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187 results for “Neuroscience”
Synthetic and real EEG datasets for closed-loop neuroscience
<p>The dataset is made primarily for the task of real-time low latency filtering of the EEG data in the closed loop neuroscience experiments and for EEG forecasting task. The dataset consists of a real data and 5 options of the synthetic data of varying difficulty.</p><p>The real dataset consists of 25 people involved into the P4 alpha neurofeedback training. Its total size is about 16.3 hours. A more detailed instruction for this file is provided in the file Real dataset instructions.txt.</p><p>Synthetic data is generated in 5 different ways: sine wave with white noise, sine wave with pink noise, narrow-band filtered pink noise sample with pink noise, state-space model with white noise and state-space model with pink noise. Each of these datasets has about 34.5 hours of data. It is generated similarly to (Wodeyar et al, 2021). A more detailed instruction for the synthetic dataset can be found in the file Synthetic datasets instructions.txt.<br> </p><p>In LowLatencyEEGFiltering.zip one can find a code for the models used in our paper for low-latency filtering with this data.</p><p>NOTE: Code is also published in the following GitHub repository: https://github.com/ivsemenkov/LowLatencyEEGFiltering</p><p> </p><p>If you use our data or code please cite: https://www.doi.org/10.1088/1741-2552/acf7f3</p>
Dataset from "Matthieu Delescluse and Christophe Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29."
<p>The dataset (in HDF5 format) used in Delescluse and Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29. arXiv:q-bio/0505053. See this reference for recording details. Data collected by Matthieu Delescluse. Briefly, 4 channels (data sets Channel_0,1,2,3, organized in a group called 'ExtracellularData'; extracellular recordings along the Purkinje cell layer of a young rat cerebellar cortex slice) of a linear 'Michigan' (now Neuronexus) probe and a loose cell-attached recording (data set Reference, in group 'CellAttached') from one of the Purkinje cells that is also extracellularly recorded: a 'ground truth' for spike sorting algorithms. Each group has three attributes: SamplingRate, HighPass and LowPass. The last two are the filter settings used prior to A/D conversion. These attributes have identical values for the 5 traces (2 groups): the data were sampled at 15 kHz, high-passed at 300 Hz and low-passed at 5 kHz.</p>
Data set from Pouzat and Chaffiol (2009) Journal of Neuroscience Methods 181:119.
<p><span>1</span></p> <p>1</p> <p>1This is the data set of Cockroach first olfactory relay recordings used in Pouzat and Chaffiol (2009) Automatic Spike Train Analysis and Report Generation. An Implementation with R, R2HTML and STAR <em>Journal of Neuroscience Methods</em> <strong>181</strong>: 119-1443. These data are also included in the R package STAR. The data are in HDF5 format.</p>
Exploring the Impact of Neuroscience Preprints: A Citation Analysis
<p>1. Neuroscience_Records_Contain_Reference_to_Preprints.Scopus.V3.xlsx</p> <p>This Excel file contains the titles, DOIs, references, and EIDs of those Neuroscience publications (journal articles, books/book chapters, conference papers, notes, etc.) from 2004 to 2022 that have at least one reference to a preprint. For example, if a Neuroscience journal article has 40 references and one of these references is a preprint, then it's included in this Excel file. These records are retrieved from Scopus through the following query:</p> <p>REFSRCTITLE ( "OSF Preprints" OR "open science foundation Preprints" OR *africarxiv* OR *agrixiv* OR *arabixiv* OR *arxiv* OR *biohackrxiv* OR *biorxiv* OR *bodoarxiv* OR *cogprints* OR *eartharxiv* OR *ecoevorxiv* OR *ecsarxiv* OR *edarxiv* OR *engrxiv* OR *frenxiv* OR "INA-Rxiv" OR *indiarxiv* OR *lawarxiv* OR "LIS Scholarship Archive" OR *marxiv* OR *mediarxiv* OR *metaarxiv* OR mindrxiv OR *nutrixiv* OR paleorxiv OR "Preprints.org" OR psyarxiv OR *repec* OR *socarxiv* OR *sportrxiv* OR "Thesis Commons" OR "CoP preprint" OR "FocUS Archive preprint" OR "PeerJ preprint" OR "Law Archive preprint" OR *medrxiv* ) AND SUBJAREA ( neur ) AND PUBYEAR < 2023</p> <p> </p> <p>2. ReferencesToPreprints.V3.txt</p> <p>References of the publications are split through a Python code (SplitReferences.py) and organized into separate lines in a text file. For example, if a publication has 40 references, all of these 40 references are split into 40 separate lines. After splitting references, those lines containing one of these words/terms ("OSF Preprints" OR "open science foundation preprints" OR africarxiv OR agrixiv OR arabixiv OR arxiv OR biohackrxiv OR biorxiv OR bodoarxiv OR cogprints OR eartharxiv OR ecoevorxiv OR ecsarxiv OR edarxiv OR engrxiv OR frenxiv OR "INA-Rxiv" OR indiarxiv OR lawarxiv OR "LIS Scholarship Archive" OR marxiv OR mediarxiv OR metaarxiv OR mindrxiv OR nutrixiv OR paleorxiv OR "Preprints.org" OR psyarxiv OR repec OR socarxiv OR sportrxiv OR "Thesis Commons" OR "CoP preprint" OR "FocUS Archive preprint" OR "PeerJ preprint" OR "Law Archive preprint" OR medrxiv) are selected (through RetrieveLinesContainingSpeceficString.py) and organized into this text file (ReferencesToPreprints.V3.txt). Each reference contains an EID (separated by ";") in order to specify which publication contains this specific reference.</p> <p>After this step, through a Python code (AddPreprintServerToEndOfLines.py) the name of a certain preprint was added to the end of each line. For example, if a line (or a reference) contains "biorxiv", the word "biorxiv" will be added to the end of this line after the "@" sign.</p>
