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644 results for “data visualization”
SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data
<p>This dataset includes three high-density sleep EEG recordings of healthy participants, downsampled to 250 Hz and stored in FIF format:</p> <ol> <li>Nap recording of a young adult participant</li> <li>Overnight recording of a young adult participant</li> <li>Overnight recording of an older adult participant</li> </ol> <p>Additionally, the dataset includes three text files for each recording:</p> <ul> <li>bad_channels.txt: Indexes of noisy channels</li> <li>annotations.txt: Onset and duration of noisy temporal intervals</li> <li>staging.txt: Sleep staging vector</li> </ul> <p>The corresponding package can be found on <a href="https://github.com/NirLab-TAU/sleepeegpy">GitHub.</a></p> <p>For citation, please use:<br>Falach, R., G. Belonosov, J. F. Schmidig, M. Aderka, V. Zhelezniakov, R. Shani-Hershkovich, E. Bar, and Y. Nir. "SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data." Computers in Biology and Medicine 192 (2025): 110232.<br><a href="https://doi.org/10.1016/j.compbiomed.2025.110232" rel="nofollow">https://doi.org/10.1016/j.compbiomed.2025.110232</a></p>
Training and test data, plus saved models for the upcoming paper `Top-down perceptual inference shaping the activity of early visual cortex'
<p>Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:</p><ul><li>'train_images': 640,000 float32 images used for model training. These are 40px images that contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. These are 40px images that contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul><p>The .zip file contains a saved model snapshot and various intermediate evaluative data. Details on these are coming soon.</p>
Supplementary material for "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences"
<p>This material supplements the following conference publication:</p> <p>Winkler, Rosenthal, Strecker (2024). "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences". Modellierung 2024.</p>
Research Data: Facial Expression Recognition under Visual Field Restriction
<p>This dataset contains the following files:</p> <p><strong>-</strong> <strong>view_trial.xlsx:</strong> Excel spreadsheet containing data from individual trials.<br><strong>-</strong> <strong>view_participant.xlsx:</strong> Excel spreadsheet containing data aggregated at the participant level.<br><strong>- consensus.xlsx:</strong> Excel spreadsheet containing consensus data analysis.<br><strong>- image_id_list.txt:</strong> Text file listing the IDs of the images used in the study from The Karolinska Directed Emotional Faces (KDEF); https://kdef.se/.</p> <p>These files provide comprehensive data used in the research project titled "Exploring the Visual Field Restriction in the Recognition of Basic Facial Expressions: A Combined Eye Tracking and Gaze Contingency Study" conducted by M. B. Urtado, R. D. Rodrigues, and S. S. Fukusima. The dataset is intended for analysis and replication of the study's findings.</p> <p>Please, when using these data, we kindly request citing the following article:<br>Urtado, M.B.; Rodrigues, R.D.; Fukusima, S.S. <strong>Visual Field Restriction in the Recognition of Basic Facial Expressions: A Combined Eye Tracking and Gaze Contingency Study</strong>. <em>Behavioral Sciences</em> <strong>2024</strong>, <em>14</em>, 355. <a href="https://doi.org/10.3390/bs14050355">https://doi.org/10.3390/bs14050355</a></p> <p>The study was approved by the Research Ethics Committee (CEP) of the University of São Paulo (protocol code 41844720.5.0000.5407). </p>
Data supplementing Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. Scientific Reports, 14, 8858.
