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Neural correlates of expectations-induced effects of caffeine intake on executive functions
<p><strong>ABSTRACT</strong></p> <p>Placebo effects (PE) are defined as the beneficial psychophysiological outcomes of an intervention that are not attributable to its inherent properties; PE thus follow from individuals’ expectations about the effects of the intervention. The present study aims aimed at examining how expectations influence neurocognitive processes.</p> <p>We will addressed this question by contrasting three double-blinded within-subjects experimental conditions in which participants are were given decaffeinated coffee, while being told they have had received caffeinated (condition i) or decaffeinated coffee (ii), and given caffeinated coffee while being told they have had received decaffeinated coffee (iii).</p> <p>After each of these three interventions, performance and electroencephalogram will bewas recorded at rest as well as during sustained attention Rapid Visual Information Processing task (RVIP) and a Go/NoGo motor inhibitory control task.</p> <p> We first aimed to confirm previous findings for caffeine-induced enhancement on these executive components and on their associated electrophysiological indexes (attentional P3 component, response conflict N2 and inhibition P3 components (ii vs iii contrast); and then to test the hypotheses that expectations also induce these effects (i vs ii), although with a weaker amplitude (i vs iii).</p> <p>Related to the behavioral findings, wWe didn’t not confirm any of our hypotheses for behavioral improvement induced by caffeine intakeon either of the investigate tasks’ measures. Regarding the neurophysiological findingsAt the electrophysiological level, however, we confirmed that caffeine effects on increased the attentional P3 and inhibition P3 components amplitude, but not on the response conflict N2 component. Additionally, wWe dodid not confirm provide evidence that expectations do not influence any of the investigate electrophysiological indexeices. Finally, we confirm that that expectations effects are smaller compared to caffeine effects but only for the Global Field Power parameter related to the attentional P3 component.</p> <p>only for one of the investigated the attentional P3 component’s parameters, and that this effect was smaller than that of</p> <p>We conclude that Hence, previously identified caffeine effects at the behavioral level may have been overestimated and that if while expectations effects have any no influence on sustained attention and inhibitory control, they are small. XXCaffeine effects at the electrophysiological level indicate that it tends to modulate brain areas underlying attentional mechanisms in both RVIP and Go/NoGo tasks rather than being specific to inhibitory control processes.</p>
Data from: "Alteration of the gut microbiota's composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters"
<p>This dataset contains all data collected and used for the publication : "Alteration of the gut microbiota’s composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters". Besides the Readme, it contains 11 files.</p> <p><br> Excel files with classification (i.e. genes according to their fold induction or repression) are provided. Data include different conditions with varying number of samples per group. Data are structured according to employed methods and then stratify the data obtained within the individual work packages.</p>
Bridging the gap between single nanoparticle imaging and global electrochemical response by correlative microscopy assisted by machine vision
<p>The data in this repository corresponds to experimental data: linear sweep voltammetry, optical movie and the database of the SEM images. They support the findings of a study discussed in the article by Godeffroy et al. published in Small Methods with the doi: http:/doi.org/10.1002/smtd.202200659. The data analysis to reproduce the results presented in the article has been carried out by homemade Python program routines also provided in this repository. The descirption of each routine is also provided in a text file.</p>
Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this correlation-based statistical dataset, i) all particle pairs with any given separation r in a N-body system are identified; ii) statistical measures are calculated over all particle pairs with the same separation r (pairwise average); iii) the redshift (z) and scale (r) dependence of all statistical measures (correlation/moment/structure/dispersion/spectrum functions for density, velocity and potential etc.) are presented. </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Inter-Chemical Correlation results for the study: NHANES20132014 (NHANES Survey 2013-2014)