Dataset for article: Jaber-Lopez, T., Garcia-Gallego, A., Perakakis, P., Georgantzis, N. (2014). Physiological and behavioral patterns of corruption. Frontiers in Behavioral Neuroscience
<p>Dataset and matlab analysis scripts for article: Dataset for article: Jaber-Lopez, T., Garcia-Gallego, A., Perakakis, P., Georgantzis, N. (2014). Physiological and behavioral patterns of corruption. Frontiers in Behavioral Neuroscience</p>
Dataset of the scientific paper "A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients" (Front. Aging Neurosci.)
<p> There are three files with the following information:<br> - data_2d.bin, binary file with information of the different parameters of the nine subjects during 2d tasks<br> - data_3d.bin, binary file with information of the different parameters of the nine subjects during 3d tasks<br> - survey.bin, binary file with the score of the System Usability Scale (SUS) survey of each subject</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>
A computational neuroscience framework for quantifying warning signals
<p>Animal warning signals show remarkable diversity, yet subjectively appear to share certain visual features that make defended prey stand out and look different from more cryptic palatable species. For example, many (but far from all) warning signals involve high contrast elements, such as stripes and spots, and often involve the colours yellow and red. How exactly do aposematic species differ from non-aposematic ones in the eyes (and brains) of their predators?</p> <p>Here we develop a novel computational modelling approach, to quantify prey warning signals and establish what visual features they share. First, we develop a model visual system, made of artificial neurons with realistic receptive fields, to provide a quantitative estimate of the neural activity in the first stages of the visual system of a predator in response to a pattern. The system can be tailored to specific species. Second, we build a novel model that defines a 'neural signature', comprising quantitative metrics that measure the strength of stimulation of the population of neurons in response to patterns. This framework allows us to test how individual patterns stimulate the model predator visual system.</p> <p>For the predator-prey system of birds foraging on lepidopteran prey, we compared the strength of stimulation of a modelled avian visual system in response to a novel database of hyperspectral images of aposematic and undefended butterflies and moths. Warning signals generate significantly stronger activity in the model visual system, setting them apart from the patterns of undefended species. The activity was also very different from that seen in response to natural scenes. Therefore, to their predators, lepidopteran warning patterns are distinct from their non-defended counterparts, and stand out against a range of natural backgrounds.</p> <p>For the first time, we present an objective and quantitative definition of warning signals based on how the pattern generates population activity in a neural model of the brain of the receiver. This opens new perspectives for understanding and testing how warning signals have evolved, and, more generally, how sensory systems constrain signal design.</p>
OpenAIRE Dataset for the SciLake neuroscience pilot
<p>This dataset is related to the subset of the OpenAIRE graph relevant to the neuroscience pilot. The dataset is built according to the <a href="https://graph.openaire.eu/docs/data-model/">data model</a> of the OpenAIRE Graph dataset.</p>
APPENDIX A - SEARCH PROTOCOLE AND PAPERS LIST - Cognitive Neuroscience's Transposition in STEAM Classroom: A Systematic Literature Review
<p>An integrative literature review was performed corresponding to PRISMA’ protocol in order to integrate issues over an innovative curriculum through cognitive sciences didactic transposition in STEAM classrooms. The search action was handled using various international databases. Each paper was selected alongside the PICO strategy and a defined selection criterion. Analysis of the literature between 2000 and 2021 is presented.</p>
Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