<p>These files supplement the publication <br>Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. <em>Scientific Reports, </em>14, 8858. https://doi.org/10.1038/s41598-024-58953-4</p> <p>The files data_expX.mat, where X is the experiment number (1-4), contain the data as described below. </p> <p>The files dataTraining_expX.mat contain the data of the first (training) block of each experiment. They are needed only for the supplemental material. </p> <p>To exemplify the usage, the functions figure2and3.m, figure4.m, figure5.m, figure6.m and Table1.m output the paper's figures and the data of Table 1, respectively; figureS2.m, figureS3.m, figureS4.m and figureS5.m output the figures of the supplemental material (figure S1 needs substantial amounts of external source code to compute the salience maps and is therefore not included).</p> <p><br>data_exp1.mat contains the following variables<br>For alert trials, variable of dimensions subjects x blocks x alert trials (20x10x64); note that only used participants and blocks with alert trials (2 through 11) are included in the data set:<br>alert_aud - the salience level of the alert tone (1-8, corresponding to 54dB(A) through 89 dB(A))<br>alert_vis - the salience level of the alert frame (1-8, corresponding to 0.10 to 8.50 Weber contrasts in logarithmic steps)<br>alert_side - the side on which the alert frame and the tone were presented (1-left, 2-right)<br>alert_fixOk - derived from eye movement data, was the first fixation closer to the alert square than to the center?<br>alert_primaryRT - primary-task reaction time (for alert trials)<br>alert_alertRT - alert-task reaction time <br>alert_correctAlert - was the response (up/down) to the alert correct?<br>alert_intrusionAlert - was there an intrusion (left/right pressed before up or down)?<br>alert_correctPrimary - was the primary task conducted correctly?<br>alert_intrusionPrimary - was there an intrusion for the primary task?<br>alert_timeToFixation - time to first fixation on alert square <br>alert_fixationToResp - time from beginning of fixation to response to the alert <br>alert_fixDur - duration of first fixation after trial onset</p> <p>For no-alert trials, variable of dimensions subjects x blocks x no-alert trials (20x10x448):<br>noalert_correctPrimary - was the primary task conducted correctly?<br>noalert_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?</p> <p>For all trials, variable of dimensions subjects x blocks x no-alert trials (20x10x512):<br>all_correctPrimary - was the primary task conducted correctly?<br>all_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?<br>all_RT - reaction time in the primary task<br>all_isAlertTrial - was the trial an alert trial? (useful to map no-alert trials and alert trials on all trials)</p> <p>In addition, there are some raw eye movement data for the alert blocks:<br>alert_eyeX, alert_eyeY - dimension 20 x 10 x 64 x 6000; x and y position in pixel coordinates relative to trial (and alert) onset, 1ms/sample, ends at conclusion of trials, filled up with NaN if duration was less than 6000ms <br>alert_eyeFixX, alert_eyeFixY, alert_eyeFixTon, alert_eyeFixDur - 20 x 10 x 64 x 15; x and y position, onset (in ms relative to trial onset) and duration of fixations during the trial (from onset to primary-task response), filled with NaN when less than 15 fixations were made. Note that the first entry of alert_eyeFixDur along the forth dimension will usually equal the alert_fixDur</p> <p><br>data_exp2.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_vis - contains only two levels (1 and 2) corresponding to Weber contrasts of 0.10 and 2.39, respectively<br>alert_dur - the level of duration of the alert frame (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_aud is not included (all tones were at 54 dB(A))<br>there are only 19 participants; hence the variables are of size 19 x ...<br>note: block 8 for subject 6 contains only 450 trials (57 alert trials), the remainder is filled with NaN.</p> <p><br>data_exp3.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_dur - the level of duration of the alert tone (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_vis is not included (all alert frames were at 0.10 contrast)</p> <p> </p> <p>data_exp4.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_vis is not included and replaced by<br>alert_condBefore - alert frame contrast level before the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)<br>alert_condAfter - alert frame contrast level after the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)</p> <p><br>dataTraining_expX.mat contains for the first (training) block of experiment X (X being 1, 2, 3 or 4) the following variables of size 20x512 (participant x trial) [19x512 in case of Experiment 2]:<br>all_correctPrimary - was the primary task conducted correctly?<br>all_RT - reaction time in the primary task<br>[Note that there are no alert trials in this block and these data are only used in the supplementary material (part 4)]</p>
