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Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 13 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the thirteenth part of 14 parts of the full dataset (13/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 35ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 7 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the seventh part of 14 parts of the full dataset (7/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 40ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
ESAA: an EEG-Speech auditory attention detection database
<p>We build a database for AAD research, which consists of competing speech stimuli and associated human neural responses, i.e, electroencephalography (EEG) recordings, namely EEG-Speech AAD (ESAA) database. This is the first AAD database with speech stimuli in a tonal language (Mandarin). Moreover, we develop an AAD baseline as a reference model for decoding which speech stream a listening subject is attending to (speaker attention detection), and a baseline for decoding which spatial locus a listening subject is attending to (speaker locus attention detection) on the ESAA database.</p> <p>We release the source code and the database for use in research purpose.</p> <p>This database consists of response data for 17 normal-hearing subjects (S1-S17). It includes:</p> <p>- 64-channel EEG data: responses to two-speaker speech stimuli<br> - Auditory stimuli data (clean): Chinese short stories narrated by a female and a male professional story teller. <br> - Auditory stimuli data (hrtf): Auditory stimuli after head-related transfer function (HRTF) filtering (simulating sound coming from ± 90 deg).<br> - Preprocessing code<br> - AAD baseline (CNN model)</p>
Selective Attention VR and PC : Data And Analysis
<p><strong>Data and Analysis Repository for</strong></p> <p><strong>Developing Virtual Reality and Computer Screen Experiments One to One Using Selective Attention as a Case Study</strong></p> <p>June 2023, Rasmus Ahmt Hansen and Marta Topor</p> <p>The current repository holds all data and analysis scripts used in the report named above. Data files are saved in .csv format and analysis scripts were written using R and R Markdown.</p> <p>The report preprint can be accessed at:</p> <p>The study aimed to develop a reliable PC control condition for a VR experiment assessing selective attention in grade 0 children.<br> The selective attention task we developed and implemented can be accessed here:</p> <ul> <li>PC : <a href="https://doi.org/10.5281/zenodo.7844487">https://doi.org/10.5281/zenodo.7844487</a></li> <li>VR : <a href="https://doi.org/10.5281/zenodo.7844593">https://doi.org/10.5281/zenodo.7844593</a></li> </ul> <p><strong>Participants</strong></p> <p>73 grade 0 children from Danish primary schools completed the selective attention test in both VR and PC environments. Performance quality was low and thus we only included 19 participants in final analyses. All data, included and excluded, are openly available in this repository.</p> <p><strong>Data</strong></p> <ul> <li>Raw data from the PC condition can be found in the RAW PC folder</li> <li>Raw data from the VR condition can be found in the RAW VR folder</li> <li>Demographic data, anonymised, can be found in the demographics.csv file</li> <li>The final data from the 19 participants included in statistical analyses can be found in the final_data_set.csv file</li> </ul> <p><strong>Analysis</strong></p> <ul> <li>Demographic analyses can be found in the demographics.R file</li> <li>Data processing, quality control and statistical analyses can be found in the full_analysis_script.Rmd</li> <li>The plots folder holds plots used in the study report</li> </ul>
Data and code for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter"
<p>This is the code and data for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter" by Müller-Hansen et al. (Global Environmental Change, 2023).</p><p>This archive contains the following materials:</p><ul><li>Data set of tweets</li><li>Table of subqueries for searching Twitter</li><li>Code for figure generation</li></ul><p>Please see the Readme for further details.</p><p> </p>
How many words is a picture worth? Attention allocation on thumbnails versus title text regions: Dataset
<p>Dataset for the following publication: https://jainlab.cise.ufl.edu/eyetrack-onlineux.html</p> <p>How many words is a picture worth? Attention allocation on thumbnails versus title text regions, Yandandul, Chaitra and Paryani, Sachin and Le, Madison and Jain, Eakta, ACM Symposium on Eye Tracking Research & Applications. (ETRA)</p> <p> </p>
Factors affecting altmetrics attention to scholarly publication in peer-reviewed journals published in Iran and Turkey
<p>The goal of this study was to trace the altmetric measures of peer-reviewed journals in two non-English speaking countries na,ely Iran and Turkey, in order to understand their correlation with some website structure and design determinants, as well as the subject and the full-text language of the journals.</p> <p> </p>
PsPM-CogSF: SCR, ECG and respiration measurements during mental arithmetic, attention, and rest
<p>This dataset includes skin conductance, ECG and respiration measurements for 20 healthy unmedicated participants (9 males and 11 females aged 23.68 +/- 2.94 years, gender information misprinted in Bach & Staib 2015) undergoing two 120-s periods of resting, attention, or mental arithmetic (adding numbers). Selection and order of the two periods is contained as group information. An event marker is recorded at the beginning and the end of each period.</p>
Data supplementing the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.