Title: NHANES Survey 2013-2014 <br>Species: Homo sapiens <br>Number of samples: 10269 <br>Number of named analytes: 218 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20072008 (NHANES Survey 2007-2008)
Title: NHANES Survey 2007-2008 <br>Species: Homo sapiens <br>Number of samples: 8529 <br>Number of named analytes: 174 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20032004 (NHANES Survey 2003-2004)
Title: NHANES Survey 2003-2004 <br>Species: Homo sapiens <br>Number of samples: 10012 <br>Number of named analytes: 427 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES19992000 (NHANES Survey 1999-2000)
Title: NHANES Survey 1999-2000 <br>Species: Homo sapiens <br>Number of samples: 9652 <br>Number of named analytes: 252 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20152016 (NHANES Survey 2015-2016)
Title: NHANES Survey 2015-2016 <br>Species: Homo sapiens <br>Number of samples: 9170 <br>Number of named analytes: 341 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20112012 (NHANES Survey 2011-2012)
Title: NHANES Survey 2011-2012 <br>Species: Homo sapiens <br>Number of samples: 10352 <br>Number of named analytes: 201 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20012002 (NHANES Survey 2001-2002)
Title: NHANES Survey 2001-2002 <br>Species: Homo sapiens <br>Number of samples: 9851 <br>Number of named analytes: 230 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20052006 (NHANES Survey 2005-2006)
Title: NHANES Survey 2005-2006 <br>Species: Homo sapiens <br>Number of samples: 9582 <br>Number of named analytes: 319 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20092010 (NHANES Survey 2009-2010)
Title: NHANES Survey 2009-2010 <br>Species: Homo sapiens <br>Number of samples: 9717 <br>Number of named analytes: 186 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: HHEARx2018-2537 (Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort.)
Title: Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort. <br>Species: Homo sapiens <br>Number of samples: 630 <br>Number of named analytes: 16 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=66 <br>
Inter-Chemical Correlation results for the study: HHEARx2017-1839 (Zika Virus Congenital Health Outcomes and the Impact of Maternal Environmental Exposures)
Title: Zika Virus Congenital Health Outcomes and the Impact of Maternal Environmental Exposures <br>Species: Homo sapiens <br>Number of samples: 2705 <br>Number of named analytes: 10 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=61 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1449 (Environmental phenols and pesticide levels in relationship to autism)
Title: Environmental phenols and pesticide levels in relationship to autism <br>Species: Homo sapiens <br>Number of samples: 842 <br>Number of named analytes: 28 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=7 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1407 (Pediatric Inner-City Environmental Exposures at School and Home and Asthma Study)
Title: Pediatric Inner-City Environmental Exposures at School and Home and Asthma Study <br>Species: Homo sapiens <br>Number of samples: 157 <br>Number of named analytes: 28 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=2 <br>
3D Reconstruction of Shoulder Muscles in Hominoid Primates: Correlating Scapular Attachment Areas with Muscle Volume
<h2><strong>How To Cite:</strong></h2> <p>If you use this data or code in your research, please cite the associated open-access <strong>manuscript, </strong>which you can find here: <a href="https://doi.org/10.1111/joa.14199">https://doi.org/10.1111/joa.14199</a><br>and this <strong>zenodo repository</strong>.</p> <h2><strong>Online Visualization:</strong></h2> <p>You can access an interactive, web-based view of the notebooks and analyses <a title="Shoulder Muscle Reconstruction Code" href="https://juliavanbeesel.github.io/ShoulderMuscleReconstructions/intro.html" target="_blank" rel="noopener">here</a>.</p> <h2><strong>Repository Description:</strong></h2> <p>This repository contains two zip files related to the analysis and visualization of 3D reconstructed muscle volumes and lengths from various hominoid specimens.