DATA SUPPORTING RESEARCH ON THE DEVELOPMENT OF PALEONTOLOGY IN BRAZIL (NETWORK VISUALIZATIONS)
<p>The documents made available present the data set that was processed in Lucas George Wendt's dissertation, presented in 2024 in the Postgraduate Program in Information Science (PPGCIN) of the Federal University of Rio Grande do Sul (UFRGS). The study is entitled: Brazilian Paleontology: a scientometric analysis based on the Lattes Curriculum. The abstract is as follows. This research sought to carry out a scientometric analysis of Paleontology in Brazil based on data collected in the Lattes Curriculum. The general objective of this dissertation is to analyze the scientific field of Paleontology diachronically and through a scientometric study - which will be explained based on the personal information of the researchers collected in their profiles and the scientific literature produced and registered in the Lattes Curriculum of the Lattes Platform. The literature review presented the concepts of Information Science, the area that, in this study, seeks to understand Paleontology through its research instruments; Scientific Communication, the main subject analyzed in this study; Metric Information Studies, the theoretical-methodological framework used in this research; Scientometrics, the theoretical scope used to understand in greater depth the constitution of the field of national Paleontology. Finally, references were also presented that help in the understanding of Paleontology in its national, South American, North American and European contexts. The research used a mixed approach of qualitative and quantitative elements. The data were generated from the CVs of researchers registered on the Lattes Platform, collected using the Brapci Bibliometric Tools tool and analyzed in specific software for metric analysis. To achieve the research objectives, data from 1,465 researcher profiles were analyzed. Regarding the full articles published in journals, 43,333 articles were considered valid. Regarding the keywords of the articles, 91,922 keywords were analyzed for word clouds and 84,771 for relationship networks. Of the academic orientations, 1,182 profiles generated 51,400 valid orientations. The aspect of the current employment relationship had 1,256 profiles considered. Regarding academic backgrounds, 1,465 profiles generated 4,556 academic backgrounds analyzed. The main contribution of this study is the realization of an unprecedented mapping of the panorama of Paleontology in Brazil, since there are no other studies that establish the same relationships that this research sought to establish. Regarding the results, based on the data collected and analyzed, the general metric indicators linked to the scientific production associated with Brazilian Paleontology were presented based on the information collected in the Lattes Curriculum; the directions of research in Paleontology that currently constitute this field in Brazil were mapped, as well as their thematic associations with other fields of knowledge; the training of PhD researchers who work with Paleontology or who have their production associated with Paleontology in terms of their academic training was characterized; and where the scientific knowledge in Paleontology or associated with Paleontology is produced was identified. The results of this study are relevant to understanding Brazilian Paleontology, highlighting its national orientation in fossil studies, doctoral training in local institutions and predominant activity in national organizations. These elements are important to consolidate Brazilian paleontological science globally. Regarding interdisciplinary relations, a clear proximity between Paleontology and Geosciences is observed, influenced by the history and current dynamics of the field. The study is available in full at this link: https://lume.ufrgs.br/handle/10183/278682.</p>
Data and analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in Vanessa cardui"
<p>Butterfly scale data and data analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in <em>Vanessa cardui</em>."</p> <p> </p> <p>It is recommended to download all files and folders into a single root folder for use in MATLAB.<br> This code was prepared for use in MATLAB R2019b, and some scripts or functions require the Image Processing Toolbox.</p>
Supplementary data for: Collaborative Program Comprehension via Software Visualization in Extended Reality
<p>Supplementary videos and images for: Collaborative Program Comprehension via Software Visualization in Extended Reality</p> <p>Videos are additionally hosted on: <a href="https://www.youtube.com/channel/UCijDvGaoZqH0LiFW3DSNH1w">https://www.youtube.com/channel/UCijDvGaoZqH0LiFW3DSNH1w</a></p>
Interview data on history-oriented theologians' recommendations for visualizations in libraries