<p>These data supplement the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.</p> <p>Use is free for academic purposes, provided the aforementioned article is appropriately cited.</p> <p>The directory contains the following files</p> <p>stimuli.tar.gz - stimuli used in this study; note that this is based on the MONS database, but some deviations from the final version of the database do exist.</p> <p>ratings.mat contains the variables<br> arousal - mean arousal rating<br> valence - mean valence rating<br> valence2 - squared mean valence rating (after subtracting midpoint)<br> motivationalValue - mean motivation rating<br> motivaionalValue2 - squared mean motivation rating (after subtracting midpoint)</p> <p>All variables are 104x3, where the first dimension is the stimulus number, and the second dimension the motivation ground truth (aversive, neutral, appetitive)</p> <p><br> Experiment 1</p> <p>fixationsExperiment1.mat contains the variables fixationX, fixationY, fixationDuration, fixaitonOnset, fixationInitial, which contain for each fixation horizontal and vertical coordinate, the duration, the time of the onset relative to the trial onset and whether it is the initial fixation. All variables have dimensions 16x104x3x50, where the first dimension is the observer, the second the scene, the third the condition and the forth a counter of fixations. Whenever there are less than 50 fixations the remainder are filled with NaN.</p> <p><br> boundingBoxesExperiment1.mat contains for each critical object the bounding box coordinates x,y of upper left corner and width and height as variables boundingBoxX, boundingBoxY, boundingBoxW, boundingBoxH respectively. Note that this is relative to the eyetracker coordinates of experiment 1 (full display 1024x768, presentation in the center) and will therefore not match the coordinates of the images in the archive or the bounding box coordinates of experiment 2. Dimensions are 104x3, the dimensions representing scene number and condition, respectively.</p> <p><br> figure2.m uses these data to computes figure 2 of the article from these data</p> <p><br> dataForExperiment1.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of table 1. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object.</p> <p><br> table1.R computes and prints the models for table 1</p> <p> </p> <p>Experiment 2</p> <p>fixationsExperiment2.mat contains fixation data for experiment 2. Variable names as in experiment 1. Dimensions are 18x99x3x3x50, where the first dimension is the observer, the second the image number, the third the visual condition, the third the motivational condition and the fifth the fixation count. Since only one visual condition was shown to each observer per motivational condition, there is an additional variable 'hasData', which is 1 if the image was presented to the observer in this condition and 0 otherwise. Since fixations can be outside the image and will therefore be excluded, there is also an additional variable fixationNumber to keep a correct count of the fixation number in the trial.</p> <p>boundingBoxesExperiment2.mat contains bounding box data for experiment 2 in image (and fixation) coordinates. Notation as for experiment 1, but coordinates refer to image and eyetracking coordinates used for experiment 2 and therefore can differ occasionally.</p> <p><br> figure3and4.m generates figures 3 and 4 of the article from these data files.</p> <p>dataForExperiment2.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of tables 2 amd 3. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. The fields imgMot and imgVis contain the motivational ground truth and the salience manipulation, respectively.</p> <p>table2.R uses the Rdata file to compute the models for table 2 of the article and print summary results</p> <p>table3.R uses the Rdata file to compute the models for table 3 of the article and print summary results. Note that the computation can take substantial time; results might deviate slightly depending on the exact version of R and its libraries used.</p> <p> </p>
Dataset supplementing Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006