<p><strong>Supplementary Data</strong> </p> <ol> <li><strong>Supplementary Data 1</strong> contains the input (non-watertight) surface meshes of the block (shown in Figure 2a) reconstructed within the context of the EPFL-KAUST collaboration, and the corresponding output (watertight) meshes generated by Ultraliser.</li> <li><strong>Supplementary Data 2 </strong>contains a set of 20 non-watertight meshes that were randomly selected from the block shown in <strong>Supplementary Figure S54</strong> and another set of the their watertight counterparts.</li> <li><strong>Supplementary Data 3</strong> contains a set of 25 neuronal morphologies with different morphological types and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 4</strong> contains a set of 25 synthetic astroglial morphologies 15 and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 5</strong> contains the vascular morphology (shown in <strong>Supplementary Fig. S83</strong>) and a corresponding multi-partitioned watertight mesh.</li> <li><strong>Supplementary Data 6</strong> contains the datasets used for the comparative analysis shown in <strong>Supplementary Section 13</strong>.<br> <br> Neuronal, astrocytic and vascular morphologies are stored in SWC, H5 and VMV file formats respectively. The file structures of the SWC and VMV formats are publicly available online. The H5 files of the complete astrocyte cells can be made available from corresponding authors upon request. All the surface meshes are stored in Wavefront OBJ files. Additional STL meshes are generated to be used for TetGen to create corresponding tetrahedral meshes. All the input and generated data files are publicly available on Zenodo (10.5281/zenodo.7105941).</li> </ol> <p><strong>Data Sources</strong> </p> <ol> <li>Cellular and subcellular NGV meshes segmented from the volume shown in Figure 2 are provided by the collaborating co-authors affiliated with KAUST.</li> <li>Neuronal meshes shown in Figure 3, Supplementary Figures S55 - S75 and Supplementary Figures S85 are publicly available from the MICrONS program.</li> <li>Neuronal morphologies shown in Figure 4, Supplementary Figures S80 - S81 and Supplementary Figure S86 are publicly available from NeuroMorpho.Org.</li> <li>Astrocytic morphologies (Figure 5 and Supplementary Figure S82) are provided by Eleftherios Zisis.</li> <li>Vascular morphologies (rat’s cerebral microvasculature) shown in Figure 6 and Supplementary Figures S83 - S84 are courtesy of Bruno Weber, University of Zürich (UZH).</li> <li>The vascular morphology of the arterial arborizations shown in Supplementary Figure S88 is available from the Brain Vasculature (BraVa) database (cng.gmu.edu/brava).</li> </ol>
Dataset: Vigil Neuroscience, Inc. (VIGL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: ProMIS Neurosciences, Inc. (PMN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Minerva Neurosciences, Inc. (NERV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Tema Neuroscience And Mental Health ETF (MNTL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
NKI Translational Neuroscience Laboratory macaque MRI dataset
<p>This dataset includes whole brain MRI data from a group of three rhesus macaques. The dataset includes functional MRI data in the form of contrast (Monocrystalline iron oxide nanoparticles (MION)) enhanced echo planar images, T1-weighted and T2-weighted anatomical images, and diffusion weighted images for each subject. The fMRI data is a mix of anesthetized resting state imaging, somatosensory stimulation, and awake movie watching. </p>
Tutorial data for Imaging in neuroscience: with a focus on MEG and EEG methods
<p>MEG/EEG/MRI tutorial data for the PhD course Imaging in neuroscience: with a focus on MEG and EEG methods at Karolinska Institutet, Stockholm, Sweden. For more information, see: https://github.com/natmegsweden/meeg_course</p> <p>Data from a single subject. The participant received 160 tactile stimulation to all five fingers of the right hand at a rate of 0.3 Hz while watching a silent movie. Continuous HPI was measured for the duration of the recording.</p> <p>MEG was recorded with a Neuromag Triux MEG scanner with 102 magnetometers and 204 planar gradiometers at a sample rate of 1000Hz. Simultaneously recorded electroencephalography (EEG) with 128 channels. Electrocardiogram (ECG) or electrooculogram (EOG) was measured together with MEG to control for artefacts from heartbeats and eye-blinks. Structural MRI.</p>
Dataset for: Effects of acute ischemic stroke on binaural perception, Dietze et al., Frontiers in Neurosciences, 2022
<p>This dataset contains the MNI-registered lesion masks of patients with strokes at different locations and the psychoacoustic results (tone in noise detection and lateralization) of stroke and control groups.<br> The dataset is described in Dietze A, Sörös P, Bröer M, Methner A, Pöntynen H, Sundermann B, Witt K and Dietz M (2022) Effects of acute ischemic stroke on binaural perception. Front. Neurosci. 16:1022354. doi: 10.3389/fnins.2022.1022354</p>
A computational neuroscience framework for quantifying warning signals
Open the record for dataset details and reuse information.
Zhang, L. & Mysore, S.P. The barn owl in systems and behavioral neuroscience: progress and promise
<p>Contains the source code and data for the metadata analysis described in Fig. 2 of the manuscript: Zhang, L. & Mysore, S.P. The barn owl in systems and behavioral neuroscience: progress and promise.</p> <p>See README (<em>ZhangEtAl_CONEUR_README.docx</em>) for more information.</p>
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.