<p>Results of an interview study with the target group "history-oriented theologians" on their recommendations for visualizations in libraries. Recommendations were retrieved with the method SHIRA (Structured Hierarchical Interviewing for Requirement Analysis)[1]. This allows generating concrete qualities and implementation suggestions out of abstract qualities.</p> <p>Feel free to contact me if you have any questions!</p> <p> </p> <p> [1] M. Hassenzahl, R. Wessler, and K.-C. Hamborg. Exploring and understanding product qualities that users desire. Conference on Human-Computer Interaction IHM-HCI’2001, 2, 2001.</p>
Interview data on experts' recommendations for visualizations in libraries
<p>Results of an interview study with twelve experts on their project processes and their recommendations for visualizations in libraries. Recommendations were retrieved with the method SHIRA (Structured Hierarchical Interviewing for Requirement Analysis)[1]. This allows generating concrete qualities and implementation suggestions out of abstract qualities.</p> <p>The file contains two pages: On the first, a meta-model was constructed out of all identified steps during library visualization projects. On the second, all SHIRA suggestions were gathered and analyzed.</p> <p>Feel free to contact me if you have any questions!</p> <p> </p> <p> [1] M. Hassenzahl, R. Wessler, and K.-C. Hamborg. Exploring and understanding product qualities that users desire. Conference on Human-Computer Interaction IHM-HCI’2001, 2, 2001.</p>
iCube data visually impaired
<p>Here, I report the codebook of the dataset for the variable names that are harder to interpret:</p> <p><strong>group</strong>: 'early-blind', 'late-blind' or 'control'</p> <p><strong>main_group</strong>: 'blind' or 'sighted'</p> <p><strong>trial_type</strong>: 'memorize' or 'recall'</p> <p><strong>conf</strong>: the configuration of pins locations used in the specific trial</p> <p><strong>subject_orientation</strong>: oreintation of the subject in absolute coordinates while doing the test (e.g. 'north' if facing North)</p> <p><strong>pin_facet_1</strong>: number of pins in facet 1</p> <p>.</p> <p>.</p> <p><strong>pin_facet_6</strong>: number of pins in facet 6</p> <p><strong>cond_facet_1</strong>: kind of face based on pin number (even or odd)</p> <p>.</p> <p>.</p> <p><strong>cond_facet_6</strong>: kind of face based on pin number (even or odd)</p> <p><strong>correct</strong>: correct response (i.e. 'same' or 'different') for that trial</p> <p><strong>response</strong>: actual response of the subject</p> <p><strong>accuracy</strong>: 1 = correct; 0 = wrong</p> <p><strong>n_tocchi_trial </strong>= number (unfiltered) of active cells of the cube in the whole trial</p> <p><strong>n_tocchi_facet_1 </strong>= number (unfiltered) of active cells in face #1 of the cube in the whole trial</p> <p>.</p> <p>.</p> <p><strong>n_tocchi_facet_6 </strong>= number (unfiltered) of active cells in face #6 of the cube in the whole trial</p> <p><strong>filt_n_tocchi_trial</strong> = number (filtered, i.e., explorative touches only) of active cells of the cube in the whole trial</p> <p><strong>filt_n_tocchi_facet_1</strong> = number (filtered, i.e., explorative touches only) of active cells in face#1 of the cube in the whole trial</p> <p>.</p> <p>.</p> <p><strong>filt_n_tocchi_facet_6</strong> = number (filtered, i.e., explorative touches only) of active cells in face#6 of the cube in the whole trial</p> <p><strong>touch_density_trial</strong> = unfiltered touch frequency (active cells/second) in the whole trial</p> <p><strong>touch_density_facet_1</strong> = unfiltered touch frequency (active cells/second) in face#1 in the whole trial</p> <p>.</p> <p>.</p> <p><strong>touch_density_facet_6</strong> = unfiltered touch frequency (active cells/second) in face#6 in the whole trial</p> <p><strong>filt_touch_density_trial</strong> = filtered (i.e. explorative touches only) touch frequency (active cells/second) in the whole trial</p> <p><strong>filt_touch_density_facet_1</strong> = filtered touch frequency (active cells/second) in face#1 in the whole trial</p> <p>.</p> <p>.</p> <p><strong>filt_touch_density_facet_6</strong> = filtered touch frequency (active cells/second) in face#6 in the whole trial</p> <p><strong>duration_trial</strong> = duration of exploration in s</p> <p><strong>dur_facet_1</strong> = duration of exploration in face#1 in the whole trial</p> <p>.</p> <p>.</p> <p><strong>dur_facet_6</strong> = duration of exploration in face#6 in the whole trial</p> <p><strong>filt_dur_facet_1</strong> = filtered (explorative touches only) duration of exploration in face#1 in the whole trial</p> <p>.</p> <p>.