</p> <ol> <li> <p><strong>MeshFiles.zip:</strong></p> <ul> <li><strong>Contents:</strong> This zip file includes all <code>.obj</code> files for 3D reconstructed muscle volumes and associated anatomical structures. Specifically, it contains: <ul> <li><strong>Muscles:</strong> Supraspinatus, Infraspinatus, Subscapularis, Teres Major, Teres Minor</li> <li><strong>Bones:</strong> Scapula and Humerus</li> <li><strong>Attachment Sites</strong></li> </ul> </li> <li><strong>Organization:</strong> The files are organized into folders by specimen. There are 9 hominoid specimens from the following species: <ul> <li><em>Hylobates lar</em></li> <li><em>Symphalangus syndactylus</em></li> <li><em>Pongo pygmaeus</em></li> <li><em>Pongo abelii</em></li> <li><em>Gorilla gorilla</em></li> <li><em>Pan troglodytes</em></li> <li><em>Homo sapiens</em></li> </ul> </li> <li><strong>Surface Scans of Muscle Geometry: </strong>The specimens <em>Pongo</em> (ID 3) and <em>Symphalangus </em>(ID 122) also contain surface scans that depict the muscle geometry of the listed muscles. These surface scans can be used for training with the iterative polygonal modelling approach. The scans are stored as <code>.obj</code>, <code>.mtl</code> and <code>.png</code> files. To view textures on these meshes, keep all three files together in the same folder.</li> <li><strong>Additional Details:</strong> Muscle reconstructions were performed for different arm positions. Each folder contains multiple humerus files, with each file representing a humerus in a specific position aligned with the corresponding muscles. The humerus file names indicate the muscles the humerus is aligned with.<br><br></li> </ul> </li> <li> <p><strong>DataAndCode.zip:</strong></p> <ul> <li><strong>Contents:</strong> <ul> <li><strong>Excel File:</strong> The original data used for analysis, presented in Table 2 of the manuscript.</li> <li><strong>Jupyter Notebook Files: </strong>These notebooks provide the analyses and figures as described in the manuscript: <ul> <li><em>Accuracy_Muscle_Length_Reconstruction:</em> Analysis of muscle length measurement comparisons, detailed in Supplementary Information Section 3: <em>Accuracy of estimating Muscle Length from 3D reconstructions</em>.</li> <li><em>Accuracy_Muscle_Volume_Reconstruction:</em> Analysis of muscle volume measurement comparisons, detailed in Results Section 3.2: <em>Accuracy of Muscle Volume and Length Reconstruction</em>.</li> <li><em>Correlation_Analysis_SIS:</em> Correlation analysis of muscle origin area to volume for the supraspinatus, infraspinatus, and subscapularis muscles, detailed in Results Section 3.3:<em> Correlation Analysis</em>.</li> <li><em>Correlation_Analysis_TT:</em> Correlation analysis of muscle origin area to volume for the teres major and minor muscles, detailed in Supplementary Information Section 1: <em>Correlation results of teres major and minor</em>.</li> </ul> </li> <li><strong>Requirements.txt:</strong> A file listing the necessary packages required to run the Jupyter notebooks.</li> </ul> </li> <li><strong>Purpose:</strong> The Python files include code for performing statistical analyses and generating figures as described in the manuscript.</li> </ul> </li> </ol> <h2><strong>Usage Instructions:</strong></h2> <ul> <li>For analyzing muscle volumes and lengths, refer to the Jupyter notebooks included in the <code>DataAndCode.zip</code>. Ensure all dependencies listed in the <code>requirements.txt</code> file are installed.</li> <li>The <code>MeshFiles.zip</code> contains the 3D models necessary for visualizing muscle and bone reconstructions, organized by specimen and arm position.</li> </ul>
Datasets for "The Search for Topographic Correlations within the Reiner Gamma Swirl"
<p>Topographic data at 0.8 m/pixel resolution with 16-bit integer values for the subregion are available as a GeoTiff file. Definition files for the K-Means and MLC algorithms in classifying swirl units for the subregion are available as ASCII text files. Masking definition files for the study region and subregion are available as shapefiles. See README file for further details.</p> <p>Data used in the research article:</p> <p>Weirich, J.R., D.L. Domingue, F.C. Chuang, A.A. Sickafoose, M.D. Richardson, Eric E. Palmer, and R.W. Gaskell, 2023. The Search for Topographic Correlations within the Reiner Gamma Swirl. The Planetary Science Journal, 4:212. DOI: 10.3847/PSJ/ace2b8</p>
Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies
<p>Dataset of process simulations results of the natural gas sweetening and flue gas treatment (first and second sheet, respectively as indicated by the sheet name in the .xlsx file). The dataset refers to the publication <em>Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO<sub>2</sub> Capture Technologies </em>by V. Negri, Vàzquey D., Sales-Pardo, Marta, Guimerà, R. and Guillén-Gosàlbez, G. The training and testing dataset are used to generate the figures in the main manuscript and supplementary information. </p> <p> </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.