Distal Radius EET Diameter for Prediction of ETT Size
ClinicalTrials.gov study NCT06166927. IPD Sharing: NO. Countries: 1. Publications: 0.
Mind-wandering and Predictive Processes in Narcolepsy: a Putative Mechanism Through Covert REM Intrusions
ClinicalTrials.gov study NCT06457945. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Multicenter Prospective Observational Study of Computer-aided Risk Perception and Prognosis Prediction in the Whole Process of Laparoscopic Hepatobiliary and Pancreatic Surgery
ClinicalTrials.gov study NCT06647264. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Predicting Language Recovery in Acute Stroke Patients in the Neurovascular Intensive Care Unit: An Exploratory Study With the Core Assessment of Language Processing.
ClinicalTrials.gov study NCT06817642. IPD Sharing: NO. Countries: 1. Publications: 0.
Prediction AlgoriThm for regeneraTive Medicine Approach in knEe OA: New Decision-making Process Based on Patient pRofiliNg (PATTERN)
ClinicalTrials.gov study NCT05447767. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: The Yin and the Yang of Prediction: an fMRI study of semantic predictive processing
Open the record for dataset details and reuse information.
Data from: EEG theta dynamics within frontal and parietal cortices for error processing during reaching movements in a prism adaptation study altering visuo-motor predictive planning
Open the record for dataset details and reuse information.
Genome-wide prediction and analysis of yeast RNase III-dependent snoRNA processing signals
GEO Series GSE65111. Saccharomyces cerevisiae. 8 samples. Type: Expression profiling by array.
A β-catenin-regulated RNA processing transcriptional program imposes chemotherapy resistance and predicts T-ALL patient survival associated with high BRCA1 levels [RNA-Seq]
GEO Series GSE196985. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Data and code used in the article "Integrating multiphysics processes with deep learning for the Prediction of coupled water-vapor-heat water fluxes in unsaturated zone"
<p>《Integrating multiphysics processes with deep learning for the Prediction of coupled water-vapor-heat water fluxes in unsaturated zone》 (the paper has been submitted to <em>Water Resources Research</em>). The data and code used in the paper, specifically, the water content data and temperature data from in-situ observations from July 1, 2020 to August 29, 2020 (with a frequency of observations of 5 min), and the sample code for implementing cpinn using python (mainly the tensorflow library). These data can help readers better understand and replicate our study. All data and code have been uploaded.</p> <p>The paper has been submitted to <em>Water Resources Research</em>, if the paper is accepted for publication, all data will be made freely available.</p> <p>.</p> <p> </p>
Dataset and Code for the Research Paper: "A Smile is All You Need: Predicting Limiting Activity Coefficients from SMILES with Natural Language Processing."
<p><strong>Dataset and Code for the Research Paper: "A Smile is All You Need: Predicting Limiting Activity Coefficients from SMILES with Natural Language Processing."</strong></p> <p>For detailed instructions on how to utilize the resources, please refer to the <code>README.md</code> and see the original publication: https://doi.org/10.1039/D2DD00058J</p>
A β-catenin-regulated RNA processing transcriptional program imposes chemotherapy resistance and predicts T-ALL patient survival associated with high BRCA1 levels [ChIP-Seq]
GEO Series GSE180650. Homo sapiens. 29 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Making Predictions using Large Scale Gaussian Processes
One of the key problems that arises in many areas is to estimate a potentially nonlinear function [tex] G(x, \theta)[/tex] given input and output samples [tex] ( X,y ) [/tex] so that [tex]y approx G(x, \theta)[/tex]. There are many approaches to addressing this regression problem. Neural networks, regression trees, and many other methods have been developed to estimate [tex]$G$[/tex] given the input output pair [tex] ( X,y ) [/tex]. One method that I have worked with is called Gaussian process regression. There many good texts and papers on the subject. For more technical information on the method and its applications see: http://www.gaussianprocess.org/ A key problem that arises in developing these models on very large data sets is that it ends up requiring an [tex]O(N^3)[/tex] computation where N is the number of data points and the training sample. Obviously this becomes very problematic when N is large. I discussed this problem with Leslie Foster, a mathematics professor at San Jose State University. He, along with some of his students, developed a method to address this problem based on Cholesky decomposition and pivoting. He also shows that this leads to a numerically stable result. If ou're interested in some light reading, I’d suggest you take a look at his [recent paper]( ) (which was accepted in the Journal of Machine Learning Research) posted on dashlink. We've also posted code for you to try it out. Let us know how it goes. If you are interested in applications of this method in the area of prognostics, check out our [new paper](/dashlink/resources/51/) on the subject which was published in IEEE Transactions on Systems, Man, and Cybernetics.
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.