</p> <p><strong>filt_dur_facet_6</strong> = filtered (explorative touches only) duration of exploration in face#6 in the whole trial</p> <p><strong>rot_abs_total</strong> = amount of rotation in deg in the whole trial</p> <p><strong>raw_mean_rot_velocity</strong> = unfiltered rotation velocity (deg/s)</p> <p><strong>filtered_mean_rot_velocity</strong> = filtered (removed rotations < 1deg/s) rotation velocity (deg/s)</p> <p><strong>facce_esplorate_0.8s</strong> = sequence of explored faces</p> <p><strong>orient_face_deg</strong> = faces orientation in spherical coordinates at the beginning of exploration of the faces reported in 'facce_esplorate_0.8s'. All cube faces are considered (i.e. list of 6 couples of coordinates for each initial timepoint of exploration of a face)</p> <p><strong>orient_face_world_coord</strong> = same as above but translated to world labels (e.g. 'north', 'up', 'west', etc.)</p> <p><strong>orient_face_local</strong> = same as above but translated to labels relative to the participants (e.g. 'left', 'down', 'rear'...)</p> <p><strong>timepoints_facce_esplorate_0.8s</strong> = timepoints in s of the beginning of exploration of a face (see 'facce_esplorate_0.8s')</p> <p><strong>mean_delta_timepoints</strong> = mean time to move from one explored face to the next (mean of 'timepoints_facce_esplorate_0.8s')</p> <p><strong>std_delta_timepoints</strong> = standard deviation of 'mean_delta_timepoints'</p> <p><strong>transition_local</strong> = position of explored face (see 'facce_esplorate_0.8s') relative to the participants</p> <p><strong>trans_matrices</strong> = matrices expressing the probability (prop) of moving from one location to another (e.g. from 'front' to 'left')</p> <p><strong>ritorni_0.8s</strong> = number of returns to already explored faces expressed as array (e.g. [0 0 0 2 0 0] = face#4 explored twice)</p> <p><strong>ritorni</strong> = total number of returns to already explored faces for that trial (sum of the array coded in ‘ritorni_0.8s’)</p>
Data from: Texas field crickets (Gryllus texensis) use visual cues to place learn but perform poorly when intra- and extra-maze cues conflict
<p>Central place foraging field crickets are an ideal system for studying the adaptive value of learning and memory, but more research is needed on ecology-relevant cognition in these invertebrates. Here, we test the visuospatial place learning of Texas field crickets (<em>Gryllus texensis</em>) in a radial arm maze. Our study expands previous work on <em>G. texensis</em> cognition for accuracy measures and extends our previous findings on females to both sexes. Additionally, our study examines whether crickets use intra- or extra-maze cues to locate a food reward using a maze rotation putting the cues in conflict. We found that male and female crickets improved performance over trials when measured by accuracy variables but not latency variables; thigmotaxis negatively impacted performance in both sexes. In a reward-absent trial, both male and female crickets demonstrated place memory. When intra- and extra-maze cues conflicted during a rotation trial, crickets' performance was not better than chance. Our rotation results suggest that crickets may experience reciprocal overshadowing of conflicting cues – a result most often seen in other taxa with conflicting multi-modal cues. We conclude that crickets do not rely solely on: (1) a single-cue association; (2) route-following; or (3) their own scent cues to navigate the maze. Instead, male and female Texas field crickets seem to learn the location of the reward using a combination of proximal and distal cues. The possibility to test large numbers of wild-caught or laboratory-reared individuals opens the door to future investigations on the evolutionary ecology of visuospatial learning in these invertebrates.</p>
Data and code for Zheng et al. Contrasting coloured ventral wings are a visual collision avoidance signal in birds
<p>This repository contains codes and data for Zheng et al. Contrasting coloured ventral wings are a visual collision avoidance signal in birds. We have three folders, each containing one of the three datasets of contrast scores of avian ventral wings. These include the mean manual contrast ventral wing scores for 1780 species, a subset of 1745 diurnal species, 648 species with high-resolution museum ventral images, and the mean Root-Mean-Square (RMS) contrast ventral wing scores for the same 648 species. We tested the collision avoidance hypothesis for each dataset by assessing the relationships between the contrast scores and ecological traits. We used the Bayesian Generalized Linear Mixed Models in MCMCglmm with considering the phylogenetic relatedness among species and the uncertainties of 100 phylogenetic trees (downloaded in birdtree.org). We included body mass, flock size, coloniality (colonial vs. non-colonial breeding species), activity time (nocturnal vs. diurnal), the number of sympatric predators, and the interaction between coloniality and body mass as the predictors. In each folder, we included four files, including an R source file, a dataset containing the contrast scores and the ecological traits of the corresponding species, and a tree file containing 100 randomly sampled phylogenetic trees among these species. See the Methods of the paper for detail. </p>
Data from two studies of learning in visual span working memory tasks.
