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122 results for “Information processing”
Data, multiscale dataset and supplementary information for 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process'
<p>This repository contains the analytical Supplementary Information, the data, the multiscale dataset and the codes used to construct the dataset and plot figures used in the manuscript 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process' (accepted in Earth and Planetary Science Letters). </p> <p> </p> <p> </p>
Data from Hesse et al. 2017: Preattentive Processing of Numerical Visual Information, Front Hum Neurosci., 11:70, 2017. doi: 10.3389/fnhum.2017.00070.
<p><strong>Data related to the following publication: </strong></p> <p>Hesse Philipp N., Schmitt Constanze, Klingenhoefer Steffen, Bremmer Frank (2017). Preattentive Processing of Numerical Visual Information. Frontiers in Human Neuroscience, 11: 70. doi: 10.3389/fnhum.2017.00070</p> <p><strong>Brief description of dataset:</strong></p> <p>The stimulus was presented on a TFT monitor (size: 41,8° x 24,3°) 52 cm in front of the participants in a dark, sound attenuated and electrically shielded room. During the experiment EEG was recorded continuously. We used 64 Ag/AgCl active electrodes located according to the extended international 10-20 system. </p> <p>The numerosity stimulus consisted of a continuously displayed black fixation target in the center of the gray screen. Additionally in each trial either one, two or three circular white patches were shown 200 ms after trial onset. These were presented for a random duration between 400 ms and 500 ms either in the left or right visual field. Two different types of patches were presented: i) the radius of the patches had the same value (0.65°) and therefore the patch size was the same (“SizeCon”) ii) the total area of the patches was conserved which resulted in the same total luminance independent of the number of patches (“LumCon”). After a random time between 400 ms and 700 ms after stimulus offset the trials ended.</p> <p>In this study we conducted an oddball experiment with an oddball-ratio of 1:4. In each block consisting of 30 trials a standard-amount of patches (one, two or three) was presented in 80% of all trials (24 trials). The two remaining quantities of patches were shown in 10% (3 trials) of the trials each. This presentation scheme allowed us to compare trials with identical physical properties because each amount of patches served as deviant and standard trial in different blocks. Attention of the participants was drawn off the white patches by a demanding detection task at the fixation target. A total number of 432 blocks consisting of 30 trials was presented to each of the 10 participants.</p> <p>EEG data were evaluated offline. The mastoids (TP9 and TP10) were chosen as new reference. A second-order, zero phase shift Butterworth filter with cutoff frequencies 0.5 and 40 Hz was applied to the continuously recorded data before it was sliced in individual trials that had a time range from 200 ms before to 500 ms after stimulus onset. A baseline correction was performed using with the signals from -110 ms to 0 ms. As a last step trials with eye movement artifacts or electrode signals that exceeded a difference of ±100 µV within an interval of 100 ms were excluded in an artifact rejection step.</p> <p> </p>
Figure 3. Combination of Neural and Symbolic Information Processing Strategies
<p>The second model developed is a model for human-like machine perception based on<br> research findings in neuroscience and neuro-psychology. The principal idea of the model is to use<br> so-called neuro-symbols as basic processing units. This concept is inspired by the fact that the brain is made up of neurons but we think in term of symbols. In analogy to the brain, starting from sensor<br> values, the sensory information is combined and condensed in a modular hierarchical manner to<br> more and more complex neuro-symbolic information until this results in a complete, unitary,<br> multimodal perception of the environment (see figure 3).</p>
Data of the publication: Recent Advances in Rare Earth Doped Inorganic Crystalline Materials for Quantum Information Processing
<p>Data corresponding to the figures of the publication "Recent Advances in Rare Earth Doped Inorganic Crystalline Materials for Quantum Information Processing" by N. Kunkel and Ph. Goldner (https://doi.org/10.1088/1361-648X/aa529a). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
Process Models obtained from event logs with with different information-preserving abstractions