<p>These data supplement the publication</p> <p>Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006</p> <p>and be used freely for scientific purposes provided the aforementioned paper is appropriately cited.</p> <p>Note that the image files cannot be provided on this site due to copyright restrictions.</p> <p>The dataset contains the following files:</p> <p>maps_01.mat - maps_72.mat:</p> <p>For each image the 6 maps used in the paper are contained, the maps of experiment 1 are labelled as in the paper (AWS, OOM, nOOM, PVL,UNI), AWS2 is the AWS map for the modified stimuli of experiments 2 and 3.</p> <p>exp?_fixations.mat contains all fixations of the respective experiment.</p> <p>For experiment 1, there are the variables xFix, yFix, durFix, which contain the x position, the y condition, and the fixation duration of each fixation. Dimensions are images x subjects x fixation number, where the first fixation is the 0th (initial) fixation. The variable condition (image x subject) contains the condition in which the respective image was shown to the subject. For the main analysis only the "0" condition was used, refer to the paper's appendix for the other conditions.</p> <p>For experiment 2 and 3, variables are called xFixByImage, yFixByImage, dFixByImage and the dimensions are subject x image x fixation number. In addition tFixByImage contains the start of the fixation relative to trial onset (negative for the 0th fixation).<br> In both cases, empty entries are filled with nans.</p> <p><br> computeROC.m is a helper function called by other functions.</p> <p><br> figure1.m through figure7.m reproduce the figures from the paper to exemplify data usage.</p> <p> </p>
Dataset supplementing Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Annals of the New York Academy of Sciences. 1339, 138-153.
<p>Data supplementing the paper Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. <em>Annals of the New York Academy of Sciences. 1339, </em>138-153. doi: 10.1111/nyas.12575 The files can be freely used for scientific purposes, provided this reference is appropriately cited.</p> <p>Files contain the behavioral data, the model can be found at https://doi.org/10.5281/zenodo.573026</p> <p> </p> <p>The following files are contained in this folder:</p> <p>dataExp1.mat contains the data of experiment 1</p> <p>The variables durationLeft and durationRight contain 5 x 6 x 6 cell arrays with the dominance durations for the left and right grating, respectively. Dimensions are subject x contrast level left x contrast level right.</p> <p><br> dataExp2.mat contains the data of experiment 2</p> <p>Variables buttonStart, buttonEnd and whichButton contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain the start time and end time of each button press, and which button (1/2) was pressed, respectively.</p> <p>Variables presStart and presEnd contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain start and end of each blank period. All time stamps refer to the onset of the first blanking trial (end of continuous presentation)</p> <p>Variable prevPerz contains the percept (button) that was pressed at the end of the continuous presentation period.</p> <p><br> figure3_human.m, figure4_human.m and figure6_human.m exemplify the usage of the data by re-plotting the figures containing human data of the aforementioned paper</p>
Attentional Bias for Uncertain Cues of Shock in Human Fear Conditioning: Evidence for Attentional Learning Theory
<p>Eye tracking data and statistical analysis of:</p> <p>Koenig, S., Uengoer, M., & Lachnit, H. (2017). Attentional bias for uncertain cues of shock in human fear conditioning: Evidence for attentional learning theory. Frontiers in Human Neuroscience. doi: 10.3389/fnhum.2017.00266.</p> <p>Abstract: We conducted a human fear conditioning experiment in which three different color cues were followed by an aversive electric shock on 0, 50, and 100% of the trials, and thus induced low (L), partial (P), and high (H) shock expectancy respectively. The cues differed with respect to the strength of their shock association (L < P < H) and the uncertainty of their prediction (L < P > H). During conditioning we measured pupil dilation and ocular fixations to index differences in the attentional processing of the cues.<br> After conditioning, the shock-associated colors were introduced as irrelevant distracters during visual search for a shape target while shocks were no longer administered and we analyzed the cues’ potential to capture and hold overt attention automatically.<br> Our findings suggest that fear conditioning creates an automatic attention bias for the conditioned cues that depends on their correlation with the aversive outcome. This bias was exclusively linked to the strength of the cues’ shock association for the early<br> attentional processing of cues in the visual periphery, but additionally was influenced by the uncertainty of the shock prediction after participants fixated on the cues. These findings are in accord with attentional learning theories that formalize how associative learning shapes automatic attention.</p>
Context modulation of learned attention deployment.