<p>Data from two working memory span tasks. The span set size could be up to six, and each row in each .csv is one response (i.e., one "click" on an item). Thus, each trial's data is spread out on multiple rows, with accuracy being a binary variable.</p>
PCF05: fMRI data in a Pavlovian delay threat conditioning task with four visual CS with different rates of electrical US
<p>This data set includes selected functional magnetic resonance imaging (fMRI) data supplementing an article. The data include: </p> <ol> <li>Untresholded Statistical Parametric Maps (SPMs) and beta images (BOLD signal estimates) for relevant contrasts for the GLMs reported in the article</li> <li>Region-of-interest (ROI) masks: anatomical ROIs with combined hemispheres, and masks created from significant BOLD signal clusters from the whole-brain analyses</li> <li>Summary data files for mean beta (BOLD signal estimate) values and their within-subject errors for each ROI</li> <li>A compilation Excel sheet of condition-wise BOLD parameter estimates and standard errors from previous axiomatic aversive prediction error studies, and associated effect sizes as well as sample sizes from a power analysis. </li> </ol> <p>Details of the experimental paradigm as well as of the fMRI data acquisition and analysis can be found in the associated article. </p>
Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data
<p>This dataset is provided for the paper "Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data".</p> <p>Please refer to the readme in the zipped file for additional documentation.</p> <p>The zipped file contains two CSV files for every country in Africa obtained by queries "[country name]" and "[country name + people]".</p>
Figure 3. Data visualization-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Data from: Evaluation of a low-cost staining method for improved visualization of sweet potato whitefly (Bemisia tabaci) eggs on multiple crop plant species
<p>The sweet potato whitefly (Bemisia tabaci) is a damaging insect pest that feeds on hundreds of crop plants. Oviposition rate is a useful metric to screen plants for whitefly resistance. Whitefly eggs are small and translucent, and can therefore be hard to count on the leaves of some crops. In this research, we tested a selective egg staining process on five crop species to determine if egg staining can improve the visualization and quantification of whitefly eggs. By comparing the egg counts before and after staining using two-sample Wilcoxon signed-rank tests (a non-parametric test for paired analyses). Two individuals counted the eggs, and for both these counters we found a significant increase in the number of visible eggs after staining on melon, tomato, and cowpea. This method could be applied to improve phenotyping for whitefly resistance in plant breeding applications.</p>
pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection (Supp data)
<p>Supplemental tables for article: <strong>pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection </strong></p>
Data Set for the Journal Article "Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks"
<p>This data archive contains all data newly created in the following publication:</p> <p>Charlotte H. Müller, Miguel Steiner, Jan P. Unsleber, Thomas Weymuth, Moritz Bensberg, Katja-<br>Sophia Csizi, Maximilian Mörchen, Paul L. Türtscher, and Markus Reiher, "Heron: Visualizing and<br>Controlling Chemical Reaction Explorations and Networks", in preparation.</p> <p>The directory contents are as follows:</p> <ul> <li>steered_eschenmoser.tar.xz: Dump of the database created during the steered exploration</li> <li>steered_exploration_protocol_chemoton_3.1.json: Protocol used for the steered exploration</li> </ul>
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.