<p>This dataset contains results of the experiment to analyze information preservation and recovery by different event log abstractions in process mining described in: Sander J.J. Leemans, Dirk Fahland "Information-Preserving Abstractions of Event Data in Process Mining"<br> Knowledge and Information Systems, ISSN: 0219-1377 (Print) 0219-3116 (Online), accepted May 2019</p> <p>The experiment results were obtained with: https://doi.org/10.5281/zenodo.3243981</p>
ViSAPy-generated test data from Lee JH., et al. Advances in Neural Information Processing Systems 30 (NIPS 2017), pp4002--4012
<p>This dataset corresponds to the simulated test data for spike-sorting algorithms in Figure 3 of:</p> <p>Lee, Jin Hyung and Carlson, David E and Shokri Razaghi, Hooshmand and Yao, Weichi and Goetz, Georges A and Hagen, Espen and Batty, Eleanor and Chichilnisky, E.J. and Einevoll, Gaute T. and Paninski, Liam. YASS: Yet Another Spike Sorter. Advances in Neural Information Processing Systems 30 (NIPS 2017). Editors I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett, year 2017, pp4002-4012.<br> publisher: Curran Associates, Inc. URL http://papers.nips.cc/paper/6989-yass-yet-another-spike-sorter.pdf</p>
Processed data from "Chromatin information content landscapes inform transcription factor and DNA interactions"
<p><strong>Chromatin information content landscapes inform transcription factor and DNA interactions</strong></p> <p>Authors: Ricardo D’Oliveira Albanus, Yasuhiro Kyono, John Hensley, Arushi Varshney, Peter Orchard, Jacob O. Kitzman, Stephen C. J. Parker</p> <p><a href="https://doi.org/10.1101/777532">https://doi.org/10.1101/777532</a></p> <p> </p> <p>This record contains the processed data used in our manuscript. For instructions on how to use or regenerate this data, please refer to <a href="https://github.com/ParkerLab/chromatin_information">https://github.com/ParkerLab/chromatin_information</a>.</p>
Fig. 2 in Hierarchical establishment of information sources during foraging decision-making process involving Acromyrmex subterraneus (Forel, 1893) (Hymenoptera, Formicidae)
Fig. 2. Decision time (s) spent by the A. subterraneus target worker according to number of trips (n) made by the respective target worker and the concentration of pheromone manipulated on the branch that does not lead to the food, estimated by total flow of foragers.
Fig. 1. Y in Hierarchical establishment of information sources during foraging decision-making process involving Acromyrmex subterraneus (Forel, 1893) (Hymenoptera, Formicidae)
Fig. 1. Y-trail system with branches of equal length (225 mm). Branches arranged at an angle of 60◦ connected to a bifurcation. Decision lines (LD) established at fixed points 140 mm far from the bifurcation center on the right and left branches, and 25 mm from the base branch to calculate the A. subterraneus workers' frequency of passage when half of their bodies had crossed each LD.
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
Text-fig. 9. Tachyglossus aculeatus. Transverse section through the orbital region of the head of a juvenile specimen. ms: sphenobturatory membrane; plb: palatine bone; psw: primary endocranial sidewall; sll: secondary lateral lamella of Kuhn (1971); this endocranial process (blue) is here interpreted as a derivative of the cartilago teniformis. (From Kuhn and Zeller 1987.) in Cartilago Teniformis And Its Derivatives: Additional Information On The Basic Composition And Evolution Of The Skull
Text-fig. 9. Tachyglossus aculeatus. Transverse section through the orbital region of the head of a juvenile specimen. ms: sphenobturatory membrane; plb: palatine bone; psw: primary endocranial sidewall; sll: secondary lateral lamella of Kuhn (1971); this endocranial process (blue) is here interpreted as a derivative of the cartilago teniformis. (From Kuhn and Zeller 1987.)
Text-fig. 6. Ornithorhynchus anatinus. Transverse section through the otic region of the head of a 180-mm specimen. ls: supracapsular lamina; lsc: lateral semicircular canal; mt: musculus temporalis; opa: parietal bone; opl: pluteal bone; ppe: parietal process of endocranium (blue). (Modified from Zeller 1989.) in Cartilago Teniformis And Its Derivatives: Additional Information On The Basic Composition And Evolution Of The Skull
Text-fig. 6. Ornithorhynchus anatinus. Transverse section through the otic region of the head of a 180-mm specimen. ls: supracapsular lamina; lsc: lateral semicircular canal; mt: musculus temporalis; opa: parietal bone; opl: pluteal bone; ppe: parietal process of endocranium (blue). (Modified from Zeller 1989.)