<p>Eye tracking data and statistical analysis of:</p> <p>Uengoer, M., Pearce, J. M., Lachnit, H., & Koenig, S. (2017). Context modulation of learned attention deploymentReward draws the eye, uncertainty holds the eye: Associative learning modulates distractor interference in visual search. Learning & Behavior. doi:10.3758/s13420-017-0277-y</p> <p>Abstract: In three experiments, we investigated the contextual control of attention in human discrimination learning. In each experiment, participants initially received discrimination training in which the cues from Dimension Awere relevant in Context 1 but irrelevant in Context 2, whereas the cues from Dimension B were irrelevant in Context 1 but relevant in Context 2. In Experiment 1, the same cues from each dimension were used in Contexts 1 and 2, whereas in Experiments 2 and 3, the cues from each dimension were changed across contexts. In each experiment, participants were subsequently shifted to a transfer discrimination involving novel cues from either dimension, to assess the contextual control of attention. In Experiment 1, measures of eye gaze during the transfer discrimination revealed that Dimension A received more attention than Dimension B in Context 1, whereas the reverse occurred in Context 2. Corresponding results indicating the contextual control of attention were found in Experiments 2 and 3, in which we used the speed of learning (associability) as an indirect marker of learned attentional changes. Implications of our results for current theories of learning and attention are discussed.</p> <p><br> Please see related identifier 10.5281/zenodo.583223 for analysis of:<br> Koenig, S., Uengoer, M., & Lachnit, H. (2017). Attentional bias for uncertain cues of shock in human fear conditioning: Evidence for attentional learning theory. Frontiers in Human Neuroscience. doi: 10.3389/fnhum.2017.00266.</p> <p>Please see related identifier 10.5281/zenodo.583233 for analysis of:<br> Koenig, S., Kadel, H., Uengoer, M., Schubö, A., & Lachnit, H. (2017). Reward Draws the Eye, Uncertainty Holds the Eye: Associative Learning Modulates Distractor Interference in Visual Search. Frontiers in Behavioral Neuroscience,11, 128. doi: 10.3389/fnbeh.2017.00128.</p> <p> </p> <p> </p>
Code and Data for: Donor activity is associated with US legislators' attention to political issues
<p>Contains data, code, and annotations for:</p> <blockquote> <p>Goel P, Malkin N, Gaynor SW, Jojic N, Miler K, Resnik P (2023) Donor activity is associated with US legislators’ attention to political issues. PLoS ONE 18(9): e0291169. https://doi.org/10.1371/journal.pone.0291169</p> </blockquote>
Basic visual functions of children and adolescents with Autism, Attention Deficit Hyperactivity Disorder, and Dyslexia
<p>Data for the manuscript Basic visual functions of children and adolescents with Autism, Attention Deficit Hyperactivity Disorder, and Dyslexia</p> <div> </div>
Affective, physiological, and attention restoration at a wooden desk: A pilot study (Datasets and R analysis code)
<p>This entry contains datasets and R processing and analysis code for the article <em>Affective, physiological, and attention restoration at a wooden desk: A pilot study.</em><br> <br> The analysis primarily investigates how people respond to the Mental Arithmetic Task (MAT) in terms of their affective states and physiological arousal, and how their cognitive performance changes between two task administrations. The analysis also checks whether affective, physiological, and cognitive responses differ between settings furnished with or without wood. We base our analysis on self-reported affective states, captured physiological (electrodermal and cardiovascular) activity, and results on the cognitive task (i.e., MAT).</p>
Figure 3 in Original specimens and type localities of early described polychaete species (Annelida) from Norway, with particular attention to species described by O.F. Müller and M. Sars
Figure 3. Example of text page from Zoologia Danica prodromus for polychaetes with armed mouth ('ore forcipato') and with eversible pharynx ('ore proboscideo'). From Müller (1776).
Figure 2 in Original specimens and type localities of early described polychaete species (Annelida) from Norway, with particular attention to species described by O.F. Müller and M. Sars
Figure 2. Text page and plate for descriptions of Scoletoma fragilis (= Lumbricus fragilis) and Scoloplos armiger (= Lumbricus armiger) from Zoologia Danica Vol. I (Müller, 1777–84).
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.