Process Mining from Information-SeekingConversations
<p>This deposit contains the results from the paper "Process Mining from Information-Seeking Conversations" submitted to WCCI 2020. The results are in .xes format, and ready to be analyzed in a process mining tool. </p>
Supporting information for 'Towards better understanding of the ferrofluid impregnation process and potential artefacts – a prerequisite for reliable interpretation of magnetic pore fabrics'
<p>These tables contain data for the manuscript 'Towards better understanding of the ferrofluid impregnation process and potential artefacts – a prerequisite for reliable interpretation of magnetic pore fabrics'</p>
Processing of haptic texture information over sequential exploration movements
<p>Where textures are defined by repetitive small spatial structures, exploration covering a greater extent will lead to signal repetition. We investigated how sensory estimates derived from these signals are integrated. In Experiment 1 participants stroked with the index finger one to eight times across two virtual gratings. Half of the participants discriminated according to ridge amplitude, the other half according to ridge spatial period. In both tasks just noticeable differences (JNDs) decreased with an increasing number of strokes. Those gains from additional exploration were over 3 times smaller than predicted for optimal observers who have access to equally reliable, and therefore equally weighted estimates for the entire exploration. We assume that the sequential nature of the exploration leads to memory decay of sensory estimates. Thus, participants compare an overall estimate of the first stimulus, which is affected by memory decay, to stroke-specific estimates during the exploration of the second stimulus. This was tested in Experiments 2 & 3. The spatial period of one stroke across either the first or second of two sequentially presented gratings was slightly discrepant from periods in all other strokes. This allowed calculating weights of stroke-specific estimates in the overall percept. As predicted, weights were approximately equal for all strokes in the first stimulus, while weights decreased during the exploration of the second stimulus. A quantitative Kalman filter model of our assumptions was consistent with the data. Hence, our results support an optimal integration model for sequential information given that memory decay affects comparison processes.</p>
Supporting Information for the submitted manuscript by Rajič et al. The origin of tectonic mélanges and implication for the subduction interface processes
<p>Files shared here contain supporting information for the submitted manuscript by Rajič et al.</p> <p>Appendix 1 file contains Text A.1, Tables A.1-2 and Figures A.1-8.</p> <p>Appendix 2 file contains all raw Raman spectra acquired in this study, along with READ ME text file.</p>
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
<p><strong>Introduction</strong></p> <p>The Robot Inverse Dynamics Dataset is a collection of trajectories and joint torque measurements of two robotic manipulators, a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL. Additionally, the dataset contains the inverse dynamical models and other useful quantities learned to reproduce the results reported on our reference paper "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification". The proposed model relies on a novel multidimensional kernel, called Lagrangian Inspired Polynomial (LIP) kernel.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~700MB.</li> <li>The dataset contains</li> <ul> <li>collections of joint trajectories and joint torque measurements of two robot manipulators: a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL.</li> <li>models of the inverse dynamics of the two manipulators learned on the datasets</li> </ul> <li>The main directories are</li> <ul> <li>Simulated_PANDA/ contains the trajectories, models and results obtained on different configurations of a Franka Emika PANDA robot, simulated in sympybotics.</li> <li>Robots/ contains the data, models and results obtained on two real robots, a Franka Emika PANDA and a Mitsubishi Electric MELFA RV4FRL</li> </ul> <li>See the README.md file for a detailed description of the directories.</li> </ul> <p><strong>Other Resources</strong></p> <p>Python code to train the models and reproduce the results in the paper are available <a href="https://github.com/merlresearch/LIP4RobotInverseDynamics">here</a>.</p> <p><strong>Citation</strong></p> <p>If you use the Robot Inverse Dynamics dataset in your research, please cite our contribution:</p> <pre><code>@InProceedings{ title={A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification}, author={Giacomuzzo, G., Dalla Libera, A., Romeres, D.,}, booktitle={IEEE Transaction on Robotics}, year={2024} } </code></pre> <p><strong>License</strong></p> <p>The Robot Inverse Dynamics dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
Raw data corresponding to Huestegge, S. M., Raettig, T., & Huestegge, L. (2019). "Are face-incongruent voices harder to process? Effects of face-voice gender incongruency on basic cognitive information processing." Journal: Experimental Psychology.
<p>Raw data file prior to subject-based aggregation. Variables and values are decribed within the file. For further reference and specifications please also refer to the original publication in the journal Experimental Psychology.</p>
Modality-specific dysfunctional neural processing of social-abstract and non-social-concrete information in schizophrenia
<p>Schizophrenia is characterized by marked communication dysfunctions encompassing potential impairments in the processing of social-abstract and non-social-concrete information, especially in everyday situations where multiple modalities are present in the form of speech and gesture. To date, the neurobiological basis of these deficits remains elusive. In a functional magnetic resonance imaging (fMRI) study, 17 patients with schizophrenia or schizoaffective disorder, and 18 matched controls watched videos of an actor speaking, gesturing (unimodal), and both speaking and gesturing (bimodal) about social or non-social events in a naturalistic way. Participants were asked to judge whether each video contains person-related (social) or object-related (non-social) information. When processing social-abstract content, patients showed reduced activation in the medial prefrontal cortex (mPFC) only in the gesture but not in the speech condition. For non-social-concrete content, remarkably, reduced neural activation for patients in the left postcentral gyrus and the right insula was observed only in the speech condition. Moreover, in the bimodal conditions, patients displayed improved task performance and comparable activation to controls in both social and non-social content. To conclude, patients with schizophrenia displayed modality-specific aberrant neural processing of social and non-social information, which is not present for the bimodal conditions. This finding provides novel insights into dysfunctional multimodal communication in schizophrenia, and may have potential therapeutic implications.</p>
Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes
<p>Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management, and infodemic controlling. However, predicting the scale of an information cascade in a long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the follower-followee network, retweet network, tweet content, and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension to information diffusion process and may assist in the control of such process.</p>
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.